Visible/Near-Infrared Spectra for Linear and Nonlinear Calibrations: A Case to Predict Soluble Solids Contents and pH Value in Peach

• Published in: Food and bioprocess technology 2011-11, Vol.4 (8), p.1376-1383
• Main Authors: Shao, Yongni, Bao, Yidan, He, Yong
• Format: Article
• Language: English
• Subjects: Agriculture; bias; Biotechnology; Calibration; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Correlation coefficient; Correlation coefficients; Food Science; I.R. radiation; Independent component analysis; Independent variables; Infrared spectra; Least squares; linear models; Mean square errors; Near infrared radiation; near-infrared reflectance spectroscopy; near-infrared spectroscopy; Nonlinear analysis; nonlinear models; Original Paper; Peaches; pH effects; prediction; Regression analysis; Root-mean-square errors; soluble solids; Spectroscopy; Spectrum analysis; Support vector machines; ultraviolet-visible spectroscopy
• ISSN: 1935-5130; 1935-5149
• DOI: 10.1007/s11947-009-0227-6

Two sensitive wavelength (SWs) selection methods combined with visible/near-infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) and pH value in peaches, including latent variables analysis (LVA) and independent component analysis (ICA). A total of 100 samples were prepared for the calibration (n = 70) and prediction (n = 30) sets. Calibration models using SWs selected by LVA and ICA were developed, including linear regression of partial least squares (PLS) analysis and nonlinear regression of least squares-support vector machine (LS-SVM). In the nonlinear models, four SWs selected by ICA achieved the optimal ICA-LS-SVM model compared with LV-LS-SVM and both of them better than linear model of PLS. The correlation coefficients (r p and r cv), root mean square error of cross validation, root mean square error of prediction, and bias by ICA-LS-SVM were 0.9537, 0.9485, 0.4231, 0.4155, and 0.0167 for SSC and 0.9638, 0.9657, 0.0472, 0.0497, and −0.0082 for pH value, respectively. The overall results indicated that ICA was a powerful way for the selection of SWs, and Vis/NIR spectroscopy incorporated to ICA-LS-SVM was successful for the accurate determination of SSC and pH value in peach.