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

Rapid detection of adulterated lamb meat using near infrared and electronic nose: A F1-score-MRE data fusion approach

文献类型： 外文期刊

作者： Jia, Wenshen 1 ; Qin, Yingdong 1 ; Zhao, Changtong 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Inst Qual Stand & Testing Technol, Beijing 100097, Peoples R China

2.Minist Agr & Rural Affairs, Dept Risk Assessment Lab Agro Prod Beijing, Beijing 100097, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Urban Agr North China, Beijing 100097, Peoples R China

4.Anhui Inst Innovat Ind Technol, Luan Branch, Luan 237100, Peoples R China

5.11,Shuguang Garden Middle Rd, Beijing, Peoples R China

关键词： Electronic nose; Near-infrared spectroscopy; Data fusion; F1-score-MRE; Machine learning

期刊名称：FOOD CHEMISTRY （ 影响因子：8.5； 五年影响因子：8.2 ）

ISSN： 0308-8146

年卷期： 2024 年 439 卷

页码：

收录情况： SCI

摘要： Individual detection techniques cannot guarantee accurate and reliable results when combatting the presence of adulterated lamb meat in the market. Here, we propose an approach combining the electronic nose and nearinfrared spectroscopy fusion data with machine learning methods to effectively detect adulterated lamb meat (mixed with duck meat). To comprehensively analyse the data from both techniques, the F1-score-based Model Reliability Estimation (F1-score-MRE) data fusion method was introduced. The obtained results demonstrate the superiority of the F1-score-MRE method, achieving an accuracy rate of 98.58% (F1-score: 0.9855) in detecting adulterated lamb meat. This surpasses the performance of the traditional data fusion and feature concatenation methods. Furthermore, the F1-score-MRE data fusion method exhibited enhanced stability and accuracy compared with the single electronic nose and near-infrared data processed by the self-adaptive BPNN model (accuracy: 94.36%, 93.66%; F1-score: 0.9435, 0.9368). This study offers a promising solution to address concerns regarding adulterated lamb meat.

  • 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