Performance Of E-nose Research Articles

Prediction of total volatile basic nitrogen (TVB-N) in fish meal using a metal-oxide semiconductor electronic nose based on the VMD-SSA-LSTM algorithm.

The total volatile basic nitrogen (TVB-N) is the main indicator for evaluating the freshness of fish meal, and accurate detection and monitoring of TVB-N is of great significance for the health of animals and humans. Here, to realize fast and accurate identification of TVB-N, in this article, a self-developed electronic nose (e-nose) was used, and the mapping relationship between the gas sensor response characteristic information and TVB-N value was established to complete the freshness detection. The TVB-N variation curve was decomposed into seven subsequences with different frequency scales by means of variational mode decomposition (VMD). Each subsequence was modelled using different long short-term memory (LSTM) models, and finally, the final TVB-N prediction result was obtained by adding the prediction results based on different frequency components. To improve the performance of the LSTM, the sparrow search algorithm (SSA) was used to optimize the number of hidden units, learning rate and regularization coefficient of LSTM. The prediction results indicated that the high accuracy was obtained by the VMD-LSTM model optimized by SSA in predicting TVB-N. The coefficient of determination (R2), the root-mean-squared error (RMSE) and relative standard deviation (RSD) between the predicted value and the actual value of TVBN were 0.91, 0.115 and 6.39%. This method improves the performance of e-nose in detecting the freshness of fish meal and provides a reference for the quality detection of e-nose in other materials. © 2024 Society of Chemical Industry.

A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology

IntroductionElectronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer. MethodsThis phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2–0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models. ResultsBased on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001). ConclusionsIn the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a “multiomics” platform.

Enhancing classification rate of electronic nose system and piecewise feature extraction method to classify black tea with superior quality

This study introduced a metal-oxide-semiconductor (MOS) based electronic nose (E-nose) to perform on-the-spot classification of superior-quality black tea. A piecewise feature method based on a line-fitting model was introduced to extract comprehensive features of E-nose sensor response curves. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used for data dimensionality reduction and structure visualization. Support vector machine (SVM) with a Radial kernel function was used to assess the performance of E-nose. The results indicated that the SVM model coupled with the piecewise feature method performed better and achieved the best classification rates of 99.50 %, 95.30 %, and 96.50 %, for training, validation, and testing datasets respectively, with testing sensitivity and specificity of up to 98.60 % and 99.10 %. The E-nose result was further correlated with compound concentrations in the black tea, measured using gas chromatography-mass spectrometry (GC–MS). Based on its enhanced performance evaluation, the introduced lab-built E-nose system yielded promising results in assessing superior-quality black tea.

Assessing over Time Performance of an eNose Composed of 16 Single-Type MOX Gas Sensors Applied to Classify Two Volatiles

This paper assesses the over time performance of a custom electronic nose (eNose) composed of an array of commercial low-cost and single-type miniature metal-oxide (MOX) semiconductor gas sensors. The eNose uses 16 BME680 versatile sensor devices, each including an embedded non-selective MOX gas sensor that was originally proposed to measure the total volatile organic compounds (TVOC) in the air. This custom eNose has been used previously to detect ethanol and acetone, obtaining initial promising classification results that worsened over time because of sensor drift. The current paper assesses the over time performance of different classification methods applied to process the information gathered from the eNose. The best classification results have been obtained when applying a linear discriminant analysis (LDA) to the normalized conductance of the sensing layer of the 16 MOX gas sensors available in the eNose. The LDA procedure by itself has reduced the influence of drift in the classification performance of this single-type eNose during an evaluation period of three months.

A Fast Pearson Graph Convolutional Network Combined With Electronic Nose to Identify the Origin of Rice

The quality of rice produced in different origins is different, and the gas reflects the external sensory information of rice. Based on the electronic nose (e-nose) instrument, the gas information of rice from different origins is obtained. An effective feature processing method is a key issue to improve the detection performance of e-nose. In this work, a fast pearson graph convolutional network (FPGCN) is proposed to identify the features extracted by the e-nose sensors and realize the origin tracking of rice. Based on the pearson correlation coefficient (PCC) value, the correlation between the features is quantified to construct the graph Laplacian matrix of graph convolutional network (GCN). The Chebyshev polynomial is introduced to reduce the computational complexity and parameters of GCN, and combine the binary tree method to speed up the pooling calculation. A multi-layer structure of FPGCN is designed to achieve the gas identification of rice. Compared with the traditional feature processing method, the FPGCN has a better classification result of 98.28&#x0025;, the best F₁-score is 0.9829, and the best Kappa coefficient is 0.9799. In conclusion, the FPGCN provides an effective theoretical method to improve the detection performance of e-nose and a new technology to track the rice quality.

Assessment of high pressure processed mandarin juice in the headspace by using electronic nose and chemometric analysis

High Pressure Processing (HPP) is a high efficient method for food preservation. As an innovative inspection method, electronic nose (E-nose) was applied to assess the HPP mandarin juices in the headspace. In this work, mandarin juices were processed by HPP at range of 0–500 MPa and diagnosed by E-nose. To improve the efficiency of E-nose, locality preserving projections (LPP) was introduced to extract information of E-nose data and compared with principal component analysis (PCA) and linear discriminant analysis (LDA). Three data extractions (stable value, max value and area value), three data reductions (PCA, LDA and LPP) and two classification algorithms (support vector machine and extreme learning machine) were applied to improve the diagnostic accuracy of E-nose. This study shows that the performance of E-nose can be enhanced by appropriate data extraction and data reduction and also gives a hint of E-nose inspection for HPP fruit juice. HHP is a high efficient technology for food preservation, including volatile compounds and solid contents. E-nose, which detect the components changes in sample's headspace, has been widely applied for the food inspection as an innovative detection method. According to authors' best knowledge, researches about E-nose detection for HHP products are not very common. In this study, E-nose is applied to detect the HHP-processed mandarin juices in the headspace. Besides, three data extractions, three data reductions and two classification algorithms were applied to improve the accuracy of E-nose determination in two HHP-process cases. This paper describes the possible application of E-nose to discriminate HHP-processed juices at different pressures. E-nose combined with appropriate pattern recognition could be used as a rapid and high efficient method to discriminate HHP-processed fruit juices.

An optimization of the MOS electronic nose sensor array for the detection of Chinese pecan quality

In this research, an embedded metal oxide semiconductor (MOS) electronic nose (e-nose) was designed to detect Chinese pecan quality. To improve the performance of e-nose, three types of features were extracted to form initial feature matrix, including mean-differential coefficient value, stable value, and response area value. Furthermore, followed by the non-search feature selection strategy, optimized feature matrix was obtained through the procedure of mean analysis, variation coefficient analysis, cluster analysis and correlation analysis. It was observed that pecans were better classified after the optimization of initial feature matrix, shown by principal component analysis (PCA) score plot. And also the regression models of optimized feature matrix established by partial least squares regression (PLSR) (R2 = 0.9377) and back propagation neural networks (BPNN) (R2 = 0.9787) presented a better prediction capacity than these of initial one (PLSR: R2 = 0.8887; BPNN: R2 = 0.9093). In conclusion, the optimization method not only reduced data dimensionality but also improved electronic nose performance.

Hybrid feature matrix construction and feature selection optimization-based multi-objective QPSO for electronic nose in wound infection detection

Purpose – The purpose of this paper is to improve the performance of E-nose in the detection of wound infection. Feature extraction and selection methods have a strong impact on the performance of pattern classification of electronic nose (E-nose). A new hybrid feature matrix construction method and multi-objective binary quantum-behaved particle swarm optimization (BQPSO) have been proposed for feature extraction and selection of sensor array. Design/methodology/approach – A hybrid feature matrix constructed by maximum value and wavelet coefficients is proposed to realize feature extraction. Multi-objective BQPSO whose fitness function contains classification accuracy and a number of selected sensors is used for feature selection. Quantum-behaved particle swarm optimization (QPSO) is used for synchronization optimization of selected features and parameter of classifier. Radical basis function (RBF) network is used for classification. Findings – E-nose obtains the highest classification accuracy when the maximum value and db 5 wavelet coefficients are extracted as the hybrid features and only six sensors are selected for classification. All results make it clear that the proposed method is an ideal feature extraction and selection method of E-nose in the detection of wound infection. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring, and is beneficial for realizing the clinical application of E-nose.

A novel sensor feature extraction based on kernel entropy component analysis for discrimination of indoor air contaminants

Component analysis techniques for feature extraction in multi-sensor system (electronic nose) have been studied in this paper. A novel nonlinear kernel based Renyi entropy component analysis method is presented to address the feature extraction problem in sensor array and improve the odor recognition performance of E-nose. Specifically, a kernel entropy component analysis (KECA) as a nonlinear dimension reduction technique based on the Renyi entropy criterion is presented in this paper. In terms of the popular support vector machine (SVM) learning technique, a joint KECA–SVM framework is proposed as a system for nonlinear feature extraction and multi-class gases recognition in E-nose community. In particular, the comparisons with PCA, KPCA and ICA based component analysis methods that select the principal components with respect to the largest eigen-values or correlation have been fully explored. Experimental results on formaldehyde, benzene, toluene, carbon monoxide, ammonia and nitrogen dioxide demonstrate that the KECA–SVM method outperforms other methods in classification performance of E-nose. The MATLAB implementation of this work is available online at http://www.escience.cn/people/lei/index.html

Improving the performance of electronic nose for wound infection detection using orthogonal signal correction and particle swarm optimization

Purpose – The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose. Design/methodology/approach – Mice are used as experimental subjects. Orthogonal signal correction (OSC) is applied to preprocess the response of Enose. Radical basis function (RBF) network is used for discrimination, and the parameters in RBF are optimized by particle swarm optimization. Findings – OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection. Research limitations/implications – Further research is required to sample wound infection dataset of human beings and to demonstrate that the Enose with proper algorithms can be used to detect wound infection. Practical implications – In this paper, Enose is used to detect wound infection, and OSC is used to improve the performance of the Enose. This widens the application area of Enose and OSC. Originality/value – The innovative concept paves the way for the application of Enose.

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

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.

Multiclass classification of n-butanol concentrations with k-nearest neighbor algorithm and support vector machine in an electronic nose

An electronic nose (e-nose) is a machine used for sensing and recognizing odors by using chemical sensors. The performance of e-nose depends on choosing correct sensor and correct pattern recognition algorithm according to application fields and kinds of the odors. In this study, different n-butanol concentrations sensed by 12 metal oxide gas sensors are classified by using multiclass support vector machine methods (SVM) and k-nearest neighbor (k-NN) algorithm. Focus in this paper is that the performances of these algorithms are increased with a decision tree structure. Therefore the proposed decision tree structure is applied to the electronic nose data for sensor subset selection and classification of the n-butanol concentrations. SVM and k-NN algorithms are tested for classification of different concentrations in this decision tree structure and ordinary structure. In addition to these, cross-validation technique is used for both increasing success of classification algorithms and assessing the results objectively. This study shows that the success of classification algorithms increase from 87% to 93% and 86% to 96% by using data of two sensors selected with the proposed decision tree structure for the k-NN and the SVM methods, respectively.