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QDA和电子鼻子与机器学习方法相结合,可以快速预测消费者对切达奶酪的偏好
Ying Wang1, Xinying Xu1, Yadong Wang1
1School of Food and Health, Beijing Technology and Business University, Beijing 100048, China.
Journal of dairy science
|November 29, 2025
概括
消费者对切达奶酪的偏好取决于香味. 通过结合感官分析和机器学习,准确地预测中国消费者对奶酪风味特征的偏好.
科学领域:
- 食品科学 食品科学 食品科学
- 感官科学 感官科学
- 数据科学数据科学数据科学
背景情况:
- 消费者对切达奶酪的接受与其香味密切相关.
- 了解中国消费者的口味偏好对于乳制品行业至关重要.
研究的目的:
- 使用定性描述性分析 (QDA) 和电子鼻子 (e-nose) 技术,系统地描述切达奶酪的风味特征.
- 用机器学习算法预测中国消费者对切达奶酪的偏好.
主要方法:
- 定性描述性分析 (QDA) 评估了6种气味属性.
- 电子鼻子 (e-nose) 技术捕获了挥发性风味化合物.
- 四个机器学习模型 (逻辑回归,高斯 SVM,k-NN,决策树) 用于对152个消费者的分类和预测.
主要成果:
- 主要组件分析显示,电子鼻子数据解释了84.3%的差异.
- 最初的机器学习模型达到高达87.5%的准确性.
- 优化的模型,不包括QDA指标,达到了92.9%的准确性,显示出更好的性能.
结论:
- 通过将感官奥米克 (QDA和e-nose) 与机器学习相结合,可以有效预测奶酪消费偏好.
- 这种方法为开发适合中国消费者口味偏好的奶酪产品提供了宝贵的见解.
- 该研究突出了乳制品行业风味导向监管的实际应用.
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