Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora

Abstract

Compared with traditional gas chromatography–mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors’ response to the odors’ exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models’ performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose.

Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani

“…Electronic noses have been proposed as a rapid and non-invasive diagnostic tool in many industries and agriculture applications. In our previous papers [53,54] we demonstrated successful applications of these noses in differentiating between two species of pathogenic oomycetes, Phytophthora and Pythium. In the current paper, we reported further development of the constructed low-cost electronic nose device with an application to pathogenic fungal differentiation (Fusarium oxysporum and Rhizoctonia solani).…”

“…The first low-cost electronic nose created by our team (PW4) [53,54] consisted of six Figaro Inc. TGS series gas sensors. The main idea behind developing the new e-nose (PW6) was another approach to selecting the sensors included in the sensor array.…”

“…The operation of an electronic nose relies on measuring the resistances of non-specific gas sensors exposed to the studied odour sample. In the previous papers [53,54]" we reported the measurements performed by the electronic nose PW4 in which six MOX commercially available gas sensors of Figaro Inc (Japan) were used. The list and target gases of these sensors are presented in Table 1.…”

“…The number of reads collected during one measurement of a sample was 3600 for the PW4 e-nose and 6000 for the PW6 e-nose device-each represents the number of sensors multiplied by the number of reads of sensor resistance magnitude in both the gas adsorption and desorption phases, as one can observe in Figure 2.The common practice [69] is to extract from the sensor response curves a smaller number of features, which allows reducing the dimensionality of the problem. In the present studies, we used a similar list of the modelling features as reported in previous papers [53,54,70]. We can enumerate several groups of the features types used in our data analysis.…”

Electronic Nose Differentiation between Quercus robur Acorns Infected by Pathogenic Oomycetes Phytophthora plurivora and Pythium intermedium

“…It would be helpful to follow this idea and use an artificial device for a similar task. This is an ambitious goal and our first experiments, reported in reference [7], were designed and performed in vitro under controlled laboratory conditions. We prepared sample growth on a traditional medium.…”