title: Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging.
abstract: In this study a near-infrared (NIR) hyperspectral imaging technique was investigated for non-destructive determination of chemical composition of intact and minced pork. Hyperspectral images (900-1700 nm) were acquired for both intact and minced pork samples and the mean spectra were extracted by automatic segmentation. Protein, moisture and fat contents were determined by traditional methods and then related with the spectral information by partial least-squares (PLS) regression models. The coefficient of determination obtained by cross-validated PLS models indicated that the NIR spectral range had an excellent ability to predict the content of protein (R(2)(cv)=0.88), moisture (R(2)(cv)=0.87) and fat (R(2)(cv)=0.95) in pork. Regression models using a few selected feature-related wavelengths showed that chemical composition could be predicted with coefficients of determination of 0.92, 0.87 and 0.95 for protein, moisture and fat, respectively. Prediction of chemical contents in each pixel of the hyperspectral image using these prediction models yielded spatially distributed visualisations of the sample composition.
authors: 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.
authors: Douglas F
authors: DF
authors: Barbin
authors: Gamal
authors: G
authors: ElMasry
authors: Da-Wen
authors: DW
authors: Sun
authors: Paul
authors: P
authors: Allen
mesh_terms: D000818:Animals; D005223:Fats / Q000737:chemistry; D005511:Food Handling; D008460:Meat / Q000032:analysis*; D011506:Proteins / Q000737:chemistry; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013552:Swine
chemical_list: D005223:Fats; D011506:Proteins