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Application of Characteristic NIR Variables Selection in Portable Detection of Soluble Solids Content of Apple by Near Infrared Spectroscopy

文献类型： 外文期刊

作者： Fan Shu-xiang 1 ; Huang Wen-qian 2 ; Li Jiang-bo 2 ; Guo Zhi-ming 2 ; Zhao Chun-jiang 1 ;

作者机构： 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： Apple;Variable selection;Soluble solids content;Portable detection

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2014 年 34 卷 10 期

页码：

收录情况： SCI

摘要： In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were proposed to select effective way. elength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS-SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (R5) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403 degrees Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.

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