A Low Cost Electronic Nose with a GMM-UBM Approach for Wood Species Verification

Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1–10.5 μm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO2 and CH4), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO2 and CH4. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications.

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Freshness Classification of Horse Mackerels with E-Nose System Using Hybrid Binary Decision Tree Structure

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420500032 ◽

2019 ◽

Vol 34 (03) ◽

pp. 2050003

Author(s):

Selda Güney ◽

Ayten Atasoy

Keyword(s):

Decision Tree ◽

Electronic Nose ◽

Low Cost ◽

Storage Conditions ◽

Tree Structure ◽

Training Dataset ◽

Support Vector ◽

Binary Decision ◽

Linear Discriminant ◽

Binary Decision Tree

The aim of this study is to test the freshness of horse mackerels by using a low cost electronic nose system composed of eight different metal oxide sensors. The process of freshness evaluation covers a scala of seven different classes corresponding to 1, 3, 5, 7, 9, 11, and 13 storage days. These seven classes are categorized according to six different classifiers in the proposed binary decision tree structure. Classifiers at each particular node of the tree are individually trained with the training dataset. To increase success in determining the level of fish freshness, one of the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and Bayes methods is selected for every classifier and the feature spaces change in every node. The significance of this study among the others in the literature is that this proposed decision tree structure has never been applied to determine fish freshness before. Because the freshness of fish is observed under actual market storage conditions, the classification is more difficult. The results show that the electronic nose designed with the proposed decision tree structure is able to determine the freshness of horse mackerels with 85.71% accuracy for the test data obtained one year after the training process. Also, the performances of the proposed methods are compared against conventional methods such as Bayes, k-NN, and LDA.

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Design and Test of a Low-Cost Electronic Nose System for Identification of Salmonella enterica in Poultry Manure

Transactions of the ASABE ◽

10.13031/trans.58.11023 ◽

2015 ◽

pp. 819-826 ◽

Cited By ~ 1

Keyword(s):

Electronic Nose ◽

Salmonella Enterica ◽

Low Cost ◽

Poultry Manure ◽

Electronic Nose System

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Integration of a low‐cost electronic nose and a voltammetric electronic tongue for red wines identification

Food Science & Nutrition ◽

10.1002/fsn3.1730 ◽

2020 ◽

Vol 8 (8) ◽

pp. 4330-4339

Author(s):

Fangkai Han ◽

Dongjing Zhang ◽

Joshua H. Aheto ◽

Fan Feng ◽

Tengfei Duan

Keyword(s):

Electronic Nose ◽

Low Cost ◽

Electronic Tongue ◽

Red Wines ◽

Voltammetric Electronic Tongue

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Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Fermentation ◽

10.3390/fermentation6040104 ◽

2020 ◽

Vol 6 (4) ◽

pp. 104

Author(s):

Claudia Gonzalez Viejo ◽

Sigfredo Fuentes

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Electronic Nose ◽

Near Infrared ◽

Raw Materials ◽

Low Cost ◽

Consumer Preference ◽

Sensor Technology ◽

Type Model ◽

Data Prediction

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

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Wood identification by a portable low-cost polymer-based electronic nose

RSC Advances ◽

10.1039/c6ra22246c ◽

2016 ◽

Vol 6 (111) ◽

pp. 109945-109949 ◽

Cited By ~ 2

Author(s):

Juliana R. Cordeiro ◽

Rosamaria W. C. Li ◽

Érica S. Takahashi ◽

Gustavo P. Rehder ◽

Gregório Ceccantini ◽

...

Keyword(s):

Electronic Nose ◽

Low Cost ◽

Wood Identification ◽

Illegal Logging ◽

Protected Species ◽

Reliable Identification

The rapid and reliable identification of woods is a difficult task, yet extremely necessary for the control of illegal logging or trade of protected species.

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Portable Low-Cost Electronic Nose Based on Surface Acoustic Wave Sensors for the Detection of BTX Vapors in Air

Sensors ◽

10.3390/s19245406 ◽

2019 ◽

Vol 19 (24) ◽

pp. 5406 ◽

Cited By ~ 2

Author(s):

Daniel Matatagui ◽

Fabio Andrés Bahos ◽

Isabel Gràcia ◽

María del Carmen Horrillo

Keyword(s):

Acoustic Wave ◽

Surface Acoustic Wave ◽

Electronic Nose ◽

Low Cost ◽

Excellent Performance ◽

Low Concentrations ◽

Acoustic Wave Sensors ◽

Benzene Toluene ◽

Wave Sensors ◽

First Time

A portable electronic nose based on surface acoustic wave (SAW) sensors is proposed in this work to detect toxic chemicals, which have a great potential to threaten the surrounding natural environment or adversely affect the health of people. We want to emphasize that ferrite nanoparticles, decorated (Au, Pt, Pd) and undecorated, have been used as sensitive coatings for the first time in these types of sensors. Furthermore, the proposed electronic nose incorporates signal conditioning and acquisition and transmission modules. The electronic nose was tested to low concentrations of benzene, toluene, and xylene, exhibiting excellent performance in terms of sensitivity, selectivity, and response time, indicating its potential as a monitoring system that can contribute to the detection of toxic compounds.

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Detection of Beef Adulterated with Pork Using a Low-Cost Electronic Nose Based on Colorimetric Sensors

Foods ◽

10.3390/foods9020193 ◽

2020 ◽

Vol 9 (2) ◽

pp. 193 ◽

Cited By ~ 3

Author(s):

Fangkai Han ◽

Xingyi Huang ◽

Joshua H. Aheto ◽

Dongjing Zhang ◽

Fan Feng

Keyword(s):

Electronic Nose ◽

Low Cost ◽

Back Propagation ◽

Quantitative Detection ◽

Chemical Compositions ◽

Ann Model ◽

Linear Discriminant ◽

Minced Beef ◽

Learning Machine ◽

Colorimetric Sensors

The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef–pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.

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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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