A Rapid Extraction Method Combined with a Monoclonal Antibody-Based Immunoassay for the Detection of Amatoxins

Amatoxins (AMAs) are lethal toxins found in a variety of mushroom species. Detection methods are needed to determine the occurrence of AMAs in mushroom species suspected in mushroom poisonings. In this manuscript, we report the generation of novel monoclonal antibodies (mAbs, AMA9G3 and AMA9C12) and the development of a competitive, enzyme-linked immunosorbent assay (cELISA) that is sensitive at 1 ng mL−1 and shows selectivity for α-amanitin (α-AMA) and γ-amanitin (γ-AMA), and less for β-amanitin (β-AMA). In order to decrease the overall time needed for analysis, the extraction procedure for mushrooms was also simplified. A rapid (1 min) extraction procedure of AMAs using solvents as simple as water alone was successfully demonstrated using Amanita mushrooms. Together, the extraction method and the mAb-based ELISA represent a simple and rapid method that readily detects AMAs extracted from mushroom samples.

Triangular Test of Amanita Mushrooms by Using Electronic Nose and Sensory Panel

This work aims to advance understanding of the differentiation of mushroom species through electronic devices that use sensors of various technologies and techniques for pattern recognition, comparing mainly volatile substances that emanate from them. In this first phase, the capacity of human olfaction to differentiate between the smell released by different wild mushrooms of the genus Amanita was analyzed by means of a triangular sensory test, comparing later the data to those obtained for the same samples with an electronic nose in a similar test. The results, still very preliminary, encourage imagining the wide application that these techniques will have and the feedback that this application can suppose for the training of the sense of human olfaction.

Identification of Poisonous Mushrooms by Means of a Hand-Held Electronic Nose

This work presents a practical application of an electronic nose to fast and efficient discrimination of different species of Amanita mushrooms. The electronic nose instrument were utilized for investigation of discrimination capability with respect to odour profile of these fungi. The home-made prototype was based on MOS-type chemical sensors and headspace sampling method. Samples were cut into thin sheets, placed in glass vials and maintained at a constant temperature using a thermostatic bath, the headspace of which was subjected to analysis. The data were analysed using multivariate methods: PCA, LDA and Artificial Neural Networks. The obtained results confirmed legitimacy of application of the electronic nose technique to identification and discrimination of fungi species. Results show a correct classification of the fungi species at the level of 80–100%.