Method and device for performing analytical measurements - Patents.com

Abstract

Dynamic coefficient of variation limits (Cv TR、lim A method for determining a reagent test area coefficient of variation (Cv) is disclosed, the method comprising: a) providing a training set (111) of optical test strips (112); b) providing a training set (121) of mobile devices (122); c) providing at least one color reference card (126); d) capturing a training set of images, each image of the training set of images including at least a portion of at least one reagent test area (114) of an optical test strip (112) of the training set (111) of optical test strips (112) and at least a portion of at least one color reference field (128) of the color reference card (126); e) capturing a reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) and f) determining a training set of pairs of the corresponding measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation (Cv TR、lim derive relationships for determining the dynamic coefficient of variation limit (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max Also disclosed is a mobile device (122) having a camera (124) and a processor (138), a determination system (110), a mobile device (122), a computer program, and a computer readable storage medium to perform an analytical measurement based on a color forming reaction.

Description

[Technical field]

[0001] The present application relates to a determination method for determining a dynamic coefficient of variation limit for evaluating the validity of an optical test strip usable for an analytical measurement based on a color-forming reaction. The present invention also relates to a measurement method for performing an analytical measurement. The present invention further relates to a determination system for determining a dynamic coefficient of variation limit for evaluating the validity of an optical test strip usable for an analytical measurement based on a color-forming reaction. The present invention further relates to a mobile device, a computer program, and a computer-readable storage medium. The present invention can be used in medical diagnostics, in particular for the qualitative or quantitative detection of one or more analytes in one or more body fluids. However, other application fields of the present invention are also possible. [Background technology]

[0002] In the field of medical diagnostics, one or more analytes must often be detected from a sample of a body fluid, such as blood, interstitial fluid, urine, or saliva. Examples of analytes to be detected are glucose, triglycerides, lactate, cholesterol, or other types of analytes that are normally present in these body fluids. Depending on the concentration and / or presence of the analytes, appropriate treatments may be selected as required. Without narrowing the scope, the invention may be specifically described with respect to blood glucose measurements. However, it should be noted that the invention may also be used for other types of analytical measurements using test elements.

[0003] Generally, devices and methods known to those skilled in the art utilize a test element that includes one or more test chemistries that can exhibit one or more detectable detection reactions, such as optically detectable detection reactions, in the presence of the analyte to be detected.For test chemistries, see, for example, The Technology Behind Glucose Meters: Test Strips, Diabetes Technology & Therapeutics, Volume 10, Supplement 1, 2008, S-10 to S-26.Other types of test chemistries are possible and can be used to implement the present invention.

[0004] In analytical measurements, especially those based on color-forming reactions, one technical challenge is the evaluation of the color change caused by the detection reaction.In addition to using dedicated analytical devices such as handheld blood glucose meters, the use of commonly available consumer electronic devices such as smartphones, tablets, or other mobile devices has become more widespread in recent years.Therefore, the cameras included in these mobile devices can be used to measure the color change of the detection reaction.

[0005] Korean Patent Application Publication No. 2019 / 0091325 discloses a method, device, and system for detecting the gastrointestinal (GI) tract.

[0006] WO 2013 / 062487 discloses an embodiment of a method for identifying an object that includes receiving an image, the image having an object in front of a background, segmenting the image using a segmentation technique into a segmented image, the segmented image having a foreground component that indicates at least a portion of the object and a background component that indicates at least a portion of the background, determining at least one characteristic of the foreground component of the segmented image, and matching the at least one property of the foreground component to a database of identified objects having a corresponding at least one property to identify the object.

[0007] Despite these advantages realized by these known methods and devices, various technical challenges remain. Typically, mobile devices such as smartphones or tablets are not specifically dedicated to analytical measurements. They are usually personal consumer electronic devices that serve various purposes. Only one of the many purposes for which a mobile device may be used may be to take an image. For this reason, the quality of such images is typically low compared to analytical measurement settings. Furthermore, not all users may use the same type of mobile device, e.g., the same smartphone, and the characteristics of the mobile device may differ from device to device. Thus, specifically, the noise of the image may differ for different mobile devices. As an example, the resolution of the cameras of different smartphones may vary, and as a result, the analytical measurements may be subject to systematic deviations. Even when using the same mobile device, the quality of the images captured by the mobile device's camera may vary, for example due to different lighting. Thus, the quality of the image may depend on various environmental conditions in the user's daily life. In particular, the environmental conditions in the user's daily life are also often not suitable for capturing high-quality images. All these factors may have a negative impact on the accuracy and reliability of the analytical measurements. Summary of the Invention [Problem to be solved by the invention]

[0008] It would therefore be desirable to provide methods and devices that at least partially address the above-mentioned technical challenges, and in particular to provide methods and devices that aid users in ensuring accurate and reliable analytical measurements when using mobile devices such as consumer electronic devices.

[0009] This problem is addressed by methods and devices having the features of the independent claims. Advantageous embodiments, which may be implemented alone or in any combination, are set out in the dependent claims as well as in the entire specification.

[0010] The terms "have", "comprise" or "include" used below, or any grammatical variants thereof, are used non-exclusively. Thus, these terms may refer both to the situation where no further features are present in the entity described in this context, in addition to the features introduced by these terms, and to the situation where one or more additional features are present. As an example, the expressions "A has B", "A comprises B" and "A includes B" may both refer to the situation where, apart from B, no other elements are present in A (i.e., A consists solely and exclusively of B), and to the situation where, apart from B, one or more further elements are present in the entity A, such as element C, elements C and D, or further elements.

[0011] Furthermore, it should be noted that the terms "at least one," "one or more," or similar expressions indicating that a feature or element may be present one or more times are typically used only once when introducing each feature or element. In the following, in most cases, when referring to each feature or element, the expressions "at least one" or "one or more" will not be repeated, regardless of the fact that each feature or element may be present one or more times.

[0012] Furthermore, when used below, the terms "preferably", "more preferably", "particularly", "more particularly", "particularly", "more particularly" or similar terms are used with any feature without limiting the possibility of substitution. Thus, the features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. The invention may be implemented by using alternative features, as recognized by those skilled in the art. Similarly, features introduced by "in an embodiment of the invention" or similar expressions are intended to be optional features, without limitations on alternative embodiments of the invention, without limitations on the scope of the invention, and without limitations on the possibility of combining the feature so introduced with other optional or non-optional features of the invention.

[0013] In a first aspect of the present invention, a dynamic coefficient of variation limit (Cv TR、lim A determination method is disclosed for determining the number of times the number of the first and second inputs of the second input is determined. The determination method includes, by way of example, the following steps, which may be performed in a given order. It should be noted, however, that a different order may generally be possible. Furthermore, it may be possible for one or more of the method steps to be performed only once or repeatedly. Furthermore, it may be possible for two or more of the method steps to be performed simultaneously or overlapping in time. The determination method may include further method steps not listed.

[0014] The method of determination is as follows: a) providing a training set of optical test strips, each optical test strip having a reagent test area, at least two of the optical test strips being non-spoiled and at least two of the optical test strips being spoiled; b) providing a training set of mobile devices, each mobile device having at least one camera; c) providing at least one color reference card having a plurality of color reference fields having known reference color values; d) capturing a training set of images using the mobile device of the training set of mobile devices, each image of the training set of images including at least a portion of at least one reagent test area of ​​an optical test strip of the training set of optical test strips and at least a portion of at least one color reference field of the color reference card; e) Specifically, using at least one processor, more specifically at least one processor of a mobile device, a reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min) training set of pairs, each reagent test area coefficient of variation (Cv TR ) is determined by measuring the color variation within the reagent test area, and the coefficient of variation (Cv RF ) is determined by measuring the color variation within the color reference field and the corresponding reagent test area coefficient of variation (Cv TR ) minimum color reference field coefficient of variation (Cv RF、min ) is the color reference field coefficient of variation (Cv RF ), determining, determining, and f) Reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets, the corresponding measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation limits (Cv TR、lim derive relationships for determining the dynamic coefficient of variation limit (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max ), including defining and deriving

[0015] As used herein, the term "variation" is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, at least one of the following: statistical uncertainty, the statistical width of the distribution and the variance of the distribution, or the variation or deviation within the distribution. The variation may be described by various mathematical parameters commonly known to those skilled in the art of statistics. The distribution may include a distribution over one dimension, or a multidimensional space, such as, for example, a two- or three-dimensional space, or a distribution over time or over space and time. Specifically, the variation may include spatial color variation on an image. Thus, the distribution may refer to the spatial distribution of colors in an image, and color values, for example, expressed using RGB color space or any other color coordinate space, may vary spatially across the pixels of the image. The variation may be quantitatively and specifically expressed, for example, by any statistical parameter that describes the variation. As an example, the variation may be described by using at least one parameter that describes the statistical deviation of color values ​​from the mean of the distribution. The average may include, for example, an average, for example an arithmetic mean or root mean square, or a median. As an example, for a color distribution in an image, one color channel may be considered, for example the red channel in an RGB color space, and the red values ​​of all pixels in the image may be used to determine the average red value. Other color channels, other color spaces or other probabilistic evaluation methods may also be applicable and are known to those skilled in the art. The variation may be more generally quantitatively represented by using the variance of the distribution as commonly applied in the field of probability theory, or a quantity derived from the variance, such as the standard deviation σ. In particular, the variation may be quantitatively represented using the coefficient of variation, also called the relative standard deviation. Further options may also be feasible.

[0016] The term "coefficient of variation" (Cv) as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or special meaning. This term may refer, in particular, but not exclusively, to a standardized measure of the variance of a distribution, and the coefficient of variation may be specifically standardized by using the mean of the distribution. Furthermore, the coefficient of variation may be specifically a relative measure of the variance of a distribution, specifically a relative measure to the mean of the distribution. The coefficient of variation may be specifically a dimensionless number. More specifically, the coefficient of variation may be defined as the ratio of the standard deviation σ of the distribution divided by the mean μ of the distribution.

number

[0017] As those skilled in the art know, other statistical parameters may also be used as the coefficient of variation. Thus, the coefficient of variation may generally describe the variation, such as the variation of the color of an image, for example, as described above, in one or more dimensions. As further shown, the image may determine the average and standard deviation of the color channels, for example, by using the RGB values ​​of the pixels of the image or a similar method. Then, by way of example, the coefficient of variation of the image may be determined by dividing the determined standard deviation by the determined average.

[0018] The term "limit" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, at least one of a threshold, a boundary, a margin, a limit, and a restriction of an entity. The entity may include at least one physical quantity or at least one quantity derived therefrom, specifically the coefficient of variation. Thus, the coefficient of variation limit may specifically refer to a limit of the coefficient variation. The coefficient of variation limit may separate optical test strips suitable for use in analytical measurements from optical test strips not suitable for use in analytical measurements. The coefficient of variation limit may specifically limit or restrict the coefficient of variation, specifically upward. The coefficient of variation limit may include at least one extreme value of the coefficient of variation, specifically the maximum value. In general, the limit may include at least one inclusive endpoint, meaning that the entity may be equal to the limit. Additionally or alternatively, the limit may include at least one exclusive endpoint, meaning that the entity may not be equal to the limit. The limit may be a dynamic limit. The limit may be a defined limit. Specifically, the limits may be defined by using at least one relationship, e.g., a function, as outlined in more detail below. Thus, the limits may be the output of a function depending on at least one input variable, and the limits may specifically vary with varying input variables.

[0019] The term "dynamic" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, at least one of variable, adaptable, adjustable, customizable, changeable, and modifiable. In contrast to a static or fixed entity, a dynamic entity may be changed, particularly as needed or desired. Specifically, a dynamic entity may be adaptable in response to various environmental conditions. Thus, a dynamic entity may be particularly better responsive to various environmental conditions. As shown, a dynamic entity, such as a dynamic limit, may be a relationship that relates the dynamic entity to at least one environmental condition. Specifically, the dynamic entity may be a function that depends on at least one input variable. The input variable may specifically represent at least one environmental condition. Additionally or alternatively, specifically in the context of a dynamic coefficient of variation limit described in more detail below, the input variable may specifically be a type of mobile device. In general, an environmental condition may relate to any entity that affects or influences the dynamic entity.

[0020] Thus, the term "dynamic coefficient of variation limit" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, the dynamic limit of the coefficient of variation. The dynamic coefficient of variation limit may particularly allow for adapting to changing environmental conditions in order to better respond to the current situation. In general, different coefficient of variation limits may be more suitable for different situations. Thus, using a static coefficient of variation regardless of changing environmental conditions may be detrimental. As an example, suppose that two images, a first image and a second image, can be captured and used to evaluate the validity of an optical test strip that can be used for analytical measurements based on color formation reactions. The first image may, for example, have lower image noise or be sharper than the second image, for example, because it was captured with a different camera with higher resolution or under better lighting. As described above, a first coefficient of variation may be determined for the first image, and a second coefficient of variation may be determined for the second image. The static coefficient of variation limit may then equally limit both the first and second coefficients of variation. The dynamic coefficient of variation limit may specifically treat the first and second images differently, taking into account different environmental conditions that existed when the images were captured, such as the use of different cameras or different lighting. To evaluate the validity of optical test strips that can be used for analytical measurements based on color-forming reactions, the dynamic coefficient of variation limit may specifically set appropriate limits according to the current situation. Thus, particularly spoiled optical test strips that produce erroneous measurement results may be more accurately identified by using the dynamic coefficient, which may be life-saving in some cases, especially in the context of blood glucose measurement.

[0021] As used herein, the term "validity" is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, the acceptability or allowability of an entity. Validity may be or include at least one criterion for evaluating an entity, such as a quality criterion. Depending on the result of the evaluation, the entity may be considered valid or acceptable for one or more follow-up processes, for example, if the entity meets the criterion, or may be considered invalid or unacceptable if the entity does not meet the criterion. Evaluating validity may specifically include, for example, a comparison with at least one limit, specifically a dynamic coefficient of variation limit. Specifically, evaluating validity may include determining whether the coefficient of variation is below, at, or above the dynamic coefficient of variation limit. As an example, a coefficient of variation above, or optionally even at, the dynamic coefficient of variation limit may be considered invalid, which may cause a process to be stopped, such as using an optical test strip for analytical measurement. Thus, spoiled optical test strips can be identified and prevented from being used in an analytical measurement.

[0022] The term "analytical measurement" as used herein is a broad term and should be given its ordinary and customary meaning to one of skill in the art and should not be limited to a special or special meaning. The term may specifically, but not limited to, refer to a quantitative and / or qualitative determination of at least one analyte in any sample or aliquot of a body fluid. Furthermore, the analytical measurement may include determining the concentration of the analyte of the body fluid. The term "body fluid" may also be referred to as "bodily fluid". For example, the body fluid may include one or more of blood, interstitial fluid, urine, saliva or other types of body fluid. The result of the concentration determination may be, by way of example, the concentration of the analyte and / or the presence or absence of the analyte to be determined. Specifically, by way of example, the determination may be a blood glucose measurement and thus the result of the determination may be, for example, a blood glucose concentration. In particular, an analytical measurement result value may be determined by the analytical measurement. The analytical measurement may specifically be performed by using at least one optical test strip.

[0023] The term "optical test strip" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically, but not limited to, refer to any element or device configured to perform a color-forming reaction. An optical test strip may also be referred to as a test strip or a test element, and all three terms may refer to the same element. An optical test strip may have, among other things, a reagent test area (also simply referred to as a "test field") that includes at least one test chemical for detecting at least one analyte. By way of example, an optical test strip may include at least one substrate, such as at least one carrier, having at least one test field applied thereto or incorporated therein. In particular, an optical test strip may further include at least one white area, such as a white field, specifically adjacent to the test field, e.g., surrounding or surrounding the test field. The white area may be a separate field independently disposed on the substrate or carrier. However, additionally or alternatively, the substrate or carrier itself may be or comprise a white area. For example, at least one carrier may be in the form of a strip, making the test element a test strip. These test strips are widely used and available to the public. A test strip may carry a single test field or multiple test fields with the same or different test chemistries contained therein.

[0024] As shown, the optical test strip may be spoiled or otherwise non-spoiled. The term "spoiled" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, at least one of damage, defects, or defects. Thus, the term "non-spoiled" may specifically refer to at least one of complete, undamaged, or free of defects, specifically at least up to a predetermined acceptable state. A spoiled entity, specifically a spoiled optical test strip, may be adversely affected to an unacceptable extent.

[0025] A spoiled optical test strip may specifically be a test strip having at least one parameter that is outside a predetermined or determinable tolerance range that defines a non-spoiled optical test strip. The parameter may be an intrinsic or extrinsic parameter. Among them, the intrinsic parameter may refer, by way of example, to a characteristic of the optical test strip itself, and the extrinsic parameter may refer, by way of example, to a parameter describing the handling of the optical test strip or the use of the optical test strip. As a result, a spoiled test strip may spoil itself and / or spoil due to improper handling. The improper handling may refer, by way of example, to a storage condition, just as the extrinsic parameter may refer, by way of example, to a parameter describing one or more storage conditions, such as storage temperature and / or storage time. Additionally or alternatively, the improper handling may refer to a time that has elapsed since application of at least one sample of at least one bodily fluid to the optical test strip, in particular to at least one reagent test area. As an example, the time that has elapsed between application of the sample to the reagent test area and capturing at least one image that includes at least a portion of the at least one reagent test area to which the sample has been applied may have to be within a predetermined tolerance range. Thus, as soon as the time falls outside the acceptable range, e.g., the time elapsed since the sample was applied passes the time window within which an acceptable measurement must be made, i.e., within which at least one image must be captured, the optical test strip will spoil.

[0026] Thus, in general, a spoiled optical test strip may be, but is not limited to, an optical test strip that has been spoiled beforehand or itself, and / or a test strip that has been mishandled by using the test strip in an incompatible manner, for example, by capturing a respective image at an inappropriate time, etc. There are two spoilage situations generally considered, which may also occur in combination with the following:

[0027] a) the optical test strip spoils itself due to degradation, poor material properties, the effects of adverse environmental effects such as humidity, expired shelf life, etc.; and / or b) the optical test strip becomes spoiled due to improper handling and / or use, e.g. by being used in a measurement performed with one or more measurement parameters outside at least one acceptable range, e.g. by capturing at least one image including at least a portion of at least one test area of ​​the optical test strip at an inappropriate time, e.g. outside a time acceptable range, e.g. with the time elapsed between application of the sample and capturing of the image being greater than a maximum delay, and / or due to the prior use of the optical test strip being for single use only.

[0028] As an example, a spoiled optical test strip may have been exposed to a harmful environment that adversely affects the optical test strip. As an example, a spoiled optical test strip may have been exposed to prior use, which may in particular render the optical test strip unusable for accurate and reliable analytical measurements. Further examples and further details are provided below. The harmful environment may have altered at least one property of the optical test strip, particularly the property of the reagent test area of ​​the optical test strip, and may render the optical test strip unsuitable for reliable analytical measurements. A non-spoiled optical test strip may provide accurate and reliable results when used in an analytical measurement. A spoiled optical test strip may provide erroneous and misleading results when used in an analytical measurement. Thus, it may often be important to identify a spoiled optical test strip and stop the analytical measurement before it potentially produces erroneous results.

[0029] The term "reagent test area" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to a concentrated amount of test chemical, such as, but not limited to, a field or area, such as a circular, polygonal or rectangular shaped field having one or more layers of material with at least one layer of the test field containing the test chemical. The reagent test area may also be referred to as a test field.

[0030] As shown, the analytical measurement may specifically be based on color formation of the reagent test area. Thus, the analytical measurement may include inducing a test reaction between a test chemical and a sample of a body fluid or a portion thereof, such as at least one analyte, specifically an analyte-specific test reaction, the test reaction including a color change of the reagent test area indicating the extent of the test reaction and / or indicating the presence or concentration of the analyte. The color formation may include any change in at least one optical property of the optical test strip or specifically the reagent test area, which change may be optically measured or determined by using a camera. Specifically, the analytical measurement may be or may include a color formation reaction in the presence of at least one analyte to be determined.

[0031] The term "color-forming reaction" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may particularly refer to, but is not limited to, a chemical, biological or physical reaction in which the color, specifically the reflectance, of at least one element involved in the reaction changes as the reaction progresses. The color formation may be detected by the mobile device, such as by a processor of the mobile device, and may be quantitatively evaluated, such as by deriving from the at least one image at least one parameter that quantifies or characterizes the color formation of the reagent test area due to the presence of the analyte in the sample of bodily fluid. The mobile device, specifically the processor of the mobile device, may be configured to determine the color change that occurs due to the detection reaction. For such purposes, at least one of the color spaces mentioned above, for example the RGB color space, may be used. By way of example, the processor may be configured to determine and process the RGB values ​​of the pixels of the image. Further options, specifically alternative color spaces, may also be feasible. At least one analyte concentration value may be determined from the color formation of the reagent test area. An analyte concentration value may be a numerical indication of the result of an analytical measurement, such as, for example, indicating the concentration of at least one analyte in a sample, such as a blood glucose concentration.

[0032] As mentioned above, the determination method includes providing a training set of optical test strips. As used herein, the term "training" is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a special or special meaning. The term may specifically, but not limited to, refer to a process of determining at least one relationship, specifically by learning and / or determining at least one parameter of the relationship, such as, for example, the parameters of a functional equation, by experiment. The term "training" may further specifically, but not limited to, refer to a process of building a model, such as a trainable model, and in particular the process of determining the parameters of the model, especially the weights. As outlined in more detail below, the relationship may include, for example, a lookup table or a model or a function. As an example, the lookup table may be determined by writing the values ​​that belong together. As a further example, the model may be determined by determining the weights of the model or regression parameters. As a further example, the function may be determined by determining the functional equation. In general, training may include at least one optimization process or tuning process or fitting process, and the combination of parameters is determined, in particular, by using a training set.

[0033] As used herein, the term "training set" is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a special or special meaning. This term may refer, in particular, but not limited to, data used or available for training purposes. The training set may be known or pre-determined. In particular, the training set may be or include past experimental data, such as data previously experimentally determined. Additionally or alternatively, the training set may include theoretical data, such as theoretically calculated data, specifically simulated data, etc. As an example, the training set may include known function pairs, which refer to elements of the domain of the function and corresponding elements of the co-domain of the function. Based on this, a function that maps the function pairs may be determined by fitting the function pairs to a polynomial, such as a linear function, using known regression analysis methods. Various other options are also feasible and are generally known to those skilled in the art. As the skilled person will further know in advance, the training set may be a sufficiently large training set, for example including many function pairs, for better training, for example to determine a function with less statistical deviation from the function pairs. In particular, the training set may include more than just one entity, as this may make fitting impossible. The training set of optical test strips includes providing at least two non-corrupted optical test strips and at least two corrupted optical test strips. Thus, at least two non-corrupted optical test strips and at least two corrupted optical test strips may be available for training purposes. This may be particularly suitable for proper training based on sufficient statistics.

[0034] The method of determining further includes providing a training set of mobile devices. The training set of mobile devices may specifically include at least two mobile devices, each having at least one camera. Thus, at least two mobile devices may be used for training purposes. Specifically, at least two mobile devices of the training set of mobile devices may differ in terms of image noise. More specifically, at least two mobile devices of the training set of different types of mobile devices, for example, two different types of smartphones, may differ in terms of image noise. Thus, the mobile devices of the training set of mobile devices may be compared to one another to determine a reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) and the minimum color reference field coefficient of variation (Cv RF、min ) in particular. The term "mobile device" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, a mobile electronic device, more specifically a mobile communication device such as a mobile phone or a smartphone. Additionally or alternatively, as outlined in more detail below, a mobile device may also refer to a tablet computer or another type of portable computer having at least one camera. A mobile device may specifically include consumer electronic devices, such as consumer electronic devices used by individual users in their daily lives.

[0035] The term "camera" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically, but not by way of limitation, refer to a device having at least one imaging element configured to record or capture spatially resolved one-dimensional, two-dimensional, or even three-dimensional optical data or information. By way of example, a camera may comprise at least one camera chip, such as at least one CCD chip and / or at least one CMOS chip, configured to record an image. As used herein, but without limitation, the term "image" may specifically relate to data recorded using a camera, such as a plurality of electronic readings from an imaging device, such as the pixels of a camera chip. Thus, the image itself may include pixels, and the pixels of an image may, for example, correlate to the pixels of a camera chip. Thus, when referring to a "pixel," reference is made directly to either a unit of image information generated by a single pixel of the camera chip, or a single pixel of the camera chip. An image may include raw pixel data. For example, the image may include data in RGB or RGGB space, monochromatic data from one of the R, G, or B pixels, a Bayer pattern image, etc. The image may include evaluated pixel data, such as a full color image or an RGB image. The raw pixel data may be evaluated, for example, by using a demosaic algorithm and / or a filtering algorithm. These techniques are generally known to those skilled in the art.

[0036] The camera may comprise, besides at least one camera chip or imaging chip, one or more further elements, such as one or more optical elements, for example one or more lenses. By way of example, the camera may be a fixed focus camera with at least one lens that is fixedly adjusted relative to the camera. Alternatively, however, the camera may also comprise one or more lenses, such as variable lenses, which may also be adjusted automatically or manually. The invention is particularly applicable to cameras that are typically used in mobile applications, such as notebook computers, tablets, or mobile phones, in particular smartphones. Thus, in particular, the camera may be part of a mobile device that, besides at least one camera, comprises one or more data processing devices, such as one or more data processors. However, other cameras are also feasible.

[0037] The camera may in particular be a color camera. Thus, color information such as color values ​​of the three colors R, G, B for each pixel etc. may be provided or generated. A different number, for example a larger number of color values, for example four color values ​​for each pixel such as R, G, G, B, etc., is also feasible. Color cameras are generally known to those skilled in the art. Thus, by way of example, a camera chip may be composed of three or more different color sensors, for example color recording pixels such as one pixel for red (R), one pixel for green (G) and one pixel for blue (B). Depending on the intensity of the respective color, for each pixel such as R, G, B, etc., a value may be recorded by the pixel, such as a digital value ranging from 0 to 255. By way of example, instead of using three colors such as R, G, B, four colors such as R, G, G, B may be used. The color sensitivity of the pixel may be generated by a color filter or by a suitable inherent sensitivity of the sensor element used in the camera pixel. These techniques are generally known to those skilled in the art.

[0038] Besides the camera, the mobile device may further comprise at least one processor. The term "processor" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may refer specifically, but not limited to, any logic circuitry configured to perform the basic operations of a computer or system, and / or generally to a device configured to perform calculations or logical operations. In particular, the processor may be configured to process basic instructions that run a computer or system. By way of example, the processor may include at least one arithmetic logic unit (ALU), at least one floating point unit (FPU), such as a numeric coprocessor or numeric coprocessor, a number of registers, specifically registers configured to supply operands to the ALU and store the results of the operations, and memories, such as L1 and L2 cache memories. In particular, the processor may be a multi-core processor. In particular, the processor may be or include a central processing unit (CPU). Additionally or alternatively, the processor may be or include a microprocessor, and thus specifically, the elements of the processor may be included in a single integrated circuit (IC) chip. Additionally or alternatively, the processor may be or include one or more chips, such as one or more application specific integrated circuits (ASICs) and / or one or more field programmable gate arrays (FPGAs) and / or one or more tensor processing units (TPUs) and / or dedicated machine learning optimization chips. The processor may be specifically configured, such as by software programming, to perform one or more evaluation operations.

[0039] The method further includes providing at least one reference card having a plurality of color reference fields, the color reference fields having known reference color values. The term "color reference card" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to any item having, disposed within, or disposed on at least one surface, such as having one or more colored fields having known colors, such as known color values ​​or known color coordinates. By way of example, the color reference card may be a flat card comprising at least one substrate having at least one color reference field having known color values ​​or color coordinates disposed on at least one surface. The substrate may specifically have a flat surface on which the color reference field is disposed. By way of example, the substrate may be or include one or more of a paper substrate, a cardboard substrate, a plastic substrate, a ceramic substrate, or a metal substrate. Laminate substrates are also possible. The substrate may be, by way of example, sheet-like or flexible. However, it should be noted that the substrate may be implemented into an article of use, such as a wall of a box, a vial, a container, a medical consumable such as a test strip, etc. Thus, the color reference card may also be fully or partially integrated into an optical test strip, as outlined in more detail below. Thus, at least one image of at least a portion of the color reference card may fully or partially include an image of at least a portion of an optical test strip having at least one reagent test area.

[0040] Furthermore, the color reference card may comprise at least one marker. The at least one marker may be or may include at least one of an identifier for identifying the color reference card and / or a type of the color reference card, such as at least one of a designator specifying details of the color reference card, such as a reference color value, such as using at least one of a label, a barcode or a QR code, a label, a barcode or a QR code, a position marker and / or an orientation marker, such as at least one of a reference mark, an ArUco code, etc. Specifically, the at least one marker may be located at at least one corner of the color reference card. Thus, the mobile device may be configured to detect and / or read the marker, specifically by optically detecting the marker on the at least one image and / or optionally by obtaining information from the marker, such as information regarding the type, characteristics or orientation of the color reference card.

[0041] The term "color reference field" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to any item having known optical properties, such as, but not limited to, known reference color values. Specifically, the color reference fields included in the color reference card may be two-dimensional structures, such as rectangles, squares, polygons, circles and / or ellipses, having uniform color values. The color values ​​of the color reference fields may specifically be one or more of predetermined, known, or determinable. The color reference fields may specifically be constituted by and / or arranged within a surface of the color reference card such that at least one color reference field is visible in a captured image of the color reference card. Furthermore, the color reference fields may have color values ​​within a subspace of a color coordinate system that corresponds to the color space of the color-forming reaction of the reagent test area. The color reference fields of the color reference card may specifically be arranged in a regular pattern on the surface of the color reference card, such as, for example, a rectangular pattern, such as a rectangular matrix pattern. The pattern arrangement may in particular allow for identifying the color reference field, such as by searching at a predetermined distance in the x and / or y directions from one or more of the markers.

[0042] The term "color value" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, a numerical representation of the color of a pixel, an object, etc. Specifically, a color value may be or include at least one color coordinate in a color coordinate system. The term "color coordinate system" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, any coordinate system that may mathematically or physically characterize the color of an object, such as the color of a test field or the color of an image recorded by a camera. Various color coordinate systems are commonly known to those skilled in the art, such as the color coordinate system defined by the CIE. The color coordinates may span or define a color space as a whole, such as by defining three or four basis vectors. For example, the color coordinates may include R, G, B color coordinates. Thus, a color value may be a color triple having R, G, B color coordinates. Color coordinates in other color coordinate systems, such as those defined by CIE 1931, are also possible.

[0043] The term "known reference color value" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, a predetermined, actual, or true color value of a color reference field. Specifically, the known reference color value may include one or more known color coordinates, such as at least three known color coordinates, such as at least one color coordinate for each R, G, B color. The known reference color value of each color reference field may be stored in a data storage device of the mobile device, for example, by a look-up table, a register, a database, or the like. The known reference color values ​​may have been determined by measuring the respective color values, specifically, by measuring the color values ​​in a controlled laboratory environment, such as using a spectrometer. Measurement of the color reference field using a spectrometer may define the respective known reference color values.

[0044] Further, the known reference color values ​​of the one or more second color reference fields may also be determined from the known reference color values ​​of the first color reference field. Specifically, a predetermined and / or known relationship may relate the known reference color values ​​of the first color reference field to the known reference color values ​​of the second reference field. Thus, the color reference card may comprise color reference fields having known reference color values, which may be related to each other. For example, the second color reference field may have a known reference color value including at least one color coordinate, such as one of R, G, B color coordinates, that is 10% higher than the at least one color coordinate included in the known reference color value of the first color reference field. Furthermore, the third color reference field may have a known reference color value including at least one color coordinate, such as one of R, G, B color coordinates, that is 20% higher than the at least one color coordinate included in the known reference color value of the first color reference field. Thus, the known reference color values ​​of the first color reference field may be known and may be further used to determine the known reference color values ​​of the one or more second color reference fields.

[0045] The term "color channel" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may refer in particular, but not exclusively, to the primary colors of a color space, which may be used to digitally store an image, such as the wavelengths at which the image was captured. The primary colors of the color space may be used to generate additional colors of the color space, particularly by combining or mixing them. As an example, the RGB color space includes three color channels, namely a red channel (R), a green channel (G), and a blue channel (B). Other options are also feasible.

[0046] The method further includes capturing a training set of images by using a mobile device of the training set of mobile devices. As used herein, the term "capturing a training set of images" or any grammatical variant thereof is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to capturing images forming a training set of images, without being limited thereto. Specifically, the images of the training set of images may be captured by using different mobile devices of the training set of mobile devices, specifically at least two mobile devices of the training set of mobile devices. More specifically, each mobile device of the training set of mobile devices may be used to capture at least one image of the training set of images. As an example, all mobile devices of the training set of mobile devices may contribute the same number of images to the training set of images. However, the mobile devices of the training set of mobile devices may also contribute different numbers of images to the training set of images, at least in part. As an example, the training set of mobile devices may include three mobile devices, where a first mobile device and a second mobile device may each contribute 10 images to the training set of images, and a third mobile device may contribute 20 images to the training set of images. Specifically, the training set of images may include at least one captured image for each mobile device in the training set of mobile devices. Other options are possible.

[0047] The term "capturing at least one image" or any grammatical variation thereof as used herein is a broad term and should be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to a special or special meaning. The term may specifically refer to one or more of imaging, image recording, image acquisition, image capture, without limitation. The term "capturing at least one image" may include capturing a single image and / or multiple images, such as a series of images. For example, capturing an image may include recording a series of images consecutively, such as a video or movie. The capturing of the at least one image may be initiated by a user action or may be initiated automatically, for example, upon automatic detection of the presence of at least one object within the camera's field of view and / or within a predefined sector of the field of view. These automatic image capture techniques are known in the field of automatic barcode readers, for example, automatic barcode reading apps. The image capture may be performed, by way of example, by a camera capturing a stream or "live stream" of images, one or more of which are stored and used as at least one first image or at least one second image, respectively, either automatically or by user interaction such as pressing a button. Image capture may be supported by a processor of the mobile device, and image storage may occur within a data storage device of the mobile device.

[0048] Each image of the training set of images includes at least a portion of at least one reagent test area of ​​an optical test strip of the training set of optical test strips. Each image of the training set of images further includes at least a portion of at least one color reference field of a color reference card. Specifically, each image of the training set of images may include a complete reagent test area and at least one complete color reference field. More specifically, each image of the training set of images may include a complete reagent test area and a complete color reference card. The color reference card may comprise, for example, 20 color reference fields. By way of example, when capturing the images, the reagent test area and the color reference card may be within the field of view of the camera, and therefore at least a portion of the reagent test area and at least a portion of the color reference card may be visible in at least one image. When capturing the images, the optical test strip and the color reference card may be specifically positioned adjacent to each other, for example adjacent to each other. By way of example, the optical test strip may be placed over a color reference card, and / or the color reference card may include one or more windows, and the color reference card having one or more windows is placed over the optical test strip such that the reagent test area is visible through the window. As also outlined below, the optical test strip may be more specifically attached to the color reference card prior to capturing the image.

[0049] The method includes determining a reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs. As described above, each image in the training set of images includes at least a portion of at least one reagent test area of ​​an optical test strip in the training set of optical test strips. At least a portion of the images that show at least a portion of the at least one reagent test area of ​​an optical test strip may be identified, for example, by image recognition. Specifically, for each image in the training set of images, a reagent test area coefficient of variation (Cv TR The reagent test area coefficient of variation (Cv TR) is determined by measuring the color variation within the reagent test area. By way of example, the RGB values ​​of the pixels of the image, e.g. the red value of the red channel, may be used for this purpose, as outlined above. Thus, as outlined above, at least a portion of the images showing at least a portion of at least one reagent test area of ​​the optical test strip may be identified, e.g. by image recognition, and the color variation within said portion of the respective image may be determined. Similarly, as described above, each image of the training set of images further includes at least a portion of at least one color reference field of the color reference card. In particular, for each image of the training set of images, a minimum color reference field coefficient of variation (Cv RF、min Additionally or alternatively, for example, for each image in the training set of images, the average color reference field coefficient of variation (Cv RF、ave ) can be determined and further used. Other indices than the minimum and average are also feasible. However, without narrowing the scope, the present invention provides a minimum color reference field coefficient of variation (Cv RF、min ) can be specifically explained. RF ) is determined by measuring the variation of color within a color reference field. The color reference field coefficient of variation (Cv RF ) is specifically the reagent test area coefficient of variation (Cv TR ) can be determined in the same way. By way of example, here too at least a part of an image showing at least a portion of at least one color reference field of the color reference card may be identified, for example by image recognition, and the color variation within said portion may be determined. However, in principle other methods are also applicable. As shown, the color reference card may comprise more than one color reference field. The color reference field variation coefficient (Cv RF ) can in particular be determined for all color reference fields of a color card or at least for all color reference fields of an image.

[0050] The coefficient of variation (Cv TR ) for the minimum color reference field coefficient of variation (Cv RF、min) is the color reference field coefficient of variation (Cv RF Specifically, for each image of the reagent test area with the color reference card, one minimum color reference field coefficient of variation (Cv RF、min ) can then be determined. RF、min ) was calculated by the coefficient of variation (Cv TR ) to determine the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) can form a pair.

[0051] The determination method is the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets, the corresponding measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation limits (Cv TR、lim ) and thus ultimately determining the measured minimum color reference field coefficient of variation (Cv RF、min ) based on the dynamic coefficient of variation limit (Cv TR、lim ) to determine, for example, calculate. As used herein, the term "relationship" is a broad term and should be given its ordinary and customary meaning to one of ordinary skill in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, an allocation or assignment of at least one first entity to at least one second entity. As also outlined below, the relationship may specifically include at least one of a look-up table, a model, an algorithm, and a function, specifically a linear function. Specifically, the determination method may involve a measured minimum color reference field coefficient of variation (Cv RF、min ) based on the dynamic coefficient of variation limit (Cv TR、lim) can be determined. However, other options are also feasible.

[0052] Dynamic Coefficient of Variation Limit (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max Therefore, the maximum coefficient of variation (Cv TR、max ) is specifically the coefficient of variation (Cv TR ) that may be considered acceptable for using the optical test strip for analytical measurements. Additionally, the maximum coefficient of variation (Cv TR、max ) is specifically the coefficient of variation (Cv TR ), which may be considered unacceptable for using optical test strips for analytical measurements. Specifically, the dynamic coefficient of variation limit (Cv TR、lim ) below the reagent test area coefficient of variation (Cv TR ) may be considered acceptable for analytical measurements. TR、lim ) exceeding the reagent test area coefficient of variation (Cv TR ) may be considered unacceptable for analytical measurements. TR、lim The reagent test area coefficient of variation (Cv TR ) may be considered either acceptable or unacceptable for an analytical measurement depending on predefined default settings.

[0053] As shown, the dynamic coefficient of variation limit (Cv TR、lim ) is the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、minAs further shown, the determination method includes providing a training set of optical test strips in which at least two of the optical test strips are non-spoiled and at least two of the optical test strips are spoiled. Specifically, the spoiled optical test strips are derived by using a training set of pairs of reagent test area coefficients of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pair and distinguishable reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min In other words, the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs are used to determine the coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs. Further options, especially in the field of machine learning, are possible and generally known to those skilled in the art.

[0054] Specifically, the coefficient of variation (Cv TR ) is the reagent test area coefficient of variation (Cv TR ) can be distinguished from the reagent test area coefficient of variation (Cv TR ) is the reagent test area coefficient of variation (Cv TR) as an example. A spoiled optical test strip may be an optical test strip selected from the group consisting of optical test strips stored under inappropriate conditions such as high temperature, inappropriately high moisture, optical test strips that have exceeded a given shelf life, impure optical test strips, optical test strips that have not been used and / or have been used before. Other ways of spoiling an optical test strip and thereby making it unsuitable for use in an analysis can be achieved, for example, by subjecting the optical test strip to any treatment that deviates from a given standard treatment, such as a given storage and / or handling condition, before use in an analytical measurement. As an example, a spoiled optical test strip may be an optical test strip that has already been used before, in particular by applying a sample of a body fluid, for example blood, to the reagent test area. This may again increase the color non-uniformity of the reagent test area. Typically, the applied blood generates a grid pattern in this case in the previously used reagent test area. Finally, this results in a greater coefficient of variation (Cv TR ) may increase. Also, as outlined below, the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs may also be pre-labeled with information regarding whether each optical test strip in the training set of optical test strips is spoiled or non-spoiled.

[0055] In addition, the coefficient of variation of the reagent test area (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs may be clustered for different mobile devices of the training set of mobile devices, e.g., for different smartphone cameras. Additionally or alternatively, similar environmental conditions, such as similar lighting when capturing the images, may also contribute to cluster formation. Specifically, the minimum color reference field coefficient of variation (Cv RF、min) may be distinguishable for different mobile devices. As an example, a first smartphone camera with higher image noise may have a larger minimum color reference field coefficient of variation (Cv RF、min ) which may result in different minimum color reference field coefficients of variation (Cv RF、min ) is the appropriate dynamic coefficient of variation limit (Cv TR、lim This reflects the fact that different environmental conditions may be relevant for determining the dynamic coefficient of variation (Cv TR、lim ) is specifically the currently measured minimum color reference field coefficient of variation (Cv RF、min ) may also be based on

[0056] Overall, the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of reagent test area coefficients of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs can be distinguished. Therefore, each minimum color reference field coefficient of variation (Cv RF、min ) for the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs and calculate the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min The dynamic coefficient of variation limit (Cv TR、lim ) can be derived. Further options, particularly in the field of machine learning, are feasible and generally known to those skilled in the art. Also, as outlined below, the default settings may derive, for example, the reagent test area coefficient of variation (Cv TR) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs, the dynamic coefficient of variation limit (Cv TR、lim ) is excluded or the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs, the dynamic coefficient of variation limit (Cv TR、lim ) may be allowed or defined.

[0057] The determination method may further comprise a step g) of applying a sample of bodily fluid to a reagent test area of ​​the optical test strip. Step g) may be specifically performed before step d). Thus, steps d) to f) may be performed by using a reagent test area to which a sample of bodily fluid has been applied in particular close time. Specifically, step g) of applying a sample of bodily fluid to the reagent test area of ​​the optical test strip may be performed less than 1 minute, more specifically less than 30 seconds, most specifically less than 10 seconds before capturing an image of the reagent test area according to step d). Specifically, the bodily fluid applied in step g) may be important for at least one embodiment of the measurement method using a mobile device having a camera and a processor to perform an analytical measurement based on a color-forming reaction, as disclosed in more detail below. By way of example, the bodily fluid applied in step g) may be blood that may be subject to a blood glucose measurement that may be performed at a later time as an analytical measurement, and the blood glucose measurement may be performed using the described determination method.

[0058] The method may further include step h) of mounting at least one optical test strip of the training set of optical test strips on a color reference card including a plurality of color reference fields having known reference color values. Step h) may be performed before step d) and optionally before step g). The color reference card may comprise at least one receiver for the optical test strip. The color card may comprise at least a window. The window and / or receiver may be positioned such that the test reagent area is visible through the window when the optical test strip is received in the receiver.

[0059] A spoiled optical test strip may spoil due to at least one of the following: a prior exposure to at least one spoilage environment, a prior device of a fluid sample, in particular a bodily fluid sample, for more than 10 minutes, in particular more than 2 hours, more particularly more than 1 day, and a time lapse since application of the fluid sample, in particular the time between applying the sample and taking the image, being outside of an acceptable range. Prior device of a bodily fluid sample may be performed unintentionally, in particular as opposed to step g), which is the application of a sample of bodily fluid to the reagent test area of ​​the optical test strip, and / or may be performed well before capturing an image of the reagent test area according to step d), in particular more than 15 minutes, more particularly more than 1 hour, more particularly more than 1 day. In particular, prior device of a fluid sample may refer to a prior, already completed analytical measurement. As an example, a diabetic patient, such as the elderly and forgetful, may perform a first blood glucose measurement by using an optical test strip. At a later time, for example, one day later, the diabetic patient may want to perform a second blood glucose measurement by accidentally using the optical test strip again, which may result in a false and misleading result because the optical test strip is corrupted by the blood of the previous device in the first blood glucose measurement. Therefore, in this exemplary case, it may be desired to stop and warn the diabetic patient at the second blood glucose measurement. As mentioned above, the optical test strip may also be corrupted by prior exposure to at least one corrupting environment. The corruptible environment may be selected from the group consisting of a humid environment, specifically an environment with a humidity of more than 60%, more specifically an environment with a humidity of more than 80%, and a bright environment, specifically an environment with an illuminance of more than 1000 lm / m2, more specifically an environment with an illuminance of more than 1500 lm / m2.

[0060] Step e) determines the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min) of the training set of pairs of reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs. Labeling may be specifically considered in step f). As used herein, the term "labeling", including all grammatical variations thereof, is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, providing at least one entity with at least one item of information regarding at least one characteristic of the entity. At least one item of information regarding at least one property of the entity is also called a "label". The at least one property of the entity may be or include, by way of example, at least one of a sign of recognition, a sign of identification, and a sign of information, all of which may be called a label. The label may include at least one item of information regarding at least one property of the labeled entity, and the item of information may specifically identify the labeled entity. The label may specifically be or include a digital label. Thus, in step f), a dynamic coefficient of variation limit (Cv TR、lim When deriving the relationship for determining the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs first may no longer be required.

[0061] Thus, step f) may include using at least one of supervised and semi-supervised learning architectures, for example by using machine learning models and / or trainable models, specifically at least one deep learning architecture. More details are provided below, specifically with respect to trainable models. As used herein, the term "deep learning" is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a special or special meaning. The term may specifically, but is not limited to, a method of using artificial intelligence (AI) for automatic model building. Deep learning may specifically include at least one neural network, which may include at least one input layer, one output layer, and at least one hidden layer between the input layer and the output layer. As used herein, the term "supervised learning" is a broad term and should be given its ordinary and customary meaning to those skilled in the art, and should not be limited to a special or special meaning. The term may specifically, but is not limited to, a deep learning method that uses a fully labeled training dataset. As used herein, the term "semi-supervised learning" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to any special or special meaning. The term may particularly refer to, but is not limited to, learning methods that use partially labeled training datasets such that portions of the training dataset must be independently assigned based on the constructed model.

[0062] However, in addition to using machine learning, other options are feasible, so by way of example other methods may be used to derive the relationships in step f) using manual, automatic or semi-automatic methods.

[0063] In step f), the dynamic coefficient of variation limit (Cv TR、lim) may exclude at least 90%, particularly at least 95%, and more particularly at least 99% of the corrupted optical test strips of the training set of optical test strips. In step f), the dynamic coefficient of variation limit (Cv TR、lim ) may allow for the acceptance of at least 80%, particularly at least 90%, more particularly at least 95%, more particularly at least 97%, and more particularly at least 99% of the uncorrupted optical test strips of the training set of optical test strips. In other words, in step f), the dynamic coefficient of variation limit (Cv TR、lim ) may not exclude more than 5%, more specifically more than 3%, more specifically more than 1% of the non-corrupted optical test strips of the training set of optical test strips. Therefore, in step f), the dynamic coefficient of variation limit (Cv TR、limPredefined default settings may be specifically used as boundary conditions when deriving relationships for determining the presence or absence of a smeared optical test strip. As used herein, the term "exclude", including all grammatical variations thereof, is a broad term and should be given its ordinary and customary meaning to one skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, rejecting, discarding, excluding, or disqualifying an entity. Specifically, excluding a spoiled optical test strip may refer to classifying or assessing the spoiled optical test strip as invalid for accurate and reliable analytical measurements. Thus, analytical measurements that may use spoiled optical test strips may be discontinued to prevent users from receiving potentially erroneous and misleading measurement results. As used herein, the term "allow", including all grammatical variations thereof, is a broad term and should be given its ordinary and customary meaning to one skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to, but is not limited to, permitting, authorizing, or accepting an entity. Specifically, accepting or accepting an unspoiled optical test strip may refer to classifying or evaluating the unspoiled optical test strip as valid or acceptable or suitable for accurate and reliable analytical measurements, such that an analytical measurement may then be performed using the unspoiled optical test strip.

[0064] The relationship derived in step f) may include at least one of a look-up table, a model, an algorithm, and a function, including, for example, one or more one-dimensional, two-dimensional, or three-dimensional graphs or surfaces, such as one or more hypersurfaces. By way of example, the look-up table may include, for example, a minimum color reference field coefficient of variation (Cv RF、min) and the derived dynamic coefficient of variation limits (Cv TR、lim ) As already indicated, the model may specifically include at least one of a regression model and a classifier model. The model may specifically include at least one trainable model. Further details of the trainable model are outlined below. The algorithm may specifically include a minimum measured color reference field coefficient of variation (Cv RF、min ) based on the dynamic coefficient of variation limit (Cv TR、lim The instructions may include, for example, at least one of a computation operation, such as addition or multiplication, and a comparison operation, such as a comparison between two entities, or a request if a particular criterion is met for an entity.

[0065] As mentioned above, the relationship derived in step f) may include a function. As used herein, the term "function" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically, but not exclusively, refer to a relationship that assigns one or more elements from a first set to elements from at least one second set, where each element of the first set is associated with exactly one element of the second set. The first set may be referred to as the domain of the function. The second set may be referred to as the co-domain of the function. As already indicated, the function may include at least one mathematical function, specifically at least one function equation. The function equation may define at least one computational operation, for example addition or multiplication. The function, specifically the function equation, may define a dynamic coefficient of variation limit (Cv TR、lim ) as the function variable for determining the measured minimum color reference field coefficient of variation (Cv RF、min ) may be used. The function may be represented graphically, for example as a graph in a two-dimensional or three-dimensional coordinate system. The function may be a linear function. Thus, the function may be represented graphically as a geometric line. The function may specifically be:

number

[0066] where parameter "a" may be the slope of the function and parameter "b" may be the offset of the function. As shown, parameters "a" and "b" are determined by using a training set of images, specifically the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets. Further, by way of example, at least one machine learning algorithm may be used to determine the parameters "a" and "b". The linear function may have a slope of 1. Thus, the parameter "a" may be equal to 1. Thus, the dynamic coefficient of variation limit (Cv TR、lim ) is specifically the measured minimum color reference field coefficient of variation (Cv RF、min ) plus an offset.

[0067] Dynamic Coefficient of Variation Limit (Cv TR、lim ) is a predetermined maximum coefficient of variation limit (Cv TR、max、事前定義 In other words, a predetermined maximum coefficient of variation limit (Cv TR、max、事前定義 ) is the dynamic coefficient of variation limit (Cv TR、lim ) at the maximum specified maximum coefficient of variation limit (Cv TR、max、事前定義 ) so that the dynamic coefficient of variation limit (Cv TR、lim ) can be the maximum limit. Therefore, the dynamic coefficient of variation limit (Cv TR、lim ) is greater than or equal to at least one minimum colorimetric field coefficient of variation (Cv RF、min ) for a given maximum coefficient of variation limit (Cv TR、max、事前定義 ) greater than the dynamic coefficient of variation limit (Cv TR、lim If the relationship can be followed to output the dynamic coefficient of variation (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max、事前定義 ) which can also ensure accurate and reliable analytical measurements.

[0068] Step f) includes using at least one machine learning algorithm, specifically, determining the coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min) pair training set to train the trainable model. As used herein, the term "trainable model" is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically, but not exclusively, refer to a mathematical model for interpreting data, such as classifying data and / or evaluating data, where the trainable model is trainable with at least one training set. In the trainable model, one or more parameters may remain variable, and the setting of the parameters depends on the training process. The trainable model may provide a model of the environment, where the model responds to stimuli from the environment in an appropriate manner and may be adapted to adjust the model according to observed deviations so that in subsequent runs the model responds to the stimuli in a more appropriate manner. The trainable model may be trained with at least one training set and, as an example, may be configured to predict at least one target variable for at least one input variable, such that at least one input variable forms the stimulus and the output target variable forms the response of the trainable model. A number of trainable models are generally known to those skilled in the art and will be further referred to throughout this application. Furthermore, the term "trainable model" may specifically refer to a model for predicting accuracy that can be trained on at least one training set, also referred to as training data, but is not limited to this. The method may include at least one training step, in which the trained model is trained on at least one training set. The trained model may include at least one model selected from the group consisting of a linear regression model including transformed features, such as logarithmic transformation or polynomials, at least one nonlinear artificial neural network (ANN), in particular at least one deep learning architecture, such as convolutional NN, recurrent NN, long short-term memory NN, at least one support vector machine (SVM), at least one kernel-based method, tree regression, random forest. The trainable model after performing the training step may also be referred to as a "trained model".

[0069] Therefore, step f) is the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets. By way of example, step f) may include training the trainable model by calculating the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) as it relates to the spoiled optical test strip or as it relates to the non-spoiled optical test strip, specifically the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) are not labeled. Thus, by way of example, step f) may include classifying pairs of the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) and applying a classifier model to the pair of the test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) by applying at least one regression model to the classification or labeled pairs of TR、lim ) relationship.

[0070] In step e), the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、 min) can be determined for at least two color channels, specifically at least two color channels selected from the group consisting of the green channel, the blue channel, and the red channel. More specifically, the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min) may be determined for the green channel and / or the red channel. In step f), the dynamic coefficient of variation limit (Cv TR、lim ) is the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) can be derived for at least two color channels determined in step e). By way of example, in step e), a reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、 min) for the green channel, and in step f) a dynamic coefficient of variation limit (Cv TR、lim ) may be derived for the green channel. Additionally or alternatively, for example, in step e), the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、 min) for the red channel, and in step f) the dynamic coefficient of variation limit (Cv TR、lim ) may be derived for the red channel. In step f), the dynamic coefficient of variation limit (Cv TR、lim ) are derived for two or more color channels, e.g., the green and red channels, the dynamic coefficient of variation limits (Cv TR、lim ) minimum dynamic coefficient of variation limit (Cv TR、lim ) to determine the maximum coefficient of variation (Cv TR、max ) can be defined. As an example, for each color channel, individual dynamic coefficient of variation limits (Cv TR、lim ) may be derived first, and individual dynamic coefficient of variation limits (Cv TR、lim ) can then be compared to each other.

[0071] However, specifically, for each color channel, individual dynamic coefficient of variation limits (Cv TR、lim ) can be derived, and for each color channel, the reagent test area coefficient of variation (Cv TR ) to the individual dynamic coefficient of variation limits (Cv TR、lim) for unspoiled optical test strips, the reagent test area coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or, optionally, equal to the reagent test area coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or optionally equal to it. As an example, one may consider the green and red channels. Then, as an example, for a non-corrupted optical test strip, the coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or arbitrarily equal to the coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or arbitrarily equal to . In other words, for a spoiled optical test strip, the coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or arbitrarily equal to the coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ), or optionally equal. Further options are possible.

[0072] The determination method may be at least partially, in particular at least one of steps e) and f), performed by a computer. With respect to computer-implemented aspects of the present invention, one or more of the method steps of the determination method according to one or more of the embodiments disclosed herein, or even all of the method steps, may be performed by using a computer or a computer network. Thus, in general, any method step including providing and / or manipulating data may be performed by using a computer or a computer network. In general, these method steps may include any method step, except for method steps that typically require manual work, such as providing a sample and / or certain aspects of performing the actual measurement.

[0073] In a further aspect of the present invention, a measurement method is disclosed, which uses a mobile device having a camera and a processor to perform an analytical measurement based on a color-forming reaction. The measurement method includes the following steps, which may be performed in a given order, as an example. However, it should be noted that a different order is generally possible. Furthermore, it may be possible to perform one or more of the method steps only once or repeatedly. Furthermore, it may be possible to perform two or more of the method steps simultaneously or overlap in time. The measurement method may include further method steps that are not listed.

[0074] The measurement method is as follows: i) providing at least one optical test strip having at least one reagent test area, the optical test strip being in particular of the same type as the optical test strips of the training set used in step a) of the determination method according to any one of the embodiments described above or below with further detailed reference to the determination method; ii) providing at least one color reference card having a plurality of color reference fields having known reference color values; iii) capturing, using a camera, at least one image of at least a portion of the reagent test area to which the at least one sample of the at least one bodily fluid has been applied and at least a portion of at least one of the color reference fields of the color reference card; iv) Specifically, using a processor, for at least one color channel of a camera of the mobile device, a color reference field coefficient of variation (Cv RF ) and determining the color reference field coefficient of variation (Cv RF ) is determined by measuring color variation in at least one color reference field; v) Specifically, using a processor, the at least one color reference field coefficient of variation (Cv RF ) to determine the minimum color reference field coefficient of variation (Cv RF、min ) to determine vi) Specifically, using a processor, the minimum color reference field coefficient of variation (Cv RF、min ) and the dynamic coefficient of variation limit (Cv TR、lim ) to determine the dynamic coefficient of variation limit (Cv TR、lim ), wherein the relationship is determined by performing a determination method according to any one of the embodiments described above or below in more detail with reference to the determination method; vii) Specifically, using a processor, for at least one color channel, a reagent test area coefficient of variation (Cv TR ) and determining the reagent test area coefficient of variation (Cv TR ) is determined by measuring a color variation in a reagent test area; viii) The reagent test area coefficient of variation (Cv TR ) to the dynamic coefficient of variation limits (Cv TR、lim ) and ix) Reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), the optical test strip is deemed to be spoiled and the measurement method is discontinued; and x) Reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), considering the optical test strip to be unspoiled, and determining a concentration of at least one analyte of the sample of the bodily fluid by using at least one color formation value for color formation of a reagent test area of ​​the optical test strip where at least one sample of the at least one bodily fluid is applied to the reagent test area of ​​the optical test strip, for at least one color channel.

[0075] As outlined above, the determination method may be performed by, for example, a manufacturer or provider, under controlled conditions, specifically using a training set of optical test strips and using a training set of mobile devices. Meanwhile, the measurement method may be performed by a user in the art, specifically using the user's personal mobile device and one or more optical test strips. The personal mobile device may be, for example, of a specific type that is among one or more types of mobile devices in the training set of mobile devices used in the determination method. The method may, for example, verify that this is the case, for example, by checking whether the user's personal mobile device is listed in a list of acceptable mobile devices. The method may further include aborting the measurement and / or issuing a warning to the user's method if this is not the case. Additionally or alternatively, the personal mobile device may be of a specific type that is not among one or more types of mobile devices in the training set of mobile devices used in the determination method. The method may further ensure that the personal mobile device can be used for reliable and accurate analytical measurements, for example, by checking whether the user's personal mobile device is listed in a list of unacceptable mobile devices.

[0076] As described above, in step ix), the reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), the optical test strip is deemed to be spoiled. Furthermore, in step x), the reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), the optical test strip is considered to be non-spoiled. Therefore, the reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim), the optical test strip may be considered spoiled or the optical test strip may be considered not spoiled, for example, depending on predefined default settings. In other words, the measurement method is based on the reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), considering the optical test strip to be spoiled and aborting the measurement method, or considering the optical test strip to be not spoiled and determining the concentration of at least one analyte in the sample of the body fluid by using, for at least one color channel, at least one color formation value for color formation of the reagent test area having at least one sample of the at least one body fluid applied to the reagent test area of ​​the optical test strip.

[0077] The measurement method may further include at least one step of applying at least one sample of at least one bodily fluid to a reagent test area of ​​the optical test strip, and the measurement method may be performed according to one of the following methods.

[0078] - Dynamic coefficient of variation limits (Cv TR、lim iii) is determined using a relationship derived when performing a determination method according to any one of the embodiments disclosed above or below in more detail with reference to the determination method in which at least one sample of at least one bodily fluid is applied to a reagent test area of ​​an optical test strip of a training set of optical test strips, the measurement method comprising applying a sample of the bodily fluid to the reagent test area of ​​the optical test strip prior to performing step iii); or - Dynamic coefficient of variation limits (Cv TR、lim) is determined using a relationship derived when performing the determination method described in any one of the embodiments disclosed above or below in further detail with reference to the determination method in which at least one sample of the bodily fluid is not applied to the reagent test area of ​​the optical test strip of a training set of optical test strips, the measurement method comprising performing steps iii) to viii) without the at least one sample of the bodily fluid being applied to the reagent test area of ​​the optical test strip, the method further comprising applying at least one sample of the bodily fluid to the reagent test area of ​​the optical test strip before or during performing step x).

[0079] Thus, for example, the determination method may be carried out with a sample of bodily fluid applied to a reagent test area as described in optional step g) of the determination method, and the measurement method may be performed within the dynamic coefficient of variation (Cv TR、lim Alternatively, the determination method may be performed without applying a sample of the body fluid to the reagent test area, and the dynamic coefficient of variation limit (Cv TR、lim ) to determine the concentration of at least one analyte in a sample of bodily fluid, and performing a measurement method using the relationship to determine the measured reagent test area coefficient of variation (Cv TR ) was determined as the dynamic coefficient of variation limit (Cv TR、lim A sample of the body fluid may be applied to the reagent test area after comparing the measured reagent test area coefficient of variation (Cv TR ) specifically, the appropriate dynamic coefficient of variation limit (Cv TR、lim ) of the reagent test area to which the body fluid sample is applied. TR ) is the dynamic coefficient of variation limit (Cv TR、lim ) can only be compared to the measured reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim) is all that is required.

[0080] The measurement method may further comprise step xi) of attaching the optical test strip to a color reference card. Step xi) may be performed before step iii).

[0081] The measurement method may be at least partially, in particular at least one of steps iv) to x), performed by a computer. With respect to computer-implemented aspects of the present invention, one or more of the method steps according to one or more of the embodiments disclosed herein, or even all of the method steps of the measurement method, may be performed by using a computer or a computer network. Thus, in general, any of the method steps including providing and / or manipulating data may be performed by using a computer or a computer network. In general, these method steps may include any method steps, except for those that typically require manual work, such as providing a sample and / or certain aspects of performing the actual measurement.

[0082] For further definitions and / or embodiments regarding the measurement method, reference may be made to the definitions and / or embodiments regarding the determination method. In a further aspect of the present invention, the dynamic coefficient of variation limits (Cv TR、lim A determination system is disclosed for determining A) a training set of optical test strips, each optical test strip having a reagent test area, at least two of the optical test strips being non-spoiled and at least two of the optical test strips being spoiled; B) A training set of mobile devices, each of which has at least one camera. C) at least one color reference card having a plurality of color reference fields having known reference color values; D) at least one processor, - searching a training set of images, the training set of images including images captured by a camera, each image in the training set of images including at least a portion of a reagent test area of ​​an optical test strip of the training set of optical test strips and at least a portion of at least one color reference field of the color reference card; - for at least one color channel of the camera of a mobile device of the training set of mobile devices, the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) training set of pairs, each reagent test area coefficient of variation (Cv TR ) is determined by measuring the color variation within the reagent test area, and the coefficient of variation (Cv RF ) is determined by measuring the color variation within the color reference field and the corresponding reagent test area coefficient of variation (Cv TR ) minimum color reference field coefficient of variation (Cv RF、min ) is the color reference field coefficient of variation (Cv RF ), determining, determining, and - Reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets, the corresponding measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation limits (Cv TR、lim derive relationships for determining the dynamic coefficient of variation limit (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max ) and a processor configured for defining, deriving, or

[0083] The term "system" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically, but not limited to, refer to any set of interacting or interdependent components or parts that form a whole. Specifically, the components may interact with each other to perform at least one common function. At least two components may be treated independently or may be combined or connectable. Thus, the term "decision system" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically, but not limited to, refer to a system configured to perform at least one decision operation. The decision system may be configured to perform at least steps e) and f) of the decision method according to any one of the embodiments disclosed above or below in further detail with reference to the decision method, specifically, to perform at least steps e) and f) of the decision method according to any one of the embodiments disclosed above or below in further detail with reference to the decision method.

[0084] As mentioned, the decision system may comprise at least one processor. The processor may be configured to connect to the mobile device of the training set of mobile devices and / or to read the mobile device of the training set of mobile devices, in particular to retrieve the training set of images. The processing of the training set of images may be at least partially cloud-based. Thus, the decision system or at least a part thereof, in particular the processor or at least a part thereof, may be cloud-based. The term "cloud-based" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may refer in particular, but not limited to, outsourcing of a system or device or a part thereof to an interconnected external device, in particular a computer or computer network having a larger computing power and / or data storage capacity. The external devices may be optionally spatially distributed. The external devices may change over time, in particular as required. The external devices may be interconnected, for example, by using the Internet. Each of the external devices may comprise at least one interface, such as a wireless interface and / or a wired interface, in particular at least one communication interface. Thus, by way of example, the processor may be constituted, at least in part, by one or more mobile devices, e.g., one or more mobile devices of a training set of mobile devices. However, the processor may also be a computer, e.g., a computer used in a laboratory. Other options are possible.

[0085] For further definitions and / or embodiments regarding the determination system, reference may be made to the definitions and / or embodiments regarding the determination method and / or measurement method.

[0086] In a further aspect of the invention a computer program is disclosed, which comprises instructions which, when the program is executed by a decision system, in particular by a decision system according to any one of the embodiments described in more detail above or below with reference to the decision system, cause the decision system to execute at least steps e) and f) of the decision method according to any one of the embodiments described in more detail above or below with reference to the decision method.

[0087] The computer program may further comprise instructions, when the program is executed by the decision system, in particular by a decision system according to any one of the embodiments described in more detail above or below with reference to the decision system, for controlling the execution of step d) of the decision method according to any one of the embodiments described in more detail above or below with reference to the decision method. The computer program may further comprise instructions, when the program is executed by the decision system, in particular by a decision system according to any one of the embodiments described in more detail above or below with reference to the decision system, for causing the decision system to execute steps a) to c) of the decision method according to any one of the embodiments described in more detail above or below with reference to the decision method.

[0088] A computer program may also be embodied as a computer program product. As used herein, a computer program product may refer to a program as a tradeable product. The product may generally be present in any format, such as a paper format, or on a computer-readable data carrier and / or on a computer-readable storage medium. In particular, the computer program product may be distributed over a data network.

[0089] In a further aspect of the present invention, a computer readable storage medium is disclosed, which comprises instructions which, when executed by a decision system, in particular by a system according to any one of the embodiments described in more detail above or below with reference to the decision system, cause the decision system to perform at least steps e) and f) of the decision method according to any one of the embodiments described in more detail above or below with reference to the decision method.

[0090] The term "computer-readable storage medium" as used herein is a broad term and should be given its ordinary and customary meaning to those skilled in the art and should not be limited to a special or special meaning. The term may specifically refer to a non-transitory data storage means such as, but not limited to, a hardware storage medium on which computer-executable instructions are stored. The computer-readable data carrier or storage medium may specifically be or comprise a storage medium such as a random access memory (RAM) and / or a read-only memory (ROM) and / or a flash memory.

[0091] The computer readable storage medium may further include instructions which, when executed by the decision system, in particular by a decision system according to any one of the embodiments described above or below in more detail with reference to the decision system, control the execution of step d) of the decision method according to any one of the embodiments described above or below in more detail with reference to the decision method. The computer readable storage medium may further include instructions which, when executed by the decision system, in particular by a decision system according to any one of the embodiments described above or below in more detail with reference to the decision system, prompt the decision system to prompt a user to execute steps a) to c) of the decision method according to any one of the embodiments described above or below with more detailed reference to the decision method.

[0092] In a further aspect of the present invention, a mobile device is disclosed having at least one camera and at least one processor, the mobile device being configured to perform at least steps iv) to x) of the measurement method according to any one of the embodiments described above or below in more detail with reference to the measurement method.

[0093] For further definitions and / or embodiments relating to the mobile device, reference may be made to the definitions and / or embodiments relating to the determination method and / or the measurement method and / or the determination system.

[0094] In a further aspect of the invention a computer program is disclosed, the computer program comprising instructions which when executed by a mobile device having a camera and a processor, in particular by a mobile device according to any one of the embodiments described above or below with further reference to the mobile device, cause the mobile device to perform at least steps iv) to x) of the measurement method as described in any one of the embodiments described above or below with further reference to the measurement method.

[0095] The computer program may further comprise instructions for controlling the execution of step iii) of the measurement method according to any one of the embodiments described above or below with further reference to the mobile device when the program is executed by the mobile device, in particular by a mobile device according to any one of the embodiments described above or below with further reference to the mobile device. The computer program may further comprise instructions for prompting the mobile device, in particular by a mobile device according to any one of the embodiments described above or below with further reference to the mobile device, to execute steps i) and ii) of the measurement method according to any one of the embodiments described above or below with further reference to the measurement method.

[0096] A computer program may also be embodied as a computer program product. As used herein, a computer program product may refer to a program as a tradeable product. The product may generally be present in any format, such as a paper format, or on a computer-readable data carrier and / or on a computer-readable storage medium. In particular, the computer program product may be distributed over a data network.

[0097] In a further aspect of the present invention, a computer readable storage medium is disclosed, the computer readable storage medium comprising instructions which when executed by a mobile device having a camera and a processor, in particular by a mobile device according to any one of the embodiments described above or below with further reference to the mobile device, cause the mobile device to perform at least steps iv) to x) of the measurement method according to any one of the embodiments described above or below with further reference to the measurement method.

[0098] The computer-readable storage medium may further include instructions that, when executed by a mobile device, in particular by a mobile device according to any one of the embodiments described above or below with further reference to the mobile device, control the execution of step iii) of the measurement method described in any one of the embodiments described above or below with further reference to the measurement method. The computer-readable storage medium may further include instructions that, when executed by a mobile device, in particular by a mobile device according to any one of the embodiments described above or below with further reference to the mobile device, prompt a user to execute steps i) and ii) of the measurement method described in any one of the embodiments described above or below with further reference to the measurement method.

[0099] The method and device according to the present invention provide a number of advantages over similar methods and devices known in the art. First, the method and device described herein may be particularly useful for users, as it may enable the user to perform analytical measurements, such as blood glucose measurements, by using a mobile device, such as a personal smartphone or tablet. Moreover, the method and device described herein may particularly facilitate accurate and reliable analytical measurements when using such mobile devices. Thus, the method and device described herein may generally increase the safety of measurements, particularly by providing an effective fail-safe mechanism for identifying spoiled optical test strips. Spoiled optical test strips, such as optical test strips already in use, may result in erroneous and misleading results of analytical measurements. This may be prevented by using the proposed method and device. In particular, the method and device described herein may include preventing the determination of the concentration of an analyte of a body fluid if an optical test strip used for this purpose is identified as spoiled. In particular, with respect to blood glucose measurements or similar health-related analytical measurements, this may prevent serious health damage and potentially be life-saving.

[0100] The methods and devices described herein may be more broadly applicable to a variety of different mobile devices, such as, for example, different smartphones with different cameras. The methods and devices may further take into account different environmental conditions, for example, different lighting when taking an image. Thus, the proposed methods and devices may be specifically applicable to each analytical measurement individually. In particular, they may enable individual reliability and safety assessment of each analytical measurement. Furthermore, the proposed methods and devices may enable improved user handling and improved user usability of analytical measurements by allowing a safe analytical measurement to be performed by capturing only one image, instead of capturing at least two images. In particular, they may reduce the overall time required to perform an analytical measurement, compared to known methods and devices. Furthermore, the proposed methods and devices may reduce the user requirements for performing an analytical measurement, which may be important, especially for users with poor health or elderly users.

[0101] In summary, without excluding further embodiments, the following embodiments may be envisaged:

[0102] Example 1: Dynamic coefficient of variation limits (Cv TR、lim ), comprising the steps of: a) providing a training set of optical test strips, each optical test strip having a reagent test area, at least two of the optical test strips being non-spoiled and at least two of the optical test strips being spoiled; b) providing a training set of mobile devices, each mobile device having at least one camera; c) providing at least one color reference card having a plurality of color reference fields having known reference color values; d) capturing a training set of images using the mobile device of the training set of mobile devices, each image of the training set of images including at least a portion of at least one reagent test area of ​​an optical test strip of the training set of optical test strips and at least a portion of at least one color reference field of the color reference card; e) Specifically, using at least one processor, more specifically at least one processor of a mobile device, a reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) training set of pairs, each reagent test area coefficient of variation (Cv TR ) is determined by measuring the color variation within the reagent test area, and the coefficient of variation (Cv RF ) is determined by measuring the color variation within the color reference field and the corresponding reagent test area coefficient of variation (Cv TR ) minimum color reference field coefficient of variation (Cv RF、min ) is the color reference field coefficient of variation (Cv RF ), determining, determining, and f) Reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets, the corresponding measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation limits (Cv TR、lim derive relationships for determining the dynamic coefficient of variation limit (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max ) determining method, including defining or deriving.

[0103] Embodiment 2: The method of embodiment 1, wherein the method further comprises step g) of applying a sample of the bodily fluid to a reagent test area of ​​the optical test strip, step g) being specifically performed before step d).

[0104] Embodiment 3: The method of any one of embodiments 1 or 2, wherein the method further comprises step h) of attaching at least one optical test strip of the training set of optical test strips to a color reference card including a plurality of color reference fields having known reference color values, and step h) is performed before step d) and optionally step g).

[0105] Embodiment 4: The method of any one of embodiments 1 to 3, wherein the spoiled optical test strip is spoiled by at least one of the following: prior exposure of the device to at least one spoiling environment prior to the fluid sample, particularly the bodily fluid sample, for more than 10 minutes, particularly more than 2 hours, more particularly more than 1 day; the time elapsed since application of the fluid sample, particularly the time between application of the sample and taking the image, being outside of an acceptable range.

[0106] Embodiment 5: The method of embodiment 4, wherein the spoilage-prone environment is selected from the group consisting of humid environments, specifically environments with a humidity of more than 60%, more specifically environments with a humidity of more than 80%, and bright environments, specifically environments with an illuminance of more than 1000 lm / m2, more specifically environments with an illuminance of more than 1500 lm / m2.

[0107]

[0023] Embodiment 6: Step e) comprises: determining the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) of the training set of pairs of reagent test area coefficients of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min6. The method of any one of the preceding claims, further comprising labelling pairs of (a) and (b), wherein the labelling is specifically taken into account in step f).

[0108] In step f) of embodiment 7, the dynamic coefficient of variation limit (Cv TR、lim 7. The method of any one of the preceding claims, wherein said determining method excludes at least 90%, particularly at least 95%, more particularly at least 99% of spoiled optical test strips of the training set of optical test strips.

[0109] In step f) of embodiment 8, the dynamic coefficient of variation limit (Cv TR、lim 8. The method of any one of claims 1 to 7, wherein the method allows acceptance of at least 80%, particularly at least 90%, more particularly at least 95%, more particularly at least 97%, and more particularly at least 99% of the non-corrupted optical test strips of the training set of optical test strips.

[0110] Embodiment 9: The method of any one of embodiments 1 to 8, wherein the relationship derived in step f) comprises at least one of a lookup table, a model, an algorithm, and a function.

[0111] Embodiment 10: A method according to any one of embodiments 1 to 9, wherein the relationship derived in step f) comprises a function, and the function is a linear function.

[0112] Embodiment 11: A method according to any one of embodiments 1 to 10, wherein the linear function has a slope of 1.

[0113] Embodiment 12: Dynamic Coefficient of Variation Limit (Cv TR、lim ) is a predetermined maximum coefficient of variation limit (Cv TR、max、事前定義 12. The method of any one of claims 1 to 11, comprising:

[0114] Embodiment 13: Step f) comprises using at least one machine learning algorithm, in particular, the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min 13. The method of any one of claims 1 to 12, comprising training a trainable model using a training set of pairs of:

[0115] In embodiment 14, step e), the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、 14. The method according to any one of the preceding claims, wherein the pair of (min) is determined for at least two color channels, in particular at least two color channels selected from the group consisting of a green channel, a blue channel and a red channel.

[0116] In step f), the dynamic coefficient of variation limit (Cv TR、lim ) is the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min 15. The method of any one of the preceding claims, wherein a pair of (a) and (b) is derived for the at least two color channels determined in step e).

[0117] Embodiment 16: A method of performing an analytical measurement based on a color-forming reaction using a mobile device having a camera and a processor, comprising: i) providing at least one optical test strip having at least one reagent test area, the optical test strip being in particular of the same type as the optical test strips of the training set used in step a) of the determination method according to any one of embodiments 1 to 15; ii) providing at least one color reference card having a plurality of color reference fields having known reference color values; iii) capturing, using a camera, at least one image of at least a portion of the reagent test area to which the at least one sample of the at least one bodily fluid has been applied and at least a portion of at least one of the color reference fields of the color reference card; iv) Specifically, using a processor, for at least one color channel of a camera of the mobile device, a color reference field coefficient of variation (Cv RF ) and determining the color reference field coefficient of variation (Cv RF ) is determined by measuring color variation in at least one color reference field; v) Specifically, using a processor, the at least one color reference field coefficient of variation (Cv RF ) to determine the minimum color reference field coefficient of variation (Cv RF、min ) to determine vi) Specifically, using a processor, the minimum color reference field coefficient of variation (Cv RF、min ) and the dynamic coefficient of variation limit (Cv TR、lim ) to determine the dynamic coefficient of variation limit (Cv TR、lim ), wherein the relationship is determined by performing a determination method according to any one of embodiments 1 to 15; vii) Specifically, using a processor, for at least one color channel, a reagent test area coefficient of variation (Cv TR ) and determining the reagent test area coefficient of variation (Cv TR ) is determined by measuring a color variation in a reagent test area; viii) The reagent test area coefficient of variation (Cv TR ) to the dynamic coefficient of variation limits (Cv TR、lim ) and ix) Reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), the optical test strip is deemed to be spoiled and the measurement method is discontinued; and x) Reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), determining the optical test strip as not spoiled if the difference is less than 0.01 mm, and determining a concentration of at least one analyte of the sample of the bodily fluid by using at least one color formation value for color formation of a reagent test area of ​​the optical test strip, where at least one sample of the at least one bodily fluid is applied to the reagent test area of ​​the optical test strip, for at least one color channel.

[0118] Embodiment 17: The method further comprises at least one step of applying at least one sample of at least one bodily fluid to a reagent test area of ​​the optical test strip, the method comprising: - Dynamic coefficient of variation limits (Cv TR、lim 17) is determined by using a relationship derived when performing the determination method of any one of embodiments 1 to 16, referring to the determination method in which at least one sample of at least one bodily fluid is applied to a reagent test area of ​​an optical test strip of a training set of optical test strips, the measurement method comprising applying a sample of the bodily fluid to the reagent test area of ​​the optical test strip before performing step iii); or - Dynamic coefficient of variation limits (Cv TR、lim17. The measurement method of embodiment 16, wherein the relationship between the at least one sample of the body fluid and the reagent test area of ​​the optical test strip is determined by using a relationship derived when performing the determination method of any one of embodiments 1 to 16 referring to a determination method in which no sample of the body fluid is applied to the reagent test area of ​​the optical test strip of a training set of optical test strips, the measurement method comprising performing steps iii) to viii) without the at least one sample of the body fluid being applied to the reagent test area of ​​the optical test strip, the method further comprising applying at least one sample of the body fluid to the reagent test area of ​​the optical test strip before or during performing step x).

[0119] Embodiment 18: A measurement method described in any one of embodiments 1 to 17, wherein the measurement method further comprises step xi) of attaching the optical test strip to a color reference card, and step xi) is performed before step iii).

[0120] Embodiment 19: Dynamic coefficient of variation limits (Cv TR、lim ), comprising: A) a training set of optical test strips, each optical test strip having a reagent test area, at least two of the optical test strips being non-spoiled and at least two of the optical test strips being spoiled; B) A training set of mobile devices, each of which has at least one camera. C) at least one color reference card having a plurality of color reference fields having known reference color values; D) at least one processor, - searching a training set of images, the training set of images including images captured by a camera, each image in the training set of images including at least a portion of a reagent test area of ​​an optical test strip of the training set of optical test strips and at least a portion of at least one color reference field of a color reference card; - for at least one color channel of the camera of a mobile device of the training set of mobile devices, the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) training set of pairs, each reagent test area coefficient of variation (Cv TR ) is determined by measuring the color variation within the reagent test area, and the coefficient of variation (Cv RF ) is determined by measuring the color variation within the color reference field and the corresponding reagent test area coefficient of variation (Cv TR ) minimum color reference field coefficient of variation (Cv RF、min ) is the color reference field coefficient of variation (Cv RF ), determining, determining, and - Reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets, the corresponding measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation limits (Cv TR、lim derive relationships for determining the dynamic coefficient of variation limit (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max ). A decision system including a processor configured for defining, deriving.

[0121] Embodiment 20: A decision system as described in any one of embodiments 1 to 19, configured for use in a decision method according to any one of embodiments 1 to 19 referring to the decision method, in particular configured to perform at least steps e) and f) of the decision method according to any one of embodiments 1 to 19 referring to the decision method.

[0122] Embodiment 21: A computer program comprising instructions, when the program is executed by a decision system, in particular by a decision system according to any one of embodiments 1 to 20 referring to the decision system, that cause the decision system to execute at least steps e) and f) of the decision method according to any one of embodiments 1 to 20 referring to the decision method.

[0123] Embodiment 22: A computer-readable storage medium comprising instructions, when executed by a decision system, in particular by a system according to any one of embodiments 1 to 21 referring to the decision system, causing the decision system to execute at least steps e) and f) of the decision method according to any one of embodiments 1 to 21 referring to the decision method.

[0124] Embodiment 23: A mobile device having at least one camera and at least one processor, the mobile device being configured to perform at least steps iv) to x) of the measurement method described in any one of embodiments 1 to 22, referring to the measurement method.

[0125] Embodiment 24: A computer program comprising instructions, when executed by a mobile device having a camera and a processor, in particular by the mobile device described in embodiment 23, causing the mobile device to perform at least steps iv) to x) of the measurement method described in any one of embodiments 1 to 23 referring to the measurement method.

[0126] Embodiment 25: A computer-readable storage medium comprising instructions that, when executed by a mobile device having a camera and a processor, specifically by the mobile device described in embodiments 1 to 23 referring to a mobile device, cause the mobile device to perform at least steps iv) to x) of the measurement method described in any one of embodiments 1 to 24 referring to the measurement method. [Brief description of the drawings]

[0127] Further optional features and embodiments are disclosed in more detail in the following description of the embodiments, preferably in conjunction with the dependent claims. In it, each optional feature can be realized separately and in any possible combination, as understood by a person skilled in the art. The scope of the present invention is not limited by the preferred embodiments. The embodiments are illustrated diagrammatically in the figures, in which the same reference numbers in these figures refer to the same or functionally equivalent elements.

[0128] [Figure 1] 1 illustrates a schematic diagram of an exemplary embodiment of a decision system according to the invention; [Diagram 2] 1 illustrates a schematic diagram of an exemplary embodiment of a mobile device according to the present invention; [Diagram 3] 2 shows a flow chart of an embodiment of a determination method according to the present invention; [Figure 4] 1 shows experimental data relating to an embodiment of a determination method according to the present invention. [Diagram 5] 2 shows a flow chart of an embodiment of a measurement method according to the present invention. [Figure 6] 1 shows experimental data for an embodiment of a measurement method according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0129] FIG. 1 illustrates the dynamic coefficient of variation (Cv TR、lim1 shows an exemplary embodiment of a determination system 110 for determining the presence or absence of a septic blood sample. The determination system 110 comprises a training set 111 of optical test strips 112. Each optical test strip 112 has a reagent test area 114. At least two of the optical test strips 112 are not spoiled. At least two of the optical test strips 112 are spoiled. FIG. 1 exemplarily shows two non-septic optical test strips 116 and two spoiled optical test strips 118. In principle, the training set 111 of the optical test strips 112 may in particular include an even larger number of optical test strips 112, in particular for better statistics. FIG. 1 exemplarily shows a grid pattern 120 of the reagent test area 114 of the spoiled optical test strip 118. Typically, the applied blood generates such a grid pattern 120 in the reagent test area 114 if blood has already been applied to the reagent test area 114 beforehand, increasing the non-uniformity of the reagent test area 114. In other words, the grid pattern 120 typically appears when a previously used optical test strip 112 is used again. Thus, the grid pattern 120 typically indicates a prior use that corrupts the optical test strip 112. However, other options are contemplated. By way of example, the grid pattern 120 may also be the result of strong light exposure that may also corrupt the optical test strip 112.

[0130] In general, spoilage of the optical test strip 118 may be caused by at least one of the following: the device prior to the fluid sample, particularly the bodily fluid sample; prior exposure to at least one spoilage environment for more than 10 minutes, particularly more than 2 hours, more particularly more than 1 day; the time elapsed since application of the fluid sample, particularly the time between application of the sample and taking the image, being outside of an acceptable range. The spoilage-prone environment may be selected from the group consisting of a humid environment, particularly an environment with a humidity of more than 60%, more particularly an environment with a humidity of more than 80%, and a bright environment, particularly an environment with an illuminance of more than 1000 lm / m2, more particularly an environment with an illuminance of more than 1500 lm / m2.

[0131] The decision system 110 further comprises a training set 121 of mobile devices 122. Each mobile device 122 has at least one camera 124. In principle, the training set 121 of mobile devices 122 may comprise a greater number of mobile devices 122, particularly for better statistics. The mobile devices 122 may be or include at least one of a mobile phone, a smartphone, a tablet computer, and the like. The camera 124 of the mobile device 128 may be configured to record an image, in particular a color image. The camera 130 may thus be a color camera and may comprise at least three color sensors, such as at least one color sensor for R, G, B colors.

[0132] The determination system 110 further comprises at least one color reference card 126. The color reference card 126 comprises a plurality of color reference fields 128. The color reference field 128 comprises a known reference color value. The color reference field 128 may be arranged on a surface of the color reference card 126, such as a substrate of the color reference card 126. In particular, the color reference field 112 may be evenly distributed over the entire surface of the color reference card 126, such that the plurality of color reference fields 126 may be distributed over the entire surface of the color reference card 126. As an example, the color reference field 128 may be arranged in a matrix pattern, such as a rectangular matrix pattern. However, the color reference fields 128 may also be arranged in other ways, such as separately from each other. For example, the color reference card 126 may comprise a plurality of gray color reference fields 130 surrounding the color reference field 128. The color reference field 128 and the gray color reference field 130 may not overlap each other. In an exemplary embodiment of color reference card 126, color reference field 128 and gray color reference field 130 may be printed on a pre-printed gray background of color reference card 126. Thus, color reference field 128 may overlap the gray background of color reference card 126.

[0133] The color reference card 126 may further include at least one window 132. Thus, at least one optical test strip 112 or a portion thereof may be viewed through the window 132 when the color reference card 126 is placed on the optical test strip 112. Specifically, at least one reagent test area 114 included in the optical test strip 112 may be viewed through the window 132 of the color reference card 126. As another example, the color reference card 126 may comprise an optical test strip 112 having a reagent test area 114, specifically such that the reagent test area 112 is accessible and visible. Specifically, the optical test strip 112 may be attached to the color reference card 126, specifically such that the reagent test area 114 is accessible and visible. In this manner, both the color reference card 126 and the reagent test area 114 may be in the field of view of the mobile device 122 for capturing an image including at least a portion of the reagent test area 114 and at least a portion of the at least one color reference field 128 of the color reference card 126.

[0134] Furthermore, the color reference card 126 may comprise at least one marker 134. The marker 134 may be or may include at least one of a position marker, such as, for example, an ArUco code, a barcode, a QR code, a label, or a combination thereof. The marker 134 may be located at at least one corner 136 of the color reference card 126. For example, at least one marker 134 may be located at each of the corners 136 of the color reference card 126, in particular such that the marker 134 is visible with the multiple color reference fields 128. Furthermore, the marker 134 may include information regarding the orientation of the color reference card 126. For further details regarding the color reference card 126, please also refer to WO 2021 / 228730.

[0135] The determination system 110 further comprises at least one processor 138. As an example, the at least one processor 138 may be at least partially included in the at least one computer 140. Additionally or alternatively, the at least one processor 138 may be at least partially included in the at least one mobile device 122. The at least one processor 138 may be cloud-based. Thus, the at least one processor 138 may be distributed in the at least one computer 140 and / or the at least one mobile device 122, as an example. The at least one computer 140 and / or the at least one mobile device may be at least partially interconnected, such as by at least one connection 142. The at least one connection 142 may be wired and / or wireless. As an example, the at least one computer 140 may be designated to evaluate images captured by the mobile device 122. Thus, as an example, the at least one computer 140 may be connected to the mobile device 122 by the connection 142, in particular to retrieve images from the mobile device 122. The processor 138, particularly when at least a portion is included in at least one mobile device 122, may be configured to support the capture of at least one image. By way of example, the processor 138 may prompt a user of the mobile device 122 to capture an image. Additionally or alternatively, the processor 138 may be configured to automatically capture an image, for example, when the reagent test area 114 and / or the color reference card 126 are in view.

[0136] The processor 138 is configured to retrieve a training set of images. The training set of images includes images captured by the camera 124. Each image in the training set of images includes at least a portion of a reagent test area 114 of an optical test strip 112 of the training set of optical test strips 112 and at least a portion of at least one color reference field 128 of the color reference card 126. Specifically, as shown in FIG. 1, an image of the entire color reference card 126 having an optical test strip 112 mounted such that the reagent test area 114 of the optical test strip 112 is visible through a window 132 of the color reference card 126 may be captured using the mobile device 122. Thus, each image in the training set of images may specifically include the entire reagent test area 114 and the entire color reference card 126.

[0137] The processor 138 may further comprise a processor for computing a reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets. TR ) is determined by measuring the color variation within the reagent test area 114. The coefficient of variation (Cv RF ) is determined by measuring the variation in color within the color reference field 128. The coefficient of variation (Cv TR ) for the minimum color reference field coefficient of variation (Cv RF、min ) is the color reference field coefficient of variation (Cv RF ) is determined. Specifically, as shown in FIG. 1, an image of the entire color reference card 126 with the optical test strip 112 mounted such that the reagent test area 114 of the optical test strip 112 is visible through a window 132 of the color reference card 126 can be captured using the mobile device 122. In this case, the minimum color reference field coefficient of variation (Cv RF、min) may be specifically determined for the entire color reference card 126.

[0138] The processor 138 calculates the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets, the corresponding measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation limits (Cv TR、lim The method further comprises deriving a relationship for determining the dynamic coefficient of variation limit (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max The determination system 110 may be configured in particular for use in a determination method according to any one of the embodiments disclosed in more detail above or below with reference to the determination method, in particular for performing at least steps e) and f) of the determination method according to any one of the embodiments disclosed in more detail above or below with reference to the determination method.

[0139] FIG. 2 shows a schematic diagram of an exemplary embodiment of a mobile device 122 proposed herein. The mobile device 122 has at least one processor 138. The mobile device 122 is configured to perform at least steps iv) to x) of the measurement method according to any one of the embodiments disclosed above or below in more detail with reference to the measurement method. The mobile device 122 for performing the measurement method is typically not identical to any of the mobile devices 122 of the training set of mobile devices of the determination system 110. However, the mobile device 122 for performing the measurement, such as the mobile device 122 shown in FIG. 2, may be of the same type as at least one of the mobile devices 122 of the training set 121 of the mobile devices 122 shown in FIG. 1, by way of example. However, the mobile device 122 shown in FIG. 2 may also be identical to one of the mobile devices 122 of the training set 121 of the mobile devices 122 shown in FIG. 1. Additionally or alternatively, the mobile devices 122 shown in FIG. 2 may be of a particular type that is not among the one or more types of mobile devices 122 in the training set 121 of mobile devices 122 shown in FIG.

[0140] FIG. 3 shows the dynamic coefficient of variation (Cv TR、lim 3 shows a flow chart of an embodiment of a determination method for determining (a) the number of the first and second inputs of the first and second inputs of the second inputs of the first and second inputs of the second inputs of the first and second inputs of the second inputs of the first and second inputs of the second inputs of the second and third ...

[0141] The method of determination is as follows: a) providing a training set 111 of optical test strips 112 (denoted by reference numeral 144), each optical test strip 112 having a reagent test area 114, at least two of the optical test strips 112 being non-spoiled and at least two of the optical test strips 112 being spoiled; b) providing a training set 121 of mobile devices 122 (denoted by reference numeral 146), each mobile device 122 having at least one camera 124; c) providing at least one color reference card 126 having a plurality of color reference fields 128 having known reference color values ​​(indicated by reference numeral 148); d) using the mobile device 122 of the training set 121 of the mobile device 122 (denoted by reference numeral 150) to capture a training set of images, each image of the training set of images including at least a portion of at least one reagent test area 114 of an optical test strip 112 of the training set 111 of the optical test strips 112 and at least a portion of at least one color reference field 128 of the color reference card 126; e) using at least one processor 138 (denoted by reference numeral 152), more specifically at least one processor 138 of the mobile device 122, to calculate a reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) and determining a training set of pairs for each reagent test area coefficient of variation (Cv TR ) is determined by measuring the color variation within the reagent test area 114 and the coefficient of variation (Cv RF ) is determined by measuring the variation in color within the color reference field 128 and the corresponding reagent test area coefficient of variation (Cv TR ) minimum color reference field coefficient of variation (Cv RF、min) is the color reference field coefficient of variation (Cv RF ), determining, determining, and f) (shown as reference numeral 154) Reagent Test Area Coefficient of Variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) pairs of training sets, the corresponding measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation limit (Cv TR、lim derive relationships for determining the dynamic coefficient of variation limit (Cv TR、lim ) is the maximum coefficient of variation (Cv TR、max ), including defining and deriving

[0142] The method may further include step g) (indicated by reference numeral 156), which is applying a sample of the bodily fluid to the reagent test area 114 of the optical test strip 112. Step g) may specifically be performed before step d). The method may further include step h) (indicated by reference numeral 158), which is mounting at least one optical test strip 112 of the training set 111 of optical test strips 112 on a color reference card 126 that includes a plurality of color reference fields 128 having known reference color values. Step h) may be performed before step d) and optionally before step g).

[0143] Step e) determines the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) of the training set of pairs of reagent test area coefficients of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、minThe labeling may be specifically considered in step f). In step f), the dynamic coefficient of variation limit (Cv TR、lim ) may exclude at least 90%, particularly at least 95%, and more particularly at least 99% of the corrupted optical test strips 118 of the training set 111 of optical test strips 112. In step f), the dynamic coefficient of variation limit (Cv TR、lim ) may allow for the acceptance of at least 80%, particularly at least 90%, more particularly at least 95%, more particularly at least 97%, and more particularly at least 99% of the uncorrupted optical test strips 116 of the training set 111 of optical test strips 112. In other words, in step f), the dynamic coefficient of variation limit (Cv TR、lim ) may not exclude more than 5%, specifically more than 3%, more specifically more than 1% of the non-corrupted optical test strips 116 of the training set 111 of optical test strips 112. The relationship may include at least one of a lookup table, a model, an algorithm, and a function. The relationship derived in step f) may include a function. The function may be a linear function. The linear function may have a slope of 1.

[0144] Dynamic Coefficient of Variation Limit (Cv TR、lim ) is a predetermined maximum coefficient of variation limit (Cv TR、max、事前定義 ). Step f) may include using at least one machine learning algorithm, specifically, the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min In step e), the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、 min) can be determined for at least two color channels, specifically at least two color channels selected from the group consisting of the green channel, the blue channel and the red channel. In step f), a dynamic coefficient of variation limit (CvTR、lim ) is the reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min For at least two color channels, a pair of dynamic coefficient of variation limits (Cv TR、lim ) may be derived first, and individual dynamic coefficient of variation limits (Cv TR、lim ) can then be compared to each other.

[0145] However, specifically, for each color channel, individual dynamic coefficient of variation limits (Cv TR、lim ) can be derived, and for each color channel, the reagent test area coefficient of variation (Cv TR ) to the individual dynamic coefficient of variation limits (Cv TR、lim ) for an unspoiled optical test strip 116, the reagent test area coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or, optionally, equal to the reagent test area coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ), or optionally equal to. As an example, one may consider the green and red channels. Then, as an example, for a non-corrupted optical test strip 116, the coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or arbitrarily equal to the coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or, optionally, equal to . In other words, for a spoiled optical test strip 118, the coefficient of variation (Cv TR) are the individual dynamic coefficient of variation limits (Cv TR、lim ) or arbitrarily equal to the coefficient of variation (Cv TR ) are the individual dynamic coefficient of variation limits (Cv TR、lim ), or optionally equal. Further options are possible.

[0146] FIG. 4 shows experimental data for an embodiment of the determination method according to the present invention. Specifically, FIG. 4 shows the measured minimum color reference field coefficient of variation (Cv RF、min ) to determine the dynamic coefficient of variation (Cv TR、lim The reagent test area coefficient of variation (Cv TR ) and the corresponding minimum color reference field coefficient of variation (Cv RF、min ) versus the measured reagent test area coefficient of variation (Cv TR ) is the measured corresponding minimum color reference field coefficient of variation (Cv RF、min) is plotted against the number of colors 128. Specifically, a color reference card 126 including 20 color reference fields 128 is used for this purpose, and a training set of images is captured by using different mobile devices 122 of a training set 121 of mobile devices 122, each image including the entire color reference card 126 along with different reagent test areas 114 of different optical test strips 112 of the training set 111 of optical test strips 112. FIG. 4 specifically illustrates three clouds of data points that may refer, at least in part, to different mobile devices 122 of the training set 121 of mobile devices 122, more specifically, to different types of mobile devices 122 of the training set 121 of mobile devices 122, such as different smartphone types. The optical test strips 112 used in the experiment illustrated in FIG. 4 were labeled as either spoiled or non-spoiled. Data relating to non-spoiled optical test strips 116 are illustrated in FIG. 4 with dots. Crosses indicate data referring to spoiled optical test strips 118 or data referring to non-spoiled optical test strips 116 of FIG. 4. Specifically, the crosses may refer to so-called challenge experiments that may include using non-spoiled optical test strips 116 or spoiled optical test strips 118. Thus, for one challenge experiment, either non-spoiled optical test strips 116 or spoiled optical test strips 118 may be used. The challenge experiments may primarily use spoiled optical test strips 118. Specifically, at least 70% of the challenge experiments may use spoiled optical test strips 118, more specifically, at least 80% of the challenge experiments may use spoiled optical test strips 118, and most specifically, at least 90% of the challenge experiments may use spoiled optical test strips 118. Thus, at least some of the crosses shown in FIG. 4, specifically the derived dynamic coefficient of variation limits (Cv TR、lim ) may indicate that the optical test strip 116 is not actually spoiled. Based on this data, the dynamic coefficient of variation limit (Cv TR、lim ) the following relationship was derived:

number

[0147] Dynamic Coefficient of Variation Limit (Cv TR、lim ) is plotted as a geometric line, indicated by reference numeral 160 in FIG. 4. Furthermore, in FIG. 4, the range above the geometric line 160 is indicated by reference numeral 159, and the range below the geometric line 160 is indicated by reference numeral 161. Thus, the dynamic coefficient of variation limit (Cv TR、lim ) separates range 159 from range 161. With respect to subsequent use of the measurement method to perform analytical measurements, range 159 may specifically refer to optical test strips 112 that are invalid for analytical measurements, and range 161 may specifically refer to optical test strips 112 that are valid for analytical measurements. As can be seen, the determined dynamic coefficient of variation limits (Cv TR、lim ) specifically allows for the elimination of a large number of data points that may refer to a spoiled optical test strip 118 (shown in FIG. 4 by crosses) and all data points that reliably refer to an unspoiled optical test strip 116 (shown in FIG. 4 by dots). Thus, the determined dynamic coefficient of variation limit (Cv TR、lim ) may be commonly used to identify spoiled optical test strips 118 and to evaluate the validity of the optical test strips 112 for analytical measurements.

[0148] FIG. 5 shows a flow chart of an embodiment of a measurement using a mobile device 122 with a camera 124 and a processor 138 to perform an analytical measurement based on a color-forming reaction. The measurement method includes the following steps, which may be performed in a given order by way of example. It should be noted, however, that a different order is generally possible. Furthermore, it may be possible to perform one or more of the method steps only once or repeatedly. Furthermore, it may be possible to perform two or more of the method steps simultaneously or overlap in time. The measurement method may include further method steps not listed.

[0149] The measurement method is as follows: i) providing at least one optical test strip 112 having at least one reagent test area 114 (indicated by reference number 162), the optical test strip 112 being in particular of the same type as the optical test strips 112 of the training set used in step a) of the determination method according to any one of the embodiments described above or below with further detailed reference to the determination method; ii) providing at least one color reference card 126 having a plurality of color reference fields 128 having known reference color values ​​(indicated by reference numeral 164); iii) capturing, by using a camera 124 (indicated by reference numeral 166), at least one image of at least a portion of the reagent test area 114 to which the at least one sample of the at least one bodily fluid has been applied and at least a portion of at least one of the color reference fields 128 of the color reference card 126; iv) (denoted by reference numeral 168) In particular, the processor 138 is used to calculate, for at least one color channel of the camera 124 of the mobile device 122, a color reference field coefficient of variation (Cv RF ) and determining the color reference field coefficient of variation (Cv RF ) is determined by measuring color variation within at least one color reference field 128; v) (indicated by reference numeral 170) Specifically, the processor 138 is used to calculate the at least one color reference field coefficient of variation (Cv RF ) is used to obtain the minimum color reference field coefficient of variation (Cv RF、min ) to determine vi) (indicated by reference numeral 172) Specifically, the processor 138 is used to calculate the minimum color reference field coefficient of variation (Cv RF、min ) and the dynamic coefficient of variation limit (Cv TR、lim ) to determine the dynamic coefficient of variation limit (Cv TR、lim), wherein the relationship is determined by performing a determination method according to any one of the embodiments described above or below in more detail with reference to the determination method; vii) (denoted by reference numeral 174) Specifically, the processor 138 is used to calculate the reagent test area coefficient of variation (Cv TR ) and determining the reagent test area coefficient of variation (Cv TR ) is determined by measuring a color variation in the reagent test area 114; viii) (shown as reference numeral 176) for at least one color channel, the reagent test area coefficient of variation (Cv TR ) to the dynamic coefficient of variation limit (Cv TR、lim ) and ix) (shown as reference numeral 178) Reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), then the optical test strip 112 is deemed to be spoiled and the measurement method is discontinued; and x) (shown as reference numeral 180) Reagent test area coefficient of variation (Cv TR ) is the dynamic coefficient of variation limit (Cv TR、lim ), considering the optical test strip 112 as not spoiled, and determining a concentration of at least one analyte of the sample of the bodily fluid by using at least one color formation value for color formation in the reagent test area 114 of the optical test strip 112, where at least one sample of the at least one bodily fluid is applied to the reagent test area 114, for at least one color channel.

[0150] As mentioned above in the context of the determination method, the dynamic coefficient of variation limit (Cv TR、lim ) is the potential reagent test area coefficient of variation (Cv TR ) into two possible cases. First, the dynamic coefficient of variation bounds (Cv TR、lim), i.e., below the geometric line 160, represents an optical test strip 112 that can be effectively used in a measurement method for performing analytical measurements; and secondly, the dynamic coefficient of variation limit (Cv TR、lim ), i.e., the range 159 above the geometric line 160, indicates an optical test strip 112 that cannot be effectively used in a measurement method to perform an analytical measurement. TR、lim ), i.e. the geometrical line 160 itself, may be counted as part of the region 159, as part of the region 161 or may not be counted at all. In general, steps ix) and x) of the method as described therefore mean checking in which of the regions 159 and 161 the test strip 112 intended for the actual performance of the measurement method is actually located, thereby determining whether the test strip 112 can actually be effectively used, e.g. whether it is spoiled or not, and depending on said determination taking appropriate further steps as listed in steps ix) and x).

[0151] The measurement method may further include at least one step of applying at least one sample of at least one bodily fluid to the reagent test area 114 of the optical test strip 112, the measurement method being carried out according to one of the following methods:

[0152] - Dynamic coefficient of variation limits (Cv TR、lim iii) is determined using a relationship derived when performing a determination method according to any one of the embodiments disclosed above or below in more detail with reference to the determination method in which at least one sample of at least one bodily fluid is applied to the reagent-testing area 114 of the optical test strip 112 of the training set 111 of the optical test strips 112, the measurement method comprising applying a sample of the bodily fluid to the reagent-testing area 114 of the optical test strip 112 before performing step iii), or - Dynamic coefficient of variation limits (Cv TR、lim) is determined by using a relationship derived when performing the determination method described in any one of the embodiments disclosed above or below in further detail with reference to the determination method in which at least one sample of the bodily fluid is not applied to the reagent test area 114 of the optical test strip 112 of the training set 111 of the optical test strips 112, the measurement method comprising performing steps iii) to viii) without at least one sample of the bodily fluid being applied to the reagent test area 114 of the optical test strip 112, the method further comprising applying at least one sample of the bodily fluid to the reagent test area 114 of the optical test strip 112 before or during performing step x).

[0153] The measurement method may further include a step xi) of attaching the optical test strip to a color reference card, as indicated by reference numeral 182. Step xi) may be performed before step iii).

[0154] FIG. 6 shows experimental data for an embodiment of the measurement method according to the present invention. In particular, FIG. 6 shows a consensus error grid, also called the Parkes error grid, which is a tool for assessing the accuracy of blood glucose measurements. For further details on the consensus error grid, see Journal of Diabetes Science and Technology, Volume 7, Issue 5, September 2013, (Copyright) Diabetes Technology Society, “Technical Aspects of the Parkes Error Grid”, Andreas Pfutzner, MD, Ph.D., David C. Klonoff, MD, Scott Pardo, Ph.D., and Joan L. Parkes, Ph.D. FIG. 6 shows a plot of measured blood glucose (ABG) levels against a user's actual blood glucose (MBG) levels, the blood glucose levels being measured according to the measurement method of the present invention. Briefly, the consensus error grid shows different standardized zones ranging from Zone A, which is practically non-critical for the user to administer insulin, to Zone E, which is highly critical, depending on the measured blood glucose value. A large discrepancy between the measured blood glucose value and the actual blood glucose value may result in improper insulin administration and thus be very serious for the user, even life-threatening. The consensus error grid includes data obtained by using non-spoiled optical test strips 116 as well as spoiled optical test strips 118. The large circles plotted on the consensus error grid represent the dynamic coefficient of variation limits (Cv TR、lim )

number

[0155] Thus, specifically, multiple significant blood glucose measurements, for example in zone C, can be identified and the corresponding measurements can be discontinued in a timely manner. In contrast, a static coefficient of variation limit of 0.09 would have detected only one data point. [Explanation of symbols]

[0156] 110 Decision System 111 Optical Test Strip Training Set 112 Optical Test Strips 114 Reagent Testing Area 116 Non-corrupted Optical Test Strips 118 Rotten Optical Test Strips 120 Grid Pattern Training set of 121 mobile devices 122 Mobile Devices 124 Camera 126 Color Reference Card 128 color reference fields 130 Gray color reference field 132 Windows 134 Marker 136 angle 138 processors 140 Computer 142 Connections 144 Decision method step a) 146 Decision method step b) 148 Decision method step c) 150 Decision method step d) 152 Decision method step e) 154 Decision method step f) 156 Decision method step g) 158 Decision method step h) 159 Area above the geometric line 160 Geometric Lines 161 Geometric Line Area 162 Measurement method step i) 164 Measurement method step ii) 166 Measurement method step iii) 168 Measurement method step iv) 170 Measurement method step v) 172 Measurement method step vi) 174 Measurement method step vii) 176 Measurement method step viii) 178 Measurement method step ix) 180 Measurement Method Steps x) 182 Measurement method step xi)

Claims

1. The dynamic coefficient of variation (Cv ＴＲ、ｌｉｍ ) , a) providing a training set (111) of optical test strips (112), each optical test strip (112) having a reagent test area (114), at least two of the optical test strips (112) being non-spoiled and at least two of the optical test strips (112) being spoiled; b) providing a training set (121) of mobile devices (122), each mobile device (122) having at least one camera (124); c) providing at least one color reference card (126) having a plurality of color reference fields (128) having known reference color values; d) capturing a training set of images by using the mobile device (122) of the training set (121) of the mobile device (122), each image of the training set of images including at least a portion of at least one reagent test area (114) of an optical test strip (112) of the training set (111) of the optical test strips (112) and at least a portion of at least one color reference field (128) of the color reference card (126); e) calculating the reagent test area coefficient of variation (Cv ＴＲ ) and the corresponding minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) training set of pairs, each reagent test area coefficient of variation (Cv ＴＲ ) is determined by measuring the color variation within the reagent test area (114), and each color reference field coefficient of variation (Cv ＲＦ ) is determined by measuring the variation in color within the color reference field (128) and the corresponding reagent test area coefficient of variation (Cv ＴＲ ) minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) is the color reference field coefficient of variation (Cv ＲＦ ) , and f) the reagent test area coefficient of variation (Cv ＴＲ ) and the corresponding minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) pairs of the training set, the corresponding measured minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) to determine the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ deriving a relationship for determining the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ ) is the maximum coefficient of variation (Cv ＴＲ、ｍａｘ ), The method of determining the

2. 2. The method of claim 1, further comprising step g) of applying a sample of bodily fluid to the reagent test area (114) of the optical test strip (112).

3. 3. The method of claim 1, further comprising the step of attaching at least one optical test strip (112) of the training set (111) of optical test strips (112) to the color reference card (126) including a plurality of color reference fields (128) having known reference color values, and step h) is performed before step d).

4. The method of any one of claims 1 to 3, wherein the spoiled optical test strip (118) is spoiled by at least one of the following: previous application of a fluid sample; previous exposure to at least one spoilage environment for more than 10 minutes; or the time elapsed since application of the fluid sample is outside of an acceptable range.

5. The method of claim 4, wherein the spoilage environment is selected from the group consisting of a humid environment and a bright environment.

6. Step e) is a step of determining the coefficient of variation (Cv ＴＲ ) and the corresponding minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ The reagent test area coefficient of variation (Cv ＴＲ ) and the corresponding minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ 6. The method of claim 1, further comprising labeling pairs of optical test strips (112) of the training set (111) of optical test strips (112) with information regarding whether the respective optical test strips (112) of the training set (111) of optical test strips (112) are spoiled or not spoiled.

7. In step f), the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ 7. The method of claim 1, wherein said determining step excludes at least 90% of said corrupted optical test strips (118) from a training set (111) of said optical test strips (112).

8. In step f), the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ 8. The method of claim 1, wherein said method further comprises: a) allowing acceptance of at least 80% of said non-corrupted optical test strips (116) of said training set (111) of said optical test strips (112).

9. The method of claim 1 , wherein the relationship derived in step f) comprises a linear function.

10. A method of performing an analytical measurement based on a color-forming reaction using a mobile device (122) having a camera (124) and a processor (138), comprising: i) providing at least one optical test strip (112) having at least one reagent testing area (114); ii) providing at least one color reference card (126) having a plurality of color reference fields (128) having known reference color values; iii) capturing, using said camera (124), at least one image of at least a portion of said reagent test area (114) to which at least one sample of at least one bodily fluid has been applied and at least a portion of at least one of said color reference fields (128) of said color reference card (126); iv) determining a color reference field coefficient of variation (Cv ＲＦ ), wherein the color reference field coefficient of variation (Cv ＲＦ ) is determined by measuring color variation within the at least one color reference field (128); v) the at least one color reference field coefficient of variation (Cv ＲＦ ) to obtain the minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) to determine vi) the minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) and the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ ) for the reagent test area (114). ＴＲ、ｌｉｍ 10. A method for determining a relationship between a first and second vertices of a vehicle, the method comprising: vii) determining a reagent test area coefficient of variation (Cv ＴＲ ) wherein the reagent test area coefficient of variation (Cv ＴＲ ) is determined by measuring a color variation in the reagent test area (114); viii) determining the reagent test area coefficient of variation (Cv ＴＲ ) to the determined dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ ) compared to ix) the reagent test area coefficient of variation (Cv ＴＲ ) is the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ ), then deeming said optical test strip (112) to be spoiled and terminating said measurement method; and x) the reagent test area coefficient of variation (Cv ＴＲ ) is the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ ), determining the optical test strip (112) as not spoiled if the residual density is less than 0.001 μm, and determining, for the at least one color channel, a concentration of at least one analyte of the sample of at least one body fluid by using at least one color formation value for color formation of the reagent test area (114) of the optical test strip (112) to which at least one sample of at least one body fluid is applied.

11. The method further comprises at least one step of applying the at least one sample of the at least one bodily fluid to the reagent test area (114) of the optical test strip (112), the method comprising: - the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ 11) is determined using a relationship derived when performing the determination method of any one of claims 1 to 10, which refers to a determination method in which at least one sample of the at least one bodily fluid is applied to the reagent-testing area (114) of the optical test strips (112) of a training set (111) of the optical test strips (112), the measurement method comprising applying the sample of the bodily fluid to the reagent-testing area (114) of the optical test strip (112) before performing step iii), or - the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ 11) is determined by using a relationship derived when performing the determination method of any one of claims 1 to 10, referring to a determination method in which no sample of the at least one body fluid is applied to the reagent-testing area (114) of the optical test strip (112) of a training set (111) of the optical test strips (112), the measurement method comprising performing steps iii) to viii) without the application of the at least one sample of the body fluid to the reagent-testing area (114) of the optical test strip (112), the method further comprising applying the at least one sample of the body fluid to the reagent-testing area (114) of the optical test strip (112) before or during performing step x). The method of claim 10, wherein the method is carried out by one of the following methods:

12. The dynamic coefficient of variation (Cv ＴＲ、ｌｉｍ ), comprising: A) a training set (111) of optical test strips (112), each optical test strip (112) having a reagent test area (114), at least two of the optical test strips (112) being non-spoiled and at least two of the optical test strips (112) being spoiled; B) a training set (121) of mobile devices (122), each mobile device (122) having at least one camera (124); C) at least one color reference card (126) having a plurality of color reference fields (128) having known reference color values; D) at least one processor (138), - searching a training set of images, said training set of images comprising images captured by said camera (124), each image of said training set of images comprising at least a portion of a reagent test area (114) of an optical test strip (112) of said training set (111) of said optical test strips (112) and at least a portion of at least one color reference field (128) of said color reference card (126); - the reagent test area coefficient of variation (Cv ＴＲ ) and the corresponding minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) training set of pairs, each reagent test area coefficient of variation (Cv ＴＲ ) is determined by measuring the color variation within the reagent test area (114), and each color reference field coefficient of variation (Cv ＲＦ ) is determined by measuring the variation in color within the color reference field (128) and the corresponding reagent test area coefficient of variation (Cv ＴＲ ) minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) is the color reference field coefficient of variation (Cv ＲＦ ) .... - the reagent test area coefficient of variation (Cv ＴＲ ) and the corresponding minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) pairs of the training set, the corresponding measured minimum color reference field coefficient of variation (Cv ＲＦ、ｍｉｎ ) to determine the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ deriving a relationship for determining the dynamic coefficient of variation limit (Cv ＴＲ、ｌｉｍ ) is the maximum coefficient of variation (Cv ＴＲ、ｍａｘ ), the processor (138), The decision system comprising:

13. A computer-readable storage medium comprising instructions that, when executed by a decision system (110), cause the decision system (110) to perform at least steps e) and f) of the decision method according to any one of claims 1 to 12 referring to the decision method.

14. A mobile device (122) having at least one camera (124) and at least one processor (138), the mobile device (122) being configured to perform at least steps iv) to x) of the measurement method of any one of claims 1 to 13 referring to the measurement method.

15. 15. A computer-readable storage medium comprising instructions that, when executed by a mobile device (122) having a camera (124) and a processor (138), cause the mobile device to perform at least steps iv) to x) of the measurement method of any one of claims 1 to 14 referring to the measurement method.

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