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.

Detection of soil pollution by petroleum products is necessary to remedy threats to economic and human health. Pollution by hydraulic oil often occurs through leaks from forestry machinery such as harvesters. Electronic noses equipped with gas sensor arrays are promising tools for applications of pollution detection and monitoring. A self-made, low-cost electronic nose was used for differentiation between clean and polluted samples, with two types of oils and three levels of pollution severity. An electronic nose uses the TGS series of gas sensors, manufactured by Figaro Inc. Sensor responses to changes in environmental conditions from clean air to measured odor, as well as responses to changes in sensor operation temperature, were used for analysis. Statistically significant response results allowed for the detection of pollution by biodegradable oil, while standard mineral oil was difficult to detect. It was demonstrated that the TGS 2602 gas sensor is most suitable for the studied application. LDA analysis demonstrated multidimensional data patterns allowing differentiation between sample categories and pollution severity levels.

Monitoring soil contamination from petroleum products is vital for protecting human health and the environment. In forestry, hydraulic oil spills frequently result from leaks in equipment such as harvesters. This study evaluates a custom-built, inexpensive electronic nose, equipped with a Figaro TGS gas sensor array, for discriminating between pristine and contaminated soil samples. Two oil types and three pollution intensities were analyzed. The constructed electronic nose applied two sensor operation modes: (i) response to change of sensor operation condition from clean air to target odors and (ii) response to sensor heater temperature modulation. Classification was performed using Random Forest and Support Vector Machine (SVM) algorithms, and Linear Discriminant Analysis (LDA) was used to explore multidimensional data patterns. The sensor heater temperature modulation mode provided superior classification performance. Measurements at room temperature achieved an accuracy of 97%, clearly outperforming measurements on samples heated to 60 °C (75%). While the system successfully identified biodegradable oil contamination, standard mineral oil was more challenging to detect. Among the sensors tested, TGS 2602 was the most effective. These findings indicate that portable electronic noses can provide a statistically robust and cost-effective tool for assessing the severity of soil pollution.

Fusarium poae is a pathogen that is widespread in the temperate zone and poses a serious threat to crops due to its wide range of host plants (including cereals). Electronic nose measurements were performed on wheat grains infected with F. poae to evaluate the application of early detection of fungal infections. Wheat seeds were artificially inoculated to test the devices. Three same-weight but different infection level experiment variants were prepared: 3 g infected seeds with 12 g healthy seeds, 5 g infected seeds with 10 g healthy seeds, and 10 g infected seeds with only 5 g healthy seeds. The seeds were infected with fresh fragments of F. poae mycelium. Measurements were carried out for five constructive days, recording the changes in volatile odor compounds released each day. A custom-built, low-cost device based on Figaro Inc. TGS metal-oxide semiconductor gas sensors and a commercially available PEN3 electronic nose device from Airsense Analytics GmbH were used for the experiment. A non-linear sensor response for the measured sample odor was observed with both devices. Spoiled grain in a proportion of 1/15 of the sample could be detected by measuring the volatile components. However, the patterns of the sensor responses were different for the various concentrations of spoiled grain in the measured samples.