Quantitative analysis of the adulteration of orange juice with sucrose using pyrolysis mass spectrometry and chemometrics

Abstract

A 10% (w/v) beet sucrose solution was used to adulterate freshly squeezed orange juice over the range 0–20% (or 0–20 g l −1 of added sucrose). Samples were analysed by the rapid automated screening technique of Curie-point pyrolysis mass spectrometry (PyMS). To deconvolve these spectra neural cognition-based methods of multilayer perceptrons (MLPs) and radial basis functions (RBFs) and the linear regression technique of partial least squares (PLS) were studied. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the level of sucrose adulteration at levels below 1% for samples, with an accuracy of ± 1.3%, on which they had not been trained. The best results were obtained using PLS when 8 latent variables were employed for predictions. Furthermore, the inputs to MLPs could be reduced using principal components analysis (PCA) from 150 masses to 8 PC scores without any deterioration of the predictive ability of the model, highlighting that PCA is an excellent pre-processing step which has the potential to speed up neural network learning as there are fewer weights to update. Since any foodstuff can be pyrolysed in this way, the combination of PyMS with chemometrics constitutes a rapid, powerful and novel approach to the quantitative assessment of food adulteration generally.