scholarly journals Multisensory Gas Analysis System Based on Reconstruction Attractors

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 964
Author(s):  
Olga Cheremisina ◽  
Vladimir Kulagin ◽  
Suad El-Saleem ◽  
Evgeny Nikulchev
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Fractal Dimensions ◽  
Image Formation ◽  
Gas Analysis ◽  
Multisensor Systems ◽  
Multisensor System ◽  
Analysis System

The paper describes the substance image formation based on the measurements by multisensor systems and the possibility of the development of a gas analysis device like an electronic nose. Classification of gas sensors and the need for their application for the recognition of difficult images of multicomponent air environments are considered. The image is formed based on stochastic transformations, calculations of correlation, and fractal dimensions of reconstruction attractors. The paper shows images created for substances with various structures that were received with the help of a multisensor system under fixed measurement conditions.

scholarly journals Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification

2020 ◽  
Vol 66 (No. 3) ◽  
pp. 97-103
Author(s):  
Farel Ahadyatulakbar Aditama ◽  
Lalu Zulfikri ◽  
Laili Mardiana ◽  
Tri Mulyaningsih ◽  
Nurul Qomariyah ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Gas Sensors ◽  
Poor Quality ◽  
Backpropagation Algorithm ◽  
The Poor ◽  
Quality Classification ◽  
Electronic Nose System ◽  
System Prototype

The aim of the present study is the development of an electronic nose system prototype for the classification of Gyrinops versteegii agarwood. The prototype consists of three gas sensors, i.e., TGS822, TGS2620, and TGS2610. The data acquisition and quality classification of the nose system are controlled by the Artificial Neural Network backpropagation algorithm in the Arduino Mega2650 microcontroller module. The testing result shows that an electronic nose can distinguish the quality of Gyrinops versteegii agarwood. The good-quality agarwood has an output of [1 –1], while the poor-quality agarwood has an output of [–1 1].

scholarly journals Design and Validation of a Portable Machine Learning-Based Electronic Nose

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3923
Author(s):  
Yixu Huang ◽  
Iyll-Joon Doh ◽  
Euiwon Bae
Keyword(s):  
Machine Learning ◽  
Metal Oxide ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Metal Oxide Sensors ◽  
Metal Oxide Gas Sensors

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography–mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

The Effects of Drought on Leaf Gas Exchange and Growth of Three Species of Herbaceous Perennials

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 540a-540
Author(s):  
K.J. Prevete ◽  
R.T. Fernandez
Keyword(s):  
Drought Stress ◽  
Stomatal Conductance ◽  
Gas Exchange ◽  
Drought Tolerance ◽  
Transpiration Rate ◽  
Leaf Gas Exchange ◽  
Gas Analysis ◽  
Herbaceous Perennials ◽  
Effects Of Drought ◽  
Analysis System

Three species of herbaceous perennials were tested on their ability to withstand and recover from drought stress periods of 2, 4, and 6 days. Eupatorium rugosum and Boltonia asteroides `Snowbank' were chosen because of their reported drought intolerance, while Rudbeckia triloba was chosen based on its reported drought tolerance. Drought stress began on 19 Sept. 1997. Plants were transplanted into the field the day following the end of each stress period. The effects of drought on transpiration rate, stomatal conductance, and net photosynthetic rate were measured during the stress and throughout recovery using an infrared gas analysis system. Leaf gas exchange measurements were taken through recovery until there were no differences between the stressed plants and the control plants. Transpiration, stomatal conductance, and photosynthesis of Rudbeckia and Boltonia were not affected until 4 days after the start of stress. Transpiration of Eupatorium decreased after 3 days of stress. After rewatering, leaf gas exchange of Boltonia and Rudbeckia returned to non-stressed levels quicker than Eupatorium. Growth measurements were taken every other day during stress, and then weekly following transplanting. Measurements were taken until a killing frost that occurred on 3 Nov. There were no differences in the growth between the stressed and non-stressed plants in any of the species. Plants will be monitored throughout the winter, spring, and summer to determine the effects of drought on overwintering capability and regrowth.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

StatPal II pH and Blood Gas Analysis System evaluated

1994 ◽  
Vol 40 (1) ◽  
pp. 124-129 ◽  
Author(s):  
R J Wong ◽  
J J Mahoney ◽  
J A Harvey ◽  
A L Van Kessel
Keyword(s):  
Point Of Care ◽  
Blood Gases ◽  
Blood Gas Analysis ◽  
Gas Analysis ◽  
Portable Instrument ◽  
Blood Gas ◽  
Target Value ◽  
Analysis System

Abstract We evaluated a new portable instrument, the PPG StatPal II pH and Blood Gas Analysis System, designed for "point-of-care" measurements of blood gases and pH. Inaccuracy (% of target value) and imprecision (CV%) were assessed by blood tonometry and comparison with a Corning 178. Within-day results for PCO2 inaccuracy and imprecision ranged from 98.2% to 102.9% and 3.3% to 3.9%, respectively; for PO2, these were 95.5% to 102.3% and 2.3% to 3.0%, respectively. Between-day results for PCO2 inaccuracy and imprecision ranged from 99.2% to 99.3% and from 2.9% to 3.2%, respectively; for PO2, the ranges were 96.2% to 98.2% and 2.6% to 3.0%, respectively. Two PCO2 outliers (in 645 samples = 0.3%) were observed. In general, tonometry recovery, measurement stability, and pH bias results for the StatPal II and Corning 178 were comparable. We conclude that the StatPal II performs within acceptable ranges of inaccuracy and imprecision.

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