化学计量学
人工智能
人工神经网络
计算机科学
机器学习
保留时间
色谱分离
过程(计算)
支持向量机
色谱法
化学
高效液相色谱法
操作系统
作者
Yash Raj Singh,Darshil B. Shah,Dilip Maheshwari,Jignesh Shah,Shreeraj Shah
标识
DOI:10.1080/10408347.2023.2254379
摘要
AbstractRetention 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.Keywords: Analytical chemistryartificial intelligencechromatographydeep learningpharmaceutical analysisretention prediction Conflict of interestThe authors report there are no competing interests to declare.Authors contributionConceptualization: Yash Raj Singh; Methodology: Yash Raj Singh, Darshil B. Shah; Formal analysis and investigation: Darshil B. Shah, Yash Raj Singh; Writing - original draft preparation: Yash Raj Singh; Writing - review and editing: Darshil B. Shah, Yash Raj Singh; Resources: Yash Raj Singh; Supervision: Dr. Dilip G. Maheshwari, Dr. Jignesh S. Shah, Dr. Shreeraj Shah
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