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| Predicting Nutritional Components of Chicken Sausage Using Principal Component Analysis and Partial Least Squares Regression |
| DING Shuxian, ZHANG Xinyu, CAI Mengran, ZHOU Hui, XU Baocai, WANG Zhaoming |
| 1. School of Food and Biological Engineering, Hefei University of Technology, Hefei 230601, China; 2. Anhui Fuliji Roast Chicken Group Co. Ltd., Suzhou 232101, China |
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Abstract Near-infrared (NIR) spectroscopy combined with chemometrics was used to predict the moisture, fat and protein contents of chicken sausage. The NIR spectra of 120 chicken sausages were collected in the wavenumber range of 4 000–10 000 cm-1. Savitzky-Golay (SG) smoothing was used for dimensionality reduction to eliminate spectral noise, removing six abnormal samples. Finally, the NIR spectra of the remaining 114 chicken sausages were used for modeling. The samples were divided into two sets at a ratio of 7:3. In total, 80 samples were randomly assigned into the calibration set, and the other 34 samples were served as the prediction set. After dimensionality reduction by principal component analysis (PCA), partial least squares regression (PLSR) was used to establish quantitative prediction models for the moisture, fat and protein contents of chicken sausage. The results showed that the coefficient of determination of prediction, root mean square error of prediction and ratio of prediction to deviation of the prediction models were 0.914, 0.673 and 2.468 for the moisture content, 0.929, 0.068 and 2.699 for the fat content, and 0.873, 0.504 and 2.048 for the protein content, respectively. Therefore, the combination of NIR spectroscopy with PCA and PLSR has good predictive ability and enables rapid and nondestructive detection of the major nutrients in chicken sausage.
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2026, Vol. 40 
