부산대학교 도서관

Purpose: The research presented in this paper applied the chemometric analysis to the near-infrared spectral data fromline-scanned hyperspectral images of beef and fish meals in animal feeds. The chemometric statistical models weredeveloped to distinguish beef meals from fish ones. Methods: The meal samples of 40 fish meals and 15 beef meals wereline-scanned to obtain hyperspectral images. The spectral data were retrieved from each of 3600 pixels in the Region ofInterest (ROI) of every sample image. The wavebands spanning 969 nm to 1551 nm (across 176 spectral bands) wereselected for chemometric analysis. The partial least squares regression (PLSR) and the principal component analysis (PCA)methods of the chemometric analysis were applied to the model development. The purpose of the models was to correctlyclassify as many beef pixels as possible while misclassified fish pixels in an acceptable amount. Results: The results showedthat the success classification rates were 97.9% for beef samples and 99.4% for fish samples by the PLSR model, and 85.1%for beef samples and 88.2% for fish samples by the PCA model. Conclusion: The chemometric analysis-based PLSR and PCAmodels for the hyperspectral image analysis could differentiate beef meals from fish ones in animal feeds.