Advanced Artificial Intelligence for Efficient and Rapid Evaluation of Analytical Procedures Greenness and Sustainability

Abstract

Artificial intelligence (AI) has become a dominant force in many scientific and industrial domains and research advancements. However, AI's advancement in analytical chemistry has been stifled due to a lack of accurate and accessible training data. Recently, AI has been a significant factor in determining the sustainability and greenness of analytical techniques. In order to demonstrate how AI can transform the assessment of different analyses of intricate multi-step processes in fields like spectroscopy and chromatography, as well as the creation of novel, quick, and precise analytical chemistry metrics that can evaluate different sustainable forms of the analytical procedure, such as greenness, redness, blueness, and whiteness. Proposed work will examine the impact and current applications of AI in chemical analysis taking Eszopiclone (EZP) as example of drug facing challenge in its quality control process due to its small concentration labelled in tablet form. Additionally, this work demonstrates a comparison of various AI operated metrics; Complementary Green Analytical Procedure Index (Complex GAPI), analytical greenness calculator (AGREE), Blue Applicability Grade Index (BAGI) and Whiteness using the multicriteria RGB 12 model to the previously used metrics, such as Analytical Eco-Scale, Green Analytical Procedure Index (GAPI).