Raman research bundle
Wasatch Photonics’ compact, modular system for Raman spectroscopy is designed to facilitate research and industrial analysis of solid and liquid samples.According to the company, the bundle includes a WP 785 ER Raman spectrometer with an integrated 785 nm excitation laser, allowing Raman scattering measurements from 100–3600 cm-1 with high signal-to-noise.
Wasatch Photonics, Morrisville, NC. www.wasatchphotonics.com
How Raman Spectroscopy Could Transform Hematology Diagnostics
November 5th 2024A leading-edge review highlights the potential of Raman spectroscopy for fast, non-invasive diagnostics in hematology and oncology. By mapping biochemical fingerprints, this technology could one day help detect cancers, monitor treatments, and even predict immune responses.
FT-NIR and Raman Spectroscopic Methods Enhance Food Quality Control
October 28th 2024A recent study showcases the potential of Fourier transform near-infrared (FT-NIR) spectroscopy and spatially offset Raman spectroscopy (SORS) in detecting raw material defects in hazelnuts caused by improper storage conditions. FT-NIR spectroscopy proved especially effective, while SORS offered complementary insights in certain scenarios. These spectroscopic methods could modernize the speed and accuracy of hazelnut inspections in the food industry.
Breakthrough in Amino Acid Differentiation with Enhanced Raman Technology
October 8th 2024Researchers from the Shanghai Institute of Technology have developed a leading-edge method using surface-enhanced Raman scattering (SERS) to accurately differentiate between amino acid enantiomers. This breakthrough could revolutionize applications in pharmaceutical synthesis, disease diagnosis, and quality control of chiral compounds.
Next-Gen Mineral Identification: Fusing LIBS and Raman Spectroscopy with Machine Learning
September 17th 2024A pioneering study integrates laser-induced breakdown spectroscopy (LIBS) with Raman spectroscopy (RS) and applies machine learning (ML) to achieve exceptional accuracy in mineral identification. The combined approach not only leverages the strengths of both techniques but also enhances classification precision, achieving up to 98.4% accuracy.
