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

07:00 EST 13th February 2020 | BioPortfolio

Summary of "Multivariate image analysis-quantitative structure retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks."

Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. GC 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 resulted descriptors were subjected to partial least square and principal components-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 paper 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. This article is protected by copyright. All rights reserved.

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This article was published in the following journal.

Name: Journal of separation science
ISSN: 1615-9314
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