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 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.

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.

Chemical variability and antioxidant activities of the essential oils of the aerial parts of Ammoides verticillata and the roots of Carthamus caeruleus and their synergistic effect in combination

2020 ◽  
Vol 23 ◽  
Author(s):  
Imane Rihab Mami ◽  
Noria Merad-Boussalah ◽  
Mohammed El Amine Dib ◽  
Boufeldja Tabti ◽  
Jean Costa ◽  
...  
Keyword(s):  
Essential Oil ◽  
Essential Oils ◽  
Antioxidant Activities ◽  
Radical Scavenging Activity ◽  
Antioxidant Properties ◽  
Radical Scavenging ◽  
Natural Food ◽  
Chemical Compositions ◽  
Chemical Variability ◽  
Ammoides Verticillata

Aim and Objective: Oxidative stress is implicated in the development and progression of many disease. Some of appropriate actions that could be initiated to taken to resolve the problem of these diseases are search for new antioxidant substances isolated from plants. The aims of this study were to study the intraspecies variations of A. verticillata and C. caeruleus essential oils from 8 locations using statistical analysis, the in vitro antioxidant properties of collective essential oils and in combinations. Materials and Methods: The essential oils were analyzed by GC and GC-MS. The intraspecies variations of the essential oil compositions were discussed using principal component analysis (PCA) and cluster analysis (CA). The antioxidant properties were evaluated DPPH-radical scavenging activity and β-carotene bleaching test. Results: The main components of Ammoides verticillata collective essential oil (Coll EO) were thymol (30.5%), carvacrol (23.2%), p-cymene (13.1%), limonene (12.5%) and terpinene-4-ol (12.3%). While roots of Carthamus caeruleus essential oil were dominated by carline oxide (86.2%). The chemical variability allowed the discrimination of two main Groups for both Coll EOs. A direct correlation between the altitudes, climate and the chemical compositions was evidenced. Ammoides verticulata and Carthamus caeruleus Coll Eos showed good antioxidant activity. In binary mixture, the interaction both Coll Eos and between oils rich of thymol and/or carvacrol with carlina oxide produced the best synergistic effects, compared to individual essential oils and the synthetic antioxidant (BHT). Conclusion: Ammoides verticillata and Carthamus caeruleus essential oil blends can be used as a natural food preservative and alternative to chemical antioxidants.

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.

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.

scholarly journals Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition

2021 ◽  
Author(s):  
Dian Kesumapramudya Nurputra ◽  
Ahmad Kusumaatmadja ◽  
Mohamad Saifudin Hakim ◽  
Shidiq Nur Hidayat ◽  
Trisna Julian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Nose ◽  
Quantitative Polymerase Chain Reaction ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Exhaled Breath ◽  
Laboratory Equipment ◽  
Linear Discriminant

Abstract Despite its high accuracy to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach possesses several limitations (e.g., the lengthy invasive procedure, the reagent availability, and the requirement of specialized laboratory, equipment, and trained staffs). We developed and employed a low-cost, noninvasive method to rapidly sniff out the coronavirus disease 2019 (COVID-19) based on a portable electronic nose (GeNose C19) integrating metal oxide semiconductor gas sensor array, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total number of 615 breath samples (i.e., 333 positive and 282 negative COVID-19 confirmed by RT-qPCR) obtained from 83 patients in two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis (LDA), support vector machine (SVM), stacked multilayer perceptron (MLP), and deep neural network (DNN)) were utilized to identify the top-performing pattern recognition methods and to obtain high system detection accuracy (88–95%), sensitivity (86–94%), specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

scholarly journals Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 45 ◽  
Author(s):  
Huixiang Liu ◽  
Qing Li ◽  
Bin Yan ◽  
Lei Zhang ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Electronic Nose ◽  
Optimal Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fermentation Processes ◽  
Extreme Gradient Boosting

In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.

scholarly journals The Chemical Compositions of Essential Oils Derived from Cryptocarya alba and Laurelia sempervirens Possess Antioxidant, Antibacterial and Antitumoral Activity Potential

Molecules ◽  
2020 ◽  
Vol 25 (23) ◽  
pp. 5600
Author(s):  
Jorge Touma ◽  
Myriam Navarro ◽  
Betsabet Sepúlveda ◽  
Alequis Pavon ◽  
Gino Corsini ◽  
...  
Keyword(s):  
Essential Oil ◽  
Essential Oils ◽  
Antioxidant Capacity ◽  
Diffusion Method ◽  
Natural Food ◽  
Antitumoral Activity ◽  
Chemical Compositions ◽  
Natural Medicine ◽  
Agar Disc

Cryptocarya alba (Peumo; CA) and Laurelia sempervirens (Laurel; LS) are herbs native to the Chilean highlands and have historically been used for medicinal purposes by the Huilliches people. In this work, the essential oils were extracted using hydrodistillation in Clevenger apparatus and analyzed by GC-MS to determine their composition. The antioxidant capacity (AC) was evaluated in vitro. The cytotoxicity was determined using cell line cultures both non tumoral and tumoral. The toxicity was determined using the nematode Caenorhabditis elegans. The antimicrobial activity was evaluated against 52 bacteria using the agar disc diffusion method and the minimum inhibitory concentrations (MICs) were determined. The principal compounds found in C. alba essential oil (CA_EO) were α-terpineol (24.96%) and eucalyptol (21.63%) and were isazafrol (91.9%) in L. sempervirens essential oil (LS_EO). Both EOs showed antioxidant capacity in vitro. Both EO showed antibacterial activity against bacteria using. LS_EO showed more inhibitory effect on these cell lines respect to CA_EO. Both EOs showed toxicity against the nematode C.elegans at 3.12–50 mg/mL. The essential oils of CA and LS have an important bioactive potential in their antioxidant, antibacterial and cytotoxicity activity. Both essential oils could possibly be used in the field of natural medicine, natural food preservation, cosmetics, sanitation and plaguicides among others.

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.  

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.

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