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Comparison and optimization of models for SSC on-line determination of intact apple using efficient spectrum optimization and variable selection algorithm

文献类型： 外文期刊

作者： Tian, Xi 1 ; Fan, Shuxiang 2 ; Li, Jiangbo 2 ; Xia, Yu 2 ; Huang, Wenqian 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

2.Beijing Res Ctr Intelligent Equipment Agr, 11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China

3.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

4.Minist Agr, Key Lab Agriinformat, Beijing 100097, Peoples R China

5.Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

关键词： Soluble solids content; Full-transmittance; Inefficient transmittance spectra; Characteristic wavelength

期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：2.638； 五年影响因子：2.581 ）

ISSN： 1350-4495

年卷期： 2019 年 102 卷

页码：

收录情况： SCI

摘要： Higher accuracy for a prediction model is the unremitting pursuit in the field of optics nondestructive technology. The multipoint full-transmittance spectra ranging from 650 to 1000 nm were acquired at a speed of 0.5 m/s using an on-line spectrum measurement system. The combination of mean normalization and 11 points smoothing were selected as the best spectral preprocessing method for removing undesirable signal excursion and light scatters existed in the original spectra. By investigating the interference of transmittance spectral intensity on the prediction accuracy with the method of efficient spectrum optimization proposed in our study, we found that those transmittance spectra with intensity lower than 0.4 at 920 nm were inefficient for SSC prediction. Furthermore, three different variable selection algorithms were used to select characteristic band for further optimizing the prediction model, the best prediction model was built based on 45 variables selected by random flog (RF) and the performance of the best model was R-pre = 0.9043 and RMSEP = 0.4787 respectively. All mentioned above illustrated that efficient spectrum optimization method coupled with variable selection algorithms were useful for improving the accuracy and robust of prediction model.

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