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Chemometrics‐based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high‐performance liquid chromatography
Author(s) -
Usman Abdullahi Garba,
Işik Selin,
Abba Sani Isah,
Meriçli Filiz
Publication year - 2021
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.202000890
Subject(s) - chemometrics , artificial neural network , adaptive neuro fuzzy inference system , artificial intelligence , ensemble forecasting , computer science , chromatography , high performance liquid chromatography , mathematics , machine learning , fuzzy logic , biological system , chemistry , fuzzy control system , biology
In this research, two nonlinear models, namely; adaptive neuro‐fuzzy inference system and feed‐forward neural network and a classical linear model were employed for the prediction of retention time of isoquercitrin in Coriander sativum L. using the high‐performance liquid chromatography technique. The prediction employed the use of composition of mobile phase and pH as the corresponding input parameters. The performance indices of the models were evaluated using root mean square error, determination co‐efficient, and correlation co‐efficient. The results obtained from the simple models showed that subclustering‐adaptive‐neuro fuzzy inference system gave the best results in both the training and testing phases and boosted the performance accuracy of the simple models. The overall comparison of the results showed that subclustering‐adaptive‐neuro fuzzy inference system ensemble demonstrated outstanding performance and increased the accuracy of the single models and ensemble models in the testing phase, up to 35% and 3%, respectively.