Investigation of spatial distributions of components within a pyrite concretion through Raman imaging coupled with classical least squares method

Raman imaging offers powerful capabilities for geoscience research; however, its quantitative application to mineral–organic interactions remains underdeveloped. Building upon the work of Kitanaka et al. (2024), who combined Raman imaging with chemometric analysis for concretion studies, this research advances the approach by coupling Raman imaging with the classical least squares (CLS) method to visualize the compositional distributions within a pyrite concretion from Taiwan. Standard Raman spectroscopic analysis identified quartz, anatase, pyrite, and well-preserved organic matter as the principal constituents. By applying the CLS algorithm to hyperspectral Raman datasets, the method enables semi-quantitative determination and spatial mapping of both mineral and organic components with high precision. The resulting CLS-based Raman images reveal distinct co-localization of pyrite and kerogen within microstructures resembling biogenetic textures. These spatial patterns provide direct visual evidence that supports bacterial sulfate reduction (BSR) as a key microbial process mediating concretion growth. This study demonstrates that integrating Raman imaging with CLS modeling not only enhances quantitative interpretation of complex mineral–organic assemblages but also provides new insights into the microbially influenced mineralization processes in sedimentary environments. The proposed approach establishes a robust framework for non-destructive, semi-quantitative, and spatially resolved characterization of geobiological materials.