Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat

Abstract In this study, the reliability and accuracy of hyperspectral imaging technique in tandem with multivariate analyses were investigated for identification and authentication of different red meat species. Hyperspectral images were acquired from longissimus dorsi muscle of pork, beef and lamb and their spectral data were extracted and analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for recognition and authentication of the tested meat. The spectra were pre-treated by second derivative and six wavelengths (957, 1071, 1121, 1144, 1368 and 1394 nm) were identified as important wavelengths from the 2nd derivative spectra. The resulting wavelengths were used in a pattern recognition algorithms for classification of meat samples with PLS-DA yielding 98.67% overall classification accuracy in the validation sets. The developed classification algorithms were then successfully applied in the independent testing set for the authentication of minced meat. The results clearly showed that the combination of hyperspectral imaging, multivariate analysis and image processing has a great potential as an objective and rapid method for identification and authentication of red meat species. Industrial Relevance This study was carried out to investigate the potential of NIR hyperspectral imaging system for identification and authentication of red meat species for the meat industry.

[1]  I. Murray,et al.  The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes. , 2008, Meat science.

[2]  W. R. Windham,et al.  Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm , 2007 .

[3]  K. Heia,et al.  Detection of nematodes in cod (Gadus morhua) fillets by imaging spectroscopy. , 2007, Journal of food science.

[4]  Karl McDonald,et al.  The effect of injection level on the quality of a rapid vacuum cooled cooked beef product , 2001 .

[5]  Da-Wen Sun,et al.  Comparison of the performances of NH3-H2O, NH3-LiNO3 and NH3-NaSCN absorption refrigeration systems , 1998 .

[6]  David Casasent,et al.  Hyperspectral waveband selection for contaminant detection on poultry carcasses , 2008 .

[7]  Di Wu,et al.  Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM , 2008 .

[8]  Chris D. Clark,et al.  Mapping and measurement of tropical coastal environments with hyperspectral and high spatial resolution data , 1997 .

[9]  Da-Wen Sun,et al.  Modelling vacuum cooling process of cooked meat: part 1: analysis of vacuum cooling system , 2002 .

[10]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

[11]  J. Barat,et al.  Nondestructive assessment of freshness in packaged sliced chicken breasts using SW-NIR spectroscopy , 2011 .

[12]  Da-Wen Sun,et al.  Modelling vacuum cooling process of cooked meat—part 2: mass and heat transfer of cooked meat under vacuum pressure , 2002 .

[13]  Ronald G. Resmini,et al.  Mineral mapping with HYperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, U.S.A. , 1997 .

[14]  Da-Wen Sun,et al.  Effect of evacuation rate on the vacuum cooling process of a cooked beef product , 2001 .

[15]  J. Aguilera,et al.  Computer Vision and Stereoscopy for Estimating Firmness in the Salmon (Salmon salar) Fillets , 2010 .

[16]  Yidan Bao,et al.  Visible/Near-Infrared Spectra for Linear and Nonlinear Calibrations: A Case to Predict Soluble Solids Contents and pH Value in Peach , 2011 .

[17]  Gamal ElMasry,et al.  Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system , 2011 .

[18]  Aoife A. Gowen,et al.  Data handling in hyperspectral image analysis , 2011 .

[19]  Gauri S. Mittal,et al.  Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network , 2010 .

[20]  Gerald W. Heitschmidt,et al.  Detection of Ingesta on Pre-Chilled Broiler Carcasses by Hyperspectral Imaging , 2005 .

[21]  Da-Wen Sun,et al.  Pizza quality evaluation using computer vision: Part 1. Pizza base and sauce spread , 2003 .

[22]  G. Ripoll,et al.  Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. , 2008, Meat science.

[23]  Aiguo Ouyang,et al.  Improvement of Near-Infrared Spectral Calibration Models for Brix Prediction in ‘Gannan’ Navel Oranges by a Portable Near-Infrared Device , 2010, Food and Bioprocess Technology.

[24]  Franco Pedreschi,et al.  Color of Salmon Fillets By Computer Vision and Sensory Panel , 2010 .

[25]  Da-Wen Sun,et al.  Recent applications of image texture for evaluation of food qualities—a review , 2006 .

[26]  A. Peirs,et al.  Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .

[27]  Fred Godtliebsen,et al.  Ridge detection with application to automatic fish fillet inspection , 2009 .

[28]  Pengcheng Nie,et al.  Determination of Calcium Content in Powdered Milk Using Near and Mid-Infrared Spectroscopy with Variable Selection and Chemometrics , 2012, Food and Bioprocess Technology.

[29]  W. R. Windham,et al.  CALIBRATION OF A PUSHBROOM HYPERSPECTRAL IMAGING SYSTEM FOR AGRICULTURAL INSPECTION , 2003 .

[30]  Jordi Coello,et al.  Application of near infrared spectral fingerprinting and pattern recognition techniques for fast identification of Eleutherococcus senticosus , 2011 .

[31]  Da-Wen Sun,et al.  Recent developments in the applications of image processing techniques for food quality evaluation , 2004 .

[32]  Federico Pallottino,et al.  Image Analysis Techniques for Automated Hazelnut Peeling Determination , 2010 .

[33]  Jimmy T Keeton,et al.  Rapid determination of moisture and fat in meats by microwave and nuclear magnetic resonance analysis--PVM 1:2003. , 2003, Journal of AOAC International.

[34]  Da-Wen Sun,et al.  Performance characteristics of HCFC-123 ejector refrigeration cycles , 1996 .

[35]  R A Sweeney,et al.  Comparison of LECO FP-228 "nitrogen determinator" with AOAC copper catalyst Kjeldahl method for crude protein. , 1987, Journal - Association of Official Analytical Chemists.

[36]  Daniel Cozzolino,et al.  Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy , 2006 .

[37]  G. Shaw,et al.  Signal processing for hyperspectral image exploitation , 2002, IEEE Signal Process. Mag..

[38]  Gamal ElMasry,et al.  Application of NIR hyperspectral imaging for discrimination of lamb muscles , 2011 .

[39]  R. Lametsch,et al.  Analytical methods for authentication of fresh vs. thawed meat - A review. , 2008, Meat science.

[40]  Angelo Zanella,et al.  Supervised Multivariate Analysis of Hyper-spectral NIR Images to Evaluate the Starch Index of Apples , 2009 .

[41]  Chun-Chieh Yang,et al.  Development of multispectral image processing algorithms for identification of wholesome, septicemic, and inflammatory process chickens ☆ , 2005 .

[42]  N. Z. Ballin.  Authentication of meat and meat products. , 2010, Meat science.

[43]  Gamal ElMasry,et al.  High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. , 2008, Journal of agricultural and food chemistry.

[44]  Kurt C. Lawrence,et al.  Improved Hyperspectral Imaging System for Fecal Detection on Poultry Carcasses , 2007 .

[45]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[46]  Gamal Elmasry,et al.  Near-infrared hyperspectral imaging for grading and classification of pork. , 2012, Meat science.

[47]  Da-Wen Sun,et al.  Meat Quality Assessment Using a Hyperspectral Imaging System , 2010 .

[48]  Andrew R. Harvey,et al.  Hyperspectral imaging for the detection of retinal disease , 2002, SPIE Optics + Photonics.

[49]  Rakesh K. Singh,et al.  Application of Artificial Neural Networks to Predict the Oxidation of Menhaden Fish Oil Obtained from Fourier Transform Infrared Spectroscopy Method , 2011 .

[50]  Jens Petter Wold,et al.  Atlantic Salmon Average Fat Content Estimated by Near‐Infrared Transmittance Spectroscopy , 1996 .

[51]  N. Prieto,et al.  Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples. , 2008, Meat science.

[52]  Ashok Samal,et al.  Visible/near-infrared hyperspectral imaging for beef tenderness prediction , 2008 .

[53]  N. Prieto,et al.  Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. , 2006, Meat science.

[54]  S. Prasher,et al.  Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. , 2007, Meat science.

[55]  Fei Liu,et al.  Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration , 2011 .

[56]  Satha Aphornratana,et al.  Evaluation of a novel combined ejector-absorption refrigeration cycle — I: computer simulation , 1996 .

[57]  Da-Wen Sun,et al.  Recent developments and applications of image features for food quality evaluation and inspection – a review , 2006 .

[58]  M. Dugan,et al.  Near infrared reflectance spectroscopy predicts the content of polyunsaturated fatty acids and biohydrogenation products in the subcutaneous fat of beef cows fed flaxseed. , 2012, Meat science.

[59]  Moon S. Kim,et al.  Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. , 2004, Applied optics.

[60]  D. Bertrand,et al.  Application of PLS‐DA in multivariate image analysis , 2006 .

[61]  S. Prasher,et al.  Pork quality and marbling level assessment using a hyperspectral imaging system , 2007 .

[62]  Gamal ElMasry,et al.  Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging , 2011 .

[63]  R. Bro,et al.  Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches. , 2008, Journal of pharmaceutical and biomedical analysis.

[64]  Da-Wen Sun,et al.  Learning techniques used in computer vision for food quality evaluation: a review , 2006 .

[65]  K. Honikel,et al.  Reference methods for the assessment of physical characteristics of meat. , 1998, Meat science.

[66]  Da-Wen Sun,et al.  Comparative study of the performance of an ejector refrigeration cycle operating with various refrigerants , 1999 .

[67]  Moon S. Kim,et al.  Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging , 2007 .

[68]  Daniel Cozzolino,et al.  Identification of animal meat muscles by visible and near infrared reflectance spectroscopy , 2004 .

[69]  T. Kenny,et al.  Effect of rapid and conventional cooling methods on the quality of cooked ham joints. , 2000, Meat science.

[70]  Da-Wen Sun,et al.  Inspecting pizza topping percentage and distribution by a computer vision method , 2000 .

[71]  G. Nychas,et al.  Rapid monitoring of the spoilage of minced beef stored under conventionally and active packaging conditions using Fourier transform infrared spectroscopy in tandem with chemometrics. , 2009, Meat science.

[72]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .