DK177150B1 - Method for calculating food quality parameters and using a color measurement instrument - Google Patents

Abstract

A method for calculating a number of quality parameters, for example color, pigment content, fat and / or water content, of a food, in particular a food of animal origin, for example meat from fish and mammals, wherein the method comprises the steps of providing a representative individual for the food; determining the characteristic size of the individual, for example length, area, volume and / or weight; positioning the measuring lens of a color measuring instrument at a surface of the food representative of the quality parameters and measuring L * a * and b * (Chroma and Hue values) of the surface, comparing the measured values for the size of the present individual and colorimetric data with a pre-provided multivariable model representative of a population of the individual species, the multivariable model being formed by mathematical treatment of chemical analysis results, visual color assessment and individual size of the population and including correlation factors between the measured colorimetric data and a series of measured quality parameters, for there by deriving quality parameters of the present individual in standardized units of measurement.

Description

DK 177150 B1 PROCEDURE for calculating quality parameters for

FOOD AND USE OF A COLOR MEASURING INSTRUMENT

The invention relates to a method for calculating quality parameters 5 for food products, especially meat products, especially from fish. More particularly, the invention relates to the use of a color measurement instrument in combination with multivariable modeling for calculating color, chemical content of pigment, fat and water in the field, e.g. at a production facility, at a slaughterhouse or at an operating laboratory.

10 In the following, reference is made to the calculation of a number of quality parameters for fish and, in particular, salmon, but the invention is not limited to use in fish, the method being conceivable also for other food products, especially other kinds of meat, and especially food products in which color is a quality criterion that must be assessed quickly and reliably.

An individual's characteristic size is understood to mean one or more dimensions, for example length or diameter, area, volume and / or weight sufficient to give a characteristic of the individual. For a fish, the characteristic quality can be described, for example, by length and / or weight.

The measurements are made on an individual of the food, for example a slaughtered fish, and are based on the measurement of reflected, visible light in combination with information about the individual's physical characteristics, such as the length and weight of the fish and the date of sampling. Multivariable models are prepared which are calibrated against reference values.

A characteristic feature of salmonids is that the musculature has a distinct red color. The red color is essentially caused by the natural pigment 30 astaxanthin. Astaxanthin is produced in phytoplankton and finds its way through the food chain via small crustaceans, which are then eaten by the salmonids. Other pigments, such as canthaxanthin, lutein, zea-xanthin, also occur, and in the following the pigments are called carotenoids as a common term, 5 The color of red color is considered by many consumers as an important quality parameter when buying salmon in form of fillets or steaks. Salmon is also used by the processing industry for the production of, for example, smoked salmon and gravy salmon (salmon preserved in brine). An important quality parameter for smoked salmon and graved salmon is the degree of red color after processing.

10

Since the red color is important for assessing the quality of the products that reach the consumer, measuring the red color before taking fish for slaughter is also important. If the dyeing of the fish is too poor, this will result in a reduced price to the breeder and the fish will be difficult to sell, especially in markets that expect a strong red color in the fish meat.

There is some correlation between the amount of carotenoids in the fish meat, such as astaxanthin, and how many carotenoids are found in the fish's musculature. Various strategies have also been devised for how the breeder should most effectively color the fish. Thus, dyeing periods are mentioned, where, for example, the content of astaxanthin in the muscle must be increased measured as mg of astaxanthin per ml. grams of muscle, and maintenance staining, where the level of astaxanthin should only be kept stable as the fish grows. It is also known that uptake of carotenoids 25 varies with fish size, season, and that there are hereditary differences between the different releases of fish. Particularly well known is the so-called spring drop, in which the content of astaxanthin in the fish muscle falls in the spring. These various factors make it necessary for the breeder to regularly obtain samples of the fish stock in order to know the fish's state of carotenoid content and possibly adjust the amount of carotenoid in the feed for the dyeing to be in accordance with the production plan.

3 DK 177150 B1

An alternative to astaxanthin has been the use of the cantha-xanthin pigment. This gives a somewhat more yellow fish flesh than the use of the pigment astaxanthin. The two pigments have also been used together in different mixing conditions. | 5;

It is known from Norwegian Patent No. 306652 that feeding with feed with increased content of the amino acid lysine for a shorter period before slaughter gives a fish muscle with visually stronger red color without increasing the content of astaxanthin.

10

The degree of red coloring can be determined visually by comparing the red color of the meat with the red color in a standard color chart set. The color card set is a collection of colored cardboard cards where the saturation and intensity of red color increases from card to card. Best known is the so-called Roche color-15 fan (Roche SalmoFan ™), where the cards are numbered from 20 to 34. The color of the fish meat is assigned a Roche color card value based on which card is closest to the color of the fish meat. This test is basically easy to perform on fresh fish and can be performed without any means other than the color cards. The use of standardized color cards is well established and the Roche-20 color card value is established as a standard. A detailed description of the basis for the preparation of such color cards is given by Skrede, G.,

Risvik, E., Huber, M. Enersen, G. and BlOmlein, L; Developing a Color Card for Raw Flesh of Astaxanthin-fed Salmon. 1990. Journal of Food Science, 55, 361-363.

25

The use of color cards is a subjective way of determining color, where several factors influence the result. Natural light will vary with time of day and meteorological conditions. Attempts have been made to remedy this by using a standardized light box (for example, Salmon 30 Color Box, Skretting AS, Stavanger, Norway, where the light source is fluorescent).

A known disadvantage of fluorescent lamps is that the color content of the light can vary over the lifetime of the fluorescent lamp. Such a light box also has an open side where side light will fall into the sample.

A piece of fish, such as a fillet, does not have a uniformly colored surface. The fillet is made up of muscle fibers, which alternate with connective tissue and fat. This complicates visual color measurement.

It is known that a fish with relatively much fat in the musculature appears paler and scores lower than a relatively lean fish.

10

In addition, color perception is different from person to person. In practice, it has been found that different observers can graduate the same piece of fish with a difference of 3 on the Roche color chart scale, for example from 22 to 24.

Furthermore, it is important whether the observer has a financial interest in the result. Thus, a seller will tend to graduate higher than a buyer.

The problem of using color cards and a standard light box has been attempted to remedy a more complicated light box as stated in Norwegian patent NO 317714. In addition, NO 317714 specifies a method for predicting the chemical content of astaxanthin, fat and Roche color value. using a photometric technique and measuring RGB color values (R = red, G = green, B = blue). The camera is digital. The disadvantage of this approach is that it requires a large and complicated light box which is not suitable for moving, but only for indoor use. There are also strict requirements for the quality of camera optics and mechanical components, such as aperture and shutter. The patent holder also points to the fact that it is important that the so-called CCD-chip ('' Charge-Coupled Device ') is kept at a stable temperature during exposure. Another disadvantage is that the intensity of the light changes over time, necessitating follow-up and control routines.

5 DK 177150 B1

The red color can also be determined by analyzing the chemical content of astaxanthin in the fish meat. Chemical analysis of astaxanthin is complicated and can only be performed in laboratories by trained personnel. Equipment such as HPLC ("High Performance Liquid Chromatography") is also required for the analysis, so a fish breeder must send the fish or piece of fish away for analysis. The answer will come after several days and significant costs are involved in performing such analysis. .

Another approach is the use of NIR (Near-Reflected Reflance). This technique is based on a reference material in which astaxanthin and other carotenoids are determined by conventional chemical analysis. An NIR spectrum of the same material is taken and, using multivariable statistical techniques, there is a relationship between spectrum and chemical content. This correlation is expressed in an NIR equation. This NIR equation is used to predict the content of carotenoids, for example astaxanthin, when new samples are analyzed.

An NIR analysis is faster and cheaper than a chemical analysis. The instrument itself is an expensive and stationary instrument and it is not profitable for the individual fish breeder to invest in his own instrument. The fish breeder must therefore in this case also send the sample away and it takes several days before the answer is available.

Measurement of the chemical content of, among others, astaxanthin in the fish meat by means of an NIR / VIS instrument (an NIR instrument that also measures visible light) is used as the established technique, for example, by the present applicant and by others.

U.S. Patent No. 6,649,412 discloses a slaughter line for fish where a NIR-30 probe is located behind the tool which removes the intestines and flushes the abdomen of the purified fish. The probe illuminates the fish musculature from the open 6 DK 177150 B1 bug and provides information about the red color so that fish can be sorted by the red color.

The NIR instruments are available in many sizes and designs, and they are used in calibrations against parameters similar to those used by the applicant. Potentially, small portable NIR / VIS instruments can also be used for this purpose. These use a relatively wide wave range, making the calibration relatively robust. However, the instruments are generally expensive, they will be problematic to standardize in such a way that the same calibration can be applied to the entire instrument fleet, and the data processing is complicated since many variables (signals or reflections from different wavelengths) are used.

The problem with other known instrumentation is that the equipment is too heavy and 15 bulky to easily carry for analysis in the field. At the same time, the instrumentation is so expensive that it is not particularly relevant for purchases for each breeding facility. More recently, less portable NIR instruments have been developed. These overcome the problem of stationary instruments. However, the instruments are still expensive and need to be calibrated at regular intervals.

Thus, for chemical color and fat content analysis, the fish must generally be sent to centrally located laboratories or analysis instruments.

This takes time and makes it necessary to postpone decisions pending 25 that the objective analysis results are available. The delay may result in unnecessarily limiting the time available for influencing product quality.

The background of the invention is that quick and objective decisions on the quality parameters of fish are needed. Today, color can be determined manually (even in the field) using color maps under standardized light 7; The method is subjective and relatively inaccurate. The process should be so simple that it can be carried out at the production facility, that is, restricted fishing areas, or in any case in close connection with them, such as on feed fleets, work boats, quays or indoors on land.

For surface color measurement, a color meter is used which can be used to determine the position of a color in the so-called NCS system. Attempts have been made to use color gauges to determine the color of fish meat. The Far-10 meter primarily produces color values expressed as XYZ values, again expressed as L *, a * - and b * values, where L * is the brightness factor (black and white), a * is red / green chromaticity, and b * is yellow / blue chromaticity. Secondly, the color meter gives the values "Chroma" (C% b) and "Hue" (H ° ab). These values are functions of L *. a * and b * and are measures of color intensity and color composition, respectively. The functions are C * ab = (a * 2 + b * 2) 1/2 H0ab = tan'1 (b * / a *) 20 The color meter can be a handheld instrument and put or put to the test in such a way that light does not come in from the side of the surface to be measured. The color meter has an internal light source, which is continuously calibrated by software that is an integral part of the instrument.

25 Reference methodology used today is based on visual color assessment related to color maps (e.g. Roche SalmoFan ™).

The use of a color meter to determine color is established knowledge; for example, a surface can be measured on which the color of the NCS system that best fits can be found. In the fish farming industry, the color meter is used for color measurement of fish. Then the values from either L *, a * or DK 177150 B1 8 are used in b *. and attempts are made to correlate these with color maps. The correlation is usually relatively weak.

Although the color meter indicates the color in several ways, it has proved difficult to find a good correlation between the measured values and the chemical content of carotenoids, such as astaxanthin, and the visual color map value. This is partly due to the fact that natural pigments other than astaxanthin are found in salmon musculature. Yellowish pigments may also be present, such as lutein and zeaxanthin, pigments which, for example, come from corn. This affects the reading of the color meter. The presence of canthaxanthin will also affect the color meter reading. Christiansen et al. found that a colorimeter was suitable for quantifying astaxanthin content in relatively pale fish (2-4 mg astaxanthin per kg) but that the instrument could not distinguish the content of astaxanthin in samples with higher astaxanthin content. The same authors also found that the use of a color chart fan predicted the chemical color content better, but neither was this prediction satisfactory. (Christiansen, R., Struksnæs, G., Estermann, R., Torrissen, O. J. 1995: Assessment of flesh color in Atlantic salmon, Salmo salar L.

20 Aquaculture Research, 26, 311-321).

The color meter measures in visible light. Thus, it will react relatively little to the amount of fat in the sample. This makes it difficult to obtain a correlation with a visually read color map value.

25

The use of just the measured value from a color meter provides a poor correlation with a color chart value and is useless in practice. At the same time, it is impossible for a user to understand how all three values together correlate with a color chart value. For this purpose, multivariable data processing is required.

9 DK 177150 B1 Visible light measurement alone indicates to some extent the chemical properties of a sample, and even color will be better indicated by incorporating other parameters into calibrations.

The invention has for its object to alleviate or reduce at least one of the disadvantages of the prior art.

The object is achieved by features described in the following description and in the claims that follow.

10

The object of the invention is to perform an objective and rapid analysis of a fish's meat quality, with emphasis on the chemical content of carotenoids, such as astaxanthin and canthaxanthin, the chemical content of fat and the color map value of the fish musculature.

15

It is a further object that this can be done in the field, preferably far away from the floating parts of a fish farming facility, such as floating walkways and feed raft. It is a further object that the analysis should be reasonable so as to be able to be carried out repeatedly so that the breeder 20 will be assisted in planning his fish production with regard to the dyeing of the fish's meat.

It is a further object that, by providing an objective and reliable color measurement which is at least as reliable as the manual color measurement 25 using color maps, such an apparatus-based color measurement can serve as objective documentation in the purchase and sale of the fish.

The object of the invention is hereby to perform objective, rapid analyzes of the meat quality of the fish by bringing the instrument to the place where the fish is located, thereby saving time and cost of sending fish for sampling.

10 DK 177150 B1

The applicant has for several years analyzed a large number of salmon for chemical content of astaxanthin, canthaxanthin and fat, color chart values and recorded length and weight of the fish and date of killing of the fish. This extensive data material has been analyzed and a relationship 5 between length, weight and fat content has been found as shown in Figure 1 and given in the table below.

Average analogue results for Norway in 1999. Salmon (Salmo salar) Weight class K factor * SalmoFan Astaxanthin Fat (%) (kg) (mg / kg) 0-1 T? I 24 ^ 6 Ϊ9 & 2 1 -2 V> 26 ^ 0 4 ^ 8 ΓΓ5 2- 3 V3 27 / Ϊ 5 ^ 8 1 ^ 8

3- 4 V5 27 ^ 8 6A 15J

4- 5 V3 2 ^ 9 6 ^ 3 T ^ 9 5- 6 ΛΑ 28ΤΪ 6 ~ 5 16 ^ 5 10 * K-factor = (weight [g] x 100) / (length [cm]) 3

It has now surprisingly been found that by combining a colorimetric measurement of the fish meat with information on the length and weight of the fish, the chemical content of pigments such as astaxanthin and canthaxanthin can be predicted by fat and also color chart value by a mathematical model, which basically takes the L *, a * and b * values of the color meter and also the length and weight of the fish. The prediction gives a better and more reliable result for the chemical content of pigment and color chart value than the use of the values for L *, a * and b * and also the derived Chroma and Hue values alone, because the fat content is taken into account in the prediction . At the same time, the fat content is predicted more accurately than can be predicted from information about the length and weight of the fish. The basis of the prediction is a multivariable statistical method that leads to a set of mathematical calibration equations.

Furthermore, for the accuracy of the prediction, it has proved advantageous to take into account the date of sampling, as there is a seasonal variation in the color chart measurements.

A method as described also makes it possible to use a portable color measurement instrument such as a digital camera or color meter in accordance with the purpose of the invention. Such instruments, and in particular the color meter, are not dependent on standardized lighting conditions and use built-in recalibration means and are therefore independent of external calibration agents. For the calculation of the chemical content of pigment, color chart value and the chemical content of fat, calibration equations are used. These will be part of a computer's software, for example a laptop which can be placed near the color meter, or a central computer which is accessed through, for example, an Internet interface. The read L *, a * and b * values are used as input values together with the measured length and weight of the fish and the date of sampling. It will thereby be possible to make the desired prediction immediately. The results can be recorded manually, for example in a separate form or electronically.

The results will be obtained on site, without any fish being sent 25 to a centrally located analytical instrument or laboratory. This will, for example, enable analysis, evaluation and subsequent selection of the optimal fish feed when the breeder and feed consultant meet at the breeding facility. A better and faster choice can then be made. Similarly, the invention will contribute to an electronic color chart measurement that can serve as objective documentation of the fish at purchase and sale.

12 DK 177150 B1

It is obvious that the present invention can also be used in determining certain quality parameters for other food products. Thus, the invention is not limited to merely salmon.

More specifically, the invention relates to a method for calculating a variety of quality parameters, for example color, pigment content, fat and / or water content, of a food product, in particular a food product of animal origin, for example meat from mammals, characterized in that: the method comprises the steps 10 - providing a representative individual of the food; determining the characteristic size of the subject, for example length, area, volume and / or weight; positioning the measurement lens of a color measurement instrument at a surface of the food representative of the quality parameters and measuring the L *, 15 a * and b * values (Chroma and Hue values) of the surface; - comparing the measured values of the subject's size and colorimetric data with a pre-existing multivariable model representative of a population of the individual, the multivariable model being formed by mathematical processing of chemical analysis results, visual color assessment and individual size of the population and include correlation factors between the measured colorimetric data and a range of measured quality parameters, thereby deriving - the quality parameters of the subject in standardized units of measurement.

25

The color measurement instrument is preferably taken from a group consisting of color meter and digital camera.

The size of the individual is advantageously indicated by two or more characteristic sizes, including weight.

13 DK 177150 B1

The chemical analysis results advantageously include the content of carotenoids and also fat and water content.

The carotenoids are preferably taken from the group consisting of astaxanthin, canthaxanthin, lutein and zeaxanthin.

The visual color rating is preferably set in a standardized value, for example a color chart value.

Alternatively, the method includes the step of recording the measurement date of the individual, the multivariable model being also correlated with the sampling dates in the population for the chemical analysis results and the visual color assessment.

The individual is advantageously a salmon.

The inferred fan / e is advantageously specified as a Roche color map value.

The invention further relates to the use of a color measurement instrument for calculating one or more quality parameters of the food, combining the Chroma and Hue values (L *, a *, b *) with information about the characteristic characteristic of the individual and that these combined data are processed in a provided mathematical multivariable model in advance.

The following describes a non-limiting example of a preferred embodiment depicted in the accompanying drawings, in which:

Figure 1 shows observed seasonal variations of fat and pigment for Atlantic salmon of 2-4 kg; 30 Figure 2 shows predicted Roche color chart values relative to read 14 DK 177150 B1

Roche color chart values for a validation set in Example 1; Figure 3 shows predicted Roche color map values relative to read Roche color map values for the validation set of Example 2; in Figure 5 4, predicted astaxa nth in values show relative to analytical asthma! xanthine values for the validation set of Example 2; Figure 5 shows predicted fat values relative to analytical fat values for the validation set of Example 2; and Figure 6 shows predicted water content values relative to analytical water content values for the validation set of Example 2.

Instruments and software referred to below have been used in accordance with normal practice of one of ordinary skill in the art.

In a method of the invention, multivariable calibration techniques are applied to a combination of readily available data from a color measurement instrument and physical data information for a measured individual, for character size data, such as length and weight, for salmon and sampling date, as has been used in the models for incorporating the relevant seasonal variations for the calculated parameters.

The surprising effect is that in the method of the invention 25 a digital camera or color meter can be used for purposes for which it is not, as a matter of principle, suitable.

The following examples only describe experiments carried out on farmed salmon stocks (Salmo salar).

30

Normally, fat content and partial pigment content (amount of color in the fish) will not be measured well in visible light, as they can in near infrared light (NIR). But in combination with physical parameters, this gets much better. Also, determining color map values will be somewhat better when data is combined as suggested.

5

Example 1 In the example, a color meter of the type Minolta Chroma meter CR-300 with a lens diameter of 0.8 cm was used. Astaxanthin was determined chemically by an HPLC method, fat was chemically determined with Soxhlet, and water content was determined by storage in a heater at 103 ° C for 16 hours. Color was determined visually by placing the sample in a Skretting light box, which is the previously mentioned Salmon Color Box (Skretting AS, Stavanger, Norway), and subsequently comparing the color with a Roche color chart, scale from 15 20 to 34. The color level in fish varies in relation to where it is measured in the fish. A color chart measurement and color meter measurement was performed in a standardized area, which is in the so-called Norwegian Quality Cut. Pr. By definition, this is obtained by cutting the fish just behind the dorsal fin (the beef cut). The area between the spine and the dorsal fin was measured. Alternatively, measurement may be performed on a fillet at a corresponding location in the fillet, that is, just behind the dorsal fin and above the spine. Weight (roundfish) and length were recorded for each fish.

In total, data from 753 fish were included in the example. 145 randomly selected samples of which 25 were included as an external validation set. Because data is included in different units of measurement (for example, grams and centimeters), the data was standardized by dividing each date with its associated standard deviation. As a regression model, Partial Least Squares regression (PLS) was used.

An estimate of the prediction error is given as RMSEP (Root Mean Square 30 Error of Prediction): 16 MS 177150 B1 Σ (* - λ) 2 MSEP = y - & - where "i" is the sample, "n" is the number of samples in the data set, "y" is the analytical value of the sample "i", and "y" is the predicted value of the sample "i".

5

After the first modeling step, approximately 5% of the values were considered to constitute extreme observations, that is, values that are abnormal to the vast majority of samples in the dataset and were removed from the dataset before further modeling. The final model is based on four significant principal components that use 92% of the variance in the data set (L *. A *, b *, weight, length) to explain 83% of the variance in color map readings. A further analysis shows that the most important, positively correlated factors were the coefficients of a * and b *. Weight correlated negatively, whereas L * and length both contributed a smaller negative coefficient to the model. Figure 2 shows the result of the prediction versus measured Roche color map values for the validation set. The prediction error expressed as RMSEP was 1.3 units. This is somewhat high but still satisfactory on the basis that the reference value is subjectively measured by Roche color chart and that the colorimeter lens is somewhat small, limiting the measured area.

Example 2 In the example, 118 composite samples were included. The value of weight, length and 25 Roche color chart value is an average value for each of the composite samples, while astaxanthin, fat and water content was determined on the composite sample as such.

• Prediction of Roche color chart value 17 DK 177150 B1

All values were standardized. Three of the samples were considered to be extreme observations and were removed from the material. The final model is based on three principle components that use 98% of the variance in the data for weight, length, L *, a * and b * to explain 95% of the variance in the color chart-5 values. The most important parameters of the model that correlate positively are a * and b *. L * correlated negatively, and length and weight correlated negatively with a small regression coefficient.

Figure 3 shows predicted value relative to read Roche color chart value for the 10 validation set. The prediction error expressed as RMSEP was 0.7 units, which is extremely good, especially when viewed in relation to the fact that the reference value is derived from a subjective reading.

• Prediction of astaxanthin content 15

All values were standardized. Three of the samples were considered to be extreme observations and were removed from the material. The final model is based on two principle components that use 90% of the variance in the data for weight, length, L *, a * and b * to explain 94% of the variance in chemical asta-20 xanthine content. The most important parameters of the model that correlate positively are a * and b *. L * correlated negatively, whereas length and weight correlated positively, but with a somewhat smaller contribution than a * and b *.

Figure 4 shows the predicted value relative to analytical astaxanthin value 25 for the validation set. The prediction error expressed as RMSEP was 0.6 mg / kg, which is extremely good.

• Fat content prediction 30 All values were standardized. Seven of the samples were considered to be extreme observations and were removed from the material. The final model is based on four principle components, which use 98% of the variance in the data for weight, length, L *, a * and b * to explain 93% of the variance in chemical fat content. The most important parameter of the model that correlates positively is weight. Length has a negative regression coefficient. The regression coefficients of L * and a * are smaller and negative, whereas that of b * is smaller and positive.

Figure 5 shows the predicted value versus analytical fat value for the validation set. The prediction error expressed as RMSEP was 0.8%, which is 10 extremely good.

• Predicting water content

All values were standardized. Four of the samples were considered to constitute 15 extreme observations and were removed from the material. The final model is based on four principle components that use 98% of the variance in the data for weight, length, L *, a * and b * to explain 90% of the variance in water content. The most important parameter of the model that correlates negatively is weight. Length has a positive coefficient of regression. The regression coefficients of L * and b * are smaller and positive, whereas that of a * is smaller and negative.

Figure 6 shows the predicted value relative to the analytical fat value of the validation set. The prediction error expressed as RMSEP was 0.8%, which is extremely good.

Although the foregoing mainly described methods for determining the quality criteria color, pigment, fat and water content of salmon, it is clear that the method will be applicable to other fish species and other animal species, where such quality parameters are descriptive for product quality. It is also clear that the method can be used for quality assessment of fruit, for example. It is also inherent in the case that the method can be used for calculating quality parameters other than the above. Multivariable models for different needs are prepared according to the same methodology as that described above, and color measurement data and distinctive individual data are entered into the multivariable model to produce desired quality parameter sizes.

Claims (16)

20 DK 177150 B1 A method for calculating a number of quality parameters, for example, color, pigment content, fat and / or water content, of a food product, in particular a food product of animal origin, for example meat from mammals, characterized in that the process contains the steps providing a representative individual of the food; determining the characteristic size of the subject, for example length, area, volume and / or weight; positioning a color measurement instrument's measurement lens at a surface of the food representative of the quality parameters and measuring the L *, a * and b * values (Chroma and Hue values) of the surface; - comparing the measured values of the subject's size and 15 colorimetric data with a pre-existing multivariable model representative of a population of the individual, the multivariable model being formed by mathematical processing of chemical analysis results, visual color assessment and individual size for the population and includes correlation factors between the measured colorimetric data and a series of 20 measured quality parameters, thereby - deriving the quality parameters of the subject in standardized units of measurement. Method according to claim 1, characterized in that the color measuring instrument is taken from a group consisting of color meter and digital camera. Method according to claim 1, characterized in that the size of the individual is characterized by two or more characterized sizes, including weight. DK 177150 B1 21 Process according to claim 1, characterized in that the chemical analysis results include the content of carotenoids, fat and water content. Process according to claim 1, characterized in that the carotenoids are taken from the group consisting of astaxanthin, canthaxanthin, lutein and zeaxanthin. Method according to claim 1, characterized in that the visual '10' color assessment is indicated in a standardized value, for example a color map value. Method according to claim 1, characterized in that the method also includes the step of recording the measurement date of the individual, the multivariable model also being correlated with sampling dates in the population for the chemical analysis results and the visual color assessment. Method according to any one of the preceding claims, characterized in that the individual is a salmon. Method according to claim 1, characterized in that the deduced color is indicated as a Roche color map value. 10. Use of a color measurement instrument for calculating one or more quality parameters for food products, combining Chroma and Hue values (L *, a *, b *) with information about the individual's characteristic size and processing these combined data in a priori provided mathematical multi variable model. Use according to claim 10, characterized in that the color measurement instrument is taken from a group consisting of fan / meter and digital camera. Use according to claim 10, characterized in that the quality parameters are color, pigment, fat and / or water content of meat, especially fish meat. 5 1/3 Received DKn1r77450 B1 Il 5 OEC. 20II PVS 18 -; -: ----------------— r Q 17 "* 15" 5 13 ““ 3 12 "______: ___ f 2 - * ~ Grease, 11 - i - 1 — f-Astaxanthin I 10 i — i — 1 — i-1-1-1-1-μ-r-1-1 — M- O Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Fig. 1 - Observed seasonal variations in fat and pigment in Atlantic salmon of 2-4kg. 30 Jfowto & Y ................................................ .................., .. «0ΰ0 .................. Number: 143:; ; :. Slope: 0.795678:: · ίζ5.03; . Displacement: 4.664410; ; :; Jk 2B— Coincidence: 0.925435 ..........-....... I ........................ ...... J .................. fragrance. ·! ; ^ EP: '"jg; s || M" 1 .................. i ................. ........ I .................. I 22 ~: .......'........... .... ί .................. .................. * ....... ...........: * · ul8r% 73a * °° i I i: 20 J ..............: ..................: .............. .... j .................. j 13 "'....................... .................................................. .................. Read Y ---, -, ---.- 1 -.-—-] --- 1 ----- 1- I ------'--- S- _IB_JK) _22_24_76_21_30 Mlacfia D-pæii. ff-was. PC): (RaciinFaJi.4j Fig. 2 - Predicted Roche color values relative to read Roche color values for the validation set in Example 1. DK 177150 B1 2/3 Predicted V - Number: 34: t \ - Slope: 1.004967 · - · ·;; 3 (, Offset: 0.066615; ....................................... ....______; ,, ..........:. Coincidence: 0.977064.:;;; ΙΜΛΧώίτίοΟ I • RMSEP: 0.666751 I; 'I i · „„ „SEP: 0.647454 I · i - 5 ~~ Error: _0.194140 | ............: ............................ ..; ...... '^^ β ^^ P¾D' ............ 20 “: ...............: .. .............: ...............: ...... pFr ^ s ^ b ^ P '^ oo ·' 'fc '/.i&i............. ;...............· ·:::: - - W®T \ 20oo i:: - ii ::: ...............: 50 "........: ...............: .... ...........: ............... ............... I j ...... ......... | 18 ~ ^ ........................ · ............ .................................................. .............. Read Y '* 1 J_ · f, -. · »* - 4 -------- I l. I” * I * '' J I __ rB ______ 2D __ 71_ £) _25 28_ SO___32. D-punitf cc.TL'op /. ".". /(Y-vw.PC); (RoiJiaPtiiSi3} Fig. 3 - Predicted Roche color values relative to read Roche color values for the validation set of Example 2. n I Predicted Y Number: 35 "........ in ........ .... \ ............. i ............. Ϊ ......... 'ί ...... ..: ..... • Slope: 0.878775: \;:: j - Displacement: 0.382129;;;; In Coincidence: 0.970181;;: *::; RMSEP; 0.563015: 1 t: f * 7 * Tough) o - SEP: 0.561506 ........ \ .............; ............. f ........ .....; '· i .............; - -ESl! - · r:: ~ ^ Λαζ ^ & ί $, οα S5jD0' '' j 'iiiii \ i I 4 - .............: .............: ............. térzY .....: · .............. · .............: .............::::::; :;; \ ^ φίΓ · »-0? i;:; J;: ·: i; • · '8¾¾] JSj ·. 23.00: ii::: 0 J ·: _i _: _ j_: j ........ i Read Y --1 -, ------------------ ^ rp-rn-TTT ^ j . ...................... _0 1_3__3 · 4_S_6_ 7_n l ^ panktsamlapr .. ,, pr-var, Pc): (Asia, 2] Fig. 4 * Predicted astaxanthin values relative to analytical 1 astaxanthin values for the validation set of Example 2. 3/3 DK 177150 B1 Predicted Y_ Number: 32 Γ: 'Slope: 0.979518! - ·.' Offset: 0.131796; i; I g.kn : = Coincidence: 0.967396::;: 95.00- J RMSEP: 0.780873: 85oa:: SEP: 0.790676;;;;;;;, Error: _- 0.0642771::::::: 10 _: ..... ..... ..........; ..................: .............. · ................ ii::: · 49,051.00::::::: · d3.rø2 & øtn 43.00::: ii · *: · '. \ \ \ \ \ \ \ \ \ Λ 'Read Ϋ f ____ y- · "'“ "e ~” f ~ 8 9 t'o _u_12 13_ 14 IS D-pu: fc oarSapf, PC): [Fat, 4 [~ Fig. 5 - Predicted fat values versus analytical fat values for the validation set of Example 2. In Predicted Y 7Ί * “" * -; --------- 1 '1 *' ........... .,., t + 1 .. *** », **. t. * r ..... ►, ............ Qty: 33: Slope: 0.955672 ::: ::::: Displacement: 3.199410 '' j »'j Coincidence: 0.951959 1; \ j * - sep: 0.759654 :::::: 3J, tJ0 ·' - Error: 0.132876 \ \ \ \,. - ^ Τΰ ^ '' ':::: ·: ::::: :: -: 83.00:::: - \: ^ °°: ί i es- ..... ffi-tP .., .. AS ^ L (JOO ......: ...........: ...........: ...........; .. .........: ^ ................................................ .................................................. .! ......... Read Y * '............... in "" "" ........ I ..... * - «- <'j— *' V'I ^ -. ^ V ^ ·.„ I .......................... .ι ^ * η- __04 ..........., 63_CG 6 '/ GB_GG_70_71 72_73 O-pur # nails ... * (Y-ver, PC): (MoJsura, <) Fig. 6 - Predicted water content values relative to analytical astaxanthin water content values Example for the validation set of Example 2.