New Estimation Model for the Analysis of Biological Samples
Scientists from the Rollins School of Public Health at Emory University (Atlanta, Georgia) have developed a new estimation model for the analysis of biological samples through liquid chromatography-mass spectrometry.
Scientists from the Rollins School of Public Health at Emory University (Atlanta, Georgia) have developed a new estimation model for the analysis of biological samples through liquid chromatography-mass spectrometry.
The new statistical method, created by Tianwei Yu and Hesen Peng, demonstrates that peak modeling represents a core component in processing data for LC-MS studies.
“To accurately quantify partially overlapping peaks, we developed a deconvolution method using the bi-Gaussian mixture model combined with statistical model selection,” they write in their report.
This new method allows LC-MS a way to quantify metabolites, which in turn enables more complex biological samples to be analyzed. The model created by Yu and Peng was developed by carrying out an extensive series of simulations, along with the testing of real-world samples.
PNNL and BaySpec Launch Compact Mass Spectrometry System for Rapid Narcotics Detection
July 8th 2025The U.S. Department of Energy’s Pacific Northwest National Laboratory’s (PNNL) VaporID, which is a newly developed portable air sampling system incorporating a miniaturized mass spectrometer (MS), can detect trace levels of fentanyl, methamphetamine, cocaine, and even explosives like TNT with great accuracy.
Radar and Soil Spectroscopy Boost Soil Carbon Predictions in Brazil’s Semi-Arid Regions
July 7th 2025A new study published in Geoderma demonstrates that combining soil spectroscopy with radar-derived vegetation indices and environmental data significantly improves the accuracy of soil organic carbon predictions in Brazil’s semi-arid regions.
Advancing Deep Soil Moisture Monitoring with AI-Powered Spectroscopy Drones
July 7th 2025A Virginia Tech study has combined drone-mounted NIR hyperspectral imaging (400 nm to 1100 nm) and AI to estimate soil moisture at root depths with remarkable accuracy, paving the way for smarter irrigation and resilient farming.
