Development of Image and Spectrum Data Driven Analysis for Soft Materials

Abstract

Data composed of images and spectra generally contain rich and complex information, and therefore the interpretation of the data is crucial to extract essential information of samples. Multivariate analysis such as principal component analysis has been applied to such data to interpret complicated sample data. Recently, machine learning and deep learning methods have been employed in a variety of fields. In this article, the features of machine learning and deep learning methods in terms of analysis of image and spectrum data of soft materials obtained by TOF-SIMS are described by comparing those of multivariate analysis. And, the future development of machine learning and deep learning application to surface analysis data are discussed.