An innovative nanoparticle-modified carbon paste sensor for ultrasensitive detection of lignocaine and its extremely carcinogenic metabolite residues in bovine food samples: Application of NEMI, ESA, AGREE, ComplexGAPI, and RGB12 algorithms.

Abstract

Nowadays, veterinary medicine residues have been viewed as a major threat to food safety worldwide, especially when dealing with carcinogenic residues. Herein, we present the first differential pulse voltammetric method for the quantification of lignocaine and its carcinogenic metabolite 2,6-xylidine residues in bovine food samples, aided by five greenness and whiteness assessment tools, including NEMI, ESA, ComplexGAPI, AGREE, and RGB12. The method depends on the electrochemical oxidation after modification of the carbon paste sensor with recycled Al2O3-NPs functionalized multi-walled carbon nanoparticles. The produced sensor (Al2O3-NPs/MWCNTs/CPE) was characterized using XRD, FT-IR, EDX, SEM, and TEM. As expected, the active surface area and electron transfer processes were accelerated by the modification, resulting in ultra-sensitive quantification with detection limits of 19.00 and 13.94nM for lignocaine and 2,6-xylidine, respectively. In terms of greenness, whiteness, sustainability, analytical effectiveness, and economic and practical considerations, the proposed method outperforms the reported methods.