Prediction of gas chromatographic retention indices of some amino acids and carboxylic acids from their structural descriptors

Fatemi, M.; Elyasi, Maryam

doi:10.1002/jssc.201100544

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…Artificial neural networks (ANNs) as nonlinear approaches have been widely used to investigate and solve various problems in various branches of science, including QSRR [13,14]. Presently the most widely used network type in ANN‐based QSRR reports is neural networks trained by back propagation learning algorithm [15].…”

Section: Introductionmentioning

confidence: 99%

Multivariate image analysis–quantitative structure‐retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

Jalili‐Jahani¹,

Fatehi²

2020

J of Separation Science

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Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. Gas chromatography separation and experimental relative retention time evaluation of these compounds on a poly (94% methyl/5% phenyl) silicone-based capillary non-bonded and cross-linked column are time consuming and expensive. In this study, relative retention times were estimated using two-dimensional images of molecules based on a newly implemented rapid and simple quantitative structure retention relationship methodology. The resulting descriptors were subjected to partial least square and principal component-radial basis function neural networks as linear and nonlinear models, respectively, to attain a statistical explanation of the retention behavior of the molecules. The high numerical values of correlation coefficients and low root mean square errors in the case of the partial least square model, confirm the supremacy of this model as well as the linear dependency of images of molecules to their relative retention times. Evaluation of the best correlation model performed using internal and external tests and its good applicability domain was checked using a distance to the model in the X-Space plot. This study provides a practical and effective method for analytical chemists working with chromatographic platforms to improve predictive confidence of studies that seek to identify unknown molecules or impurities. K E Y W O R D Spartial least squares, polychlorinated biphenyls, quantitative structure retention relationship, radial basis function neural networks, two-dimensional images

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Section: Introductionmentioning

confidence: 99%

Multivariate image analysis–quantitative structure‐retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

Jalili‐Jahani¹,

Fatehi²

2020

J of Separation Science

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Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

Yan

Huang

et al. 2013

J of Separation Science

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Retention indices for frequently reported compounds of plant essential oils on three different stationary phases were investigated. Multivariate linear regression, partial least squares, and support vector machine combined with a new variable selection approach called random-frog recently proposed by our group, were employed to model quantitative structure-retention relationships. Internal and external validations were performed to ensure the stability and predictive ability. All the three methods could obtain an acceptable model, and the optimal results by support vector machine based on a small number of informative descriptors with the square of correlation coefficient for cross validation, values of 0.9726, 0.9759, and 0.9331 on the dimethylsilicone stationary phase, the dimethylsilicone phase with 5% phenyl groups, and the PEG stationary phase, respectively. The performances of two variable selection approaches, random-frog and genetic algorithm, are compared. The importance of the variables was found to be consistent when estimated from correlation coefficients in multivariate linear regression equations and selection probability in model spaces.

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Prediction of gas chromatographic retention indices of some amino acids and carboxylic acids from their structural descriptors

Cited by 2 publications

References 40 publications

Multivariate image analysis–quantitative structure‐retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

Multivariate image analysis–quantitative structure‐retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

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