Quantification of Soil Organic Carbon by Shifted-Excitation Raman Difference Spectroscopy with Machine Learning and Common Mode Rejection
Soil organic carbon (SOC) is an important indicator of soil
health and productivity in agricultural and natural ecosystems. Conventional measurement techniques, such as combustion analysis, yield accurate results but consume time and material resources.
Calibrated Uncertainty Estimation for Soil Organic Carbon from Raman Spectra
Identifying sources of measurement uncertainty is one of the first steps to producing reliable, usable data.
Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic–Mineral Mixtures
Raman spectroscopy applied with machine learning quantifies the amino acid constituents of complex mixtures in the presence of interfering fluorescence and mineral features
Collaboration between Miraterra Inc. and the University of British Columbia