An electrochemical molecularly imprinted sensor based on chitosan capped with gold nanoparticles and its application for highly sensitive butylated hydroxyanisole analysis in foodstuff products.

One of the most widely used synthetic antioxidants in food, butylated hydroxyanisole (BHA) has raised serious concerns due to its potential toxic effects on human health. Hence, elaboration of simple, effective and sensitive methods for BHA detection is pressing. In this regards, the present research work highlights a facile, simple, and fast synthesis approach for the development of an electrochemical sensor for the analysis of BHA in foodstuffs. In this study, the chitosan (CS) capped with gold nanoparticles (AuNPs) were self-assembled on a screen-printed carbon electrode (SPCE) and complete the elaboration of the molecularly imprinted polymer (MIP) sensor in the presence of BHA as templates. The electrochemical behaviour of the MIP sensor was investigated by using electrochemical impedance spectroscopy (EIS), differential pulse voltammetry (DPV), and cyclic voltammetry (CV). Similarly, the morphology of the electrodes surface of the different elaboration steps was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and atomic force microscopy (AFM). In addition, the obtained results demonstrate satisfactory sensitivity and selectivity to BHA compared to interfering species, including ascorbic acid and citric acid. Under optimal experimental conditions, the MIP sensor exhibits responses proportional to concentrations over a range of 0.01-20 μg mL-1, with a low detection limit (LOD) of 0.001 μg mL-1 (signal-to-noise ratio S/N = 3). Besides, the reproducibility, stability, and repeatability of the MIP sensor were proven. Taking into account all these outcomes, the MIP sensor well demonstrates its ability towards the determination of BHA in food samples with a relative standard deviation (RSD ≤ 8%). Spectrophotometry was utilized as a validation method. Partial least squares (PLS) prediction models were constructed from the MIP sensor and spectrophotometer data with a regression coefficient (R = 0.99). According to the achieved outcomes, the MIP sensor could be a viable tool for food control.