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

Adaptive Ant Colony Optimization Approach to Characteristic Wavelength Selection of NIR Spectroscopy

文献类型： 外文期刊

作者： Guo Zhi-Ming 1 ; Huang Wen-Qian 2 ; Peng Yan-Kun 1 ; Wang Xiu 1 ; Tang Xiu-Ying 1 ;

作者机构： 1.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

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

关键词： Near infrared spectroscopy; Ant colony optimization; Characteristic wavelength; Apple; Soluble solid content

期刊名称：CHINESE JOURNAL OF ANALYTICAL CHEMISTRY （ 影响因子：1.134； 五年影响因子：0.909 ）

ISSN： 0253-3820

年卷期： 2014 年 42 卷 4 期

页码：

收录情况： SCI

摘要： Ant colony optimization algorithm combined with partial least squares (ACO-PLS) was employed to select the characteristic wavelength of near infrared (NIR) spectra for soluble solid content (SSC) in apple from different geographical region. The aim was to improve its accuracy and applicability, and to simplify the NIR prediction model. After collection of apple sample from three major apple. production regions of China, the original spectra of the apple in wavelength range of 3800 - 14000 cm(-1) was acquired, and SSC was determined for reference measurements by standard method. Based on the features of heuristic global search and the random selection mechanism of Monte Carlo roulette, ACO explored optimally the efficient wavelength from the NIR spectroscopy of the apple to develop models for predicting the SSC of the apple. Experimental results showed that the performance of ACO-PLS model was superior to the performances from traditional PLS and GA-PLS models with the least variables. Good prediction performance was obtained for SSC with correlation coefficients of 0.9708, and root mean square errors of prediction 0.5144, respectively. The study demonstrates that adaptive ant colony optimization could effectively select the characteristic wavelengths of NIR spectral to improve the model robustness and applicability.

  • 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