Abstract:Low-dimensional metal oxides-based electronic noses have been applied in various fields, such as food quality, environmental assessment, coal mine risk prediction, and disease diagnosis. However, the applications of these electronic noses are limited for conditions such as precise safety monitoring because electronic nose systems have problems such as poor recognition ability of mixed gas signals and sensor drift caused by environmental factors. Advanced algorithms, including classical gas recognition algorith… Show more
“…[53] and lyophilized [54] and fresh garlic cultivars [55] and for the early detection of garlic artificially inoculated with several fungal pathogenic isolates [56]. The combinations of the analytical approaches used in the present work with e-nose devices, along with the availability of ever more performing algorithms, including classical gas recognition and neural network-based algorithms [57,58], will improve the practical and useful utilization of VOCs as a high-throughput detection tool for the early diagnosis of human diseases and of pest and plant diseases in the field and during storage thanks to the rapid discrimination of individual chemical species within the issued aromatic mixtures.…”
Fusarium bulb rot, caused by Fusarium proliferatum, is a worldwide disease of garlic, both in the open field and during storage. Early diagnosis of the disease during storage is difficult due to the morphology of the bulbs and cloves. Volatile organic compounds (VOCs) are secondary metabolites produced by several microorganisms, including phytopathogenic fungi and bacteria. In recent years, the development of several techniques for the detection and characterization of VOCs has prompted their use, among others, as a diagnostic tool for the early and non-destructive analysis of many diseases of species of agricultural interest. In this paper, proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and solid-phase microextraction gas chromatography/mass spectrometry (SPME-GC/MS) were successfully utilized to characterize the volatolome of commercial garlic cloves, artificially and naturally infected with F. proliferatum, for the early discrimination between diseased and healthy ones. A partial least squares discriminant analysis (PLSDA) and a principal component analysis (PCA) allowed for the separation of infected and healthy cloves and the identification of specific VOCs produced by the fungus during the infection. The results obtained in this work could be utilized for the development of simpler, more economical, and more portable devices for the early detection of infected garlic bulbs during storage.
“…[53] and lyophilized [54] and fresh garlic cultivars [55] and for the early detection of garlic artificially inoculated with several fungal pathogenic isolates [56]. The combinations of the analytical approaches used in the present work with e-nose devices, along with the availability of ever more performing algorithms, including classical gas recognition and neural network-based algorithms [57,58], will improve the practical and useful utilization of VOCs as a high-throughput detection tool for the early diagnosis of human diseases and of pest and plant diseases in the field and during storage thanks to the rapid discrimination of individual chemical species within the issued aromatic mixtures.…”
Fusarium bulb rot, caused by Fusarium proliferatum, is a worldwide disease of garlic, both in the open field and during storage. Early diagnosis of the disease during storage is difficult due to the morphology of the bulbs and cloves. Volatile organic compounds (VOCs) are secondary metabolites produced by several microorganisms, including phytopathogenic fungi and bacteria. In recent years, the development of several techniques for the detection and characterization of VOCs has prompted their use, among others, as a diagnostic tool for the early and non-destructive analysis of many diseases of species of agricultural interest. In this paper, proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and solid-phase microextraction gas chromatography/mass spectrometry (SPME-GC/MS) were successfully utilized to characterize the volatolome of commercial garlic cloves, artificially and naturally infected with F. proliferatum, for the early discrimination between diseased and healthy ones. A partial least squares discriminant analysis (PLSDA) and a principal component analysis (PCA) allowed for the separation of infected and healthy cloves and the identification of specific VOCs produced by the fungus during the infection. The results obtained in this work could be utilized for the development of simpler, more economical, and more portable devices for the early detection of infected garlic bulbs during storage.
“…After obtaining a well-structured and efficient tree, classifying a sample is a relatively simple task. This is one of the significant advantages of using this method when good results are achieved (Han et al, 2011;Wang et al, 2023).…”
The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software “Weka™” and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.
“…In order to ensure that the signal length of all samples is consistent, the signal in the equilibrium of each sensor is down-sampled to obtain a whole-process signal with a length of 300, resulting in 1800 data points for each sample. During extended data collection, the sensor's resistance value might fluctuate due to environmental temperature and humidity changes [25]. However, the sensor's response (R) remains relatively consistent.…”
The classification of mixed gases is one of the major functions of the electronic nose. To address the challenges associated with complex feature construction and inadequate feature extraction in gas classification, we propose a classification model for gas mixtures based on the Vision Transformer (ViT). The whole-process signals of the sensor array are taken as input signals in the proposed classification model, and self-attention mechanism is employed for the fusion of global information and adaptive feature extraction to make full use of the dependence of responses at different stages of the whole-process signals to improve the model’s classification accuracy. Our model exhibited a remarkable accuracy (96.66%) using a dataset containing acetone, methanol, ammonia, and their binary mixtures. In comparison, experiments conducted by support vector machine (SVM) and a one-dimensional deep convolutional neural network (1D-DCNN) model demonstrated classification accuracy of 90.56% and 92.75%, respectively. Experimental results indicate that the ViT gas classification model can be effectively combined with multi-channel time series data from the sensor array using the self-attention mechanism, thereby improving the accuracy of mixed gases classification. This advancement can be expected to become a standard method for classifying mixed gases.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.