使用一个紧和自动化的拉曼光谱系统与机器学习检测粉碎玉米中的亚毒素
Yong-Kyoung Kim1, Jianwei Qin2, Insuck Baek2
1Division of Safety Analysis, Experiment & Research Institute, National Agricultural Products Quality Management Service, Gimcheon 39660, Republic of Korea.
Current research in food science
|December 11, 2023
概括
一种新的便携式拉曼光谱法使用机器学习准确地检测玉米中的 aflatoxins. 这种快速,可在现场部署的技术有助于在收获和加工过程中选食品和料的危险污染物.
科学领域:
- 分析化学 分析化学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 非洲毒素是毒性菌毒素,经常在玉米等农作物中发现.
- 污染食品的消费对人类和牲畜造成严重的健康风险.
- 目前的检测方法往往耗时,不适合现场使用.
研究的目的:
- 开发一种便携式,非破坏性,实时的方法来检测食品中的偏毒素污染.
- 为了利用紧的拉曼光谱学与机器学习相结合,用于亚毒素识别.
- 为了在收获或加工地点早期对食品材料进行选.
主要方法:
- 一个紧的拉曼光谱仪器用于从自然污染的玉米样本中获取数据.
- 评估了各种机器学习分类模型,包括线性差异分析 (LDA) 和支持矢量机器 (SVM).
- 应用了光谱预处理技术,如萨维茨基-戈莱第二导数 (SG2),标准正常变量 (SNV) 和乘法散射校正 (MSC).
主要成果:
- 使用Savitzky-Golay第二导数 (SG2) 预处理的线性差异分析 (LDA) 实现了最高的分类准确率95.7%.
- 部分最小平方回归 (PLSR) 模型显示出高精度,确定系数 (R2C和R2V) 高达0.9998 (SNV) 和0.9916 (MSC).
- 开发的方法显示出快速准确的亚拉托克辛量化和分类的巨大潜力.
结论:
- 紧的拉曼光谱,加上化学测量和机器学习,是快速,现场选食品和料的危险物质的可行工具.
- 这项技术有助于实时检测,这对于防止受污染农产品的分销至关重要.
- 该研究强调了自动化,现场部署的系统在确保食品安全方面的潜力.
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