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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1326
Author(s):  
Piotr Borowik ◽  
Leszek Adamowicz ◽  
Rafał Tarakowski ◽  
Przemysław Wacławik ◽  
Tomasz Oszako ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Learning Algorithm ◽  
Plant Protection ◽  
Low Cost ◽  
Cost Effective ◽  
Practical Aspect ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Classification Models ◽  
Electronic Noses

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.

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

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5868
Author(s):  
Piotr Borowik ◽  
Leszek Adamowicz ◽  
Rafał Tarakowski ◽  
Przemysław Wacławik ◽  
Tomasz Oszako ◽  
...  
Keyword(s):  
Rhizoctonia Solani ◽  
Fusarium Oxysporum ◽  
Electronic Nose ◽  
Low Cost ◽  
Cost Effective ◽  
Pathogenic Fungi ◽  
Classification Performance ◽  
Electronic Noses ◽  
Machine Learning Classification ◽  
Metal Oxide Gas Sensors

Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors’ responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions.

scholarly journals Non-Invasive Detection and Staging of Colorectal Cancer Using a Portable Electronic Nose

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5440
Author(s):  
Heena Tyagi ◽  
Emma Daulton ◽  
Ayman S. Bannaga ◽  
Ramesh P. Arasaradnam ◽  
James A. Covington
Keyword(s):  
Colorectal Cancer ◽  
Electronic Nose ◽  
High Mortality ◽  
Low Cost ◽  
Area Under The Curve ◽  
Gas Chromatography Mass Spectrometry ◽  
Electronic Noses ◽  
Cancer Group ◽  
Potential Biomarker ◽  
Tof Ms

Electronic noses (e-nose) offer potential for the detection of cancer in its early stages. The ability to analyse samples in real time, at a low cost, applying easy–to-use and portable equipment, gives e-noses advantages over other technologies, such as Gas Chromatography-Mass Spectrometry (GC-MS). For diseases such as cancer with a high mortality, a technology that can provide fast results for use in routine clinical applications is important. Colorectal cancer (CRC) is among the highest occurring cancers and has high mortality rates, if diagnosed late. In our study, we investigated the use of portable electronic nose (PEN3), with further analysis using GC-TOF-MS, for the analysis of gases and volatile organic compounds (VOCs) to profile the urinary metabolome of colorectal cancer. We also compared the different cancer stages with non-cancers using the PEN3 and GC-TOF-MS. Results obtained from PEN3, and GC-TOF-MS demonstrated high accuracy for the separation of CRC and non-cancer. PEN3 separated CRC from non-cancerous group with 0.81 AUC (Area Under the Curve). We used data from GC-TOF-MS to obtain a VOC profile for CRC, which identified 23 potential biomarker VOCs for CRC. Thus, the PEN3 and GC-TOF-MS were found to successfully separate the cancer group from the non-cancer group.

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Discrimination of Volatiles of Shiitakes (Lentinula edodes) Produced during Drying Process by Electronic Nose

2020 ◽  
Vol 16 (1-2) ◽  
Author(s):  
Hui Zhang ◽  
Jing Peng ◽  
Yu-ren Zhang ◽  
Qiang Liu ◽  
Lei-qing Pan ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Lentinula Edodes ◽  
Solid Phase ◽  
Principal Component ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Drying Process ◽  
Drying Time ◽  
Discriminant Models ◽  
Accuracy Rates

AbstractThis study aimed to investigate the potential of electronic nose (E-nose) to differentiate volatiles of shiitakes produced at different drying stages. Shiitakes at different drying time slots were categorized into four groups (fresh, early, middle and late stage) by sensory evaluation. E-nose was used to analyze the volatiles and compared with headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry (HS/GC-MS). The principal component analysis results showed that shiitakes at each stage could be successfully discriminated by E-nose and HS/GC-MS. The differences in volatile organic compounds produced at each stage were mainly caused by sulfurs and alcohols, leading to apparent changes of sensors sensitive to sulfurs, alcohols and aromatic compounds. The discriminant models were established by partial least squares discriminant analysis and support vector machine classification, with accuracy rates of 91.25 % and 95.83 %, respectively. The results demonstrated the potential use of E-nose in classifying and monitoring shiitakes during drying process.

Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure

2019 ◽  
Vol 34 (03) ◽  
pp. 2050003
Author(s):  
Selda Güney ◽  
Ayten Atasoy
Keyword(s):  
Decision Tree ◽  
Electronic Nose ◽  
Low Cost ◽  
Storage Conditions ◽  
Tree Structure ◽  
Training Dataset ◽  
Support Vector ◽  
Binary Decision ◽  
Linear Discriminant ◽  
Binary Decision Tree

The aim of this study is to test the freshness of horse mackerels by using a low cost electronic nose system composed of eight different metal oxide sensors. The process of freshness evaluation covers a scala of seven different classes corresponding to 1, 3, 5, 7, 9, 11, and 13 storage days. These seven classes are categorized according to six different classifiers in the proposed binary decision tree structure. Classifiers at each particular node of the tree are individually trained with the training dataset. To increase success in determining the level of fish freshness, one of the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Bayes methods is selected for every classifier and the feature spaces change in every node. The significance of this study among the others in the literature is that this proposed decision tree structure has never been applied to determine fish freshness before. Because the freshness of fish is observed under actual market storage conditions, the classification is more difficult. The results show that the electronic nose designed with the proposed decision tree structure is able to determine the freshness of horse mackerels with 85.71% accuracy for the test data obtained one year after the training process. Also, the performances of the proposed methods are compared against conventional methods such as Bayes, k-NN, and LDA.

scholarly journals Rapid Non-Destructive Quantification of Eugenol in Curdlan Biofilms by Electronic Nose Combined with Gas Chromatography-Mass Spectrometry

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4441
Author(s):  
Lu Han ◽  
Jingyi Zhu ◽  
Xia Fan ◽  
Chong Zhang ◽  
Kang Tu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Prediction Model ◽  
Electronic Nose ◽  
Quantitative Method ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Linear Discriminant ◽  
Chromatography Mass Spectrometry ◽  
Non Destructive

Eugenol is hepatotoxic and potentially hazardous to human health. This paper reports on a rapid non-destructive quantitative method for the determination of eugenol concentration in curdlan (CD) biofilms by electronic nose (E-nose) combined with gas chromatography-mass spectrometry (GC-MS). Different concentrations of eugenol were added to the film-forming solution to form a series of biofilms by casting method, and the actual eugenol concentration in the biofilm was determined. Analysis of the odor collected on the biofilms was carried out by GC-MS and an E-nose. The E-nose data was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) in order to establish a discriminant model for determining eugenol concentrations in the biofilms. Further analyses involving the application of all sensors and featured sensors, the prediction model-based partial least squares (PLS) and support vector machines (SVM) were carried out to determine eugenol concentration in the CD biofilms. The results showed that the optimal prediction model for eugenol concentration was obtained by PLS at R2p of 0.952 using 10 sensors. The study described a rapid, non-destructive detection and quantitative method for determining eugenol concentration in bio-based packaging materials.

scholarly journals Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)

2014 ◽  
Vol 32 (No. 6) ◽  
pp. 538-548 ◽  
Author(s):  
A. Sanaeifar ◽  
S.S. Mohtasebi ◽  
M. Ghasemi-Varnamkhasti ◽  
H. Ahmadi ◽  
J. Lozano
Keyword(s):  
Electronic Nose ◽  
Low Cost ◽  
Principal Component ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
Computational Techniques ◽  
Laboratory Equipment ◽  
Linear Discriminant ◽  
Ripening Stages ◽  
Non Destructive

Potential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaningful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors.  

A Sensor Fusion Based Pan-Tilt Platform for Activity Tracking and Fall Detection

2017 ◽  
Author(s):  
Zhangjie Chen ◽  
Hanwei Liu ◽  
Yuqiao Wang ◽  
Ya Wang
Keyword(s):  
Sensor Fusion ◽  
Degrees Of Freedom ◽  
Learning Algorithm ◽  
Low Cost ◽  
Fall Detection ◽  
Support Vector ◽  
Fusion Algorithm ◽  
Activity Tracking ◽  
Term Care ◽  
Sensor Module

This paper presents a pan-tilt sensor fusion platform for activity tracking and fall-detection which can work as a reliable surveillance system with long-term care function. A low cost thermal array sensor and a distance sensor are integrated together as the sensor module. The sensor module is installed on a pan-tilt orienting mechanism with two rotation degrees of freedom to increase the field of view while reducing the number of sensors used on-board. The performance of the sensor test platform is analyzed. The location of the indoor object as well as its size can be estimated based on a novel sensor fusion algorithm. The support vector machine (SVM) based machine learning algorithm is applied for fall detection. The preliminary experiment result shows a 95% accuracy to identify falling action from similar normal indoor activity such as sitting and picking up stuff.

Development of Novel Electronic Nose Applied for Strawberry Freshness Detection during Storage

2018 ◽  
Vol 14 (7-8) ◽  
Author(s):  
Mengke Xing ◽  
Ke Sun ◽  
Qiang Liu ◽  
Leiqing Pan ◽  
Kang Tu
Keyword(s):  
Electronic Nose ◽  
Sensor Array ◽  
Oxide Semiconductor ◽  
Mass Spectrometry Analysis ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Successive Projections Algorithm ◽  
Spectrometry Analysis ◽  
Sensors Array ◽  
Post Harvesting

AbstractA newly self-developed electronic nose (E-nose) system for the detection of “Hongyan” strawberry freshness at different storage periods was studied. The system consisted of six metal oxide semiconductor sensors connected to a data acquisition system and a computer with pattern recognition software. The aroma emitted by “Hongyan” strawberry samples was detected during post-harvesting storage, and stable E-nose response values were used to develop cluster analysis and classification models. The successive projections algorithm was employed to optimize the sensors array, and the results obtained by gas chromatography–mass spectrometry analysis proved that the optimized sensor array was feasible to differentiate decayed strawberries from fresh ones. Partial least squares discriminant analysis and support vector machine (SVM) models were built. Accuracy of 94.9 % on the testing set was obtained based on the optimized sensor array, and this result was satisfactory compared to that of commercial PEN3 E-nose.

scholarly journals Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4239
Author(s):  
Zhiyuan Wu ◽  
Hanying Zhang ◽  
Wentao Sun ◽  
Ning Lu ◽  
Meng Yan ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Low Cost ◽  
Visual Observation ◽  
K Nearest Neighbor ◽  
Sampling Device ◽  
Airflow Distribution ◽  
The Government ◽  
The Cost

In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

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