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.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    The present invention claims priority from U.S. Patent Application No. 61/804,106 filed Mar. 21, 2013, which is incorporated herein by reference. 
     
    
     TECHNICAL FIELD 
       [0002]    The present invention relates to materials characterization and identification, and in particular to spectroscopic characterization of seafood. 
       BACKGROUND OF THE INVENTION 
       [0003]    A recently published report on one of the largest surveys conducted to date about seafood fraud revealed that one third of seafood species purchased at restaurants and grocery stores in cities across the United States were mislabeled. The study was conducted by Oceana, a non-profit international advocacy group, over a period of 2 years from 2010-2012, whereby over 1200 samples were collected from 674 retail outlets in 21 US states (K. Warner, W. Timme, B. Lowell, and M. Hirshfield, “Oceana Study Reveals Seafood Fraud Nationwide”, February 2013 report). DNA testing was performed on fish samples to correctly identify the fish species and uncover mislabeling. Similar conclusions could be drawn from a previous US Congressional Research Service Report regarding combating fraud and deception in seafood marketing (Congressional Research Service Report for Congress, 7-5700, www.crs.gov, RL-34124 (2010)). 
         [0004]    Substitution of a more expensive fish by a lower-cost species is illegal. It is motivated by monetary gains by perpetrators leading to negative economic, health, and environmental consequences. Consumers and honest seafood suppliers are cheated into paying higher prices for lower-cost, less-desirable substitutes. One of most commonly substituted and more expensive fish is red snapper often swapped for tilapia. Furthermore, some fish substitutes pose health hazards. For example, the above Oceana study has determined that over 90% of what is advertised as white tuna was actually escolar, which is a snake mackerel species containing toxins known to cause gastrointestinal problems. Lastly, some substituted fish may be of an overfished or threatened species. One such fish is the Atlantic cod, which was found to be swapped for Pacific cod in the same study. 
         [0005]    The supply chain “from boat to plate” is complex and unregulated, making such illegal activities difficult to track. Combating fish fraud requires traceability of fish supply across the entire supply chain, as well as and increased inspection. DNA testing for inspection is time consuming and can only be done on a sampling basis. The DNA testing requires taking samples of fish to a lab and waiting for results, —a process that can take days. 
         [0006]    Wong in U.S. Pat. No. 5,539,207 discloses a method of identifying human or animal tissue by Fourier Transform Infrared (FT-IR) spectroscopy. A mid-infrared spectrum of a tissue in question is measured and compared to a library of infrared spectra of known tissues, to find a closest match. Either a visual comparison, or a pattern recognition algorithm can be used to match the infrared spectra. In this way, various tissues, and even normal or malignant (e.g. cancerous) tissues can be identified. 
         [0007]    Detrimentally, the method of Wong is difficult to use for the purpose of seafood identification in field conditions. An FT-IR spectrometer is a complex and bulky optical device. Its core module, a scanning Michelson interferometer, uses a precisely movable large optical mirror to perform a wavelength scan. To stabilize the mirror, a heavy optical bench is used. Due to many precision optical and mechanical components, an FT-IR spectrometer requires laboratory conditions, and needs to be re-calibrated and re-aligned frequently by trained personnel. The use of an FT-IR spectrometer is dictated by the fact that the fundamental vibrational frequencies of the infrared fingerprint are present in the 2.5 to 5 micrometers region of the electromagnetic spectrum. These vibrational bands are of high resolution and high absorption levels, showing strong absorption with narrow spectral bands. 
         [0008]    Monro in U.S. Pat. No. 7,750,299 discloses a system for active biometric spectroscopy, in which a DNA film of a particular biological subject is irradiated by a frequency-tunable millimeter-wave radio transmitter, and radio waves transmitted and scattered by the DNA film are detected. Monro teaches that radio wave scattering spectra of different DNA films are different. Therefore, transmitted or scattered radio wave spectrum can detect different DNA films, which can be associated with different fish species. In this way, species of a fish sample can be identified. 
         [0009]    Detrimentally, the method of Monro cannot be applied to the fish samples themselves, because the signal from non-DNA tissues will overwhelm the DNA signal. Because of this, DNA of the fish samples have to be extracted and formed into a film. The sample preparation is time-consuming, and can only be done in lab conditions. 
         [0010]    Cole et al. in U.S. Pat. No. 7,728,296 disclose an apparatus and method for detection of explosive materials using terahertz (THz) radiation. THz radiation occupies a frequency band between infrared and millimeter radio waves. Many explosive materials have a unique spectral signature in THz frequency domain, thus affording a non-invasive, remote detection of explosives with a high sensitivity. Detrimentally, THz radiation sources are bulky and expensive, limiting their current use to security-critical applications such as at airport security checkpoints. 
         [0011]    The methods and devices of the prior art appear unsuitable for a goal of identification of seafood species in field conditions. A method and system are required that would enable a food and drug administration (FDA) official perform a quick on-the-spot seafood species identification and characterization, assisting the official in deciding whether to take a law enforcement action. Private persons, such as restaurant chefs, sushi bar patrons, and fish market customers, would also benefit from a possibility to quickly verify seafood species being purchased. 
       SUMMARY OF THE INVENTION 
       [0012]    It is a goal of the invention to provide a method and apparatus for field spectroscopic characterization of seafood. 
         [0013]    From the technology standpoint, it is preferable to perform spectroscopic measurements in wavelength bands that afford easy generation, wavelength separation, and detection of electromagnetic radiation. A near infrared (NIR) band, e.g. between 0.7 and 2.5 micrometers, satisfies this condition. Broadband light emitting diodes and even miniature incandescent sources can be used for generation of NIR light in this wavelength band. A variety of spectrally selective elements, e.g. thin-film interference filters, are available for wavelength separation. Photodiode arrays are available for detection of NIR light. 
         [0014]    Despite the convenience of working in the NIR part of the spectrum, the prior art has been largely focusing on longer, less technology-friendly wavelength bands, because main vibrational frequencies of characteristic molecular bonds of most organic compounds correspond to wavelengths longer than 2.5 micrometers (2500 nm), necessitating the use of heavy and bulky equipment to generate, wavelength-disperse, and detect electromagnetic radiation at these longer wavelengths. The inventors have realized that the multiples of the vibrational frequencies, or so called overtones, do fall within the technology-convenient NIR band and, therefore, biological substance identification information is present in the NIR spectra, although this information is hidden due to a relatively low amplitude and multiple frequencies of the overtones. 
         [0015]    When spectroscopic information is not readily available or visually identifiable from a spectrum, advanced data processing and feature or pattern extraction and modeling techniques, such as Principle Component Analysis (PCA), Soft Independent Modeling of Class Analogy (SIMCA), Partial Least Square Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM), can be used to extract the required information. Therefore, the multivariate pattern recognition and data regression enables the use of a lightweight and compact NIR spectrometer for identification and characterization of seafood species. 
         [0016]    In accordance with the invention, there is provided a method for field authentication of a seafood sample, comprising: 
         [0017]    (a) providing a portable NIR spectrometer; 
         [0018]    (b) obtaining a reflection spectrum of the seafood sample using the NIR spectrometer of step (a); 
         [0019]    (c) performing a multivariate pattern recognition analysis of the reflection spectrum of the seafood sample obtained in step (b) to determine a matching spectrum with a most similar spectral pattern by comparing the reflection spectrum to a library of known identity spectra corresponding to different species of seafood; and 
         [0020]    (d) identifying the seafood sample based on the matching spectrum bearing the most similar spectral pattern determined in step (c). 
         [0021]    These pattern recognition algorithms can also generate a confidence measure, or a probability estimate, of a likelihood of the identification result. 
         [0022]    In accordance with the invention, there is further provided a method for field determination of freshness of a seafood sample, comprising: 
         [0023]    (a) providing a portable NIR spectrometer; 
         [0024]    (b) obtaining a reflection spectrum of the seafood sample using the NIR spectrometer of step (a); 
         [0025]    (c) performing a multivariate pattern recognition analysis of the reflection spectrum of the seafood sample obtained in step (b) to determine a matching spectrum with a most similar spectral pattern by comparing the reflection spectrum to a library of known identity spectra corresponding to the freshness of the seafood sample, thereby providing a quantitative measure of the freshness of the seafood sample. 
         [0026]    The reflection spectrum can be obtained from a plurality of locations on the seafood sample to reduce the effect of surface texture of the seafood sample. The multivariate regression analysis can include e.g. Partial Least Square (PLS) and Support Vector Regression (SVR). 
         [0027]    In accordance with the invention, there is further provided an apparatus for field authentication of a seafood sample, comprising: 
         [0028]    a portable NIR spectrometer for obtaining a NIR reflection spectrum of the seafood sample, and 
         [0029]    an analyzer operationally coupled to the spectrometer and configured for performing a multivariate pattern recognition analysis of the reflection spectrum of the seafood samples to determine a matching spectrum with a most similar spectral pattern by comparing the reflection spectrum to a library of known identity spectra corresponding to different species of seafood, and to identify the seafood sample based on the matching spectrum bearing the most similar spectral pattern. 
         [0030]    The portable NIR spectrometer can include a spectrally laterally variable optical transmission filter coupled to a photodetector array, resulting in a particularly compact and lightweight structure. A mobile communication device can be configured to communicate with the NIR spectrometer and perform the multivariate analysis of the reflection spectra obtained by the portable NIR spectrometer. Furthermore, at least some data analysis and spectra pattern models building activities can be performed at a remote server in communication with the mobile device. 
         [0031]    In accordance with yet another aspect of the invention, there is further provided a non-transitory storage medium disposed in the mobile communication device and having encoded thereon the library of the known identity spectra. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0032]    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. 
           [0033]    Exemplary embodiments will now be described in conjunction with the drawings, in which: 
           [0034]      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; 
           [0035]      FIG. 2  is a side cross-sectional view of a portable handheld NIR spectrometer of the apparatus of  FIG. 1 ; 
           [0036]      FIG. 3A  is a side cross-sectional view of a light detection subassembly the portable NIR spectrometer of  FIG. 2 ; 
           [0037]      FIG. 3B  is a side cross-sectional view of a wavelength dispersive element used in the light detection subassembly of  FIG. 3A ; 
           [0038]      FIG. 3C  is a transmission spectrum of the wavelength dispersive element of  FIG. 3B ; 
           [0039]      FIG. 3D  is a three-dimensional view of the portable handheld NIR spectrometer of  FIG. 2 ; 
           [0040]      FIG. 4A  is a flow chart of a method for field authentication of a seafood sample according to the invention; 
           [0041]      FIG. 4B  is a flow chart of an exemplary multivariate analysis of the NIR spectra according to the invention; 
           [0042]      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; 
           [0043]      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; 
           [0044]      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 meat— FIG. 7 ); and samlet/salmon trout (skin and meat— FIG. 8 ), used in experimental verification of the invention; 
           [0045]      FIG. 9  is a color photograph of a prototype of the apparatus measuring a NIR spectrum of a salmon sample; 
           [0046]      FIGS. 10A and 10B  are flow charts of data collection and analysis for higher and lower quality seafood, respectively, used in the experimental verification; 
           [0047]      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; 
           [0048]      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 
           [0049]      FIGS. 13A , B;  16 A, B; and  19 A, 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 
       [0050]    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. 
         [0051]    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 λ) 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. 
         [0052]    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  37 A and coupled to the LVF  31  with a optically transmissive adhesive  38 . An electronics board  37 B is provided to support and control the CMOS processing chip  37 A. 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 . 
         [0053]    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 . 
         [0054]    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. 
         [0055]    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  34 A,  34 B, and  34 C pointing to individual transmission peaks  35 A,  35 B, and  35 C, 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  43 A to  34 C. 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. 
         [0056]    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. 
         [0057]    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. 
         [0058]    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 . 
         [0059]    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. 
         [0060]    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 . 
         [0061]    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 . 
         [0062]    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. 
         [0063]    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. 
         [0064]    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  50 A for field authentication of the seafood sample  11  is similar to the apparatus  10  of  FIG. 1 . One difference is that in the apparatus  50 A 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 . 
         [0065]    Turning now to  FIG. 5B  with further reference to  FIGS. 4A and 5A , an apparatus  50 B for field authentication of a seafood sample is similar to the apparatus  50 A of  FIG. 5A . The apparatus  50 B 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. 
       Experimental Verification 
       [0066]    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  60 A and a whole mullet  60 B ( FIG. 6 ), both skin and meat (the meat is not shown). The second set included: winter codfish skin  71 A; codfish skin  71 B; winter codfish meat  72 A; and codfish meat  72 B. The third set included: samlet skin  81 A; salmon trout skin  81 B; samlet meat  82 A; and salmon trout meat  82 B. 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  60 A,  71 A,  72 A,  81 A, and  82 A, and the “B” group includes less expensive species  60 B,  71 B,  72 B,  81 B, and  82 B. Thus, substitution of “A” species with “B” species can provide a substantial economic benefit. 
         [0067]    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. 
         [0068]    Referring now to  FIGS. 10A and 10B , flow charts  100 A and  100 B represent spectra acquisition and PCA model building steps performed for the fish samples  60 A and  60 B;  71 A and  71 B;  72 A and  72 B;  81 A and  81 B; and  82 A and  82 B, respectively. In steps  101 A and  101 B, three different individual pieces were provided for each fish sample  60 A and  60 B;  71 A and  71 B;  72 A and  72 B;  81 A and  81 B;  82 A and  82 B, respectively, of  FIGS. 6 to 8 . For mullets  60 A and  60 B; winter codfish/codfish  71 A and  71 B;  72 A and  72 B, and samlet/salmon trout  81 A and  81 B;  82 A and  82 B pairs, the skin reflection spectra were collected in steps  102 A and  102 B, respectively; and the meat reflection spectra were collected in steps  103 A and  103 B, 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  60 A;  60 B;  71 A;  71 B;  72 A;  72 B;  81 A;  81 B;  82 A; and  82 B of  FIGS. 6 to 8 . The spectra were corrected for scattering using a standard method of extended multiplicative scatter correction. 
         [0069]    Thus, the total of thirty spectra have been obtained for each fish skin type  60 A and  60 B;  71 A and  71 B;  81 A and  81 B in steps  104 A and  104 B, respectively. The total of thirty spectra have been obtained for each fish meat type  72 A and  72 B;  82 A and  82 B in steps  105 A and  105 B, respectively. The spectra have been averaged in groups of five for each of the three samples of each type in respective steps  106 A,  107 A; and  106 B,  107 B, 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  60 A;  60 B;  71 A;  71 B;  72 A;  72 B;  81 A;  81 B;  82 A; and  82 B. Then, PCA models have been established in steps  108 A,  108 B 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. 
       Red Mullet/Mullet Pair 
       [0070]    Referring to  FIG. 11  with further reference to  FIG. 6 , reflection spectra of the red mullet  60 A and mullet  60 B are shown as dependence of reflection signal in arbitrary units on the wavenumber in inverse centimeters (cm −1 ), in the range between 10900 to 6000 cm −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. 
         [0071]    Turning to  FIG. 12  with further reference to  FIGS. 10A and 10B , the results of the PCA analysis steps  108 A,  108 B ( FIG. 10B ) are presented. In  FIG. 12 , red mullet skin score points  121 A are sufficiently separated from mullet skin score points  121 B to allow easy identification, but no clear separation was achieved between red mullet meat score points  122 A and mullet meat score points  122 B. 
         [0072]    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  131 A represent calibration red mullet samples, skin and meat, used to obtain the identity spectra of red mullet; white-filled circles  131 B 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. 
         [0073]    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. 
       Winter Cod/Cod Pair 
       [0074]    Referring to  FIG. 14  with further reference to  FIG. 7 , reflection spectra of the winter cod skin  71 A, winter cod meat  72 A, cod skin  71 B, and cod meat  72 B ( FIG. 7 ) are shown as dependence of reflection signal in arbitrary units on the wavenumber in inverse centimeters (cm −1 ), in the range between 10900 to 6000 cm −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. 
         [0075]    Turning to  FIG. 15  with further reference to  FIGS. 10A and 10B , the results of the PCA analysis steps  108 A,  108 B ( FIG. 10B ) are presented. In  FIG. 15 , winter cod skin score points  151 A appear interspersed with cod skin score points  151 B, and winter cod meat score points  152 A appear interspersed with cod meat score points  152 B, so no clear distinction can be made at this stage. 
         [0076]    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  161 A represent calibration winter cod samples, both skin and meat, used to obtain the identity spectra of winter cod; white-filled circles  161 B 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. 
       Samlet/Salmon Pair 
       [0077]    Referring to  FIG. 17  with further reference to  FIG. 8 , reflection spectra of the samlet skin  81 A, samlet meat  82 A, salmon trout skin  81 B, and salmon trout meat  82 B are shown as dependence of reflection signal in arbitrary units on the wavenumber in inverse centimeters (cm −1 ), in the range between 10900 to 6000 cm −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. 
         [0078]    Turning to  FIG. 18  with further reference to  FIGS. 10A and 10B , the results of the PCA analysis steps  108 A,  108 B ( FIG. 10B ) are presented. In  FIG. 18 , samlet skin score points  181 A appear interspersed with salmon trout skin score points  181 B, and samlet meat score points  182 A appear interspersed with salmon trout meat score points  182 B, so that no clear distinction can be made at this stage. 
         [0079]    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  191 A represent calibration samlet samples, both skin and meat, used to obtain the identity spectra of samlet; white-filled circles  191 B 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. 
       Meerbarbe Filets Freshness 
       [0080]    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. 
         [0081]    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. 
         [0000]    
       
         
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                   
                 Prediction 
                 Models 
               
               
                 Method Name 
                 Success Rate 
                 building Time 
               
               
                   
               
             
             
               
                 Naive Bayes classifier 
                 83.3% 
                 &lt;0.1 sec 
               
               
                 Classification and Regression 
                     75% 
                 &lt;0.1 sec 
               
               
                 Trees (CART) 
               
               
                 TreeBagger implementation of 
                 83.3% 
                  0.3 sec 
               
               
                 bagged decision trees 
               
               
                 LIBLINEAR linear classifier 
                 81.7% 
                 &lt;0.1 sec 
               
               
                 Support Vector Machine (SVM) 
                 93.3% 
                 &lt;0.1 sec 
               
               
                 with Linear Kernel 
               
               
                 Support Vector Machine Radial 
                 81.7% 
                 &lt;0.1 sec 
               
               
                 Basis Function (SVM-RBF) 
               
               
                 Linear Discriminant Analysis 
                     85% 
                 &lt;0.1 sec 
               
               
                 (LDA) 
               
               
                 Quadratic Discriminant Analysis 
                     85% 
                 &lt;0.1 sec 
               
               
                 (QDA) 
               
               
                 Partial Least Squares Discriminant 
                 86.7% 
                     44 sec 
               
               
                 Analysis (PLS-DA) 
               
               
                 SIMCA 
                 88.3% 
                      1 sec 
               
               
                   
               
             
          
         
       
     
         [0082]    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 Naïve 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%. 
         [0083]    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. 
         [0084]    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. 
         [0085]    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.