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 (900–1700nm) 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 (R2cv=0.88), moisture (R2cv=0.87) and fat (R2cv=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.
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