Spectroscopic characterization of seafood

Abstract

A method and apparatus for field spectroscopic characterization of seafood is disclosed. A portable NIR spectrometer is connected to an analyzer configured for performing a multivariate analysis of reflection spectra to determine qualitatively the true identities or quantitatively the freshness of seafood samples.

Claims

1. A system comprising: one or more devices to: receive information associated with a first type of seafood and a second type of seafood; determine a first plurality of spectra for the first type of seafood and a second plurality of spectra for the second type of seafood; perform a first operation on the first plurality of spectra to generate a modified first plurality of spectra; perform a second operation on the second plurality of spectra to generate a modified second plurality of spectra; and generate, based on performing the first operation and the second operation, a model, information associated with the model being used to identify a seafood sample based on a reflection spectrum obtained by a spectrometer, and the spectrometer being separate from the one or more devices.

2. The system of claim 1, where the first type of seafood and the second type of seafood have similar visual features, the first type of seafood is a first species, and the second type of seafood is a second species.

3. The system of claim 1, where the first plurality of spectra includes a first quantity of spectra for a first characteristic of the first type of seafood and a second quantity of spectra for a second characteristic of the first type of seafood, and the second plurality of spectra includes a third quantity of spectra for a first characteristic of the second type of seafood and a fourth quantity of spectra for a second characteristic of the second type of seafood.

4. The system of claim 1, where the one or more devices are further to: receive information associated with a plurality of additional types of seafood, each additional type of seafood, of the plurality of additional types of seafood, being different than the first type of seafood and being different than the second type of seafood; generate a plurality of spectra for each additional type of seafood; and perform an operation on the plurality of spectra for each additional type of seafood to generate a modified plurality of spectra for each additional type of seafood, information associated with the modified plurality of spectra for each additional type of seafood being included in the model.

5. The system of claim 1, where the one or more devices are further to: perform, using the information associated with the model, multivariate pattern recognition analysis on the seafood sample; and transmit, based on performing the multivariate pattern recognition analysis, a result of the multivariate pattern recognition analysis to another device for display.

6. The system of claim 1, where information associated with the seafood sample is obtained using the spectrometer.

7. The system of claim 1, where the one or more devices are further to: identify, using the information associated with the model, a type of seafood associated with the seafood sample.

8. A method comprising: receiving, by a device, information associated with a first type of seafood and a second type of seafood; determining, by the device, a first plurality of spectra for the first type of seafood and a second plurality of spectra for the second type of seafood; performing, by the device, a first operation on the first plurality of spectra to generate a modified first plurality of spectra; performing, by the device, a second operation on the second plurality of spectra to generate a modified second plurality of spectra; and generating, by the device, and based on performing the first operation and the second operation, a model, information associated with the model being used to identify a seafood sample based on a reflection spectrum obtained by a spectrometer, and the spectrometer being separate from the device.

9. The method of claim 8, where the first type of seafood and the second type of seafood have similar visual features, the first type of seafood is a first species, and the second type of seafood is a second species.

10. The method of claim 8, where the first plurality of spectra includes a first quantity of spectra for a first characteristic of the first type of seafood and a second quantity of spectra for a second characteristic of the first type of seafood, and the second plurality of spectra includes a third quantity of spectra for a first characteristic of the second type of seafood and a fourth quantity of spectra for a second characteristic of the second type of seafood.

11. The method of claim 8, further comprising: receiving information associated with a plurality of additional types of seafood, each additional type of seafood, of the plurality of additional types of seafood, being different than the first type of seafood and being different than the second type of seafood; generating a plurality of spectra for each additional type of seafood; and performing an operation on the plurality of spectra for each additional type of seafood to generate a modified plurality of spectra for each additional type of seafood, information associated with the modified plurality of spectra for each additional type of seafood being included in the model.

12. The method of claim 8, further comprising: performing, using the information associated with the model, multivariate pattern recognition analysis on the seafood sample; and providing, based on performing the multivariate pattern recognition analysis, a result of the multivariate pattern recognition analysis for display.

13. The method of claim 8, where information associated with the seafood sample is obtained using the spectrometer.

14. The method of claim 8, further comprising: identifying, using the information associated with the model, a type of seafood associated with the seafood sample.

15. A non-transitory computer readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by a processor of a device, cause the processor to: receive information associated with a first type of seafood and a second type of seafood; determine a first plurality of spectra for the first type of seafood and a second plurality of spectra for the second type of seafood; perform a first operation on the first plurality of spectra to generate a modified first plurality of spectra; perform a second operation on the second plurality of spectra to generate a modified second plurality of spectra; and generate, based on performing the first operation and the second operation, a model, information associated with the model being used to identify a seafood sample based on a reflection spectrum obtained by a spectrometer, and the spectrometer being separate from the device.

16. The non-transitory computer readable medium of claim 15, where the first plurality of spectra includes a first quantity of spectra for a first characteristic of the first type of seafood and a second quantity of spectra for a second characteristic of the first type of seafood, and the second plurality of spectra includes a third quantity of spectra for a first characteristic of the second type of seafood and a fourth quantity of spectra for a second characteristic of the second type of seafood.

17. The non-transitory computer readable medium of claim 15, where the instructions further include: one or more instructions to receive information associated with a plurality of additional types of seafood, each additional type of seafood, of the plurality of additional types of seafood, being different than the first type of seafood and being different than the second type of seafood; one or more instructions to generate a plurality of spectra for each additional type of seafood; and one or more instructions to perform an operation on the plurality of spectra for each additional type of seafood to generate a modified plurality of spectra for each additional type of seafood, information associated with the modified plurality of spectra for each additional type of seafood being included in the model.

18. The non-transitory computer readable medium of claim 15, where the instructions further include: one or more instructions to perform, using the information associated with the model, multivariate pattern recognition analysis on the seafood sample; and one or more instructions to provide, based on performing the multivariate pattern recognition analysis, a result of the multivariate pattern recognition analysis for display.

19. The non-transitory computer readable medium of claim 15, where information associated with the seafood sample is obtained using the spectrometer.

20. The non-transitory computer readable medium of claim 15, where the instructions further include: one or more instructions to identify, using the information associated with the model, a type of seafood associated with the seafood sample.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

(2) Exemplary embodiments will now be described in conjunction with the drawings, in which:

(3) FIG. 1 is a schematic three-dimensional view of an apparatus for field authentication of a seafood sample according to the invention, superimposed with an NIR reflection spectrum measured by the apparatus;

(4) FIG. 2 is a side cross-sectional view of a portable handheld NIR spectrometer of the apparatus of FIG. 1;

(5) FIG. 3A is a side cross-sectional view of a light detection subassembly the portable NIR spectrometer of FIG. 2;

(6) FIG. 3B is a side cross-sectional view of a wavelength dispersive element used in the light detection subassembly of FIG. 3A;

(7) FIG. 3C is a transmission spectrum of the wavelength dispersive element of FIG. 3B;

(8) FIG. 3D is a three-dimensional view of the portable handheld NIR spectrometer of FIG. 2;

(9) FIG. 4A is a flow chart of a method for field authentication of a seafood sample according to the invention;

(10) FIG. 4B is a flow chart of an exemplary multivariate analysis of the NIR spectra according to the invention;

(11) FIG. 5A is a schematic view of one embodiment of the apparatus of the invention, in which a portable device in wireless communication with the NIR spectrometer is used to analyze NIR spectra obtained by the NIR spectrometer;

(12) FIG. 5B is a schematic view of another embodiment of the apparatus of the invention, in which the portable device is used to relay the measured NIR spectra to a remote server for performing the multivariate analysis;

(13) FIGS. 6 to 8 are color photographs of seafood pairs to be discriminated between, including: red mullet/mullet pair (FIG. 6); winter codfish/codfish pair (skin and meatFIG. 7); and samlet/salmon trout (skin and meatFIG. 8), used in experimental verification of the invention;

(14) FIG. 9 is a color photograph of a prototype of the apparatus measuring a NIR spectrum of a salmon sample;

(15) FIGS. 10A and 10B are flow charts of data collection and analysis for higher and lower quality seafood, respectively, used in the experimental verification;

(16) FIGS. 11, 14, and 17 are measured diffuse reflection spectra of the red mullet/mullet pair, winter codfish/codfish pair, and samlet/salmon trout pair, respectively;

(17) FIGS. 12, 15, and 18 are three-dimensional score plots of principal component analysis (PCA) models of the red mullet/mullet pair, winter codfish/codfish pair, and samlet/salmon trout pair, respectively; and

(18) FIGS. 13A, B; 16A, B; and 19A, B are Coomans plots of Soft Independent Modeling of Class Analogy (SIMCA) analyses of the red mullet/mullet pair, winter codfish/codfish pair, and samlet/salmon trout pair, respectively.

DETAILED DESCRIPTION OF THE INVENTION

(19) While the present teachings are described in conjunction with various embodiments and examples, it is not intended that the present teachings be limited to such embodiments. On the contrary, the present teachings encompass various alternatives and equivalents, as will be appreciated by those of skill in the art.

(20) Referring to FIG. 1, an apparatus 10 for field authentication of a seafood sample 11 includes a portable NIR spectrometer 12 for obtaining a diffuse NIR reflection spectrum 13 (signal power P vs. wavelength ) of the seafood sample 11. An analyzer 14 is operationally coupled e.g. via a cable 15 to the spectrometer 12. The analyzer 14 is configured to perform a multivariate analysis of the reflection spectrum 13 of the seafood sample 11 to determine at least one characteristic parameter corresponding to the reflection spectrum 13. The analyzer 14 is configured for comparing the at least one parameter to a threshold corresponding to species of the seafood sample 11, for determination of the species of the seafood sample 11. The species can be displayed on a display 16 of the analyzer 14. The at least one parameter can include two or more parameters. The two parameters can be represented graphically as a point on an XY plot called Coomans plot. A position of the point on the Coomans plot is indicative of the seafood species of which the reflection spectrum 13 was taken. Multivariate regression/pattern recognition analysis and Coomans plots will be considered in detail further below. The construction of the NIR spectrometer 12 is considered first.

(21) Referring to FIG. 2, the NIR spectrometer 12 includes a body 23, incandescent lamps 24 for illuminating the seafood sample 11, a tapered light pipe (TLP) 25 for guiding diffusely reflected light 36, a laterally variable filter (LVF) 31 for separating the reflected light 36 into individual wavelengths, and a photodetector array 37 for detecting optical power levels of the individual wavelengths. The photodetector array 37 is formed in a CMOS processing chip 37A and coupled to the LVF 31 with a optically transmissive adhesive 38. An electronics board 37B is provided to support and control the CMOS processing chip 37A. An optional pushbutton 21 is provided to initiate the spectra collection. The photodetector array 37 is aligned perpendicular to a longitudinal axis LA of the TLP 25.

(22) In operation, the incandescent lamps 24 illuminate the seafood sample 11. The TLP 25 collects the diffusely reflected light 36 and direct it towards the LVF 31. The LVF 31 separates the diffusely reflected light 36 into individual wavelengths, which are detected by the photodetector array 31. The measurement cycle can be initiated by pressing the pushbutton 21, or by an external command from the analyzer 14.

(23) The compact size of the NIR spectrometer 12 is enabled by the construction of its light detection subassembly 29. Referring to FIG. 3A, the light detection subassembly 29 is shown in XZ plane. In FIG. 3A, the light detection subassembly 29 is flipped by 180 degrees as indicated by the direction of the z-axis on the right side of FIGS. 2 and 3A. In the preferred embodiment shown in FIG. 3A, the optically transparent adhesive 38 directly couples the photodetector array 37 to the LVF 31. The optically transparent adhesive 38 needs to: be electrically non-conductive or dielectric in nature; be mechanically neutral by achieving good adhesion strength with inducing stress or destructive forces to the detector array 37; optically compatible to transmit the desired spectral content; remove reflection created at air to glass interfaces; and have reasonable coefficient of thermal expansion properties to minimize stress to the detector pixels 52 during curing and during thermal cycling. Am opaque epoxy 22 encapsulates the LVF 31, facilitating removal of stray light and protecting the LVF 31 from humidity. An optional glass window 39 is placed on top of the LVF 31 for additional environmental protection.

(24) Referring to FIGS. 3B, and 3C, the operation of the LVF 31 is illustrated. The LVF 31 is shown in YZ plane, in which the wavelengths are dispersed. The LVF 31 includes a wedged spacer 32 sandwiched between wedged dichroic mirrors 33, to form a Fabry-Perot interferometer with a laterally variable spacing between the dichroic mirrors 33. The wedge shape of the optical transmission filter 31 makes its transmission wavelength laterally variable, as shown with arrows 34A, 34B, and 34C pointing to individual transmission peaks 35A, 35B, and 35C, respectively, of a transmission spectrum 35 (FIG. 3C) shown under the variable optical transmission filter 31. In operation, the polychromatic light 36 reflected from the seafood sample 11 impinges on the variable optical filter 31, which separates the polychromatic light 36 into individual spectral components shown with the arrows 43A to 34C. The wavelength range of the NIR spectrometer 12 is preferably between 700 nm and 2500 nm, and more preferably between 950 nm and 1950 nm.

(25) Using the LVF 31 and the TLP 25 allows a considerable size reduction of the NIR spectrometer 12. The NIR spectrometer 12 is free of any moving parts for wavelength scanning. Small weight of the NIR spectrometer 12, typically less than 100 g, allows a direct placement of the NIR spectrometer 12 onto the seafood sample 11. Small weight and size also makes the NIR spectrometer 12 easily transportable e.g. in a pocket of a food inspector. The size of the NIR spectrometer 12 is illustrated in FIG. 3D. The NIR spectrometer 12 can easily be held in hand, with the pushbutton 21 conveniently located for thumb operation.

(26) Many variants of the NIR spectrometer are of course possible. For instance, the incandescent bulbs 24 can be replaced with broadband light emitting diodes or LEDs. The TLP 25 can be replaced with another optical element, such as a fiber optic plate or a holographic beam shaper. The LVF 31 can be replaced with another suitable wavelength-selective element such as a miniature diffraction grating, an array of dichroic mirrors, a MEMS device, etc.

(27) Referring to FIG. 4A with further reference to FIG. 1, a method 40 for field authentication of the seafood sample 11 includes a step 41 of providing the portable NIR spectrometer 12 described above. In a step 42, the reflection spectrum 13 of the seafood sample 11 is obtained using the NIR spectrometer 12. In a step 43, a multivariate pattern recognition analysis of the reflection spectrum 13 of the seafood sample 11 is performed to determine a matching spectrum with a most similar spectral pattern by comparing the reflection spectrum 13 to a library of known identity spectra corresponding to different species of seafood. Finally, in a step 44, the seafood sample 11 is identified based on the matching spectrum bearing the most similar spectral pattern determined in the previous step 43.

(28) Herein, the term matching spectrum does not of course denote an exact match. Instead, it denotes an identity spectrum of the library, carrying the most similar spectral pattern, as compared to the measured reflection spectrum 13. Thus, the match does not have to be exact, only the closest match of those available. The proximity of the match can be calculated based on the particular matching evaluation method used.

(29) The multivariate pattern recognition analysis 43 is performed to extract seafood species information from the reflection spectrum 13. Due to multitude of overtones of vibrational frequencies of characteristic molecular bonds, the reflection spectrum 13 can be very complex, so that individual spectral peaks cannot be visually identified. According to the invention, the multivariate pattern recognition analysis 43, also known as chemometric analysis, is performed to identify or authenticate species of the seafood sample 11.

(30) The measuring step 42 preferably includes performing repetitive spectral measurements at different locations on the seafood sample 11, and averaging the repetitive measurements, to lessen a dependence of the obtained reflection spectrum on a texture of the seafood sample 11. Extended Multiplicative Scatter Correction (EMSC) of the reflection spectrum 13 can be used to reduce dependence of the measured reflection spectrum 13 on scattering properties of the seafood sample 11.

(31) The reflection spectrum 13 can also be pre-processed using other known statistical methods, e.g. a Standard Normal Variation (SNV) of the reflection spectrum 13 can be computed before proceeding to the multivariate pattern recognition analysis step 43. The slope and/or inflection of the spectral features in the reflection spectrum 13 can be accounted for by performing Savitzky-Golay filtering of the reflection spectrum 13, and computing a first and/or second derivative of the reflection spectrum 13 to be accounted for in the multivariate pattern recognition analysis step 43. Other statistical methods, such as sample-wise normalization and/or channel-wise auto-scaling of the reflection spectrum 13, can be used to facilitate the multivariate pattern recognition analysis step 43, and to provide more stable results.

(32) The multivariate pattern recognition analysis 43 is usually performed in two stages. By way of example, referring to FIG. 4B with further reference to FIG. 1, a PCA step 45 is performed at first, to define a calibration model for each seafood type that needs to be identified. The PCA step 45 can be done in advance, before measuring the seafood sample 11, at a calibration stage of the apparatus 10. In a second step 46, similarities between the collected reflection spectrum 13 and the calibration models of different seafood species are analyzed. In the embodiment shown, soft independent modeling of class analogies (SIMCA) is used. As a result of the SIMCA step 46, two parameters are determined. These two parameters are plotted in a XY plot (Coomans plot), different areas of which correspond to different seafood species. Only one parameter is required in some cases, and this parameter can be compared to a threshold determined in the PCA step 45, to authenticate the seafood sample 11. Other multivariate pattern recognition analysis methods can be applied. Examples of these methods are considered below in the Experimental Verification section.

(33) In view of proliferation of computerized mobile communication devices such as smartphones, it is advantageous to use a mobile communication device to perform the multivariate pattern recognition analysis step 43 (FIGS. 4A and 4B). Referring to FIG. 5A with further reference to FIGS. 1 and 4A, an apparatus 50A for field authentication of the seafood sample 11 is similar to the apparatus 10 of FIG. 1. One difference is that in the apparatus 50A of FIG. 5A, a mobile communication device 54 is configured to perform the multivariate analysis step 43 and the identification step 44 of the method 40 of FIG. 4A. To that end, the mobile communication device 54 can include a non-transitory storage medium 58 having encoded thereon the library of the known identity spectra corresponding to different species of seafood, and/or computer instructions for performing the multivariate pattern recognition/data reduction analysis step 43. The mobile communication device 54 can be coupled to the NIR spectrometer 12 via a wireless link 59 such as Bluetooth, or via a wired e.g. USB communication, for communicating the obtained reflection spectrum 13 to the mobile communication device 54.

(34) Turning now to FIG. 5B with further reference to FIGS. 4A and 5A, an apparatus 50B for field authentication of a seafood sample is similar to the apparatus 50A of FIG. 5A. The apparatus 50B of FIG. 5B includes a remote server 57 in communication with the mobile communication device 54 via an RF communication link 56 to a cell tower 55 connected to the Internet 52. In operation, the reflection spectrum 13 is communicated from the mobile device 54 to the remote server 57, and the multivariate pattern recognition analysis, i.e. the step 43 of the method 40 of FIG. 4A, is performed at the remote server 57. The result of the multivariate analysis step 43 (FIG. 4A) is communicated back to the mobile device 54 (FIG. 5B) for displaying to a user, not shown. The identification step 44 (FIG. 4A) can be performed either by the mobile device 54 or by the remote server 57 (FIG. 5B). Using the computational power of a remote server frees up the resources on the mobile communication device, and as a result can speed up the overall process of seafood identification.

(35) Experimental Verification

(36) A number of experiments were performed to verify that similarly looking, but differently priced fish species can be identified using a combination of NIR spectroscopy and multivariate regression (chemometric) analysis. Referring to FIGS. 6 to 8, three sets of different fish species were used. The first set included a whole red mullet 60A and a whole mullet 60B (FIG. 6), both skin and meat (the meat is not shown). The second set included: winter codfish skin 71A; codfish skin 71B; winter codfish meat 72A; and codfish meat 72B. The third set included: samlet skin 81A; salmon trout skin 81B; samlet meat 82A; and salmon trout meat 82B. As can be seen from the photos of FIGS. 6 to 8, even for a seafood professional such as a merchant or a cook, let alone a general public customer, the visual discrimination of the whole fish and the fish filets would be rather challenging. In FIGS. 6 to 8, the A group includes more expensive species 60A, 71A, 72A, 81A, and 82A, and the B group includes less expensive species 60B, 71B, 72B, 81B, and 82B. Thus, substitution of A species with B species can provide a substantial economic benefit.

(37) Turning to FIG. 9, an apparatus 90 used in the experimental verification of the invention included MicroNIR 1700 spectrometer 92 manufactured by JDS Uniphase Corporation, Milpitas, Calif., USA. The MicroNIR spectrometer 92 was operated in a wavelength range of 950 nm to 1650 nm. The MicroNIR spectrometer 92 is a low-cost, ultra-compact portable spectrometer that weighs 60 grams and is less than 50 mm in diameter. The spectrometer 92 operates in a diffuse reflection and is constructed similarly to the spectrometer 12 of FIG. 3B, including a light source (not shown) for illuminating the seafood sample 11, the dispersing element 31, the photodetector array 37, and electronics (not shown), which are all contained in a small portable package that can be placed directly on a seafood sample 91. The spectrometer 92 is connected by a cable 95 to a laptop computer 94 running Unscrambler multivariate analysis software provided by CAMO AS, Oslo, Norway (version 9.6). For each spectral measurement, 50 scans having integration times of 5 milliseconds have been accumulated, resulting in a total measurement time of 0.25 seconds per reflection spectrum measurement.

(38) Referring now to FIGS. 10A and 10B, flow charts 100A and 100B represent spectra acquisition and PCA model building steps performed for the fish samples 60A and 60B; 71A and 71B; 72A and 72B; 81A and 81B; and 82A and 82B, respectively. In steps 101A and 101B, three different individual pieces were provided for each fish sample 60A and 60B; 71A and 71B; 72A and 72B; 81A and 81B; 82A and 82B, respectively, of FIGS. 6 to 8. For mullets 60A and 60B; winter codfish/codfish 71A and 71B; 72A and 72B, and samlet/salmon trout 81A and 81B; 82A and 82B pairs, the skin reflection spectra were collected in steps 102A and 102B, respectively; and the meat reflection spectra were collected in steps 103A and 103B, respectively. A total of ten NIR reflection spectra were obtained at different positions on each of the three pieces, resulting in thirty measurements for each fish sample 60A; 60B; 71A; 71B; 72A; 72B; 81A; 81B; 82A; and 82B of FIGS. 6 to 8. The spectra were corrected for scattering using a standard method of extended multiplicative scatter correction.

(39) Thus, the total of thirty spectra have been obtained for each fish skin type 60A and 60B; 71A and 71B; 81A and 81B in steps 104A and 104B, respectively. The total of thirty spectra have been obtained for each fish meat type 72A and 72B; 82A and 82B in steps 105A and 105B, respectively. The spectra have been averaged in groups of five for each of the three samples of each type in respective steps 106A, 107A; and 106B, 107B, resulting in two averaged spectra for each sample, and six averaged spectra for each sample type, including skin and meat. The averaging was done to lessen a dependence of the obtained reflection spectrum on a texture of respective the seafood samples 60A; 60B; 71A; 71B; 72A; 72B; 81A; 81B; 82A; and 82B. Then, PCA models have been established in steps 108A, 108B for the respective A and B samples. A SIMCA analysis was performed to identify the type of each fish sample. The results were presented in form of Coomans plots for each fish type.

(40) Red Mullet/Mullet Pair

(41) Referring to FIG. 11 with further reference to FIG. 6, reflection spectra of the red mullet 60A and mullet 60B are shown as dependence of reflection signal in arbitrary units on the wavenumber in inverse centimeters (cm.sup.1), in the range between 10900 to 6000 cm.sup.1. Twelve traces including six spectra of red mullet skin and the six spectra of mullet skin are shown at 111. Twelve traces including the respective six spectra of red mullet meat and six spectra of mullet meat are shown at 112. One can see that the spectra 111 of red mullet and mullet skin are quite similar to each other, and the spectra 112 of red mullet and mullet meat are quite similar to each other as well, so visually the spectra of red mullets cannot be differentiated from the spectra of mullets, for both skin and meat.

(42) Turning to FIG. 12 with further reference to FIGS. 10A and 10B, the results of the PCA analysis steps 108A, 108B (FIG. 10B) are presented. In FIG. 12, red mullet skin score points 121A are sufficiently separated from mullet skin score points 121B to allow easy identification, but no clear separation was achieved between red mullet meat score points 122A and mullet meat score points 122B.

(43) Referring now to FIGS. 13A and 13B, results of SIMCA analysis of red mullet/mullet pair are presented in form of Coomans plots at 5% significance. FIG. 13A shows results of red mullet sample identification. Gray-colored circles 131A represent calibration red mullet samples, skin and meat, used to obtain the identity spectra of red mullet; white-filled circles 131B represent calibration mullet samples, skin and meat, used to obtain the identity spectra of mullet; and filled (black) circles 132 represent the test sample. The total of four black circles correspond to one red mullet skin sample and one red mullet meat samples, each represented by two averaged spectra. FIG. 13B shows results of mullet sample identification. Filled (black) circles 133 represent two test samples. The total of eight black circles 133 correspond to two mullet skin samples and two mullet meat samples, each represented by two averaged spectra as explained above.

(44) Only one of the two parameters Distance to Red Mullet and Distance to Mullet can be used by comparing the parameter to a threshold. For example, if Distance to Mullet is used, the threshold is about 0.01. If Distance to Red Mullet is used, the threshold is approximately 0.0008. One can see from FIGS. 13A and 13B that red mullet, both skin and meat, are both readily identifiable. Thus, removing skin of the fish sample would not allow a potential wrongdoer to hide an illegal act of substituting red mullet with mullet.

(45) Winter Cod/Cod Pair

(46) Referring to FIG. 14 with further reference to FIG. 7, reflection spectra of the winter cod skin 71A, winter cod meat 72A, cod skin 71B, and cod meat 72B (FIG. 7) are shown as dependence of reflection signal in arbitrary units on the wavenumber in inverse centimeters (cm.sup.1), in the range between 10900 to 6000 cm.sup.1. Twelve traces including the six spectra of winter cod skin and the six spectra of cod skin are shown at 141. Twelve traces including the respective six spectra of winter cod meat and six spectra of cod meat are shown at 142. One can see that the spectra 141 of winter cod and cod skin are quite similar to each other, and the spectra of winter cod and cod meat are also very similar, so visually the spectra of winter cod cannot be differentiated from the spectra of cod, for both skin and meat samples.

(47) Turning to FIG. 15 with further reference to FIGS. 10A and 10B, the results of the PCA analysis steps 108A, 108B (FIG. 10B) are presented. In FIG. 15, winter cod skin score points 151A appear interspersed with cod skin score points 151B, and winter cod meat score points 152A appear interspersed with cod meat score points 152B, so no clear distinction can be made at this stage.

(48) Referring now to FIGS. 16A and 16B, results of SIMCA analysis of winter cod/cod pair are presented in form of Coomans plots at 5% significance. FIG. 16A shows results of cod sample identification. Gray-colored circles 161A represent calibration winter cod samples, both skin and meat, used to obtain the identity spectra of winter cod; white-filled circles 161B represent calibration cod samples, both skin and meat, used to obtain the identity spectra of cod; and filled (black) circles 162 represent the test sample. The total of eight black circles correspond to two cod skin samples and two cod meat samples, each represented by two averaged spectra as explained above. FIG. 16B shows results of winter cod sample identification. Filled (black) circles 163 represent one test sample. The total of four black circles 163 correspond to one winter cod skin sample and one winter cod meat sample, each represented by two averaged spectra. One can see from FIGS. 16A and 16B that winter cod, both skin and meat, is readily identifiable and distinguishable from cod.

(49) Samlet/Salmon Pair

(50) Referring to FIG. 17 with further reference to FIG. 8, reflection spectra of the samlet skin 81A, samlet meat 82A, salmon trout skin 81B, and salmon trout meat 82B are shown as dependence of reflection signal in arbitrary units on the wavenumber in inverse centimeters (cm.sup.1), in the range between 10900 to 6000 cm.sup.1. Twelve traces including the six spectra of samlet skin and the six spectra of salmon trout skin are shown at 171. Twelve traces including the respective six spectra of samlet meat and six spectra of salmon trout meat are shown at 172. One can see that the skin spectra 171 of samlet and salmon trout are quite similar to each other, and the meat spectra 172 of samlet and salmon trout are also very similar, so visually the spectra of samlet cannot be differentiated from the spectra of salmon trout, for both skin and meat samples.

(51) Turning to FIG. 18 with further reference to FIGS. 10A and 10B, the results of the PCA analysis steps 108A, 108B (FIG. 10B) are presented. In FIG. 18, samlet skin score points 181A appear interspersed with salmon trout skin score points 181B, and samlet meat score points 182A appear interspersed with salmon trout meat score points 182B, so that no clear distinction can be made at this stage.

(52) Referring now to FIGS. 19A and 19B, results of SIMCA analysis of samlet/salmon trout are presented in form of Coomans plots at 5% significance. FIG. 19A shows results of salmon trout sample identification. Gray-colored circles 191A represent calibration samlet samples, both skin and meat, used to obtain the identity spectra of samlet; white-filled circles 191B represent calibration salmon trout samples, both skin and meat, used to obtain the identity spectra of salmon trout; and filled (black) circles 192 represent the test sample. The total of eight black circles correspond to two salmon trout skin samples and two salmon trout meat samples, each represented by two averaged spectra. FIG. 19B shows results of samlet sample identification. Filled (black) circles 193 represent two test samples. The total of four black circles 193 correspond to two samlet skin samples and two samlet meat samples, each represented by two averaged spectra. One can see from FIGS. 19A and 19B that samlet, both skin and meat, is readily identifiable and distinguishable from salmon trout.

(53) Meerbarbe Filets Freshness

(54) A numerical study of reflection spectra of meerbarbe filets has been performed, in which various known multivariate analysis methods were used to differentiate between meerbarbe filet (both skin and skinless meat) freshness conditions.

(55) Table 1 below summarizes successful prediction rate with alternate matching methods of the mullet and red mullet performed on a typical desktop computer. The spectra were auto-scaled before being sent to multivariate pattern classifiers. The last column of Table 1 provides the time it takes to build the predictive models. The time to perform prediction based on existing models are typically in the range of milliseconds. The time to build model can become important factors when one needs to do in-situ models updating. In field, point-of-use applications, the speed of measurement and the speed of obtaining the results are important to be as short as possible. In addition, the accuracy of the results is important. From Table 1, one can see that methods such as SVM (with linear kernel) provide the best accuracy at the shortest time.

(56) TABLE-US-00001 TABLE 1 Prediction Models Method Name Success Rate building Time Naive Bayes classifier 83.3% <0.1 sec Classification and Regression .sup.75% <0.1 sec Trees (CART) TreeBagger implementation of 83.3% 0.3 sec bagged decision trees LIBLINEAR linear classifier 81.7% <0.1 sec Support Vector Machine (SVM) 93.3% <0.1 sec with Linear Kernel Support Vector Machine Radial 81.7% <0.1 sec Basis Function (SVM-RBF) Linear Discriminant Analysis .sup.85% <0.1 sec (LDA) Quadratic Discriminant Analysis .sup.85% <0.1 sec (QDA) Partial Least Squares Discriminant 86.7% .sup.44 sec Analysis (PLS-DA) SIMCA 88.3% .sup.1 sec

(57) Below, the numerical methods of Table 1 are discussed only briefly, since the methods themselves are known in the art. Each of the methods has its advantages. In the Nave Bayes method, it is assumes that all features are independent on each other, and the results can be easily interpreted. The CART method is also easy to understand and interpret; however, trees created from numeric datasets can be complex, and the method tends to have over-fitting problems. The TreeBagger Analysis and Random Forest Analysis methods usually gave very good results, and the training step of the method was relatively quick. LIBLINEAR method was very efficient in distinguishing seafood species and conditions. The SVM method with Linear Kernel, including Support Vector Classification (SVC) for qualitative analysis, and Support Vector Regression (SVR) for quantitative analysis, resulted in the prediction success rate of over 93%. In LDA method, it is assumed that all classes have identical covariance matrix and are normally distributed, and Discriminant functions are always linear. In QDA method, the classes do not necessarily have identical covariance matrix, but the normal distribution is still assumed. Partial Least Square (PLS) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of minimum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Partial least squares Discriminant Analysis (PLS-DA) is a variant used when the Y is categorial. PLS-DA methods resulted in moderate prediction rates of 85-87%.

(58) The results show that NaiveBayes, TreeBagger, SVM-linear, LDA, QDA, PLS-DA, and SIMCA can be used in the multivariate analysis for the purpose of correlating the NIR reflection spectra with seafood samples. First and second derivatives of the obtained spectra can also be used in place of, or in addition to the pretreatments of spectra, as an input data strings for the multivariate analysis.

(59) The hardware used to implement the various illustrative logics, logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but, in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Alternatively, some steps or methods may be performed by circuitry that is specific to a given function.

(60) The foregoing description of one or more embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of the invention be limited not by this detailed description, but rather by the claims appended hereto.