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

Near-Infrared Spectra Combining with CARS and SPA Algorithms to Screen the Variables and Samples for Quantitatively Determining the Soluble Solids Content in Strawberry

文献类型： 外文期刊

作者： Li Jiang-bo 1 ; Guo Zhi-ming 1 ; Huang Wen-qian 1 ; Zhang Bao-hua 1 ; Zhao Chun-jiang 1 ;

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

关键词： Variable selection;Sample selection;Near-infrared spectra;Strawberry;Soluble solids content

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： 2015 年 35 卷 2 期

页码：

收录情况： SCI

摘要： In using spectroscopy to quantitatively or qualitatively analyze the quality of fruit, how to obtain a simple and effective correction model is very critical for the application and maintenance of the developed model. Strawberry as the research object, this research mainly focused on selecting the key variables and characteristic samples for quantitatively determining the soluble solids content. Competitive adaptive reweighted sampling (CARS) algorithm was firstly proposed to select the spectra variables. Then, Samples of correction set were selected by successive projections algorithm (SPA), and 98 characteristic samples were obtained. Next, based on the selected variables and characteristic samples, the second variable selection was performed by using SPA method. 25 key variables were obtained. In order to verify the performance of the proposed CARS algorithm, variable selection algorithms including Monte Carlo-uninformative variable elimination (MC-UVE) and SPA were used as the comparison algorithms. Results showed that CARS algorithm could eliminate uninformative variables and remove the collinearity information at the same time. Similarly, in order to assess the performance of the proposed SPA algorithm for selecting the characteristic samples, SPA algorithm was compared with classical Kennard-Stone algorithm. Results showed that SPA algorithm could be used for selection of the characteristic samples in the calibration set. Finally, PLS and MLR model for quantitatively predicting the SSC (soluble solids content) in the strawberry were proposed based on the variables/samples subset (25/98), respectively. Results show that models built by using the 0. 59% and 65. 33% information of original variables and samples could obtain better performance than using the ones obtained by using all information of the original variables and samples. MLR model was the best with R-pre(2) =0. 909 7, RMSEP=0. 348 4 and RPD= 3. 327 8.

  • 相关文献

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

[2]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.

[3]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.

[4]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

[5]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.

[6]Circular Multi-Storied Culture Mode among Strawberry, Mushroom and Leaf Vegetables in Greenhouse. Chuanfei Zhong,Yuntao Zhang,Guixia Wang,Jing Dong,Linlin Chang,Lina Wang. 2012

[7]Study of Volatile Compound Contents in a Progeny Issued from Cross between 'Camarosa' and 'Benihoppe' (65-71). Guixi Wang,Yuntao Zhang,Jing Dong,Chuanfei Zhong,Linlin Chang,Lina Wang. 2012

[8]Isolation of DNA-Methyltransferase Genes and Their Promoter Sequences from Strawberry (Fragaria x ananassa Duch.). Linlin Chang,Zhihong Zhang,Yuntao Zhang,He Li,Yue Yang,Hongyan Dai. 2012

[9]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

[10]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

[11]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.

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

[13]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

[14]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

[15]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.

[16]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.

[17]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.

[18]Circular Multi-Storied Culture Mode among Strawberry, Mushroom and Leaf Vegetables in Greenhouse. Zhong, Chuanfei,Zhang, Yuntao,Wang, Guixia,Dong, Jing,Chang, Linlin,Wang, Lina. 2011

[19]Genetically Transformed Strawberry (Fragaria x ananassa Duch.) with Cold-Inducible Transcription Factor CBF1. Jin, W. M.,Dong, J.,Liu, Y.,Zhang, Y. P.,Pan, Q. H.. 2009

[20]Study of Volatile Compound Contents in a Progeny Issued from Cross between 'Camarosa' and 'Benihoppe'. Wang, Guixia,Zhang, Yuntao,Dong, Jing,Zhong, Chuanfei,Chang, Linlin,Wang, Lina. 2011

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