Near-infrared hyperspectral imaging for grading and classification of pork.

In this study, a hyperspectral imaging technique was developed to achieve fast, accurate, and objective determination of pork quality grades. Hyperspectral images were acquired in the near-infrared (NIR) range from 900 to 1700 nm for 75 pork cuts of longissimus dorsi muscle from three quality grades (PSE, RFN and DFD). Spectral information was extracted from each sample and six significant wavelengths that explain most of the variation among pork classes were identified from 2nd derivative spectra. There were obvious reflectance differences among the three quality grades mainly at wavelengths 960, 1074, 1124, 1147, 1207 and 1341 nm. Principal component analysis (PCA) was carried out using these particular wavelengths and the results indicated that pork classes could be precisely discriminated with overall accuracy of 96%. Algorithm was developed to produce classification maps of the tested samples based on score images resulting from PCA and the results were compared with the ordinary classification method. Investigation of the misclassified samples was performed and showed that hyperspectral based classification can aid in class determination by showing spatial location of classes within the samples.

[1]  PETER SYKES,et al.  Organic Molecules , 1968, Nature.

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

[3]  F. Toldrá,et al.  Sensory characteristics of cooked pork loin as affected by nucleotide content and post-mortem meat quality. , 1999, Meat science.

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

[5]  Shiv O. Prasher,et al.  Categorization of pork quality using Gabor filter-based hyperspectral imaging technology , 2010 .

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

[7]  Paolo Menesatti,et al.  Quality Evaluation of Fish by Hyperspectral Imaging , 2010 .

[8]  R. G. Kauffman,et al.  Muscle protein changes post mortem in relation to pork quality traits. , 1997, Meat science.

[9]  Y. Kosugi,et al.  Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery , 2007 .

[10]  P. Purslow,et al.  Modelling quality variations in commercial Ontario pork production. , 2008, Meat science.

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

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

[13]  Y. R. Chen,et al.  Principal component regression of near-infrared reflectance spectra for beef tenderness prediction , 2001 .

[14]  Jilu Feng,et al.  The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping , 2003 .

[15]  H B Ding,et al.  Near-infrared spectroscopic technique for detection of beef hamburger adulteration. , 2000, Journal of agricultural and food chemistry.

[16]  Michael Ngadi,et al.  Hyperspectral Image Processing Techniques , 2010 .

[17]  D. E. Chan,et al.  High Throughput Spectral Imaging System for Wholesomeness Inspection of Chicken , 2008 .

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

[19]  Jiang Fa-chao,et al.  Hyperspectral scattering profiles for prediction of beef tenderness. , 2009 .

[20]  D. V. Byrne,et al.  Warmed-over flavour in porcine meat - a combined spectroscopic, sensory and chemometric study. , 2000, Meat science.

[21]  Shiv O. Prasher,et al.  Application of Hyperspectral Technique for Color Classification Avocados Subjected to Different Treatments , 2009, Food and Bioprocess Technology.

[22]  S B Engelsen,et al.  Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy. , 2000, Meat science.

[23]  Yves Roggo,et al.  Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms , 2005 .

[24]  D. T. Witte,et al.  Net analyte signal based statistical quality control. , 2005, Analytical chemistry.

[25]  Michael Ngadi,et al.  Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network , 2007 .

[26]  J. Gómez-Sanchís,et al.  Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

[27]  P. Williams,et al.  Near-Infrared Technology in the Agricultural and Food Industries , 1987 .

[28]  M. Ruusunen,et al.  Comparison of the thermal characteristics of connective tissue in loose structured and normal structured porcine M. semimembranosus. , 2008, Meat science.

[29]  F. Toldrá,et al.  The use of muscle enzymes as predictors of pork meat quality , 2000 .

[30]  Daniel E. Guyer,et al.  Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers , 2006, Computers and Electronics in Agriculture.

[31]  Tarek El-Ghazawi,et al.  Hyperspectral image assessment of oil‐contaminated wetland , 2005 .

[32]  Ashok Samal,et al.  Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region , 2008 .

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

[34]  Nuria Aleixos,et al.  Erratum to: Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

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

[36]  V. Santé-Lhoutellier,et al.  Characterisation of PSE zones in semimembranosus pig muscle. , 2005, Meat science.

[37]  K. Jouppila,et al.  Muscle fiber properties and thermal stability of intramuscular connective tissue in porcine M. semimembranosus , 2009 .

[38]  B. Engel,et al.  Causes for variation in pork quality. , 1997, Meat science.

[39]  Seyed Mohammad Ali Razavi,et al.  Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit , 2011 .

[40]  Ding Hb,et al.  Near-infrared spectroscopic technique for detection of beef hamburger adulteration. , 2000 .

[41]  Renfu Lu,et al.  Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images , 2007 .

[42]  Chun-Chieh Yang,et al.  Machine vision system for online inspection of freshly slaughtered chickens , 2009 .

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

[44]  M. Ngadi,et al.  Protein Denaturation in Pork Longissimus Muscle of Different Quality Groups , 2011 .

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

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