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

Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging

文献类型： 外文期刊

作者： Fan, Shuxiang 1 ; Huang, Wenqian 1 ; Guo, Zhiming 1 ; Zhang, Baohua 1 ; Zhao, Chunjiang 1 ;

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

2.Northwest Agr & Forestry Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

关键词： Hyperspectral imaging;Soluble solids content;Firmness;Pear;Variable selection

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： Hyperspectral imaging technique was investigated to determine the soluble solids content (SSC) and firmness of pears. A total of 160 pear samples were prepared for the calibration (n = 120) and prediction (n = 40) sets. A hyperspectral imaging system was used to acquire hyperspectral reflectance image from each pear in visible and near infrared (400-1000 nm) regions. Mean spectra were extracted from the regions of interest for the hyperspectral image of each pear. Spectral data were first pretreated with different preprocessing techniques and analyzed using partial least square (PLS) to establish calibration models. However, the large size of spectral data contains a large number of redundant variables that lead to complexity and poor predicting ability of calibration models. Several variable selection methods were investigated to select effective wavelength variables for the determination of SSC and firmness of pear. In this study, the variables selected by successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS) and the combination of CARS and SPA were used for PLS regression. The CARS-SPA-PLS models based on 25 and 22 variables achieved the optimal performance for two internal quality indices compared with full-spectrum PLS, CARS-PLS, and SPA-PLS models. The correlation coefficient (r (pre)) and root mean square error of prediction (RMSEP) by CARS-SPA-PLS were 0.876, 0.491 for SSC and 0.867, 0.721 for firmness, respectively. The overall results indicated that the CARS-SPA was a powerful way for the selection of effective variables and the hyperspectral imaging system together with CARS-SPA-PLS model could be applied as a fast and potential method for the determination of SSC and firmness of pear.

  • 相关文献

[1]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.

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

[3]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.

[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]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.

[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]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

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

[10]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.

[11]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

[12]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

[13]Preharvest application of phellodendron bark extracts controls brown rot and maintains quality of peento-shaped peach. Feng, Xiaoyuan,Cao, Jiankang,Jiang, Weibo,Feng, Xiaoyuan,Wang, Baogang,Li, Wensheng,Shi, Lei. 2008

[14]Research on Nondestructive Measurement of Firmness and Soluble Tannin Content of 'Mopanshi' Persimmon Using Vis/NIR Diffuse Reflection Spectroscopy. Zhang, Peng,Li, J.,Feng, X.,Wang, B.,Xue, Y..

[15]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[16]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

[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]Risk assessment and ranking of pesticide residues in Chinese pears. Li Zhi-xia,Nie Ji-yun,Yan Zhen,Xu Guo-feng,Kuang Li-xue,Pan Li-gang,Pan Li-gang,Xie Han-zhong,Wang Cheng,Liu Chuan-de,Zhao Xu-bo,Guo Yong-ze. 2015

[19]Identification of seedling cabbages and weeds using hyperspectral imaging. Wei, Deng,Zhao Chunjiang,Xiu, Wang,Huang, Yanbo,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang. 2015

[20]Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging. Zhang, Dongyan,Zhang, Lifu,Zhang, Dongyan,Wang, Xiu,Zhang, Dongyan,Wang, Xiu,Lin, Fenfang,Huang, Yanbo. 2016

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