JP2009526222A - Methods for calculating qualitative parameters of food - Google Patents

Abstract

A method for calculating a range of qualitative parameters such as color, pigment content, fat and / or moisture content of foodstuffs, in particular foodstuffs of animals such as fish and mammalian meat, Providing a representative individual of the food; determining the individual's characteristic size, eg, length, area, volume and / or weight; representing the qualitative parameters of the measuring lens of the instrument that measures color Measuring the surface L ^* , a ^*, and b ^* values (Chroma and Hue values); and measuring the size of the individual and the colorimetric data to the set of species of the individual A multivariate model given in advance for representatives of colorimetric data formed and measured by chemical analysis results, visual color assessment and mathematical processing of individual size against a set Comparing to a multivariate model comprising a correlation factor between the measured and qualitative parameter ranges; and as a result, obtaining an individual qualitative parameter in a standardized unit of measurement. Method.
[Selection] Figure 2

Description

Description of the invention

  The present invention relates to a method for calculating qualitative parameters of food products, in particular meat products, in particular fish. More specifically, the present invention relates to a portable instrument combined with multivariate modeling to calculate the chemical content of colors, pigments, fats, water in places such as manufacturing facilities, slaughterhouses or test sites. Regarding use.

  In the following, the calculation of many qualitative parameters of fish, especially certain grades of salmon, is described for reference, but the invention is not limited to use in fish and other foods, especially other types of salmon. It is also a method that can be used for meat, especially foods that are qualitative criteria whose color needs to be evaluated quickly and reliably.

  An individual's characteristic size refers to one or more dimensions sufficient to provide the individual's characteristics, such as length or diameter, area, volume and / or weight. For fish, characteristic quantities may be stated, for example, by length and / or weight.

  Measurements are made on food individuals such as slaughtered fish, based on measurements of reflected visible light, information on physical characteristics of individual features such as fish length and weight, and sampling dates Combined with the information. A multivariate model is created and calibrated against a reference value.

  The salmonoid is characterized by the unique red color of the muscles. This red color is essentially due to the natural pigment astaxanthin. Astaxanthin is produced in phytoplankton and emerges through a food chain through small crustaceans, which are eaten by the salmon. There are other pigments such as canthaxanthin, lutein, zeaxanthin, and these pigments are referred to by the common term carotenoid.

  The degree of red color is considered by many consumers as an important qualitative parameter when consumers purchase salmon in the form of fillets or shreds. Salmon is also used, for example, in the processing industry to produce smoked salmon and gravlux (salted salmon). An important qualitative parameter for smoked salmon and Gravlux is the degree of redness after processing.

  It is important to measure the red color before cutting the fish for slaughter because the red color is important for assessing the quality of the product reaching the consumer. Insufficient color of fish gives the farmer a price drop that makes it difficult to sell, especially in markets where a fresh red color is required for fish freshness.

  To a certain extent, there is a correlation between the amount of carotenoids such as astaxanthin in fish food and the amount of carotenoids found in fish muscle. Different approaches have been taken to how farmers make fish the most effective color. Therefore, there is a discussion about the color development stage where the amount of astaxanthin in the muscle, for example measured as mg of astaxanthin per gram of muscle, and the maintenance of color development where the level of astaxanthin is stably maintained when the fish grows up. Has been made. Carotenoid absorption varies with fish size and season, and there are genetic differences between fish of different origins. Particularly known is the so-called spring drop, where the amount of astaxanthin in fish muscle decreases in spring. These different factors require farmers to regularly check the quality of the fish stock to know the status of the fish in terms of adjusting the carotenoid content and possibly the amount of carotenoids in the feed to develop color according to the production plan. To.

  An alternative to astaxanthin is the use of canthaxanthin dyes. This gives a somewhat yellow fish than if an astaxanthin pigment was used. The two dyes may be used together in different ratio mixtures.

  According to Norwegian Patent No. 306652, by giving the food an increased amount of lysine amino acids in a short time before slaughter, it can give fish muscles with a visually brighter red color without increasing the amount of astaxanthin Known.

  The degree of red color development can be determined visually by comparing the red color of meat with the red color of a standardized set of color cards. The set of color cards is a colored cardboard card, with the red saturation and intensity increasing from card to card. The most known is the so-called Roche color fan (Roche SalmoFan®), whose cards are numbered 20-34. The fish meat color is given a Roche color card value based on the card closest to the fish meat color. This test is basically easy to do in completely fresh fish and can be done without any method other than a color card. The use of standardized color cards is well established, and Roche color card values are established as standards. A further description of the basis for the use of such color cards is Skrede, G., Risvik, E., Huber, M., Enersen, G. and Blumlein, L .; Developing a Color Card for raw Flesh of Astaxanthin-fed Salmon. 1990. Journal of Food Science, 55, 361-363.

  The use of a color card is a subjective way of determining color, and several factors influence the result. Natural light varies with time and meteorological conditions. Attempts have been made to improve this through the use of standardized certification boxes (eg, illumination boxes) (eg, salmon color boxes where the light source is a fluorescent tube, Skretting AS, Stavanger, Norway). A known disadvantage of fluorescent tubes is that the amount of light color can change during the lifetime of the fluorescent tube. In addition, such an illumination box has an open surface where lateral light strikes the sample.

  Fish pieces like fillets do not have a uniformly colored surface. Fillets are made of muscle fibers that alternate between connective tissue and fat. This complicates visual color measurement.

  It is known that fish with relatively high fat in muscles appear lighter in color and lower in evaluation than fish with relatively less fat.

  In addition, color recognition varies from person to person. In fact, it has been found that different observers can give the same piece of fish three different ratings on the Roche color card scale, eg 22-24. In addition, it is also important whether the observer has an economic interest in the results. Therefore, sellers tend to value higher than buyers.

  There are attempts to remedy the problem using standard lighting boxes with more sophisticated lighting boxes such as those disclosed in color cards and Norwegian Patent No. 317714. In addition, 317714 discloses a method for predicting the chemical amount of astaxanthin, fat and Roche color values by measuring photometric techniques and RGB color values (R = red, G = green, B = Blue). The camera is digital. The disadvantage of this method is that it requires a large and sophisticated lighting box that is not mobile and can only be used indoors. There are also strict requirements for the quality of mechanical components such as camera optics and apertures and shutters. The patentee also points out the fact that it is important that the so-called CCD chip (charge coupled device) is maintained at a stable temperature during irradiation. Another drawback is that the light intensity changes over time and needs to be regularly checked and adjusted.

  The red color is also determined by analyzing the chemical amount of astaxanthin in fish meat. Chemical analysis of astaxanthin is complex and can only be performed in the laboratory by a trained person. Equipment such as HPLC (High Performance Liquid Chromatography) is also required to perform the analysis. Therefore, fish farmers have to send fish or fish fragments far away for analysis. Answers are obtained in a few days, and it costs considerable to perform such an analysis.

  Another method is the use of NIR (Near Infrared Reflectance). This technique is based on reference materials and astaxanthin and other carotenoids are measured by conventional chemical analysis. NIR spectra are taken for the same material, and multivariate analysis finds a statistical relationship between the spectra and chemical quantities. This relationship is expressed by the NIR formula. This NIR equation is used to predict the content of carotenoids such as astaxanthin when a new sample is analyzed.

  NIR analysis is faster and less expensive than chemical analysis. The device itself is an expensive and stationary device and it is not beneficial for individual fish farmers to invest in their own device. Therefore, in this case as well, fish farmers need to send samples far away and it takes several days for answers to be available.

  Measuring the stoichiometric amount of astaxanthin in fish meat with a NIR / VIS apparatus (NIR apparatus also measures visible light) is used, for example, as a technique established by the present application and other literature.

  US Pat. No. 6,649,412 discloses a method for slaughtering fish, where the NIR probe is located behind a tool that removes the intestines and cleans the peeled fish abdomen. The probe illuminates the muscles of the fish from the open abdomen and provides information about the red color so that the fish is classified by red color.

  NIR instruments come in many sizes and designs and are used in calibration for parameters corresponding to those used by the applicant. In some cases, a small portable NIR-VIS device may be used for this purpose. These use a relatively wide wavelength range and perform a relatively coarse calibration. However, the instrument is generally expensive and problematic to standardize so that the same calibration is used throughout the instrument, and many variables (signals or reflections from different wavelengths) are used. In this case, data processing becomes complicated.

  The problem with other known instruments is that the device is too heavy and too large to carry easily for field analysis. At the same time, the equipment is so expensive that it is not reasonable to purchase it for personal aquaculture equipment. Recently, smaller portable NIR devices have been developed. These overcome the problems of stationary equipment. However, the equipment is expensive and has the problem that it must be calibrated at regular intervals.

  Therefore, in general, fish must be sent to a central laboratory or analytical means for chemical color and fat mass analysis. This takes time and it is necessary to decide to postpone in anticipation of obtaining objective analysis results. Deferrals result in time gaps in processes that affect unnecessarily limited product quality.

  The background of the present invention is the need for a quick and objective judgment on the qualitative parameters of fish. Today, colors are determined manually (in the field) by color cards under standardized light conditions, but this method is subjective and relatively quick. The method should be simple so that it can be carried out in a production facility, i.e. a fish enclosure or possibly a feeding boat, a work boat, a pier or indoors on the ground.

A colorimeter is used to measure the color of the surface and can be used to determine the position of the color in a so-called NCS system. Attempts have been made to use a colorimeter to measure the color of fish meat. The colorimeter first gives the color values expressed as XYZ values, then the values expressed as L ^* , a ^* and b ^* values, where L ^* is the brightness factor (black / white). ), A ^* is red / green degree, and b ^* is yellow / blue degree. Second, the colorimeter gives values of “Chroma” (C ^* _ab ) and “Hue” (H ^o _ab ). These values are functions of L ^* , a ^*, and b ^* , which are the criteria for color intensity and color composition, respectively. The function is
C ^* _ab = (a ^{* 2} + b ^{* 2} ) ^1/2
H ^o _ab = tan ⁻¹ (b ^* / a ^* ).

  The colorimeter can be a hand-held instrument and can be placed sideways or upright on the sample so that no light enters the surface to be measured from the side. The colorimeter has an internal light source and is continuously measured by software embedded in part of the instrument.

  The reference method used today is based on a visual assessment of the color associated with a color card (eg Roche SalmoFan®).

The use of a colorimeter to measure color is an established knowledge, for example, after the surface is measured, the best matching color may be found in the NCS system. In the fish farming industry, colorimeters are used to measure the color of fish. In doing so, values derived from L ^* , a ^* or b ^* are used and attempts have been made to correlate these to the color card. The correlation is usually relatively weak.

  The colorimeter shows color in several ways, but found it difficult to find a good correlation between measured values, chemical content of carotenoids such as astaxanthin, and visual color card values . This is related to the fact that there are other natural pigments in the salmon muscle other than astaxanthin. There may be yellowish pigments such as lutein and zeaxanthin, such as pigments derived from corn. This affects the colorimeter reading. The presence of canthaxanthin also affects the colorimeter reading. Christiansen et al. Found that the colorimeter is suitable for quantifying the relative amount of astaxanthin in pale fish (2-4 mg astaxanthin per kg), but the instrument is more expensive The amount of astaxanthin in the sample with astaxanthin content could not be identified. The same author found that the use of color card fans better predicted the amount of chemical color, but this prediction was not satisfactory (Christiansen, R., Struksnaes, G., Estermann , R., Torrissen, OJ 1995: Assessment of flesh color in Atlantic salmon, Salmo salar L. Aquaculture Research, 26, 311-321).

  A colorimeter measures visible light. Therefore, it is not very responsive to the amount of fat in the sample. This makes it difficult to achieve a correlation with the visually read color card value.

  Using only the values measured with a colorimeter gives a very poor correlation with the color card and cannot be used in practice. At the same time, it is impossible for the user to understand how all three values correlate with the color card values. Multivariate data processing is required for this purpose.

  Visible light alone measurements show the chemical properties of the sample to some limited extent, and the color is better shown by incorporating other parameters into the calibration.

The present invention aims to ameliorate or reduce at least one of the disadvantages of the prior art.
The object is achieved by the characteristics described in the following description and the appended claims.

  The purpose of the present invention is to provide an objective and rapid analysis of fish flesh quality, with the chemical amount of carotenoids such as astaxanthin and canthaxanthin, the chemical amount of fat and the color value of the fish muscle color card. I put emphasis.

  A further object is to be able to do in the field, preferably in floating parts of fish farming facilities such as floating walkways or bait barges. A further objective is that the analysis is not expensive, which allows it to be performed repeatedly and helps the farmer plan fish production in terms of fish color.

  A further object is to provide an objective and reliable color measurement that is at least as reliable as manual color measurement using a color card, thereby making color analysis based on such a device an objective in purchasing and selling fish. It is to serve as a practical material.

  Therefore, an object of the present invention is to provide an objective and quick analysis of the quality of fish by transporting the equipment to where the fish are, thereby reducing the time and cost of sending the fish for sampling. Can do.

  For several years, applicants have analyzed many salmons for chemical amounts of astaxanthin, canthaxanthin and fat, color card values, and recorded the length and weight of the fish, and the date the fish was killed. This large data was analyzed and a relationship between body length, weight and fat mass was found as shown in FIG. 1 and the table below.

Average analysis results in 1999, Norway. Salmosalar.

* K factor = (weight [g] x 100) / (body length [cm]) ³
Surprisingly, by combining colorimetric measurements of fish meat with information about fish length and weight, chemical amounts of pigments such as astaxanthin and canthaxanthin, fat mass, and color card values can also be used as a starting point. It can be predicted by a mathematical model that utilizes the L ^* , a ^* and b ^* values of the colorimeter and the length and weight of the fish. The prediction is because the fat mass is taken into account in the prediction, rather than using only the values for L ^* , a ^* and b ^* and the resulting Chroma and Hue values, the chemical amount of the pigment and the color card value Provide better and more reliable results. At the same time, fat mass is predicted more accurately than expected from information about fish body length and weight. The basis for prediction is a multivariate statistical method, which leads to a series of mathematical calibration equations.

  Due to seasonal variations in color card measurements, it has proved beneficial to further take into account sampling dates for the accuracy of the predictions.

The described method also makes it possible to use a portable color measuring device, such as a digital camera or a colorimeter, according to the object of the invention. Such instruments and especially colorimeters do not rely on standardized light conditions, use an integrated means for recalibration and are therefore independent of external calibration means. Calibration formulas are used for the calculation of the chemical amount of the dye, the value of the color card and the chemical amount of fat. These are the part of the software of the computer, for example a laptop, adjacent to the colorimeter, or the part of the central computer software that is reached, for example via the internet interface. The L ^* , a ^* and b ^* values read are used as input values along with the measured fish length and weight and the date of sampling. This makes it possible to make a desired prediction quickly. The results may be recorded manually, for example as a separate form, or electronically.

  Results are provided on-the-fly without sending fish to a centrally located analytical instrument or laboratory. This allows, for example, analysis, evaluation and thereby the selection of the optimal fish food when the fishermen and food consultants meet at the farm. Better and faster selection is possible. Similarly, the present invention contributes to electronic color card measurements that can serve as objective data for fish in purchases and sales.

  Obviously, the invention can also be used to determine certain qualitative parameters of other foods. Therefore, the present invention is not limited to salmon.

More specifically, the present invention covers a range of qualitative parameters such as color, pigment content, fat and / or moisture content of foods, especially foods derived from animals such as fish and mammalian meat. Regarding the method of calculating, the method
-Providing a representative individual of the food;
-Determining the characteristic size of the individual, eg length, area, volume and / or weight;
Placing the measuring lens of the instrument for measuring the color on the surface of the food representing the qualitative parameter and measuring the L ^* , a ^* and b ^* values (Chroma and Hue values) of the surface;
A multivariate model in which measurements and colorimetric data on the size of individuals are given in advance for representatives of a set of individual species, with chemical analysis results, visual color assessment and individual size relative to the set Comparing to a multivariate model comprising a correlation factor between the measured colorimetric data and the measured qualitative parameter range formed by mathematical processing of:
-As a result, obtaining individual qualitative parameters in standardized units of measurement.

The instrument for measuring the color is preferably selected from the group consisting of a colorimeter and a digital camera.
The individual size is preferably represented by two or more characteristic sizes including weight.

The chemical analysis results advantageously include carotenoid content, and also fat and moisture content.
The carotenoid is preferably selected from the group consisting of astaxanthin, canthaxanthin, lutein and zeaxanthin.

  The visual color rating is preferably indicated by a standardized value, for example a color card value.

  Alternatively, the method includes the step of recording the date of measurement for an individual, and the multivariate model correlates with a sampling date and visual color assessment in a set for chemical analysis results.

Preferably, the individual is salmon.
Preferably, the resulting color is shown as a Roche color card value.

The present invention further relates to the use of a device that measures color for the calculation of one or more qualitative parameters of a food product, and Chroma and Hue values (L ^* , a ^* , b ^* ) are mathematical multivariate models. Is processed.

  The equipment and software shown below are used according to the usual methods for those skilled in the art.

  According to the method described in the present invention, the multivariate calibration technique uses data easily obtained from a colorimeter, measured physical data (in the case of salmon, characteristic quantities such as body length and weight). As well as the sampling date combination used in the model to incorporate relevant seasonal changes to the calculated parameters.

  The surprising effect of the method according to the invention is that a digital camera or colorimeter can be used for applications that are basically not well suited.

  Normally, fat and especially pigment content (amount of color in fish) is not measured well in visible light, but is measured in near infrared light (NIR). However, it is better when combined with physical parameters. Also, the determination of color card values is somewhat better when the data is combined as described above.

Example 1
In the examples, a Minolta Chroma meter CR-300 type colorimeter having a lens having a diameter of 0.8 cm was used. Astaxanthin was measured chemically by HPLC, fat was measured chemically by Soxhlet, and moisture content was measured by storage in a thick cabinet at 103 ° C. for 16 hours. The color is measured visually by placing the sample in a Skretting illumination box, ie the salmon color box described above (Skretting AS, Stavanger, Norway), after which the color is Roche on a scale of 20-34. Compare with color card. The level of color in the fish varies depending on where the fish is measured. Color card measurements and measurements with a colorimeter were carried out in a standardized area located in the so-called Norwegian Quality Cut. By definition, this occurs by cutting the fish right behind the dorsal fin ("chopping"). The area between the vertebra and the dorsal fin was measured. Alternatively, measurements may be made at corresponding positions on the fins, i.e., right back of the dorsal fin and on the vertebrae. Weight (whole fish) and body length were recorded for each fish.

In total, data for 753 fish were included in the examples, of which 145 arbitrarily selected samples were included as an external validation group. Since different units of measurement data (eg grams and centimeters) are included, it was standardized by dividing each data by its standard deviation. PLS regression analysis was used as the regression model. The estimation of the prediction error is given as RMSEP (Square root mean square error of prediction).

Where “i” is the sample, “n” is the number of samples in the group of data, “y _i ” is the analytical value of sample “i”, and “y ^ _i ” is sample “i”. Is the predicted value.

After the first modeling step, about 5% of the values were considered “outliers”, ie abnormal values for the main sample in the data set, and were removed from the data set prior to further modeling. The last model is based on 4 significant principal components and in the data group (L ^* , a ^* , b ^* , weight, body length) to explain 83% of the variation in color card reading. Use 92% of the variation. Further analysis shows that the most important positively correlated factor is the coefficient of a ^* and b ^* . Weight is negatively correlated, while both L ^* and length contribute to the model with smaller negative coefficients. FIG. 2 shows a comparison between the predicted results and the Roche color card values measured for the validation group. The expected error was expressed as RMSEP and was 1.3 units. This is a somewhat high value, but is good considering that the reference value is measured subjectively by the Roche color card, the colorimeter lens is somewhat small and limits the measurement area.

Example 2
In example 118, a composite sample is included. Weight values, body lengths and Roche color card values are average values for each sample of the composite, while astaxanthin, fat and water content were measured on the composite sample itself.

• Prediction of Roche color card values All values have been standardized. Three samples were considered outliers and were removed from the data. The final model is built on three main elements and illustrates 98% variation in data for weight, length, L ^* , a ^* and b ^* to account for 95% of the variation in color card values. Use%. The most important parameters of the positively correlated model are a ^* and b ^* . L ^* was negatively correlated, and body length and weight were negatively correlated with a small regression coefficient.

  FIG. 3 shows a comparison between the predicted value and the Roche color card value read for the validation group. The prediction error expressed as RMSEP is 0.7 units, which is very good, especially when viewed in terms of the fact that the reference value is obtained as a result of subjective reading.

• Prediction of astaxanthin content All values were standardized. Three samples were considered outliers and were removed from the data. The model of interest was built on two main elements and variability in data on weight, length, L ^* , a ^* and b ^* to account for 94% of variability in chemical astaxanthin content 90% of the The most important positively correlated parameters of the model are a ^* and b ^* . L ^* is negatively correlated, while magnitude and weight are positively correlated with somewhat less contribution than a ^* and b ^* .

  FIG. 4 shows a comparison of predicted values and analytical astaxanthin values for the validation group. The prediction error expressed as RMSEP is 0.6 mg / kg, which is very good.

• Prediction of fat content All values have been standardized. Seven samples were considered outliers and were removed from the data. The model of interest was built on four main elements and in the data for weight, size, L ^* , a ^* and b ^* to account for 93% of the variation in chemical fat content Use 98% of the variation. The most important positively correlated parameter of the model is weight. The magnitude has a negative regression coefficient. The regression coefficients for L ^* and a ^* are smaller and negative, while the regression coefficients for b ^* are smaller and positive.

  FIG. 5 shows a comparison between predicted values and analytical fat values for the validation group. The prediction error expressed as RMSEP is 0.8%, which is very good.

• Prediction of moisture content All values have been standardized. Four samples were considered outliers and were removed from the data. The model of interest was built on four main elements and 98 variability in data for weight, size, L ^* , a ^* and b ^* to account for 90% of variability in moisture content. % Is used. The most important negatively correlated parameter of the model is weight. The magnitude has a positive regression coefficient. The regression coefficients for L ^* and b ^* are smaller and positive, while the regression coefficients for a ^* are smaller and negative.

  FIG. 6 shows a comparison between the predicted value for the validation group and the analytical fat value. The prediction error expressed as RMSEP is 0.8%, which is very good.

  In the above, the main methods for determining the color, pigment, fat and moisture content that are the criteria for quality in salmon have been described, but it is clear that the method can also be used in other species of fish and other animal species. , Such qualitative parameters describe the quality of the product. It is clear that the method can also be used for quality evaluation of things like fruits, for example. Of course, the method can also be used in the calculation of other qualitative parameters than those described here. Multivariate models for different requirements are performed according to the same methodology as described above, and colorimetric measurement data and characteristic individual data are inserted into the multivariate model to produce the desired qualitative parameter quantities.

Seasonal changes in fat and pigment observed for 2-4 kg Atlantic salmon. Roche color card values read for predicted Roche color card values and validation groups (Example 1). Expected Roche color card values and Roche color card values read for validation groups (Example 2). Analyzed astaxanthin value for predicted astaxanthin value and validation group (Example 2). Analyzed fat values for predicted fat values and validation groups (Example 2). Predicted moisture content value and analysis moisture content value for validation group (Example 2).

Claims (12)

A method for calculating a range of qualitative parameters such as color, pigment content, fat and / or moisture content of foodstuffs, in particular foodstuffs from animals such as fish and mammalian meat,
Providing a representative individual of the food;
Determining an individual's characteristic size, eg, length, area, volume and / or weight;
Positioning the measuring lens of the color measuring instrument on the surface of the food representing the qualitative parameters and measuring the surface L ^* , a ^* and b ^* values (Chroma and Hue values);
A measure of individual size and colorimetric data is a multivariate model given in advance for representatives of a set of individual species, including chemical analysis results, visual color assessment and individual size for the set. Comparing with a multivariate model comprising a correlation factor between the measured colorimetric data and the measured qualitative parameter range formed by mathematical processing;
As a result, obtaining a qualitative parameter of an individual in a standardized unit of measure.   2. The method of claim 1, wherein the instrument for measuring color is selected from the group consisting of a colorimeter and a digital camera.   2. The method of claim 1, wherein the size of the individual is characterized by two or more characteristic sizes including weight.   2. The method of claim 1, wherein the chemical analysis results include carotenoid content and fat and water content.   2. The method of claim 1, wherein the carotenoid is selected from the group consisting of astaxanthin, canthaxanthin, lutein and zeaxanthin.   The method according to claim 1, wherein the visual color assessment is indicated by a standardized value, for example a color card value.   The method of claim 1, further comprising the step of recording a date on which an individual is measured, wherein the multivariate model is a population for chemical analysis results and visual color assessment. How to correlate with the date of sampling in.   The method according to any one of claims 1 to 7, wherein the individual is salmon.   The method of claim 1, wherein the resulting color is indicated as a Roche color card value. Use of a color measuring instrument for the calculation of one or more qualitative parameters of food, wherein Chroma and Hue values (L ^* , a ^* , b ^* ) are processed in a mathematical multivariate model .   Use according to claim 10, characterized in that the device for measuring the color is selected from the group consisting of a colorimeter and a digital camera.   Use according to claim 10, characterized in that the qualitative parameter is the color, pigment, fat and / or moisture content of meat, in particular fish meat.

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