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An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium: F. avenaceum, F. langsethiae, F. poae, and F. sporotrichioides. The electronic nose prototype is a low-cost device based on commercially available TGS series sensors from Figaro Corp. Two types of gas sensors that respond to the perturbation are used to collect signals useful for discriminating between the samples under study. First, an electronic nose detects the transient response of the sensors to a change in operating conditions from clean air to the presence of the gas being measured. A simple gas chamber was used to create a sudden change in gas composition near the sensors. An inexpensive pneumatic system consisting of a pump and a carbon filter was used to supply the system with clean air. It was also used to clean the sensors between measurement cycles. The second function of the electronic nose is to detect the response of the sensor to temperature disturbances of the sensor heater in the presence of the gas to be measured. It has been shown that features extracted from the transient response of the sensor to perturbations by modulating the temperature of the sensor heater resulted in better classification performance than when the machine learning model was built from features extracted from the response of the sensor in the gas adsorption phase. By combining features from both phases of the sensor response, a further improvement in classification performance was achieved. The E-nose enabled the differentiation of F. poae from the other fungal species tested with excellent performance. The overall classification rate using the Support Vector Machine model reached 70 per cent between the four fungal categories tested.

The massive import of uncontrolled technical grain from the East into the European Community poses a risk to public health when it ends up in the mills to be used as flour for food purposes instead of being burnt (biofuel). In fungal infections of wheat, the most dangerous species belong to the genus Fusarium. F. poae is a pathogen that is most commonly isolated from cereals worldwide and causes various types of diseases in animals and humans due to the numerous toxins it produces. The manuscript reports an attempt to distinguish between four species of Fusarium, F. avanceum, F. langsethiae, F. poae, and F. sporotrichioides, in wheat grains by measuring the volatiles emitted. The patterns obtained from the signals captured by the electronic nose PEN3 were used to build the Random Forests classification model. The recall and precision of the classification performance for F. poae reached 91 and 87%, respectively. The overall classification accuracy reached 70%. Gas chromatography coupled with mass spectrometry (GC–MS) was used to analyze the chemical composition of the emitted volatiles. The patterns found in the GC–MS results allowed an explanation of the main patterns observed when analyzing the electronic nose data. The mycotoxins produced by the Fusarium species analyzed were detected. The results of the reported experiment confirm the potential of the electronic nose as a technology that can be useful for screening the condition of the grain and distinguishing between different pathogenic infestations.