机器学习支持的多源数据融合能够准确地区分毛泰风味白生产技术
Longyuan Lin1, Ziying Ruan1, Na Zhang1
1College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Food research international (Ottawa, Ont.)
|February 18, 2026
概括
这项研究开发了一个综合分析框架,使用多源风味分析和机器学习来验证Maotai-Flavor Baijiu (MFB) 造过程的真实性. CatBoost模型在区分Kunsha,Suisha和Fansha过程中达到97%的准确性.
科学领域:
- 食品化学 食品化学
- 分析化学 分析化学
- 机器学习应用 机器学习应用
背景情况:
- 毛泰-风味Baijiu (MFB) 的质量严重依赖于其造过程,特别是Kunsha (KSJ),Suisha (SSJ) 和Fansha (FSJ) 的阶段.
- 强大的身份验证方法对于确保MFB质量和可追溯性至关重要.
研究的目的:
- 根据其独特的造工艺,开发一个综合分析框架来验证MFB的真实性.
- 使用多源风味分析和机器学习,区分KSJ,SSJ和FSJ生产方法.
主要方法:
- 使用电子鼻子 (e-nose),电子舌头 (e-tongue),GC-FID和HS-SPME-GC-MS对80个MFB样本进行了全面的表征.
- 通过贝叶斯式超参数调开发和优化九个机器学习分类器.
- 应用SHAP框架对关键的区分特征进行解释性分析.
主要成果:
- 在三种造工艺中观察到挥发性概况和味道属性的显著差异.
- 电子鼻子和电子舌头分析确定了关键的分辨感官属性.
- 在区分造过程中,CatBoost机器学习模型实现了97.0%的高分类精度.
- 关键的芳香活性化合物,如乙酸乙烯和2,3,5,6-四甲基被确定为对工艺的歧视至关重要.
结论:
- 结合多源数据和优化机器学习的综合分析框架为MFB流程可追溯性提供了强大的数据驱动策略.
- 这种方法促进了MFB生产中的有效质量控制和认证.
- 结果与造化学的既定原则保持一致,验证了分析方法.
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