A comprehensive review on the applications of chemometrics in analytical chemistry
DOI:
https://doi.org/10.69857/joapr.v13i3.861Keywords:
Chemometrics, Data-driven Methodologies, Multivariate Analysis, Artificial Intelligence, Machine Learning in ChemometricsAbstract
Background: This article presents a review of the various applications of chemometrics in analytical chemistry. Chemometrics is essential to analytical chemistry because it provides sophisticated methods for extracting, analyzing, and interpreting chemical data. To maximize analytical procedures, enhance data reliability, and extract insights, this field combines statistical and mathematical techniques with chemical research. Chemometrics provides analytical chemists with the means to manage the massive datasets generated by contemporary analytical methods, such as spectroscopy and chromatography. Methodology: This review combines data from previous research articles that have elaborated and described the various applications of chemometrics in the analytical chemistry sector. Results and Discussion: The combination of chemometrics with artificial intelligence and machine learning offers more advanced analytical and predictive modelling possibilities. It is anticipated that these developments will transform analytical chemistry by enhancing researchers' ability to manage complex datasets and gain deeper insights from their investigations. This is especially important in industries where precise data interpretation is critical, such as pharmaceuticals, ecological surveillance, and food safety. Conclusion: Chemometrics is essential to contemporary analytical chemistry because it provides methods and instruments that enhance quality control, facilitate innovative research advancements, and improve data analysis capabilities. The accuracy and efficacy of chemical analyses are expected to continue to improve as this sector develops.
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