Abstract

Ponceau 4R (E124) and carmoisine (CMS; E122) are frequently utilized azo synthetic dyes in the food industry owing to their aesthetically pleasing coloration and broad consumer acceptability. It is imperative to prioritize environmentally favorable technologies for quantifying these dyes, as excessive consumption of these poses significant health risks. The primary objective of this research was to establish a reversed-phase (RP)-HPLC method that could simultaneously detect Ponceau 4R and CMS, implementing green analytical chemistry (GAC) and analytical quality by design (AQbD), using an ultrasound-assisted extraction (UAE) technique in commercial food samples. An Agilent Eclipse Plus column (C18, 250 × 4.6 mm id, 5 µm) was utilized for effective separation with a mobile phase of ethanol-acetate buffer pH 5 (60:40, v/v), flow rate of 1 mL/min, and detection wavelength of 515 nm. Critical variables selected for method optimization were ethanol percentage and flow rate, determined using central composite design (CCD). In order to adhere to the 12 principles of green chemistry, hazardous solvents were substituted with ethanol, which is distinguished by its ease of use, effectiveness, and ecological sustainability. The greenness assessment was conducted utilizing the green analytical procedure index (GAPI), analytical eco-scale (AES), and analytical greenness metrics (AGREE). The respective retention times for Ponceau 4R and CMS were 2.276 and 3.450 min. The recovery rate of Ponceau 4R and CMS fluctuated between 70% and 102% and 80% and 102%, respectively, across various marketed food samples. The procedure passed validation in accordance with the International Conference on Harmonization Q14 guidelines. The devised method demonstrates that the validation parameters like linearity, precision, sensitivity, and reproducibility are within the specified limits of ICH guidelines. The greenness assesment tools GAPI, AES, and AGREE produced the most favorable results. In future, environmentally sustainable, solvent-based, robust AQbD methodologies for assessing varieties of food colorants may be adopted and improved commercially.

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