For a complimentary listing of your new product in Spectroscopy's annual review of new products, please download and complete the form below. All information will be kept confidential until publication in the May 2020 issue. PLEASE NOTE:
For a complimentary listing of your new product in Spectroscopy's annual review of new products, please download and complete the form below. All information will be kept confidential until publication in the May 2020 issue.
PLEASE NOTE:
Deadline: February 7, 2020
Send completed form to: Cindy Delonas at CDelonas@mmhgroup.com
Please note: Cindy has a new email, reflecting our current corporate ownership. Emails to her old UBM address will not go through!
Careful consideration will be given to all surveys received. Submitted information will be held in confidence until publication. Final decisions regarding what details are included for publication is based on editorial judgment, reader interest, and available space.
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.