Time-dependent evaluation of test strips for differentiating hemoglobin and myoglobin in urine

Abstract

The invention relates to methods, devices, and software for distinguishing hemoglobin (Hb) from myoglobin (Mb) in urine samples using peroxidase-reactive test strips. A time-resolved video of the color change on the test pad is recorded, and hue values are extracted and analyzed over time. Diagnostic differentiation between Hb and Mb is achieved by evaluating the hue-time curve, in particular via endpoint hue values and the ratio of integrated hue values in distinct time intervals. The invention further comprises a standardized optical setup for recording, a software-based evaluation algorithm, and a refillable cartridge for in-situ urine analysis devices containing appropriate test fields. The invention enables combined testing with additional urine parameters and is suitable for medical and non-clinical applications such as sport and home diagnostics.

Description

[0001] TIME-DEPENDENTEVALUATION OFTESTSTRIPS FORDIFFERENTIATINGHEMOGLOBIN ANDMYOGLOBIN IN URINE Myoglobinuria, the presence of myoglobin in urine, is a clinical condition that can indicate muscle damage and is associated with various causes, including muscle trauma, immobilisation, and intense physical exercise. Current detection methods for myoglobin are complex and unsuitable for rapid diagnosis. Urine dipsticks are sensitive to myoglobin and Haemoglobin due to the peroxidase activity of these globin proteins. The study investigates whether urine dipsticks, which detect blood via the peroxidase activity of haemoglobin, can differentiate between haemoglobin and myoglobin based on their colour change patterns. The research was conducted with the model-based experimental analysis method. It included modelling the reaction kinetics in Python and experimental work based on the knowledge gained from the model. The experimental approach analyses different highly concentrated solutions with either pure haemoglobin, pure myoglobin, or a mixture of both. A method is proposed that allows the discrimination between samples containing pure haemoglobin or samples containing myoglobin based on the hue value and the ratio of the area under the hue values over time. The results also indicate that better discrimination between haemoglobin and myoglobin and a mixture of both is possible with further research. The findings could have significant implications for developing a simple, rapid, cost-effective diagnostic tool for myoglobinuria.1. Introduction1.1.Problem statement The appication project aims to further the development of a urine analysis system for thedetermination of haemoglobin and myoglobin. Conventional urine dipsticks for detecting blood in the urine are based on the detection of haemoglobin, either free or from intact erythrocytes. The peroxidase activity of haemoglobin and myoglobin leads to the oxidation of the colour indicator present in the dipstick. Theapplicant noticed that the colour change or the colour pattern will differ betweenhaemoglobin and myoglobin. This indicates that a more refined diagnosis could be possible.1.2.Theoretical Background 1.2.1. Myoglobin and HaemoglobinMyoglobin (Mb) is a globin protein found in the human body with a molecular weight between 16,500 and 17,500 Daltons(David 2000) (Grover, Atta et al.2004). It consists of one folded polypeptide chain of 154 amino acids. The oxygen binding site of Mb is a slit in the structure with a single heme group with a central iron atom (David 2000). The heme residue is a porphyrin ring: iron ion complex. Mb plays an essential role in the respiratory system; its primary function in the human body is the reversible binding of oxygen and its storage in muscle tissue (Ordway and Garry 2004). About 2-3 mg of Mb per gram of wet tissue can be found in the skeletal and cardiac muscle vertebrates (Milne 1988). Mb is related to Haemoglobin (Hb); the heme groups in Mb and Hb are identical and can bind oxygen in a 1:1 ratio. Hb consists of four polypeptide chains, each with a haem group attached. The four chains can be divided into two pairs; one pair is the so-called α-chains, slightly shorter than the β-chains of the other pair (Huehns and Shooter 1965). Hb can bind four oxygen molecules, while Mb can only bind one. Due to the higher oxygen affinity of Mb, oxygen can be carried from Hb through the bloodstream and then bind to Mb, resulting in an oxygen flux from the blood to the muscle tissue(David 2000). Besides its primary function as an oxygen transporter, Hb possesses different enzymatic activities, including the pseudo peroxidase activity (Dejia Li 2006). The molecular weight of Hb is estimated to be between 64,000 and 64,500 Daltons (BALDWIN 1975, Grover, Atta et al. 2004). Hb can be present in the urine due to various factors. These factors include menstrual blood, injuries to the urinary tract, or other conditions such as inflammation, kidney stones, or cancer in the kidneys, ureter, bladder, or prostate. (Jimbo 2010). 1.2.2. Medical relevance Mb can be present in the urine in small quantities, typically less than five ng / ml; an excessive amount of Mb in the urine is called myoglobinuria. Clinical symptoms of myoglobinuria include weakness and swelling of the muscle and a colour change of the urine from yellowish to brownish. Myoglobinuria can have a variety of causes, from hereditary to acquired. An example of hereditary causes is metabolic myopathies, such as disorders of glycolysis or diseases characterised by mitochondrial respiratory chain dysfunction. Acquired causes include a variety of mechanical or chemical injuries of the muscle tissue. The terms myoglobinuria and rhabdomyolysis are often used interchangeably, but the breakdown of skeletal muscle tissue is medically called rhabdomyolysis and results in myoglobinuria (David 2000). Through the breakdown of the muscle tissue is, the intercellular contain, such as creatine kinase, glutamic oxalacetic transaminase, lactate dehydrogenase, and aldolase, myoglobin is the heme pigment, electrolytes such as potassium and phosphates, are released in the bloodstream and later into the urine. Large amounts of Mb can lead to renal tubular obstruction, direct nephrotoxicity, and acute renal failure (Khan 2009). The causes of rhabdomyolysis include a range of possible causes, starting with trauma and crush syndrome, which include direct injuries to the muscle tissue from burns or electric shocks. Chemical agents, including alcohol or carbon monoxide, or the use of drugs, can also cause rhabdomyolysis. Immobilization of a patient can also cause Mb to be present in the urine. Weather conditions that lead to heat strokes or hypothermia can both be caused by it (Kagen, 1978 ). In adults, the most common cause is drug abuse, including medical drug and alcohol abuse, muscle diseases, trauma, neuroleptic malignant syndrome (NMS), seizures, and immobility. In children, on the other hand, the most common causes are viral myositis, trauma, connective tissue disorder, exercise and drug overdose (Khan 2009). The symptoms of rhabdomyolysis include a bright spectrum that can range from an asymptomatic rise in plasma creatine kinase (CK) through massive increases in enzyme activity, which are associated with acute renal failure (ARF), severe alterations in electrolytes and disseminated intravascular coagulation (DIC) (Cervellin, Comelli et al.2010). The visible colour change of the urine through the present Mb occurs when the Mb concentration exceeds 250 μg / ml, corresponding to the destruction of more than 100g of muscle tissue(Lane and Phillips 2003). Exertional rhabdomyolysis (ER) is a type of rhabdomyolysis caused by intense physical exercise, leading to prolonged and / or repetitive muscle overload. ER is more common in professions based on intense physical labour, such as professional athletes and military members (Nye, Kasper et al. 2021). The number of cases of ER, outside of professional athletics and the military, is rapidly growing due to the rising number of people interested in a healthier lifestyle. The exact number is complex to evaluate due to the range of presentations and inconsistent case definitions (Carneiro, Viana-Gomes et al. 2021). Some studies suggest that the presence of Mb in the bloodstream is a normal reaction to intense exercise. This can lead to the leakage of the Mb from the bloodstream into the urine. In most cases, the presence of Mb in blood or urine should go back to ‘acceptable’ levels after around 24 hours (Milne 1988). The likelihood of developing an ER decreases with the rise of the individual's fitness level; less fit individuals have a higher chance than well-conditioned athletes. Environmental conditions, such as excessive wetness or dryness, hotness, or coldness, can also increase the risk level. Genders are subjected to be a possible risk factor; studies have found that more patients with ER are male than female. Possible causes could be the more significant amount of muscle tissue in a male body or more males taking part in high-intensity exercise than women (Carneiro, Viana-Gomes et al.2021). The most commonly useddiagnostic marker to identify rhabdomyolysis is an increase of CK concentration up to 1,000 U / l or five times the average value. A severe case of rhabdomyolysis is defined as a CK concentration of 10,000 U / l (Gaik C 2020). 1.2.3. Conventional detection Methods of Myoglobin in urineDifferent methods can identify the presence of Mb in urine. Quantitative methods like immunohistochemistry, spectrophotometry, and radioimmunoassay are more available to assess myoglobin in the urine, but they are not commonly required in clinical practice (Anwar MY and V.2023). Immunochemical methods are probably the most sensitive approach. The antibodies used to purify Mb do not react with Hb, which allows the quantitation of Mb levels in urine and serum (David 2000). Radioimmunoassays are sensitive but time-consuming and, therefore, not suitable for emergencies (Powell, Friedlander et al. 1984). The use of spectrophotometry for detecting Mb in urine has several problems. The positive identificationof the met- or ferry-form of Mb mostly present in urine is complicated based on the spectra.The presence of Hb and Mb in the sample can further complicate the identification. Mb or Hb could also lose their characteristic spectrum by being irreversibly denatured. Electrophoresis can be used, but the denaturation of Mb in urine can change the electrophoretic mobility and the apparent molecular size, and the sensitivity is limited. The Immunochemical methods have the disadvantage of complex techniques and minor sensitivity (Kagen 1978). A urine dipstick combined with another method is another way to verify the presence of Mb in urine. Therefore, the urine is tested with a conventional peroxidase-based dipstick, and a positive result leads to the second step differentiation between Mb and Hb. In the ultrafiltration / dipstick method, the urine is centrifugated through a micro concentrator membrane with a 30000-Da cutoff. The cutoff of 30000 Da allows Mb, 16500 Da, to pass through the membrane while stopping Hb, 64000 Da. The Hb-free urine can then be analysed for the presence of haem pigment. The analysis can be done again with a conventional peroxidase-based dipstick. A problem with this method is that in some devices, the membrane partially stops Mb and Hb (Grover, Atta et al.2004). Another study observed that the amount of Mb in 25 urine samples using this technique varied from <1 to 38% (Loun, Copeland et al. 1996). The microscopic urine analysis to check for red blood cells is another method to evaluate if the positive urine dipstick is due to the presence of Hb or Mb (Anwar MY and V. 2023). 1.2.4. Urine dipsticksUrine dipsticks are a cheap and widely used tool for bedside urine analysis. The general set-up of a urine test strip is a carrier material, either plastic or paper. The test pads are mounted on top of the carrier material, consisting of the reagent paper and a nylon net. A urine test strip can either be a single stick, which measures only one specific parameter, or a multi-stick with more than one test pad. Typical parameters that can be measured with a urine test strip are specific gravity, pH value, leukocyte, nitrite, protein, glucose, keratin, urobilin, bilirubin and blood—the urinalysis procedure with a test strip. The test strip and the urine sample come into contact, which starts the reaction. Depending on the parameter, the waiting time until the result is reliable is typically between 60 and 120 seconds. The response of the test pad to the urine sample leads to a colour change in the presence of the parameter of interest. After the appropriate waiting time, the colour of the test pad is compared with the colour scale on the package (Neymeyer 2014). 1.2.5. Test pad for bloodThe test pad of interest for the feasibility study is for detecting blood in urine. The general content of these test pads are an organic hydrogen peroxide, a chromogen (and often a buffer). The detection is based on pseudo peroxidase properties of Hb and Mb. Chromogen is a chemical that appears colourless but can be converted through a reaction to a chemical compound that is ‘’coloured’’. This test pad's most commonly used chromogens are -3.3', 5.5'-Tetramethylbenzidine (TMB) and o-thodolin. Free Hb and Mb in a urine sample catalyse theoxidation of the present chromogen through the organic hydrogen peroxide in the test pad. Depending on the chromogen, the resulting colour can differ, but the oxidation of TMB leads to a blue colour. Conventional dipsticks can detect free haemoglobin and the presence of intact erythrocytes. The presence of intact erythrocytes is shown as dots of colour on the test strip. That is due to the lysing properties of the test paper. The lysed erythrocytes release their containing the Hb onto the paper, which interacts with the paper the same way as the free Hb. Free Hb and Mb are shown on the test strip with an even colour change due to skipping the lysing step (Neymeyer 2014). Conventional test pads for blood estimate the quantities of blood present instead of a pure negative or positive. The quantity is calculated based on the intensity of the colour change. The results are categorised into four groups from no presence of blood, negative, to huge amounts of blood, represented by ‘+++’. The specific ranges of the groups can differ depending on the brand of test strip. The interpretation of the colour compared to the colour scale has the possibility of divergence due to the difference in colour perception of one person to another. To avoid this, companies developed tabletop instruments that analyse the test strips. The interpretation of colour, especially between two categories, can differ from person to person (Analyticon).1.2.6. Peroxidase reaction mechanismThe general reaction mechanism of heme peroxidases consists of three sequential steps. (Equation 1, Equation 2, Equation 3). Equation 1 Enzyme + ^^^^ ^ Compound 1 + ^^^Equation 2 Compound 1 + Substrate Compound 2 + ^^^^^^^^^°Equation 3 Compound 2 + Substrate Enzyme + ^^^^^^^^^° + ^^^At the start of the reaction, the ferric heme of the resting protein is oxidised by hydrogen peroxide, which causes a heterolytic cleavage. The heterolytic cleavage leads to the release of a water molecule and the formation of an intermediate called Compound 1. Compound 1 is 2- electron-equivalents more oxidised than the resting protein. The nature of Compound 1 was studied intensely in spectroscopic studies, which resulted in the establishment that Compound 1 is made of an oxo-ferryl species (Fe (IV)=O) and a delocalised radical on the heme ring, in most cases, to form a porphyrin π-cation and, in some cases, on the side chain of a b amino acid. In some Heme peroxidases, the formation of another intermediate has been implicated in the so-called Compound 0. The presence of a substrate leads to the reduction of Compound 1 by one electron to Compound 2, with the loss of the heme / protein-based radical (Equation 2). The third step is the oxidation of another substrate molecule by Compound 2, which converts the enzyme back into the resting enzyme. To summarise, the reaction mechanism consists of three steps and proceeds through the formation of two intermediate compounds. Compound 1 has a radical and an oxy-ferryl species, while Compound 2 only has an oxo-ferryl species (GUMIERO 2011). 1.2.7. Colour perceptionHuman vision is based on three components that can produce colour perception. Full-colour display devices used in computer graphics work with the same principle by mixing three primary colours to create their full-colour range. The human retina contains three types of colour photoreceptor cone cells. The three cone types in the eye, named L, M, and S, are differentiated mainly by the spectrum of visible light, where the sensitivity is at a maximum. L cones are most sensitive to low-frequency light waves (around 555–565 nm), M cones respond best to middle-frequency light waves (around 530–537 nm), and S cones are most responsive to high-frequency light waves (around 415–430 nm) (Mustafi, Engel et al.2009). When a beam of light mainly consists of short wavelengths, the blue radiation stimulates the cone cells with a maximum of 430 nanometers more than the other two types of cone cells.The activation of only two of the three types of cone cells leads to a limited range of coloursthat can be perceived. The human eye can perceive primary subtractive colours as yellow in two ways. One way is the stimulation of red and green comes through monochromatic yellow light ca.580 nanometres, both respond equally due to the similar absorption spectral overlap. Alternatively, the same yellow colour perception can be achieved by separately stimulating red and green cone cells with a mix of distinct red and green wavelengths. The wavelengths were selected to avoid significant overlap in their absorption spectra (Spring;,; et al.). Conditions or physical abnormalities in the human eye can lead to an impairment of colour perception. The absence of different types of cones leads to different types of colour blindness. The missing red or green cones result in red-green colour blindness, where differentiating from red to green is hard. The total absence of cones is known as trichromatic colour blindness. Partial colour blindness, or dichromatic vision, can occur when only two pigments are present, leading to poor colour discrimination(Gordon 1998). A colour space is a concept in which colour occupies a three-dimensional volume. The primary colours define the RGB cube (Joblove and Greenberg 1978). 1.2.7.1. HSV colour spaceThe HSV colour model stands for ‘’hue’’, ‘’saturation’’ and ‘’value’’, Smith introduced it. Hue stands for the position of the colour in the spectrum, for example, green or red. The term saturation describes the range of vividness from fully saturated to desaturated. The value corresponds to the lightness of the colour. The HSV space is derived by projecting an RGB colour cube onto a plane, where hue and saturation are polar coordinates, and the value is the length of the cube's diagonal (Schwarz, Cowan et al.1987). 1.2.7.2. CIE / LAB colour spaceThe CIELAB or CIE L*a*b system is a three-dimensional colour space with three axes. The ‘L’ axis is greyscale, starting at 0 (black) and ending at 100 (white). The axis of ‘a’ is the red / green axis; negative corresponds to green and positive a corresponds to red. A colour's yellow and blue components are represented on the ‘b’ axis, where positive ‘b’ values correspond to yellow and negative ‘b’ corresponds to blue. The centre of the plane is neutral, and the distance between the inner and outer parts is represented as ‘C’, the saturation of the colour. The hue is represented by the angle on the chromaticity axes (Ly, Dyer et al.2020).2. Method and MaterialThe chapter method and material is divided into several parts. Starting with the method section and the method used for the overall appication, the methods used for the simulation and the methods used for the experiment. The material part is also divided into the material used for the simulation and the material used in the experimental part. 2.1.Method 2.1.1. Model-based Experimental Analysis / MEXA

[0002] Figure 1: Model-based Experimental Analysis (Marquardt 2005)The appication project was done following the model-based experimental analysis or shortMEXA approach. MEXA is based on the tight integration of experimental techniques,mathematical modelling, and model identification. Therefore, prior knowledge is used todevelop a mathematical model of the experiment and the measurement before experimenting, rather than using the previous knowledge just to design a suitable experiment and select appropriate measurement techniques. Mathematical modelling is used to provide insights into the suitability of the experimental set-up. Improvements of the experimental set-up can be done before time-consuming experimental work. It is to take into consideration theinevitable model uncertainty concerning major model assumptions. The sensitivity analysis of the mathematical model is used to improve the understanding of the variations in the model and their impact. Model identification involves selecting the most suitable model structure, including parameter estimation and model structure identification. The initial model may not accurately reflect the natural phenomena; therefore, the model can be adjusted during the experimental phase. The MEXA work process is mainly used for challenging kinetic modelling problems such as reaction kinetics in homogeneous and heterogeneous reactive systems, multi-component diffusion in liquids and mass transfer to a single liquid droplet levitated in a liquid phase (Marquardt 2005). 2.1.2. Set-up of the Simulation The MEXA approach was applied in this study by first creating a model of the reaction taking place in the test pad with Hb and Mb. 2.1.2.1. Reaction KineticsBased on the reaction mechanism shown in equations 1 to 3. The reaction pattern of a mixture of Hb and Mb reacting with hydrogen peroxide and, as the second substrate, a chromogen. Compound 1 is represented by Com 1 and either Hb or Mb in front of it, depending on the ground state. Compound 2 is represented similarly, just shortened to Com 2. The reactions of Hb and Mb are parallel competitive reactions which give the following reaction pattern: Hb + Hydrogen peroxide Hb(Com 1) + water Hb(Com 1) + Chromogen ^ Hb(Com 2) + oxidized ChromogenHb(Com 2) + Chromogen ^ Hb + oxidized Chromogen + waterMb + Hydrogen peroxide Mb(Com 1) + water Mb(Com 1) + Chromogen Mb(Com 2) + oxidized ChromogenMb(Com 2) + Chromogen Mb + oxidized Chromogen + waterBased on the kinetic reaction, a system of ordinary differential equations (ODEs) was created that described the concentration of components involved in the response over time. The set of ODEs is shown in equations 4 to 13. The nomenclature of the ODEs is that the different components are represented by upper key letters, and the reaction constants are represented by a ‘k’ with a number and either a lower key ‘m’ or ‘h’. The number represents the reaction step, so it could have been either a one, a two or a three, while the lower key letter stands for either ‘m’ as Mb or ‘h’ as Hb and shows which pseudo peroxidase is involved in the reaction. The square brackets are used to represent a concentration with the unit mol per L of the upper key letter in the brackets. In the ODEs, the Hb is represented in its starting conformation as ‘A’; as Compound 1, it is represented as ‘D’; and as Compound 2, it is represented as ‘H’. In its starting conformation, Mb is represented as ‘C’, and as Compound 1, it is represented as ‘E’, and Compound 2 as ‘I’. The organic hydrogen peroxide in the ODEs is represented as ‘B’ and water as ‘F’. The chromogen is represented by ‘G’, and the oxidised chromogen is represented by ‘J’. 2.1.2.2. EstimationA model is a simplified version of reality and includes assumptions and estimations. The assumptions made in this model are shown below. The kinetic constants, denoted as ^^^and ^^^, represent the constants for the reaction of Hb / Mb with hydrogen peroxide, acting as proxies for the kinetic constants in reactions with organic hydrogen peroxide(González-Sánchez, García-Carmona et al.2011), (David A. Moffet 2000). The reaction constants, denoted as ^^^and ^^^, for the reaction between the first intermediate Compound 1 of Mb and Hb and the chromogen could not be found during the literature search. But the research suggested that the oxo-ferryl state, with a radical equalling to Compound 1, is a short-lived protein radical, which decays fast through the electron transfer to a more able oxo-ferryl Mb (Ostdal, Skibsted et al.1997). Similarly, the globin radical of the Compound 1 state of Hb is highly unstable and tends to swiftly decay to a more enduring form of oxo-ferrylHb—radical-free. The oxo-ferryl Hb with a protein radical is unstable with a half-life of 50 seconds. This radical-free form is analogous to the peroxidase Compound2 (Gebicka and Banasiak 2009). The reaction constants for the third step, the reaction of Compound 2 with the substrate, could also be found. However, the constants ^^^specific to Mb are derived from its reaction with hydrogen peroxide and ABTS, where ABTS is a chromogen distinct from the TMB used inthis experiment ^^^ is an estimation reflecting the typical magnitude of the reaction in step3 (Gebicka and Banasiak 2009).2.1.2.2.1. The steady-state approximationBased on the short-lived nature of Compound 1 of Hb and Mb, the steady-state approximation was used to simplify the set of ODEs. The new set of ODEs follows the same nomenclature as the first set. 2.1.2.3. Sensitivity analysisA sensitivity analysis assessed the sensitivity of ordinary differential equations (ODEs). Thestudy involved calculating the partial derivatives of each ODE concerning various components,such as A, to determine which part of the ODE had the most significant impact on the respective concentration over time. The sensitivity analysis results indicated key influencers within the ODEs related to Hb. In the initial state, hydrogen peroxide and its corresponding reaction constant demonstrated the highest influence on Hb concentration over time. Similarly, for Hb Compound 2, theunoxidised chromogen and its reactive constant played a crucial role. This pattern wasmirrored in the case of Mb and Mb Compound 2.Regarding the unoxidised chromogen, the ODEs revealed that the most influentialcomponents were either Hb Compound 2, Mb Compound 2, or a combination of both, along with their respective reaction constants. Importantly, this aligns with the expected behaviour. 2.1.2.4. ConcentrationThe starting concentrations of the two substrates were based on the concentration found in the dip strip from Medi-Test Combi 10® from in-virto-Diagnostikum. The concentration of Hb and Mb in the sample mixture is based on the testing limits of the CombiScreen®test strips. The insert of the Medi-Test Combi 10® states that 5 to 10 erythrocytes / µl equal around 0.015mg / dL of Hb or respectively Mb (In-vitro-Diagnostikum). For the calculation, it was assumed that 10 erythrocytes / µl is equivalent to 0.015 mg / dl. Based on that, around 50 erythrocytes / µl would be approximately 0.075 mg / dL, and for 300 Erythrocytes / µl would be about 4.5 mg / dL. The concentration converted into g / L gave the new concentration of 0.00015 g / L, 0.00075 g / L and 0.0045 g / L. The concentration needed for the simulation is molar. So, based on the molar weight of Hb and Mb, the unit was converted. The molar weightused for Hb was 64500 Da, and for Mb, it was 17500 Da. The reactive components of pseudoperoxidase reaction are the heme groups of Hb and Mb and not the whole molecule. Hb consists of four heme groups, and Mb only consists of one heme group. To consider that, the initial molar concentration of Hb represented by the letter A is multiplied by four. ^^^^ ^^ ^ ^^^^^^^^^ Equation 22: molar mass=^^^^^^ ^^ ^^^^^ ^^ ^ ^^^^^^^^^= M = ^ ^^^^ ^^ ^ ^^^^^^^^^ Equation 23 concentration = ^^^^^^ = c = ^ The chromogen and organic hydrogen concentration found in the medi-test Combi 10 test strips was 59 µg / cm^of Tetramethylbenzidine and 253 µg / cm^of cumolhydrogenperxide. The test pad has a size of 0.5 cm*0.5 cm*ca*0.1cm. Based on the size of the test field, the amount of chromogen and organic hydrogen present in the test pad was estimated to be 0.1475 µg of chromogen and 6.325 µg of organic peroxide. The molecular weight of tetramethylbenzidine is 240.34 g / mol (Information), and that of cumolhydrogenperxide is 152.19 g / mol (Information), together with the equation. In the simulation, the organic peroxide is replaced with a hydrogen peroxide with the same molecular concentration as calculated for the organic peroxide. 2.1.2.5. ParameterThe set parameters used to do the simulation framework are the kinetic constants and the concentration of the components of the test pad at the beginning of the reaction. The concentration of the pseudo peroxidases Mb and Hb were variables that were changed repeatedly to study the impact. The following kinetic constants were used: Table 1: reaction constants used in the model. Reaction constant Unit Reference ^^^ 300 (González-Sánchez, García-Carmona et al.2011) ^^^ 540 (David A. Moffet 2000) ^^^ 100 (Gebicka and Banasiak 2009) ^^^ 0.000088 (David A. Moffet 2000) The following starting concentrations were used:FARAGO P11610DE.PROV17 Table 2: initial concentration used in the model. Component Concentration Unit ReferenceA Varies mol / LB 9.297 e-6 mol / L (In-vitro-Diagnostikum)C Varies mol / LD 0 mol / LE 0 mol / LF 0 mol / LG 3.068 e-7 mol / L (In-vitro-Diagnostikum)H 0 mol / LI 0 mol / LJ 0 mol / L2.1.2.6. ImplementationThe simulation was based on the ODEs mentioned above and used to portray the concentration change of the involved components over time. The ODEs were transferred into the programming language ‘Python,’ and the command ‘odeint’ was employed to solve these ODEs. The concentration change was visualised by plotting the concentration of components involved based on the ODEs over time. This plot was used to investigate the plausibility of each component's concentration change over time. This investigation can be found in 2.1.2.7. A secondary plot was added that showed the involved component's concentration change over time as curves of relative concentration.FARAGO P11610DE.PROV18 The colour change in the test pad is based on the oxidised chromogen; therefore, the change in concentration of the oxidised chromogen was of interest, and the code was changed to exclude the visual representation of the change in concentration of all components except the oxidised chromogen. The option of having more than one set of starting concentrations visualised was added to make possible the comparison of the behaviour based on different starting concentrations of Hb and Mb. The time frame to investigate the behaviour and the influence of Hb and Mb was from 0 to 60 seconds. This time frame was based on the time frame for evaluating the dip strips. An integration of the area under the generated curves that had time limits equal to the ones used in the visualisation was added. The last step was the correct labelling of the axis, the title of the visualisation, and the inclusion of a legend in the outer parameter of the plot so that no overlap would hide the behaviour of the curves. 2.1.2.7. Checking plausibilityThe plot shows the curves of all concentrations of the components over time for a random mixture of Hb and Mb. To check the plausibility of the simulation. The simulation of all ODEs was done to compare the behaviour of the curves over timewith the expected behaviour of the reaction. This is done to ensure no mistakes were madein creating the ODEs and implementing them. The expected behaviour of the Hb is a decrease of the Hb in the ground state over time and an increase of Compound 2 in a reverse mirror of the decrease of Hb, while the concentration of Compound 1 should stay zero. The behaviour expected of the Mb in the different states is the same as the one of Hb. The expected behaviour of the hydrogen peroxide is a steady decrease while the water concentration should increase similarly. The concentration of the chromogen is expected to decrease, while the concentration of the oxidised chromogen should increase.FARAGO P11610DE.PROV19 The hydrogen peroxide has a higher concentration than the rest, so a second plot with all components except hydrogen was needed to study the behaviour of the curves. The behaviour of the concentration curves created by the simulation shows the behaviour as expected. The decreased slope and, accordingly, the increased slope of the Mb ground state and Mb compound 1 are sharper than the respective curves of Hb. The increase in water concentration is equal to the increase in the concentration of the oxidised chromogen. 2.1.2.8. MeasurementsThe simulation was first run with varying starting concentrations of Mb and Hb in the possible measurement range of the dipstick. The resulting insights or patterns were investigated by trying to find a pattern. The final measurements were conducted the following way, the starting concentration was divided into the three categories of the urine dipstick. The set of starting concentrations consists of pure Hb and Mb samples to study if pure samples could be distinguished. A sample where the mixture is equal parts Mb and Hb and two samples of a mixture where either Hb is higher than Mb or the other way around.FARAGO P11610DE.PROV20 2.1.2.9. Relative concentrationTo evaluate how fast the change of chromogen to oxidised chromogen was. Where allcomponents are divided by the highest concentration present, in this case, Hydrogen peroxide, to get the relative concentration. This was used to plot the change in concentrationsover time. The change of concentration of interest were the chromogen and the oxidisedchromogen concentrations. 2.1.3. ExperimentA series of preliminary experiments were conducted to ensure an optimal setup in preparation for the main experiment. 2.1.3.1. Preparation Experiments2.1.3.1.1. Primary pre-experimentThe primary objective of the initial experiment was to evaluate the behaviour of Hb and Mb in distilled water, focusing mainly on comparing the overall reaction trend with a simulated scenario. The experiment involved preparing various solutions with different Hb and Mb concentrations, emphasising identifying general trends rather than specific concentrations. 1. Concentration Preparation: Concentrated solutions of Hb and Mb (designated as Hb1and Mb1) were formulated by transferring a small spatula tip of powder into glass beakers and dissolving them in distilled water, resulting in solutions of 200ml each. 2. Utilizer Analysis: CombiScreen®dip strips were coated with Hb1 and Mb1 solutionsand subjected to analysis using the Urilyzer® 100 Pro. Results were recorded, and visual evaluations were conducted to assess the reactions. 3. Dilution Process: Solutions Hb2 and Mb2, representing less concentrated solutions,were created by diluting Hb1 and Mb1.1 ml of each solution was transferred to new beakers and filled with distilled water to achieve a total volume of 100 ml.FARAGO P11610DE.PROV21 4. Utilizer Analysis (Diluted Solutions): Dip strips were coated with Hb2 and Mb2solutions, followed by analysis using the Urilyzer® 100 Pro. Results were recorded, and visual evaluations were conducted to observe any variations in the reaction dynamics. 5. Intermediate Concentration Solutions: To obtain solutions with signal intensitiesbetween H / Mb1 and H / Mb2, 2ml of Hb1 and Mb1 were transferred to new beakers and diluted with distilled water to reach a total volume of 100ml. 6. Utilizer Analysis (Intermediate Solutions): Dip strips were coated with Hb3 and Mb3solutions, and the Urilyzer® 100 Pro was employed for analysis. Results were documented, and visual evaluations were conducted to capture nuanced changes in the reaction. 7. Mixture Preparation: Two mixed solutions of Hb and Mb (Mix1 and Mix2) wereprepared. Mix1 combined 0.5ml of Hb1 and 0.5ml of Mb1 with distilled water up to 100ml. Mix2 was created by mixing 1ml of Hb1 and 1ml of Mb1 in a new beaker and filling it with distilled water up to 100ml. Additionally, a negative control of pure distilled water was prepared. 8. Utilizer Analysis (Mixed Solutions): Dip strips were coated with Mix1, Mix2, and thenegative control solutions, followed by analysis using the Urilyzer® 100 Pro. Results were recorded, and visual evaluations were conducted to assess any distinctive features in the mixed solutions. The results showed that the intensity of the colour change increased with the concentration of the pseudo-peroxidase active component in the mixture. The speed of the reaction also increased, even though the human eye could not find a difference in the speed or intensity between the mixtures of pure Mb and Hb. 2.1.3.1.2. Secondary pre-experimentFARAGO P11610DE.PROV22 The second preparational experiment aimed to investigate the influence of artificial urine on the reaction with Hb and Mb and the urine dipsticks compared to distilled water-based ones. 1. Artificial urine (UA): The UA was prepared based on the following recipe (Khan, Readet al.2017): Material needed Used unit1 1 LDistilled water Up to 1 L Di-sodium hydrogen phosphate 1,14 1.142 gSodium chloride 8 8.02 gPotassium chloride 0,2 0.19 gPotassium dihydrogen phosphate 0,2 0.2 gQuinoline yellow 0,015 0.017 g2. UA-based solutions: HB1 and Mb1 from the first preparational experiment were usedto prepare the UA-based solution. For UHb2 and UHb3, 1ml or 2ml of Hb1 were transferred in new beakers, respectively. For UMb2 and UMb3, 1ml or, respectively, 2ml of Mb1 were transferred in new beakers. All beakers were filled with UA up to 100ml. For UMix2, 1ml of Hb1 and 1ml of Mb1 were mixed in a new beaker and filled with UA up to 100ml; for UMix1, 50 ml of UMix2 were mixed in a new beaker with UA up to 100ml. For UHb1 and UMb1, a small spatula tip of powder is poured into glass beakers and dissolved in distilled water, resulting in solutions of 200ml each.FARAGO P11610DE.PROV23 3. Water-based solutions: The solution Hb1, Hb2, Hb3, Mb1, Mb2 and Mb3 were usedfrom the first preparational experiment. For Mix21ml of Hb1 and 1ml of Mb1 were mixed with distilled water up to 100ml. For Mix150ml of Mix2 were transferred into a new beaker and mixed with distilled water up to 100ml. 4. Negative control: The negative control of the UA-based solution was 100ml of pureUA labelled UNeg. For the distilled water-based solution, 100ml of distilled water marked neg was used. 5. Measurement: Each solution was used to coat a urine dipstick, which was analysedwith the Urilyzer® 100 Pro and evaluated by the human eye. All results were noted down. The findings from this experiment indicate that there is no discernible variation in the reactivity of the test strip with solutions, whether based on uric acid (UA) or distilled water. Consequently, the inclusion of UA in the primary experiment is deemed unnecessary, and the use of distilled water alone is deemed satisfactory. 2.1.3.1.3. Tertiary pre-experimentThe third preparational experiment aimed to study the effect of the storage time of the solutions. Therefore, solutions that give the signals ‘+’, ‘++’, and ‘+++’ for Hb-based and Mb- based are needed.^ Stock solution: A stock solution of Hb and Mb, each having a concentration of 0.013 g / l,was prepared by mixing 100ml of distilled water with 0.0013 g of Hb or Mb, respectively.^ Solutions: By diluting the stock solution of Hb, the Hb+ and Hb++ solutions withconcentrations of 0.00026 and 0.00039 g / l were prepared, and the undiluted solution is used as Hb+++. The dilution of the Mb stock solution is used to prepare Mb+ with a concentration of 0.00026 g / L and Mb++ with a concentration of 0.000312 g / L and Mb+++ as the undiluted stock solution.FARAGO P11610DE.PROV241. Measurements: Each solution was used to coat a urine dipstick, which was analysed withthe Urilyzer® 100 Pro and evaluated by the human eye. The results were noted down. Each measurement was done in the following time steps directly after mixing, after 1 hour, 2 hours, 3 hours, 4 hours, and the next day. There was no observed loss of reactivity when the solutions were stored in the refrigerator throughout this experiment. Consequently, it is established that the solutions for the primary experiments can be conveniently prepared in advance, eliminating the necessity for immediate measurement following the mixing process. 2.1.3.1.4. Quadri pre-experimentThe fourth experiment aimed to optimise the setup for the main experiment. By testing different angles, light settings, and set-up plates. 1. Initial set-up test: The first try of the set-up was a metal rod of 47 cm with the set-upplate version 1 in the middle of the lightbox. On top of the lightbox was a cardboard lid with a hole in the shape of the smartphone's camera. The light setting was on the maximum lighting settings on both sides. Access to the dip strip was gained through one of the sides of the lightbox. 2. Measurements: To test the set-up, different dip-strips were tested and recorded inthe set-up with solutions from the third experiment. 3. Optimizing: Different positions of the cardboard, metal rod, and lighting settings weretried, and what was going well and where problems were found were evaluated. 4. Second set-up test: The second set-up differed from the initial by using the optimisedand marked positions of the metal rod and the camera. And the second version of the set-up plate with the improvements. 5. The measurements and optimising steps were repeated.FARAGO P11610DE.PROV25 6. Third set-up test: The third set-up used the optimised version of the cardboard andthe third version of the set-up plate. 7. Marking: The best working position, settings and so on were written down andmarked to ensure they could be found again. Set-up plate version 1 the fix points for the cover. The cover was a rectangular, 3D-printed part with 11 holes that are equivalent to the 11 test pads on the dipstick that can be placed on top and covered with parts of the dipstick. That increased thecontrast between the coloured test pads and the surroundings. The cover and the plate arenot permanently connected and can be separated to ensure that cleaning any remaining solution is easy and reduces the risk of contamination. The paper tag to identify the solutions can be placed on the plate's upper part, as seen in Figure 3 A. On the right side of the plate was space left to glue the reference scale provided on the package of the CombiScreen® dipsticks. On the other side of the set-up plate is a mechanism consisting of four small rods that interlock with the metal rod used to shorten the distance between the camera and the set-up plate. The metal rod has a height of 47 cm. Set-up plate version 2 The second version of the set-up plate was newly printed to improve the quality of the experimental set-up. The size of the set-up plate stayed the same, except the depth of theFARAGO P11610DE.PROV26 cavity for the dipstick decreased. The most significant change was made on the left side of the dipstick cavity; a funnel-shaped channel was created next to the position of the fived testpad, the test pad of interest. This channel was added to improve the transfer of the samplesto the test pad. The cover was also first enhanced by including the channel mirroring the channel on the plate. The thickness of the cover was decreased to reduce the shadowing in the holes of the cover for the test pad. By testing the set-up plate version 2, the need for further improvements was found. Firstly, the channel that was added did not allow an even coating of the test pad, resulting in a colour change in only parts of the test pad, for example, the right corner. The visibility of the colour change was challenging for low concentrated solution to increase the visibility, the concentration of the total reactive was increased from 0.00015 to 0.0002 g / L. The problems found were used to create set up plate version three, with a funnel added on top of the cover over the test pad of interest. 2.1.3.2. Main ExperimentIn line with the MEXA approach, the knowledge gained from the simulation is used to design the experimental approach. The experimental approach is described in the following section, starting with the preparation of the samples, followed by the experimental process, the editing of the recorded video, and the analysis of the videos. 2.1.3.2.1. Preparation of the samplesThe samples were created by creating a highly concentrated stock solution of Hb and Mb to decrease the uncertainty of measuring each sample's Hb and Mb powder weight, reducing the needed resources. The stock solution was made by mixing distilled water with the powder. For the Hb stock solution, 0.0051 grams were mixed with 10 ml of distilled water, which created a stock solution with a concentration of 0.5 g / l of Hb. The Mb stock solution was done similarly by mixing 10 ml of distilled water and 0.0050 grams of Mb to create a solution with a concentration of 0.5 g / l of Mb.FARAGO P11610DE.PROV27 The needed dilutions were pre-calculated in an Excel sheet to ensure an accurate amount of stock solution was used for each solution. Each solution was labelled methodically with the letter of the set and a number. The amounts of stock solution and distilled water used, and the label can be found in Table 3. Table 3: Solutions prepared and their labelling. Name Signal Total reactiveCon. Hb Con Mb Stock Hb Stock Mb Distilled water [g / l] [g / l] [g / l] [µl] [µl] [ml] A1 + 0.0002 0.0002 0 40 0 99.96A2 + 0.0002 0 0.0002 0 40 99.96A3 + 0.0002 0.0001 0.0001 20 20 99.96A4 + 0.0002 0.00015 0.00005 30 10 99.96A5 + 0.0002 0.00005 0.00015 10 30 99.96B1 ++ 0.00075 0.00075 0 150 0 99.85B2 ++ 0.00075 0 0.00075 0 150 99.85B3 ++ 0.00075 0.000375 0.000375 75 75 99.85B4 ++ 0.00075 0.0005 0.00025 100 50 99.85B5 ++ 0.00075 0.00025 0.0005 50 100 99.85C1 +++ 0.0045 0.0045 0 900 0 99.1C2 +++ 0.0045 0 0.0045 0 900 99.1C3 +++ 0.0045 0.00225 0.00225 450 450 99.1C4 +++ 0.0045 0.003 0.0015 600 300 99.1FARAGO P11610DE.PROV28 C5 +++ 0.0045 0.0015 0.003 300 600 99.1Neg. control Neg. 0 0 0 0 0 100S1 Similar curve 0.002 0.00188 0.00013 376 26 99.59S2 Similar curve 0.00150 0.00050 0.00100 100 200 99.70S5 Similar curve 0.00075 0.00070 0.00005 141 9 99.85S6 Similar curve 0.00062 0.00044 0.00018 88 37 99.762.1.3.3. ProcedureThe measurements were taken with two different tools. First, use the Urilyzer® 100 Pro to analyse the test strips coated with the different solutionsto ensure the reaction's intensity is as expected. Therefore, a drop of solution was transferredfrom the bottle to the test strip, which was coated. Then, the test strip was placed into the stray of the Urilyzer® 100 Pro, and the analysis process was started by pressing the start button. The test strip was then automatically analysed after 60 seconds, and the result was noted—the measuring principle of the Urilyzer® 100 Pro based on the relative reflectance value. The analyser starts by reading the reference pad, followed by the test pads on the dipstick. The LEDs in the optical unit emit light at various wavelengths onto the surface of the test pad; this light is reflected more or less intensely depending on the colour change. The reflected light is detected and converted into digital form and relative reflectance values. The reluctant values were then compared with the defined range limits. The measurements were done with the set-up plate. The measurements with the set-up plate were done by removing the solution needed from the fridge. The label of the solution, with the type of solution and the number of samples, is placed on the set-up plate. The 100µl pipette was set to 50µl. The test strip wasFARAGO P11610DE.PROV29 placed on setup plate version 3 and put under the cover so that all test pads were visible. The video recording app was set to the following settings: ^Frame Interval: 0.5 seconds^ Video Dura^on: 7 seconds^ Recording ^me: 1 min 45 seconds^ Speed: 15xThe phone was then placed in the marked posi^on on top of the lightbox, and therecording started. With the pipe^e, 50µl of the solution was transferred to the test pad for blood. After the recording, the video was checked, and the setup plate was cleaned. This process was repeated with each solution three times to minimise outside effects that could influence the reaction. 2.1.3.4. Video editingThe video recording and editing process included several steps to improve the test fields' visibility. Therefore, the videos were rotated so that the reference scale is straight; in mostcases, the rotation is around 90 degrees. The videos were also zoomed in so that the test padof interest, the test pad for blood, and the reference scale for that test pad were in focus. The videos were also shortened; the part of the video from the beginning to the moment ofcontact between the solution and the test pad was carefully removed. This editing wasintended to ensure that the visual analysis focused on the critical phase of interaction between the solution and the test field. For the analysis of the videos, the videos were transformed from videos to a series of frames. This also allowed for removing frames if necessary to ensure that the first frame is the point of contact between the solution and the test pad. 2.1.3.5. AnalysingFARAGO P11610DE.PROV30 The video analysis code was developed in Python by the company Medipee. The code is tailored for the analysis of the video data, extracting the relevant information, and correlatingit with the concentration values. The code extracts the frames from the video file first in thecolour format RGB and is then converted into the HSV colour format. The provided HSV values of the picture are used to discriminate between the five areas of interest and the surroundings. The five most significant areas are selected, and a binary image representing the reaction fields based on the specific colour range in HSV is created. Based on the knowledge of the position of the field in the images, the differentiation between the different fields is based on the position on the x-axis. Then, the mean values of the reaction field, like the position, area, and colour statistics, are calculated. The mean values of the reaction field calculated in a range are visualised in a plot. The code gives an output of five plots. The plot shows the discrimination between the different areas of interest and the surroundings; this facilitates the evaluation of the precision of the discrimination. Based on the hue values of the test field, two plots were generated for the visualisation of the estimated concentration of the solution by correlating the concentration in ery / µl and signal. Using an exponential fit, a curve is created that marks the mean end value of hue in the reference field and the test field. The test field value is shown as a red dot. The other plot based on the hue value is a plot of the concentration in ery / µl over time, where theconcentration at 60 seconds is marked with a red dot.The calculated hue values of the solutions were used to calculate the mean hue values of each solution to reduce the influence of the surroundings. This means that hue values of thesamples were visualized in the measurement sets A, B, C and S. The use of ‘raw’ data isalways preferable, especially due to the uncertainty in the calculation of the concentration.The resulting plots are shown in Figure 10 for the solution sets ‘A’,’B’ and ‘C’. Figure 11 showsthe results for the similar curves. It was done to evaluate if the hue value is used fully for the discrimination between Hb and Mb. The device-specific noise adds a lot of uncertainty in the case that the measurements were repeated with a different device. To reduce this factor and make the results more applicableFARAGO P11610DE.PROV31 to measurements done with different devices. Therefore, the tripoidal rule is used to integrate the area under the curve from 0 to 30 seconds, area 1, and the area under the curve from 30 to 60 seconds, area 2. These calculations were done in Python with the command np. traps and a dx value of 0.5 because the measurements were done in a 0.5-second time step. The ratios calculated were used to find a relationship between the concentration of grams per Liter. The concentration of the total reactive and the corresponding ratios of the area under the curve were used to create a plot. The ratios corresponding to the reference concentrations of pure Mb were related to a fit curve; the same was done for pure Hb. The other ratios were plotted, too. For the discrimination between pure Hb and samples with Mb of an unknown concentration, the relation between concentration and the ratio of the area under the curveis not functional. Therefore, was a plot created that visualizes the relation between the meanhue value at 60 seconds of the samples and the ratio of the area under the curve. 2.2.Material The material section is divided into two parts. The first is about the tools used for the creation of the model. These tools are commands and libraries of the programming language Python. The second part describes the equipment and the material used in the experimental work. 2.2.1. SimulationThe programming language used for developing the forward model was Python due to its easy use, and the code was implemented in JupyterNotebook, an open-source web application designed for creating and sharing documents enriched with code. Originating from the Jupyter project, JupyterNotebook was installed via the Anaconda platform. The libraries used in Python for the simulation were numpy and scipy.integrate, matplotlib.pyplot and scipy.special. These libraries are essential tools for scientific calculations and data analysis in Python.FARAGO P11610DE.PROV32 NumPy provides the basis for the numerical operations. Scipy extends the capabilities withadditional functions. Scipy.integrate is a module in Scipy used for numerical integration andsolving ordinary differential equations. Scipy.special is a SciPy module that providesmathematical functions that are not part of the standard Python math library. The matplotlib is used for the visualisation in a MATLAB-like way. 2.2.2. Experiment2.2.2.1. Set-upFigure 4: Set-up of the main experiment. The overall experimental set-up of the main experiment was in the lightbox. In the lightbox was a metal rod with a height of 47 cm placed, and a white backdrop was set on the bottom of the lightbox. The set-up plate version 3 was placed on top of the metal rod. The top of the lightbox was partially covered with a cardboard piece glued with tape on the lightbox. In the cardboard was a triangular hole in the shape of the smartphone camera, and with a pen, the outline of the smartphone was drawn to show its location. Inside the lightbox were two lights, which were both set to maximum brightness. The urine dipstick was placed in its designated space on the set-up plate, and the uncovered space at the top of the lightbox was used to access the test pad with the pipette. The smartphone was placed on top of the lightbox in its designated outline so the camera could see the set-up plate clearly.FARAGO P11610DE.PROV33 Set-up plate version 3 reference scale for the test pads. The colour column next to the funnel was the one of interest. 2.2.2.2. Equipment2.2.2.2.1. LightboxThe light box is big with integrated lighting, which can change intensity. The inside of the light box is a reflective silver material. On the top of the lightbox is an opening, and on theside is another opening with a zipper. The lightbox has a height of 64 cm, a length of 64 cm,and a width of 64 cm. The lightbox used is from the company ‘Yorbay’. 2.2.2.2.2. Urilyzer® 100 ProThe Urilyzer® 100 Pro stands out as a semi-automated urine test strip analyser developed by Analyticon. Specifically designed to work seamlessly with CombiScreen® 5SYS PLUS, CombiScreen® 7SYS PLUS, CombiScreen® 11SYS PLUS, CombiScreen® 11SYS, and CombiScreen® mALB / CREA, this device streamlines the analysis of urine samples. This analyser allows the examining of one test strip at a time. The process involves placing the test strip onto a movable tray coated with the urine sample. After a waiting period of 60 seconds, the Urilyzer® 100 Pro analyses the test strip. The results are presented in a clear and accessible manner, expressed through a signal range from '+' to '+++'. Furthermore, the device estimates the quantity of the specific chemical being measured. This dual reporting system enhances the interpretability of the results for healthcare professionals and users alike. The Urilyzer® 100 Pro's user interface displays the results of each test pad in the sequence correspondingFARAGO P11610DE.PROV34 to their arrangement on the test strip. This ensures a systematic and straightforward interpretation of the data (Analyticon). 2.2.2.2.3. ScaleThe scale used in the experiment is a VMR® LA Classic analysis scale. The measurementprecision rang at 0.0001 g.2.2.2.2.4. Dip stripsThe experiment utilised CombiScreen® plus 11 dipsticks by Analyticon. These strips are equipped with test pads designed to assess various parameters, including Glucose, Ascorbic acid, Ketones, Protein, pH value, Blood, Nitrite, Leucocytes, Specific Gravity, Bilirubin, and Urobilinogen. Specifically, the test pad designed for blood measurement contains tetramethylbenzidine-dihydrochloride at a concentration of 2.0%. 2.2.2.2.5. SmartphoneThe smartphone used in this experiment was a ‘Fairphone 4’, on it installed was the Foto app ‘framelaps’. This app has a function to set a timer, which stops video filming after a set time. And the frames per second can be set up. 2.2.2.2.6. Video EditorThe videos were edited with the open-source video editor OpenShot Video Editor. The Video editor was created in 2008 and is available on Linux, Mac and Windows. 2.2.2.2.7. Haemoglobin and Myoglobin samplesBoth chemical substances utilised in the experiment were procured from the reputableSigma-Aldrich company. Hb was obtained by extracting it from bovine blood, while Mb employed in the experiment was sourced as Mb from equine skeletal muscle lyophilized powder. 2.2.2.2.8. Negative ControlFARAGO P11610DE.PROV35 The negative control was the distilled water without any addition. The label of the negative control was neg. This solution was needed to ensure no contamination in the distilled water, causing a reaction in the test strip. 2.2.2.2.9. Tools used in the Analysing code.The analysing code written in Python used various tools provided by Python, including NumPy, Matplotlib, SciPy, OS, Time, OpenCV, JSON, Pickle and Lzma. The combination of these libraries in the Python code provides the ability to analyse video frames quickly, distinguishes between regions of interest, calculates the mean values of different colour models, and allows data analysis to be visualised and used to estimate concentration. The use of libraries simplifies the code and the interaction between the operating system and the user. OpenCV is a vision library whose main functions involve image and / or video processing tasks. JSON is a standard format for the data exchange between Python and othercomponents, it can be used either for the storage or for the transfer of data. Using the Pickelcomplex, data structures can be saved efficiently or used for loading the data. The library Lzma is used to compress or decompress data, decreasing storage requirements on the computer. The model OS enables the interaction with the operating system; the time-related functions used in the code were provided from the module Time.3. ResultsThe result section is divided into two parts: first, the results of the simulation and its insights for the experimental design, and the second part is the experiment results. 3.1.Simulation 3.1.1. Plots of the oxidised chromogen concentrationThe simulation results are visualised through python-generated plots of the change of the oxidised chromogen concentration over a timeframe of 60 seconds. The concentration testedFARAGO P11610DE.PROV36 was divided into three sets; each set had five different mixed solutions that, in total, had the same amount of total reactive. The first set is called ‘A’ and has a total reactive concentrationof 0.00015 g / L with an expected analysing result of ‘+’ with the Urilyzer® 100 Pro 100 Pro.The second set of curves labelled as ‘B’ had a total reactive concentration of 0.00075 g / L and an expected result of ‘++’. The third set of curves labelled as ‘C’ had the desired outcome of ‘+++’ and a total reactive concentration of 0.0045 g / L. Each set of concentrations consists of five solutions, indicated by a number. The solutions with pure Hb were labelled as one; two signified the solution of pure Mb. The three labelled solutions have equal amounts of Hb and Mb. Numbers four and five characterise curves where Hb and Mb are present, with four indicating a higher Hb concentration than Mb and five indicating the reverse. This detailed nomenclature distinguishes and categorises the Figure 6: expected oxidized chromogen concentration over time. simulation results based on their respective concentrations and compositions. 3.1.2. The relative concentrationFARAGO P11610DE.PROV37 The plot in Figure 6 shows the relative concentration of the chromogen and the oxidised chromogen over a 10-second interval. The y-axis of the plots is the molar concentration in mol per Liter, and the x-axis is the time in seconds. The chromogen is represented in three plots as cyan and the oxidised chromogen as green. The intersection of the curves marks the point where 50 percent of the chromogen is oxidised. Plot A represents the curves with the initial concentration matching ‘A3’. Similarly, plot B is based on the starting concentration reaching ‘B3’, and plot C uses the starting concentration matching ‘C3’. Figure 7: Chromogen and oxidized chromogen concentration over time 3.1.3. Similar curvesDuring the model's try-out phase, curves with different initial concentrations of Hb and Mb but a similar or identical curve of the change of concentration of the oxidised chromogen were found. These curves were labelled ‘S’; the starting concentration of each curve of Hb and Mb in grams per Liter and in mol per Liter and the total reactive in grams per Liter arepresented in Table 4. The two curves that build a pair are shown in the table right after eachother; for example, S1 and S2 are a pair. Table 4: Similar curves found and the initial concentration of Hb and Mb Total reactive [g / L] Hb [g / L] Mb [g / L] Hb [mol / L] Mb [mol / L]FARAGO P11610DE.PROV38 S1 0.00202 1.88E-03 1.31E-04 1.17E-07 7.42 E-09S2 0.00150 5.00E-04 9.98E-04 3.10E-08 5.7E-08S3 0.00293 2.74E-03 1.93E-04 1.70E-07 1.1E-08S4 0.00249 1.66E-03 8.26E-04 1.03E-07 4.72E-08S5 0.00075 7.03E-04 4.69E-05 4.36E-08 2.68E-09S6 0.00062 4.40E-04 1.84E-04 2.73E-08 1.05E-08S7 0.00249 2.34E-03 1.56E-04 1.45E-07 8.92E-09S8 0.00200 1.00E-03 9.98E-04 6.2E-08 5.7E-08S9 0.00163 1.09E-03 5.43E-04 6.74E-08 3.1E-08S10 0.00150 7.50E-04 7.49E-04 4.65E-08 4.28E-08S11 0.00300 2.00E-03 9.99E-04 1.24E-07 5.71E-08S12 0.00280 1.40E-03 1.40E-03 8.68E-08 7.99E-08The plot with the corresponding concentration curves. The Y-axis is the concentration given; the X-axis is the time in seconds from 0 to 60 seconds.

[0003] FARAGO P11610DE.PROV39 Figure 7: Similar curves of oxidized chromogen concentration. 3.1.4. Insights for the ExperimentBased on the knowledge gained from the simulation following, essential insights were taken for the experimental design. The experiment must be designed so that the first 10 seconds of the reaction are recorded, due to the fact that at least half of the chromogen is oxidised in the first 10 seconds of the reaction. The simulation suggests that over time, the oxidised chromogen concentration curve can be used to distinguish between Mb and Hb and a mixture of both, which is expected from some curves found where the same curves were based on different startingconcentrations. The existence of these curves can be evaluated in the experimental approachto assess possible limitations. 3.2.Experiment 3.2.1. Optical evaluationThe optical evaluation of the colour change was done by the human eye and the reference scale of the package (Figure 9A). Showed that concentrations that were evaluated with theFARAGO P11610DE.PROV40 Urilyzer® 100 Pro and gave the result ‘+++’ did not correspond with the reference colour ‘+++’ on the package. The colour was perceived to be darker than the reference colour for ‘++’ andlighter than the colour of the ‘+++’ reference colour. An example of such a colour is seen inFigure 9B. During the optical evaluation, the colour of the reference colour ‘+++’ was never found. Figure 8: Comparison of colour seen and reference scale. 3.2.2. Colour change in the test padThe evaluation of the images of the test field after 60 seconds showed that the colour changeon the test pad is often not even and can be spotty. Especially in the set ‘A’ solutions, the colour changes from yellow to light green, uneven and irregular. But almost all solutions resulted in a colour change that was not even. The least uneven is the test pad, resulting in highly concentrated solutions. Examples of these spooty colour changes can be seen in Figure 9. FARAGO P11610DE.PROV41 3.2.3. Estimation of the concentration based on the hue value.Table 5 shows the estimated concentrations for the different solution, their mean and the result of the analysing with the Urilyzer® 100 Pro. The estimation are based on the hue values at 60 seconds and the hue value of the reference scale colours. Table 5: estimated ery / µl concentration based on the h value and results of the Urilyzer® 100 Pro. Urilyzer® Try 1 Try 2 Try3 mean100 Pro Con. [ery / µl] Con. [ery / µl] Con. [ery / µl] Con. [ery / µl] Con. [ery / µl]A1 4 3 4 3.67 5-10A2 5 7 5 5.67 5-10A3 4 4 6 4.67 5-10A4 4 3 4 3.67 5-10A5 5 4 5 4.67 5-10B1 7 9 9 8.33 50B2 12 15 7 11.33 50B3 14 10 6 10.00 50B4 11 6 8 8.33 50B5 9 11 9 9.67 50C1 53 43 44 46.67 300FARAGO P11610DE.PROV42 C2 65 36 71 57.33 300C3 50 61 59 56.67 300C4 57 60 49 55.33 300C5 78 71 52 67.00 3003.2.4. Mean hue-values over timeThe curves in Figure 11 are the visualisation of the results of the analysing code. The huevalue on the y-axis are corresponding to the concentration of the oxidised chromogen versus the time in seconds on the x-axis. The time range is from 0 to 100 seconds. Different shapes represent different solutions. In all three plots are the solutions of the pure Mb represented by the circle, and the diamond represents the solutions of the pure Hb. The square visualises the solutions with equal amounts of Hb and Mb. The two triangles with varying orientations are the solutions with uneven amounts of Hb and Mb. The triangle with the tip pointing to the left is the solution of more Mb than Hb. The triangle pointing to the right represents the solutions with more Hb than Mb.

[0004] FARAGO P11610DE.PROV43 The hue values of the solution measured increase the fastest in the first 10 seconds, the solution of the ‘C’ set increases the fastest and the hue values of the A set increase the slowest. 3.2.4.1. Set ‘A’The highest hue values in the ‘A’ set are measured in the solution of pure Mb; these hue values peak at around 40 seconds at a hue value of approximately 32 and slowly start to decrease. The second highest hue values are measured in the solution of equal amounts of Hb and Mb. The peak is also at 40 seconds, at around 31.8 and decreases drastically. The hue values of the solution of higher Hb than Mb start as the second lowest, overtaking the hue values of the solution of higher Mb than Hb, and peaks around 45 seconds around 31.5 andFARAGO P11610DE.PROV44slowly decreases. The hue values of the solution of higher Mb than Hb peak at around 20seconds at 31.5 and reduce drastically. The lowest hue values of the solution of pure Hb peak around 38 at approximately 30.8 and start to decrease. 3.2.4.2. Set ‘B’The highest hue values in the ‘B’ set are measured in the solution of pure Mb; these hue values peak at around 40 seconds at a hue value of approximately 39.8 and slowly start todecrease. The second highest hue values are measured in the solution of equal amounts ofHb and Mb. The peak is also at 40 seconds, at around 38 and decreases. The other hue values are close to each other, and hard to distinguish from another. All hue values of the three solutions peak around 35 seconds at a hue value of 36.5 and decrease. 3.2.4.3. Set ‘C’The hue values of the c set do not decrease in the time measured. The highest hue value is around 67 and belongs to the solution of pure Mb; the second highest hue value is around 65 and belongs to the solution of higher Hb than Mb. The hue value of the solution with equal parts Hb and Mb at the end of the measurement is approximately 62. These values intersect with the hue values of the solution with more Mb than Hb at around 30 seconds. The highest hue value of the solution of more Mb than Hb is approximately 60. The lowest hue values are measured with the pure Hb solution, and the highest is estimated at around 57. 3.2.5. The mean hue values of the similar curve solutionsThe hue values in the figure above are from the solutions with similar expected colourchanges. An expected pair with a similar colour change were S1 and S2 visualized in the plotas the diamond and as a triangle oriented to the left. The hue values of S1 are higher than the h values of the S2 solutions. The hue values of the S5 and S6 pair are closer to getter and lower than the other pair, but still distinguishable.FARAGO P11610DE.PROV45 3.2.6. RaitoTo make the results more universally applicable and eliminate the device-specific noise, a ratio of the integral of the values from 0 to 30 seconds and from 30 to 60 seconds is used. The ratios calculated are shown in the table 5. Table 6: ratios and the corresponding concentrations of Hb and Mb in the starting solution AB C SHb Mb ratio Hb Mb ratio Hb Mb ratio Hb Mb ratio0 0.0002 1.036 0 0.00075 1.088 0 0.0045 1.216 0.00044 0.00018 1.080.00005 0.00015 1.019 0.00025 0.0005 1.069 0.0015 0.003 1.207 0.0005 0.00099 1.1330.0001 0.0001 1.028 0.000375 0.000375 1.087 0.00225 0.00225 1.221 0.0007 0.000047 1.0750.00015 0.00005 1.031 0.0005 0.00025 1.08 0.003 0.0015 1.237 0.0014 0.0014 1.1550.0002 0 1.025 0.00075 0 1.066 0.0045 0 1.164 0.0018 0.00013 1.1553.2.7. Relation of Ratio and ConcentrationFARAGO P11610DE.PROV46 Figure 12 is ued to viszulise the relation between the raito of the area under the curve, y- axis, and the concentration in grams per liter thex a-xis. The ratios of the pure Hb and pure Mb hue are represented with a circle for Mb and a square for pure Hb. With a fit line, illustrated with a dotted line, are the values for pure Mb connected. The values for Hb were also connected with a fit line. The diamonds in the different colours are representing the mixed solutrion of Hb and Mb. Green are the data of the ‘C’ set, orange is the ‘B’set, blue is the ‘A’ set and the red are the ‘S’ set data. Figure 12: ratio of the area under the curve over-concentration in grams per Liter. 3.2.8. Relation between the ratio of the area under the curve and hue valueThe Figure 13 is a visual representation of the relation between the mean hue value and the ratio of the area under the curve of the solution. The ratios of the pure Hb and pure Mb hue are represented with a circle for Mb and a square for pure Hb. With a fit line, illustrated with a dotted line, are the values for pure Mb connected. The values for Hb were also connected with a fit line. The diamonds in the different colours are representing the mixed solutrion of Hb and Mb. Green are the data of the ‘C’ set, orange is the ‘B’set, blue is the ‘A’ set and the red are the ‘S’ set data.FARAGO P11610DE.PROV47 4. DiscussionThe discussion chapter first deals with interpreting the simulation and the experiment results and their comparison. Followed by the limits and challenge phased in the study and the possible impact of the findings. The section is wrapped up with the conclusion. 4.1.Interpretation of the results 4.1.1. SimulationThe simulation results show the relationship between the initial concentration and the expected oxidized chromogen concentration. The oxidized chromogen correlates to the colour change on the test pad. The curves shown in Figure 5 are used to visualize the differentiation of Hb and Mb with a conventional urine test strip. The curves show that solutions with higher concentrations of Mb exhibit faster reactions across all three concentration ranges, leading to a higher expected concentration of the oxidized chromogen compared to other solutions. Accordingly, the solutions with a higher Hb concentration than Mb demonstrate slower reactions and a lower presence of oxidized chromogen. This observation suggests that Mb significantly influences the concentration of the oxidized chromogen.FARAGO P11610DE.PROV48 Similar curves found in the simulation were mainly located between the measurement ranges ‘++’ and ‘+++’, but not exclusively curves were also found between ‘+’ and No clear pattern was found, but it is noticeable that in half of the cases, the pair includes a curve with a starting concentration where Mb and Hb are almost equal. This is the case for the curves S8, S10 and S12. The trend that the higher the starting concentration, the higher the concentration of the oxidised chromogen was also seen in the set of similar curves. By having the S11 and S12, the highest total reactive concentration and the fastest increasing curve. The slowest increasing concentration curve was found with the lowest total reactive concentration, S5 and S6. 4.1.2. ExperimentThe optical evaluation showed that the colour of the reference field ‘+++’ could not be reproduced in the experimental work. That could either indicate that the reference colour scale is not representative or the concentrations used were not high enough to reproduce the exact colour. The uncertainties in the calculations of the concentration could cause this. The fact that the colour change on the test pad was often uneven, can influence the resulting hue value. This could be caused by the experimental setup, which leaves room for improvement. The results shown in Table 4 highlight that the estimated concentrations based on the hue values are significantly lower than the results of the Urilyzer® 100 Pro suggest. This indicates that a direct conversion of the hue value into concentration based on the colour of the reference scale is not applicable. The curves in Figure 10 indicate that hue values over time are unsuitable for discriminationof the Mb, Hb, and Mixture solutions. The pure Hb and Mb hue values are in all concentration sets and are suitable to distinguish from each other. The hue values of the pure Mb are the highest in all three concentration sets, and Hb is the lowest. Discrimination between the mixed solution is not possible, and no clear trend can be found for the positioning. The onlytrend is that the mixed solution's hue values are between the pure Mb and the pure Hb.FARAGO P11610DE.PROV49 Figure 11 The curves of the hue value over time for the set ‘S’ solution showed that eventhough the hue values of the pairs are similar to each other, the curves were not identical or as close as in the model. The graph shown in Figure 12 shows that the minimum and maximum ranges of the pureHb and the pure Mb values do not overlap. The data points of the mixed samples are located above the black line representing the Hb values, expected for the solution of the ‘A’ set. The ‘A’ set is the set of measurements with the lowest concentration and the lowest colour change, increasing the influence of any outer factors. The data points of the mixed solution are partially on the Mb value dotted line. Based on that, a discrimination between solution with known total reactive concentration, of pure Hb and solution containing Mb can be done. Figure 13 shows the relationship between the mean hue value at 60 seconds and the ratioof the area under the curve. The values of the mixtures of the sets ‘B’, ‘C’ and ‘S’ are all under the fit curve of the pure Hb values. The exception is the ‘A’ set values, they are above the fitcurve of the Hb data. The device-specific noise has the biggest influence on the ‘A’-setsolutions since the hue values of this set are low, so the percentage of the noise is proportionally more significant. 4.2.Comparison of the experiment and the simulation The most noticeable difference between the expected curves of the estimated chromogen concentration and the raw hue values over time is that the value in the measured result first increases and then decreases. These decreases indicated that the reaction kinetic is more complex than used in the model. The model does not include a reaction of the oxidized chromogen with another component back to the unoxidized chromogen. The other big difference is that the trend that the higher the initial Mb concentration in the mixed solutions, the higher the expected oxidized chromogen could not be found in the measurement data.FARAGO P11610DE.PROV50 The model and the measurement data agree that the rate of the colour change is the highest in the first seconds of the reaction and that pure Mb gives a faster and more intense colour change than pure Hb. 4.3.Limitations and challenges The limits and challenges encountered during this research must be discussed to better understand the results. 4.3.1. Technological LimitationThe interpretation of the results depends intensely on the accuracy of conventional urine dipsticks and is influenced by the quality of the reagents used and the precision of the reference scale provided. The variability in colour perception among different people can lead to differences in the interpretation of the result; this was addressed using the Urilyzer® 100 Pro. The performance of the Urilyzer® 100 Pro is limited by the reference field provided in the machine. 4.3.2. Limitation of the repeatabilityThe setting for each measurement was tried to be constant by using a lightbox. But the light settings can still differ slightly. The camera’s sharpness can also influence the repeatability of the measurements taken. 4.3.3. Limitation of InformationThe information found in the literature limited the precision of the molecular weight used in the calculation. The molecular weights of Hb and Mb differed depending on the literature, which prevented precise results of calculations involving the molecular weights. The limitation on the precision of numbers was also found in the conversion of erythrocytes per µl into Hb and Mb in mg / dl. The value found in the package insert ofFARAGO P11610DE.PROV510.015mg / dl of Hb or Mb being equal to 5- 10 erythrocytes per µl leads to a considerabledifference in concentration based on interpretation. For Example. when it's assumed that 5 erythrocytes per µl are equal to 0.015 mg / dl and based on that, the concentrations of the other measurement ranges are calculated. That would result in a conversion of 50 erythrocytes per µl into a concentration of 0.15 mg / dl of Hb and Mb. This concentration is double the concentration used in the current model. The gap in knowledge about the kinetic constants of the reaction of Hb and Mb in the literature is a limitation of the precision of the model. No information was found about the reaction constants between Compound 1 of Hb and Mb and the chromogen TMB. 4.3.4. ChallengesExperimental set-up A challenge found in the experimental set-up was the position of the pipette tip during the coating of the test pad with the sample solution. The pipette tip often covered the test pad, this problem was tried to reduce with the addition of the funnel. However, the first frames were still often compromised, which could mean that the time axis is potentially shifted. 4.4.Outlook for the Applicant’s Technological Application Based on these findings, future research should include improved measurement and an elaborate evaluation process. The evaluation process could include the use of artificial intelligence and an improved analysis code. The measurements could be improved in a variety of ways. One way to improve the measurement could be through further developing the experimental setup. The coating procedure on the test pad is currently still covering the test pad in some cases. A new design to eliminate this problem would improve the measurements. Another way to develop the measurement set-up could be to design it in the direction to be included in the measuring device from Medipee.FARAGO P11610DE.PROV52 Another way to improve the measurements would be by improving the precision of the calculations. One way could be by addressing the limits of the calculation and comparing the results of a model based on the calculations done with the different molecular weights foundfor Hb and Mb with each other and the results of the experiment. A similar way could addressthe uncertainty around converting erythrocytes per µl into Hb and Mb in mg / dl. The model could also be improved by studying the possible back reaction of the oxidized chromogen that was not included in the current model. And studying the literature gap for the reaction constant. Primarily, the reaction kinetics involving compound 1 of Hb and Mb, by gathering more experimental information about the reaction kinetics, could drastically improve the precision of the current model. 4.5.Impact of the result The possibility of the discrimination of pure Hb and a sample containing Mb and the possibility of developing a procedure to further discriminate with conventional urine dipstick could impact a range of fields, from the medical to lifestyle. First, the impacts on the medical field are discussed. The detection of myoglobinuria in the clinical context is often done with an immunochemical method, which needs a laboratory, the time and knowledge of the person performing the technique. The pure use of urine dipsticks would reduce the requirements of these resources. The performance of urinalysis is already common bedside practice, and the procedure is well known. This could also be useful for the intense care unit or short ICU, where most patients are on bed rest or otherwise immobilised, such as comatose patients. These patients are at a higher risk of developing rhabdomyolysis due to the drastic reduction of muscle mass. They are often not able to verbally communicate other symptoms of rhabdomyolysis, such as swelling and muscle pain. Regularly testing these patients' urine could help monitor and take measurements of the first signs of rhabdomyolysis. The risks of rhabdomyolysis are also rising due to global warming, which causes more extreme weather conditions. Heat strokes and hypothermia are possible causes of rhabdomyolysis, especially for the elderly or children. Based on this, a regular checkup of the Mb in the urine in retirement homes could be advisable.FARAGO P11610DE.PROV53 Secondly, the possibility of Mb detection in urine that needs fewer resources could benefit environments with limited access to resources, such as a laboratory. Examples of these groups are military members, especially in the context of active duty. As mentioned in the theoretical background, members of the military have a statistically higher chance of developing exercise-caused rhabdomyolysis due to their intense physical labour. A method for determining the presence of Mb in the urine without the need for a laboratory or heavy and expensive equipment would be beneficial. The same goes for people in countries where hospital and laboratory access can be spared. Professional athletes could use urine dipsticks to optimise their training and prevent overtraining. During exercises, the muscle tissue can be injured, which depending on the intensity leads to Mb in the urine. By routinely checking the urine for the presence of Mb, rest days can be put in till the muscle tissue is recovered, which would give a more beneficial training outcome and decrease the risk of injuries. However, the use in the lifestyle field for training optimisation may even be more significant. Professional athletes are often closely monitored by doctors. They are in good physical shape overall, while hobby athletes or people who want to increase their fitness often do that in a less monitored environment and probably know less about the risks of overtraining. The rehabilitation exercise could also be closely monitored. Further development of the research based on the results could reduce the cost of pharmaceutical studies or the research for genetic disorders that cause the presence of Mb in the urine. In summary, the findings of this study and its potential further development could simplify the diagnosis of myoglobinuria in medical settings but also optimize the training for members of military, and athletes in the lifestyle contexts. 4.6.Conclusion The results of this study conclude that the reaction patterns of Hb and Mb differ. However, the calculation of the initial concentration of Hb and Mb based on the colour change with theFARAGO P11610DE.PROV54 current data is not possible. A discrimination is possible between samples containing pure Hb or samples containing Mb, either pure Mb or a mixture of Hb and Mb for higher concentration than the ‘A’ set. This is what is needed to have an invention ready for filing. The method proposed for the discrimination between Hb samples and samples containing Mb, is the measurement of the colour change and the hue values of this colour change. Theratio of the resulting curve of the hue values over time of the integration from 0 to 30 and 30to 60 seconds. These values are plotted as seen in Figure 13 and checked to see if the data ofthe sample is below the line of the pure Hb value or on the pure Hb line. Depending on the location, contains the sample either pure Hb or Mb, either pure or in combination with Hb. In view of the findings and experimental proof as described above, the Applicant describes and claims as follows: The present invention relates to the field of in vitro diagnostics, and more specifically to the detection and differentiation of biologically relevant analytes in liquid bodily fluids, in particular urine. The invention addresses the extracorporeal analysis of chemical or pseudo- enzymatic reactions that produce a visible color change, and relates to the time-resolved, algorithmically supported interpretation of such reactions, which occur in a test pad or similar reactive substrate when exposed to a sample containing a target substance. In particular, the invention enables the identification and differentiation of two diagnostically relevant substances—hemoglobin (Hb) and myoglobin (Mb)—based on distinct temporal characteristics of their respective color reactions in a common test environment. Diagnostic dipsticks for urinalysis have long been used to detect the presence of various components such as proteins, glucose, leukocytes, nitrites, and blood. Among these, blood detection typically relies on the pseudo-peroxidase activity of hemoglobin or myoglobin, which catalyze the oxidation of a chromogenic substrate, resulting in a visible color change. The most commonly used chromogen for such tests is tetramethylbenzidine (TMB), which, when oxidized, changes its spectral properties in a manner perceptible to the human eye. Traditional test strips include a visual color comparison scale printed on the packaging or in accompanying instructions, which the user employs to interpret the result after a fixedFARAGO P11610DE.PROV55 waiting time. However, these systems suffer from a number of disadvantages. Firstly, both hemoglobin and myoglobin trigger nearly identical reactions in conventional test fields,making it impossible to distinguish between them using color alone. Secondly, the subjectivenature of visual interpretation introduces a high degree of variability and limits the applicability of such tests in non-clinical or unsupervised environments. Thirdly, conventional tests rely on endpoint color evaluation and ignore the temporal evolution of the reaction, even though the time-dependent behavior of the color change contains valuable diagnostic information. It is therefore the object of the present invention to provide a method and associated system that allow the differentiation of components in a fluid, particularly the distinction between hemoglobin and myoglobin in urine, based on the time-resolved analysis of the color reaction behavior of a reactant. The invention aims to provide a solution that does not rely on visual color comparison scales, but instead uses algorithmic processing of optical data acquired at multiple time points following the initiation of the reaction. The invention further seeks to make such analysis feasible in decentralized or mobile settings, such as at-home testing, sports monitoring, or rehabilitation, using commonly available imaging devices such as smartphone cameras. This object is achieved by a method as defined in claim 1, wherein a reactant capable of undergoing a color change in the presence of a target component is immobilized in a carrier medium and brought into contact with a fluid sample at a defined start time. An optical sensor system is then used to record the colorimetric response of the reactant at a plurality of measurement time points. The resulting time-dependent data is subjected to algorithmic analysis to determine the progression of the color change. This temporal profile is mathematically compared to reference data that characterizes known response behaviors of the components of interest, enabling an inference as to the presence or predominance of a specific component. The method is particularly suited to distinguish between substances that produce similar colorimetric outcomes but differ in the kinetics of their reactions, as is the case for hemoglobin and myoglobin.FARAGO P11610DE.PROV56 In regard of the terms used, the following definitions are to be understood: a component refers to a chemically or biologically active substance within the fluid sample, in particular hemoglobin or myoglobin. A fluid is any liquid medium, particularly excreted urine, containing the analytes of interest. A reactant is a chemical substance, typically a chromogen such as tetramethylbenzidine, that exhibits a color change when exposed to the component. The carrier medium is a test pad or porous substrate in which the reactant is embedded. A defined start time is the moment at which the sample first contacts the reactant and initiates the chemical reaction. An optical sensor system refers to any imaging device, such as a smartphone camera or dedicated sensor, capable of capturing colorimetric data at different times. A measurement time point is one of at least two distinct moments during the reaction at which data is recorded. Algorithmic processing involves the mathematical or computational analysis of the colorimetric data, including the generation of time-dependent curves and their comparison to stored reference datasets. Reference data includes pre-recorded or statistically modeled reaction profiles corresponding to specific components. Inference refers to the logical or computational determination, based on these comparisons, of the identity or relative amount of the component present in the sample. The following advantages might be reached: The invention enables the non-invasive, extracorporeal detection and differentiation of analytes in biological fluids using simple testhardware combined with algorithmic evaluation of temporal data. It allows the distinctionbetween hemoglobin and myoglobin—previously indistinguishable by colorimetric means— by leveraging the kinetic differences in their respective reactions. The method improves diagnostic accuracy, reduces user error, and supports application in decentralized, mobile, or home-based environments. It eliminates the reliance on subjective visual comparison, and instead provides objective, quantitative results. The system can be integrated into digital health infrastructures, supporting telemedical applications, longitudinal health monitoring, and data-driven decision-making in fields such as sports science, clinical diagnostics, and rehabilitation.FARAGO P11610DE.PROV57 The following variations might be considered: The reactant may consist of any chromogen capable of showing differential time-response to the analytes of interest, and may be used inconjunction with catalysts or stabilizing agents. The test pad may be part of a disposable stripor an integrated cartridge compatible with a dedicated analysis device. The optical sensor may be fixed in a housing that includes a standardized illumination environment to eliminate lighting variability. The video data may be analyzed using threshold-based or machine- learning models trained on validated datasets. Additional preprocessing steps may be applied, such as background correction, hue normalization, or region-of-interest segmentation. The invention may be extended to allow simultaneous detection of multiple parameters by including additional test fields on the same substrate, thereby producing a comprehensive urinalysis profile. Results may be transmitted wirelessly to a mobile application or cloud-based platform for visualization, storage, or further interpretation. According to the first aspect of the invention, which can be found as according to claim 1, the object of the invention is solved by a method for extracorporeal detection of a component ina fluid, in particular a component in a liquid, especially excreted urine, comprising the use ofa reactant that exhibits a color reaction property in response to the component, wherein the reactant is provided in a carrier medium, wherein the reactant is brought into contact with the fluid at a defined start time, whereafter an optical sensor system is used to detect a colorimetric property of the reactant at a plurality of, in particular at least two, measurement time points, whereafter a time-dependent progression of the color change behavior is determined or approximated by means of algorithmic processing, whereafter the temporal profile of the color reaction is mathematically compared to reference data, thereby enabling or performing an inference regarding the presence or relative predominance of the component in the fluid, preferably without using a visual color comparison scale, in particular for distinguishing two different components based on differing temporal characteristics of the color response of the reactant in their presence. In regard of the terms used, the following definitions are to be understood: A component is a target analyte capable of inducing or participating in a chemical or pseudo-enzymatic reaction, in particular hemoglobin or myoglobin, which exhibit peroxidase-like activity. A fluidFARAGO P11610DE.PROV58 refers to a biological liquid sample, especially urine, that may contain such a component. A reactant is a color-change-capable substance embedded in a test matrix, such as a chromogen that responds visibly to oxidation, preferably tetramethylbenzidine. The carrier medium is a physical substrate, typically a test pad, capable of holding the reactant in position and allowing fluid interaction. The defined start time marks the beginning of the chemical reaction, typically the moment of initial fluid-reactant contact. An optical sensor system is a device capable of detecting colorimetric properties, preferably including a camera or photodiode array configured to record images or data over time. A measurement time point is any discrete moment at which such a detection occurs, with at least two being required to establish a temporal progression. The colorimetric property is a measurable characteristic such as hue, saturation, brightness, or spectral shift. A time-dependent progression is amathematically described curve or data sequence representing changes in this property overtime. Algorithmic processing refers to the application of software or computational routines to extract and model this progression. Reference data are pre-defined temporal profiles or mathematical models based on known component behavior under standardized conditions. An inference is a computational or logical conclusion derived from comparing the observedprofile to such reference data. A visual color comparison scale refers to a printed or displayedchart against which a user would otherwise subjectively evaluate color; its avoidance signifies the automated nature of the method. The following variations might be considered: The reactant may be immobilized in various matrix materials including cellulose, nitrocellulose, or polymer membranes. The fluid sample may be applied by capillary action, pipetting, or automated dispensing. The optical sensor system may include built-in calibration routines or be pre-aligned within a measurement enclosure. Time points may be spaced linearly or non-linearly depending on expected kinetic behavior. Algorithmic processing may use hue-time curve fitting, moving average filters, spline interpolation, or time-series classification algorithms. Reference data may be encoded as statistical models, lookup tables, or multidimensional feature maps. The inference process may employ binary classification, fuzzy logic, or supervised machine learning. The method may be implemented in point-of-care devices, smartphone-based diagnostics, or laboratoryFARAGO P11610DE.PROV59 instruments. The result may be output in raw numerical form, categorized labels, or integrated into patient monitoring dashboards. The following advantages might be reached: The method enables a reliable, objective, and non-invasive assessment of specific analytes in urine or other fluids without dependence onuser interpretation or visual scales. It overcomes the indistinguishability of hemoglobin andmyoglobin in traditional peroxidase-based tests by leveraging their differing reaction kinetics. The approach supports decentralization of diagnostic testing, enabling use in personal, athletic, or remote medical contexts. Algorithmic analysis ensures reproducibility, scalability, and potential for integration into digital health ecosystems. The detection process is compatible with low-cost consumables and common imaging hardware, reducing barriers towidespread deployment. By focusing on the reaction’s temporal characteristics, the methodenhances diagnostic specificity and opens the way for kinetic fingerprinting of other analytes beyond those mentioned. According to another aspect of the invention, which can be found as according to claim 2, the object of the invention is solved by a method according to claim 1, wherein the component comprises hemoglobin (Hb) and / or myoglobin (Mb), and wherein a time-resolved video of the color change of a peroxidase-reactive test pad on a urine dipstick is captured after contact with the sample, and hue values are extracted and analyzed over time to determine the presence of Hb or Mb based on characteristic response patterns, so that the method is a method for distinguishing hemoglobin (Hb) from myoglobin (Mb) in a urine sample, comprising: capturing a time-resolved video of the color change of a peroxidase-reactive test pad on a urine dipstick after contact with the sample; extracting hue values over time from said video; analyzing the hue-time curve to determine the presence of Hb or Mb based on characteristic response patterns. In regard of the terms used, the following definitions are to be understood: Hemoglobin is a heme-containing protein predominantly found in red blood cells and capable of catalyzing oxidative reactions due to its peroxidase-like activity. Myoglobin is a structurally related protein found in muscle tissue, likewise exhibiting pseudo-peroxidase activity but differing inFARAGO P11610DE.PROV60 molecular structure and release kinetics. A urine dipstick is a test device comprising a strip or panel with immobilized test pads that change color in the presence of specific analytes. A peroxidase-reactive test pad contains a chromogen, such as tetramethylbenzidine, that undergoes an oxidation-induced color change when exposed to Hb or Mb. A time-resolved video is a sequence of image frames captured at known intervals over the duration of the reaction, preferably using a digital camera or mobile device. Hue values refer to the color angle in the HSV color space, representing the perceived color irrespective of intensity or brightness. A hue-time curve is a graph or dataset showing the variation in hue over time following the start of the reaction. Characteristic response patterns are time-dependent colorimetric trajectories that differ between Hb and Mb due to their respective reaction kinetics. The following variations might be considered: The video recording may begin immediately after the fluid contacts the test pad or be triggered by a sensor detecting sample presence. The test pad may be enclosed in a standardized lighting environment to ensure consistent image capture. The hue extraction may focus on the region of interest within the test field and apply spatial averaging to reduce noise. Curve analysis may include derivative assessment, peak detection, time-to-maximum hue, or ratio metrics. Classification may use template matching, decision trees, or probabilistic models trained on reference datasets. The device used for video acquisition may include autofocus, exposure lock, and image stabilization features. The method may be configured to output either a binary result (e.g., Hb or Mb) or a probabilistic distribution representing confidence levels. Additional tests may be co-located on the dipstick but excluded from the hue analysis zone to prevent interference. The following advantages might be reached: The method allows for specific differentiation between hemoglobin and myoglobin in urine samples using a single peroxidase-reactive testpad, which is not possible using traditional static endpoint color analysis. By evaluating huevalues over time, the system extracts kinetic information unique to each analyte, thereby enabling targeted diagnostics for muscle injury (Mb) versus bleeding (Hb). The use of video enables high temporal resolution and accurate tracking of dynamic changes, while the hue- based approach ensures robustness against brightness and exposure variations. The methodFARAGO P11610DE.PROV61 can be deployed using standard mobile phones, making it accessible, portable, and suitable for non-clinical use cases such as sports medicine, rehabilitation monitoring, or field diagnostics. According to another aspect of the invention, which can be found as according to claim 3, the object of the invention is solved by a method according to any of claims 1 or 2, wherein the fluid is urine. In regard of the terms used, the following definitions are to be understood: Urine is a biological fluid excreted by the kidneys, typically sterile at the point of production, andcontaining a mixture of electrolytes, metabolites, proteins, and potentially diagnosticbiomarkers. In the context of the present invention, urine serves as the carrier of the target components, particularly hemoglobin or myoglobin, and may originate from individuals undergoing physical exertion, medical treatment, or clinical evaluation. The urine sample may be freshly voided, midstream, or collected according to standardized sampling protocols. It is assumed that no pre-treatment or centrifugation is necessary, and that the sample is applieddirectly to the test pad. The specific composition of urine may vary depending on hydration,diet, activity level, and physiological status, which is accounted for in the system through robust colorimetric modeling. The following variations might be considered: The urine sample may be used undiluted or diluted, depending on the application setting or sensor sensitivity. The method may include a calibration step to account for sample turbidity, pH, or background coloration. Sample application may be performed via immersion of the dipstick, dropwise pipetting, or via capillary flow from a collection aid. The method may be implemented in settings where urine is directly analyzed in-situ, such as toilet-integrated sensors or clinical receptacles, or ex-situ via collected test strips. The invention may be extended to include user instructions for first- morning urine, time-of-day sampling, or use following physical activity to enhance Mb- specific diagnostics. The following advantages might be reached: By specifying urine as the fluid of interest, the invention is positioned for high-impact diagnostic use in a wide range of health-relatedFARAGO P11610DE.PROV62 contexts, from sports science to nephrology. Urine is a non-invasively accessible sample matrix, allowing for repeatable and comfortable testing without the need for trained personnel. The ability to distinguish myoglobin and hemoglobin in urine opens new pathways for the early detection of rhabdomyolysis, trauma, or renal stress. The method supports both clinical and at-home usage scenarios and may be integrated with patient self-monitoring applications or telemedical services for real-time health tracking. According to another aspect of the invention, which can be found as according to claim 4, the object of the invention is solved by a method according to any of claims 1 to 3, wherein the reactant comprises a chromogen, in particular tetramethylbenzidine (TMB), capable of exhibiting peroxidase-like color changes in the presence of hemoglobin and / or myoglobin. In regard of the terms used, the following definitions are to be understood: A chromogen is a chemical compound that undergoes a visible color change when oxidized or otherwise chemically transformed in response to an analyte or enzymatic activity. Tetramethylbenzidine, abbreviated TMB, is a well-established chromogenic substrate that exhibits a blue to green oxidation product in the presence of peroxidase or pseudo-peroxidase activity. The term peroxidase-like color change refers to the visible shift in color resulting from the oxidation of the chromogen by hydrogen peroxide, catalyzed by the pseudo-enzymatic activity of hemoglobin or myoglobin. The inclusion of TMB or a comparable chromogen within the reactant matrix enables sensitive and specific detection of even trace amounts of these target components. The chromogen may be stabilized with buffering agents or deposited in dried form onto the test pad. The following variations might be considered: The chromogen may be used alone or in combination with additional reactants to tune the response kinetics or visual contrast. Other suitable chromogens may include o-tolidine, benzidine derivatives, 3,3ʹ-diaminobenzidine, or proprietary compounds optimized for digital imaging. The concentration, pH sensitivity, and particle size of the chromogen formulation may be adjusted to optimize time-response characteristics. The chromogen may be immobilized on paper, polymer, or nanocomposite carriers to enhance stability and control wetting behavior. Additives may be included toFARAGO P11610DE.PROV63 modulate reaction rate, suppress background signals, or extend shelf life. The chromogen may further be applied in multilayer structures or microencapsulated formats for use in single-use disposable cartridges. The following advantages might be reached: The use of a chromogen such as TMB ensures a strong and well-characterized colorimetric response to hemoglobin and myoglobin, providinga robust foundation for hue-time analysis. The predictable oxidation behavior of TMBsupports reproducible time-course measurements essential for kinetic differentiation. Its compatibility with visible light detection enables the use of inexpensive imaging systems such as smartphone cameras. The chromogen’s sensitivity allows for detection of low analyte concentrations, improving the diagnostic utility of the test. Moreover, the visual clarity and rapid onset of the TMB color change improve user confidence and facilitate integration into both consumer and professional diagnostic workflows. According to another aspect of the invention, which can be found as according to claim 5, the object of the invention is solved by a method according to any of claims 1 to 4, wherein the colorimetric property is a hue value derived in the HSV color space, and optionally additionally includes saturation or value components. In regard of the terms used, the following definitions are to be understood: A colorimetric property refers to a quantifiable characteristic of color as perceived and recorded by a sensor,particularly in digital image formats. The hue value is the angular component of the HSV (hue,saturation, value) color space, representing the pure color tone regardless of brightness or intensity. The HSV model separates chromatic information (hue) from lightness (value) andvividness (saturation), which is advantageous for analyzing color changes under variablelighting conditions. Hue is typically expressed in degrees from 0 to 360, with each segment corresponding to a primary or secondary color region. Saturation denotes the purity or intensity of the color, while value reflects the brightness. By prioritizing the hue value, the invention focuses on the dimension most directly linked to chemical reaction progression, while optionally also considering saturation and value to improve robustness and signal quality.FARAGO P11610DE.PROV64 The following variations might be considered: Colorimetric data may be extracted from raw video frames, converted from RGB to HSV space via standard mathematical transformations. The analysis may use only hue, or include multidimensional metrics combining hue with saturation or value to distinguish complex color dynamics. Calibration steps may be applied to normalize sensor-specific color profiles. Hue measurements may be averaged across regions of interest, corrected for background, or filtered over time to reduce noise. Additional color spaces such as CIELAB or YUV may be considered for advanced modeling, with HSV remaining the preferred representation for hue-based tracking. In some cases, non-visual spectral properties may be incorporated to enhance analysis precision. The following advantages might be reached: Focusing on hue as the primary colorimetric parameter allows the system to extract the most relevant and consistent signal from the colorreaction, independent of lighting intensity or shadows. The HSV color model simplifies thecomputational process of isolating meaningful color change from irrelevant brightness fluctuations, making it ideal for use with mobile phone cameras under uncontrolled ambient conditions. By optionally incorporating saturation and value, the system can improve differentiation in borderline cases and enhance resistance to artifacts. The method thus supports more accurate, robust, and interpretable data for downstream analysis, contributing to reliable analyte identification and improved diagnostic outcomes. According to another aspect of the invention, which can be found as according to claim 6, the object of the invention is solved by a method according to any of claims 1 to 5, wherein the time-dependent progression of the colorimetric property is evaluated by calculating the area under the hue-time curve in a first time interval (0 to 30 seconds) and a second time interval (30 to 60 seconds), and a ratio of said areas is computed. In regard of the terms used, the following definitions are to be understood: The time- dependent progression refers to the continuous or discrete measurement of hue values as a function of time, resulting in a hue-time curve that reflects the kinetics of the chemical reaction. The area under the curve (AUC) is a numerical integration of this curve over a specified time interval, representing the cumulative color change within that period. The firstFARAGO P11610DE.PROV65 and second time intervals are defined temporal segments post initial fluid-reactant contact, chosen to capture early and late phases of the reaction, respectively. The 0 to 30 second interval typically captures the primary oxidation event, while the 30 to 60 second interval reflects the continued or plateau phase of the reaction. The ratio of these areas is a diagnostic metric that quantifies the relative shape and timing of the color change, which differscharacteristically between hemoglobin and myoglobin responses.The following variations might be considered: Time intervals may be adjusted depending on reaction speed, ambient temperature, or specific formulation of the test pad. Additional time intervals may be included for higher-resolution profiles. Numerical integration may use methods such as the trapezoidal rule, Simpson’s rule, or spline-based estimation. The ratio may be used alone or combined with other parameters such as the endpoint hue or curve slope to form composite diagnostic models. Integration may be performed over raw hue data or over noise-reduced and baseline-corrected values. In some cases, logarithmic or normalized versions of the AUC values may be used to enhance inter-sample comparability. The following advantages might be reached: The use of integral ratios as a diagnostic metric provides a mathematically robust and reproducible way to differentiate between analytes with similar color reactions but distinct kinetic signatures. Hemoglobin and myoglobin exhibit differences in reaction onset and progression, which are effectively captured by comparing the early-phase and late-phase hue integrals. This approach reduces reliance on single time points and minimizes sensitivity to momentary artifacts or fluctuations. It also enables algorithmic classification using simple thresholding or regression models. The method is computationally efficient, compatible with real-time analysis, and provides a quantitative basis for decision-making that improves diagnostic confidence across diverse usage scenarios. According to another aspect of the invention, which can be found as according to claim 7, the object of the invention is solved by a method according to any of claims 1 to 6, wherein the determined time-dependent profile is compared to reference data corresponding to known reactions of Hb and Mb, optionally using threshold-based classification or regression models, and optionally in the context of sport, rehabilitation, or clinical diagnostics, and / or the videoFARAGO P11610DE.PROV66 is captured using a mobile device fixed in a standardized geometry relative to the test pad, and / or the dipstick comprises a chromogen based on tetramethylbenzidine (TMB), and / or the test pad color change is induced by interaction with myoglobin, hemoglobin, or a mixture thereof in the sample, and / or an image segmentation is used to isolate the test field region of interest before hue analysis, and / or a preprocessing step is applied to correct for lighting conditions, camera color response, or background interference. In regard of the terms used, the following definitions are to be understood: Reference data refers to pre-recorded hue-time curves or mathematical models derived from known reactions of pure hemoglobin, pure myoglobin, and defined mixtures thereof, under standardized conditions. Threshold-based classification is a rule-based method that compares measured parameters, such as integral ratios or endpoint hue values, to fixed numericalboundaries. Regression models are statistical or machine learning algorithms that mapcontinuous inputs to continuous or categorical outputs, trained on annotated data. A standardized geometry is a mechanically fixed or guided spatial relationship between themobile device camera and the test pad, ensuring consistent image framing and perspective.Image segmentation refers to the computational process of identifying and isolating the region of interest (ROI) corresponding to the active test field within the image frame. Preprocessing includes algorithmic corrections such as white balance adjustment, brightness normalization, shadow suppression, and removal of irrelevant background pixels. The following variations might be considered: Reference data may be updated dynamically based on batch-specific calibration or adapted to user populations. Classification may use decision trees, support vector machines, neural networks, or ensemble methods. The mobile device may be inserted into a dock, cradle, or foldable structure to achieve standardized geometry. Segmentation may be based on contour detection, color thresholds, fiducial markers, or deep learning object recognition. Preprocessing may include rotation correction, perspective flattening, or lens distortion compensation. The dipstick may include printed markers or alignment zones to aid positioning. Chromogen formulations may vary slightly totune the reaction for environmental or demographic contexts.FARAGO P11610DE.PROV67 The following advantages might be reached: The comparison to reference data enables precise differentiation between Hb and Mb despite overlapping peroxidase-like reactions, using quantitative, repeatable, and explainable methods. Threshold and regression models can be optimized for specific use cases, such as sports diagnostics or chronic condition monitoring. Fixing the geometry of the mobile device relative to the test pad ensures high reproducibility and reduces operator variability. Image segmentation and preprocessing improve signal quality, reduce noise, and eliminate confounding factors due to ambient lighting or inconsistent sample application. These technical enhancements support the development of a reliable, scalable, and user-friendly diagnostic system deployable in a broad range of environments. According to another aspect of the invention, which can be found as according to claim 8, the object of the invention is solved by a method, especially according to any of the preceding claims, comprising: integrating the hue-time curve over a first time interval (0 to 30 seconds) and a second time interval (30 to 60 seconds); computing a ratio of said integrals as a diagnostic parameter for distinguishing Hb and Mb. In regard of the terms used, the following definitions are to be understood: Integration of the hue-time curve refers to the numerical calculation of the area under the curve representing hue values plotted against time, capturing the cumulative colorimetric response in a given interval. The first and second time intervals are predefined durations starting from the moment of test initiation, typically corresponding to the early and late reaction phases,selected to reflect differential kinetic behavior. The ratio of the two integrals is a calculatedvalue that quantifies the shift in the reaction dynamic, serving as a signature metric for classification. The diagnostic parameter is a scalar indicator used in downstream interpretation, which may feed into either deterministic or probabilistic models to infer the dominant component. The following variations might be considered: The exact length and boundaries of the intervals may be adjusted based on ambient temperature, chromogen formulation, or sensor frame rate. Integration may be performed using trapezoidal, Simpson’s, or higher-orderFARAGO P11610DE.PROV68 polynomial approximations. The integrals may be normalized before comparison to mitigate variation in lighting or camera gain. The ratio may be calculated as a simple quotient, logarithmic value, or transformed using z-score or other statistical normalization. Additional metrics such as curve slope, curvature, or moment analysis may be combined with the integral ratio to enhance classification fidelity. The following advantages might be reached: Integrating the hue-time curve provides a robust and noise-tolerant representation of reaction kinetics that captures not only the magnitude but also the tempo of the color response. The ratio of early to late integrals enables a compact and informative descriptor that effectively separates reaction profiles characteristic of hemoglobin and myoglobin. This method enhances reproducibility, is readily implemented in resource-limited hardware, and reduces the dependency on real-time supervision. It also allows for flexible adaptation to different sensor technologies and operating conditions. According to another aspect of the invention, which can be found as according to claim 9, the object of the invention is further solved in that said ratio is used to assign a test result to one of the categories: “Hb only”, “Mb only”, or “mixed presence”. In regard of the terms used, the following definitions are to be understood: The ratio described previously serves here as a discriminant feature for categorization into discrete diagnostic outcomes. “Hb only” refers to a sample where the observed color reaction matches reference behavior predominantly associated with hemoglobin. “Mb only” signifies a match to myoglobin-specific reaction kinetics. “Mixed presence” indicates a sample in which both components contribute to the observed profile, typically occupying a transitional or blended position between the two reference curves. Assignment is the computational act of selecting one category based on predefined rules, thresholds, or model output. The following variations might be considered: The assignment algorithm may use simple rule- based logic, for example comparing the ratio against two thresholds defining upper and lower bounds for Mb and Hb classification, with values in between assigned as mixed. Alternatively, soft classification or probabilistic models may provide confidence scores for each category. The system may allow optional overrides, repeat testing, or cross-validation with additionalFARAGO P11610DE.PROV69 parameters such as endpoint hue or reaction onset delay. Categories may be expanded in future applications to include “undetermined” or “invalid sample” if data quality is insufficient. The following advantages might be reached: Categorizing samples into interpretable diagnostic groups facilitates clinical decision-making and simplifies communication to users. The ternary classification model corresponds to practical real-world outcomes and supports longitudinal monitoring by providing consistent outcome labels. This structure also enables statistical aggregation and risk scoring across populations. Automated assignment ensuresobjectivity and reduces cognitive load for users or healthcare providers, making the systemaccessible in self-testing scenarios. According to another aspect of the invention, which can be found as according to claim 10, the object of the invention is further solved in that the ratio is compared to pre-defined thresholds derived from reference curves. In regard of the terms used, the following definitions are to be understood: Pre-defined thresholds are numerical cut-off values established through experimental calibration using reference samples containing known concentrations of Hb, Mb, or both. These thresholds delineate the boundaries between diagnostic categories based on empirical or statistically modeled distributions. Reference curves are hue-time profiles collected under controlled conditions and annotated with ground-truth analyte identities. The comparison is the mathematical operation by which the measured integral ratio is positioned relative to these thresholds, thereby determining the category assignment. The following variations might be considered: Thresholds may be fixed or dynamically adjusted based on batch, temperature, device model, or user-specific calibration. They may be encoded as static parameters in firmware or updated via software. Threshold logic may be one-dimensional (based solely on the ratio) or multi-dimensional, incorporating additional features such as hue endpoint or response latency. Threshold calibration may use ROC analysis, clustering, or machine learning optimization techniques. Thresholds may differ by use case—for example, sports screening versus hospital diagnostics.FARAGO P11610DE.PROV70 The following advantages might be reached: Using well-defined thresholds provides transparent and verifiable decision criteria, enhancing trust and regulatory acceptability. It allows for rapid execution of classification routines with minimal computational overhead. Threshold-based logic supports offline operation and is compatible with embedded systems. It also facilitates validation, benchmarking, and certification of the diagnostic method across different deployment contexts. According to another aspect of the invention, which can be found as according to claim 11, the object of the invention is solved by a method according to any of claims 8 to 10, wherein the hue integration is performed using trapezoidal numerical approximation. In regard of the terms used, the following definitions are to be understood: Trapezoidal numerical approximation refers to a method of estimating the area under a curve by summing the areas of a series of adjacent trapezoids formed between successive data points along the hue-time curve. This method is computationally efficient and suitable for real-time or embedded processing, especially when the time intervals between measurements are uniform. It balances simplicity with acceptable accuracy for most reaction profiles encountered in the context of the invention. The following variations might be considered: The trapezoidal rule may be implemented over evenly or unevenly spaced time points, with optional error correction applied at endpoints. Higher-order integration methods, such as Simpson’s rule or spline-based techniques, may be used as alternatives in more complex systems. The implementation may also include smoothing or interpolation between frames to reduce digitization noise. The trapezoidal method may be combined with weighting factors that account for varying confidence levels across time windows.FARAGO P11610DE.PROV71 The following advantages might be reached: Trapezoidal integration is easy to implement, stable under typical noise conditions, and well-suited to hue data sampled at discrete frame intervals. It allows fast and low-power computation on mobile devices or dedicated microcontrollers. Its mathematical transparency also supports validation and regulatory compliance, making it ideal for diagnostic use cases. According to another aspect of the invention, which can be found as according to claim 12, the object of the invention is further solved by a method according to any of claims 8 to 11, further comprising computing a hue value at a predefined endpoint time and combining it with the integral ratio in a multivariate decision function. In regard of the terms used, the following definitions are to be understood: A predefined endpoint time is a fixed time point, such as 60 seconds after test initiation, at which the hue value is recorded as a reference for final reaction state. The hue value at this point represents the stabilized or maximum extent of the color change. A multivariate decision function refers to a mathematical or algorithmic routine that uses two or more input variables—in this case, the integral ratio and the endpoint hue value—to determine the most probable classification of the sample. This function may be expressed as a weighted combination, a decision surface, or a statistical classifier. The following variations might be considered: The endpoint time may be chosen based on empirical reaction duration or adapted dynamically according to sample conditions. The hue value may be taken as a mean over several frames surrounding the endpoint to reduce frame- level variation. The decision function may be a linear discriminant, logistic regression, neural network, or ensemble model. Additional inputs such as early hue slope, saturation, or camera metadata may be included to enhance accuracy. The function may operate locally or be part of a remote inference engine accessed via network.FARAGO P11610DE.PROV72 The following advantages might be reached: Combining kinetic and static colorimetric features provides a more comprehensive diagnostic signature than either alone. The integral ratio captures the temporal dynamics, while the endpoint hue reflects total chromogenic response, jointly increasing diagnostic precision. A multivariate function allows for nuanced distinctions in complex cases and improves classification robustness under variable sample or environmental conditions. According to another aspect of the invention, which can be found as according to claim 13, the object of the invention is further solved by a method, especially according to any of the preceding claims, comprising: simultaneously analyzing the final hue value at 60 seconds and the integral ratio from 0–30 and 30–60 seconds; classifying the sample based on a two- dimensional fit model derived from reference data. In regard of the terms used, the following definitions are to be understood: Simultaneous analysis refers to the concurrent use of multiple diagnostic metrics within the same classification routine. A two-dimensional fit model is a predictive algorithm that maps pairs of input variables—here, endpoint hue and integral ratio—into a coordinate space where decision boundaries separate classes of analyte presence. Reference data denotes a dataset of previously measured samples with known ground truth, used to derive the model parameters by fitting techniques such as linear regression, decision surfaces, or clustering. The following variations might be considered: The fit model may be linear or nonlinear, continuous or segmented, depending on the distribution of reference data. Outlier correction, dimensionality reduction, or data augmentation may be used during model training. Classification boundaries may be hard-coded or adapted through online learning.FARAGO P11610DE.PROV73 The model may operate on pre-processed input data normalized by environmental conditions or device calibration factors. Visualization of classification regions may be provided to aid user understanding or expert review. The following advantages might be reached: Using a two-dimensional model allows for clear, interpretable classification logic that can be visualized and validated against experimental results. It accommodates interaction effects between hue intensity and kinetic behavior, improving classification reliability. The approach is scalable, easy to extend to higher- dimensional models, and lends itself to automated, real-time implementation on constrained devices or in networked diagnostic platforms. According to another aspect of the invention, which can be found as according to claim 14, the object of the invention is further solved by a method according to claim 13, wherein said two-dimensional fit model is derived from machine learning regression of validated training data. In regard of the terms used, the following definitions are to be understood: Machine learning regression is a computational method by which an algorithm learns to approximate a mappingfrom input variables—such as hue and integral ratio—to output variables such as probabilityof analyte presence, based on examples from a training set. Validated training data refers to experimentally obtained samples with verified component content (Hb, Mb, or both), measured under standardized conditions and annotated for use in model development. The fit model is thus not predetermined, but learned from data using optimization techniques that minimize classification error or maximize predictive accuracy.FARAGO P11610DE.PROV74 The following variations might be considered: Learning algorithms may include ridge regression, support vector regression, random forests, or deep learning models. Training data may be expanded over time with new samples, including user-contributed anonymized data sets. Cross-validation, bootstrapping, or Bayesian inference may be used to assess generalization performance. The model may be implemented directly on-device or in the cloud, with regular updates based on usage statistics or population trends. Training may include domain-specific constraints or regularization to improve interpretability and control overfitting. The following advantages might be reached: Machine-learned models provide adaptive, high- performance classification with minimal human parameter tuning. They can capture complex, nonlinear relationships in real-world data that are not evident from theoretical assumptions. The ability to learn from empirical examples improves robustness to sample variability, environmental influence, and sensor heterogeneity. This results in more accurate, reliable diagnostics across a broad range of users and use cases. According to another aspect of the invention, which can be found as according to claim 15, the object of the invention is further solved by a method according to claim 13 or 14, wherein said model includes corrections for total pseudo-peroxidase concentration. In regard of the terms used, the following definitions are to be understood: Total pseudo- peroxidase concentration refers to the aggregate concentration of peroxidase-active substances in the sample, including both hemoglobin and myoglobin, which jointly contribute to the overall oxidation of the chromogen. Corrections in the model are adjustments made to account for the intensity or amplitude of the colorimetric reaction, thereby normalizing kinetic patterns for fair comparison across samples with differing absolute analyte loads. These corrections may be embedded in the model as weighting factors, normalization terms, or scaling functions.FARAGO P11610DE.PROV75 The following variations might be considered: Corrections may be based on the maximum hue value, total area under the hue-time curve, or optical density measurements derived from full-color analysis. The correction may be global, affecting all input variables equally, or feature-specific. It may be applied before classification or as a post-processing adjustment. In certain implementations, pseudo-peroxidase activity may be inferred from secondary chemical indicators or derived from reference channels. Correction functions may be learned or hard-coded depending on system complexity. The following advantages might be reached: Including correction for total pseudo-peroxidase concentration improves model accuracy by reducing bias due to concentration-dependent effects. It allows better differentiation between similar kinetic shapes at different magnitudes and reduces false positives in highly concentrated samples. This leads to more consistent results across a wide range of biological and environmental conditions, strengthening the clinical reliability and applicability of the diagnostic method.

[0005] FARAGO P11610DE.PROV76 According to another aspect of the invention, which can be found as according to claim 16, the object of the invention is solved by a device for the standardized acquisition of urine dipstick color reaction data. This device is particularly adapted to carry out any of the methods described in the preceding claims. It is constructed in such a way that it comprises a light- sealed enclosure equipped with internal fixed illumination, a replaceable setup plate that holds a urine dipstick in a predefined and repeatable orientation, a funnel structure designed to direct liquid samples precisely onto the reactive area of the test pad, and a camera fixture that maintains a mobile device or imaging unit in a constant geometric relationship with respect to the test pad. In regard of the terms used, the following definitions are to be understood: A light-sealed enclosure is a structural casing designed to eliminate the influence of external light sources during the image capture process. This enclosure ensures that all optical measurements are performed under stable and reproducible lighting conditions, which is essential for precise hue extraction over time. The internal fixed illumination refers to light elements, such as high color rendering index LEDs, which are permanently mounted within the enclosure and configured to provide homogeneous lighting that does not fluctuate across test runs. The replaceable setup plate is a mechanically dockable component that allows for the consistent placement and easy replacement of test strips, ensuring each strip is aligned in the exact same position during each measurement cycle. The funnel structure serves to guide the applied urine sample directly and evenly to the active zone of the test pad, thereby avoiding samplemisapplication, splashing, or uneven wetting, which could all compromise data integrity. Thecamera fixture is a mechanical holding structure that secures a mobile phone or imaging sensor in a fixed position with controlled angle and focal length, thereby ensuring that the recorded image of the test pad remains geometrically stable across repeated measurements. The construction of such a device may involve the use of polymeric materials with integrated optical baffles or coatings to enhance internal reflectivity or absorption, depending on theFARAGO P11610DE.PROV77 desired light distribution. The camera fixture may be adjustable to accommodate a range of smartphone models, or it may use a universal clamp system that ensures alignment regardless of camera position. The setup plate may be encoded with alignment cues, such as contrast lines or reference symbols, to aid automated region detection during image processing. The funnel may optionally include flow regulators or pre-wetted absorbent pads to control the timing and uniformity of fluid contact. The technical advantages realized through this design are significant. The device creates a controlled microenvironment for optical measurements, drastically reducing variability introduced by ambient light, manual camera handling, and inconsistent strip placement. This controlled geometry and illumination condition are essential prerequisites for algorithmic analysis of time-resolved colorimetric reactions, particularly when relying on subtle hue dynamics. The use of off-the-shelf mobile imaging hardware further promotes cost efficiency and accessibility, enabling the system to be deployed in both clinical and non-clinical settings without specialized training or equipment. According to another aspect of the invention, which can be found as according to claim 17, the object of the invention is further solved in that the device comprises a printed reference color scale positioned adjacent to the test pad. This reference is visible within the image field during analysis and provides a stable calibration standard against which the measured color values can be adjusted. The term printed reference color scale refers to a set of chromatic fields or patches, each exhibiting a known and predefined hue and saturation, typically produced using highly stableprinting techniques or deposited pigments. These reference fields serve as visual anchorswithin the image data, allowing the processing software to correct for variations in device-FARAGO P11610DE.PROV78 specific color response, sensor noise, or minor illumination shifts, even when internal lighting is nominally constant. In certain configurations, the reference scale may be integrated directly into the replaceable setup plate, ensuring it is always located at a consistent distance and angle from the test field.In other embodiments, the reference may be printed on the test strip itself, provided thisdoes not interfere with sample application. The materials used for the reference scale must be resistant to chemical contamination, humidity, and mechanical wear to preserve their chromatic accuracy over multiple uses. The presence of this reference scale enables dynamic calibration, making it possible to correct every test in real time without requiring batch-specific factory calibration of the device or camera. This ensures that results are comparable not only within one user’s device but also across different devices, test kits, and environmental conditions. It enhances trust in the system and lays the foundation for clinical-grade reproducibility. According to another aspect of the invention, which can be found as according to claim 18, the object of the invention is further solved in that the internal surfaces of the enclosure are designed using reflective or absorptive materials to achieve optimal lighting uniformity. These surfaces are selected and positioned so that light emitted from the internal illuminationsources is distributed evenly across the entire area of the test pad, eliminating local hotspots,dark zones, and edge shadows. In this context, reflective materials are those which scatter or diffuse incident light efficiently, typically featuring matte white or light-gray surface coatings with low gloss and high reflectance across the visible spectrum. They are used to redirect light from the sourcesFARAGO P11610DE.PROV79 toward the test area, ensuring angular homogenization of illumination. Absorptive materials, by contrast, are used to suppress stray reflections that might otherwise distort the apparent color of the test pad. These are typically matte black coatings or structured surfaces that absorb incident light and prevent unintended optical feedback. By carefully engineering the spatial arrangement and optical properties of these materials, it becomes possible to shape the internal light field within the enclosure to achieve excellent flat-field illumination. This enhances the quality of image data acquired from the test pad and significantly reduces the computational burden of post-capture correction, resulting in faster and more accurate test evaluation. According to another aspect of the invention, which can be found as according to claim 19, the object of the invention is further solved in that the camera fixture includes a guide or positioning slot, which ensures automated alignment of the camera with respect to the test field. This alignment mechanism ensures that each recorded video sequence or image series captures the test pad from the same perspective and at the same focal distance. The term guide or positioning slot refers to a mechanical structure, such as a molded recess, track, or adjustable clamp, which accepts a mobile phone or similar device and holds it in a defined position. This prevents rotational or translational displacement during test execution and ensures that the region of interest appears consistently in the image field. Depending on the design, the fixture may incorporate tactile feedback or locking features to confirm correct alignment. This structural solution eliminates the need for the user to manually aim or frame the camera before starting a test. It supports automatic region detection during image processing andFARAGO P11610DE.PROV80 improves the accuracy of hue extraction by eliminating geometric distortion. Moreover, it reduces setup time and enables consistent operation even by untrained users, contributing to the scalability and robustness of the diagnostic system. According to another aspect of the invention, which can be found as according to claim 20, the object of the invention is further solved in that the funnel structure includes a built-in channel or absorbent element that ensures even wetting of the test pad upon sample application. This mechanism serves to distribute the liquid uniformly across the reaction zone, thereby avoiding uneven coloration or incomplete activation of the chromogenic field. A channel in this context is a molded or embedded conduit designed to direct the sample flow from the point of introduction to the reactive surface. Its geometry is selected to promotelaminar flow and minimize turbulence. An absorbent element is typically a porous material,such as a structured cellulose sponge or nonwoven fiber layer, positioned within the channel to slow down the sample’s advance and promote uniform lateral dispersion before reaching the test pad. Together, the channel and absorbent features ensure that the color reaction begins simultaneously and progresses evenly across the whole test surface. This uniformity is essential for time-resolved color analysis, as uneven wetting would otherwise lead to spatial and temporal gradients that complicate or invalidate algorithmic evaluation. The inclusion of these fluid-handling features thus enhances both user convenience and measurement fidelity. According to another aspect of the invention, which can be found as according to claim 21, the object of the invention is further solved by a computer program product stored on a non-FARAGO P11610DE.PROV81 transitory medium, wherein the program is configured to perform all computational steps necessary for the automated analysis of the colorimetric test. These steps include receiving a time-resolved video sequence of the test pad, identifying and segmenting the region of interest within each video frame, extracting HSV color values from the segmented region over time, calculating hue-time curves as well as derived metrics such as endpoint hue and area- under-the-curve values, and classifying the sample with respect to the presence of hemoglobin, myoglobin, or both. In this context, a computer program product refers to software stored on a digital medium such as a solid-state drive, flash memory, or cloud-based container, which is executable by a general-purpose processor or mobile computing unit. Segmentation involves identifying the pixel coordinates corresponding to the test field and excluding background, alignment marks, or unrelated regions. HSV extraction denotes the transformation of RGB pixel values into the hue, saturation, and value dimensions of the color space, where hue is prioritized for its correlation with chromogen oxidation state. Classification refers to the final interpretation of the measured hue progression based on predefined or learned models. Through its architecture, this software enables the full automation of a complex diagnostic process that would otherwise require trained personnel. It eliminates human subjectivity, reduces operational error, and allows for consistent application of interpretive criteria across tests and users. Furthermore, it enables the integration of the test system into broader digital health infrastructures, supporting data export, cloud storage, and remote monitoring. According to another aspect of the invention, which can be found as according to claim 22, the object of the invention is further solved in that the classification module of the software includes both threshold-based and regression-based models. These two approaches offer complementary strengths in processing hue-time data.FARAGO P11610DE.PROV82 Threshold-based classification relies on predefined numerical cut-off values which segment the diagnostic feature space into discrete categories. This model is transparent, easy to validate, and highly efficient computationally. Regression-based classification, on the other hand, uses learned mathematical functions that map input features to continuous outputs or probabilities, enabling finer distinctions and increased adaptability to complex, real-world data. By including both classification strategies, the system can balance interpretability and precision. Simple cases may be resolved with fast rule-based logic, while borderline or ambiguous cases benefit from the nuance and flexibility of regression-based inference. The hybrid approach also allows continuous improvement of diagnostic accuracy through model refinement over time. According to another aspect of the invention, which can be found as according to claim 23, the object of the invention is further solved in that the software includes a preprocessing stage which performs rotation correction, scaling, and color calibration on the incoming video data. This step enhances the consistency and reliability of the extracted hue data by normalizing the visual input prior to diagnostic analysis. Rotation correction realigns any tilted or skewed image frames so that the region of interest appears in a standard orientation. Scaling adjusts the resolution or field-of-view to match reference parameters, ensuring consistent pixel density and image framing. Color calibration aligns the captured color data with reference standards, either through comparison to printed patches or through embedded device profiles, compensating for sensor-specific or environmental shifts.FARAGO P11610DE.PROV83 These preprocessing routines ensure that variations in how the test was recorded do not compromise the quality of the analysis. They reduce the burden on downstream classification algorithms and improve the robustness of the system under diverse usage conditions. According to another aspect of the invention, which can be found as according to claim 24, the object of the invention is further solved in that the software displays analysis results graphically and optionally provides means for exporting the data. The results are presented to the user in an intelligible and informative way, helping to interpret the test outcome and share it with third parties if needed. The graphical display may include plots of the hue-time curve, indication of classification outcome, confidence scores, and trend history. Export functionality allows the user to store test results in standardized formats or transmit them securely to healthcare providers, electronic health records, or cloud storage platforms. This enhances transparency, trust, and utility of the diagnostic system, transforming it from a mere color reader into a full-featured decision-support tool. According to another aspect of the invention, which can be found as according to claim 25, the object of the invention is further solved in that all analysis is performed in real time directly on a mobile device. This eliminates the need for server-side processing and enables the system to function in offline or bandwidth-limited settings.FARAGO P11610DE.PROV84 Real-time analysis refers to the execution of image acquisition, processing, feature extraction, and classification during or immediately following the test procedure, without perceptible delay. The mobile device serves as the host for the full diagnostic pipeline, leveraging its onboard computational and imaging capabilities. This on-device autonomy accelerates feedback, protects user privacy by avoiding data uploads, and ensures that the test can be used anywhere, anytime, even in resource- constrained environments. According to another aspect of the invention, which can be found as according to claim 26, the object of the invention is solved by a method for non-clinical detection of myoglobinuria, wherein the method corresponds to any of the preceding claims 1 to 15 and is applied specifically to a urine sample obtained from an individual engaged in strenuous physical activity, military service, or rehabilitation training. This application context extends the scope of the invention beyond formal clinical diagnostics, enabling the monitoring of muscle stress and early detection of muscle breakdown in real-world, physically demanding environments. In regard of the terms used, the following definitions are to be understood: The term non- clinical detection refers to diagnostic or monitoring procedures performed outside ofinstitutional medical settings, such as hospitals or laboratories. This includes but is not limitedto sports facilities, military field deployments, outpatient rehabilitation centers, and private homes. Myoglobinuria describes the pathological presence of myoglobin in the urine, typically resulting from the degradation of muscle tissue following excessive strain, trauma, or pathological muscle damage. It is of particular concern in endurance athletes, military personnel undergoing load-bearing training, or patients recovering from orthopedic or neurological impairment. The detection method as described in the present invention is especially suited for this context, as it requires no invasive sampling, laboratory infrastructure, or technical expertise. Instead, it operates entirely on a urine sample obtained throughFARAGO P11610DE.PROV85 normal voiding and analyzed using the standardized colorimetric evaluation techniques as previously disclosed. The application of the method under such conditions enables real-time, portable, and repeatable screening for rhabdomyolysis, overtraining, or muscle fatigue syndromes. The invention is thus particularly valuable in scenarios where timely intervention may prevent further injury or facilitate individualized training regimens. The diagnostic system can be implemented in mobile units, field clinics, or integrated into wearable monitoring protocols, providing actionable insights in settings traditionally underserved by laboratory diagnostics. According to another aspect of the invention, which can be found as according to claim 27, the object of the invention is further solved in that the detection as described in claim 26 is conducted by the user at home using a smartphone and a dipstick kit. This embodiment represents a fully self-administered diagnostic protocol, bringing advanced colorimetric and algorithmic analysis capabilities into the domain of consumer health technology. In regard of the terms used, the following definitions are to be understood: The user is understood to be a non-professional individual with no formal training in medical diagnostics, but capable of following structured instructions for sample collection and device handling. The phrase at home signifies that the procedure is performed in a domestic setting without supervision by healthcare personnel. The smartphone, as used in this context, is a commercially available mobile communication device equipped with a camera, processor, and display, which executes the software described in earlier claims to perform video capture, hue extraction, data processing, and classification. The dipstick kit comprises a disposable diagnostic strip incorporating the peroxidase-reactive test pad as previously described, packaged together with optional instructions, sample containers, or alignment aids.FARAGO P11610DE.PROV86 This embodiment brings together simplicity of use and diagnostic sophistication, creating a self-contained system for the early detection and tracking of myoglobin-related anomalies inthe urine. The combination of low barrier to entry, immediate feedback, and integration withdigital health tools makes the method ideal for sports enthusiasts, elderly patients, or anyone requiring remote or frequent monitoring. According to another aspect of the invention, which can be found as according to claim 28, the object of the invention is further solved in that longitudinal results are collected to form a recovery or training readiness profile. This extends the use of the invention from a single- time-point diagnostic method to a time-series monitoring system that reflects the dynamic state of muscle recovery and stress resilience over time. In regard of the terms used, the following definitions are to be understood: Longitudinal results refer to repeated measurements obtained from the same individual across differenttime points, typically recorded in a structured and comparable format. These results areanalyzed to identify trends, deviations, or patterns that correspond to physiological recovery after exertion, or preparedness for renewed physical activity. A recovery profile denotes a representation, quantitative or qualitative, of how the body metabolizes and clears myoglobin following muscular strain. A training readiness profile further interprets this data in the context of planned physical activity, indicating whether the individual is sufficiently recovered or at risk of cumulative damage. The system may store past results locally on the device, or transmit them securely to cloud- based services where analytic models evaluate the trajectory of hue-time parameters, integral ratios, or endpoint values. These trajectories can then be visualized and interpretedFARAGO P11610DE.PROV87 through intuitive dashboards, aiding athletes, coaches, or healthcare providers in making informed decisions regarding workload, rest periods, and medical evaluation. The ability to generate individualized baselines and monitor deviations in real time represents a major innovation in preventive and personalized health management. It moves the invention beyond static diagnostics and into the realm of adaptive, behavior-sensitive biofeedback. According to another aspect of the invention, which can be found as according to claim 29, the object of the invention is further solved in that the results of the method are integrated into a remote health monitoring platform. This creates a systemic interface between the locally performed urine analysis and centralized medical or coaching systems that can evaluate, interpret, and act upon the diagnostic data. In regard of the terms used, the following definitions are to be understood: A remote health monitoring platform is a digital ecosystem, typically cloud-based, that aggregates data from multiple individuals and sources, enabling centralized storage, analysis, and alerting. Integration in this context means that the output from the colorimetric test—such as diagnostic classifications, hue curves, or kinetic indices—is formatted, encrypted, and transmitted via secure protocols to this platform, where it becomes accessible to authorized stakeholders. These stakeholders may include medical professionals, sports physiologists, rehabilitation coordinators, or even automated rule-based systems capable of issuing alerts or recommendations. The utility of such integration is particularly pronounced in cohort management scenarios, where many individuals are subject to similar physical demands and require collectiveFARAGO P11610DE.PROV88 surveillance for early signs of overtraining, dehydration, or renal strain. The system supports population-wide analytics, anomaly detection, and intervention triage, thereby contributing to scalable preventive health strategies. According to another aspect of the invention, which can be found as according to claim 30, the object of the invention is further solved by a cartridge for refilling an in-situ urine analysis device, wherein the cartridge comprises a substrate with at least one peroxidase-reactive test field adapted for hue-based discrimination of hemoglobin and myoglobin, and optionally further comprises additional test fields selected from the group consisting of glucose, leukocytes, pH, nitrite, protein, ketones, or specific gravity. In regard of the terms used, the following definitions are to be understood: A cartridge is a modular, replaceable component intended to be inserted into an analysis device, such as a smart toilet, urinalysis reader, or point-of-care system. The cartridge includes a substrate, typically a plastic or polymeric carrier, on which chemically treated test fields are arranged. The peroxidase-reactive test field corresponds functionally to those already described in the earlier method claims, and is specifically formulated to support hue-based discrimination via time-resolved analysis. The optional test fields provide additional biochemical measurements based on colorimetric or electrochemical detection methods, enabling a more comprehensive health screening. The cartridge may include printed alignment guides, fluid routing layers, or optical windows, depending on the configuration of the host device. Each test field is positioned and engineered to interact optimally with the fluid stream and the device’s imaging or sensing components.FARAGO P11610DE.PROV89 The use of such a refillable cartridge supports scalable deployment of the system in semi- automated or fully automated sanitary installations. It enables high-throughput, contact-free monitoring in hygiene-sensitive environments such as hospitals, elder care facilities, or athletic institutions. The optional inclusion of additional analytes expands the diagnostic utility beyond myoglobin and hemoglobin, potentially transforming the system into a full- spectrum urinalysis platform. Importantly, the cartridge format supports standardization, traceability, and ease of replacement, thereby enhancing both operational convenience and long-term usability. According to another aspect of the invention, which can be found as according to claim 31, the object of the invention is further solved in that the cartridge as described in claim 30comprises test fields that are pre-arranged to correspond to the optical paths of anautomated image analysis system. This configuration ensures that, once inserted into the analysis device, each reactive zone aligns precisely with the field of view or designated sensor axis of the imaging system, thereby enabling fully automated, parallel acquisition of multiple diagnostic signals. In regard of the terms used, the following definitions are to be understood: The term pre- arranged in this context refers to a deliberate spatial configuration of the test fields on thecartridge substrate, in accordance with the fixed optical architecture of the host system. Thismeans that each color-reactive zone is placed at a predefined position such that, when illuminated and imaged by the internal optics of the device—be it via a camera, a photodiodearray, or any other optical sensor—it appears at a known coordinate in the captured datastream. The optical path refers to the trajectory of light from the test field to the sensor, including the effects of lenses, mirrors, filters, or diffusers that may be present in the imaging assembly.FARAGO P11610DE.PROV90 The advantage of this spatial matching lies in the ability to process each test field independently and accurately without requiring active adjustment or software-based region detection. This streamlines the analytical workflow, reduces error due to misalignment, and accelerates test interpretation. Furthermore, it supports the implementation of cartridges with multiple fields without increasing system complexity, enabling true multiplex diagnostics in compact, modular formats. According to another aspect of the invention, which can be found as according to claim 32, the object of the invention is further solved in that each test field on the cartridge as described in claim 30 or 31 is individually encapsulated to avoid cross-contamination. This encapsulation ensures that the reaction taking place in one test zone remains chemically and optically isolated from adjacent zones, thereby preserving the specificity and reliability of each individual measurement. In regard of the terms used, the following definitions are to be understood: Individual encapsulation means that each test pad or reactive area is surrounded by a physical barrier, a fluidic isolation zone, or a hydrophobic moat that prevents the lateral migration of sample fluid or diffusion of reagents. This may be achieved using molded plastic wells, thermally bonded films, or printed barrier layers, depending on the manufacturing method. Cross- contamination in this context refers not only to the physical mixing of fluid between adjacent test areas, but also to the optical or chemical interference caused by chromogenic bleed-over, reagent migration, or overlapping color development. By ensuring that each field is chemically isolated, the integrity of test results is preserved even under conditions of high sample load, prolonged wetting, or turbulent flow. This is particularly important in cartridges containing peroxidase-reactive fields, as these reactions are sensitive to oxygen, pH, and co-reactants that may be unintentionally introduced from neighboringFARAGO P11610DE.PROV91 regions. The encapsulated architecture therefore provides both analytical accuracy and robustness in complex test environments. According to another aspect of the invention, which can be found as according to claim 33, the object of the invention is further solved by a combined method for urine analysis, wherein the method comprises performing the previously described hue-time-based discrimination of hemoglobin and myoglobin and simultaneously performing at least one additional urinalysis test using other test pads on the same dipstick or cartridge, with the goal of generating a comprehensive result that includes both peroxidase-based and additional analyte data. In regard of the terms used, the following definitions are to be understood: A combined method refers to a diagnostic procedure in which multiple independent analyses are conducted in parallel or in immediate succession, using the same physical sample and substrate. The simultaneous performance of hemoglobin and myoglobin discrimination with further urinalysis involves applying the urine sample to a test strip or cartridge that incorporates multiple zones, each dedicated to detecting a specific chemical, physical, or biological parameter. These additional tests may include, but are not limited to, glucose concentration, leukocyte esterase activity, nitrite presence, proteinuria, ketone levels, urine pH, or specific gravity. The image analysis system used to evaluate the test may capture the entire cartridge area in a single optical pass, extract relevant colorimetric properties from each zone, and compute a multivariate result set. The inclusion of both time-resolved kinetic data and single-point colorimetric evaluations enables the generation of a unified diagnostic profile that reflects the user’s physiological state across several metabolic dimensions.FARAGO P11610DE.PROV92 The value of such a combined method lies in its capacity to identify not only isolated events, such as muscle degradation, but also systemic or comorbid conditions. For example, the concurrent detection of myoglobinuria and elevated protein levels may suggest renal involvement, while simultaneous glucose and ketone presence may point toward diabetic ketoacidosis. The integration of these diverse test results into one cohesive output facilitates faster, more informed medical decision-making. According to another aspect of the invention, which can be found as according to claim 34, the object of the invention is further solved in that the results of the combined method as described in claim 33 are time-aligned to provide synchronous color reaction data. This means that the interpretation of multiple test zones is performed with reference to a common temporal framework, enabling kinetic correlations and enhancing analytical coherence. In regard of the terms used, the following definitions are to be understood: Time alignment refers to the process of correlating the measurement time points for all relevant test fields toa shared starting point, typically the moment of sample application or reaction initiation. Inthe case of hue-time analysis, the time axis is critical, as the kinetics of the color reaction carry diagnostic meaning. In traditional urinalysis, static endpoint values suffice; however, with multiple kinetic and non-kinetic fields operating simultaneously, a unified temporal framework ensures consistency in data capture and interpretation. This synchronized approach allows the analysis software to consider interdependencies between different test zones—for example, whether a delay in peroxidase reaction corresponds with delayed glucose oxidation or slow leukocyte activation. It also ensures that all measurements reflect the same physiological time window, improving the relevance and accuracy of composite diagnostics.FARAGO P11610DE.PROV93 The benefit of time-aligned analysis is most evident in longitudinal or comparative applications, where temporal integrity across different analyte responses becomes a key quality parameter. It also enhances visualization, as users and clinicians can assess how multiple parameters evolve in parallel across a shared time axis. According to another aspect of the invention, which can be found as according to claim 35, the object of the invention is further solved in that the analysis platform integrates results from both visual test pads and additional sensors. This constitutes a hybrid diagnostic system that expands beyond colorimetric analysis by incorporating electronic, optical, or biochemical sensors capable of measuring supplementary parameters. In regard of the terms used, the following definitions are to be understood: An analysis platform, in this context, refers to the combination of hardware and software components that process input data from the test cartridge and deliver a diagnostic output. Visual test pads are those whose results are interpreted by analyzing visible color changes, as in the case of the hue-time curves discussed throughout this disclosure. Additional sensors may include pH electrodes, conductivity meters, refractometers, temperature sensors, or fluorescence detectors, which provide data that are not accessible through colorimetric means alone. The integration of visual and non-visual data sources requires a coherent data processing architecture, capable of synchronizing signals, resolving conflicts, and generating composite interpretations. This multi-sensor integration enhances diagnostic depth, as some parameters—such as osmolarity or hormone concentrations—may not produce a visible chromatic response, but can significantly influence the clinical picture. The ability to combine traditional dipstick chemistry with modern sensor technologies elevates the system from a single-modality tool to a full-spectrum point-of-care laboratory. It opens the door to machine learning–based inference models that correlate visual patterns with sensor readings to improve predictive power. It also prepares the platform for futureFARAGO P11610DE.PROV94 scalability, as new sensors and test formats may be introduced without altering the underlying analytical logic According to another aspect of the invention, which can be found as according to claim 36, the object of the invention is solved by a device for the in-situ analysis of mitigated urine in a toilet vessel, comprising a sensor and a remote or local computational processing unit, wherein the system is configured to execute a method according to any of the preceding method claims and / or to comprise a computer program according to any of the preceding software claims, and / or to embody a device according to any of the earlier device claims. This embodiment extends the scope of the invention into a sanitary context where diagnostic testing is integrated directly into toilet infrastructure, thereby enabling passive, repeated, and user-unobtrusive health monitoring during routine urination. In regard of the terms used, the following definitions are to be understood: A device for in- situ analysis is a diagnostic system that performs the detection and evaluation of analytes directly at the point of urination, without requiring the user to actively collect or transfer a sample. In this embodiment, the device is built into or physically associated with a toilet bowl, urinal, or equivalent fixture that is used for the natural excretion of urine. The term mitigated urine denotes urine that may be altered or diluted as a result of its interaction with the ambient environment, including contact with flush water, surface wetting, or aeration. Despite such mitigation, the diagnostic process must remain sufficiently robust to extract meaningful signals from the sample. The sensor integrated into the device may be an optical sensor capable of colorimetric detection, such as a digital camera, photodiode, or spectrophotometric array, configured to monitor a chromogenic test field located within oradjacent to the bowl. The computational processing unit may be either embedded within thehousing of the toilet or situated remotely, for example in a control box, smartphone, or network server, and is tasked with executing the image processing and classification algorithms described throughout this disclosure.FARAGO P11610DE.PROV95 This embodiment requires a carefully coordinated architecture that ensures that the urine stream reliably reaches the reactive test zone under conditions that preserve the diagnostic value of the chromogenic reaction. The location and design of the test field must ensure sufficient exposure to the fluid, while also avoiding contamination from splashing, detergent residue, or intermittent flush cycles. The sensor must be positioned such that it maintains a clear optical path to the test field, free from obstructions or fogging, and protected against moisture ingress or soiling. In parallel, the processing unit must support the extraction of hue values over time, the generation of kinetic descriptors such as integral ratios and endpoint values, and the classification of the result with respect to the presence or relative predominance of hemoglobin or myoglobin. The advantages of this embodiment are numerous. By embedding the diagnostic process into a toilet, the invention allows for completely passive health monitoring, requiring no additional effort or behavioral change on the part of the user. This enables high-frequency testing in populations where regular screening would otherwise be impractical, such as in geriatric care, rehabilitation wards, or military training facilities. The unobtrusive nature of the setup also facilitates longitudinal analysis, as results can be collected over time to identify trends, acute events, or chronic changes. Moreover, by linking the measurement process to a frequently used sanitary fixture, the invention creates a diagnostic platform that is inherently scalable, hygienic, and seamlessly integrated into daily life. The robustness of the underlying colorimetric and algorithmic principles ensures that even mitigated samples yield reliable and clinically meaningful results. In other words, one might describe as follows: The present invention relates to a method and a device for the non-stationary evaluation of test strips used for the qualitative and / or quantitative detection of analytes in a fluid sample, particularly in a biological sample such as urine. The invention further provides a novel diagnostic approach that enables the distinction between analytes based on their temporal colorimetric reaction patterns, using conventional or modified test strips, without the need for specialized laboratory equipment or trained personnel.FARAGO P11610DE.PROV96 According to one aspect of the invention, the method comprises the following steps: First, a test strip is provided which comprises one or more reactive test pads impregnated with a reagent formulation. These reagents are designed to undergo a detectable color change when they come into contact with a specific analyte or group of analytes, wherein the intensity, hue, or kinetics of the color change are indicative of the presence and / or concentration of the target substance. In the next step, the test pad is brought into contact with the sample, such as through immersion or dropwise application of urine, thereby activating the chromogenic reaction. Following activation, at least two images of the test pad are acquired at defined time points using an optical recording device, preferably a digital camera or imaging sensor. These images may be taken at predefined intervals or continuously in video form, depending on the configuration of the system. Subsequently, the colorimetric properties of the test pad are evaluated based on the captured image data. This evaluation may involve the measurement of hue values, brightness, saturation, or any other optically detectable parameter that reflects the progress of the underlying chemical reaction. Preferably, hue values are extracted in the HSV color space, as this format separates chromatic information from brightness and thereby supports robust analysis under variable lighting conditions. The extracted values are used to construct a time-dependent progression curve — for example, a hue-time curve — which describes theevolution of the color response over the monitored interval. This progression curve is then mathematically compared to reference curves or modelled reaction profiles that correspond to known concentrations and compositions of analytes, particularly hemoglobin and myoglobin. Based on this comparison, the analyte composition of the sample is approximated and, optionally, quantified. One specific strength of this method lies in its ability to distinguish between hemoglobin and myoglobin, even when bothFARAGO P11610DE.PROV97 analytes activate the same peroxidase-sensitive color reaction. Because the temporal dynamics of the chromogenic response differ between these two proteins, the method enables differentiation not only between presence and absence, but also between individual and mixed analyte states. In a preferred application, the invention provides a method and device for detecting myoglobin in urine using test strips that are sensitive to both hemoglobin and myoglobin, but which are evaluated using time-resolved hue analysis in order to separate their signals. After the test pad has been contacted with the sample, the hue value of the blood test field is measured over time, and the area under the hue-time curve is calculated for two or more defined time intervals. These area values are then combined into a diagnostic ratio that is characteristic of the analyte’s kinetic profile. When compared to stored reference values obtained from controlled reactions with pure hemoglobin and pure myoglobin, the system can classify the sample accordingly and estimate the analyte concentration. The invention also relates to a device for carrying out the method described above. The device comprises a urine test strip with a test pad containing an organic peroxide, such as cumene hydroperoxide or tert-butyl hydroperoxide, and a chromogen, such as tetramethylbenzidine (TMB), o-tolidine, or ABTS, that is oxidized in the presence of hemoglobin or myoglobin. The test strip is supported in a holder that ensures stable positioning and allows precise application of the urine sample onto the test pad. A camera or imaging system is arranged in fixed geometric relation to the test field and is capable of recording images or video sequences of the color change as it unfolds. The optical system may include supplemental elements such as a standardized illumination source, optical filters, or adjustable focusing optics to enhance image quality and reproducibility.FARAGO P11610DE.PROV98 The recorded image data is analyzed by a processor, which may be embedded in the device or externally connected, such as in a smartphone, tablet, or computer. The processor executes image analysis routines that convert the RGB image data to HSV format, extract hue values from the region of interest, and compute diagnostic parameters such as area ratios or endpoint hues. These calculated metrics are then compared to reference values stored in amemory unit associated with the processor. The memory may reside locally within the deviceor be cloud-based, and it contains validated hue-time profiles for known concentrations and mixtures of hemoglobin and myoglobin. Based on this comparison, the processor determines whether myoglobin is present in the urine sample and, if so, estimates its concentration or contribution relative to hemoglobin. The result of the analysis is displayed to the user via a user interface, which may consist of a screen, LED indicator, acoustic signal, or app-based visualization on a paired device. The system may also include connectivity functions for storing results, generating logs, or transmitting data to healthcare providers for further evaluation. The invention is not limited to the detection of myoglobin or hemoglobin. Rather, the disclosed principles apply broadly to the detection of any analyte that can be made to react with a chromogenic reagent on a test pad and exhibit a color change with analyte-specific kinetics. In particular, the invention may be extended to the diagnosis of conditions such as diabetes, kidney dysfunction, urinary tract infection, or bladder cancer, by applying the sametemporal analysis techniques to test pads for glucose, protein, nitrite, leukocytes, or pH,respectively. The method offers the significant advantage of being usable in both professional and layperson settings. It eliminates the need for visual comparison with printed color scales and removes the requirement for specialized analytical equipment or trained operators.FARAGO P11610DE.PROV99 Myoglobinuria, the presence of myoglobin in urine, is often an indicator of muscle tissue breakdown and may be associated with a wide spectrum of underlying causes, including trauma, metabolic disorders, seizures, prolonged immobilization, heat stroke, or drug- induced rhabdomyolysis. Because myoglobin shares structural and chemical features withhemoglobin — both being globin proteins containing heme groups — their reactions withchromogenic peroxidase substrates such as TMB are nearly indistinguishable in endpoint color. However, as has been newly recognized and technically implemented by the present invention, their reaction kinetics differ in a reproducible manner. This insight forms the core of the invention’s diagnostic capability and enables a previously unavailable separation of signals on a shared test pad. By converting hue dynamics into time-indexed metrics, and matching those against a library of known profiles, the invention introduces a novel class of non-invasive diagnostics that are simple, cost-effective, and well suited for real-world use. This includes deployment in clinical environments, home settings, athletic training facilities, military installations, and other contexts where early detection of muscle injury, strain, or metabolic disorder is desirable.Through its combination of established test strip chemistry and advanced time-based dataanalysis, the invention bridges the gap between traditional colorimetric testing and modern algorithmic diagnostics.

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Claims

FARAGO P11610DE.PROV104 Patent Claims 1. A method for extracorporeal detection of a component in a fluid, in particular a component in a liquid, especially excreted urine, comprising the use of a reactant that exhibits a color reaction property in response to the component, wherein the reactant is provided in a carrier medium, wherein the reactant is brought into contact with the fluid at a defined start time, whereafter an optical sensor system is used to detect a colorimetric property of the reactant at a plurality of, in particular at least two, measurement time points, whereafter a time-dependent progression of the color change behavior is determined or approximated by means of algorithmic processing, whereafter the temporal profile of the color reaction is mathematically compared to reference data, thereby enabling or performing an inference regarding the presence or relative predominance of the component in the fluid, preferably without using a visual color comparison scale, in particular for distinguishing two different components based on differing temporal characteristics of the color response of the reactant in their presence.

2. The method according to claim 1, wherein the component comprises hemoglobin(Hb) and / or myoglobin (Mb), and wherein a time-resolved video of the color change of a peroxidase-reactive test pad on a urine dipstick is captured after contact with the sample,FARAGO P11610DE.PROV105 and hue values are extracted and analyzed over time to determine the presence of Hb or Mb based on characteristic response patterns, so that the method is a method for distinguishing hemoglobin (Hb) from myoglobin (Mb) in a urine sample, comprising: ^capturing a time-resolved video of the color change of a peroxidase-reactive test pad on a urine dipstick after contact with the sample; ^extracting hue values over time from said video;^ analyzing the hue-time curve to determine the presence of Hb or Mbbased on characteristic response patterns3. The method according to claim 1 or 2, wherein the fluid is urine.

4. The method according to any of claims 1 to 3, wherein the reactant comprises achromogen, in particular tetramethylbenzidine (TMB), capable of exhibiting peroxidase-like color changes in the presence of hemoglobin and / or myoglobin5 The method according to any of claims 1 to 4, wherein the colorimetric propertyis a hue value derived in the HSV color space, and optionally additionally includes saturation or value components.

6. The method according to any of claims 1 to 5, wherein the time-dependentprogression of the colorimetric property is evaluated by calculating the area under the hue-time curve in a first time interval (0 to 30 seconds) and a second time interval (30 to 60 seconds), and a ratio of said areas is computed.

7. The method according to any of claims 1 to 6, wherein^ the determined time-dependent profile is compared to reference datacorresponding to known reactions of Hb and Mb, optionally using threshold-based classification or regression models, and optionally in the context of sport, rehabilitation, or clinical diagnosticsFARAGO P11610DE.PROV106 and / or ^the video is captured using a mobile device fixed in a standardizedgeometry relative to the test pad. and / or ^the dipstick comprises a chromogen based on tetramethylbenzidine(TMB) and / or ^the test pad color change is induced by interaction with myoglobin,hemoglobin, or a mixture thereof in the sample and / or ^an image segmentation is used to isolate the test field region of interestbefore hue analysis and / or ^a preprocessing step is applied to correct for lighting conditions, cameracolor response, or background interference.

8. A method,especially according to any of the preceding claims, comprising: integrating the hue-time curve over a first time interval (0 to 30 seconds) and a second time interval (30 to 60 seconds); computing a ratio of said integrals as a diagnostic parameter for distinguishing Hb and Mb.FARAGO P11610DE.PROV1079. The method according to claim 8, wherein said ratio is used to assign a test resultto one of the categories: “Hb only”, “Mb only”, or “mixed presence”.

10. The method according to claim 8 or 9, wherein the ratio is compared to pre-defined thresholds derived from reference curves.

11. The method according to any of claims 8 to 10, wherein the hue integration isperformed using trapezoidal numerical approximation.

12. The method according to any of claims 8 to 11, further comprising computing ahue value at a predefined endpoint time and combining it with the integral ratio in a multivariate decision function.

13. A methodespecially according to any of the preceding claims, comprising: simultaneously analyzing the final hue value at 60 seconds and the integral ratio from 0–30 and 30–60 seconds; classifying the sample based on a two-dimensional fit model derived from reference data.

14. The method according to claim 13, wherein said two-dimensional fit model isderived from machine learning regression of validated training data.

15. The method according to claim 13 or 14, wherein said model includes correctionsfor total pseudo-peroxidase concentration.

16. A device for standardized acquisition of urine dipstick color reaction data,especially comprising means for executing a method according to any of the preceding claims,FARAGO P11610DE.PROV108 the device comprising: a light-sealed enclosure with internal fixed illumination; a replaceable setup plate configured to hold a urine dipstick in a predetermined orientation; a funnel structure integrated with the setup plate for precise liquid application to a defined test pad area; a camera fixture configured to mount a mobile device or camera in a fixed spatial relationship to the test pad.

17. The device according to claim 16, further comprising a printed reference colorscale located adjacent to the test pad.

18. The device according to claim 16 or 17, wherein the enclosure includes reflectiveor absorptive materials for lighting uniformity.

19. The device according to any of claims 16 to 18, wherein the camera fixtureincludes a guide or positioning slot for automated alignment.

20. The device according to any of claims 16 to 19, wherein the funnel structureincludes a channel or absorbent element to promote even wetting of the test pad.

21. A computer program product stored on a non-transitory medium, configured to:receive a time-resolved video of a peroxidase-reactive test pad; identify and segment regions of interest in individual frames; extract HSV color values from said regions over time; compute hue-time curves and calculate endpoint and area-under-curve metrics; classify the sample with respect to Hb, Mb, or both.FARAGO P11610DE.PROV10922. The computer program product according to claim 21, wherein the classificationincludes both threshold-based and regression-based decision models.

23. The computer program product according to claim 21 or 22, wherein video pre-processing includes rotation correction, scaling, and color calibration.

24. The computer program product according to any of claims 21 to 23, whereinanalysis results are displayed graphically and optionally exported.

25. The computer program product according to any of claims 21 to 24, whereinanalysis is performed in real-time on a mobile device.

26. A method for non-clinical detection of myoglobinuria, comprising performing themethod of any of claims 1 to 15 on a urine sample of an individual engaged in strenuous physical activity, military service, or rehabilitation training.

27. The method according to claim 26, wherein said detection is conducted by theuser at home using a smartphone and dipstick kit.

28. The method according to claim 26 or 27, wherein longitudinal results arecollected to form a recovery or training readiness profile.

29. The method according to any of claims 26 to 28, wherein results are integratedinto a remote health monitoring platform.

30. A cartridge for refilling an in-situ urine analysis device, comprising:a substrate with at least one peroxidase-reactive test field adapted for hue-based discrimination of Hb and Mb; optionally further test fields selected from: glucose, leukocytes, pH, nitrite, protein, ketones, or specific gravity.

31. The cartridge according to claim 30, wherein the test fields are pre-arranged tocorrespond to the optical paths of an automated image analysis system.FARAGO P11610DE.PROV11032. The cartridge according to claim 30 or 31, wherein each test field is individuallyencapsulated to avoid cross-contamination.

33. A combined method for urine analysis, comprising:performing the method according to any of claims 1 to 15 for Hb / Mb discrimination; simultaneously performing at least one additional urinalysis test using other test pads on the same dipstick or cartridge; generating a comprehensive result including both peroxidase-based and additional analyte data.

34. The method according to claim 33, wherein results are time-aligned to providesynchronous color reaction data.

35. The method according to claim 33 or 34, wherein the analysis platform integratesresults from both visual test pads and additional sensors.

36. A device for the in-situ analysis of mitigated urine in a toilet vessel, comprising asensor and a remote or local computational processing unit, arranged to execute a method according to any of the preceding method claims and / or comprising a computer program according to any of the preceding computer program claims, and / or according to any of the preceding device claims.