Abstract

Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed chromatographic techniques. Numerous approaches were reported for retention prediction in different chromatographic techniques, and consistent results demonstrated that the accuracy and effectiveness of deep learning models outclassed the linear machine learning models, mainly in liquid and gas chromatography, as ML algorithms use fewer complex data to train and predict information. Support Vector machine-based neural networks were found to be most utilized for the prediction of retention factors of different compounds in thin-layer chromatography. Cheminformatics, chemometrics, and hybrid approaches were also employed for the modeling and were more reliable in retention prediction over conventional models. Quantitative Structure Retention Relationship (QSRR) was also a potential method for predicting retention in different chromatographic techniques and determining the separation method for analytes. These techniques demonstrated the aids of incorporating QSRR with AI-driven techniques acquiring more precise retention predictions. This review aims at recent exploration of different AI-driven approaches employed for retention prediction in different chromatographic techniques, and due to the lack of summarized literature, it also aims at providing a comprehensive literature that will be highly useful for the society of scientists exploring the field of AI in analytical chemistry.

摘要

通过基于人工智能 (AI) 的技术进行保留预测已获得指数级增长，因为它们能够处理复杂的数据集并简化大多数常用色谱技术中化合物识别和分离的关键任务。报道了多种不同色谱技术中保留预测的方法，一致的结果表明，深度学习模型的准确性和有效性优于线性机器学习模型，主要是在液相色谱和气相色谱中，因为机器学习算法使用较少的复杂数据来训练和预测信息。人们发现基于支持向量机的神经网络最常用于预测薄层色谱中不同化合物的保留因子。化学信息学、化学计量学和混合方法也用于建模，并且在保留预测方面比传统模型更可靠。定量结构保留关系 (QSRR) 也是预测不同色谱技术中的保留和确定分析物分离方法的潜在方法。这些技术展示了将 QSRR 与人工智能驱动技术相结合的帮助，以获得更精确的保留预测。本综述旨在最近探索不同色谱技术中用于保留预测的不同人工智能驱动方法，并且由于缺乏总结性文献，它还旨在提供全面的文献，对于探索该领域的科学家协会非常有用。分析化学人工智能领域。