Development and Validation of Ecofriendly AQbD Aided Analytical Methods for the Quantification of Food Additives in Commercial Food Samples

Abstract

Food additives have been employed by diverse cultures and civilizations to augment or preserve the nutritional quality of food, enhance freshness, prolong shelf-life, and guarantee the year-round availability of seasonal foods, offering convenience through economical, time-saving, ready-to-eat alternatives. Prioritizing environmentally sustainable solutions for quantifying these food additives is essential, as their excessive use presents considerable health hazards. Developing a waste-reducing, eco-friendly method for HPLC/HPTLC compound analysis is a challenging and labour-intensive process. A singular approach can be both ecologically sustainable and resilient by integrating the principles of sustainable chemistry with analytical quality by design. The initiative seeks to investigate sustainable analytical methodologies for the selected food additives, focusing on green analytical techniques for method development and validation. newlineThis research involves the development of analytical techniques for quantifying food additives by the proposed HPLC and HPTLC approach, utilizing environmentally benign compounds. Stress analyses were conducted on all the proposed HPLC and HPTLC methodologies. Sample preparation is a critical and intricate step in the procedure, requiring careful formulation to reduce potential disruptions in matrix and ensure suitability for food analysis. Thus, it is essential to create extraction methods that are both straightforward and environmentally friendly. Food additives have been extracted from diverse food samples via the green UAE approach. The UAE ensures the integrity and safety of the extracts while enhancing the recovery rate from food samples. The sustainability of the proposed methods was determined using the Analytical-Eco-Scale (AES), Green-Analytical-Procedure-Index (GAPI), and AGREE metrics. Meanwhile, Blue-Applicability-Grade-Index (BAGI) was used to evaluate blueness, and the Red-Green-Blue (RGB) model was applied to analyse whiteness newline