Edibility and species discrimination of wild bolete mushrooms using FT-NIR spectroscopy combined with DD-SIMCA and RF models

Mushroom poisoning is a highly publicized food safety concern, and commercial fraud by adulterating cheap species in the wild mushroom supply chain often occurs. To safeguard the lives and property of consumers, it is necessary to develop accurate methods to discriminate the edibility and species of wild mushrooms. Near-infrared (NIR) spectroscopy has become a popular analytical tool due to its key advantages. This work aimed to investigate the feasibility of discriminating the edibility and species of wild bolete mushrooms using NIR spectroscopy-based two-dimensional correlation spectroscopy (2DCOS) combined with Red-Green-Blue (RGB) image analysis and multivariate analysis methods. The results showed that the data driven version of soft independent modeling of class analogy (DD-SIMCA) model had excellent sensitivity (0.98) but the specificity was only 0.59. This model was not suitable to accurately discriminate the edibility of boletes, but can be used for screening a potentially poisonous species of boletes (Caloboletus calopus). As for species discrimination, random forests (RF) model had great classification and generalization ability and the accuracy of the training set and test set was 97.22% and 100%, respectively. Therefore, FT-NIR spectroscopy combined with DD-SIMCA and RF models had the potential to discriminate the edibility and species of wild bolete mushrooms. •Seven major boletes species were collected from 14 regions in southwestern China.•2DCOS combined with RGB and multivariate analysis methods to discriminate boletes.•First discrimination of wild boletes edibility by using FT-NIR and DD-SIMCA model.•Combined unsupervised (PCA and HCA) and supervised methods (PLS-DA, RF and SVM).