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

Summary 5‐ 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 ( R 2 ) of 0.926 and a root‐mean‐square error of prediction ( RMSEP ) of 0.4168 in EN ; there were R 2 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.