Method for assessing stability and quality of dairy products and plant-based milk substitutes during storage

Abstract

A method is proposed for assessing the stability and quality of dairy products, primary and primary milk-based products (including cheese) or plant-based milk substitutes during storage under specified conditions by aging dairy products, primary and primary milk-based products (including cheese) or plant-based milk substitutes, regularly sampling and analyzing using different parameters. The data set is analyzed using principal component analysis (PCA) and partial least squares (PLS) calibration methods. The calculated age is compared to an actual sample age and a calibration line is provided that can be used to determine an unknown sample age.

Description

Field of the Invention

[0001] The present invention is in the field of food technology and relates to a method for early assessment of the stability and quality, in particular age, of dairy products, primary and primary milk-based products - including cheese - or their plant-based milk substitutes, by collecting defined quality and composition data and evaluating them statistically. Background of the Invention

[0002] One of the greatest requirements in the food industry is not only to manufacture and market its products in perfect quality, but also to ensure this quality and technical functionality for a sufficiently long period of time. In addition to the microbiological shelf life that can be ensured by appropriate processes such as heating, aseptic filling and packaging, and preservation, chemical or physical changes in the product during storage must also be avoided, since these changes can at best only lead to changes in taste, but at worst spoil the food and have a negative impact on its technical functionality. One of the most important factors affecting the quality of food is the penetration of substances, especially oxygen, through the packaging material, which can affect the color, taste, technical functionality or, due to oxidation, all three factors of the food. The packaging used must therefore guarantee optimal product and functional protection and meet high product-specific requirements in terms of material selection and packaging design. In particular, the perfect quality and technical functionality of the product must be ensured within the declared minimum shelf life.

[0003] In addition to material tests and theoretical calculation models, standard qualification tests, such as real-time product storage tests, can also be used to determine quality, functionality and, as a result, minimum shelf life. The packaging material to be tested is filled under controlled conditions and stored until the specified shelf life. However, this means that sufficiently reliable statements about the suitability of an existing product packaging design or, conversely, the suitability of a new product development for an existing packaging type can only be made after the expected minimum shelf life has expired. Test cycles that can in some cases last for several months directly highlight the main disadvantages of this test method. Competition among product suppliers, due to the growing variety of products, increasing transport routes, ever-shorter product cycles and high consumer expectations regarding product quality and safety, requires that the required development times be shortened. Prior art

[0004] Related methods are known in the prior art, which can be used to indirectly and imprecisely determine the shelf life of food in a package. For example, EP 1626275 B1 (WILD) proposes a method for evaluating the shelf life of a product in a packaging material, the method comprising the following steps: (a) placing a sealed packaging device (4) containing a product in a pressurized chamber (1), (b) storing the packaging material (4) in the pressurized chamber (1) in a test gas atmosphere at a positive pressure p1 and a specific temperature T1 for a period of time t1, wherein a specified amount Q of the test gas permeates into the packaging material, (c) storing the packaging material (4) under the influence of heat and / or light for a specific period of time t2, and (d) Analytical and / or sensory examination of the filling material.

[0005] The disadvantage of this method is the relatively high cost of the equipment; moreover, the functional principle - simulation of the amount of oxygen introduced into the filling material and the acceleration of the reaction by increasing the pressure - ultimately allows only very rough estimates, which are generally not suitable for qualitative statements, especially since it is known that for many polymer / gas systems the solubility and diffusion coefficient of the permeating species in the polymer (e.g. plastic bottles) may vary greatly from one polymer to another (e.g. plastic bottles) and become concentration- and pressure-dependent if the studies are carried out below the glass transition temperature Tg of the polymer and / or the critical temperature of the gas [ K. "Oxygen permeability of plastic bottles and caps" Dissertation TU München (2003)].

[0006] EP 2378354 B1, EP 2581785 B1 and WO 2009 056591A1 (UNI ) are the subject of completely different approaches to visually indicate the shelf life of packaged foods to the consumer. These involve sensor devices, in particular electrochemical processors, which short-circuit when the package is opened. An aluminum oxide layer is formed, the progression of which is proportional to the shelf life of the product. For example, an increase in temperature accelerates the formation of the oxide, so naturally it also reflects the decrease in the freshness of the product. However, this solution requires a separate sensor for each individual package, which is also technically complex and often shows unstable results in the case of multiple or single openings.

[0007] EP 3875953 B1 (DMK) describes an accelerated storage test for determining the quality and shelf life of food products, wherein a first sample is stored at a temperature T1 and a similar second sample is stored at a significantly higher temperature T2, whereby the samples are subjected to a sensory evaluation and compared with one another via a calibration curve. Since the sensory evaluation of products always contains a subjective component, a method based entirely on objective parameters would be more advantageous. Purpose of the Invention

[0008] It is therefore an object of the present invention to provide a test method which, by means of a calibration chart, can quickly and reliably indicate certain properties, such as age or quality, of unknown dairy products, primary and primary milk-based products, including cheese or its milk substitutes. Summary of the invention

[0009] The present invention relates to a method for assessing the stability and quality of dairy products, primary or primary milk-based products (including cheese) and plant-based milk substitutes over storage time, comprising or consisting of the following steps: (a) provide a first set of samples of dairy products, primary or principal dairy-based products including cheese, or plant-based dairy alternatives. P1.0, P2.0...Pn.0); (b) aging the samples from step (a) for a specified period of time to obtain an nth group of samples (P1.1, P1.2 ... P2.1, P2.2 ... Pn.x); (c) creating a set of test parameters; (d) applying the specified test parameters to each individual sample in the first group and the nth group of samples to generate a data set; (e) using principal component analysis (PCA) to examine the correlation of the data in the data set, obtain a coordinate system (score map), and determine the largest variance of the data as the first component and the second largest variance of the data as the second component; (f) Partial least squares analysis (PLS) was performed on the data; (g) generating a partial least squares (PLS) calibration model to obtain a PLS response plot (calibration line) in which the calculated response for each data point is determined relative to the corresponding measured response for each data point; and (h) Evaluate external samples using the calibration levels according to the specified evaluation standards.

[0010] Surprisingly it has been found that the age and quality of in particular dairy products, primary and main milk-based products (including cheese) or plant milk alternatives can be easily and reliably determined based on selected chemical and physical data - which in any case are usually collected as part of quality control.

[0011] The method according to the invention is divided into four parts: -Selection of appropriate analysis parameters; - Data generation; - Check the data for correlation via PCA; and - Calculate product age using PLS. Dairy products, primary and primary milk-based products (including cheese) or their plant-based milk alternatives

[0012] The choice of dairy products, primary and main milk-based products (including cheese) or plant-based milk substitutes thereof to which the method according to the invention can be applied is not important per se. Typically, the product is selected from the group consisting of whole milk, skimmed milk, ultra-high temperature sterilized milk, milk powder, cream, whey, curd, cheese and yogurt, but also primary and main milk-based products, such as various desserts. Also included are plant-based milk substitutes, such as plant-based beverages, spreads, cream substitutes, yogurt substitutes and cheese substitutes. Sample set

[0013] Before a data set can be created, samples must first be prepared. The first sample set includes samples at time t=0, i.e. fresh samples, hereinafter referred to as P1, P2, P3 to PN. The first group of samples typically includes 3 to 10, preferably 4 to 7 samples, wherein these samples are packaged in containers typically having a volume of 10 mL to 100 L.

[0014] To prepare the second and further groups ("Nth group") of samples, the original samples are stored ("aged") under specified conditions, preferably at a temperature of 4 to 20°C, protected from light. The storage period can cover from 1 to 25 weeks, but a longer storage period of 4 to 22 weeks is preferred. During this period, more samples are taken regularly, usually every 1-2 weeks. After a first aging period of 2 weeks, the original sample 2 becomes the aged sample 2 after the first aging period, marked as P2.1. Sample P4.11 will become the name of the original sample 4, which has undergone 11 aging periods, i.e. 22 weeks. Test Parameters

[0015] A key feature of the method according to the invention is the selection of the test parameters based on which the sample is evaluated. The following factors are preferably taken into account for this purpose: - dissolved oxygen content; - pH value; - Lumisizer; - Lightness color value white / black (L*); - Color coordinates red / green (a*); - Color coordinates yellow / blue (b*); - Free amino nitrogen (PAN); - ammonia content; - urea content; - Calcium content; -acidity; - Rheological behavior; -strength; - the degree of syneresis; -Thermal behavior (DSC)

[0016] This list does not claim to be complete. Depending on the dairy product, primary and main milk-based product (including cheese) or milk substitute used, more parameters can usually be defined. The information value of the method according to the invention depends on the parameter selection and the number of products tested. It has proven useful to select at least 5, preferably at least 7 parameters. The larger the scope of the set, the less important the information value of a single parameter.

[0017] Most parameters, such as pH, dissolved oxygen and the contents of free amino nitrogen, urea, calcium and acid, can be determined using conventional analytical methods which are well known to those skilled in the art of food analysis and dairy science and need not be described in further detail here.

[0018] The instability index is determined using the LUMISIZER. This device measures particle and droplet velocities during emulsification and sedimentation and determines the particle size distribution according to DIN ISO 13318-2. A description of the device and technology can be found here: https: / / www.lum- gmbh.com / lumisizer_de.html#:~:text=Der%20LUMiSizer%20ist%20das% 20einzige,Systeme%20%E2%80%93%20Sie%20haben%20die%20Wahl

[0019] The lightness color value L* and the two color coordinates a* and b* belong to the so-called L*a*b* color system, also known as the CIELAB system. It is currently the most commonly used color measurement system and is widely used in almost all fields of application. It was defined by the CIE in 1976 as one of the equidistant color spaces in order to solve the main problem of the original Yxy system: equal geometrical differences in the x,y color triangle do not lead to equal perceived 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 sign indicates the color direction: +a* indicates the red component and -a* points to the green component. Thus, +b* represents yellow and -b* represents blue. At the origin of the coordinates (the intersection of the axes) is a neutral gray without any chromaticity. As the a*b* value increases, i.e. the color position is further from the center, the chromaticity increases. The CR 400 or 410 devices have proven to be suitable for determining color values ​​in the context of the method according to the invention. Further information can be found in the consumer information "Exact colorcommunications" from Konica Minolta. Principal Component Analysis (PCA)

[0020] The data pool obtained in this way is evaluated in a further step by means of a so-called “principal component analysis” (PCA), also known as principal component analysis.

[0021] This is an established statistical method for analyzing large data sets with a large number of parameters per observation, for improving the interpretability of the data while retaining a maximum amount of information, and for visualizing multidimensional data. Formally speaking, PCA is a statistical technique for reducing the dimensionality of a data set. This is done 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 the original data. In the method according to the invention, only the first two principal components are used to represent the two-dimensional data and to visually identify clusters of closely related data points.

[0022] In PCA data analysis, the first principal component of a set of P variables, assuming a joint normal distribution, becomes the derived variable, which is formed as a linear combination of the original variables and explains the maximum variance. The second principal component explains the maximum variance remaining when the effect of the first component is eliminated, and we can continue with P iterations until all variance is explained. PCA is most often used when many variables are highly correlated with each other and their number needs to be reduced to independent sets. Therefore, the first principal component or the second principal component can be defined as the direction that maximizes the variance of the projected data. PCA is the simplest truly eigenvector-based multivariate analysis and is closely related to factor analysis. A detailed description of the method can be found, for example, at https: / / en.wikipedia.org / wiki / Principal_component_analysis.

[0023] In short, PCA provides a coordinate system where the component with the largest variance or deviation for each data point (the "first component") is determined relative to the component with the second largest variance (the "second component"). The more data points are distributed on the coordinate field, the greater the distance between the aged sample data points and the fresh sample data points, the more pronounced the aging effect is overall, and the more meaningful the results of the method are overall. Therefore, a specific purpose of PCA is to determine whether the selected test parameters are actually suitable for mapping the hypothesized changes of the samples over time. If this is not the case, the set of test parameters must be changed or supplemented. Partial Least Squares (PLS) Calibration Method

[0024] Partial least squares path modeling or partial least squares structural equation modeling (PLS-PM, PLS-SEM) is a method for structural equation modeling that is able to estimate complex causal relationships in path models with latent variables.

[0025] It is a component-based estimation method that differs from covariance-based structural equation modeling. In contrast to these structural equation modeling methods, instead of fitting a joint factor model to the data, PLS-PM fits a composite model that maximizes the amount of variance explained. In addition, PLS-PM is also able to consistently estimate certain parameters of the common factor model through an adjustment called consistent PLS-PM (PLSC-PM). Another related development is factor-based PLS-PM (PLSF), which is a variant that uses PLSC-PM as the basis for estimating factors in the common factor model; this approach significantly increases the number of common factor model parameters that can be estimated and effectively bridges the gap between classical PLS-PM and covariance-based structural equation modeling.

[0026] The PLS-PM structural equation model consists of two sub-models: the measurement model and the structural model. The measurement model represents the relationship between the observed data and the latent variables. The structural model represents the relationship between the latent variables.

[0027] The iterative algorithm solves the structural equation model by alternately estimating latent variables using the measurement model and the structural model, hence the name of the method: partial. The measurement model estimates the latent variables as a weighted sum of the manifest variables. The structural model estimates the latent variables using simple or multiple linear regression between the latent variables estimated by the measurement model. The algorithm is repeated until convergence is achieved [see also Avkiram et.al, "Partial Least Squares Structural Equation Modeling", Springer Verlag (2018)].

[0028] In short, PLS also provides a coordinate system based on the same data set, where for each data point the component with the largest variance or deviation (the "first component") is determined relative to the component with the second largest variance (the "second component"). But at the same time, it calculates the value of an evaluation criterion (the "response"), such as the age of the sample, for each data point. The calculated values ​​are then plotted in a calibration graph against the actual values ​​of the samples (e.g. known age). A regression line is then laid through the data cloud, which acts as a calibration line and can then be used to classify unknown samples according to their age based on their physicochemical parameters. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 : shows the score diagram for the third step of the method according to the present invention, i.e. when the fat content is 0.3 wt%. Figure 2 : shows the fourth step of the method according to the present invention, i.e. the PLS-score diagram when the fat content is 0.3 wt%. Figure 3: shows the fourth step of the method according to the invention, namely the PLS-response diagram. Example Example 1A / B Parameter selection and data generation

[0029] The method according to the invention for skimmed milk (fat content 0.3 wt %) is explained below. In the first step, the following parameters are selected from the available parameters: -Dissolved oxygen -pH -InI=Lumisizer - Lightness color value white / black (L*) - Color coordinates red / green (a*) -Color coordinates yellow / blue (b*) -Free amino nitrogen (PAN) - Ammonia content - Urea content

[0030] A total of 4 skimmed milk samples were collected in the form of 0.2 liter or 1 liter; these constituted the first set of samples (P1 to P4). Selected parameters of the samples analyzed (sample age: 0 weeks)

[0031] Each of these samples was then stored at 20°C, protected from light, for a period of 22 weeks. Every two weeks, a new sample (P1.1, P2.1 ... P1) was taken from each of the original samples and analyzed according to the selected parameters. In this way, 11 aging stages were mapped for each of the original 4 samples. Thus, in the table below, sample 3 represents the 3rd sample from the new bottling, while P3.9 represents the same sample at aging stage 9 (ie after 18 weeks of storage). The data set determined in this way is summarized in Table 1. Table 1 Quality measurement dataset Example 1C Use PCA = Principal Component Analysis to check for correlation in the data

[0032] The third step of the method according to the invention is as follows Figure 1 The previously obtained data set is processed using principal component analysis. PCA provides a coordinate system formed by the first and second principal components. Figure 1, principal component 1 explains 46.8% of the variance. The second principal component explains 23.6%. Overall, PCA explains 70.4% of the variance in the original data. Knowing which parameters characterize the two principal components is irrelevant to this procedure, as this step only involves adjusting the variance to a scalable size.

[0033] As can be seen from the figure, there is a large shift relative to the original data set at t=0. This means that there is in fact an aging effect, which will be examined in more detail in the fourth step below. If the data points are all close to the original t=0 value, this would mean, on the contrary, that there is no time-dependent effect; therefore, the procedure would be terminated at this point. Therefore, this step serves to determine whether the method according to the invention can be applied to the product to be checked. If not, the parameters used to collect the data set must be supplemented or changed. Example 1D Set PLS = Partial Least Squares calibration model

[0034] The fourth step of the method according to the invention is as follows Figure 2 and Figure 3 After confirming in the previous step that the selected set of test parameters produces a data set that actually reflects the changes in the sample over time, the same data set is prepared this time using the partial least squares calibration method. This again provides a graph where the largest and second largest variances span the coordinate system ( Figure 2 ), but also provides a calculated measurement of a given parameter (the "response") - in this case, the age of the sample. If the calculated measurement (i.e., the calculated sample age) is determined relative to the actual and known age of the sample, a calibration curve can be plotted through the array of points ( Figure 3 ). For example, the calibration curve can be used to determine the age of an undated sample.

Claims

1. A method for assessing the stability and quality of dairy products, primary or primary milk-based products (including cheese) and plant-based milk substitutes over storage time, comprising or consisting of the following steps: (a) provide a first set of samples of dairy products, primary or main milk-based products including cheese, or plant-based milk substitutes (P1.0, P2.0...Pn.0); (b) aging the samples from step (a) for a specified period of time to obtain an nth group of samples (P1.1, P1.2 ... P2.1, P2.2 ... Pn.x); (c) creating a set of test parameters; (d) applying the specified test parameters to each individual sample in the first group and the nth group of samples to generate a data set; (e) using principal component analysis (PCA) to examine the correlation of the data in the data set, obtain a coordinate system (score map), and determine the largest variance of the data as the first component and the second largest variance of the data as the second component; (f) Partial least squares analysis (PLS) was performed on the data; (g) generating a partial least squares (PLS) calibration model to obtain a PLS response plot (calibration line) in which the calculated response for each data point is determined relative to the corresponding measured response for each data point; and (h) Evaluate external samples using the calibration levels according to the specified evaluation standards.

2. The method according to claim 1, characterized in that The dairy product or plant-based milk substitute is selected from the group consisting of whole milk, skimmed milk, ultra-high temperature pasteurized milk, milk powder, cream, whey, curd, cheese, yogurt or plant-based substitutes thereof.

3. The method according to claim 1 and / or 2, characterized in that The first set of samples included 3 to 10 samples.

4. The method according to at least one of claims 1 to 3, characterized in that The second set of samples included samples from the first set of samples, each sample having a storage period ranging from 1 to 25 weeks.

5. The method according to at least one of claims 1 to 4, characterized in that Sample volumes range from 10 mL to 100 L.

6. The method according to at least one of claims 1 to 5, characterized in that The set of test parameters includes at least two of the following parameters: - dissolved oxygen content; - pH value; - Lumisizer; - Lightness color value white / black (L*); - Color coordinates red / green (a*); - Color coordinates yellow / blue (b*); - Free amino nitrogen (PAN); - ammonia content; - urea content; - Calcium content; -acidity; - Rheological behavior; -strength; - the degree of syneresis; -Thermal behavior (DSC) 7. The method according to claim 6, characterized in that The set of test parameters comprises at least 5, preferably at least 7 parameters.

8. The method according to at least one of claims 1 to 7, characterized in that The age of the sample was chosen as the evaluation criterion used.

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