Unsupervised hyperspectral data mining and bioimaging by information entropy and self-modeling curve resolution

TL;DR

This paper introduces a new unsupervised algorithm combining self-modeling curve resolution and information entropy to estimate the number of biochemical species and extract their spectra from hyperspectral data, applicable across various biomedical and biochemical fields.

Contribution

The paper presents a novel versatile algorithm that improves hyperspectral data analysis by accurately estimating the number of biochemical components and extracting their spectra without supervision.

Findings

Successfully estimated the number of biochemical species in diverse datasets

Extracted representative spectra of pure and mixed biochemical components

Demonstrated applicability across biomedical and remote sensing data

Abstract

Unsupervised estimation of the dimensionality of hyperspectral microspectroscopy datasets containing pure and mixed spectral features, and extraction of their representative endmember spectra, remains a challenge in biochemical data mining. We report a new versatile algorithm building on semi-nonnegativity constrained self-modeling curve resolution and information entropy, to estimate the quantity of separable biochemical species from hyperspectral microspectroscopy, and extraction of their representative spectra. The algorithm is benchmarked with established methods from satellite remote sensing, spectral unmixing, and clustering. To demonstrate the widespread applicability of the developed algorithm, we collected hyperspectral datasets using spontaneous Raman, Coherent Anti-stokes Raman Scattering and Fourier Transform IR, of seven reference compounds, an oil-in-water emulsion, and tissue-engineered extracellular matrices on poly-L-lactic acid and porcine jejunum-derived small intestine submucosa scaffolds seeded with bovine chondrocytes. We show the potential of the developed algorithm by consolidating hyperspectral molecular information with sample microstructure, pertinent to fields ranging from gastrophysics to regenerative medicine.