scholarly journals Gas Sensor Array and Classifiers as a Means of Varroosis Detection

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 117 ◽  
Author(s):  
Andrzej Szczurek ◽  
Monika Maciejewska ◽  
Beata Bąk ◽  
Jakub Wilk ◽  
Jerzy Wilde ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Chemical Properties ◽  
Support Vector ◽  
True Negative ◽  
Data Set ◽  
Gas Sensor Array ◽  
Efficient Detection ◽  
Bee Colonies ◽  
Selection Of

The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

Onion sour skin detection using a gas sensor array and support vector machine

2009 ◽  
Vol 3 (4) ◽  
pp. 193-202 ◽  
Author(s):  
Changying Li ◽  
Ron Gitaitis ◽  
Bill Tollner ◽  
Paul Sumner ◽  
Dan MacLean
Keyword(s):  
Support Vector Machine ◽  
Gas Sensor ◽  
Sensor Array ◽  
Support Vector ◽  
Skin Detection ◽  
Gas Sensor Array ◽  
Sour Skin

Development of Electronic Nose with Low-Cost Dynamic Headspace for Classifying Vegetable Oils and Animal Fats

2015 ◽  
Vol 771 ◽  
pp. 50-54 ◽  
Author(s):  
Kuwat Triyana ◽  
M. Taukhid Subekti ◽  
Prasetyo Aji ◽  
Shidiq Nur Hidayat ◽  
Abdul Rohman
Keyword(s):  
Gas Sensor ◽  
Vegetable Oils ◽  
Electronic Nose ◽  
Sensor Array ◽  
Low Cost ◽  
Measurement Data ◽  
Dynamic Headspace ◽  
Data Set ◽  
Animal Fats ◽  
Gas Sensor Array

A portable electronic nose (e-nose) using low-cost dynamic headspace and commercially metal oxide gas sensors has been developed. This paper reports evaluation on the performance of the e-nose to classify vegetable oils (sunflower and grape seed oils) and animal fats (mutton, chicken and pig fats). The e-nose consists of a dynamic headspace sampling, a gas sensor array and a real-time data acquisition system based on ATMega-16 microcontroller. The dynamic headspace can divided into two chambers, i.e. sample and gas sensor array room. It is also equipped with three small fans for adjusting sensing and purging processes. Principal component analysis (PCA) was used for measurement data analysis after all features being extracted. The first two principal components were kept because they accounted for 91.1% of the variance in the data set (first and second principals accounted for 72.9, 18.2% of the variance, respectively). This results show that the e-nose can distinguish vegetable oils and animal fats. This work demonstrates for the future that the e-nose with low-cost dynamic headspace technique may be applied to the identification of oils and fats in halal authentication.

scholarly journals Gas sensor array to clasify the chicken meat with E. coli contaminant by using random forest and support vector machine

2021 ◽  
pp. 100083
Author(s):  
Suryani D. Astuti ◽  
Mohammad H. Tamimi ◽  
Anak A.S. Pradhana ◽  
Kartika A. Alamsyah ◽  
Hery Purnobasuki ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Gas Sensor ◽  
Sensor Array ◽  
Chicken Meat ◽  
Support Vector ◽  
E Coli ◽  
Gas Sensor Array

scholarly journals Data set from gas sensor array under flow modulation

Data in Brief ◽  
2015 ◽  
Vol 3 ◽  
pp. 131-136 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Jordi Fonollosa ◽  
Luis Fernández ◽  
Agustín Gutiérrez-Gálvez ◽  
Santiago Marco ◽  
...  
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Data Set ◽  
Flow Modulation ◽  
Gas Sensor Array

scholarly journals A Robust and Low-Complexity Gas Recognition Technique for On-Chip Tin-Oxide Gas Sensor Array

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Farid Flitti ◽  
Aicha Far ◽  
Bin Guo ◽  
Amine Bermak
Keyword(s):  
Gas Sensor ◽  
Tin Oxide ◽  
Sensor Array ◽  
Clustering Algorithm ◽  
Low Complexity ◽  
Recognition System ◽  
Training Data ◽  
Worst Case ◽  
Data Set ◽  
Gas Sensor Array

Gas recognition is a new emerging research area with many civil, military, and industrial applications. The success of any gas recognition system depends on its computational complexity and its robustness. In this work, we propose a new low-complexity recognition method which is tested and successfully validated for tin-oxide gas sensor array chip. The recognition system is based on a vector angle similarity measure between the query gas and the representatives of the different gas classes. The latter are obtained using a clustering algorithm based on the same measure within the training data set. Experimented results on our in-house gas sensors array show more than98%of correct recognition. The robustness of the proposed method is tested by recognizing gas measurements with simulated drift. Less than1%of performance degradation is noted at the worst case scenario which represents a significant improvement when compared to the current state-of-the-art.

scholarly journals Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3917 ◽  
Author(s):  
Shurui Fan ◽  
Zirui Li ◽  
Kewen Xia ◽  
Dongxia Hao
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Recognition Rate ◽  
Principal Component ◽  
High Sensitivity ◽  
Pso Algorithm ◽  
Support Vector ◽  
Gas Sensor Array ◽  
Multicomponent Gas ◽  
Difficult Challenge

The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.

scholarly journals LOCATING GAS SOURCES USING A GAS SENSOR ARRAY

1991 ◽  
Vol 7 (Supple) ◽  
pp. 1565-1568 ◽  
Author(s):  
Yukio Hiranaka ◽  
Hiro Yamasaki
Keyword(s):  
Gas Sensor ◽  
Sensor Array ◽  
Gas Sensor Array
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