scholarly journals Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples

2019 ◽  
Author(s):  
Wenshen Jia ◽  
Gang Liang ◽  
Hui Tian ◽  
Jing Sun ◽  
Cihui Wan
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Penicillium Expansum ◽  
Support Vector ◽  
Linear Discriminant ◽  
Pattern Recognition Methods ◽  
Group A ◽  
Group B

In this paper, PEN3 electronic nose was used to detect and recognize fresh and moldy apples (inoculated with Penicillium expansum and Aspergillusniger) taken Golden Delicious apples as model subject. Firstly, the apples were divided into two groups: apples only inoculated with different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined, which can simplify the analysis process and improve the accuracy of results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM) and radial basis function neural network (RBFNN), were then applied to analyze the data obtained from the characteristic sensors, respectively, aiming at establishing the prediction model of flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W and W2W in the PEN3 electronic nose exhibited strong signal response to the flavor information, indicating were most closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors was used for modeling. The results of the four pattern recognition methods showed that BPNN presented the best prediction performance for the training and validation sets for both the Group A and Group B, with prediction accuracies of 96.29% and 90.00% (Group A), 77.70% and 72.00% (Group B), respectively. Therefore, it first demonstrated that PEN3 electronic nose can not only effectively detect and recognize the fresh and moldy apples, but also can distinguish apples inoculated with different molds.

scholarly journals Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1526 ◽  
Author(s):  
Wenshen Jia ◽  
Gang Liang ◽  
Hui Tian ◽  
Jing Sun ◽  
Cihui Wan
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Electronic Nose ◽  
Gas Sensors ◽  
Penicillium Expansum ◽  
Support Vector ◽  
Linear Discriminant ◽  
Pattern Recognition Methods ◽  
Group A ◽  
Group B

In this study, the PEN3 electronic nose was used to detect and recognize fresh and moldy apples inoculated with Penicillium expansum and Aspergillus niger, taking Golden Delicious apples as the model subject. Firstly, the apples were divided into two groups: individual apple inoculated only with/without different molds (Group A) and mixed apples of inoculated apples with fresh apples (Group B). Then, the characteristic gas sensors of the PEN3 electronic nose that were most closely correlated with the flavor information of the moldy apples were optimized and determined to simplify the analysis process and improve the accuracy of the results. Four pattern recognition methods, including linear discriminant analysis (LDA), backpropagation neural network (BPNN), support vector machines (SVM), and radial basis function neural network (RBFNN), were applied to analyze the data obtained from the characteristic sensors, aiming at establishing the prediction model of the flavor information and fresh/moldy apples. The results showed that only the gas sensors of W1S, W2S, W5S, W1W, and W2W in the PEN3 electronic nose exhibited a strong signal response to the flavor information, indicating most were closely correlated with the characteristic flavor of apples and thus the data obtained from these characteristic sensors were used for modeling. The results of the four pattern recognition methods showed that BPNN had the best prediction performance for the training and testing sets for both Groups A and B, with prediction accuracies of 96.3% and 90.0% (Group A), 77.7% and 72.0% (Group B), respectively. Therefore, we demonstrate that the PEN3 electronic nose not only effectively detects and recognizes fresh and moldy apples, but also can distinguish apples inoculated with different molds.

scholarly journals ELECTRONIC NOSE FOR ANCHOVY FRESHNESS MONITORING BASED ON SENSOR ARRAY AND PATTERN RECOGNITION METHODS: PRINCIPAL COMPONENTS ANALYSIS, LINEAR DISCRIMINANT ANALYSIS AND SUPPORT VECTOR MACHINE

2014 ◽  
pp. 61-67
Author(s):  
A. Amari ◽  
N. El Bari ◽  
B. Bouchikhi
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Tin Dioxide ◽  
Principal Component ◽  
Support Vector ◽  
Current Pattern ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

An electronic nose based system, which employs an array of six inexpensive commercial gas sensors based on tin dioxide (Figaro Engineering Inc., Japan), has been used to analyse the freshness states of anchovies. Fresh anchovies were stored in a refrigerator at 4 ± 1°C over a period of 15 days. Electronic nose measurements need no sample preparation and the results indicated that the spoilage process of anchovies could be followed by using this technique. Conductance responses of volatile compounds produced during storage of anchovy were monitored and the result were analysed by multivariate analysis methods. In this paper principal component analysis (PCA) and linear discriminant analysis (LDA) were used to investigate whether the electronic nose was able to distinguishing among different freshness states (fresh, moderated and non-fresh samples). The loadings analysis was used to identify the sensors responsible for discrimination in the current pattern file. Therefore, the support vector machines (SVM) method was applied to the new subset, with only the selected sensors, to confirm that a subset of a few sensors can be chosen to explain all the variance. The results obtained prove that the electronic nose can discriminate successfully different freshness state using LDA analysis. Some sensors have the highest influence in the current pattern file for electronic nose. Support vector machine (SVM) model, applied to the new subset of sensors show the good performance.

Evaluation of Semanotus bifasciatus (Motschulsky) Infestation in Platycladus orientalis Plants Using E-Nose and GC-MS

2020 ◽  
Vol 63 (6) ◽  
pp. 1629-1637
Author(s):  
Zhenhe Wang ◽  
Yubing Sun ◽  
Jun Wang ◽  
Yongwei Wang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Electronic Nose ◽  
Support Vector ◽  
List Type ◽  
Linear Discriminant ◽  
Platycladus Orientalis ◽  
Volatile Organic

HighlightsE-nose was employed for evaluation of Semanotus bifasciatus infestation based on four time-domain features.Plant VOCs were analyzed by GC-MS, and the results proved the feasibility of E-nose detection.PNN, BPNN, SVM, and PLSR were introduced to classify and predict Semanotus bifasciatus infestation numbers.Abstract. Trunk-boring insects such as Semanotus bifasciatus (Motschulsky) are difficult to detect because the larvae are hidden inside the trunks. In this study, the variation of volatile organic compounds (VOCs) in Platycladus orientalis after S. bifasciatus infestation was evaluated using an electronic nose (E-nose). VOCs from sample plants were observed with gas chromatography - mass spectrometry (GC-MS), and the results indicated that uninfected and infected groups differed both qualitatively and quantitatively, which proves the feasibility of E-nose evaluation. To extract features of the E-nose response signals, four feature extraction methods were applied, and their performances were compared based on linear discriminant analysis (LDA). Three classification models, including back-propagation neural network (BPNN), support vector machine (SVM), and probabilistic neural network (PNN), were established to identify the severity of infestation based on the optimal feature extraction method (75th second value). The classification results of BPNN, PNN, and SVM based on the calibration set were 96.43%, 91.07%, and 100%, respectively, and the results based on the validation set were 91.67%, 91.67%, and 100%, respectively. In addition, partial least squares regression (PLSR) and BPNN were used to predict the larvae density and achieved highly reliable results. It can be concluded that combining E-nose with GC-MS is a potential technique for evaluating trunk-borer infestation and can be used for pest management. Keywords: Electronic nose, Feature extraction, Pest evaluation, Semanotus bifasciatus, Volatile organic compounds.

scholarly journals Mid-Infrared Spectroscopy for Coffee Variety Identification: Comparison of Pattern Recognition Methods

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Chu Zhang ◽  
Chang Wang ◽  
Fei Liu ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Yunnan Province ◽  
Variety Identification ◽  
Support Vector ◽  
Hainan Province ◽  
Mid Infrared ◽  
Arabica Coffee ◽  
Robusta Coffee ◽  
Pattern Recognition Methods

The potential of using mid-infrared transmittance spectroscopy combined with pattern recognition algorithm to identify coffee variety was investigated. Four coffee varieties in China were studied, including Typica Arabica coffee from Yunnan Province, Catimor Arabica coffee from Yunnan Province, Fushan Robusta coffee from Hainan Province, and Xinglong Robusta coffee from Hainan Province. Ten different pattern recognition methods were applied on the optimal wavenumbers selected by principal component analysis loadings. These methods were classified as highly effective methods (soft independent modelling of class analogy, support vector machine, back propagation neural network, radial basis function neural network, extreme learning machine, and relevance vector machine), methods of medium effectiveness (partial least squares-discrimination analysis,Knearest neighbors, and random forest), and methods of low effectiveness (Naive Bayes classifier) according to the classification accuracy for coffee variety identification.

scholarly journals Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Haifeng Sun ◽  
Ling Wang ◽  
Shun Wang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Near Infrared ◽  
Scatter Correction ◽  
Recognition Rate ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Pattern Recognition Methods ◽  
Gelatin Gels

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

Nano Sensor Using One Dimensional Porous Indium Oxide and Pattern Recognition Method of Its Electronic Information

2021 ◽  
Vol 16 (2) ◽  
pp. 255-263
Author(s):  
Qinghong Wu ◽  
Wanying Zhang
Keyword(s):  
Pattern Recognition ◽  
Indium Oxide ◽  
Electronic Nose ◽  
Sensor Array ◽  
Dimensional Space ◽  
Ordinary Least Squares ◽  
Acquisition Time ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Linear Discriminant

Due to its high sensitivity, low price and fast response speed, gas sensors based on metal oxide nanomate-rials have attracted many researchers to modify and explore the materials. First, pure indium oxide (In2O3) nanotubes (NTs)/porous NTs (PNTs) and Ho doped In2O3 NTs/PNTs are prepared by electrospinning and calcination. Then, based on the prepared nanomaterials, the 6-channel sensor array is obtained and used in the electronic nose sensing system for wine product identification. The system obtains the frequency signals of different liquor products by means of 6-channel sensor array, analyzes the extracted electronic signal characteristic information by means of ordinary least squares, and introduces the pattern recognition method of moving average and linear discriminant to identify liquor products. In the experiment, compared with pure In2O3 NTs sensor, pure In2O3 PNTs sensor has higher sensitivity to 100 ppm ethanol gas, and the sensitivity is further improved after mixing Ho. Among them, 6 mol% Ho + In2O3 PNTs have the highest sensitivity and the shortest response time; based on the electronic nose system composed of prepared nanomaterial sensor array, frequency signals of different Wu Liang Ye wines are collected. With the extension of acquisition time, the corresponding frequency first decreases and then becomes stable; the extracted liquor characteristic signal is projected into two-dimensional space and three-dimensional space. The results show that the pattern recognition system based on this method can extract the characteristic signals of liquor products and distinguish them.

scholarly journals Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2936 ◽  
Author(s):  
Xianghao Zhan ◽  
Xiaoqing Guan ◽  
Rumeng Wu ◽  
Zhan Wang ◽  
You Wang ◽  
...  
Keyword(s):  
Herbal Medicine ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Herbal Medicines ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Convenient Means ◽  
Electronic Nose System ◽  
Insight Into

As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were >0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

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