scholarly journals Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

Chemosensors ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 243
Author(s):  
Mansour Rasekh ◽  
Hamed Karami ◽  
Alphus Dan Wilson ◽  
Marek Gancarz
Keyword(s):  
Performance Analysis ◽  
Essential Oils ◽  
Electronic Nose ◽  
Sensor Array ◽  
Oxide Semiconductor ◽  
Regulatory Quality ◽  
Analysis Methods ◽  
Artificial Neural Network Ann ◽  
Mos Sensor ◽  
Ann Classification

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

scholarly journals Classification and Identification of Essential Oils from Herbs and Fruits Based on a MOS Electronic-Nose Technology

Chemosensors ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 142
Author(s):  
Mansour Rasekh ◽  
Hamed Karami ◽  
Alphus Dan Wilson ◽  
Marek Gancarz
Keyword(s):  
Essential Oil ◽  
Essential Oils ◽  
Electronic Nose ◽  
Sensor Array ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
Natural Food ◽  
Gaseous Emissions ◽  
Edible Plant ◽  
Plant Sources

The frequent occurrence of adulterated or counterfeit plant products sold in worldwide commercial markets has created the necessity to validate the authenticity of natural plant-derived palatable products, based on product-label composition, to certify pricing values and for regulatory quality control (QC). The necessity to confirm product authenticity before marketing has required the need for rapid-sensing, electronic devices capable of quickly evaluating plant product quality by easily measurable volatile (aroma) emissions. An experimental MAU-9 electronic nose (e-nose) system, containing a sensor array with 9 metal oxide semiconductor (MOS) gas sensors, was developed with capabilities to quickly identify and classify volatile essential oils derived from fruit and herbal edible-plant sources. The e-nose instrument was tested for efficacy to discriminate between different volatile essential oils present in gaseous emissions from purified sources of these natural food products. Several chemometric data-analysis methods, including pattern recognition algorithms, principal component analysis (PCA), and support vector machine (SVM) were utilized and compared. The classification accuracy of essential oils using PCA, LDA and QDA, and SVM methods was at or near 100%. The MAU-9 e-nose effectively distinguished between different purified essential oil aromas from herbal and fruit plant sources, based on unique e-nose sensor array responses to distinct, essential-oil specific mixtures of volatile organic compounds (VOCs).

scholarly journals Application of Electronic Nose for Evaluation of Wastewater Treatment Process Effects at Full-Scale WWTP

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 251 ◽  
Author(s):  
Grzegorz Łagód ◽  
Sylwia M. Duda ◽  
Dariusz Majerek ◽  
Adriana Szutt ◽  
Agnieszka Dołhańczuk-Śródka
Keyword(s):  
Wastewater Treatment ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Sensor Array ◽  
Treatment Plant ◽  
Full Scale ◽  
Oxide Semiconductor ◽  
Pollution Level ◽  
Gas Sensor Array

This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; thus, the samples could be classified in terms of their similarity and the analyzed gas-fingerprint could be related to the pollution level expressed by physical and biochemical indicators. Principal component analysis was employed for dimensionality reduction, and cluster analysis for grouping observation purposes. Supervised learning techniques confirmed that the obtained data were applicable for the classification of wastewater at different stages of the purification process.

Sensor array optimization and discrimination of apple juices according to variety by an electronic nose

2017 ◽  
Vol 9 (6) ◽  
pp. 921-928 ◽  
Author(s):  
Hao Wu ◽  
TianLi Yue ◽  
Zhijiao Xu ◽  
Chen Zhang
Keyword(s):  
Metal Oxide ◽  
Electronic Nose ◽  
Chemical Sensors ◽  
Apple Juice ◽  
Sensor Array ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Array Optimization ◽  
Semiconductor Type

An electronic nose (PEN3) containing 10 metal oxide semiconductor type chemical sensors was used to discriminate between eight varieties of apple juice.

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.

PENELITIAN DAN PENGEMBANGAN SENSOR AROMATIK SEBAGAI ALAT DETEKSI ALKOHOL PADA PARFUM DAN WEWANGIAN UNTUK AUTENTIKASI PRODUK PARFUM HALAL

2017 ◽  
Vol 18 (2) ◽  
pp. 302-322
Author(s):  
Fajar Hardoyono
Keyword(s):  
Sensor Array ◽  
Principal Component ◽  
Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Response Patterns ◽  
Data Recording ◽  
Analysis Method ◽  
Sample Group ◽  
Array Pattern ◽  
Group Data

Abstract: The development of aromatic sensor array instrument for the detection of alcohol in perfume. The research was conducted by developing the sensor array using 8 sensors made of metal oxide semiconductor. The sensor types used in this study consisted of TGS 813, TGS 822, TGS 2600, TGS 826, TGS 2611, TGS 2620, TGS 2612 and TGS 2602. Response patterns of 8 sensors formed a sensor array pattern used to detect the aroma of 2 groups of samples perfume made from the essential oil of ginger. The first sample group is pure ginger atsiri oil without mixed alcohol. The second sample group was made from the ginger atsiri oil mixed with alcohol with a level of 0.02 M. The results of the data recording show that the developed instrument is able to dissect the first sample group with the second sample group. Data analysis using principal component analysis method (PCA shows that the instrument is able to distinguish the contaminated alcohol perfume group 0.2 M with the alcohol-free perfume group with 100% accuracy. Keywords: Sensor Aroma, Perfume.

Field hockey from the performance analyst’s perspective: A systematic review

2021 ◽  
pp. 174795412110089
Author(s):  
Felicity Lord ◽  
David B Pyne ◽  
Marijke Welvaert ◽  
Jocelyn K Mara
Keyword(s):  
Systematic Review ◽  
Performance Analysis ◽  
Best Practice ◽  
Holistic Approach ◽  
Field Hockey ◽  
Sport Science ◽  
Analysis Techniques ◽  
Analysis Methods ◽  
Temporal Variables ◽  
Spatio Temporal

Field hockey is an evolving sport, but it is unclear whether performance analysis techniques are reflective of current best practice. The objective of this review was to identify performance analysis methods used in field hockey, assess their practicality, and provide recommendations on their implementation in the field. A systematic search of the databases SPORTDiscus, Web of Science, Scopus, MEDLINE and PubMed was performed. Key words addressed performance analysis methods and field hockey, with all other disciplines of sport science excluded. A total of 8 articles were identified from the systematic review. Three studies explored patterns of play in relation to goal scoring opportunities, two articles examined penalty corner strategies and three compared specific actions in hockey. The limited performance analysis research in field hockey has focused on game actions in patterns of play. However, greater insights may be gained by analysing hockey using a holistic approach that incorporates spatio-temporal variables and player-opposition interactions. There is an opportunity to employ novel performance analysis techniques in hockey which provide more practical and effective approaches for analysing strategies and tactics.

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

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