Chemo-Metric Optimization for Robust and Green Chromatographic Methods

Developing chromatographic methods usually encounters challenges of achieving analytical robustness and adhering to the principles of greenness through Green Analytical Chemistry (GAC). Traditional One-Variable-at-a-Time (OVAT) optimization techniques often fail to capture complex factor interactions, resulting in inefficient methods and subsequent environmental impacts. This paper introduced chemometrics, which encompasses multivariate analysis (MVA), Design of Experiments (DoE), and Monte Carlo simulations as a data-driven and systematic solution. Chemometric optimization helps to identify Critical Method Parameters (CMPs) and facilitates the development of Method Operable Design Region (MODR), within which analytical performance is reliable and consistent. The integration of GAC metrics, such as the Analytical GREEnness (AGREE) calculator, would promote sustainability through chemometric approaches by reducing solvent consumption, lowering energy demands, minimizing waste generation, and shorter analysis times. Thus, the relationship between chemometrics and GAC provides a framework for developing efficient, robust, and environmentally responsible chromatographic methods that comply with regulatory expectations and Quality-by-Design (QbD) principles.