Vector Machine Regression Model least squares support for prediction of the crystallinity of cracking catalysts by infrared spectroscopy

Abstract

The recently introduction of the least squares support vector machines method for regression purposes in
the field of Chemometrics has provided several advantages to linear and nonlinear multivariate calibration methods. The
objective of the paper was to propose the use of the least squares support vector machine as an alternative multivariate
calibration method for the prediction of the percentage of crystallinity of fluidized catalytic cracking catalysts, by means
of Fourier transform mid-infrared spectroscopy. A linear kernel was used in the calculations of the regression model. The
optimization of its gamma parameter was carried out using the leave-one-out cross-validation procedure. The root mean
square error of prediction was used to measure the performance of the model. The accuracy of the results obtained with
the application of the method is in accordance with the uncertainty of the X-ray powder diffraction reference method. To
compare the generalization capability of the developed method, a comparison study was carried out, taking into account
the results achieved with the new model and those reached through the application of linear calibration methods. The
developed method can be easily implemented in refinery laboratories