机器学习辅助的便携式微等离子体光学发射光谱仪用于食品安全监测
Tian Ren1, Yao Lin2, Yubin Su1
1Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, China.
Analytical chemistry
|March 21, 2024
概括
使用微等离子体光学发射光谱和机器学习的便携式设备提供了快速,低成本的食品安全分析. 这项技术可以准确地评估肉类的新鲜度,并在现场检测食品的改.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 食品科学 食品科学 食品科学
背景情况:
- 当前的食品安全方法往往缺乏在现场分析的速度,便携性和成本效益.
- 越来越需要快速的现场分析工具,以确保食品的新鲜性和检测伪造.
研究的目的:
- 开发一款便携式设备,用于现场评估食品的新鲜度和检测 adulteration.
- 将点放电微等离子体光学发射光谱学 (μPD-OES) 与机器学习相结合,以提高分析能力.
主要方法:
- 一个便携式μPD-OES设备与模块化注射单元 (头部空间固体相微提取和头部空间净化) 集成.
- 来自食品样本 (肉类,咖啡) 的芳香分子通过微等离子体激发和光学发射光谱学进行分析.
- 机器学习算法被用来解释得到的辐射光谱.
主要成果:
- 该设备在评估肉的新鲜度方面取得了很高的准确性:牛肉96.0%,猪肉98.7%,肉94.7%.
- 它成功地识别了用肉造的牛肉 (准确率为99.5%),并没有错误地对咖啡种类进行了分类.
- 微PD-OES系统为不同的香味分子生成了特定的原子和分子发射光谱.
结论:
- 机器学习和μPD-OES的结合为食品芳香分析提供了一个简单,便携且具有成本效益的策略.
- 这项技术显示出对食品安全现场监测的巨大潜力,包括新鲜度评估和改检测.
- 开发的方法为实时食品质量控制和监管合规提供了一个有希望的解决方案.
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