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

Maryam Juybar; Mohammadreza Khanmohammadi Khorrami; Amir Bagheri Garmarudi; Shima Zandbaaf

doi:10.1016/j.saa.2019.117539

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

Spectrochim Acta A Mol Biomol Spectrosc. 2020 Mar 5:228:117539. doi: 10.1016/j.saa.2019.117539. Epub 2019 Oct 30.

Authors

Maryam Juybar¹, Mohammadreza Khanmohammadi Khorrami², Amir Bagheri Garmarudi², Shima Zandbaaf²

Affiliations

¹ Chemistry Department, Faculty of Science, Imam Khomeini International University, P.O. box 3414896818, Qazvin, Iran. Electronic address: maryamjuybar.gu@gmail.com.
² Chemistry Department, Faculty of Science, Imam Khomeini International University, P.O. box 3414896818, Qazvin, Iran.

PMID: 31748157
DOI: 10.1016/j.saa.2019.117539

Abstract

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 ~3612 cm^-1 region. The successive projection algorithm (SPA) was conducted for the uninformative variable elimination and feature selection strategies. The effect of pre-processing methods (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 × 10^-2 to 2.19 × 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.

Keywords: Acidity; Artificial neural network; FTIR spectrometry; Multiplicative scatter correction; Successive projection algorithm; Zeolite.