Lab-Made Electronic Nose for Fast Detection of Listeria monocytogenes and Bacillus cereus

The aim of this study is to determine the performance of a lab-made electronic nose (e-nose) composed of an array of metal oxide semiconductor (MOS) gas sensors in the detection and differentiation of Listeria monocytogenes (L. monocytogenes) and Bacillus cereus (B. cereus) incubated in trypticsoy broth (TSB) media. Conventionally, the detection of L. monocytogenes and B. cereus is often performed by enzyme link immunosorbent assay (ELISA) and polymerase chain reaction (PCR). These techniques require trained operators and expert, expensive reagents and specific containment. In this study, three types of samples, namely, TSB media, L. monocytogenes (serotype 4b American Type Culture Collection (ATCC) 13792), and B. cereus (ATCC) 10876, were used for this experiment. Prior to measurement using the e-nose, each bacterium was inoculated in TSB at 1 × 103–104 CFU/mL, followed by incubation for 48 h. To evaluate the performance of the e-nose, the measured data were then analyzed with chemometric models, namely linear and quadratic discriminant analysis (LDA and QDA), and support vector machine (SVM). As a result, the e-nose coupled with SVM showeda high accuracy of 98% in discriminating between TSB media and L. monocytogenes, and between TSB media and B. cereus. It could be concluded that the lab-made e-nose is able to detect rapidly the presence of bacteria L. monocytogenes and B. cereus on TSB media. For the future, it could be used to identify the presence of L. monocytogenes or B. cereus contamination in the routine and fast assessment of food products in animal quarantine.

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

10.21203/rs.3.rs-750988/v1 ◽

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.

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

Sensors ◽

10.3390/s19010045 ◽

2018 ◽

Vol 19 (1) ◽

pp. 45 ◽

Cited By ~ 19

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.

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

Czech Journal of Food Sciences ◽

10.17221/113/2014-cjfs ◽

2014 ◽

Vol 32 (No. 6) ◽

pp. 538-548 ◽

Cited By ~ 21

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.  

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

International Journal of Food Engineering ◽

10.1515/ijfe-2018-0111 ◽

2018 ◽

Vol 14 (7-8) ◽

Cited By ~ 2

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.

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

Chemosensors ◽

10.3390/chemosensors9060142 ◽

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).

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The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

Chemosensors ◽

10.3390/chemosensors7030029 ◽

2019 ◽

Vol 7 (3) ◽

pp. 29 ◽

Cited By ~ 7

Author(s):

Shidiq Nur Hidayat ◽

Kuwat Triyana ◽

Inggrit Fauzan ◽

Trisna Julian ◽

Danang Lelono ◽

...

Keyword(s):

Electronic Nose ◽

External Validation ◽

Principal Component ◽

Black Tea ◽

Oxide Semiconductor ◽

Support Vector ◽

Quality Level ◽

Validation Data ◽

Signal Preprocessing

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production); and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry.

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Comparison of SNP based and cgMLST based typing of Listeria monocytogenes isolates from Seeliger’s historical “Special Listeria Culture Collection”

10.26226/morressier.56d5ba29d462b80296c962da ◽

2016 ◽

Cited By ~ 1

Author(s):

Patrick Hyden

Keyword(s):

Listeria Monocytogenes ◽

Culture Collection

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Advanced Killing Potential of Thymol against a Time and Temperature Optimized Attached Listeria monocytogenes Population in Lettuce Broth

Biomolecules ◽

10.3390/biom11030397 ◽

2021 ◽

Vol 11 (3) ◽

pp. 397

Author(s):

Dimitra Kostoglou ◽

Parthena Tsaklidou ◽

Ioannis Iliadis ◽

Nikoletta Garoufallidou ◽

Georgia Skarmoutsou ◽

...

Keyword(s):

Listeria Monocytogenes ◽

Benzalkonium Chloride ◽

Planktonic Cells ◽

Antimicrobial Efficiency ◽

Fresh Vegetables ◽

Viable Bacteria ◽

Steel Surfaces ◽

Polymerase Chain ◽

Killing Action ◽

Foodborne Infections

Fresh vegetables and salads are increasingly implicated in outbreaks of foodborne infections, such as those caused by Listeria monocytogenes, a dangerous pathogen that can attach to the surfaces of the equipment creating robust biofilms withstanding the killing action of disinfectants. In this study, the antimicrobial efficiency of a natural plant terpenoid (thymol) was evaluated against a sessile population of a multi-strain L. monocytogenes cocktail developed on stainless steel surfaces incubated in lettuce broth, under optimized time and temperature conditions (54 h at 30.6 °C) as those were determined following response surface modeling, and in comparison, to that of an industrial disinfectant (benzalkonium chloride). Prior to disinfection, the minimum bactericidal concentrations (MBCs) of each compound were determined against the planktonic cells of each strain. The results revealed the advanced killing potential of thymol, with a concentration of 625 ppm (= 4 × MBC) leading to almost undetectable viable bacteria (more than 4 logs reduction following a 15-min exposure). For the same degree of killing, benzalkonium chloride needed to be used at a concentration of at least 20 times more than its MBC (70 ppm). Discriminative repetitive sequence-based polymerase chain reaction (rep-PCR) also highlighted the strain variability in both biofilm formation and resistance. In sum, thymol was found to present an effective anti-listeria action under environmental conditions mimicking those encountered in the salad industry and deserves to be further explored to improve the safety of fresh produce.

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Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Sensors ◽

10.3390/s19020419 ◽

2019 ◽

Vol 19 (2) ◽

pp. 419 ◽

Cited By ~ 11

Author(s):

Dongdong Du ◽

Jun Wang ◽

Bo Wang ◽

Luyi Zhu ◽

Xuezhen Hong

Keyword(s):

Prediction Models ◽

Extraction Methods ◽

Oxide Semiconductor ◽

Soluble Solids ◽

Support Vector ◽

Least Squares Regression ◽

Accuracy Rate ◽

Linear Discriminant ◽

The Difference ◽

Ripe Stage

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

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

Sensor Review ◽

10.1108/sr-02-2013-630 ◽

2014 ◽

Vol 34 (3) ◽

pp. 304-311 ◽

Cited By ~ 14

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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