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机器学习增强的风味组学:识别关键的香味化合物,并预测汁风味baijiuu的感觉质量

Shuai Li¹, Yueran Han², Li Wang²

¹College of Liquor and Food Engineering, Key Laboratory of Fermentation Engineering and Biological Pharmacy of Guizhou Province, Guizhou University, Guiyang, Guizhou 550025, China; Key Laboratory of Fermentation Engineering and Biological Pharmacy of Guizhou Province, China.

|February 14, 2025

概括

风味学和机器学习揭示了味的baijiuu.

科学领域:

食品科学与技术食品科学与技术
分析化学分析化学
计算化学的计算化学

背景情况:

传统的方法来评估香香味baijiu的质量是不够的.
感官特征和关键的芳香化合物对于酒质量至关重要.

研究的目的:

在不同生产周期中探索味白的感觉特征和香味化合物.
开发机器学习模型以准确预测baijiu质量等级.
识别关键的气味活性化合物及其度值.

主要方法:

风味学技术,包括气色谱-质谱 (GC-MS) 和感官评估.
机器学习算法 (MLP,XGBoost,随机森林) 与集群 (HCA,K-means,GMM) 结合在一起.
使用绝对乙基二甲 (AEDA) 和气味活性值 (OAV) 等技术分析挥发性有机化合物 (VOC).
沙普利添加剂解释 (SHAP) 模型用于识别关键的芳香化合物及其对质量的贡献.

主要成果:

感官配置文件从早期轮 (BJ1-BJ2) 的"酸性"演变为中期轮 (BJ3-BJ5) 的""和晚期轮 (BJ6-BJ7) 的"焦糖".
确定了18种关键的气味活性化合物,包括乙酸乙烯和乙酸.
机器学习模型实现了很高的预测准确度:XGBoost+K-means.的97%.
SHAP模型确定了20种关键的芳香化合物,如二甲基酸盐和四甲基酸,以及它们的质量值.

结论:

风味学和机器学习为汁风味baijiu的质量评估提供了强大而准确的方法.
了解感官特征和关键芳香化合物的演变对于风味控制至关重要.
这种方法可以实现精确的质量分级,并可以指导白生产以获得最佳的风味配置.

关键词:

度值是指一个度值.这就是Flavoromics.机器学习机器学习质量等级质量等级质量等级质量等级汁风味的baijiuu是什么意思感官特征感官特征感官特征

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