METHOD FOR PREDICTING RAW MATERIAL FUNCTIONALITY IN THE FINAL PRODUCT

Description

AREA OF INVENTION

[0001] The present invention is in the field of food technology and relates to a method for predicting the raw material functionality in end products, in particular in dairy products, products based primarily and mainly on milk including cheeses, their plant-based milk alternatives and corresponding hybrid products, by collecting defined analytical and raw material data and their statistical evaluation. TECHNOLOGICAL BACKGROUND

[0002] The trend towards veganism has arrived in Germany. Around 1.3 million people follow a vegan diet. By comparison, approximately 8 million are vegetarian. However, these numbers are rising daily. This also increases interest in plant-based raw materials suitable for replacing animal products. In the dairy sector, the search is on for substitutes for the important milk proteins. Candidates include plant-based proteins, such as those derived from peas, broad beans, or oats. However, one problem is that milk proteins cannot simply be replaced one-to-one with any other protein type. This is because, firstly, recipes are designed around milk as the protein source, and secondly, the proteins themselves can have very different structures and functionalities.The same applies to the substitution of animal and / or plant macronutrients such as fat and carbohydrates. Another aspect that can affect the functionality of the raw materials in the final product is that raw material manufacturing processes vary depending on the supplier, resulting in different raw material qualities and batches. Consequently, even with a weight-equal substitution of raw materials, the resulting products may suddenly exhibit a mealy, rubbery, or dull texture instead of a structured one.

[0003] To counteract this, it has previously been necessary to first incorporate potentially suitable candidates into various end products and verify the resulting desired properties. Given that the market now offers hundreds of different raw materials and additives for influencing the composition, structure, and stability of end products, both dairy and plant-based, the required pre-selection is extremely time-consuming. Therefore, a method that allows for a rapid prediction of the expected properties in the end products would be desirable. STATE OF THE ART

[0004] Assessment methods are known from the prior art that can be used, for example, to predict the shelf life of food in packaging. For instance, EP 1626275 B1 (WILD) proposes a method for assessing product shelf life in packaging, which comprises the following steps: a) Inserting a sealed packaging (4) filled with contents into a pressure chamber (1), b) Storing the packaging (4) in the pressure chamber (1) for a period of time t 1 at overpressure p 1 and a specific temperature T 1 in a test gas atmosphere, whereby a defined quantity Q of test gas permeates into the packaging, c) Storing the packaging (4) for a specific period of time t 2 under the influence of heat and / or light, and d) analytical and / or sensory examination of the contents.

[0005] A disadvantage of this method is the high cost of the equipment; moreover, the operating principle – simulating the amount of oxygen introduced into the material and accelerating the reaction through increased pressure – ultimately only allows for a rough estimate, which is often unsuitable for a qualitative assessment, especially since it is known that for many polymer / gas systems, the solubility and diffusion coefficients of the permeating substance in the polymer (e.g., a plastic bottle) become dependent on concentration and pressure when the investigation is carried out below the glass transition temperature (Tg) of the polymers and / or the critical temperature of the gas. [ MÜLLER, K. "Oxygen Permeability of Plastic Bottles and Closures" Dissertation TU Munich (2003) )]

[0006] A completely different method of visually indicating the shelf life of a packaged food to the consumer is the subject of patent protection EP 2378354 B1. ,EP 2581785 B1 and WO 2009 056591 A1 (University of Münster) concern a sensor device, specifically an electrochemical processor, which is short-circuited when the packaging is opened. An aluminum oxide layer forms, the progression of which is proportional to the product's shelf life. For example, an increase in temperature accelerates oxide formation and thus naturally reflects the decreasing freshness of the product. However, this solution requires equipping each individual package with a separate sensor, which is also technically complex.

[0007] EP 3875953 B1 (DMK) describes an accelerated storage test for determining the quality and shelf life of foodstuffs, in which a first sample is stored at a temperature T1 and an identical second sample at a significantly higher temperature T2, with the samples being assessed sensorially and compared using a calibration curve. Since this sensory assessment of products always contains a subjective component, a method based exclusively on objective parameters would be more advantageous.

[0008] None of these existing investigation methods deals with the early, targeted prediction of the raw material functionality in the final product.

[0009] Furthermore, JP2022121218 discloses a method for predicting raw material functionality in butter, comprising the steps of: providing raw milk samples of different qualities; producing butter from these raw milk samples; recording and evaluating the product properties; analyzing the data using principal component analysis to obtain a score plot; assigning the raw material qualities to the data points in the score plot; and identifying clusters in the score plot to recognize which raw materials lead to similar or identical product properties of the butter. TASK OF INVENTION

[0010] Therefore, the object of the present invention was to provide a test method which, through the correlated consideration and statistical evaluation of various raw material properties, allows the reliable early and reproducible prediction of the raw material functionality in end products such as dairy products, products based primarily and mainly on milk including cheeses, their plant-based milk alternatives and corresponding hybrid products. DESCRIPTION OF THE INVENTION

[0011] The invention relates to a method for predicting the raw material functionality in end products such as dairy products, products primarily and mainly based on milk including cheeses, their plant-based milk alternatives and corresponding hybrid products, comprising or consisting of the following steps (a) Providing a set of raw materials of varying qualities; (b) Creating a set of test parameters; (c) Applying the defined test parameters to each of the individual samples from step (a) to generate a data set; (d) Incorporating the raw materials of varying qualities from step (a) into finished products such as dairy products, products primarily and mainly based on milk, including cheeses, their plant-based milk alternatives, and corresponding hybrid products; (e) Recording and evaluating the product characteristics; (f) Verifying the relationship of the data in the data set using Principal Component Analysis (PCA) to obtain a coordinate system (score plot) and determining the largest variance of the data as the first component and the second largest variance of the data as the second component; (g) Assigning the product characteristics found in step (e) to the data points in the coordinate system from step (f);and (h) identifying clusters in the score plot to identify raw materials which, when used in the final product, lead to the desired similar or identical product characteristics.

[0012] Surprisingly, it was found that, with the aid of the inventive method, new raw materials can be assessed in advance with regard to different properties and functionalities that they produce when incorporated into different dairy products, products based primarily and mainly on milk including cheeses, their plant-based milk alternatives and corresponding hybrid products, based on physicochemical properties that are mostly already recorded during the incoming inspection of the raw materials, but are not used for a described prediction.

[0013] The method according to the invention is divided into four sections: Selection of suitable analysis parameters, data generation, verification of the correlation of the data through PCA, and identification of clusters in the score plot to identify raw materials that, when used in the product, lead to the desired similar or identical product properties. Dairy products, products primarily and mainly based on milk including cheeses or their plant-based milk alternatives and corresponding hybrid products

[0014] The selection of products to which the process according to the invention can be applied is not inherently critical. Typically, the products are selected from the group consisting of whole milk, skimmed milk, UHT milk, milk powders, cream, quark, cheese, and yogurt, but also products primarily and mainly based on milk, such as numerous desserts. Also included are their plant-based milk alternatives, such as plant-based drinks, spreads, cream alternatives, yogurt alternatives, and cheese alternatives. The process can also be applied to hybrid products in which only some of the animal milk components have been replaced by plant-based alternatives, or vice versa (replacement of plant-based components with animal-based raw materials or ingredients). This list is not exhaustive. Raw materials

[0015] The raw materials, whose properties and functionalities in the final products are to be predicted, can be selected from the group consisting of raw materials such as proteins, fats, carbohydrates, but also semi-finished products such as whey, milk permeates and milk retentates, their mixtures, as well as their corresponding plant-based substitutes or raw materials. They can be in solid, powdery, or liquid and concentrated form.

[0016] Typically, for the desired prediction of the raw material functionality in the final product, a raw material set of 5 to 50, preferably 7 to 15 raw materials is required, which preferably leads to at least three different final product properties. Test parameters

[0017] A key feature of the method according to the invention is the selection of the test parameters on the basis of which the evaluation of the samples is to be carried out. The following are preferably suitable for this purpose: pH value; solubility; thermal behavior (DSC); particle size distribution (D10, D50, D90, specific surface area); fat absorption capacity (rapeseed oil); water absorption capacity; dissolved oxygen content; instability index (luminizer); brightness color value white / black (L*); color coordinate red / green (a*); color coordinate yellow / blue (b*); free amino nitrogen (PAN); ammonia content; urea content; calcium content; acidity; rheological behavior; strength; and degree of syneresis.

[0018] Both DSC dataThese are the results obtained from differential thermal analysis (DSC). This is a thermal analysis method used to measure the amount of heat released or absorbed by a sample during heating, cooling, or an isothermal process. An encapsulated container with a sample (5-40 mg) and a second identical container without contents (reference) or a reference sample are placed together in a temperature chamber and subjected to the same temperature change program. Due to the heat capacity of the sample and any exothermic or endothermic processes or phase changes such as melting or evaporation, temperature differences arise between the sample and the reference, as thermal energy flows into or out of the sample during the process under investigation. DSC is primarily used to determine the degree of crystallinity, the melting or decomposition point, and the degree of protein denaturation. Among other things, the following are measured:The temperature at which the thermally induced reaction begins (onset) and ends (endset), the temperature at which the reaction reaches its maximum (peak), and the height of the peak are determined. The reaction enthalpy is also determined.

[0019] The specific surface The particle size distribution of the powders was determined by static laser light scattering (Horiba LA-960). According to the inventive method, the specific surface area of ​​the powders is a volume-related quantity Sv with the unit m2 / m3. However, the mass-related specific surface area SMv is usually determined, as this is easier to perform experimentally. The two values ​​can be converted using the factor ρ: S V = ρ S M

[0020] The particle size distribution includes the specification of the D10, D50 and D90 values, i.e., the specification of the particle size ranges that comprise 10, 50 or 90 wt.% of all powder particles.

[0021] The Fat absorption capacity(here the absorption capacity of rapeseed oil) as well as the what storage capacity These values ​​relate to the swelling capacity of the powders and are given in g of fat or g of water per g of powder.

[0022] The Brightness color value L* as well as the two Color coordinates a* and b*These colors belong to the so-called L*a*b* color system, also known as the CIELAB system. It is the most widely used system for color measurement today and has become prevalent in almost all fields of application. It was defined by the CIE in 1976 as one of the equidistant color spaces to address the main problem of the original Yxy system: Equal geometric difference distances in the x,y color triangle do not result in perceptually equal color differences. The color space of the L*a*b* system is characterized by the lightness L* and the color coordinates a* and b*. The signs indicate the color direction: +a* indicates a red component, while -a* points towards green. Accordingly, +b* stands for yellow, and -b* for blue. At the origin (intercept of the axes) is a neutral gray without any chromaticity. As the a*b* values ​​increase, i.e., the further the color point is from the center, the greater the chromaticity.For determining color values, the CR 400 or 410 device has proven effective within the framework of the inventive method. Further information can be found in the consumer information leaflet "Exact Color Communication" from Konica Minolta.

[0023] Preferably, the set of test parameters should contain at least 5, preferably at least 7 parameters. Principal Component Analysis (PCA)

[0024] The resulting data pool is then evaluated using the so-called "Principal Component Analysis" (PCA), also known as principal component analysis.

[0025] This is an established statistical method for analyzing large datasets with a high number of parameters per observation, improving the interpretability of data while maintaining a maximum amount of information, and visualizing multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is achieved by linearly transforming the data into a new coordinate system in which (most of) the variation in the data can be described with fewer dimensions than in the original data. Within the framework of the method according to the invention, only the first two main components are used to represent the data in two dimensions and to visually identify clusters of closely related data points.

[0026] In PCA data analysis, the first principal component of a set of p variables, assumed to be jointly normally distributed, becomes the derived variable, formed as a linear combination of the original variables, and explains the largest variance. The second principal component explains the largest variance in what remains after removing the effect of the first component, and we can continue with p iterations until all the variance is explained. PCA is most commonly used when many of the variables are highly correlated and it is desirable to reduce their number to an independent set. The first and second principal components can be defined, respectively, as directions that maximize the variance of the projected data. PCA is the simplest of the true eigenvector-based multivariate analyses and is closely related to factor analysis.A detailed description of the method can be found, for example, in. https: / / en.wikipedia.orgi / wiki / Principal component analysis

[0027] In short, PCA provides a coordinate system in which, for each data point, the component with the greatest variance or deviation ("First Component") is plotted against the component with the second greatest variance ("Second Component"). In the final step, each data point in the coordinate system is assigned its respective previously determined product property.

[0028] Surprisingly, the analysis revealed clustering that can be clearly correlated with the properties and functionalities under investigation. These properties and functionalities include, for example, texture, creaminess, syneresis, mouthfeel, firmness, emulsion stability, synergistic effects, and combinations of two, three, or more of these properties.

[0029] This means that an unknown raw material sample only needs to be analyzed for the aforementioned test parameters in order to assign it a coordinate using PCA (Product Analysis). Based on the location of this coordinate, the expected functionality of the raw material in the final formulation can then be determined quickly, reliably, and potentially automatically. An even faster simulation based on the initial values ​​and future prediction and selection criteria for raw material selection using AI (artificial intelligence) in an even accelerated approach is therefore easily conceivable and feasible as a second stage. Furthermore, the early and rapid selection of suitable alternative raw materials is possible in any industrial application. Example 1A / B Parameter selection and data generation

[0030] The inventive method is explained below using the evaluation of various plant-based powders. First, the protein fraction was extracted from the fruits of various plants (pea, broad bean, chickpea, oat) and then brought into a powdered state. Then, the following parameters were selected from the available parameters: pH value, solubility, DSC data (onset, endset, peak, peak height, reaction enthalpy), particle size distribution (D10, D50, D90, specific surface area), fat absorption capacity (rapeseed oil), water absorption capacity, color coordinates (L*, a*, b*) and the corresponding measured values ​​are determined; these are in Table 1 summarized. Subsequently, the various samples of plant-based powders were incorporated into a standard formulation for vegan plant-based quark, and the texture of the resulting products was determined. The results are also available in Table 1, last column. Table 1 Data set for quality determination Example. origin pH* Solubility* Onset [°C] End set [°C] Peak [°C] Reaction enthalpy [J / g] Peak height [mW] Specific surface area [cm² / cm³] 1 pea 7,77 0,4 56,275 70,7 63,46 -0,585 0,02963 2029,9 2 pea 7,7 0,3 52,65 72,485 64,965 -2,265 0,103005 2359,45 3 pea 7,77 0,325 52,825 70,12 62,64 -1,305 0,05987 1386,25 4 pea 7,62 0,375 52,44 69,49 60,47 -0,69 0,048685 1021,1 5 pea 7,98 0,4 53,27 71,08 62,725 -1,095 0,069085 1316,45 6 pea 7,76 0,375 59,37 68,31 63,065 -1,345 0,080835 1130,9 7 pea 7,39 0,2 55,54 66,67 61,425 -0,775 0,09297 545,137705 8 pea 7,67 0,275 51,74 68,445 60,94 -1,71 0,11 814,445 9 broad bean 7,21 0,4 55,18 66,86 61,375 -0,305 0,02612 1756 10 broad bean 7,08 0,25 54,27 70,105 63,055 -4,64 0,185 7011,15 11 chick-pea 7,76 0,4 56,555 73,115 64,875 -1,42 0,06824 1654,15 12 Oats 7,74 0,3 53,505 69,175 62,065 -0,97 0,04052 1371,2 *) 1 wt% solution Example. origin D10 [µm] D50 [µm] D90 [µm] FA** WA*** L* a* b* texture 1 pea 16,41019 36,679675 73,50104 0,695 3,295 82,4 2,89 19,36 structured 2 pea 16,04886 28,1959 50,241605 0,61 1,2 85,3 0,77 25,19 unstructured 3 pea 24,950615 49,37172 121,441215 0,66 2,775 80,68 2,8 18,17 structured 4 pea 31,059785 72,287075 183,973495 1,005 2,49 82,32 4,24 21,51 rubbery 5 pea 27,636675 51,3814 98,7448 0,45 2,46 79,85 3,67 19,15 structured 6 pea 31,891985 59,106525 117,480105 0,63 2,34 81,41 3,49 18,9 structured 7 pea 24,270895 98,484615 204,588875 0,785 1,155 77,18 6,08 19,02 unstructured 8 pea 36,621365 117,186405 222,080115 0,86 1,155 75,76 7,32 19,94 unstructured 9 broad bean 19,44992 41,04005 84,19119 0,63 2,27 87,52 0,46 13,18 mealy 10 broad bean 5,51351 8,9755 15,646215 0,945 1,365 89,53 0,54 13,05 unstructured 11 chick-pea 20,88119 42,092875 87,612445 0,68 2,91 83,79 1,42 16,25 structured 12 Oats 24,35504 51,666535 119,96077 0,79 1,505 78,96 3,01 17,21 Dull, mealy ** Fat absorption [g fat / g powder] *** Water absorption [g water / g powder] Example 1C Verification of the correlation of the data using PCA = Principal Component Analysis

[0031] The third step of the inventive method is in Figure 1 reproduced. The previously acquired dataset is processed using Principal Component Analysis. PCA provides a coordinate system in which the scores for the first two principal components are plotted for each data point. Figure 1The explained variance of principal component 1 is 40.8%, and that of the second principal component is 26.1%. Overall, PCA explains 66.9% of the variance of the original data. Knowing which parameters determine the two principal components is irrelevant to the procedure, as this step only aims to scale the variance to a manageable size.

[0032] As can be seen from the diagram, the data points are distributed more or less evenly across the coordinate system. However, after assigning the actual textures found to the data points, it becomes apparent that the diagram contains clusters of the same or similar structure. The desired structured textures are found exclusively in the x-coordinate range -2.5 to 2.5 and the y-coordinate range +1 to +3. Samples not located in this cluster were unstructured; their textures ranged from mealy and creamy to rubbery.

[0033] Figure 1This can therefore be understood as a raw material selection diagram. To predict the expected functional properties of a raw material, it is only necessary to record the physicochemical data according to the selected test parameters – which are usually either specified or recorded during incoming inspection – and to calculate the corresponding data point as described above. The position of the data point in the diagram then allows conclusions to be drawn about the expected raw material functionality in the final product. Example 2

[0034] The inventive method is explained below using the evaluation of 17 different milk protein powders; the following parameters were selected from the available parameters: pH value, solubility, DSC data (onset, endset, peak), particle size distribution (10, D50, D90, specific surface area), fat absorption capacity (rapeseed oil), water absorption capacity, color coordinates (L*, a*, b*) and the corresponding measured values ​​are determined; these are in Table 2 summarized.

[0035] Subsequently, the various samples of milk protein powder were incorporated into a standard formulation for high-protein milk semolina, and the texture of the resulting products was determined. The results are also shown in Table 2, last column. A PCA (Product Combination Analysis) was then performed according to Example 1C. The score plot showing the cluster of substances with desired properties can be found in Figure 2 . Table 2 Data set for quality determination Example. pH* Solubility* Onset [°C] End set [°C] Peak [°C] Specific surface area [cm² / cm³] 1 7,78 0,30 0,00 0,00 0,00 7,78 2 7,85 0,48 60,93 72,87 64,35 7,85 3 7,38 0,65 0,00 0,00 0,00 7,38 4 7,83 0,65 0,00 0,00 0,00 7,83 5 7,84 0,40 61,21 66,48 64,03 7,84 6 7,85 0,40 52,84 59,18 55,63 7,85 7 7,60 0,50 0,00 0,00 0,00 7,60 8 7,68 0,60 0,00 0,00 0,00 7,68 9 7,61 0,50 61,51 67,92 67,92 7,61 10 7,75 0,63 61,35 77,76 66,28 7,75 11 7,47 0,40 56,88 72,93 66,29 7,47 12 7,88 0,65 62,19 71,66 66,83 7,88 13 7,57 0,30 0,00 0,00 0,00 7,57 14 7,82 0,40 0,00 0,00 0,00 7,82 15 7,73 0,05 0,00 0,00 0,00 7,73 16 7,74 0,01 0,00 0,00 0,00 7,74 17 7,91 0,15 58,15 61,80 60,36 7,91 Example. D10 [µm] D50 [µm] D90 [µm] FA** WA*** L* a* b* texture 1 23,98 55,87 167,72 2,23 1,37 93,34 -1,21 9,14 pasty, creamy, with noticeable semolina grains 2 35,52 86,49 222,32 2,36 3,64 91,91 -0,74 9,92 less firm than standard, creamy 3 23,04 61,05 168,18 2,23 4,42 92,75 -1,14 9,88 too firm / compact 4 17,70 41,80 88,29 2,30 4,65 94,18 -1,71 9,42 comparable to standard. 5 31,96 171,26 322,69 1,31 3,62 92,13 -1,34 12,29 too firm / compact 6 27,75 69,27 186,03 2,08 3,22 92,54 -1,26 9,35 firmer and stickier than standard. 7 26,17 93,50 250,93 1,73 3,73 92,75 -1,14 9,88 Slightly firmer than standard; okay. 8 25,48 62,62 193,51 2,42 4,53 92,12 -1,36 12,65 slightly too thin / watery 9 33,96 74,92 225,91 2,60 3,55 92,98 -1,80 10,75 slightly too thin / watery 10 27,10 69,60 197,09 2,36 3,89 92,47 -0,28 7,77 slightly too thin / watery 11 21,24 64,05 190,68 2,39 3,98 92,50 -0,69 9,35 slightly firmer than standard. 12 28,66 71,08 207,07 1,58 3,19 93,18 -2,12 11,92 firmer than standard. 131 25,64 53,25 102,00 3,59 2,25 90,93 -1,65 12,86 firmer and stickier than standard. 14 28,19 65,22 185,75 2,62 3,19 92,29 -1,84 12,10 comparable to standard, slightly creamier 15 42,96 175,53 368,09 2,09 0,30 88,26 -0,57 13,74 Slightly softer than standard; okay. 16 58,08 187,30 392,03 2,64 0,14 87,71 -0,37 13,87 Slightly softer than standard; okay. 17 18,25 50,40 111,51 2,14 1,15 93,39 -2,34 12,61 In order * 1% solution ** Fat absorption [g fat / g powder] *** Water absorption [g water / g powder]

Claims

1. A method for predicting raw material functionality in end products such as dairy products, primary and main milk-based products including cheese, their plant-based milk alternatives and corresponding hybrid products, comprising or consisting of the following steps (a) providing a set of raw materials of different qualities; (b) establishing a set of test parameters; (c) applying the set of test parameters to each of the individual samples from step (a) to generate a data set; (d) incorporating the raw materials of different qualities from step (a) into final products, such as dairy products, products based primarily and principally on milk, including cheeses whose plant-based milk alternatives and corresponding hybrid products; (e) recording and evaluation of product characteristics; (f) checking the correlation of the data in the data set using Principal Component Analysis (PCA) to obtain a coordinate system (score plot) and determining the largest variance of the data as the first component and the second largest variance of the data as the second component; (g) assigning the product properties found from step (e) to the data points in the coordinate system from step (f); and (h) determining clusters in the score plot to identify raw materials which, when used in the end product, lead to the desired similar or identical product properties.

2. The method of Claim 1, wherein the end products used are milk products, products based primarily and mainly on milk, including cheeses, their plant-based milk alternatives and corresponding hybrid products, which are selected from the group formed by whole milk, skimmed milk, UHT milk, milk powders, cream, curd, cheese and yoghurt, and cheese and their plant-based substitute products and applications such as, among others including plant-based drinks, spreads, cream alternatives, yoghurt alternatives and cheese alternatives.

3. The method of Claim 2, wherein milk- and / or plant-based raw materials / ingredients / ingredients are used which are selected from the group formed by proteins, fats, carbohydrates, acid whey, milk permeates and milk retentates as well as mixtures and concentrates thereof4. The method of Claim 1, wherein raw material sample set comprising 5 to 50 samples is used.

5. The method of Claim 1, wherein sample set is used which comprises raw materials of at least three different qualities.

6. The method of Claim 1, wherein a set of test parameters is used which comprises at least two of the following parameters: - pH value; - solubility; - thermal behavior (DSC) of the powder; - particle size distribution (D10, D50, D90, specific surface area); - fat absorption capacity (rapeseed oil); - water absorption capacity; - dissolved oxygen quantity; - instability index (Lumisizer); - lightness color value white / black (L*); - color coordinate red / green (a*); - color coordinate yellow / blue (b*); - free amino nitrogen (PAN); - ammonia content; - urea content; - calcium content; - acidity; - rheological behavior; - firmness; and - degree of syneresis.

7. The method of Claim 6, wherein a set of test parameters is used which comprises at least 5, preferably at least 7 parameters.

8. The method of Claim 1, wherein properties are predicted which are selected from the group formed by texture, creaminess, syneresis, mouthfeel, firmness, emulsion stability, synergy effects as well as combinations of two, three or more of these properties and prediction of their application properties.