预测发酵牛奶中的功能生物活性,使用液体染色学-质谱学代谢学上的深度学习
Falah Awwad1, Ghassan Al-Sumaidaee2, Aya Eltayeb1
1Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, UAE.
Journal of dairy science
|January 25, 2026
概括
深度学习 (DL) 从代谢数据准确预测发酵牛奶的生物活性. 这为功能性食品开发提供了可扩展,高效的方法,即使样本大小有限.
科学领域:
- 食品科学与技术 食品科学与技术
- 生物技术是生物技术.
- 计算生物学 计算生物学
背景情况:
- 发酵乳制品提供营养和健康益处.
- 通过传统测试来评估这些功能性质是耗时的,并且限制了可扩展性.
- 开发高效的选方法对于功能性食品的开发至关重要.
研究的目的:
- 调查深度学习 (DL) 作为评估发酵乳制品生物活性的快速有效替代方案的潜力.
- 开发和验证一种能够从代谢数据预测生物活性得分的DL模型.
- 探索DL在功能性食品开发中的实用性,采用有限的样本大小.
主要方法:
- 液体染色学-质谱学 (LC-MS) 代谢学被用来分析18个发酵牛奶样本.
- 进行了体外生物活性测定 (抗氧化剂,酶抑制,抗癌).
- 在预处理的LC-MS数据上训练了一维卷积神经网络 (1D-CNN),包括数据增强和规范化.
主要成果:
- 1D-CNN模型在所有生物活性预测中实现了0.548±0.089的平均绝对误差,证明了强大的概括性.
- 主要成分分析显示,根据牛奶类型和发酵条件,样品分别被分组.
- 在DL方法成功地预测了功能生物活性从代谢签名,尽管一个小的数据集.
结论:
- 深度学习,结合强大的预处理和数据增强,可以从代谢数据准确预测发酵乳制品的功能生物活性.
- 这种基于DL的方法提供了一个可扩展和高效的替代传统生物活性测试.
- 这些发现表明DL辅助查在功能性食品的开发中是一个有希望的途径.
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