CA2642482C - Method of calculating quality parameters of foodstuffs - Google Patents

Abstract

A method for calculating a range of quality parameters, for example colour, pigment content, fat and/or water content, of a foodstuff, in particular a foodstuff of animal origin, for example meat from fish and mammals, the method comprising the steps of providing a representative individual of the foodstuff; determining the characteristic size of the individual, for example length, area, volume and/or weight; positioning the measuring lens of a colour measuring instrument at a surface of the foodstuff representative of the quality parameters, and measuring the L*, a* and b* values (Chroma and Hue values) of the surface; comparing the measured values for the size of the present individual and colorimetric data to a multivariate model provided in advance, representative of a population of the species of the individual, the multivariate model being formed by mathematical processing of chemical analysis results, visual colour evaluation and individual size for the population and comprising correlation factors between the measured colorimetric data and a range of measured quality parameters, in order thereby to derive the quality parameters of the present individual in standardized units of measurement.

Description

METHOD OF CALCULATING QUALITY PARAMETERS OF FOODSTUFFS
The invention relates to a method for calculating quality parameters of foodstuffs, particularly meat products, especially. from fish. More specifically, the invention relates to the use of a portable instrument in combination with multivariate modelling for calculating colour, chemical contents of pigment, fat and water in the field, for example in a production facility, in a slaughterhouse or in a works laboratory.

In what follows, reference is made to the calculation of a number of quality parameters of fish, and to a particular degree salmon, but the invention is not limited to the use on fish, as it is conceivable for the method to be used also for other foodstuffs, particularly other sorts of meat, and particularly foodstuffs in which colour is a quality criterion which has to be evaluated quickly and reliably.

By the characteristic size of an individual is meant one or more dimensions, for example length,or diameter, area, volume and/or weight, sufficient to provide a characteristic of the individual. For a fish the characteristic quantity may be described for example by length and/or.weight.

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

A characteristic feature of salmonoids is that the muscles have a distinct red colour. The red colour is caused essentially by the natural pigment astaxanthin. Astaxanthin is produced in phytoplankton and finds it way up through the food chain through small crustaceans, which are then eaten by salmonoids. There are also other pigments, such as canthaxanthin, lutein, zeaxanthin, and in what follows, the pigments are also called, by a collective term, carotenoids.
The degree of red colouring is considered by many consumers as an important quality parameter when they are to buy salmon in the form of fillets or chops. Salmon is also used by the processing industry for producing, for example, smoked salmon and gravlax .(brine-cured salmon). An important quality parameter of smoked salmon and gravlax is the degree of red colouring after processing.

As the red colour is important to the evaluation of the quality of the products that reach the consumer, measuring the red colour before removing fish for slaughtering is also important. If the colouring of the fish is too poor, this will give a deduction in the price to the farmer and the fish will be difficult to sell, in particular in markets expecting a bright red colour in the fish flesh.

To a certain degree, there is a connection between the amount of carotenoids in the fish feed, such as astaxanthin, and how much carotenoids is found in the muscles of the fish.
Different strategies have also been worked out for how the farmer should most efficiently colour the fish. Thus, there is talk about-colouring periods, in which the astaxanthin content, for example, of the muscles is to be increased, measured as mg of astaxanthin per gram of muscle, and maintenance colouring, in which. the level of astaxanthin is only to be kept stable as the fish grows. It is also known that the absorption of carotenoids varies with fish size, season and that there are hereditary differences between the different releases of fish. Known in particular is the so-called spring drop, in which the astaxanthin content in the fish muscle decreases in spring. These different factors make it necessary for the farmer to sample the fish stock regularly to know the state of the fish as regards the carotenoid content and possibly adjust the amount of carotenoid in the feed in order for the colouring to be in accordance with the production plan.

An alternative to astaxanthin has been the use of the pigment canthaxanthin. This gives a somewhat yellower fish flesh than the use of the pigment astaxanthin. The two pigments have also been used together in different proportions of mixture.
From Norwegian patent No. 306652 is known that feeding feeds with elevated contents of the amino acid lysine for a shorter period before slaughter gives a fish muscle with a visually brighter red colour without the astaxanthin content being elevated.

The degree of red colouring can be determined visually by comparing the red colour of the flesh to the red colour of a standardized set of colour cards. The set of colour cards is a collection of coloured cardboard cards, in which red colour saturation and intensity increase from card to card. Best known is the so-called Roche colour fan (Roche SalmoFanTM) in which the cards are numbered from 20 to 34. The colour of the fish flesh is given a Roche colour card value based on which card is the closest to the colour of the fish flesh. This test is basically easy to perform on completely fresh fish and can be carried out without any other means than the colour cards. The use of standardized colour cards is well established, and the Roche colour card value is established as a standard. A further description of the basis for working out such colour cards are given by 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 colour cards is a subjective way of determining colour, in which several factors affect the result. Natural light will vary with time of day and the meteorological conditions. Attempts have been made to remedy this by the use of a standardized illumination box (for instance the Salmon Colour Box, Skretting AS, Stavanger, Norway in which the light source is fluorescent tubes). A known disadvantage of fluorescent tubes is that the colour content of the light may vary over the life span of the fluorescent tube. Further, such an illumination box has an open side, where lateral light will enter to fall on the sample.

A piece of fish, such as a fillet, does not have an evenly coloured surface. The fillet is built of muscle fibres alternating between connective tissue and fat. This .complicates the visual colour measuring.

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

In addition, the perception of colour is different from person to person. It has turned out in practice that different observers may grade the same piece of fish with a difference of 3 on the Roche colour card scale, for example from 22 to 24. In addition it is of importance whether the observer has an economic interest in the result. Thus, a seller will have a tendency to grade higher than a buyer.
There has been an attempt to remedy the problem with using colour cards and a standardized illumination box with a more elaborate illumination box as disclosed in Norwegian patent NO 317714. In addition NO 317714 discloses a method of predicting the chemical contents of astaxanthin, fat and Roche colour value by means of a photometric technique and measurement of RGB colour values (R = red, G = green, B =
blue). The camera is digital. The drawback of this method is that it requires a large and elaborate illumination box which is not suited for being moved, but only for use indoors.
There are also strict requirements as to the quality of the camera optics and mechanical components like aperture and shutter. The patent owner also points out the fact that it is important that the so-called CCD chip ("Charge-Coupled Device") is kept at a stable temperature during exposure.
Another drawback is that the intensity of the light changes over time, which necessitates routines for following up and control.

The red colour can also be determined by analysing the chemical contents of astaxanthin in the fish flesh. Chemical analysis of astaxanthin is complicated and can only be carried out in laboratories by trained personnel. Equipment like HPLC ("High Performance Liquid Chromatography") is also required for the analysis to be carried out. Thus, a fish farmer must send away the fish or piece of fish to be analysed. The answer will come after several days, and there is a considerable cost in having'such an analysis carried out.

Another method is the use of NIR ("Near-Infrared Reflectance"). This technique is based on a reference material, in which astaxanthin and other carotenoids are determined by conventional chemical analysis. An NIR spectrum is taken of the same material, and by means of multivariate, statistical techniques connections are found between=spectrum and chemical contents. This connection is expressed in an NIR
equation. This NIR equation is used to predict the contents of carotenoids, for example astaxanthin, when new samples are analysed.

An NIR analysis is quicker and less expensive than a chemical analysis. The instrument itself is an expensive and stationary instrument and it is not remunerative for the individual fish farmer to invest in an instrument of his own.
Therefore, also in this case, the fish farmer must send the sample away, and it takes several days before the answer is available.

Measuring the chemical contents of, among other things, astaxanthin in the fish flesh by means of an NIR/VIS
instrument (an NIR instrument also measuring visible light) is used as the established technique for example by the present applicant and by others.

In the patent document US 6,649,412 is disclosed a slaughter line for fish, in which an NIR probe is placed behind the tool that removes the intestines from and cleans the belly of the gutted fish. The probe illuminates the fish muscle from the open belly and provides. information about the red colour, so that fish may be sorted according to the red colour.

NIR instruments come in many sizes and designs, and they are used in calibrations against parameters corresponding to those used by the applicant. Potentially, small portable NIR-VIS instruments may also be used for the purpose. These use a relatively wide wave range, which makes the calibration relatively robust. But the instruments are generally expensive, they will be problematic to standardize in such a way that the same calibration may be used on the entire instrument park, and the data processing is complicated as many variables (signals or reflections from different wave lengths) are used.

The problem with other known instrumentation is that the equipment is too heavy and voluminous to be transported along easily for field analyses. At the same time the instrumentation is so expensive that it is not very relevant for purchase for the individual farming facility. Recently, smaller portable NIR instruments have been developed. These overcome the problem of stationary instruments. But it still remains that the instruments are expensive and that they have to be calibrated at regular intervals.

Thus, in general, for the analysis of chemical colour and fat content the fish must be sent to laboratories or analysing instruments that are located centrally. This takes time and makes it necessary for decisions to be postponed in anticipation of the objective analysis results becoming available. The postponement may result in the time space at disposal for influencing the product quality being limited unnecessarily.

The background of the invention is that there is a need for quick and objective decisions on the quality parameters of fish. Today colour can be determined manually (also in the field) by means of colour cards under standardized light conditions, but the method is subjective and relatively inaccurate. The method should be so simple that it can be carried out at the production facility, that is to say the fish enclosures, or in any case in close connection thereto, such as on feeding barges, work boats, piers or indoors on land.

A colorimeter is used for colour measuring of surfaces and can be used to determine the position of a colour in the so-called NCS system. Attempts have been made to use colorimeters to determine the colour of fish flesh. The colorimeter primarily gives colour values expressed as XYZ
values, again expressed as L*, a* and b* values, L* being the lightness factor (black/white), a* being red/green chromaticity and b* yellow/blue chromaticity. Secondarily the colorimeter gives the values "Chroma" (C*ab) and "Hue" (H ab) These values. are functions of L*, a* and b* and are measures of colour intensity and colour composition respectively. The functions are C*ab = (a*2 + b*2) 1/2 H ab = tan-1(b* /a* ) The colorimeter may be a hand-held instrument and is placed to lie or stand on the sample in such a manner that light does not enter from the side onto the surface to be measured.
The colorimeter has an internal light source and this is calibrated continuously by software which is an integrated part of the instrument.

Reference methodology used today is based on visual evaluation of colour which is related to colour cards (for example Roche SalmoFanTm).

The use of a colorimeter to determine colour is established knowledge; for example, a surface may be measured, and then the colour in the NCS system which is the best match may be found. Within the fish farming trade colorimeters are used for colour measuring in fish. Then the values from either L*, a* or b* are used, and an attempt is made to correlate these to colour cards. The correlation is then normally relatively weak.

Even though the colorimeter indicates colour in several ways, it has turned out to be difficult to find a good correlation between the measured values and the chemical contents of carotenoids, for example astaxanthin, and the visual colour card value. This is connected to, among other things, the fact that there are other natural pigments than astaxanthin in the salmon muscle. Yellowish pigments may also be present, such as lutein and zeaxanthin, pigments coming from maize, for example. This affects the reading of the colorimeter. The occurrence of canthaxanthin will also affect the reading of the colorimeter. Christiansen et al. found that a colorimeter was suitable for quantifying the astaxanthin content in relatively pale fish (2-4 mg of astaxanthin per kg.), but that the instrument could not distinguish between the astaxanthin content in samples having higher astaxanthin content. The same authors found that the use of a colour card fan predicted the chemical colour content better, but this prediction was not satisfactory either. (Christiansen, R., Struksnaes, G., Estermann, R., Torrissen, O.J. 1995:
Assessment of flesh colour in Atlantic salmon, Salmo salar L.
Aquaculture Research, 26,'311-321) The colorimeter measures within visible light. Thus, it will respond relatively little to the amount of fat in the sample:
This makes it difficult to achieve a correlation with a visually read colour card value.

The use of only the measured value from a colorimeter gives too poor a correlation with a colour card value and is unusable in practice. At the same time it is impossible for a user to understand how all three values together correlate with a colour card value. For this purpose multivariate data processing is needed.

Measuring in visible light alone describes to a somewhat limited degree the chemical properties of a sample, and even colour will be described better by incorporating other parameters into calibrations.

The invention has as its object to remedy or reduce at least one of the drawbacks of the prior art.

The object is achieved through-features described in the description below and in the claims-that follow.

The object of the invention is to perform an objective and quick analysis of the flesh quality of a fish, with an emphasis on the chemical contents of carotenoids like astaxanthin and canthaxanthin, chemical content of fat and colour card value of the fish muscle.

It is a further object that this may be done in the field, preferably way out on the floating parts of a fish farming facility, like floating walkways and feeding barges. It is a further object that the analysis should be reasonable, thereby allowing it to be carried out repeatedly, so that the farmer will have some help in planning his fish production as regards the colouring of the flesh of the fish.

It is a further object that by providing an objective and reliable colour measurement which is at least just as reliable as the manual colour measurement using colour cards, such apparatus-based colour measurement could serve as objective documentation in the buying and selling of the fish.

The object of the invention is thereby to carry out objec-tive, quick analyses of the flesh quality of the fish by bringing the instrument to where the fish are, thereby saving time and costs of sending fish for sampling.

More specifically, the invention relates to a method of cal-culating a range of quality parameters, for example colour, pigment content, fat and/or water content, of a foodstuff, in particular a foodstuff of animal origin, for example meat from fish and mammals, characterized in that the method com-prises the steps of - providing a representative individual of the foodstuff;
determining the characteristic size of the individual, for example length, area, volume and/or weight;

positioning the measuring lens of a colour measuring in-strument at a surface of the foodstuff representative of the quality parameters, and measuring the L*, a* and b* values, and the Chroma and Hue values, of the surface;
comparing the measured values for the size of the pre-sent individual and colorimetric data to a multivariate model provided in advance, representative of a population of the species of the individual, the multivariate model being formed by mathematical processing of chemical analysis re-sults, visual colour evaluation and individual size for the population and comprising correlation factors between the measured colorimetric data and a range of measured quality parameters, in order thereby to derive the quality parameters of the present individual in standardized units of measurement.

The colour-measuring instrument is preferably taken from a group consisting of colorimeter and digital camera.

The size of the individual is preferably described by means of two or more characteristic sizes, including weight.

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

The carotenoids are preferably taken from the group consist-ing of astaxanthin, canthaxanthin, lutein and zeaxanthin.
The visual colour evaluation is preferably indicated in a standardized value, for example a colour card value.

Alternatively, the method includes the step of recording the date of measuring for the individual, the multivariate model being correlated also with the sampling dates in the popula-tion for the chemical analysis results and the visual colour evaluation.

Advantageously, the individual is a salmon.
Advantageously, the derived colour is indicated as a Roche colour card value.

The invention further relates to the use of a colour-measuring instrument for the calculation of one or more qual-ity parameters of the foodstuff, the Chroma and Hue values and the L*, a* and b* values being processed in a mathemati-cal multivariate model.

In what follows is described a non-limiting example of a pre-ferred embodiment which is visualized in the accompanying drawings, in which:

Figure 1 shows observed seasonal variations in fat and pig-ment for Atlantic salmon of 2-4 kg;

Figure 2 shows predicted Roche colour card values versus read Roche colour card values for a validation set in example 1;

Figure 3 shows predicted Roche colour card values versus read Roche colour card values for the validation set in example 2;

Figure 4 shows predicted astaxanthin values versus analyti-cal astaxanthin values for the validation set in example 2;

Figure 5 shows predicted fat values versus analytical fat values for the validation set in example 2; and Figure 6 shows predicted water content values versus ana-lytical water content values for the validation set in example 2.

For several years the applicant has analysed a large number of salmon for chemical contents of astaxanthin, canthaxanthin and fat, colour card values, and recorded length and weight of the fish and date of killing of the fish. This extensive data material has been analysed and a connection has been found between length, weight and fat content as shown in fig-ure 1 and quoted in the table below.

Average analysis results for Norway 1999. Salmon (Salmo salar).

Weight class K factor* SalmoFan Astaxanthin Fat (%) (kg) (mg/kg) 0 - 1 1.1 24.6 3.9 8.2 1 - 2 1.2 26.0 4.8 11.5 2 - 3 1.3 27.1 5.8 13.8 3 - 4 1.3 27.8 6.4 15.1 4 - 5 1.3 27.9 6.3 15.9 - 6 1.4 28.1 6.5 16.2 * K factor = (weight [g] x 100)/(length [CM]) 3 It has now surprisingly turned out that by combining a col-orimetric measurement of the fish flesh with information about the length and weight of the fish, the chemical con-tents of pigments like astaxanthin and canthaxanthin, fat and also colour card value can be predicted by means of a mathe-matical model that takes as a starting point the L*, a* and b* values of the colorimeter and also the length and weight of the fish. The prediction provides a better and more reli-able result for the chemical contents of pigment and colour card 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 consideration in the prediction. At the same time the fat content is predicted more accurately than what may be predicted from information about the length and weight of the fish. The basis for the prediction is a multi-variate, statistical method leading to a set of mathematical calibration equations.

For the accuracy of the prediction it has further turned out to be advantageous to take into account the date of the sam-pling as there is a seasonal variation in the colour card measurements.

A method as described also makes it possible to use a port-able colour-measuring instrument like a digital camera or a colorimeter in accordance with the object of the invention.
Such instruments and the colorimeter in particular are not dependent on standardized light conditions and make use of built-in means for recalibration and are therefore independ-ent of external means of calibration. For the calculation of the chemical contents of pigment, colour card value and chemical contents of fat the calibration equations are used.
These will be part of the software of a computer, for example a laptop which may be located adjacent to the colorimeter, or of a central computer which is reached via, 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 date of sampling. It will thereby be possible to carry out the desired prediction immediately. The results may be recorded manually in a separate form for example, or elec-tronically.

The results will be provided there and then, without any fish having to be sent to a centrally located analysing instrument or laboratory. This will enable, for example, analysis, evaluation and subsequently the choice of the optimum fish feed when the farmer and feed consultant meet at the farming facility. A better and quicker choice can then be made. Simi-larly, the invention will contribute to an electronic colour card measurement which may serve as objective documentation of the fish in buying and selling.

It is obvious that the present invention could be used when determining certain quality parameters also of other food-stuffs. Thus, the invention is not limited to comprising salmon only.

Instruments and software referred to in what follows, are used in accordance with a practice normal to a skilled per-son.

By a method according to the invention multivariate calibra-tion techniques are applied to a combination of easily acces-sible data from a colorimeter instrument and information on physical data of a measured individual, for salmon characteristic quantity data like length and weight, and sampling date used in the models to incorporate the relevant seasonal variations for the parameters calculated.

The surprising effect is that in the method according to the invention a digital camera or a colorimeter can be used for purposes, for which it is basically not well suited.

The examples that follow, exclusively describe experiments carried out on stocks of reared salmon (Salmo salar).
Normally, fat and partially pigment content (amount of colour in the fish) will not be measured well in visible light, which they can be in near-infrared light (NIR). But in combination with physical parameters'this becomes much better. Also the determination of colour card values will be somewhat better when data are combined as suggested.

Example 1 In the example was used a Minolta Chroma meter CR-300 type colorimeter with a lens diameter of 0.8 cm.'Astaxanthin was determined chemically by an HPLC method, fat was determined chemically by Soxhlet, and water content was determined by storing in a hot cabinet at 103 C for 16 hours. Colour was determined visually by placing the sample in a Skretting illumination box, that is the previously mentioned Salmon Colour Box (Skretting AS, Stavanger, Norway) and then comparing the colour to Roche colour card, scale from 20 to 34. The colour-level in fish varies in relation to where in the fish it is measured. A colour card measurement and measurement with colorimeter were carried out in a standardized area lying in the so-called Norwegian Quality Cut. Per definition, this is produced by cutting the fish right behind the dorsal fin ("chop cut"). The area between the vertebra and dorsal fin was measured. Alternatively, measuring can be done on a fillet at a corresponding place in the fillet, that is to say right behind the dorsal fin and above the vertebra. Weight (round fish) and length were recorded for each fish.

Altogether, data from 753 fish were included in the example..
145 randomly picked samples thereof were included as an external validation set. As data in different units of measurement (for example grams and centimetres) are included, the data were standardized by dividing each datum by its associated standard deviation. As a regression model was used Partial Least Squares regression (PLS). An estimate of the, error of prediction is given as the RMSEP (Root Mean Square Error of Prediction):

n ~~)) /(Yi Ji) RMSEP = ii i=1 in which "i" is the sample, "n" is the number of samples in the set of data, "yi" is the analytical value of the sample "i" and "yi" is the predicted value of the sample "i".
After the first modelling step approximately 5 % of the values were considered to be "outliers", that is to say values that were abnormal relative to the great majority of samples in the data set, and were removed from the data set before further modelling. The end model is based on four significant principal components using 92 % of the variance in the data set (L*, a*, b*, weight, length) to explain 83 %
of the variance in colour card 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 by a smaller negative coefficient to the model. Figure 2 shows the result of the prediction versus the measured Roche colour card values for the validation set. The error of prediction, expressed as the RMSEP, was 1.3 units. This is somewhat high, but still satisfactory on the basis that the reference value is measured subjectively by means of Roche colour cards and that the lens of the colorimeter is somewhat small, which limits the area measured.

Example 2 In the example 118 composite samples are included. The value of weight, length and Roche colour card value is an average value for each of the samples of the composite, whereas astaxanthin, fat and water content were determined on the composite sample as such.

= Prediction of Roche colour card value All values were standardized. Three of the samples were considered to be'outliers and were removed from the material.
The end model is built on three principal components utilizing 98 % of the variance in the data for weight, length, L*, a* and b* to explain 95 % of the variance in the colour card values. The most important parameters of the model correlating positively, are a* and b*. L* correlated negatively, and length and weight correlated negatively by a small coefficient of regression.

Figure 3 shows predicted value versus read Roche colour card value for the validation set. The error of prediction expressed as the RMSEP was 0.7 units, which is very good, in particular when seen in relation to the fact that the reference value results from a subjective reading.

= Prediction of astaxanthin content All values were standardized. Three of the samples were considered to be outliers and were removed from the material.
The end model is built on two principal components using 90 %
of the variance in the data for weight, length, L*, a* and b*
to explain 94 % of the variance in chemical astaxanthin content. The most important parameters of the model correlating positively, are a* and b*. L* correlated negatively, whereas length and weight correlated positively, but by a somewhat smaller contribution than a* and b*.
Figure4 shows the predicted value versus analytical astaxanthin value for the validation set. The error of prediction expressed as the RMSEP was 0.6 mg/kg, which is very good.

= Prediction of fat content All values were standardized. Seven of the samples were considered to be outliers and were removed from the material.
The end model is built on four principal components using 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 correlating positively is weight. Length has a negative. coefficient of regression. The coefficients of regression of L* and a* is 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 error of prediction expressed as the RMSEP was 0.8 %, which is very good.

= Prediction of water content All values were standardized. Four of the samples were considered to be outliers and were removed from the material.

The end model is built on four principal components using 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 correlating negatively is weight. Length has a positive coefficient of regression. The coefficients of regression of L* and b* is smaller and positive whereas that of a* is smaller and negative.

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

Even though,, in the above, there are described in the main methods of determining the quality criteria colour, pigment, fat and water content in salmon, it is obvious that the method could be used on other species of fish and other species of animals, in which such quality parameters are descriptive of product quality. It is also obvious that the method could be used for quality assessment of, for example, fruit. It also lies in the nature of the case that the method could be used in calculating other quality parameters than those mentioned here. Multivariate models for different needs are worked out according to the same methodology as that described above, and colorimeter measurement data and characteristic individual-data are inserted into the multivariate model, so that desired quality parameter quantities are generated.

Claims (17)

C1aims

1. A method for calculating a range of quality parameters of a foodstuff, characterized in that the method comprises the steps of providing a representative individual of the foodstuff;

- determining the characteristic size of the individual;

- positioning the measuring lens of a colour-measuring instrument at a surface of the foodstuff representative of the quality parameters, and measuring the L*, a* and b* values, and the Chroma and Hue values, of the surface; and - comparing the measured values for the size of the present individual and colorimetric data to a multivariate model provided in advance, representative of a population of the species of the individual, the multivariate model being formed by mathematical processing of chemical analysis results, visual colour evaluation and individual size for the population and comprising correlation factors between the measured colorimetric data and a range of measured quality parameters, in order thereby to derive the quality parameters of the present individual in standardized units of measurement.

2. The method in accordance with claim 1 wherein the quality parameters are chosen from a group consisting of colour, pigment content, fat and water content.

3. The method in accordance with claim 1 or claim 2 characterized in that the foodstuff is of animal origin.

4. The method in accordance with claim 3 characterized in that the foodstuff of animal origin is meat from fish and mammals.

5. The method in accordance with claim 1, characterized in that the colour-measuring instrument is taken from a group consisting of colorimeter and digital camera.

6. The method in accordance with claim 1, characterized in that the size of the individual is characterized by means of two or more characteristic sizes, including weight.

7. The method in accordance with claim 1, characterized in that the chemical analytical results include the contents of carotenoids and also fat and water content.

8. The method in accordance with claim 1, characterized in that the carotenoids are taken from the group consisting of astaxanthin, canthaxanthin, lutein and zeaxanthin.

9. The method in accordance with claim 1, characterized in that the visual colour evaluation is indicated in a standardized value.

10. The method in accordance with claim 8 characterized in that the standardized value is a colour card value.

11. The method in accordance with claim 1, characterized in that the method also includes the step of recording the date of measuring for the individual, the multivariate model being correlated also with the sampling dates in the population for the chemical analysis results and the visual colour evaluation.

12. The method in accordance with any one of claims 1 to 11, characterized i n that the individual is a salmon.

13. The method in accordance with claim 1, characterized in that the derived colour is indicated as a Roche colour card value.

14. Use of a colour-measuring instrument for the calculation of one or more quality parameters of foodstuffs, Chroma and Hue values and the L*, a* and b* values being processed in a mathematical multivariate model provided in advance.

15. The use according to claim 14, characterized in that the colour-measuring instrument is taken from a group consisting of colorimeter and digital camera.

16. The use according to claim 14, characterized in that the quality parameters calculated are one or more of the parameters selected from the group consisting of colour, pigment, fat and water content of meat.

17. The method in accordance with claim 16, characterized in that the meat is fish meat.