Multivariate Data Analysis in Analytical Chemistry Using Artificial Intelligence Techniques (Article Review) - Khalil, Ahmed Moussa (American Journal of Biomedicine and Pharmacy, 2026)
In recent years, artificial intelligence (AI) and machine learning (ML) have revolutionized multivariate data analysis in analytical chemistry. Traditional chemometric analyses (e.g., PCA, PLSR) remain essential methods for dimension reduction and exploratory investigation. Nevertheless, AI methods like SVM (support vector machine), Random Forest model, Deep Learning models can enhance the prediction performance automatically extract features and process non- linear more complex datasets effectively. Applications are extremely diverse ranging from spectroscopy, chromatography, electrochemical sensors to pharmaceutical quality control and environmental monitoring. “Preprocessing and feature engineering are very important for detection quality of the model. This article aims to review the recent evolution of, applications for and synergy between AI‐based and classical chemometric tools towards contemporary analytical processes.