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

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.

Deriving biomedical diagnostics from NMR spectroscopic data.

Biomedical spectroscopic experiments generate large volumes of data. For accurate, robust diagnostic tools the data must be analyzed for only a few characteristic observations per subject, and a large number of subjects must be studied. We describe here two of the current data analytic approaches applied to this problem: SIMCA (principal component analysis, partial least squares), and the statistical classification strategy (SCS). We demonstrate the application of the SCS by three examples of its use in analyzing 1H NMR spectra: screening for colon cancer, characterization of thyroid cancer, and distinguishing cancer from cholangitis in the biliary tract.