Regularized sliced inverse regression for determination of the percentage of crystallinity in FCC catalysts

This paper proposes a new calibration model based on regularized sliced inverse regression (RSIR) for predicting the percentage of crystallinity of fluidized catalytic cracking catalysts (FCC) using Fourier transform mid-infrared spectroscopy (FT-MIR). RSIR is an effective dimension-reduction tool that looks for a proper dimension-reduction subspace without requiring a pre-specified functional form for the relation between independent and dependent variables. Combinations of RSIR with linear and nonlinear learning algorithms like multiple linear regression (MLR) and Support vector regression (SVR) were applied to the preprocessed data set. A comparison of performance among the different approaches, including previous results reached using PLS, was done. RSIR–MLR achieved the highest prediction accuracy, leading to a simple calibration model.