您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of 'Ya' Pear

文献类型： 外文期刊

作者： Li, Jiangbo 1 ; Huang, Wenqian 1 ; Chen, Liping 1 ; Fan, Shuxiang 1 ; Zhang, Baohua 1 ; Guo, Zhiming 1 ; Zhao, Chunjia 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 spectroscopy;Monte Carlo-uninformative variable elimination;Successive projections algorithm;Variable selection;Soluble solids content;'Ya' pear

期刊名称：FOOD ANALYTICAL METHODS （ 影响因子：3.366； 五年影响因子：3.07 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Informative variable selection or wavelength selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra because the modern spectroscopy instrumentations usually have a high resolution and the obtained spectral data sets may have thousands of variables and hundreds or thousands of samples. In this study, a new combination of Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA; MC-UVE-SPA) was proposed to select the most effective variables. MC-UVE was firstly used to eliminate the uninformative variables in the raw spectra data. Then, SPA was applied to determine the variables with the least collinearity. A case study was done based on the NIR spectroscopy for the non-destructive determination of soluble solids content (SSC) in 'Ya' pear. A total of 160 samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. Three calibration algorithms including linear regressions of partial least square regression (PLS) and multiple linear regression (MLR), and nonlinear regression of least-square support vector machine (LS-SVM) were used for model establishment by using the selected variables by SPA, UVE, MC-UVE, UVE-SPA, and MC-UVE-SPA, respectively. The results indicated that linear models such as PLS and MLR were more effective than nonlinear model such as LS-SVM in the prediction of SSC of 'Ya' pear. In terms of linear models, different variable selection methods can obtain a similar result with the RMSEP values range from 0.2437 to 0.2830. However, combination of MC-UVE and SPA was helpful for obtaining a more parsimonious and efficient model for predicting the SSC values in 'Ya' pear. Twenty-two effective variables selected by MC-UVE-SPA achieved the optimal linear MC-UVE-SPA-MLR model compared with other all developed models by balancing between model accuracy and model complexity. The coefficients of determination (r (2)), root mean square error of prediction, and residual predictive deviation by MC-UVE-SPA-MLR were 0.9271, 0.2522, and 3.7037, respectively.

  • 相关文献

[1]Near-Infrared Hyperspectral Imaging Combined with CARS Algorithm to Quantitatively Determine Soluble Solids Content in "Ya" Pear. Li Jiang-bo,Chen Li-ping,Huang Wen-qian,Peng Yan-kun. 2014

[2]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

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

[4]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

[5]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.

[6]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

[7]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.

[8]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.

[9]Comparative analysis of models for robust and accurate evaluation of soluble solids content in 'Pinggu' peaches by hyperspectral imaging. Chen, Liping. 2017

[10]Characteristic Wavelengths Selection of Soluble Solids Content of Pear Based on NIR Spectral and LS-SVM. Fan Shu-xiang,Zhao Chun-jiang,Fan Shu-xiang,Huang Wen-qian,Li Jiang-bo,Zhao Chun-jiang,Zhang Bao-hua. 2014

[11]Assessment of Influence Detective Position Variability on Precision of Near Infrared Models for Soluble Solid Content of Watermelon. Qian Man,Fan Shu-xiang,Chen Li-ping,Qian Man,Huang Wen-qian,Wang Qing-yan,Fan Shu-xiang,Zhang Bao-hua,Chen Li-ping,Qian Man,Huang Wen-qian,Wang Qing-yan,Fan Shu-xiang,Zhang Bao-hua,Chen Li-ping,Qian Man,Huang Wen-qian,Wang Qing-yan,Fan Shu-xiang,Zhang Bao-hua,Chen Li-ping. 2016

[12]Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data. Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Liu, Chen,Huang, Wenqian,Tian, Xi.

[13]A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. Li, Jiangbo,Huang, Wenqian,Zhao, Chunjiang,Zhang, Baohua.

[14]Effect of spectrum measurement position variation on the robustness of NIR spectroscopy models for soluble solids content of apple. Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Huang, Wenqian,Wang, Chaopeng,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Huang, Wenqian,Wang, Chaopeng,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Huang, Wenqian,Wang, Chaopeng,Fan, Shuxiang,Zhang, Baohua,Li, Jiangbo,Huang, Wenqian,Wang, Chaopeng.

[15]Research on Error Reduction of Path Change of Liquid Samples Based on Near Infrared Trans-Reflective Spectra Measurement. Wang Ya-hong,Dong Da-ming,Zheng Wen-gang,Wang Wen-zhong,Wang Ya-hong,Zhou Ping,Ye Song,Wang Wen-zhong. 2014

[16]Study on the Near Infrared Model Development of Mixed Liquid Samples by the Algorithm of OSC-PLS. Wang, Dong,Ye, Shengfeng,Min, Shungeng,Wang, Dong,Ma, Zhihong. 2011

[17]Study on the Influence of Outlier Elimination in the Quantitative Near-Infrared Models of Paprika Quality. Wang, Dong,Wang, Jihua,Wang, Dong,Ma, Zhihong,Pan, Ligang,Wang, Jihua,He, Hongju,Wang, Dong,Min, Shungeng.

[18]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.

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台