title: Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis.
abstract: The potential of visible and near infrared hyperspectral imaging was investigated as a rapid and nondestructive technique for classifying fresh and frozen-thawed meats by integrating critical spectral and image features extracted from hyperspectral images in the region of 400-1000 nm. Six feature wavelengths (400, 446, 477, 516, 592 and 686 nm) were identified using uninformative variable elimination and successive projections algorithm. Image textural features of the principal component images from hyperspectral images were obtained using histogram statistics (HS), gray level co-occurrence matrix (GLCM) and gray level-gradient co-occurrence matrix (GLGCM). By these spectral and textural features, probabilistic neural network (PNN) models for classification of fresh and frozen-thawed pork meats were established. Compared with the models using the optimum wavelengths only, optimum wavelengths with HS image features, and optimum wavelengths with GLCM image features, the model integrating optimum wavelengths with GLGCM gave the highest classification rate of 93.14% and 90.91% for calibration and validation sets, respectively. Results indicated that the classification accuracy can be improved by combining spectral features with textural features and the fusion of critical spectral and textural features had better potential than single spectral extraction in classifying fresh and frozen-thawed pork meat.
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Hongbin
authors.initials: H
authors.lastname: Pu
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China; Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems Engineering, University College Dublin, National University of Ireland, Agriculture & Food Science Centre, Belfield, Dublin 4, Ireland. Electronic address: dawen.sun@ucd.ie.
authors.forename: Da-Wen
authors.initials: DW
authors.lastname: Sun
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Ji
authors.initials: J
authors.lastname: Ma
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Jun-Hu
authors.initials: JH
authors.lastname: Cheng
mesh_terms: D000465:Algorithms; D000818:Animals; D002138:Calibration; D005511:Food Handling; D005615:Freezing; D006801:Humans; D008460:Meat / Q000032:analysis* / Q000145:classification; D009132:Muscles; D019265:Spectroscopy, Near-Infrared / Q000379:methods; D013552:Swine
keywords: Fresh pork; Frozen-thawed pork; Hyperspectral imaging; Probabilistic neural network; Texture