Physics-Informed Machine Learning Overcomes Particle-Size Nonlinearities That Confound the Raman Spectrometry of Granular Carbonaceous Materials
Raman spectroscopy supports multivariate regression models that can offer a rapid and nondestructive approach to quantifying the composition of granular materials. Soils constitute a class of such materials for which chemical analysis provides vital information for agriculture and environmental stewardship.
Quantifying Soil Total Inorganic Carbon Using Shifted-Excitation Raman Difference Spectroscopy (SERDS) and Machine Learning
Accurate quantification of soil inorganic carbon, which is stored predominantly in carbonate minerals, is essential for understanding the soil chemistry, agricultural productivity, and carbon sequestration processes.
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.
Soil Carbon Quantification by Shifted-Excitation Raman Difference Spectroscopy with Common-Mode Rejection
Quantifying soil organic carbon (SOC) has a vital role to play in advancing soil health, optimizing agricultural productivity, and helping mitigate climate change.
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