Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks

Jalali‐Heravi, Mehdi; Masoum, Saeed; Shahbazikhah, Parviz

doi:10.1016/j.jmr.2004.08.011

Cited by 34 publications

(19 citation statements)

References 20 publications

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“…The overtraining causes the ANN to loose its prediction power. 32 Therefore, during training of the networks, it is desirable that iterations are stopped when overtraining begins. To control the overtraining of the network during the training procedure, the values of RMSET and RMSEV were calculated and recorded to monitor the extent of the learning in various iterations.…”

Section: Resultsmentioning

confidence: 99%

Prediction Partial Molar Heat Capacity at Infinite Dilution for Aqueous Solutions of Various Polar Aromatic Compounds over a Wide Range of Conditions Using Artificial Neural Networks

Habibi‐Yangjeh

Esmailian²

2007

Bulletin of the Korean Chemical Society

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Artificial neural networks (ANNs), for a first time, were successfully developed for the prediction partial molar heat capacity of aqueous solutions at infinite dilution for various polar aromatic compounds over wide range of temperatures (303.55-623.20 K) and pressures (0.1-30.2 MPa). Two three-layered feed forward ANNs with back-propagation of error were generated using three (the heat capacity in T = 303.55 K and P = 0.1 MPa, temperature and pressure) and six parameters (four theoretical descriptors, temperature and pressure) as inputs and its output is partial molar heat capacity at infinite dilution. It was found that properly selected and trained neural networks could fairly represent dependence of the heat capacity on the molecular descriptors, temperature and pressure. Mean percentage deviations (MPD) for prediction set by the models are 4.755 and 4.642, respectively.

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Section: Resultsmentioning

confidence: 99%

Prediction Partial Molar Heat Capacity at Infinite Dilution for Aqueous Solutions of Various Polar Aromatic Compounds over a Wide Range of Conditions Using Artificial Neural Networks

Habibi‐Yangjeh

Esmailian²

2007

Bulletin of the Korean Chemical Society

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“…For these reason in recent years, ANNs have been used to a wide variety of chemical problems such as simulation of mass spectra, ion interaction chromatography, aqueous solubility and partition coefficient, simulation of nuclear magnetic resonance spectra, prediction of bioconcentration factor, solvent effects on reaction rate, prediction of normalized polarity parameter in mixed solvent systems and dissociation constant of acids. [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] The main aim of the present work is to develop a QSPR model based on molecular descriptors using ANN for modeling and prediction of E T N values for various solvents (including 216 solvents) with diverse chemical structures. In the first step, a MLR model was constructed.…”

Section: -18mentioning

confidence: 99%

“…If a network is left to train for too long, however, it will overtrain and will lose the ability to generalize. [34][35][36][37] Methods and Procedure Data set. As first step for developing the MLR and ANN models, the molecular descriptors should be generate.…”

Section: Theorymentioning

confidence: 99%

Artificial Neural Network Prediction of Normalized Polarity Parameter for Various Solvents with Diverse Chemical Structures

2007

Bulletin of the Korean Chemical Society

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Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (E T N ) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q + ), total charge in molecule (q t ), molecular volume of solvent (V m ), dipole moment (µ) and polarizability term (π I ) are input descriptors and its output is E T N . It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the E T N values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the E T N of solvents shows non-linear correlations with the molecular descriptors.

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“…It is useful in spectral analyses because the simultaneous inclusion of multiple spectral intensities can greatly improve the precision and applicability of quantitative spectral analysis of multicomponent mixtures that can not be resolved by conventional spectrometry. In recent years multivariate calibration has become an important tool in resolution of mixtures of components in many different fields including biomedical 20,21 environmental 22,23 and drug analysis 24,25 . This paper describes an analytical methodology for simultaneous determination of Sulfamethoxazole and Trimethoprim using spectrophotometric method with preprocessing by direct orthogonal signal correction (DOSC).…”

Section: Introductionmentioning

confidence: 99%

Direct Orthogonal Signal Correction-Partial Least Squares for Simultaneous Spectrophotometric Determination of Sulfamethoxazole and Trimethoprim in Pharmaceutical Formulation and Synthetic Samples

Goodarzi

Shahbazikhah

Sohrabi

et al. 2009

J. Chil. Chem. Soc.

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This work describes a very simple, novel and selective spectrophotmetric method for simultaneous determination of sulfamethoxazole and trimethoprim in pharmaceutical preparation and synthetics samples using direct orthogonal signal correction-partial least squares (DOSC-PLS). The all factors affecting on the sensitivity were optimized and the linear dynamic range for determining of these drugs was found. The PLS modeling was used for the multivariate calibration of the spectrophotometric data and DOSC was used for preprocessing of data matrices and the prediction results of model. The experimental calibration matrix was designed by measuring the absorbance over the range 200-400 nm for 32 samples of 2.0-15.0 and 0.4-6.0 μg mL -1 of sulfamethoxazole and trimethoprim, respectively. The root mean square error of prediction for sulfamethoxazole and trimethoprim using DOSC-PLS and PLS were 0.02212, 0.02363 and 0.17014, 0.01660,μg mL -1 respectively. Application of the method to synthetic samples of standard solutions and in pharmaceutical formulation was performed and satisfactory results were obtained.

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Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks

Cited by 34 publications

References 20 publications

Prediction Partial Molar Heat Capacity at Infinite Dilution for Aqueous Solutions of Various Polar Aromatic Compounds over a Wide Range of Conditions Using Artificial Neural Networks

Prediction Partial Molar Heat Capacity at Infinite Dilution for Aqueous Solutions of Various Polar Aromatic Compounds over a Wide Range of Conditions Using Artificial Neural Networks

Artificial Neural Network Prediction of Normalized Polarity Parameter for Various Solvents with Diverse Chemical Structures

Direct Orthogonal Signal Correction-Partial Least Squares for Simultaneous Spectrophotometric Determination of Sulfamethoxazole and Trimethoprim in Pharmaceutical Formulation and Synthetic Samples

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