Detection of Tuber melanosporum Using Optoelectronic Technology

Area of Science:

• Agricultural Science
• Sensor Technology
• Mycology

Background:

• The underground detection of black truffles (Tuber melanosporum) is challenging due to the absence of reliable, non-invasive methods.
• Truffles possess significant economic and ecological importance, necessitating improved detection techniques.

Purpose of the Study:

• To develop and validate a portable optoelectronic nose for the in vitro identification of Tuber melanosporum.
• To assess the device's efficacy in detecting truffles at various depths and in complex soil matrices.

Main Methods:

• Integration of nine optical sensors (colorimetric and fluorogenic) and one electrochemical sensor.
• Utilized UVM-7, alumina, and silica as sensor supports.
• Employed Partial Least Squares Discriminant Analysis (PLS-DA) and artificial neural network (ANN) models for data analysis.

Main Results:

• Achieved robust discrimination between soil, compost, and truffles with 0.91 accuracy.
• Demonstrated high accuracy (0.94) for truffle detection at a depth of 30 cm.
• ANN models showed optimal performance in binary classification tasks.

Conclusions:

• The developed optoelectronic nose shows significant potential as an accurate, non-invasive tool for truffle detection.
• The system is suitable for application in the agronomic management of truffle orchards.
• Further development could enhance underground truffle detection capabilities.