机器学习辅助的香味特征预测在江风味的baijiuu
Min Zhu1, Mingyao Wang1, Junfeng Gu2
1College of Biomass Science and Engineering, Sichuan University, Chengdu 610065, China.
Food chemistry
|March 9, 2025
概括
机器学习通过分析风味化合物,准确地预测江风味Baijiu (JFB) 香味. 这种风味学方法识别了关键的化学驱动因素,增强了JFB的感觉概况.
科学领域:
- 食品化学 食品化学
- 感官科学 感官科学
- 计算化学计算化学
背景情况:
- 江味白 (JFB) 具有复杂的芳香特征,由多种相互作用的化学化合物造成.
- 由于这种复杂性,精确的感官感知和预测JFB的全球香味是具有挑战性的.
研究的目的:
- 利用机器学习开发一种用于预测JFB全球芳香特征的新策略.
- 为了确定影响JFB整体芳香特征的关键化学参数.
主要方法:
- 对27个代表性的JFB样本进行了风味学和感官分析.
- 整合了包括神经网络在内的五个机器学习算法来预测香味.
- 进行了模型剖析和通过尖端和遗漏测试进行验证.
主要成果:
- 神经网络 (NN) 模型在预测JFB香味方面表现出卓越的表现.
- 18个化学参数被确定为对整体芳香特征的重要贡献者.
- 尖端和遗漏测试证实了这些参数的影响,并改善了感官体验.
结论:
- 机器学习,特别是NN,提供了一个精确的方法来预测JFB的味道.
- 这项研究为JFB风味形成机制提供了宝贵的见解.
- 这些发现可以指导开发改进的JFB生产和质量控制.
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