ATR-FTIR光谱和深度学习用于红葡萄酒的化学分析和地理分类
Shengwei Wang1, Keda Chen1, Yize Wang1
1School of Computer Science and Engineering, Northwest Normal University, Lanzhou, China.
Journal of food science
|January 23, 2026
概括
这项研究使用红外光谱学和一种新的AI模型来验证红葡萄酒的来源. 该方法准确地确定葡萄酒化学成分的区域差异,确保可靠的地理可追溯性.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 机器学习用于光谱学.
背景情况:
- 地理来源认证对于葡萄酒质量控制和消费者信任至关重要.
- 葡萄酒原产地分析的传统方法可能耗时且缺乏精度.
- 开发快速,准确和强大的葡萄酒地理可追溯性方法至关重要.
研究的目的:
- 开发和验证一种使用光谱学和机器学习验证红葡萄酒地理原产地认证的新方法.
- 调查减弱总反射里埃变换红外光谱 (ATR-FTIR) 对于捕获特定区域的光谱指纹的潜力.
- 评估残余复合卷积神经网络 (Res-MCNN) 的性能,以准确地分类红葡萄酒来源.
主要方法:
- 使用ATR-FTIR光谱学分析来自中国不同地区 (新疆和河北) 的200个红葡萄酒样本.
- 应用先进的预处理技术:竞争性自适应重量化采样 (CARS) 用于波长选择和决策树聚合用于特征优化.
- 开发和实施一个新的残余复合卷积神经网络 (Res-MCNN) 用于分类.
主要成果:
- ATR-FTIR成功捕获了特定区域的光谱指纹,标化酸和化合物被确定为关键的区分标记.
- 拟议的Res-MCNN模型实现了高分类准确度 (97.975%),灵敏度 (98.2%),特异性 (97.6%) 和AUC (0.989).
- 有控制的实验表明,尽管酒精和糖含量略有变化,但该模型保持稳定的可追溯性性能.
结论:
- 结合ATR-FTIR光谱学和Res-MCNN模型,为红葡萄酒的地理原产地认证提供了一种高度有效和可靠的方法.
- 开发的模型在识别区域光谱特征方面表现强,有助于葡萄酒的真实性和可追溯性.
- 这种方法为葡萄酒行业提供了一个有前途的工具来打击欺诈和确保产品的来源.
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