对数据和模型进行系统审查,以预测食品的风味和质地
Michael Gunning1,2,3, Ilias Tagkopoulos1,2,3
1Department of Computer Science, University of California, Davis, 95616, USA.
Current research in food science
|July 21, 2025
概括
使用计算模型预测食物的味道和质地正在进步. 机器学习显示了味道和气味化合物识别的前景,尽管纹理数据需要更多的开发.
科学领域:
- 食品科学 食品科学 食品科学
- 计算化学的计算化学
- 感官科学 感官科学
背景情况:
- 味道和质地是关键的食物属性.
- 预测建模为食品开发和质量控制提供了潜力.
- 当前的数据资源和感官预测模型正在不断发展.
研究的目的:
- 系统地审查数据资源和计算模型,以预测食品的风味和质地.
- 识别感官预测研究中的挑战和机遇.
- 突出未来的研究方向和应用.
主要方法:
- 对计算模型和数据资源的系统文献综述.
- 机器学习方法对味道,气味和纹理预测的分析.
- 评估现有数据库及其在模型开发中的作用.
主要成果:
- 由于定义的分子分类,口味预测更加成熟.
- 气味预测面临着来自不同气味的挑战以及缺乏标准化指标的挑战.
- 机器学习,包括图形神经网络和深度学习,显示了识别味道和气味化合物的潜力.
- 纹理预测研究不那么先进,但对于食品质量控制至关重要.
- 专业数据库越来越多地可用于支持模型开发和基准测试.
结论:
- 在风味预测方面取得了重大进展,特别是在味道方面.
- 需要进一步开发强大的数据集和标准化指标,以预测气味和纹理.
- 提高模型可解释性和整合受体级数据是未来的关键方向.
- 应用范围不仅仅是食品设计,还包括香水,制药和环境监测.
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