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在LC-HRMS中预测保留指数,以加强水中的有机微污染物的结构识别:基于选择性的过

Ardiana Kajtazi¹, Marin Kajtazi², Maike Felipe Santos Barbetta³

¹Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.

Analytical chemistry

|January 3, 2025

概括

一个新的机器学习算法有助于识别水中的有机污染物,通过使用两个不同的染色学阶段的保留数据来预测分子结构. 该工具可以改善水质分析,并确保获得更安全的饮用水.

科学领域:

环境化学环境化学
分析化学分析化学
计算化学计算化学

背景情况:

全球范围内确保获得安全饮用水是一个重大挑战,特别是在发展中国家,由于持久的有毒有机污染物.
高性能液体染色学与高分辨率质谱学 (HPLC-HRMS) 结合,对于污染物识别至关重要,但与复杂的结构和不可靠的数据库作斗争.
对未知有机微污染物的精确结构阐明对于有效的水质管理至关重要.

研究的目的:

开发一种机器学习算法,支持对水中的小型有机污染物 (C,H,O基,<500 Da) 的结构阐明.
通过整合来自多个染色体相的实验和预测保留数据来提高污染物识别的准确性.
为在水样中更快,更可靠地识别未知的有机微污染物提供一个工具.

主要方法:

开发一种机器学习算法,比较可能的分子结构的实验和预测保留时间.
使用了两种逆相静止相 (八基,C18和五基,F5),具有不同的选择性.
将保留时间转换为仪器独立性的保留指数,并采用多重线性回归 (MLR) 模型 (MLR-C18,MLR-F5) 具有16个有影响力的分子描述符.

主要成果:

对于保留指数,MLR-C18模型实现了高预测准确度 (R2 = 0.97,RMSE = 36,MAE = 26),MLR-F5显示了可比性能 (R2 = 0.96,RMSE = 44,MAE = 34).
使用两种静止相数据的新型交叉式过可以消除超过70%的给定元素组成的不正确结构公式.
开发的模型成功地用于在真实饮用水样本中识别污染物,证明了其实际实用性.

结论:

机器学习算法有效预测保留指数,并大大减少有机污染物的错误结构识别.
结合使用两个不同的静止阶段,提高了HPLC-HRMS分析中结构阐明的可靠性.
该工具为水质管理提供了有前途的进步,可以更快,更准确地识别未知的有机微污染物.

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