应用到NIR光谱数据的统计机器学习技术,以快速检测黄粉中的苏丹染料-I,并优化预处理和波长选择
Saumita Kar1, Bipan Tudu1, Rajib Bandyopadhyay1
1Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt Lake Campus, Block LB, Sector III, Plot 8, Salt Lake, Kolkata, 700 098 India.
Journal of food science and technology
|September 17, 2024
概括
近红外 (NIR) 谱学有效地检测和量化黄粉中的苏丹染料I杂物. 机器学习模型,特别是部分最小平方回归 (PLSR),为食品安全提供了准确和可靠的分析.
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:
- 黄粉是一种广泛消费的香料,易受改.
- 苏丹染料I是一种合成染料,在食品中存在时会对健康造成风险.
研究的目的:
- 开发和验证一种使用NIR光谱检测和量化黄粉中的苏丹染料I的方法.
- 为此分析,比较各种机器学习回归技术的性能.
主要方法:
- 机器学习对近红外 (NIR) 光谱数据的系统应用.
- 用不同度的苏丹染料I (1-30%) 添加的黄样品的制备.
- 使用主要组件分析 (PCA) 进行可视化和部分最小平方回归 (PLSR) 进行定量分析,与集树回归 (ENTR),支向量回归 (SVR) 和主要组件回归 (PCR) 进行比较.
主要成果:
- 部分最小平方回归 (PLSR) 显示出卓越的性能.
- PLSR实现了高精度,确定系数 (R2) >0.97和根平均平方误差 (RMSE) <0.93.
- 使用从净分析信号 (NAS) 理论衍生出的优点图 (FOM) 来验证模型的稳定性.
结论:
- 与机器学习相结合的NIR光谱是检测和量化黄中的苏丹染料I的可靠技术.
- 开发的PLSR模型为食品质量控制和安全保证提供了令人满意的准确性和稳定性.
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