Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous determination of potassium, calcium, nitrate and chloride in mixtures of potassium and calcium chlorides and ammonium nitrate. The measurements for the Ca2+/Cu2+ determinations were done with a pH-glass electrode and calcium and copper ion-selective electrodes; results were accurate to ±8%. For the K+/Ca2+NO?3/Cl? determinations, the measurements were made with the relevant ion-selective electrodes and a glass electrode; the mean relative error was ±6%, and for the worst cases the error did not exceed 20%. 相似文献
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. 相似文献
Artificial neural network (ANN) classifiers have been successfully implemented for various quality inspection and grading tasks of diverse food products. ANN are very good pattern classifiers because of their ability to learn patterns that are not linearly separable and concepts dealing with uncertainty, noise and random events. In this research, the ANN was used to build the classification model based on the relevant features of beer. Samples of the same brand of beer but with varying manufacturing dates, originating from miscellaneous manufacturing lots, have been represented in the multidimensional space by data vectors, which was an assembly of 12 features (% of alcohol, pH, % of CO(2) etc.). The classification has been performed for two subsets, the first that included samples of good quality beer and the other containing samples of unsatisfactory quality. ANN techniques allowed the discrimination between qualities of beer samples with up to 100% of correct classifications. 相似文献
Understanding relationships between the structure and composition of molecular mixtures and their chemical properties is a main industrial aim. One central field of research is oil chemistry where the key question is how the molecular characteristics of composite hydrocarbon mixtures can be associated with the macroscopic properties of the oil products. Apparently these relationships are complex and often nonlinear and therefore call for advanced spectroscopic techniques. An informative and an increasingly used approach is two-dimensional nuclear magnetic resonance (2D NMR) spectroscopy. In the case of composite hydrocarbons the application of 2D NMR methodologies in a quantitative manner pose many technical difficulties, and, in any case, the resulting spectra contain many overlapping resonances that challenge the analytical work. Here, we present a general methodology, based on quantitative artificial neural network (ANN) analysis, to resolve overlapping information in 2D NMR spectra and to simultaneously assess the relative importance of multiple spectral variables on the sample properties. The results in a set of 2D NMR spectra of oil samples illustrate, first, that use of ANN analysis for quantitative purposes is feasible also in 2D and, second, that this methodology offers an intrinsic opportunity to assess the complex and nonlinear relationships between the molecular composition and sample properties. The presented ANN methodology is not limited to the analysis of NMR spectra but can also be applied in a manner similar to other (multidimensional) spectroscopic data. 相似文献
Predicting potentially dangerous chemical reactions is a critical task for laboratory safety. However, a traditional experimental investigation of reaction conditions for possible hazardous or explosive byproducts entails substantial time and cost, for which machine learning prediction could accelerate the process and help detailed experimental investigations. Several machine learning models have been developed which allow the prediction of major chemical reaction products with reasonable accuracy. However, these methods may not present sufficiently high accuracy for the prediction of hazardous products which particularly requires a low false negative result for laboratory safety in order not to miss any dangerous reactions. In this work, we propose an explainable artificial intelligence model that can predict the formation of hazardous reaction products in a binary classification fashion. The reactant molecules are transformed into substructure-encoded fingerprints and then fed into a convolutional neural network to make the binary decision of the chemical reaction. The proposed model shows a false negative rate of 0.09, which can be compared with 0.47–0.66 using the existing main product prediction models. To provide explanations for what substructures of the given reactant molecules are important to make a decision for target hazardous product formation, we apply an input attribution method, layer-wise relevance propagation, which computes the contributions of individual inputs per input data. The computed attributions indeed match some of the existing chemical intuitions and mechanisms, and also offer a way to analyze possible data-imbalance issues of the current predictions based on relatively small positive datasets. We expect that the proposed hazardous product prediction model will be complementary to existing main product prediction models and experimental investigations.An explainable neural network model is developed to predict the formation of hazardous products for chemical reactions. An input attribution method, layer-wise relevance propagation, is used to explain the decision-making process.相似文献
Additive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 μm. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R-square (R2) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method. 相似文献
The simple linear neural network model was investigated as a method for automated interpretation of infrared spectra. The model was trained using a database of infrared spectra of organic compounds of known structure. The model was able to learn, without any prior input of spectrum-structure correlations, to recognize and identify 76 functional groupings with accuracies ranging from fair to excellent. The effect of network input parameters and of training set composition were studied, and several sources of spurious correlations were identified and corrected.Dedicated to Professor W. Simon on the occasion of his 60th birthday 相似文献
This work reports on further development of an inhibition electrochemical sensor array based on immobilized bacteria for the preliminary detection of a wide range of organic and inorganic pollutants, such as heavy metal salts (HgCl2, PbCl2, CdCl2), pesticides (atrazine, simazine, DDVP), and petrochemicals (hexane, octane, pentane, toluene, pyrene, and ethanol) in water. A series of DC and AC electrochemical measurements, e.g., cyclic voltammograms and impedance spectroscopy, were carried out on screen-printed gold electrodes with three types of bacteria, namely Escherichia coli, Shewanella oneidensis, and Methylococcus capsulatus, immobilized via poly l-lysine. The results obtained showed a possibility of pattern recognition of the above pollutants by their inhibition effect on the three bacteria used. The analysis of a large amount of experimental data was carried out using an artificial neural network (ANN) programme for more accurate identification of pollutants as well as the estimation of their concentration. The results are encouraging for the development of a simple and cost-effective biosensing technology for preliminary in-field analysis (screening) of water samples for the presence of environmental pollutants.
In this work, a combination of complementary metal-oxide semiconductor (CMOS) microchip system with capillary array electrophoresis (CAE) is demonstrated as a system for optimizing conditions for enzymatic reaction. Dimethylacridinone (DDAO)-phosphate substrate and alkaline phosphatase conjugate were selected for the enzymatic reaction, which was applicable to the enzyme-linked immunosorbent assay (ELISA) technique. Laser-induced fluorometry with a miniature semiconductor laser was used to detect the enzymatic products. The speed of the enzymatic reaction between the DDAO-phosphate and the alkaline phosphatase conjugate was investigated as a function of reaction time. The microchip-CAE detection system could determine the pH condition and the concentration of enzyme that are suitable for rapid and low-cost analysis. This result shows the feasibility of using the microchip-CAE system for application to miniaturized screening systems. 相似文献
A force field for liquid water including polarization effects has been constructed using an artificial neural network (ANN). It is essential to include a many-body polarization effect explicitly into a potential energy function in order to treat liquid water which is dense and highly polar. The new potential energy function is a combination of empirical and nonempirical potentials. The TIP4P model was used for the empirical part of the potential. For the nonempirical part, an ANN with a back-propagation of error algorithm (BPNN) was introduced to reproduce the complicated many-body interaction energy surface from ab initio quantum mechanical calculations. BPNN, described in terms of a matrix, provides enough flexibility to describe the complex potential energy surface (PES). The structural and thermodynamic properties, calculated by isobaric-isothermal (constant-NPT) Monte Carlo simulations with the new polarizable force field for water, are compatible with experimental results. Thus, the simulation establishes the validity of using our estimated PES with a polarization effect for accurate predictions of liquid state properties. Applications of this approach are simple and systematic so that it can easily be applied to the development of other force fields besides the water-water system. 相似文献
A multi-layer neural network is constructed to describe the thermal decomposition of rhodium acetate. Critical analysis of
the residual, trained, interpolated and extrapolated errors, with the number of neurons, indicates the efficiency of the present
approach. It was possible, within this framework, to improve the An model, with a better correlation between the results. A new value of the activation energy, Ea, and frequency factor, Z, are calculated for the decomposition process. Since the neural network is more precise than a particular
model, the calculated values for these quantities are believed to be more precise. The computed values are Ea=194.0 kJ mol-1 and Z=5.23·1016 s-1. The neural network eliminates the step to decide, among the available models, the one that best fit the data. An agreement
up to four significant figures can be achieved even for data not used in the training process, both in the interpolated and
extrapolated regions. This method suggests, therefore, an important alternative tool for the experimentalists. The present
approach can also be adapted to other systems and to data in two dimensions.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
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. 相似文献