使用碎片化质谱与机器学习模型预测水中的未识别化学品的气味感官属性
Yuanxi Huang1, Lingjun Bu1, Kuan Huang2
1Hunan Engineering Research Center of Water Security Technology and Application, Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha 410082, China.
Environmental science & technology
|June 15, 2024
概括
这项研究引入了一种新的机器学习模型,使用分子结构或MS2光谱来预测水中的气味感知. 该模型准确地识别了未知的化学品的气味属性,有助于水质管理.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 在水中追踪气味需要了解众多气味剂的气味感官属性.
- 对水中的气味剂的实验性确定是复杂的,对于大规模分析而言并不务实.
研究的目的:
- 开发第一个机器学习 (ML) 模型,用于预测气味感知和水中的气味剂值.
- 为了能够使用分子结构或MS2光谱作为输入特征进行预测.
主要方法:
- 开发并验证了一种用于气味预测的机器学习模型.
- 使用分子结构和MS2光谱作为输入特征.
- 应用非目标分析,从真实的水样中获得MS2光谱.
主要成果:
- ML模型在预测气味感知和值方面表现出卓越的准确性.
- MS2光谱为预测提供了与分子结构几乎相当的性能.
- 该模型成功地预测了真实水样中的未识别化学品的气味属性.
结论:
- 机器学习模型可以有效地预测水中的气味剂的气味感官属性.
- MS2光谱为分子结构提供了可行的替代品,用于气味预测,特别是未识别的化合物.
- 了解功能组相互作用和物理化学性质是嗅觉机制的关键,促进水质监测和控制.
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