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.     

Full-range optical pH sensor array based on neural networks

A neural network multivariate calibration is used to predict the pH of a solution in the full-range (0–14) from hue (H) values coming from imaging an optical pH sensor array based on 11 sensing elements with immobilized pH indicators. Different colorimetric acid-base indicators were tested for membrane preparation fulfilling the following conditions: 1) no leaching; 2) change in tonal coordinate by reaction and 3) covering the full pH range with overlapping between their pH responses. The sensor array was imaged after equilibration with a solution using a scanner working in transmission mode. Using software developed by us, the H coordinate of the colour space HSV was calculated from the RGB coordinates of each element.The neural network was trained with the calibration data set using the Levenberg–Marquardt training method. The network structure has 11 input neurons (each one matching the hue of a single element in the sensor array), 1 output (the pH approximation value) and 1 hidden layer with 10 hidden neurons. The network provides an MSE = 0.0098 in the training data, MSE = 0.0183 in the validation data and MSE = 0.0426 in the test data coming from a set of real water samples. The resulting correlation coefficient R obtained in the Pearson correlation test is R = 0.999.     

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.     

Kinetic determination of organic vapor mixtures with single piezoelectric quartz crystal sensor using artificial neural networks

A single piezoelectric quartz crystal coated with one kind of crown ether was applied to the simultaneous determination of binary acid and amine vapor mixtures. From the adsorption and desorption curves of analytes, which were somewhat different in shape, frequency shifts from ten time windows were taken as inputs for artificial neural networks (ANN). Prediction results were satisfactory for ANN in both sample sets. The average relative errors, for formic acid and acrylic acid were 5%, for n-butylamine and aniline, they were 3% with ANN respectively. The effects of number of neurons in the hidden layer of ANN on the performance of the network are also discussed.     

基于一维双螺旋链的锌配合物的合成与荧光性能

选择配体N,N'-双(3-吡啶)丙二酰胺(3-bpma)、1,4-对苯二乙酸(H₂pda)和硝酸锌在水热条件下,自组装制备了一个基于双螺旋链的三维超分子锌配合物[Zn(3-bpma)(pda)]_n(1),并通过红外光谱、元素分析、热重分析和X射线单晶衍射分析进行了晶体结构表征。 单晶结构分析表明标题锌配合物是正交晶系,Pna21空间群,晶胞参数a=1.62512(11) nm,b=1.15947(8) nm,c=1.19282(8) nm,α=90°,β=90°,γ=90°,V=2.2476(3) nm³,M_r=513.80,D_c=1.518 g/cm³,Z=4,F(000)=1056,R₁=0.0381,wR₂=0.0669。 金属锌离子被两种桥连配体3-bpma和pda连接形成一种一维双螺旋链状结构,相邻的链间进一步通过氢键作用拓展成为三维超分子网络结构。 标题锌配合物具有强荧光发射特性,而且其对不同的有机溶剂分子和金属离子有显著的荧光传感特性,可以作为检测硝基苯的高灵敏性荧光传感材料。 CCDC:1811967     

An electronic tongue using potentiometric all-solid-state PVC-membrane sensors for the simultaneous quantification of ammonium and potassium ions in water

The simultaneous determination of NH₄⁺ and K⁺ in solution has been attempted using a potentiometric sensor array and multivariate calibration. The sensors used are rather non-specific and of all-solid-state type, employing polymeric (PVC) membranes. The subsequent data processing is based on the use of a multilayer artificial neural network (ANN). This approach is given the name "electronic tongue" because it mimics the sense of taste in animals. The sensors incorporate, as recognition elements, neutral carriers belonging to the family of the ionophoric antibiotics. In this work the ANN type is optimized by studying its topology, the training algorithm, and the transfer functions. Also, different pretreatments of the starting data are evaluated. The chosen ANN is formed by 8 input neurons, 20 neurons in the hidden layer and 2 neurons in the output layer. The transfer function selected for the hidden layer was sigmoidal and linear for the output layer. It is also recommended to scale the starting data before training. A correct fit for the test data set is obtained when it is trained with the Bayesian regularization algorithm. The viability for the determination of ammonium and potassium ions in synthetic samples was evaluated; cumulative prediction errors of approximately 1% (relative values) were obtained. These results were comparable with those obtained with a generalized regression ANN as a reference algorithm. In a final application, results close to the expected values were obtained for the two considered ions, with concentrations between 0 and 40 mmol L^–1.     

迭代目标转换因子分析与人工神经网络法用于邻、间、对硝基甲苯的分光光度同时测定的比较研究

本文将迭代目标转换因子分析与人工神经网络法用于分光光度法同时测定邻、间、对硝基甲苯,并与目标转换因子分析的结果进行了比较.结果表明,迭代目标转换因子分析法与线性网络法的效果都很好.其相对误差分别为1.3％和1.2％,而目标转换因子分析法的预测误差较大,其相对误差为10.4％.     

Identification of combustible material with piezoelectric crystal sensor array using pattern-recognition techniques

A promising way of increasing the selectivity and sensitivity of gas sensors is to treat the signals from a number of different gas sensors with pattern recognition (PR) method. A gas sensor array with seven piezoelectric crystals each coated with a different partially selective coating material was constructed to identify four kinds of combustible materials which generate smoke containing different components. The signals from the sensors were analyzed with both conventional multivariate analysis, stepwise discriminant analysis (SDA), and artificial neural networks (ANN) models. The results show that the predictions were even better with ANN models. In our experiment, we have reported a new method for training data selection, 'training set stepwise expending method' to solve the problem that the network can not converge at the beginning of the training. We also discussed how the parameters of neural networks, learning rate eta, momentum term alpha and few bad training data affect the performance of neural networks.     

Electronic Noses Using Quantitative Artificial Neural Networ

The present paper covers a new type of electronic nose(e-nose) with a four-sensor array,which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error(RE) in changing quantitative analysis into the artificial neural network (ANN).. Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, liquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.     

Electronic Noses Using Quantitative Artificial Neural Network

The present paper covers a new type of electronic nose (e-nose) with a four-sensor array, which has been applied to detecting gases quantitatively in the presence of interference. This e-nose has adapted fundamental aspects of relative error (RE) in changing quantitative analysis into the artificial neural network (ANN). Thus, both the quantitative and the qualitative requirements for ANN in implementing e-nose can be satisfied. In addition, the e-nose uses only 4 sensors in the sensor array, and can be designed for different usages simply by changing one or two sensor(s). Various gases were tested by this kind of e-nose, including alcohol vapor, CO, iiquefied-petrol-gas and CO2. Satisfactory quantitative results were obtained and no qualitative mistake in prediction was observed for the samples being mixed with interference gases.     

Usage of Artificial Neural Network (Back Propagation) in Optimising Salicylic Acid Determination with Ferric(III) Nitrate

Abstract

The use of artificial neural networks (ANN) in optimizing salicylic acid (SA) determination is presented in this paper. A simple and rapid spectrophotometric method for salicylic acid (SA) determination was carried out based on the complexation of salicylic acid–ferric(III) nitrate, SAFe(III). The SA forms a stable purple complex with ferric(III) nitrate at pH 2.45. The useful dynamic linear range is 0.01–0.35 g/L. It has a maximum absorption at 524 nm and the stability is more than 50 hours. The results were used for artificial neural networks (ANNs) training to optimize data. For training and validation purposes, a back‐propagation (BP) artificial neural network (ANN) was used. The results showed that ANN technique was very effective and useful in broadening the limited dynamic linear response range mentioned to an extensive calibration response (0.01–0.70 g/L). It was found that a network with 22 hidden neurons was highly accurate in predicting the determination of SA. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.0078 and 0.00427 g/L, respectively.     

Homogeneous liquid-liquid microextraction via flotation assistance coupled with gas chromatography-mass spectrometry for determination of myclobutanil in cucumber,tomato, grape,and strawberry using genetic algorithm

Facile and potent homogeneous liquid–liquid microextraction via flotation assistance method (HLLME-FA) combined with gas chromatography-mass spectrometry was proposed for determination of trace amounts of myclobutanil in fruit and vegetable samples. The paramount parameters, such as extraction and homogeneous solvent types and volumes, ionic strength and extraction time were studied. Under optimum conditions, the detection limit of 0.005 ng g^?1, the linear range of 0.05–100 ng g^?1, and the precision of 3.8% were acquired. A three-layer arti?cial neural network (ANN) model was used with 10 neurons and tan-sigmoid function at hidden layer and a linear transfer function at output layer were developed to predict the process. The results indicated that the proposed ANN model could perfectly predict the process with the mean square error of 0.89%. Then genetic algorithm was utilised to optimise the parameters. The proposed procedure showed satisfactory results for analysis of cucumber, tomato, grape, and strawberry.     

Synthesis and crystal structure of two novel nickel (imidazole) complexes having hydrogen-bonded networks

Two nickel (imidazole) complexes, Ni(im)₆Cl₂·4H₂O (1) and Ni(im)₆(NO₃)₂ (2) (im=imidazole) have been synthesized and characterized by elemental analysis, IR, UV, TG and single crystal X-ray diffraction. 1 crystallizes in the triclinic space group P-1 with a=8.800(6) Å, b=9.081(6) Å, c=10.565(7) Å, =75.058(9)°, β=83.143(8)°, γ=61.722(8)°, V=718.3(8) Å³, Z=1 and R₁ (wR₂)=0.0469 (0.1497). 2 crystallizes in the trigonal space group R-3 with a=12.370(6) Å, b=12.370(6) Å, c=14.782(14) Å, =90.00°, β=90.00°, γ=120.00°, V=1959(2) Å³, Z=3 and R₁ (wR₂)=0.0358 (0.0955). 1 and 2 exhibit different supramolecular network due to their different counter anions and different hydrogen bonding connection. In compound 1, [Ni(im)₆]²⁺ cation and counter anions Cl⁻ alternatively array in an ABAB fashion via N–HCl hydrogen bonding. In compound 2, the plane of each NO₃²⁻ is almost parallel and each NO₃²⁻ connect three different [Ni(im)₆]²⁺ cations via N–HO hydrogen bonding.     

烃类混合气体的神经网络模型检测

八十年代末科学家模仿生物鼻研制一种传感器阵列与计算机模式识别的气体检测系统．传感器阵列相当于生物鼻的嗅觉细胞，计算机模式识别系统相当于嗅泡和大脑「‘］．传感器阵列对气体的响应是一个多维空间的响应模式，这种响应模式经过一定的数学处理后可以实现气体的种类识别或浓度检测［’－‘j．传感器的响应和混合气体浓度之间呈非线性关系，这一特性给定量检测多组分气体混合物造成很大的限制．应用人工神经元网络技术（ANN）可以克服这一缺陷，并使检测气体的选择性大大提高．本工作运用ANN中的反向传播（BP）算法识别由16个不同…     

Neural network modelling of adsorption isotherms

This paper examines the possibility to use a single neural network to model and predict a wide array of standard adsorption isotherm behaviour. Series of isotherm data were generated from the four most common isotherm equations (Langmuir, Freundlich, Sips and Toth) and the data were fitted with a unique neural network structure. Results showed that a single neural network with a hidden layer having three neurons, including the bias neuron, was able to represent very accurately the adsorption isotherm data in all cases. Similarly, a neural network with four hidden neurons, including the bias, was able to predict very accurately the temperature dependency of adsorption data.     

一种新型钼钒多酸化合物的合成、结构与荧光性能

采用水热技术,合成了一种新型四帽Keggin结构多酸化合物[H₃Mo₈^ⅥV₈^ⅣO₄₀(AsO₄)](en)₂(4,4-bipy)₇·9H₂O(en:乙二胺;bipy:联吡啶)(1),并对化合物进行了元素分析、红外光谱、X射线光电子能谱和X射线单晶结构分析。 晶体结构分析表明, 化合物属三斜晶系,P1空间群,晶胞参数a=1.47395(5) nm,b=1.48172(6) nm,c=1.62881(7) nm,α=66.16(3)°,β=87.15(2)°,γ=63.42(1)°,V=2.8723(2) nm³,Z=1,R₁=0.0728,wR₂=0.2014。 化合物由四帽Keggin多酸阴离子、4,4'-联吡啶、乙二胺和结晶水分子构成,化合物分子间存在大量的氢键,使化合物1形成3-D超分子结构。 荧光测试表明,化合物1能发出较强的荧光,有可能成为潜在的光活性材料。     

Structural and spectroscopic properties of Hexamethylenetetramine cobalt(II) complex: [Co(NCS)2(hmt)2(H2O)2][Co(NCS)2(H2O)4] (H2O)

Synthesis, structure, spectroscopy and thermal properties of complex [Co(NCS)₂(hmt)₂(H₂O)₂][Co(NCS)₂(H₂O)₄] (H₂O) (I), assembled by hexamethylenetetramine and octahedral Co(II) metal ions, are reported. Crystal data for I: F_w 387.34, a=9.020(8), b=12.887(9), c=7.95(1) Å, =96.73(4), β=115.36(5), γ=94.16(4)°, V=820(1) Å³, Z=2, space group=P−1, T=173 K, λ(Mo-K)=0.71070 Å, ρ_calc=1.718567 g cm⁻³, μ=17.44 cm⁻¹, R=0.088, R_w=0.148. An interesting two-dimensional network is assembled via hydrogen bonds through coordinated and free water molecules. The d–d transition energy levels of Co(II) ion are determined by UV–vis spectroscopy and calculated by ligand field theory. The calculated results agree well with experiment ones.     

Use of an Electronic Tongue Based on All‐Solid‐State Potentiometric Sensors for the Quantitation of Alkaline Ions

An array of eight nonspecific potentiometric sensors was used in combination with multivariate calibration for the simultaneous determination of NH , K⁺ and Na⁺ ions. The sensors were of the all‐solid‐state type and employed a PVC polymer membrane. Signals were processed by using a multilayer artificial neural network (ANN). The ANN configuration used was optimized by using 8 neurons in the input layer, 5 in the hidden layer and 3 in the output layer. Use of the Bayesian Regularization algorithm allowed a quick building of an accurate model, as confirmed by random multi‐starting of network weights. The system was used to analyze synthetic and river water, waste water and fertilizer samples. Correct results were obtained for the three ions in synthetic and real water samples; in fertilizers, ammonium ion can be determined, while sodium and potassium show biased results.     

A simple method for predicting the frequency of the infrared inactive carbonyl stretching mode in LM(CO)5 molecules with C4V symmetry

The paper presents a new method for predicting the frequency of the b₁ mode, which is infrared-inactive, in complexes of the type LM(CO)₅ belonging to C_4V point group. The method was based on the relation λ₃=λ₄+[(1−δ/δ)](λ₁−λ₂), where δ=(λ₁−λ₂)/(λ₁−λ₂+λ₃−λ₄), λ₁, λ₂, λ₃ and λ₄ are the λ parameters of the , , b₁ and e modes, respectively. For a large numbers of complexes of the type LM(CO)₅ the average value of δ was found to be 0.80, with a standard deviation of 0.02. With the use of average value of δ, the frequencies of b₁ mode were estimated. The result obtained indicated that there exists a rather good fit between observed and calculated frequencies, with a mean error of 2.7 cm⁻¹. In addition, it was shown that the δ parameter can be used as a criterion of the correct band assignment for the complexes understudy.