Spectral Systems is the leader in precise infrared optical components, coatings, systems integration, and services from original concept through final production.
With over 32 years of experience we can ensure your optics are made to meet your specifications. Our facility is equipped with state of the art polishing, coating, and inspection equipment to ensure your optics are fabricated consistently and efficiently.
We have the most comprehensive capabilities to provide optical solutions for the entire infrared spectral range, from the vacuum UV to the far-IR, no one else can cover the infrared spectrum better than Spectral Systems. We routinely work with over 16 infrared materials and have a broad range of proprietary coating solutions to meet all your needs.
Headquartered in Hopewell Junction, New York, with other offices in Connecticut and Wisconsin.
Spectral Systems LLC
35 Corporate Park Drive
Hopewell Junction, NY 12533
TELEPHONE
(845) 896-2200
FAX
(845) 896-2203
E-MAILinfo@spectral-systems.com
WEB SITEwww.spectral-systems.com
NUMBER OF EMPLOYEES
50+
YEAR FOUNDED
1983
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.