Chemometric algorithms for analyzing high dimensional temperature dependent near infrared spectra

High dimensional data analysis has gained widespread acceptance with the rapid development of analytical instruments and experimental techniques. Benefiting from the second order advantage, high order chemometric algorithms have shown a great ability to match the nature of data and extract the latent components from the data. In this study, multiway principal component analysis (NPCA), parallel factor analysis (PARAFAC) and alternating trilinear decomposition (ATLD) were employed, respectively, to extract the information from temperature dependent near infrared (NIR) spectra of alcohol aqueous solutions. The variations of the structure induced by temperature and concentration in the solutions were analyzed by the three algorithms. Spectral features can be observed from the loadings obtained by NPCA, which explain the maximum variances. Spectral profiles computed by PARAFAC and ATLD contain the spectral information of the components. The former prefers to show the information of ethanol, water and ethanol water cluster, while the latter opts for describing the information of the ethanol and different water clusters in the solution. However, all the three algorithms are able to capture the quantitative information from the spectra. Therefore, high order chemometric algorithms may provide powerful tools for analyzing temperature dependent NIR spectra to obtain the structural and quantitative information of the aqueous solutions.