Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration
AbstractIn the production of calcined kaolin, the soluble Al2O3 content is used as a quality control criterion for some speciality applications. The increasing need for automated quality control systems in the industry has brought the necessity of developing techniques that provide (near) real-time data. Based on the understanding that the presence of water in the calcined kaolin detected using infrared spectroscopy can be used as a proxy for the soluble Al2O3 measurement, in this study, a hand-held infrared spectrometer was used to analyse a set of calcined kaolin samples obtained from a production plant. The spectra were used to predict the amount of soluble Al2O3 in the samples by implementing partial least squares regression (PLS-R) and support vector regression (SVR) as multivariate calibration methods. The presence of non-linearities in the dataset and the different types of association between water and the calcined kaolin represented the main challenges for developing a good calibration. In general, SVR showed a better performance than PLS-R, with root mean squared error of the cross-validation (RMSECV)
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Guatame-Garcia, A.; Buxton, M. Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration. Minerals 2018, 8, 136.
Guatame-Garcia A, Buxton M. Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration. Minerals. 2018; 8(4):136.Chicago/Turabian Style
Guatame-Garcia, Adriana; Buxton, Mike. 2018. "Prediction of Soluble Al2O3 in Calcined Kaolin Using Infrared Spectroscopy and Multivariate Calibration." Minerals 8, no. 4: 136.
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