A kinetic spectrophotometric method for the determination of ternary mixtures of reducing sugars with the aid of artificial neural networks and multivariate calibration

Abstract

A differential spectrophotometric method has been developed for the simultaneous quantitative determination of glucose (GLU), fructose (FRU) and lactose (LAC) in food samples. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K 3Fe(CN) 6) as the oxidant. The reaction data were recorded at the analytical wavelength (420 nm) of the K 3Fe(CN) 6 spectrum. Since the kinetic runs of glucose, fructose and lactose overlap seriously, the condition number was calculated for the data matrix to assist with the optimisation of the experimental conditions. Values of 80 °C and 1.5 mol l −1 were selected for the temperature and concentration of sodium hydroxide (NaOH), respectively. Linear calibration graphs were obtained in the concentration range of 2.96–66.7, 3.21–67.1 and 4.66–101 mg l −1 for glucose, fructose and lactose, respectively. Synthetic mixtures of the three reducing sugar were analysed, and the data obtained were processed by chemometrics methods, such as partial least square (PLS), principal component regression (PCR), classical least square (CLS), back propagation-artificial neural network (BP-ANN) and radial basis function-artificial neural network (RBF-ANN), using the normal and the first-derivative kinetic data. The results show that calibrations based on first-derivative data have advantages for the prediction of the analytes and the RBF-ANN gives the lowest prediction errors of the five chemometrics methods. Following the validation of the proposed method, it was applied for the determination of the three reducing sugars in several commercial food samples; and the standard addition method yielded satisfactory recoveries in all instances.