Comparative Molecular Surface Analysis (CoMSA) for Modeling Dye—Fiber Affinities of the Azo and Anthraquinone Dyes.

Polański, Jarosław; Gieleciak, Rafał; Wyszomirski, Miroslaw

doi:10.1002/chin.200405233

Cited by 2 publications

(2 citation statements)

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“…The method evaluates the reliability of each variable in the model based on analysis of regression coefficients of PLS and selection criterion. It has been widely applied in analytical chemistry for removing the low-frequency varying background and the high-frequency noise [10], retention prediction of peptides [12], and analysis of steroids [13]. In these researches, they proposed to use an artificial random variable matrix with very small amplitude, added to the original data set to estimate the cutoff.…”

Section: Introductionmentioning

confidence: 99%

A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy

Jiang

Liu

2012

Sensors

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A novel method which is a combination of wavelet packet transform (WPT), uninformative variable elimination by partial least squares (UVE-PLS) and simulated annealing (SA) to extract best variance information among different varieties of lubricants is presented. A total of 180 samples (60 for each variety) were characterized on the basis of visible and short-wave infrared spectroscopy (VIS-SWNIR), and 90 samples (30 for each variety) were randomly selected for the calibration set, whereas, the remaining 90 samples (30 for each variety) were used for the validation set. The spectral data was split into different frequency bands by WPT, and different frequency bands were obtained. SA was employed to look for the best variance band (BVB) among different varieties of lubricants. In order to improve prediction precision further, BVB was processed by UVE-PLS and the optimal cutoff threshold of UVE was found by SA. Finally, five variables were mined, and were set as inputs for a least square-support vector machine (LS-SVM) to build the recognition model. An optimal model with a correlation coefficient (R) of 0.9850 and root mean square error of prediction (RMSEP) of 0.0827 was obtained. The overall results indicated that the method of combining WPT, UVE-PLS and SA was a powerful way to select diagnostic information for discrimination among different varieties of lubricating oil, furthermore, a more parsimonious and efficient LS-SVM model could be obtained.

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Section: Introductionmentioning

confidence: 99%

A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy

Jiang

Liu

2012

Sensors

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“…2,3 Therefore, the QSPR methods can save resources and accelerate the development of dye molecules. Some QSPR studies have been carried out on the properties of dyes, including affinity, [4][5][6][7] spectral properties, [8][9][10][11][12] solubility, [13][14][15] toxicity, 16 half-wave potential, 17 and photovoltaic performance, 18 which are of great significance for exploring the dyeing mechanism. 1,2,19 However, there are only a few reports on QSPR models for the color fastness of dyes.…”

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confidence: 99%

Support vector machine classification model for color fastness to ironing of vat dyes

Wang

2021

Textile Research Journal

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Investigations of quantitative relationships between structure and color fastness of dyes are crucial in seeking novel dyes. For the first time, this work reported a classification model based on a quantitative structure–property relationship to predict color fastness to ironing of vat dyes. By performing binary classification analysis based on a support vector machine (SVM) and genetic algorithm, 56 vat dyes in the training set together with seven molecular descriptors were used to develop the classification model, which was validated with 59 vat dyes in the test set. The optimal SVM model ( C = 208.465 and γ = 5.9692) possesses overall accuracy of 91.1% for the training set and 83.1% for the test set, which is more accurate than those from the binary logistic regression model (87.5% and 81.4%, respectively). Furthermore, the mechanism of molecular descriptors correlated with color fastness to ironing of vat dyes is discussed.

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Comparative Molecular Surface Analysis (CoMSA) for Modeling Dye—Fiber Affinities of the Azo and Anthraquinone Dyes.

Cited by 2 publications

References 1 publication

A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy

A Non-Destructive Distinctive Method for Discrimination of Automobile Lubricant Variety by Visible and Short-Wave Infrared Spectroscopy

Support vector machine classification model for color fastness to ironing of vat dyes

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