An IGA-PLSP method for FT-NIR wavelength selection for measuring soluble solid content of citrus fruits

作者：Zhao, Xin; Zhu, Qibing^*; Huang, Min; Cen, Haiyan

来源：Analytical Methods, 2013, 5(18): 4811-4817.

DOI：10.1039/c3ay40916c

• GENETIC ALGORITHM
• VIS/NIR SPECTROSCOPY
• DIFFUSE-REFLECTANCE
• QUALITY
• PREDICTION
• FIRMNESS
• JUICE

Near-infrared spectroscopy is a useful technique for fast, noninvasive quality measurement of fruits. The research was aimed to select the optimal wavelengths from Fourier transform near-infrared reflectance (FT-NIR) spectroscopy for the soluble solid content (SSC) evaluation of citrus. A total of 220 Gongchuan citrus were analyzed in this experiment. Standard genetic algorithm, immune genetic algorithm (IGA), and IGA combined with partial least squares projection algorithm (IGA-PLSP) were used for the optimal wavelength selection. The partial least squares (PLS) and least squares support vector machine (LS-SVM) were used for the prediction of SSC. Prediction models using the optimal wavelengths (280 wavelengths) selected by IGA-PLSP significantly improved the prediction results compared with the models using the full spectra. The standard errors of prediction (RMSEP) were reduced by 14.8% and 34.0% for the PLS and LS-SVM model, respectively. In addition, the LS-SVM model based on the IGA-PLSP method achieved better prediction results (correlation coefficient of prediction or R-p = 0.923 and RMSEP 0.66 degrees Brix) compared to the PLS model (R-p = 0.914 and RMSEP = 0.69 degrees Brix). This research demonstrated that the IGA-PLSP algorithm provides an effective means for the optimal wavelength selection, and improves the FT-NIR spectral prediction of citrus SSC.

• 出版日期2013
• 单位江南大学