Edinburgh Instruments has become one of the world's largest manufacturers of leading edge spectroscopic instrumentation and gas detection solutions.
Edinburgh Instruments has over 30,000 sq. ft. of manufacturing and office space just outside Edinburgh, where it employs over 75 people. The company is involved in the development, manufacture, and sale of a wide range of high technology products for the scientific research and industrial markets. Product ranges include lasers and analytical spectrometers supplied by the Photonics Division and gas detection and monitoring products supplied by the Sensors Division.
Academia and fundamental research in a wide range of fields including photochemistry, photobiology, various applications in life science and physical chemistry as well as industrial applications such as food science, environment/water monitoring, and solar cells.
Research grade fluorescence spectrometers, analytical spectrofluorometers, dedicated fluorescence lifetime spectrometers, pulsed diode lasers and LEDs, terahertz, and CO2 gas lasers.
Edinburgh Instruments are part of the Techcomp Europe group, with all manufacturing facilities in Scotland. Sales, service, and applications facilities are located around the world.
USA: 2
UK: 75
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.