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

Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics

文献类型： 外文期刊

作者： Long, Yuan 1 ; Huang, Wenqian 1 ; Wang, Qingyan 1 ; Fan, Shuxiang 1 ; Tian, Xi 1 ;

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

2.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Minist Agr, Key Lab Agriinformat, Beijing 100097, Peoples R China

4.Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

关键词： Raman hyperspectral imaging; Maize kernel; Fungal spore quantity; Textural features; Nondestructive detection

期刊名称：FOOD CHEMISTRY （ 影响因子：9.231； 五年影响因子：8.795 ）

ISSN： 0308-8146

年卷期： 2022 年 372 卷

页码：

收录情况： SCI

摘要： Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity.

  • 相关文献
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