在植物油中通过理论分析和实验恢复AFB1内在光,并通过矩阵的持续变化进行理论分析和实验
Meng Wang1, Xiaoqi Zhao1, Xiaoyun Yang1
1College of Food Science and Engineering/Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing University of Finance and Economics, Nanjing 210023, China.
Food chemistry
|December 5, 2024
概括
一种新方法使用光光谱学和人工智能准确检测植物油中的阿弗拉托辛B1 (AFB1),即使使用复杂的样本矩阵. 这种方法可以恢复毒素的真实光,从而可靠地对食品安全进行查.
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B1 (AFB1) 是农业产品中的有毒污染物.
- 光光谱技术提供非破坏性的AFB1检测.
- 食品矩阵使精确的AFB1光测量变得复杂.
研究的目的:
- 开发一种方法来恢复内在的阿弗拉托xin B1光.
- 在植物油中创建精确的AFB1度预测模型.
- 证明拟议方法在传统方法上的优越性.
主要方法:
- 使用双整合球 (DIS) 进行吸收和散射测量.
- 使用激光诱导光 (LIF) 来捕捉光强度.
- 开发了一个6参数分析模型与1D-CNN恢复AFB1光.
- 使用扰乱和恢复光数据校准的预测模型.
主要成果:
- 从复杂的植物油矩阵中成功恢复了AFB1内在光.
- 使用回收的光,实现了准确的AFB1度预测.
- 与使用扰乱光的模型相比,模型性能显著改善.
结论:
- 拟议的方法有效地纠正光光谱学中的矩阵效应.
- 基于1D-CNN的分析模型为AFB1量化提供了强大的解决方案.
- 该技术通过可靠,非破坏性的AFB1查来提高食品安全.
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