AB SCIEX and ARUP Laboratories Collaborate
AB Sciex will team up with Arup Laboratories to develop new test methods to improve hormone analysis using mass spectrometry.
AB Sciex (Foster City, California) will team up with Arup Laboratories (Salt Lake City, Utah), the clinical reference laboratory for the University of Utah’s Department of Pathology, to develop new test methods to improve hormone analysis. This collaborative effort, intended to enable hormone analysis using mass spectrometry, should make it possible to produce results previously unattainable in clinical research.
"The shift to mass spectrometry-based clinical research continues to rapidly accelerate,” said Joe Anacleto, Vice President and General Manager of the Applied Markets and Clinical Research Business for AB Sciex, in a statement. Unlike immunoassay-based approaches that produce inaccurate measurements because of interferences, the new technology provides a higher level of sensitivity, allowing researchers to measure low-abundance hormones more precisely. Furthermore, these new test methods will be developed using technology that combines quantitative analysis with qualitative capabilities.
"Our mission in the mass spectrometry lab of Arup is to push the limits of quantitation as low as we can, so we can deliver better results for clinical research,” said Alan Rockwood, Ph.D., Scientific Director of the Mass Spectrometry Lab at Arup Laboratories, in a statement.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.
