Calibrated Uncertainty Estimation for Soil Organic Carbon from Raman Spectra

Machine learning (ML) is a powerful tool for inferring chemometric properties from Raman spectra, expanding the information extractable from high-dimensional spectral data. A growing application is the estimation of soil organic carbon (SOC), where ML models relate overlapping Raman and fluorescence features to chemical composition. However, these models typically lack calibrated, prediction-level uncertainty estimates that limit their utility in decision-critical contexts.