Advances and Applications of Chemometrics: The Future of Data-Driven Analysis- A Review

Chemometrics, first introduced by Svante Wold in the ۱۹۷۰s, integrates mathematics, statistics, and computer science to extract meaningful information from complex chemical data. Initially applied to enhance analytical techniques such as Near-Infrared Spectroscopy (NIR) calibration, High-Performance Liquid Chromatography (HPLC) resolution, and spectral deconvolution, chemometrics has since evolved into a cornerstone of data-driven chemical analysis. This review explores research from ۱۹۸۸–۲۰۲۵, highlighting its applications in analytical chemistry, food science, pharmaceutical analysis, forensic investigations, and environmental monitoring. Fundamental principles such as data preprocessing, multivariate analysis, calibration, and predictive modeling are discussed, with emphasis on recent advances incorporating across diverse fields, chemometrics enables efficient experimental design, rapid interpretation of large datasets, and sustainable solutions by reducing material and energy usage while improving accuracy and reliability. Limitations such as overfitting, interpretability challenges, and dependence on rigorous validation remain, but rapid progress continues to enhance its practicality. By transforming raw data into actionable chemical insight, chemometrics is poised to be a foundational pillar for scientific innovation and sustainable practices in the future.

کلیدواژه ها:

chemometrics ، Data Analysis ، analytical chemistry ، statistical