机器学习辅助的香味特征预测在基于风味基因的西红泥
Zhihui Hu1, Yufei He2, Sirou Gu1
1College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an 710119, Shaanxi, China.
Food chemistry
|November 7, 2025
概括
这项研究结合了风味学和机器学习,以预测番茄的风味质量. 一个多层感知子模型准确地预测了基于挥发性化合物的感觉属性,从而实现实时质量控制.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 味道对于番茄泥质量至关重要.
- 传统的风味评估方法缺乏速度和准确性.
- 风味学和机器学习提供了潜在的解决方案.
研究的目的:
- 整合风味学和机器学习,用于番茄的风味分析.
- 描述热处理过程中的感觉和挥发性变化.
- 开发一种感官质量的预测模型.
主要方法:
- 头部空间-固相微提取-气相色谱-质谱 (HS-SPME-GC-MS) 用于挥发性化合物识别.
- 感官分析,以评估风味配置文件.
- 机器学习模型,包括多层感知器 (MLP),用于预测建模.
主要成果:
- 确定了71种挥发性化合物.
- 观察到的感官变化从"新鲜","果实",和"花"到"煮熟"和"酸味"随着热量的增加.
- MLP模型实现了R2>0.99,根据挥发物精确预测了基于挥发物的感觉质量.
- 确定了影响特定感官描述者的关键挥发性物质.
结论:
- 开发的模型提供了一个强大的框架,用于预测番茄的风味质量.
- 这种方法可以在生产过程中实时监测和控制风味.
- 风味学和机器学习的整合增强了食品加工中的质量评估.
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