在小麦面粉中识别和定量检测非法添加剂,基于近红外光谱学与化学测量相结合
Xinyi Dong1, Ying Dong2, Jinming Liu3
1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
概括
一种新型号快速检测非法面粉添加剂,如亚二碳胺 (ADA),粉和石膏粉,使用近红外光谱和AI. 这通过准确识别小麦面粉中的污染物来确保食品安全.
科学领域:
- 食品科学与技术 食品科学与技术
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 小麦面的质量和安全性对公共健康至关重要.
- 面粉中的非法添加剂对健康构成重大风险.
- 需要快速准确的检测方法来确保食品的完整性.
研究的目的:
- 开发一个快速识别和检测三个非法面粉添加剂的模型:亚二碳胺 (ADA),粉和石膏粉.
- 将近红外光谱学 (NIRS) 与先进的化学测量方法相结合,以提高检测.
- 引入新的波长选择技术,以提高模型性能.
主要方法:
- 使用近红外光谱学 (NIRS) 结合化学测量方法.
- 采用一维卷积神经网络 (1D-CNN) 来减少数据的维度.
- 应用支持矢量机 (SVM) 用于非法添加剂的非线性分类.
- 引入了两个级联波长选择方法:BiPLS-IBPSO和CARS-IBPSO.
- 开发了用于定量检测的部分最小平方 (PLS) 回归模型.
主要成果:
- 1D-CNN-SVM模型实现了添加剂识别的高精度 (F1验证得分为98.81%).
- 在CARS-IBPSO方法有效地选择特征波长进行定量检测.
- 使用CARS-IBPSO选择波长的PLS模型显示出出色的性能 (例如,ADA的R2为0.9786).
- 开发的方法使得在小麦面粉中快速准确地检测出非法添加剂.
结论:
- 与1D-CNN和SVM相结合的NIRS提供了一个强大的方法,用于快速识别非法添加剂.
- 与PLS回归相结合的CARS-IBPSO特征选择为定量检测提供了一个有效的策略.
- 这项研究提出了一种新,高效和准确的方法来确保小麦面粉的安全性.
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