频谱多尺度注意力融合网络用于快速检测黑茶伪造,使用手持式光谱仪
Jiawei Tang1,2, Yongyan Chen1, Qing Meng3
1Chinese-Hungarian Cooperative Research Centre for Food Science, College of Food Science, Southwest University, Chongqing 400715, China.
Foods (Basel, Switzerland)
|December 30, 2025
概括
一种新方法使用近红外 (NIR) 光谱和光谱多尺度注意力融合网络 (SMAFNet) 来检测黑茶中微量的人造色素,确保食品安全.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 黑茶与人工染料的改会给健康带来风险.
- 近红外 (NIR) 光谱是一种快速,非破坏性的分析方法.
- 通过NIR检测色素的微量水平是具有挑战性的,因为低度和自然色素干扰.
研究的目的:
- 开发一种快速,非破坏性的方法来检测黑茶中的微量人工染料.
- 为了提高NIR光谱的灵敏度,以检测低级别的改.
- 引入和验证一个新的光谱多尺度注意力融合网络 (SMAFNet).
主要方法:
- 使用手持式近红外光谱仪进行样本分析.
- 开发和实施了光谱多尺度注意力融合网络 (SMAFNet).
- SMAFNet集成了光谱预处理,多尺度特征提取和跨尺度注意力融合.
主要成果:
- 与传统的机器学习模型相比,SMAFNet表现优越,特别是在低改水平 (0.1到0.5g·kg-1) 上.
- 精度从97.22%到100%不等,F1分数从0.9879到1.00,并回忆达到100%检测日落黄色,tartrazine和Ponceau 4R.
- 这种方法被证明是可行的,并且对微量色素检测具有稳定性.
结论:
- 将NIR光谱与SMAFNet相结合,为黑茶中人工染料的快速和有区别检测提供了一个实用的框架.
- 这种方法支持现场监测食品安全和质量控制.
- 该研究强调了先进的深度学习模型在食品中进行微量分析的潜力.
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