Methodology for the selection of suitable sensors for incorporation into a gas sensor array

An investigation into suitable mathematical techniques which can be used to select sensor components for a gas sensor array is reported. Data from a tin dioxide Taguchi semiconductor sensor array were obtained individually for various organic solvents and analysed using multivariate techniques, including principal component analysis, cluster analysis and star symbol plots. It was shown that the array data produced a series of characteristic response patterns for the analytes. It was also found that analytes of similar chemical nature had similar response patterns, indicating a correlation between sensor interaction and the chemical functional groups of the analyte. The multivariate techniques used proved to be very useful in enabling a suitable selection of the components of the array to be made by identifying which of the components or sensors were acting independently.     

Discrimination of liquor aromas by pattern recognition analysis of responses from a gas sensor array

A sensor array composed of six semiconductor gas sensors was applied to the discrimination of liquor aromas. A semi-automatic headspace concentrator utilizing porous polymers was used for pretreatment of sample aromas in order to remove excess amounts of ethanol and to standardize aroma introduction to the sensor array. The differences in response patterns for liquor samples were not so conspicuous because of the non-selectivity of the gas sensors. After normalizing the sensor responses to eliminate the effects of absolute magnitude, pattern recognition analysis was applied to the resulting six-dimensional data matrices. Cluster analysis succeeded in classifying eight liquors. Five spirits (cognac and four different brands of whisky) were correctly classified by both linear discriminant analysis and cluster analysis.     

Quartz crystal microbalance sensor array for the detection of volatile organic compounds

A sensor array system consisting of five quartz crystal microbalance (QCM) sensors (four for measuring and one for reference) and an artificial neural network (ANN) method is presented for on-line detection of volatile organic compounds. Three ionic liquids, 1-butyl-3-methylimidazolium chloride (C₄mimCl), 1-butyl-3-methylimidazolium hexafluorophosphate (C₄mimPF₆), 1-dedocyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (C₄mimNTf₂), and silicone oil II, which is widely used as gas chromatographic stationary phase, have been selected as sensitive coatings on the quartz surface allowing the sensor array effective to identify chemical vapors, such as toluene, ethanol, acetone and dichloromethane. The success rate for the qualitative recognition reached 100%. Quantitative analysis has also been investigated, within the concentration range of 0.6-6.1 mg/L for toluene, 0.9-7.5 mg/L for ethanol, 2.8-117 mg/L for dichloromethane, and 0.7-38 mg/L for acetone, with a prediction error lower than 8%.     

Electrochemical solid phase micro-extraction and determination of salicylic acid from blood samples by cyclic voltammetry and differential pulse voltammetry

The electrochemical solid phase micro-extraction of salicylic acid (SA) at graphite-epoxy-composed solid electrode surface was studied by cyclic voltammetry. SA was oxidized electrochemically in pH 12.0 aqueous solution at 0.70 V (vs. saturated calomel electrode) for 7 s. The oxidized product shows two surface-controlled reversible redox couples with two proton transferred in the pH range of 1.0∼6.0 and one proton transferred in the pH range of 10.0∼13.0 and is extracted on the electrode surface with a kinetic Boltzman function of i _p = 3.473–4.499/[1 + e^{(t − 7.332)/6.123}] (χ ² = 0.00285 μA). The anodic peak current of the extracted specie in differential pulse voltammograms is proportional to the concentration of SA with regression equation of i _p = −5.913 + 0.4843 c (R = 0.995, SD = 1.6 μA) in the range of 5.00∼200 μM. The detection limit is 5.00 μM with RSD of 1.59% at 60 μM. The method is sensitive and convenient and was applied to the detection of SA in mouse blood samples with satisfactory results.     

Detection of methamphetamine using aptamer-based biosensor chip and cyclic voltammetry technique

In this study, polycarbonate coated with Au nanoparticles and anti-methamphetamine aptamer is used for the detection of methamphetamine. Immobilization of aptamer on Au nanoparticle modified polycarbonate surface, and monitoring of methamphetamine were evaluated with AFM, ATR-FTIR, and DRS techniques. FT-IR reflection spectrum shows a pick at 3300–3500 ​nm range (related to amine groups of aptamer bases). After immobilization of aptamer on Au nanoparticle modified polycarbonate, 8% decrease in reflection was observed. To evaluate of detection power of the designed nanobiosensor, its sensitivity for the detection of different concentrations of methamphetamine was measured with cyclic voltammetry. The detection limits of the designed nanobiosensor in two methods of FT-IR and cyclic voltammetry were 2 and 5 ​μM, respectively. Notably, the designed nanobiosensor in two different methods didn't show any response to glucose.     

Application of an artificial neural network (ANN) and piezoelectric chemical sensor array for identification of volatile organic compounds

A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds.     

Simultaneous determination of fermented milk aroma compounds by a potentiometric sensor array

The paper reports on the application of an electronic tongue for simultaneous determination of ethanol, acetaldehyde, diacetyl, lactic acid, acetic acid and citric acid content in probiotic fermented milk. The αAstree electronic tongue by Alpha M.O.S. was employed. The sensor array comprised of seven non-specific, cross-sensitive sensors developed especially for food analysis coupled with a reference Ag/AgCl electrode. Samples of plain, strawberry, apple-pear and forest-fruit flavored probiotic fermented milk were analyzed both by standard methods and by the potentiometric sensor array. The results obtained by these methods were used for the development of neural network models for rapid estimation of aroma compounds content in probiotic fermented milk.The highest correlation (0.967) and lowest standard deviation of error for the training (0.585), selection (0.503) and testing (0.571) subset was obtained for the estimation of ethanol content. The lowest correlation (0.669) was obtained for the estimation of acetaldehyde content. The model exhibited poor performance in average error and standard deviations of errors in all subsets which could be explained by low sensitivity of the sensor array to the compound. The obtained results indicate that the potentiometric electronic tongue coupled with artificial neural networks can be applied as a rapid method for the determination of aroma compounds in probiotic fermented milk.     

The use of cyclic voltammetry for wine analysis: Determination of polyphenols and free sulfur dioxide

The use of cyclic voltammetry to characterize wines and wine polyphenols in a pH 3.3 model wine solution has been extended to take into account the effects of sulfur dioxide and polyphenol adsorption processes. A good correlation was obtained between a cyclic voltammetric measure, based upon the response produced before and after acetaldehyde additions, and the concentration of free sulfur dioxide in eight white wines (r² = 0.974). By the addition of acetaldehyde to the white wines, an important new step in the methodology, the area under the anodic scan in the potential range from −100 to 1200 mV (Ag/AgCl) closely matched the spectroscopic measure of total polyphenols (absorbance at 280 nm) for the white wines, when both were measured in terms of caffeic acid equivalents (r² = 0.949). The anodic peak area accounted for about 70% of the 280 nm total phenols measure, in catechin equivalents, for the red wines, and a good linear correlation was also obtained (r² = 0.942). The level of catechol and galloyl-containing polyphenols in the wines was calculated by measuring the size of the first anodic peak at around 450 mV after treatment of the wines with acetaldehyde; the peak current correlated well with the total caffeic acid derivatives in the white wines determined by HPLC (r² = 0.982). The concentration of flavonols was estimated by selective adsorption of these compounds onto the carbon electrode and determining the anodic peak current at 1120 mV, with good correlations obtained when compared to total flavonols as measured by HPLC (r² = 0.984 for the red wines, and r² = 0.987 for the white wines).     

Characterization of an electrochemical mercury sensor using alternating current,cyclic, square wave and differential pulse voltammetry

Here we report the characterization of an electrochemical mercury (Hg²⁺) sensor constructed with a methylene blue (MB)-modified and thymine-containing linear DNA probe. Similar to the linear probe electrochemical DNA sensor, the resultant sensor behaved as a “signal-off” sensor in alternating current voltammetry and cyclic voltammetry. However, depending on the applied frequency or pulse width, the sensor can behave as either a “signal-off” or “signal-on” sensor in square wave voltammetry (SWV) and differential pulse voltammetry (DPV). In SWV, the sensor showed “signal-on” behavior at low frequencies and “signal-off” behavior at high frequencies. In DPV, the sensor showed “signal-off” behavior at short pulse widths and “signal-on” behavior at long pulse widths. Independent of the sensor interrogation technique, the limit of detection was found to be 10 nM, with a linear dynamic range between 10 nM and 500 nM. In addition, the sensor responded to Hg²⁺ rather rapidly; majority of the signal change occurred in <20 min. Overall, the sensor retains all the characteristics of this class of sensors; it is reagentless, reusable, sensitive, specific and selective. This study also highlights the feasibility of using a MB-modified probe for real-time sensing of Hg²⁺, which has not been previously reported. More importantly, the observed “switching” behavior in SWV and DPV is potentially generalizable and should be applicable to most sensors in this class of dynamics-based electrochemical biosensors.     

Recognition of adulteration of Italian wines by thin-film multisensor array and artificial neural networks

The paper reports the use of a chemoresistive multisensor array for recognition of some adulterated Italian wines (two white, four red, two rosè) added with methanol, ethanol or other same-colour wine. A multisensor array constituted by four thin-film semiconducting metal oxide sensors, surface-activated by Pt, Au, Pd, Bi metal catalysts, has been used to generate the chemical pattern of the volatile compounds present in the wine samples. The responses of the multisensor array towards wines tested by headspace sampling have been evaluated. Multivariate analysis including principal component analysis (PCA) as well as back-propagation method trained artificial neural networks (ANNs) have been applied to analytical data generated from the multisensor array to identify both the adulteration of wines and to determine the added content of adulterant agent of methanol or ethanol up to 10 vol.%. The cross-validated ANNs provide the highest achieved percentage of correct classification of 93% and the highest achieved correlation coefficient of 0.997 and 0.921 for predicted-versus-true concentration of methanol and ethanol adulterant agent, respectively.     

Detection of acetone in the human breath as diabetes biomarker based on PPy/WO3 nanocomposite sensor by FFT continuous cyclic voltammetry method

This research represents a novel detection method of acetone level in the exhaled breath samples (RH=88 %) based on polypyrrole/tungsten oxide (PPy/WO₃) nanocomposite sensor. The PPy/WO₃ sensor was fabricated by the deposition of nanocomposite on/between interdigitated electrodes (IDEs) through electrospray coating and was then characterized by FE-SEM imaging. In this detection method, the coulometric signal of the sensor was calculated using Fast Fourier Continuous Cyclic Voltammetry (FFTCCV), where cyclic voltammetry (CV) was applied to the sensor in the defined potential rang and then charge changes of the sensor was obtained by integration of the current in all scanned potential ranges. FFTCCV method enhances the sensitivity of the sensor when exposed to the gas mixtures containing acetone. In addition to its fast coulometric response time (≤5 s) in the two linear ranges of 0.7–2.8 ppm and 2.8–28.2 ppm (R²=0.99), FFTCCV method provides the low detection limit of 70 ppb, and high sensitivity toward acetone at the optimum values of the parameters. The fabricated sensor showed great selectivity toward acetone when exposed to humid air and some exhaled gas like carbon dioxide, ammonia, methanol, ethanol and alkyl amines. The results were very satisfying as the sensor was capable to detect different acetone levels in human exhaled breath as non-invasive diagnosis of diabetes with a good correlation (R²≃0.9) to the routine blood sugar test taken by different commercial glucometers results.     

A complete numerical simulation of the techniques of alternating current linear sweep and cyclic voltammetry: analysis of a reversible process by conventional and fast Fourier transform methods

We describe the method of achieving the first completely general simulation of ac linear sweep and cyclic voltammetry making use of the fully implicit Richtmyer modification (FIRM) method. The simulation technique is applied to a reversible process under conditions where a sinusoidal waveform of any amplitude is superimposed onto the dc potential which is swept at a finite scan rate. Results, where possible, are compared with the existing theory derived at constant dc potential to confirm the fidelity of the simulation. In particular, we demonstrate excellent agreement with the results of Engblom et al. [J. Electroanal. Chem. xxx (1999) xxx] for large amplitude ac voltammetry described in the companion paper immediately preceding this article. The use of conventional and Fourier transform methods of data analysis are compared to highlight the advantages of the use of the fast Fourier transform algorithm in ac voltammetry.     

A piezoelectric quartz crystal sensor array self assembled calixarene bilayers for detection of volatile organic amine in gas

P-tert-butylcalix[n]arenes (n=4, 6, 8, abbreviated as CA[4], CA[6], CA[8], respectively) were immobilized on the Au surface of the piezoelectric quartz crystal by the reaction between CA[n] and the acid chloride terminated mercaptoacetic acid (MAA) self assembled monolayers to form MAA/CA[n] bilayers. The sensing films were not only immobilized easily and reproducibly, but also used to improve the reversibility of the sensor signal. The response characteristics show the response of CA to organic amine attributes to specific interaction between CA[n] (host) and organic amines (guest). The frequency shifts of n-butylamine and iso-butylamine are much larger than tert-butylamine and diethylamine because of shape-selection and hydrogen bonding. Compared to CA[6] and CA[4], CA[8] has highest sensitivity to organic amine due to having more flexibility to accommodate guest molecules. A sensor array with three-layer back-propagation neural network was applied to detect the binary mixture of n-butylamine in the range of 7.14–142 μl l⁻¹ and iso-butylamine in the range of 7.14–57 μl l⁻¹. The optimum values of learning rate (0.15) and momentum term (0.8) were determined by experiment. The best epoch of training was 1098. The root mean square error of prediction was 1.69 (μl l⁻¹) for n-butylamine, and 1.42 (μl l⁻¹) for iso-butylamine.     

Label-free monitoring of site-specific DNA cleavage by EcoRI endonuclease using cyclic voltammetry and electrochemical impedance

Cyclic voltammetry (CV) combined with electrochemical impedance spectroscopy (EIS) were proposed to monitor the site-specific DNA cleavage by EcoRI endonuclease without using external label. The alteration of CV and EIS signal demonstrated that double-strands (dsDNA) contain recognition sequence was cleaved by EcoRI endonuclease. Real-time monitoring indicated that the dsDNA was cleaved by EcoRI more than 90% after 2 h of enzyme digestion time. Control experiment showed that the DNA cleavage by EcoRI endonuclease is site-specific for DNA sequence. Experimental results demonstrated that the efficiency of EcoRI cleavage was highly dependent on the concentration of EcoRI concentration in the range from 0.04 to 0.4 U μL⁻¹ with one almost linear relationship.     

Design and fabrication of a low-cost flow-through cell for the determination of acetaminophen in pharmaceutical formulations by flow injection cyclic voltammetry

A low-cost electrochemical flow-through cell is designed and fabricated to use in conjunction with a flow injection (FI) system. This detector cell used a centrosymmetric radial flow thin-layer geometry with a stainless steel auxiliary electrode and a reference electrode (Ag/AgCl) without a salt bridge. The 5H pencil lead electrode used as a working electrode in the home-made cell is an extremely inexpensive electrode which performs as well as the expensive commercial glassy carbon electrode. Optimum conditions for determining acetaminophen using the proposed FI manifold was investigated. Appropriate volume of sample and/or standard solution containing acetaminophen in pH 2.2 Mcllvaine buffer solution was injected into the proposed FI system and mixed with the flowing stream of supporting electrolyte (pH 2.2 Mcllvaine buffer solution) at an optimum flow rate of 1 ml min⁻¹. The cyclic voltammograms were recorded over the potential range from −0.5 to +2.0 V with a scan rate of 40 mV s⁻¹. Linear calibration curve over the range of 0.1–5 mM acetaminophen was established with the regression equation Y=3.68X+1.0157 (r²=0.9964). The recommended method has been applied to the determination of acetaminophen in 8 commercial pharmaceutical preparations. The percentage recoveries of the spiked acetaminophen in four tablet samples were ranging from 103 to 112 with the relative standard deviation in the range of 0.1–1.3%.     

Electrochemical behaviors of native and thermally denatured fish DNA in the presence of cytosine derivatives and porphyrin by cyclic voltammetry using boron-doped diamond electrode

The electrochemical behaviors of native and thermally denatured fish DNA was investigated using boron-doped diamond (BDD) film electrode by cyclic voltammetry. The BDD electrode afforded us to measure weak current less than muA for the DNA solution in 100 microl. The mixture of acetic acid and sodium acetate solution (0.2 M) was used as a supporting electrolyte. Two oxidation peaks were observed at about +1.1 V and +1.3 V at pH 4.6 for thermally denatured fish DNA. This is due to the oxidation of guanine and adenine in the denatured fish DNA, respectively. In contrast, the native fish DNA showed ill-defined peaks at +1.1 V. Furthermore, the electrochemical behaviors of thermally denatured fish DNA were studied in the presence of cytosine, cytidine, cytidine-5-monophosphate, tetrakis(1-methypyridinium-4-yl)porphyrin (H(2)(TMPyP)(4+)) and Ru(II)(TMPyP)(4+). The oxidation peak intensity at +1.1 V gradually decreased with the increase of the concentrations of the above compounds. Based on the above studies, electrochemical behaviors of the thermally denatured fish DNA at BDD electrode is discussed.     

Application of an artificial neural network (ANN) and piezoelectric chemical sensor array for identification of volatile organic compounds

A piezoelectric chemical sensor array was developed using four quartz crystals. Gas chromatographic stationary phases were used as sensing materials and the array was connected to an artificial neural network (ANN). The application of the ANN method proved to be particularly advantageous if the measured property (mass, concentration, etc.) should not be connected exactly to the signal of the transducers of the piezoelectric sensor. The optimum structure of neural network was determined by a trial and error method. Different structures were tried with several neurons in the hidden layer and the total error was calculated. The optimum values of primary weight factors, learning rate (η=0.15), momentum term (μ=0.9), and the sigmoid parameter (β=1) were determined. Finally, three hidden neurons and 900 training cycles were applied. After the teaching process the network was used for identification of taught analytes (acetone, benzene, chloroform, pentane). Mixtures of organic compounds were also analysed and the ANN method proved to be a reliable way of differentiating the sensing materials and identifying the volatile compounds.     

Studies on monitoring the composition of the copolymer by cyclic voltammetry and in situ spectroelectrochemical analysis

Electrochemical copolymerization of diphenylamine (DPA) with ortho-toluidine (OT) was carried out in 4 M sulphuric acid medium by cyclic voltammetry. Cyclic voltammograms (CVs) of the copolymer films were recorded to deduce the electrochemical characteristics. In situ UV-visible spectroelectrochemical studies on copolymerization were carried out using indium tin oxide (ITO) coated glass plate as working electrode for different feed ratios of DPA and OT. UV-visible spectral characteristics show clear dependencies on the molar feed composition of DPA or OT used in electropolymerization. Derivative cyclic voltabsorptogram (DCVA) was deduced at the wavelength corresponding to the absorption by the intermediate species and used to identify the intermediates generated during the electropolymerization. The molar composition of DPA and OT units in the copolymer for the copolymers synthesized with different molar feed ratios of DPA and OT was determined by UV-visible spectroscopy. Reactivity ratios of DPA and OT were deduced by using Fineman-Ross and Kelen-Tudos methods and the observed differences in the composition of DPA/OT in the copolymers were correlated with CV characteristics and results obtained from in situ spectroelectrochemical studies.     

Use of sequential injection analysis to construct an electronic-tongue: application to multidetermination employing the transient response of a potentiometric sensor array

An electronic tongue based on the transient response of an array of non-specific-response potentiometric sensors was developed. A sequential injection analysis (SIA) system was used in order to automate its training and operation. The use of the transient recording entails the dynamic nature of the sensor's response, which can be of high information content, of primary ions and also of interfering ions; these may better discriminated if the kinetic resolution is added. This work presents the extraction of significant information contained in the transient response of a sensor array formed by five all-solid-state potentiometric sensors. The tool employed was the Fourier transform, from which a number of coefficients were fed into an artificial neural network (ANN) model, used to perform a quantitative multidetermination. The studied case was the analysis of mixtures of calcium, sodium and potassium. Obtained performance is compared with the more traditional automated electronic tongue using final steady-state potentials.     

VOCs分子的半导体型传感器识别检测研究进展

具有体积小、功耗低、灵敏度高、硅工艺兼容性好等优点的金属氧化物半导体(MOS)气体传感器现已广泛地应用于军事、科研和国民经济的各个领域。然而MOS传感器的低选择性阻碍了其在物联网(IoT)时代的应用前景。为此,本文综述了解决MOS传感器选择性的研究进展,主要介绍了敏感材料性能提升、电子鼻和热调制三种改善MOS传感器选择性的技术方法,阐述了三种方法目前所存在的问题及其未来的发展趋势。同时,本文还对比介绍了机器嗅觉领域主流的主成分分析(PCA)、线性判别分析(LDA)和神经网络(NN)模式识别/机器学习算法。最后,本综述展望了具有数据降维、特征提取和鲁棒性识别分类性能的卷积神经网络(CNN)深度学习算法在气体识别领域的应用前景。基于敏感材料性能的提升、多种调制手段与阵列技术的结合以及人工智能(AI)领域深度学习算法的最新进展,将会极大地增强非选择性MOS传感器的挥发性有机化合物(VOCs)分子识别能力。