共查询到20条相似文献,搜索用时 31 毫秒
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Ran Y Jain N Yalkowsky SH 《Journal of chemical information and computer sciences》2001,41(5):1208-1217
The revised general solubility equation (GSE) is used along with four different methods including Huuskonen's artificial neural network (ANN) and three multiple linear regression (MLR) methods to estimate the aqueous solubility of a test set of the 21 pharmaceutically and environmentally interesting compounds. For the selected test sets, it is clear that the GSE and ANN predictions are more accurate than MLR methods. The GSE has the advantages of being simple and thermodynamically sound. The only two inputs used in the GSE are the Celsius melting point (MP) and the octanol water partition coefficient (K(ow)). No fitted parameters and no training data are used in the GSE, whereas other methods utilize a large number of parameters and require a training set. The GSE is also applied to a test set of 413 organic nonelectrolytes that were studied by Huuskonen. Although the GSE uses only two parameters and no training set, its average absolute errors is only 0.1 log units larger than that of the ANN, which requires many parameters and a large training set. The average absolute error AAE is 0.54 log units using the GSE and 0.43 log units using Huuskonen's ANN modeling. This study provides evidence for the GSE being a convenient and reliable method to predict aqueous solubilities of organic compounds. 相似文献
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The newly developed algorithm of evolving factor analysis has been supplemented by iterative refinement. It allows the completely model-free calculation of concentration profiles and spectra from spectrophotometric and other spectroscopic data. Not even implicit use is made of the law of mass action. The results are practically identical with those based on a specific chemical model and classical least-squares refinement. Iterative evolving factor analysis is based on applying factor analysis successively to the set of the first 1,2 M spectra of a spectrometric titration. The analysis is repeated from the opposite end and the eigenvalues thus calculated are combined into “concentration profiles” of completely abstract “species”. These “concentration profiles” are iteratively refined by normalization, calculation of the absorption spectra from the normalized concentrations and recalculation of the concentration profiles from the absorption spectra. Evolving factor analysis is not restricted to spectrometric titrations, and can also be applied to peak resolution in chromatography using a multiwavelength (diode array) photometric or mass-spectrometric detection system, or to any other ordered set of multichannel data. 相似文献
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This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths. 相似文献
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S. Dragović A. Onjia 《Russian Journal of Physical Chemistry A, Focus on Chemistry》2007,81(9):1477-1481
The artificial neural network (ANN) data analysis method was used to recognize and classify soils of an unknown geographic
origin. A total of 103 soil samples were differentiated into classes according to the regions in Serbia and Montenegro from
which they were collected. Their radionuclide (226Ra, 238U, 235U, 40K, 134Cs, 137Cs, 232Th, and 7Be) activities detected by gamma-ray spectrometry were then used as inputs to ANN. Five different training algorithms with
different numbers of samples in training sets were tested and compared in order to find the one with the minimum root mean
square error (RMSE). The best predictive power for the classification of soils from the fifteen regions was achieved using
a network with seven hidden layer nodes and 2500 training epochs using the online back-propagation randomized training algorithm.
With the optimized ANN, most soil samples not included in the ANN training data set were correctly classified at an average
rate of 92%.
The text was submitted by the authors in English. 相似文献
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An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pKa and log Kow values. 相似文献
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Kumar Rohit Raj Abhishek Kardam Jyoti Kumar Arora Shalini Srivastava 《Journal of Radioanalytical and Nuclear Chemistry》2010,283(3):797-801
The tendency of selenium to interact with heavy metals in presence of naturally occurring species has been exploited for the
development of green bioremediation of toxic metals from soil using Artificial Neural Network (ANN) modeling. The cross validation
of the data for the reduction in uptake of Hg(II) ions in the plant R. sativus grown in soil and sand culture in presence of selenium has been used for ANN modeling. ANN model based on the combination
of back propagation and principal component analysis was able to predict the reduction in Hg uptake with a sigmoid axon transfer
function. The data of fifty laboratory experimental sets were used for structuring single layer ANN model. Series of experiments
resulted into the performance evaluation based on considering 20% data for testing and 20% data for cross validation at 1,500
Epoch with 0.70 momentums The Levenberg–Marquardt algorithm (LMA) was found as the best of BP algorithms with a minimum mean
squared error at the eighth place of the decimal for training (MSE) and cross validation. 相似文献