title: Near-infrared hyperspectral imaging for grading and classification of pork.
abstract: 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.
authors.affiliation: Food Refrigeration and Computerised Food Technology, School of Agriculture, Food Science & Veterinary Medicine, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland.
authors.forename: Douglas
authors.initials: D
authors.lastname: Barbin
authors.forename: Gamal
authors.initials: G
authors.lastname: Elmasry
authors.forename: Da-Wen
authors.initials: DW
authors.lastname: Sun
authors.forename: Paul
authors.initials: P
authors.lastname: Allen
mesh_terms: D000818:Animals; D008460:Meat / Q000145:classification* / Q000592:standards*; D013055:Spectrophotometry, Infrared / Q000379:methods*; D013552:Swine