Multivariate Analysis Applied to Polymer Imaging Data Obtained by Near-Field Infrared Microscopy

Chemical imaging techniques such as mass spectrometry (MS) imaging and imaging spectroscopy have grown to be important in a variety of fields. Infrared spectrum information, for example is essential to evaluate organic and biological samples. Recently, near-field spectroscopy techniques have been developed that enable higher spatial resolution above the one usually obtainable due to wavelength limitations. In terms of chemical imaging for organic materials, time-of-flight secondary ion mass spectrometry (TOF-SIMS) is one of the powerful techniques because of extremely high sensitivity and high spatial resolution of approximately 100 nm. Since TOF-SIMS does not always provide complete information on complex samples, a complementary technique of similar spatial resolution is required. Near-field infrared microscope (NFIR) is the most promising candidate for a complementary analysis method along with TOF-SIMS. It is, however, often difficult to interpret NFIR data because of the low signal intensity in near-field infrared. Multivariate analysis techniques such as principal component analysis (PCA), which have successfully been applied to TOF-SIMS imaging data, would also likely be helpful for NFIR data interpretation. In this study, a multicomponent model polymer sample was measured with NFIR and then the image data along with the complex NFIR spectra were analysed by PCA. As a result, the components in the model sample can be separately displayed based on groups of peaks specific to every component indicated by PCA. [DOI: 10.1380/ejssnt.2017.19]