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Near-Infrared Hyperspectral Imaging Combined with CARS Algorithm to Quantitatively Determine Soluble Solids Content in "Ya" Pear

文献类型： 外文期刊

作者： Li Jiang-bo 1 ; Peng Yan-kun 2 ; Chen Li-ping 1 ; Huang Wen-qian 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

关键词： Near-infrared hyperspectral imaging;SSC;'Ya' pear;Variable selection;CARS

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 5 期

页码：

收录情况： SCI

摘要： The present study proposed competitive adaptive reweighted sampling (CARS) algorithm to be used to select the key variables from near-infrared hyperspectral imaging data of "Ya" pear. The performance of the developed model was evaluated in terms of the coefficient of determination(r(2)), and the root mean square error of prediction (RMSEP) and the ratio (RPD) of standard deviation of the validation set to standard error of prediction were used to evaluate the performance of proposed model in the prediction process. The selected key variables were used to build the PLS model, called CARS-PLS model. Comparing results obtained from CARS-PLS model and results obtained from full spectra PLS, it was found that the better results (r(pre)(2) = 0. 908 2, RMSEP=0. 312 0 and RPD=3. 300 5) were obtained by CARS-PLS model based on only 15. 6% information of full spectra. Moreover, performance of CARS-PLS model was also compared with PLS models built by using variables got by Monte Carlo-uninformative variable elimination (MC-UVE) and genetic algorithms (GA) method. The result found that CARS variable selection algorithm not only can remove the uninformative variables in spectra, but also can reduce the collinear variables from informative variables. Therefore, this method can be used to select the key variables of near-infrared hyperspectral imaging data. This study showed that near-infrared hyperspectral imaging technology combined with CARS-PLS model can quantitatively predict the soluble solids content (SSC) in "Ya" pear. The results presented from this study can provide a reference for predicting other fruits quality by using the near-infrared hyperspectral imaging.

  • 相关文献

[1]Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of 'Ya' Pear. Li, Jiangbo,Huang, Wenqian,Chen, Liping,Fan, Shuxiang,Zhang, Baohua,Guo, Zhiming,Zhao, Chunjiang,Li, Jiangbo.

[2]基于CARS特征波段筛选的冬小麦植株氮浓度监测. 陶婷,孟炀,杜晓初,梅新,赵培钦,梅广源,赵倩,杨小冬. 2024

[3]A bi-layer model for nondestructive prediction of soluble solids content in apple based on reflectance spectra and peel pigments. Huang, Wenqian. 2018

[4]The Detection of Soluble Solid Contents and Conductivity of Apple Juice by Homemade Near Infrared Spectrometer. Zhu, Dazhou,Ma, Zhihong,Lu, Anxiang,Zhao, Liu,Wang, Cheng,Pan, Ligang,Zhu, Dazhou,Ma, Zhihong,Lu, Anxiang,Zhao, Liu,Pan, Ligang,Tu, Zhenhua.

[5]Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging. Huang Wen-qian,Li Jiang-bo,Chen Li-ping,Guo Zhi-ming. 2013

[6]An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor. Qu, Fangfang,Ren, Dong,Wang, Jihua,Zhang, Zhong,Lu, Na,Meng, Lei,Wang, Jihua. 2016

[7]Near-Infrared Spectra Combining with CARS and SPA Algorithms to Screen the Variables and Samples for Quantitatively Determining the Soluble Solids Content in Strawberry. Li Jiang-bo,Guo Zhi-ming,Huang Wen-qian,Zhang Bao-hua,Zhao Chun-jiang. 2015

[8]Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging. Fan, Shuxiang,Huang, Wenqian,Guo, Zhiming,Zhang, Baohua,Zhao, Chunjiang,Fan, Shuxiang,Zhao, Chunjiang.

[9]Application of Characteristic NIR Variables Selection in Portable Detection of Soluble Solids Content of Apple by Near Infrared Spectroscopy. Fan Shu-xiang,Zhao Chun-jiang,Fan Shu-xiang,Huang Wen-qian,Li Jiang-bo,Guo Zhi-ming,Zhao Chun-jiang. 2014

[10]Application of Long-Wave Near Infrared Hyperspectral Imaging for Measurement of Soluble Solid Content (SSC) in Pear. Li, Jiangbo,Tian, Xi,Huang, Wenqian,Zhang, Baohua,Fan, Shuxiang,Li, Jiangbo,Tian, Xi,Huang, Wenqian,Zhang, Baohua,Fan, Shuxiang,Li, Jiangbo,Huang, Wenqian,Li, Jiangbo,Huang, Wenqian.

[11]Using Vis/NIR Diffuse Transmittance Spectroscopy and Multivariate Analysis to Predicate Soluble Solids Content of Apple. Fan, Shuxiang,Guo, Zhiming,Zhang, Baohua,Huang, Wenqian,Zhao, Chunjiang,Fan, Shuxiang,Guo, Zhiming,Zhang, Baohua,Huang, Wenqian,Zhao, Chunjiang,Fan, Shuxiang,Guo, Zhiming,Zhang, Baohua,Huang, Wenqian,Zhao, Chunjiang,Fan, Shuxiang,Guo, Zhiming,Zhang, Baohua,Huang, Wenqian,Zhao, Chunjiang.

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