Review on unravelling the analytical signatures of fluoroquinolone antibiotics: Exploring diverse matrices through chemometric modelling

Abstract

Globally, fluoroquinolones are the third largest antimicrobial category. These molecules can enter natural biota either in unmetabolized or partially metabolised form and undergoes further transformation depending on biotic and abiotic factors in aqueous and terrestrial ecosystems, which can lead to antimicrobial resistance. This requires timely monitoring and prediction of fluoroquinolone and metabolite changes. The physiochemical flexibility of fluoroquinolones, complicated sampling combinations, and matrix interference in sample preparation and detection could give misleading quantifying results. These complex and massive data sets require rigorous statistical and mathematical data processing approaches to detect analytical fingerprints/patterns, and point - nonpoint source discrimination. This paper has critically reviewed the use of predictive and exploratory chemometric models to identify the patterns and resolve overlapping, asymmetric peaks and multicollinearity fluoroquinolone spectrum data raised from several separative and non-separative detection techniques. Moreover, this review also highlights the crucial parameters involved in determining fluoroquinolones in real-time samples, challenges, and research gaps associated with current analytical techniques. The approach also prioritises the integration of clustering, classification and regression-based chemometrics to achieve justifiable accurate results. This review will address fluoroquinolone detection challenges and help the government and research community to develop better regulatory policies, analytical methods, and mitigation strategies to protect life-saving antibiotics.