基于peptidomics技术和机器学习方法的乳制品苦味的识别和预测
Yang Yu1, Shengchi Liu2, Xinchen Zhang2
1CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China.
Food chemistry
|September 8, 2023
概括
这项研究引入了一种新的工作流程,使用体学和机器学习来有效选苦味 (BPs). 开发的模型准确地预测了BP,大大加快了药物和营养领域的研究.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 食品科学 食品科学 食品科学
背景情况:
- 苦味 (BPs) 在药物和营养研究中至关重要.
- 目前针对BP的大规模查方法是低效的,昂贵的和耗时的.
研究的目的:
- 开发一种全面的工作流程,用于对苦味的高通量选.
- 为准确的BP预测创建和验证机器学习模型.
主要方法:
- 采用了体学技术进行类分析.
- 使用光梯度增强机算法开发了一种新的分类预测模型 (CPM-BP).
- 纳入了扩展的数据集和BP的独特特征因素,用于模型培训.
主要成果:
- 在预测BP时,CPM-BP模型实现了90.3%的准确性.
- 从腐烂和新鲜的UHT牛奶样本中确定了180个潜在的BPs.
- 通过实验验证了三个新的BP,证明它们能够激活人类苦味受体T2R4 (hT2R4).
结论:
- 开发的体学和机器学习工作流提供了一种有效和高效的选苦味的方法.
- 该CPM-BP模型在识别潜在的BP方面表现出高准确性和实用性.
- 这一进步有助于进一步研究苦味感知,药物开发和营养科学.
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