A characteristic absorption peak interval method based on subspace partition for FTIR microscopic imaging classification

Abstract

A spatial distribution of an image that retains all of the multicomponent sample information in the spectral channels can be obtained using multivariate analysis methods, such as principal component analysis (PCA). Most multivariate methods build classifiers based on the spectral features extracted from high-dimensional space. However, such mathematical models have been devoted to exploring the spectral features contained in full spectrum bands, and lack the chemical specificity of the mid-infrared spectrum. In this report, we present the results of a novel characteristic absorption peak interval (CAPI) method to extract spectral band characteristics. This CAPI method extracts absorption peak bands from the spectral dimension by implementing four developed strategies of subspace partition (SP), thereby capturing the subspace characteristic information in multiple adjacent functional group areas. Finally, stacked absorption peak bands are utilized to obtain more efficient distribution information from the multiple subspace feature sets, and then an extreme learning machine is used to perform classification. Experimental results show that the proposed all-sub-band randomization strategy, subspace randomization strategy, probabilistic principal component analysis of the subspace partition, and optimum index factor of the subspace partition are all effective for mid-infrared spectroscopy microscopic image classification. Compared to PCA, 2D principal component analysis, probabilistic principal component analysis, kernel principal component analysis, and PCA linear discriminant analysis, experimental results for three Fourier transform infrared spectroscopy (FTIR) microscopy imaging datasets show that the proposed CAPI method outperforms the other multivariate methods, achieves a higher overall and average classification accuracy, and retains the physical meaning of the spectrum.

Sigma-zrandom forest, classification and confidence

“…The techniques developed in the machine learning context represent a powerful option for dealing with classification problems [4,5], diagnosis [6,7], forecasting [8] and many more, mostly concerning decision-making processes. In general, these take require a human classification strategy to feed the decision-making algorithm.…”