基板Vis-NIR光谱与机器学习相结合,用于Hongmeiren的多任务分析:地理来源识别和抗氧化成分量化
Tao Jiang1, Jianjun Ding1, Shaofeng Yuan1
1State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University University, Wuxi, Jiangsu Province, China; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, China.
Food chemistry
|June 7, 2025
概括
桌面可见和近红外 (Vis-NIR) 光谱学与机器学习相结合,有效地识别了Hongmeiren的起源并量化了抗氧化剂. 这种方法为水果提供了有效的,同时进行的质量评估和地理真实性验证.
科学领域:
- 农业化学 农业化学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 经济动机的原产地欺诈是香港梅林 (HMR) 果的担忧,因为它具有地理标志的优势.
- 不同的HMR来源之间存在抗氧化成分的显著差异.
研究的目的:
- 开发一种方法,同时识别HMR的地理来源.
- 为了量化HMR类中关键的抗氧化成分.
- 评估基板Vis-NIR光谱学与机器学习相结合的可行性.
主要方法:
- 使用了长板可见和近红外 (Vis-NIR) 光谱学.
- 机器学习模型用于分类 (原产地识别) 和回归 (抗氧化剂量化).
- 应用了数据预处理 (Savitzky-Golay,区域规范化) 和特征选择 (Boruta算法) 来优化模型性能.
主要成果:
- 送神经网络模型在地理来源识别方面实现了88.7%的分类准确性,AUC为0.943.
- 该模型表现出强大的回归性能,用于量化 Askorbic 酸 (R2 = 0.875),总 (R2 = 0.856) 和总黄 (R2 = 0.806).
- 预处理和特征选择技术显著改善了模型性能.
结论:
- 基板Vis-NIR光谱是一种实用和高效的工具,用于水果的多任务分析.
- 这种方法可以同时进行质量评估和地理真实性识别.
- 开发的方法可以帮助打击原产地欺诈,确保水果的质量.
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