Determination of Protein Content of Auricularia auricula Using Near Infrared Spectroscopy Combined with Linear and Nonlinear Calibrations

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• 作者：Fei Liu ; Yong He ; Guangming Sun
• 刊名：Journal of Agricultural and Food Chemistry
• 出版年：2009
• 出版时间：June 10, 2009
• 年：2009
• 卷：57
• 期：11
• 页码：4520-4527
• 全文大小：778K
• 年卷期：v.57,no.11(June 10, 2009)
• ISSN：1520-5118

文摘

Near infrared (NIR) spectroscopy was investigated to determine the protein content of Auricularia auricula (commonly called black woody ear or tree ear) using partial least-squares (PLS), multiple linear regression (MLR), and least-squares-support vector machine (LS-SVM). The performances of different preprocessing were compared including Savitzky−Golay (SG) smoothing, standard normal variate, multiplicative scatter correction (MSC), first derivative, second derivative, and direct orthogonal signal correction. A successive projections algorithm (SPA) was applied for relevant effective wavelengths selection. The combinations of various pretreatment and calibration methods were compared based on the prediction performance. The optimal full-spectrum PLS model was achieved by raw spectra, whereas the optimal SPA-MLR, SPA-PLS, and SPA-LS-SVM models were achieved by MSC spectra. The best prediction performance was achieved by the SPA-LS-SVM model, with correlation coefficients (r) = 0.9839 and a root mean squares error of prediction (RMSEP) = 0.16. The results indicated that NIR spectroscopy combined with SPA-LS-SVM was the most successful to determine the protein content of A. auricula.