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

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 193 ◽  
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.

Fusion of a Low-cost Electronic Nose and Near Infrared Spectroscopy for Qualitative and Quantitative Detection of Beef Adulterated with Duck

2022 ◽  
Author(s):  
FangKai Han ◽  
Xingyi Huang ◽  
Joshua Harington Aheto ◽  
Xiaorui Zhang ◽  
Marwan M.A. Rashed
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Electronic Nose ◽  
Near Infrared ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Quantitative Detection ◽  
Qualitative And Quantitative ◽  
Convenient Technique ◽  
Colorimetric Sensors

A low-cost electronic nose (E-nose) based on colorimetric sensors fused with near-infrared (NIR) spectroscopy was proposed as a rapid and convenient technique for detecting beef adulterated with duck. The total...

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 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.  

scholarly journals Rapid qualitative and quantitative detection of formaldehyde in squids based on colorimetric sensor array

2021 ◽  
Vol 233 ◽  
pp. 02021
Author(s):  
Binbin Guan ◽  
Hongmei Ding ◽  
Bin Chen ◽  
Mi Zhou ◽  
Zhaoli Xue
Keyword(s):  
Sensor Array ◽  
Recognition Rate ◽  
Principal Component ◽  
Colorimetric Sensor ◽  
Quantitative Detection ◽  
Ann Model ◽  
K Nearest Neighbor ◽  
Training Set ◽  
Linear Discriminant ◽  
Colorimetric Sensor Array

The colorimetric sensor array was used to detect the volatile organic compounds (VOCs) in squids with different formaldehyde content. In order to distinguish whether the formaldehyde is artificially added in the squids, the linear discriminant analysis (LDA) and K-nearest neighbor (KNN) based on principal component analysis (PCA) were used to make qualitative judgments, the result shows that the recognition rates of the training set and prediction set of the LDA model were 95% and 85% respectively, and the recognition rates of the training set and prediction set of the KNN model were both 90%. Moreover, error back propagation artificial neural network (BP-ANN) was used to quantitatively predict the concentration of formaldehyde in squids. The result indicates that the BP-ANN model acquired a good recognition rate with the correlation coefficient (Rp) for prediction was 0.9887 when the PCs was 10. To verify accuracy and applicability of the model, paired sample t-test was used to verify the difference between the predicted value of formaldehyde in the BP-ANN model and the actual addition amount. Therefore, this approach showed well potentiality to provide a fast, accuracy, no need for a pretreatment, and low-cost technique for detecting the formaldehyde in squids.

scholarly journals Wireless Smell System for Hazardous Gases Detection

2018 ◽  
Author(s):  
Cristhian Manuel Durán Acevedo ◽  
Pablo Andrés Goméz Monsalve ◽  
Rafael Andrés Acosta Rozo
Keyword(s):  
Electronic Nose ◽  
Gas Sensing ◽  
Low Cost ◽  
Data Communication ◽  
Indoor Environments ◽  
Sample Collection ◽  
Detection Zone ◽  
Linear Discriminant ◽  
Toxic Gases ◽  
Hazardous Gases

This study describes the design and implementation of an electronic nose, which was applied to classify and identify hazardous gases generated in underground coal mines. For this purpose, an electrochemical sensor array was used to detect a set of toxic gases. This work illustrates the electronic components of a wireless multisensory system for the toxic gases detection on indoor environments (i.e., underground mines), which was performed with reduced size, low cost and low electrical consumption, in order to detect different compounds using the basic principle of operation of each component to be applied to the target gas. Furthermore, the sample collection, data communication and data processing in real time obtained an excellent performance for gas sensing and even to measure the concentration level of the chemical volatile compounds transmitted from different points of the detection zone. The results demonstrated that using a wireless electronic nose for toxic gases detection was possible to reach a success rate of discrimination of 97%, using principal components analysis (PCA) and Linear Discriminant Analysis (LDA).

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

scholarly journals HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 660 ◽  
Author(s):  
Fang Liu ◽  
Liubin Li ◽  
Yongbin Liu ◽  
Zheng Cao ◽  
Hui Yang ◽  
...  
Keyword(s):  
Kernel Method ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Industrial Applications ◽  
Performance Degradation ◽  
Support Vector ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

Fast Screening for Antioxidant Properties of Flavonoids from Pterogyne nitens Using Electrochemical Methods

2012 ◽  
Vol 95 (3) ◽  
pp. 773-777 ◽  
Author(s):  
Leonardo Luiz Okumura ◽  
Luis Octávio Regasini Regasini ◽  
Daniara Cristina Fernandes ◽  
Dulce Helena Siqueira da Silva ◽  
Maria Valnice Boldrin Zanoni ◽  
...  
Keyword(s):  
Antioxidant Activities ◽  
Low Cost ◽  
Supporting Electrolyte ◽  
Antioxidant Properties ◽  
Reference Electrode ◽  
Structural Features ◽  
Quantitative Detection ◽  
Nonaqueous Media ◽  
Electrochemical Detector ◽  
Fast Screening

Abstract A fast, low-cost, convenient, and especially sensitive voltammetric screening approach for the study of the antioxidant properties of isoquercitrin and pedalitin from Pterogyne nitens is suggested in this work. These flavonoids were investigated for their redox properties using cyclic voltammetry in nonaqueous media using N,N-dimethylformamide and tetrabutylammonium tetrafluorborate as the supporting electrolyte, a glassy carbon working electrode, Ag|AgCl reference electrode, and Pt bare wire counter electrode. The comparative analysis of the activity of rutin has also been carried out. Moreover, combining HPLC with an electrochemical detector allowed qualitative and quantitative detection of micromolecules (e.g., isoquercitrin and pedalitin) that showed antioxidant activities. These results were then correlated to the inhibition of β-carotene bleaching determined by TLC autographic assay and to structural features of the flavonoids.

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