scholarly journals Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2146 ◽  
Author(s):  
Pei Li ◽  
Zouhong Ren ◽  
Kaiyi Shao ◽  
Hequn Tan ◽  
Zhiyou Niu
Keyword(s):  
Electronic Nose ◽  
Fish Meal ◽  
Solid Phase ◽  
Classification Method ◽  
Mass Spectrometry Analysis ◽  
Support Vector ◽  
Characteristic Parameters ◽  
Accuracy Rate ◽  
Linear Discriminant ◽  
Maximum Gradient

In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.

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

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 419 ◽  
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.

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.

scholarly journals Discrimination of Volatiles of Shiitakes (Lentinula edodes) Produced during Drying Process by Electronic Nose

2020 ◽  
Vol 16 (1-2) ◽  
Author(s):  
Hui Zhang ◽  
Jing Peng ◽  
Yu-ren Zhang ◽  
Qiang Liu ◽  
Lei-qing Pan ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Lentinula Edodes ◽  
Solid Phase ◽  
Principal Component ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Drying Process ◽  
Drying Time ◽  
Discriminant Models ◽  
Accuracy Rates

AbstractThis study aimed to investigate the potential of electronic nose (E-nose) to differentiate volatiles of shiitakes produced at different drying stages. Shiitakes at different drying time slots were categorized into four groups (fresh, early, middle and late stage) by sensory evaluation. E-nose was used to analyze the volatiles and compared with headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry (HS/GC-MS). The principal component analysis results showed that shiitakes at each stage could be successfully discriminated by E-nose and HS/GC-MS. The differences in volatile organic compounds produced at each stage were mainly caused by sulfurs and alcohols, leading to apparent changes of sensors sensitive to sulfurs, alcohols and aromatic compounds. The discriminant models were established by partial least squares discriminant analysis and support vector machine classification, with accuracy rates of 91.25 % and 95.83 %, respectively. The results demonstrated the potential use of E-nose in classifying and monitoring shiitakes during drying process.

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

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.

scholarly journals Rapid Non-Destructive Quantification of Eugenol in Curdlan Biofilms by Electronic Nose Combined with Gas Chromatography-Mass Spectrometry

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4441
Author(s):  
Lu Han ◽  
Jingyi Zhu ◽  
Xia Fan ◽  
Chong Zhang ◽  
Kang Tu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Prediction Model ◽  
Electronic Nose ◽  
Quantitative Method ◽  
Gas Chromatography Mass Spectrometry ◽  
Support Vector ◽  
Linear Discriminant ◽  
Chromatography Mass Spectrometry ◽  
Non Destructive

Eugenol is hepatotoxic and potentially hazardous to human health. This paper reports on a rapid non-destructive quantitative method for the determination of eugenol concentration in curdlan (CD) biofilms by electronic nose (E-nose) combined with gas chromatography-mass spectrometry (GC-MS). Different concentrations of eugenol were added to the film-forming solution to form a series of biofilms by casting method, and the actual eugenol concentration in the biofilm was determined. Analysis of the odor collected on the biofilms was carried out by GC-MS and an E-nose. The E-nose data was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) in order to establish a discriminant model for determining eugenol concentrations in the biofilms. Further analyses involving the application of all sensors and featured sensors, the prediction model-based partial least squares (PLS) and support vector machines (SVM) were carried out to determine eugenol concentration in the CD biofilms. The results showed that the optimal prediction model for eugenol concentration was obtained by PLS at R2p of 0.952 using 10 sensors. The study described a rapid, non-destructive detection and quantitative method for determining eugenol concentration in bio-based packaging materials.

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 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 Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage

Foods ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1579
Author(s):  
Huanhuan Feng ◽  
Mengjie Zhang ◽  
Pengfei Liu ◽  
Yiliu Liu ◽  
Xiaoshuan Zhang
Keyword(s):  
Cold Storage ◽  
Electronic Nose ◽  
Principal Component ◽  
Storage Condition ◽  
Quality Parameters ◽  
Training Dataset ◽  
Support Vector ◽  
Accuracy Rate ◽  
Specific Storage ◽  
Test Dataset

Salmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic nose spoilage detection for quality parameters changes and freshness under cold storage conditions. The salmon samples were analyzed and divided into three groups in an incubator set at 0 °C, 4 °C, and 6 °C. The quality parameters, i.e., texture, color, sensory, and pH changes, were measured and evaluated at different temperatures after 0, 3, 6, 9, 12, and 14 days of cold storage. The principal component analysis (PCA) algorithm can be used to cluster electronic nose information. Furthermore, a Convolutional Neural Networks and Support Vector Machine (CNN-SVM) based algorithm is used to cluster the freshness level of salmon samples stored in a specific storage condition. In the tested samples, the results show that the training dataset of freshness is about 95.6%, and the accuracy rate of the test dataset is 93.8%. For the training dataset of corruption, the accuracy rate is about 91.4%, and the accuracy rate of the test dataset is 90.5%. The overall accuracy rate is more than 90%. This work could help to reduce quality loss during salmon cold storage.

scholarly journals Five Typical Stenches Detection Using an Electronic Nose

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2514 ◽  
Author(s):  
Wei Jiang ◽  
Daqi Gao
Keyword(s):  
Electronic Nose ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Odor Concentration ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Components Analysis ◽  
Olfactory Organs

This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.

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.  

Sign in / Sign up

Export Citation Format

Share Document