A NIR spectroscopy-based efficient approach to detect fraudulent additions within mixtures of dried porcini mushrooms

Boletus edulis and allied species (BEAS), known as “porcini mushrooms”, represent almost the totality of wild mushrooms placed on the Italian market, both fresh and dehydrated. Furthermore, considerable amounts of these dried fungi are imported from China. The presence of Tylopilus spp. and other extraneous species (i.e., species edible but not belonging to BEAS) within dried porcini mushrooms – mainly from those imported from China and sold in Italy – may represent an evaluable problem from a commercial point of view. The purpose of the present study is to evaluate near-infrared spectroscopy (NIRS) as a rapid and effective alternative to classical methods for identifying extraneous species within dried porcini batches and detecting related commercial frauds. To this goal, 80 dried fungi including BEAS, Tylopilus spp., and Boletus violaceofuscus were analysed by NIRS. For each sample, 3 different parts of the pileus (pileipellis, flesh and hymenium) were analysed and a low-level strategy for data fusion, consisting of combining the signals obtained by the different parts before data processing, was applied. Then, NIR spectra were used to develop reliable and efficient class-models using a novel method, partial least squares density modelling (PLS-DM), and the two most commonly used class-modelling techniques, UNEQ and SIMCA. The results showed that NIR spectroscopy coupled with chemometric class-modelling technique can be suggested as an effective analytical strategy to check the authenticity of dried BEAS mushrooms. [Display omitted] •NIR spectroscopy was applied to detect extraneous species within dried porcini mushrooms.•A novel class-modelling method (PLS-DM) allowed to build efficient authentication models.•A peculiar data fusion strategy was performed to enhance model performances.•Outcomes were critically compared with classical approaches.