Artificial neural networks and multivariate calibration for spectrophotometric differential kinetic determinations of food antioxidants Original Research Article

Mixtures of food antioxidants, butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT) and propyl gallate (PG), were simultaneously analyzed with spectrophotometry, based on their different kinetic properties. These antioxidants react differentially with Fe(III), and the reduced product of which, Fe(II), will be complexed by chromogenic reagent 2,2′-dipyridyl. The differential kinetic spectra were monitored and recorded at 510 nm, and the data obtained from the experiments were processed by chemometric approaches, such as artificial neural network (ANN), classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS). A set of synthetic mixtures of antioxidants was evaluated and the results obtained by the applications of these chemometric approaches were discussed and compared. It was found that the ANN method afforded better precision relatively than those of CLS, PCR and PLS. The proposed method was also applied satisfactorily to the determination of antioxidants in several commercial food products.