Nondestructive measurement of soluble solids content in apple using near infrared hyperspectral imaging coupled with wavelength selection algorithm
文献类型： 外文期刊
作者： Zhang, Dongyan 1 ; Xu, Yunfei 1 ; Huang, Wenqian 2 ; Tian, Xi 2 ; Xia, Yu 2 ; Xu, Lu 1 ; Fan, Shuxiang 2 ;
作者机构： 1.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Hefei 230601, Anhui, Peoples R China
2.Beijing Res Ctr Intelligent Equipment Agr, 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
关键词： Near infrared hyperspectral imaging; Apple; Soluble solids content; Variable selection
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 98 卷
页码：
收录情况： SCI
摘要： Hyperspectral imaging is a promising technique for nondestructive sensing of multiple quality attributes of apple fruit. This research evaluated and compared different mathematical models to extract effective wavelengths for measurement of apple soluble solids content (SSC) based on near infrared (NIR) hyperspectral imaging over the spectral region of 1000-2500 nm. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), random frog (RF), and CARS-SPA, CARS-RF combined algorithms were used for extracting effective wavelengths from hyperspectral images of apples, respectively. Based on the selected effective wavelengths, different models were built and compared for predicting SSC of apple using partial least squares (PLS) and least squared support vector regression (LS-SVR). Among all the models, the models based on the ten effective wavelengths selected by CARS-SPA achieved the best results, with R-P, RMSEP of 0.907, 0.479 degrees Brix for PLS and 0.917, 0.453 degrees Brix for LS-SVR, respectively. The overall results indicated that CARS-SPA can be used for selecting the effective wavelengths from hyperspectral data. Both PLS and LS-SVR can be applied to develop calibration models to predict apple SSC. Furthermore, the wavelengths selected by CARS-SPA algorithm has a great potential for online detection of apple SSC.
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