Determination of acidity in metal incorporated zeolites by infrared spectrometry using artificial neural network as chemometric approach

The NH3-TPD analysis is a costly and tedious method to determine zeolites acidity. Thus, to do so, FTIR spectroscopy was quantitatively used as a fast and cost-effectively method. Back-propagation artificial neural network (BP-ANN) was used for the analysis of multivariate base on the characteristic absorbance of 11 zeolite samples after metal substitution in the similar to 3612 cm(-1) region. The successive projection algorithm (SPA) was conducted for the uninformative variable elimination and feature selection strategies. The effect of pre-processingmethods (e.g. MC and MSC) was examined. It is observed after using MSC for minimizing the light scattering effect and signal-to-noise correction, the minimum mean squared error (MSE) value of the testing set data reduced from 5.36 x 10(-2) to 2.19 x 10(-4) and R-tot increases from 0.91 to 0.99. Also, the results of nonparametric Wilcoxon t-test and Sign test methods also confirmed that there is no clear difference between the zeolite acidity obtained by two conventional method and the proposed method. (c) 2019 Elsevier B.V. All rights reserved.