Green analytical chemistry: Experimental and chemometric methods for the detection of therapeutics using liquid chromatography in wastewater samples

Abstract

The present expeditious advancement of green analytical chemistry (GAC) necessitates the establishment of explicit and succinct GAC principles, which may serve as valuable guidance in the adoption of environment friendly laboratory practises. The current ideas of green chemistry and green engineering need modification to effectively address the requirements of analytical chemistry within the context of GAC. The use of multivariate curve resolution (MCR), parallel factor analysis (PARAFAC), self-weighted alternating trilinear decomposition (SWATLD), and unfolded partial least squares/residual bi-linearization (UPLS/RBL) are prevalent approaches for the examination of process data across several application domains like to detect contaminants in water samples. A special emphasis was placed on circumstances that necessitate sophisticated and customised implementations of multivariate curve resolution. This will involve addressing enhancements in pre-processing techniques, arrangements of data from multiple sets, customised constraints, challenges associated with non-ideal noise structure, and deviations from linearity. The study furthermore covers a thorough analysis of case studies and new developments within the discipline, highlighting the efficacy of green analytical chemistry in the identification of pharmaceutical substances in wastewater. This paper examines the principles, methodologies, instrumental analysis, multivariate algorithms, MCR methods, data set configurations, separation techniques and practical applications of GAC for resource minimization and sustainability.