Detection of Tuber melanosporum Using Optoelectronic Technology

Tuber melanosporum, the black truffle, is a fungus of high economic and ecological value, but its underground detection remains a challenge due to the lack of reliable, non-invasive methods. This study presents the development and proof of concept of a portable optoelectronic nose that integrates nine optical sensors and one electrochemical sensor for the in vitro identification of T. melanosporum. The optical sensors use colorimetric and fluorogenic molecular indicators supported on UVM-7, alumina, and silica. Tests were performed with truffles at different depths and in the presence of soil and compost to evaluate the device's multi-source response. Partial least squares discriminant analysis (PLS-DA) models showed robust discrimination between soil, compost, and truffles, with an accuracy of 0.91 under most conditions. Detection at 30 cm showed an accuracy of 0.94, confirming the system's ability to differentiate between sample types. Performance improved in simplified scenarios based on the presence or absence of truffles. Furthermore, the artificial neural network models achieved optimal results in binary classification. Taken together, the results support the system's potential as an accurate, non-invasive tool with possible application to the agronomic management of truffle orchards.