Detection of 5‐HMF in apple juice with artificial sensing systems

Abstract

Summary5‐HMF (5‐hydroxymethyl‐furfural) is a product of thermal treatment and is increasingly considered a food contaminant. Here, different concentrations of 5‐HMF were measured in apple juice to evaluate the performance of the electronic nose (EN) and electronic tongue (ET) as rapid detection techniques for 5‐HMF when coupled with chemometric analysis. Principal component analysis (PCA) and linear discriminant analysis (LDA) evaluated the discrimination capacity of EN and ET for 5‐HMF. Loading analysis examined the discrimination contribution of the EN sensors. Partial least square (PLS) regression analysis established a quantitative prediction model for different concentrations of 5‐HMF based on EN and ET data set. The optimal models had a coefficient of determination (R2) of 0.926 and a root‐mean‐square error of prediction (RMSEP) of 0.4168 in EN; there were R2 of 0.914 and RMSEP of 0.5836 in ET. These results demonstrate that EN and ET coupled with chemometric analysis are two promising approaches for the rapid and online detection of 5‐HMF in apple juice.