%A HUANG Wen-qian;LI Jiang-bo;CHEN Li-ping*;GUO Zhi-ming %T Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging %0 Journal Article %D 2013 %J SPECTROSCOPY AND SPECTRAL ANALYSIS %R 10.3964/j.issn.1000-0593(2013)10-2843-04 %P 2843-2846 %V 33 %N 10 %U {https://www.gpxygpfx.com/CN/abstract/article_6633.shtml} %8 2013-10-01 %X It is very important to extract effective wavelengths for quantitative analysis of fruit internal quality based on hyperspectral imaging. In the present study, genetic algorithm (GA), successive projections algorithm (SPA) and GA-SPA combining algorithm were used for extracting effective wavelengths from 400~1 000 nm hyperspectral images of Yantai “Fuji” apples, respectively. Based on the effective wavelengths selected by GA, SPA and GA-SPA, different models were built and compared for predicting soluble solids content (SSC) of apple using partial least squares (PLS), least squared support vector machine (LS-SVM) and multiple linear regression (MLR), respectively. A total of 160 samples were prepared for the calibration (n=120) and prediction (n=40) sets. Among all the models, the SPA-MLR achieved the best results, where R2p, RMSEP and RPD were 0.950 1, 0.308 7 and 4.476 6 respectively. Results showed that SPA can be effectively used for selecting the effective wavelengths from hyperspectral data. And, SPA-MLR is an optimal modeling method for prediction of apple SSC. Furthermore, less effective wavelengths and simple and easily-interpreted MLR model show that the SPA-MLR model has a great potential for on-line detection of apple SSC and development of a portable instrument.