Metal Oxide Semiconductor Sensor Array Research Articles

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

Soil organic matter determination based on artificial olfactory system and PLSR-BPNN

Soil organic matter (SOM) is a key indicator of soil fertility. For accurate measurement of SOM, a novel method based on an artificial olfactory system (AOS) was proposed. The response curves of soil volatile organic compounds (VOCs) were measured using a metal-oxide semiconductor sensor array, and four features (including maximum value, mean differential coefficient, response area, and the transient value at the 20th second) were obtained from the curves and used to build olfactory feature space. Then, prediction models were established using the pattern recognition algorithm. To further enhance the accuracy of AOS measurement, we used Monte Carlo cross-validation (MCCV) to identify and eliminate the abnormal samples of the soil olfactory feature space. Then, the dimension reduction method of the genetic algorithm (GA)back-propagation (BP) was used to find the appropriate feature vectors, and two types of hybrid models were presented. One was the support vector machine (SVM) and group method of data handling (GMDH) combined model—SVM-GMDH. The other was a combination of partial least squares regression (PLSR) and back-propagation neural network (BPNN)—PLSR-BPNN. The forecasting performances of three single models (BPNN, PLSR, support vector regression: SVR) and two combined models (PLSR-BPNN, SVM-GMDH) were comparatively evaluated. The evaluation indices included coefficient of determination (R 2), root mean square error (RMSE), ratio of performance to deviation and relative prediction error (RPE). It was found that the predictive capabilities of all five tested models were improved after elimination of abnormal samples and feature reduction. Moreover, PLSR-BPNN performed the best in predicting SOM concentrations, with R 2 = 0.952, RMSE = 1.771, PRD = 4.291, and slight variation of RPE within 0–0.185, and thus can offer a reference for predicting SOM via AOS.

Detection of Salmonella Typhimurium contamination levels in fresh pork samples using electronic nose smellprints in tandem with support vector machine regression and metaheuristic optimization algorithms.

Rapid detection and quantification of bacterial foodborne pathogens are crucial in reducing the incidence of diseases associated with meat products contaminated with pathogens. For the identification, discrimination and quantification of Salmonella Typhimurium contamination in pork samples, a commercial electronic nose with ten (10) metal oxide semiconductor sensor array is applied. Principal component analysis was successfully applied for discrimination of inoculated samples and inoculated samples at different contaminant levels. Support vector machine regression (SVMR) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms were applied for S. Typhimurium quantification. Although SVMR results were satisfactory, SVMR hyperparameter tuning (c and g) by PSO, GA and GS showed superior performance of the models. The order of the prediction accuracy based on the prediction set was GA-SVMR (R P 2 = 0.989; RMSEP = 0.137; RPD = 14.93) > PSO-SVMR (R P 2 = 0.986; RMSEP = 0.145; RPD = 14.11) > GS-SVMR (R P 2 = 0.966; RMSEP = 0.148; RPD = 13.82) > SVMR (R P 2 = 0.949; RMSEP = 0.162; RPD = 12.63). GA-SVMR's proposed approach was fairly more effective and retained an excellent prediction accuracy. A clear relationship was identified between odor analysis results, and reference traditional microbial test, indicating that the electronic nose is useful for accurate microbial volatile organic compound evaluation in the quantification of S. Typhimurium in a food matrix.

Multicapillary Gas Chromatography-Temperature Modulated Metal Oxide Semiconductor Sensors Array Detector for Monitoring of Volatile Organic Compounds in Closed Atmosphere Using Gaussian Apodization Factor Analysis.

A unique metal oxide semiconductor sensor (MOS) array detector with eight sensors was designed and fabricated in a PTFE chamber as an interface for coupling with multicapillary gas chromatography. This design consists of eight transfer lines with equal length between the multicapillary columns (MCC) and sensors. The deactivated capillary columns were passed through each transfer line and homemade flow splitter to distribute the same gas flow on each sensor. Using the eight ports flow splitter design helps us to equal the length of carrier gas path and flow for each sensor, minimizing the dead volume of the sensor's chamber and increasing chromatographic resolution. In addition to coupling of MCC to MOS array detector and other considerations in hardware design, modulation of MOS temperature was used to increase sensitivity and selectivity, and data analysis was enhanced with adapted Gaussian apodization factor analysis (GAFA) as a multivariate curve resolution algorithm. Continues air sampling and injecting system (CASI) design provides a fast and easily applied method for continues injection of air sample with no additional sample preparation. The analysis cycle time required for each run is less than 300 s. The high sample load and sharp injection with the fast separation by MCC decrease the peak widths and improve detection limits. This homemade customized instrument is an alternative to other time-consuming and expensive technologies for continuous monitoring of outgassing in air samples.

Temperature modulation of electronic nose combined with multi-class support vector machine classification for identifying export caraway cultivars

Caraway is one of the most consumed medicinal spice around the world. Different cultivars of caraway represent differences in the aromatic profiles which mark the difference in their selling prices in the international market. The present study provides a non-destructive methodology based on metal oxide semiconductor (MOS) based electronic nose (e-nose) and chemometrics for rapid classification of five different caraway cultivars (Zarand, Jupar, Kerman, Khabarbaft and Kohbanan) from Iran. To acquire enhanced aromatic profiles the study utilises temperature modulation technique in a MOS based electronic nose. The temperature modulations were performed with two voltage functions i.e. rectangular and sinusoid, and the best voltage function for classification was evaluated and reported. Measurements were performed in three phases and recorded transient responses of eight MOS sensors were considered for data analysis. To visualise the MOS sensor array data principal component analysis (PCA) was used. For classification purpose, linear discriminant analysis (LDA) and multi-class support vector machine (SVM) were performed on the PCA transformed data. Results showed that data visualisation with PCA clearly identify separate clusters over the PC transformed space. A maximum classification accuracy of 97.92 ± 3.82% was obtained for the SVM classification model on the rectangular voltage function. The performance of LDA modelling was similar to the SVM results in the case of rectangular voltage function, but in the case of the sinusoid form, the SVM results showed better accuracy to classify the caraway cultivars. As a consequence, it can be concluded that MOS based e-nose and chemometrics could support the rapid and non-destructive classification of caraway cultivars. Furthermore, the temperature modulation with rectangular voltage function can provide higher classification accuracy.

A Digital Signal Processing-Assisted Microfluidic PCB for On-Chip Fluorescence Detection

We report the use of digital signal processing (DSP) strategies to realize high-sensitivity on-chip fluorescence detection in microfluidic-opto-electronic printed circuit board (PCB) devices without requiring bulky high-quality optical components and filters. An isotachophoresis (ITP) assay was studied in a microfluidic PCB with an embedded standard complementary metal oxide semiconductor sensor array demonstrating a detection sensitivity of 10-nm initial concentration for fluorescein. The amplified ITP signal is processed with the DSP algorithms allowing the fluorescence signal extracted from noisy sensor output to achieve high signal-to-noise ratio. The DSP-assisted fluorescence detection system utilizes off-the-shelf, consumer-grade electronics and lensless imaging to achieve high-level but also scalable integration on microfluidic PCBs.

A novel framework for analyzing MOS E-nose data based on voting theory: Application to evaluate the internal quality of Chinese pecans

Metal oxide semiconductor (MOS) gas sensors have been widely used in the field of electronic nose (E-nose) for detecting the simple or complex volatile compounds. The electrical signals of MOS sensor array contain the abundant “fingerprint” information of samples. In this paper, an E-nose equipped with an array of MOS sensors was applied to detect Chinese pecans nondestructively for qualitative discrimination and quantitative prediction. Traditionally, most pattern recognition methods are based on single feature, which loses much useful information. To extract more information, a voting method, which was composed of principle component analysis (PCA) results based on 5 features (i.e., the 10th second values, the 75th second values, the area values, the maximum values and the minimum values of E-nose response curves), was proposed to classify pecan samples with different storage times. For quantitative prediction, six regression models were built on random forest (RF) algorithm to predict the contents of fatty acids in pecans. The analysis results showed that the voting method classified different pecan samples with 96% accuracy rate, and the regression models had satisfying prediction performance (R2>0.97 in calibration sets and R2>0.95 in validation sets). These results suggest that the voting method and RF algorithm would be promising analysis method for E-nose data.

An electronic nose for the detection of Sarin, Soman and Tabun mimics and interfering agents

An electronic nose system (E-nose) with metal oxide semiconductor sensors (MOS) has been designed to discriminate and quantify different chemical warfare agents (CWA) mimics. The E-nose consists of an array of commercial MOS for different gases, two sensors for temperature sensing, a sample handling system, a data acquisition system and a laptop with the data acquisition system control. With this device, discrimination studies have been carried out to detect specific CWA simulants (diethyl chlorophosphonate (DCP), diethyl cyanophosphate (DCNP), diisipropyl fluoride (DFP)), their derivatives (diethyl 1-phenylethyl phosphonate (OP-1), diethyl (2- cyanoethyl)phosphonate (OP-2), dimethyl methyl phosphonate (OP-3) and diethyl (2-oxopropyl)phosphonate (OP-4)) and some potential interfering substances (sulfuric acid, ammonia, ethanol and acetone). Principal components analyses (PCA) show that it is possible to discriminate the studied organophosphorous CWA mimics (DCP, DCNP and DFP) from the other studied derivatives and potential interfering agents. In addition, DCNP quantification studies have been done by using partial least squares (PLS) and a mathematical model has been obtained to predict DCNP concentrations in air. In this model, the coefficient of determination (R2) is 0.9567, the root mean square error of prediction (RMSEP) is 30 and the limit of detection (LOD) is 5ppm so the model is considered valid. These results suggest that this E-nose system is capable to discriminate and quantify CWA mimics and it would be a feasible system to be used in a real scenario.

Thin film measurement system for moving objects based on a laterally distributed linear variable filter spectrometer

Instantaneous measurement of optical or geometrical parameters of thin layers is an ambitious aim in many industrial applications. These layers have a variety of use-cases, such as optical bandpassing, dielectric permittivity, or lubrication. Mostly, these layers are in motion due to the production process. In order to observe process parameters, the motion usually has to be disrupted. Thus, the increase of production time due to control purposes is an undesirable drawback of this otherwise suitable technique. In this publication, we present a solution to this particular drawback of most production process monitoring systems exemplarily for film thickness measurement. We show the realization of a measurement principle which has, to our knowledge, never been published before in this application. Therefore, we exploit the advantages of the combination of a linear variable filter with a complementary metal oxide semiconductor sensor array. By an apt readout sequence, this measurement system is able to measure transmission spectra while the target is in motion. We show that this measurement system is able to measure film thicknesses of objects in the range of several 100 nm thickness at up to a velocity of 4 m/s. A reproducibility below 2 nm was acquired.

Electronic nose and Alicyclobacillus spp. spoilage of fruit juices: An emerging diagnostic tool

The application of an electronic nose equipped with a Metal Oxide Semiconductor sensor array for the detection of Alicyclobacillus acidoterrestris and A. acidocaldarius artificially inoculated in peach, orange and apple fruit juices is described in this study. Overall the system was able to detect the presence of Alicyclobacillus spp. in all the tested fruit juices at 24 h from inoculation. The electronic nose could detect bacterial concentration as low as <10 2 colony forming unit/ml and it was also able to classify bacterial contamination independently of the Alicyclobacillus species. The gas chromatography–mass spectrometry characterization of the volatile profile of orange juices confirmed the existence of quantitatively different patterns between contaminated and uncontaminated samples.

Intelligent actuation strategy via image feedbacks for a magnetically actuated autofocusing module in mobile phones

This study proposes a new miniaturized autofocusing mechanism and an intelligent autofocusing algorithm with digital images as feedbacks. An actuation scheme for autofocusing is fostered via (1) capturing image by a complementary metal-oxide semiconductor sensor array, (2) evaluating the clearness of images by the well-known method of discrete cosine transformation (DCT), and (3) actuating the lens in the designed mechanism to the best-focusing position estimated by a neural network (NN), assisted by a genetic algorithm (GA).

Illumination heating and physical raking for increasing sensitivity of electronic nose measurements with black tea

Abstract One of the most complicated components of electronic olfaction process is odour handling and delivery system capable of enabling the associated sensors to perform with acceptable sensitivity. For smell monitoring of black tea, an array of metal oxide semiconductor (MOS) sensors has been used for assessment of volatiles in the experimental set-up. In the presence of detectable vapor, the conductivity of the sensor increases depending on the concentration of odour molecules in the vapor. But, the MOS sensors are highly sensitive to moisture and water vapor. Presence of water vapor in the headspace of any sample, therefore, produces strong sensor outputs, which are essentially noise. Such overriding effect of noise caused by water vapor plays catastrophic role in terms of efficient pattern recognition by parametric and non-parametric methods. This paper presents the details of a novel sampling system based on illumination-controlled heating together with physical raking of the tea samples developed for enhancement of sensitivity of MOS sensor array. This increase in sensor outputs enhances the precision of the measurement system significantly. The efficacy of the system has been validated by comparison of performance of the system in terms of correlating electronic nose data with tea tasters’ scores using probabilistic neural network (PNN).

A Complementary Metal Oxide Semiconductor sensor array based detection system for Laser Induced Breakdown Spectroscopy: Evaluation of calibration strategies and application for manganese determination in steel

This paper describes a low cost detection system for Laser Induced Breakdown Spectroscopy based on a simple spectrograph employing a conventional diffraction grating and a non-intensified, non-gated, non-cooled 1024 pixel Complementary Metal Oxide Semiconductor linear sensor array covering the spectral range from about 250 to 390 nm. It was employed in conjunction with a 1064 nm, 5 ns pulse duration Nd:YAG laser source for analyzing steel samples using the integration of 300 analysis pulses (35 mJ each). That led to gains in the signal-to-noise ratio of approximately 3 and 16 for Mn and Fe peaks, respectively, in addition to gains in the emission intensities of about 5.3, both in comparison with the integration of just 50 analysis pulses. The acquired emission spectra were used for Mn determination, in the range from 0.214 to 0.608% m/m as previously determined by ICP OES, evaluating different univariate (at different discrete wavelengths) and multivariate (over different spectral ranges) calibration strategies. The best results, using a PLS calibration model in the spectral range from 292.9 to 294.5 nm (related to Mn emission peaks), had relative errors of prediction of the Mn concentrations, for samples not employed in the calibration, from 0.3 to 7.3%, which are similar to or better than those obtained for Mn determination in steel using higher cost detection systems. The successful analytical application of the new detection system demonstrated that the performance of low cost detection systems can be very good for specific applications and that its low resolution and sensitivity can be at least partially compensated by the use of chemometrics and the integration of analysis pulses.

Real-time label-free quantitative monitoring of biomolecules without surface binding by floating-gate complementary metal-oxide semiconductor sensor array integrated with readout circuitry

We report a label-free field-effect sensing array integrated with complementary metal-oxide semiconductor (CMOS) readout circuitry to detect the surface potential determined by the negative charge in DNA molecules. For real-time DNA quantification, we have demonstrated the measurements of DNA molecules without immobilizing them on the sensing surface which is composed of an array of floating-gate CMOS transistors. This nonimmobilizing technique allows the continuous monitoring of the amount of charged molecules by injecting DNA solutions sequentially. We have carried out the real-time quantitative measurement of 19bp oligonucleotides and analyzed its sensitivity as a function of pH in buffer solutions.