通过机器学习预测和改进复杂的酒风味
Michiel Schreurs1,2,3, Supinya Piampongsant1,2,3, Miguel Roncoroni1,2,3
1VIB-KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium.
Nature communications
|March 27, 2024
概括
机器学习模型使用化学和感官数据准确地预测酒的味道和消费者的喜好. 这种方法识别了关键化合物,使得能够创建具有更好的口味特征的增强酒品种.
科学领域:
- 食品科学 食品科学 食品科学
- 计算化学计算化学
- 感官科学 感官科学
背景情况:
- 食物口味感知是复杂的,受到众多化学和外部因素的影响,使预测变得困难.
- 了解化学成分和消费者欣赏之间的复杂关系对于食品产品的开发至关重要.
研究的目的:
- 使用机器学习开发酒风味和消费者欣赏的预测模型.
- 为了确定特定的化学化合物,驱动风味感知和消费者喜欢酒.
- 为创造具有增强感官属性的定制食品奠定基础.
主要方法:
- 对250种不同酒的200多种化学特性进行分析.
- 由训练有素的品尝小组进行的定量描述性感官分析.
- 整合了超过18万个消费者评论与化学和感官数据.
- 培训和评估10个机器学习模型,包括梯度提升.
主要成果:
- 机器学习模型,特别是渐变增强,在预测味道和消费者欣赏方面显著优于传统的统计方法.
- 确定特定的,意想不到的化学化合物作为酒风味和消费者喜好的主要驱动因素.
- 开发改性酒品种 (酒精和非酒精),通过添加已识别的化合物,明显提高消费者的欣赏度.
结论:
- 大数据和机器学习有效地阐明了食品化学,味道和消费者感知之间的复杂联系.
- 这项研究为数据驱动的新型食品设计提供了基础框架,这些新型食品具有优化的感官质量.
- 这些发现为开发定制的食品和饮料产品铺平了道路,这些产品具有优越的,可预测的消费者吸引力.
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