Mesoporosity based classification of ZSM-5 nano catalysts using DRIFT spectroscopy and chemometrics.

Abstract

DRIFT spectra were used for classification of ZSM-5 catalysts according to their mesopore volumes. The spectra were pretreated by Savitzky-Golay smoothing and standard normal variate (SNV) algorithms prior to outlier detection by Hotelling T2 statistic technique. Supervised classification was applied to the spectra using partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogies (SIMCA) algorithms. The samples were classified into three classes related to their mesopore volumes by the proposed method and the results were in accordance with N2 physisorption textural analysis using Brunauer-Emmett-Teller (BET) model. The confusion matrix and classification efficiency parameters including sensitivity, specificity, accuracy and precision were calculated. Classification accuracy of 96% and error rate of 2% was obtained using PLS-DA algorithm while SIMCA algorithm by providing 100% classification accuracy and zero error rate proved better performance in classification of ZSM-5 catalysts.