title: Rapid and non-invasive quantification of intramuscular fat content of intact pork cuts.
abstract: Having acquired near infrared (NIR) hyperspectral images of intact pork loin samples through an NIR hyperspectral imaging system, the efficiency of a variety of image processing techniques including texture pattern analysis techniques were applied to process hyperspectral images so as to determine the intramuscular fat (IMF) content non-destructively. After the segmentation of region of interest (ROI), the raw spectral, texture-based spectral and textural characteristics of pork images were extracted by spectral averaging and pattern recognition techniques namely Gabor filter and improved gray level co-occurrence matrix (GLCM), respectively. First derivatives of the non-filtered and the Gabor filtered spectra were also investigated. Full waveband partial least squares regression (PLSR) was employed to determine the optimal parameters of Gabor filter and GLCM, and to select optimal wavelengths for IMF prediction. A stepwise procedure was applied to the optimal wavelengths to further optimize them to key wavelengths. Multiple linear regression (MLR) models were built based on the key wavelengths. Mean spectra and the Gabor filtered spectra outperformed GLCM. The best result, represented by correlation coefficients of calibration (Rc), cross validation (Rcv) and prediction (Rp) of 0.89, 0.89, and 0.86, respectively, was achieved using the first derivative of Gabor filtered spectra at 1193 and 1217 nm. To visualize the IMF content in pork, the distribution maps of IMF content in pork were drawn using a mean spectra-based MLR model. These promising results highlight the great potential of NIR hyperspectral imaging for non-destructive prediction of IMF content of intact pork.
authors.affiliation: Department of Bioresource Engineering, McGill University, Macdonald Campus. 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, Canada H9X 3V9.
authors.forename: H
authors.initials: H
authors.lastname: Huang
authors.affiliation: Department of Bioresource Engineering, McGill University, Macdonald Campus. 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, Canada H9X 3V9.
authors.forename: L
authors.initials: L
authors.lastname: Liu
authors.affiliation: Department of Bioresource Engineering, McGill University, Macdonald Campus. 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, Canada H9X 3V9. Electronic address: michael.ngadi@mcgill.ca.
authors.forename: M O
authors.initials: MO
authors.lastname: Ngadi
authors.affiliation: Agriculture and Agri-food Canada, 3600 Casavant West, Saint-Hyacinthe, Quebec, Canada J2S 8E3.
authors.forename: C
authors.initials: C
authors.lastname: Gariépy
mesh_terms: D000818:Animals; D005223:Fats / Q000032:analysis*; D008461:Meat Products / Q000032:analysis*; D015999:Multivariate Analysis; D018482:Muscle, Skeletal / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared; D013552:Swine
chemical_list: D005223:Fats
keywords: DMG; DMS; Distribution map; GF; GIa; GIh; GIt; Gabor filter; Gabor-filtered mean spectra; Gin; Gray level co-occurrence matrix; Intramuscular fat content; MG; MS; Multiple linear regression; Partial least squares regression; WLD; angular second moment of GLCM; contrast of GLCM; correlation of GLCM; homogeneity; non-filtered mean spectra; the first derivative of Gabor-filtered mean spectra; the first derivative of non-filtered mean spectra; wide line detector.