Sensor Array Optimization Research Articles

Evaluation of lipid oxidation of Chinese-style sausage during processing and storage based on electronic nose

A portable electronic nose was used for extracting flavour fingerprint map of Chinese-style sausage during processing and storage, in parallel with detection of acid value (AV) and peroxide value (POV) for evaluating lipid oxidation. Sausage samples during processing and storage were divided into three and five quality phases, respectively. After comparison of sensors response to lipid oxidation, optimal sensor array was determined. Several classification and regression models were developed to classify samples into their respective quality phase and predict lipid oxidation using full and optimal sensor array. Results indicated classification accuracy for sausage samples were, respectively, above 95% and 82% during the processing and storage. For support vector machine (SVM) and artificial neural networks (ANN) regression models, good performance in predicting AV and POV were obtained, with the coefficients of determination (R2s) >0.914 and 0.814 during processing and storage, respectively. Thus, E-nose demonstrated acceptable feasibility in evaluating the degree of lipid oxidation of Chinese-style sausage during processing and storage.

Molecularly Imprinted Sol-Gel-Based QCM Sensor Arrays for the Detection and Recognition of Volatile Aldehydes.

The detection and recognition of metabolically derived aldehydes, which have been identified as important products of oxidative stress and biomarkers of cancers; are considered as an effective approach for early cancer detection as well as health status monitoring. Quartz crystal microbalance (QCM) sensor arrays based on molecularly imprinted sol-gel (MISG) materials were developed in this work for highly sensitive detection and highly selective recognition of typical aldehyde vapors including hexanal (HAL); nonanal (NAL) and bezaldehyde (BAL). The MISGs were prepared by a sol-gel procedure using two matrix precursors: tetraethyl orthosilicate (TEOS) and tetrabutoxytitanium (TBOT). Aminopropyltriethoxysilane (APT); diethylaminopropyltrimethoxysilane (EAP) and trimethoxy-phenylsilane (TMP) were added as functional monomers to adjust the imprinting effect of the matrix. Hexanoic acid (HA); nonanoic acid (NA) and benzoic acid (BA) were used as psuedotemplates in view of their analogous structure to the target molecules as well as the strong hydrogen-bonding interaction with the matrix. Totally 13 types of MISGs with different components were prepared and coated on QCM electrodes by spin coating. Their sensing characters towards the three aldehyde vapors with different concentrations were investigated qualitatively. The results demonstrated that the response of individual sensors to each target strongly depended on the matrix precursors; functional monomers and template molecules. An optimization of the 13 MISG materials was carried out based on statistical analysis such as principle component analysis (PCA); multivariate analysis of covariance (MANCOVA) and hierarchical cluster analysis (HCA). The optimized sensor array consisting of five channels showed a high discrimination ability on the aldehyde vapors; which was confirmed by quantitative comparison with a randomly selected array. It was suggested that both the molecularly imprinting (MIP) effect and the matrix effect contributed to the sensitivity and selectivity of the optimized sensor array. The developed MISGs were expected to be promising materials for the detection and recognition of volatile aldehydes contained in exhaled breath or human body odor.

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.

Sensor array optimization and discrimination of apple juices according to variety by an electronic nose

An electronic nose (PEN3) containing 10 metal oxide semiconductor type chemical sensors was used to discriminate between eight varieties of apple juice.

Characterisation and Optimization of in-process Eddy Current Sensor Arrays Using Computed Tomography

Due to increasing demands towards serial production of carbon fibre reinforced plastics (CFRP) the method development for process integrated quality control has become inevitable. The preforming step of Resin Transfer Moulding (RTM) offers the possibility for process integration of eddy current sensor arrays, enabling 100% inspection of produced components. However, the application of this concept needs to take into account that optimal designs of applied sensors may vary depending on tool shape and inspected material. Therefore it is necessary to design and create a set of CFRP material standards with varying characteristics considering geometry and material. The created material standards are inspected via industrial computer tomography by using methods of image processing in order to acquire quantitative information about the structure of inspected material. Afterwards eddy current measurement is conducted with varying sensor properties in order to determine the optimal structure of an eddy current array that can be integrated in preforming tools. The presented paper includes detailed description of the approach focusing on the definition of reference geometries, measurement via computer tomography and eddy current testing.

Sensor Array Optimization for Complexity Reduction in Electronic Nose System

An Electronic nose (E-Nose) can be used to assess food quality and fruit ripeness without personal bias. A set of relevant sensors must be identified to design an effective E-Nose and reduce implementation cost and complexity. The analysis of tropical fruit odour in terms of pattern recognition errors is carried out to determine the minimum number of sensors and their combinations. Two new methods namely 1) principal component loading and mutual information between sensor data, and 2) threshold based approach are proposed in this work to evaluate and optimize the sensor set. Four pattern recognition methods, namely multilayer perceptron neural network (MLPNN), radial basis function neural network (RBFNN), support vector machine (SVM), and k-nearest neighbour (k-NN) are also compared in terms of classification performance. The pattern recognition error of SVM with the optimal set of sensors is as low as 2.78% and that of k-NN is 9.72%. The results conclude that the pattern classification error with MLPNN, and RBFNN is higher than the error from k-NN and SVM.

Sensor Array Optimization for Mobile Electronic Nose: Wavelet Transform and Filter Based Feature Selection Approach

Mobile Electronic Nose (MoLen) is a prospective concept for Sensing as a Service (S2aaS) development. Furthermore, gas sensor array is a substantial part of MoLen. This work treats about two issues related with gas sensor array. First, commonly used resistive sensor e.g. MOS (Metal-Oxide Semiconductor) gas sensor is highly contaminated with noise. Second, a poor combination of sensor array leads to features redundancy issue. These problems cause significant performance degradation on classifier. It will get worse if the classifier runs on S2aaS environment. To deal with these issues, this study proposes the robust sensor array optimization method based on Wavelet Transform to handle the noise and the modified Fast Correlation-Based Filter (FCBF) to find the best combination of sensor array with minimizing feature redundancy. This study has the following contribution: i) reducing the noise from gas sensor array that generated irrelevant data; ii) finding the best sensor array for beef quality classification to improve the quality of input to classifier. The experimental results show that the proposed method successfully reduces the noise power at maximum 14.41% and it is able to determine the best combination of sensors in sensor array with the 16% of improvement of General Resolution Factor (GRF)that is associated with larger classification rate.

An analytical model for studying the structural effects and optimization of a capacitive tactile sensor array

This paper presents an analytical model to study the structural effects of a capacitive tactile sensor array on its capacitance changes and sensitivities. The tactile sensor array has 8 × 8 sensor units, and each unit utilizes the truncated polydimethylsiloxane (PDMS) pyramid array structure as the dielectric layer to enhance the sensing performance. To predict the capacitance changes of the sensor unit, it is simplified into a two-layered structure: upper polyethylene terephthalate (PET) film and bottom truncated PDMS pyramid array. The upper PET is modeled by a displacement field function, while each of the truncated pyramids is analyzed to obtain its stress–strain relation. Using the Ritz method, the displacement field functions are solved. The deformation of the upper electrodes and the capacitance changes of the sensor unit can then be calculated. Using the developed model, the structural effects of the truncated PDMS pyramid array and the PDMS bump on the capacitance changes and sensitivities are studied. To achieve the largest capacitance changes, the dimensions have been optimized for the sensor unit. To verify the developed model, we have fabricated the sensor array, and the average sensitivities of the sensor unit to the x-, y-, and z-axes force are 0.49, 0.50, and 0.32% mN−1, respectively, while the model predicted values are 0.54, 0.54, and 0.35% mN−1. Results demonstrate that the developed model can accurately predict the sensing performance of the sensor array and could be utilized for structural optimization.

Investigation of Chemical Sensor Array Optimization Methods for DADSS

Nowadays, most major automobile manufacturers are very interested, and actively involved, in developing driver alcohol detection system for safety (DADSS) that serves to prevent driving under the influence. DADSS measures the blood alcohol concentration (BAC) from the driver's breath and limits the ignition of the engine of the vehicle if the BAC exceeds the reference value. In this study, to optimize the sensor array of the DADSS, we selected sensors by using three different methods, configured the sensor arrays, and then compared their performance. The Wilks' lambda, stepwise elimination and filter method (using a principal component) were used as the sensor selection methods [2,3]. We compared the performance of the arrays, by using the selectivity and sensitivity as criteria, and Sammon mapping for the analysis of the cluster type of each gas. The sensor array configured by using the stepwise elimination method exhibited the highest sensitivity and selectivity and yielded the best visual result after Sammon mapping.

Selection of a piezoelectric sensor array for detecting volatile organic substances in water

Sensitive coatings for piezoelectric sensors used to assess the presence of anthropogenic volatile organic substances in the equilibrium gas phase over natural water have been selected using an array of measuring elements, and the data have been processed using principal component analysis. Groups of piezoelectric sensors with similar characteristics for substance identification have been determined based on the correlation between piezoelectric sensor responses when vapors of organic substances were detected. The stepby-step optimization of the piezoelectric sensor array has been carried out in order to identify the greatest number of organic compounds (vapors) in the sample. Volatile organic compounds can be identified in their aqueous solutions and natural water using the optimized piezoelectric sensor array.

Electronic Nose Feature Extraction Methods: A Review.

Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

Sensor Array Design for Complex Sensing Tasks.

Chemical detection in complex environments presents numerous challenges for successful implementation. Arrays of sensors are often implemented for complex chemical sensing tasks, but systematic understanding of how individual sensor response characteristics contribute overall detection system performance remains elusive, with generalized strategies for design and optimization of these arrays rarely reported and even less commonly adopted by practitioners. This review focuses on the literature of nonspecific sensor array design and optimization strategies as well as related work that may inform future efforts in complex sensing with arrays.

A modified analytical model to study the sensing performance of a flexible capacitive tactile sensor array

This paper presents a modified analytical model to study the sensing performance of a flexible capacitive tactile sensor array, which utilizes solid polydimethylsiloxane (PDMS) film as the dielectric layer. To predict the deformation of the sensing unit and capacitance changes, each sensing unit is simplified into a three-layer plate structure and divided into central, edge and corner regions. The plate structure and the three regions are studied by the general and modified models, respectively. For experimental validation, the capacitive tactile sensor array with 8 × 8 (= 64) sensing units is fabricated. Experiments are conducted by measuring the capacitance changes versus applied external forces and compared with the general and modified models’ predictions. For the developed tactile sensor array, the sensitivity predicted by the modified analytical model is 1.25%/N, only 0.8% discrepancy from the experimental measurement. Results demonstrate that the modified analytical model can accurately predict the sensing performance of the sensor array and could be utilized for model-based optimal capacitive tactile sensor array design.

A Sensor Array Optimization Method Based on Variance Difference for Machine Olfaction

In machine olfaction or electronic nose, sensor optimization is important to enhance pattern recognition efficiency and reduce redundant information. Highly correlated response of one sensor to two different odors implies less contribution of this sensor to the classification of these two odors. Variance difference is a significant index to measure the similarity of sensor responses. A sensor optimization method based on variance difference is proposed in this paper; both the average value of variance difference and cluster analysis of variance difference matrix were considered to identify several possible sensor subsets. Six Chinese herbal medicines and linear discrimination analysis (LDA) were applied to test the classification results in order to determine the best subset. LDA results indicated that the optimized sensor subset performed well in classification of the six Chinese medicines. The proposed sensor array optimization method could be applied to other kinds of odors classification as a novel method.

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.

Quantitative damage detection and sparse sensor array optimization of carbon fiber reinforced resin composite laminates for wind turbine blade structural health monitoring.

The active structural health monitoring (SHM) approach for the complex composite laminate structures of wind turbine blades (WTBs), addresses the important and complicated problem of signal noise. After illustrating the wind energy industry's development perspectives and its crucial requirement for SHM, an improved redundant second generation wavelet transform (IRSGWT) pre-processing algorithm based on neighboring coefficients is introduced for feeble signal denoising. The method can avoid the drawbacks of conventional wavelet methods that lose information in transforms and the shortcomings of redundant second generation wavelet (RSGWT) denoising that can lead to error propagation. For large scale WTB composites, how to minimize the number of sensors while ensuring accuracy is also a key issue. A sparse sensor array optimization of composites for WTB applications is proposed that can reduce the number of transducers that must be used. Compared to a full sixteen transducer array, the optimized eight transducer configuration displays better accuracy in identifying the correct position of simulated damage (mass of load) on composite laminates with anisotropic characteristics than a non-optimized array. It can help to guarantee more flexible and qualified monitoring of the areas that more frequently suffer damage. The proposed methods are verified experimentally on specimens of carbon fiber reinforced resin composite laminates.

Rapid Identification of Asteraceae Plants with Improved RBF-ANN Classification Models Based on MOS Sensor E-Nose.

Plants from Asteraceae family are widely used as herbal medicines and food ingredients, especially in Asian area. Therefore, authentication and quality control of these different Asteraceae plants are important for ensuring consumers' safety and efficacy. In recent decades, electronic nose (E-nose) has been studied as an alternative approach. In this paper, we aim to develop a novel discriminative model by improving radial basis function artificial neural network (RBF-ANN) classification model. Feature selection algorithms, including principal component analysis (PCA) and BestFirst + CfsSubsetEval (BC), were applied in the improvement of RBF-ANN models. Results illustrate that in the improved RBF-ANN models with lower dimension data classification accuracies (100%) remained the same as in the original model with higher-dimension data. It is the first time to introduce feature selection methods to get valuable information on how to attribute more relevant MOS sensors; namely, in this case, S1, S3, S4, S6, and S7 show better capability to distinguish these Asteraceae plants. This paper also gives insights to further research in this area, for instance, sensor array optimization and performance improvement of classification model.

Evaluation of red wines antioxidant capacity by means of a voltammetric e-tongue with an optimized sensor array

In this work, two sets of voltammetric sensors -prepared using different strategies- have been combined in an electronic tongue to evaluate the complete antioxidant profile of red wines. To this aim, wine samples were analyzed with the whole set of sensors. In order to reduce the large dimensionality of the data set while keeping the relevant information provided by the sensors, two different methods of feature selection and data compression were used (the kernels method and Discrete Wavelet Transform feature extraction method). Then, the coefficients obtained were used as the input variables of Principal Component Analysis (to evaluate the capability of discrimination. Partial-least squares regression (PLS) and artificial neural networks (ANNs) were performer to build the quantitative prediction models that allowed the quantification of the antioxidant capacity of the tested wines.

A sensor array optimization method of electronic nose based on elimination transform of Wilks statistic for discrimination of three kinds of vinegars

In order to discriminate three kinds of Chinese vinegars using an electronic nose (E-nose), an E-nose sensor array optimization method based on elimination transform with pivoting of Wilks Λ-statistic is put forward. With the help of the elimination transform some sensors with high discriminant ability are orderly selected step by step, and an ordered set of the selected sensors is simultaneously obtained by the selected sequence. Although the ordered set is not necessarily optimization array, the optimization array can be easily obtained by further exploring the discrimination results of sub-arrays which are composed by the first two, three, four and more sensors of the ordered set. In the discrimination of three kinds of vinegar samples, 9 sensors were selected in sequence and consisted of the ordered set using the method, and these vinegar samples could be well discriminated by principal component analysis (PCA). Furthermore, the first two sensors were selected and their PCA result was better than that of the 9 sensors. This illuminates that the array optimization method is very suitable and effective.

Design of sparse array for mad imaging based on maximizing information capacity

In past years, growing efforts have been made to the rapid interpretation of magnetic field data acquired by a sparse synthetic or real magnetic sensor array. An appealing requirement on such sparse array arranged within a specified survey region is that to make the number of sensor elements as small as possible, meanwhile without deteriorating imaging quality. For this end, we propose a novel methodology of arranging sensors in an optimal manner, exploring the concept of information capacity developed originally in the communication society. The proposed scheme reduces mathematically the design of a sparse sensor array into solving a combinatorial optimization problem, which can be resolved efficiently using widely adopted Simultaneous Perturbation and Statistical Algorithm (SPSA). Three sets of numerical examples of designing optimal sensor array are provided to demonstrate the performance of proposed methodology.