HORIBA Scientific is the world-leading manufacturer of high performance spectroscopic instrumentation and photonics components. Our products offer unsurpassed sensitivity, precision, performance, and capabilities.
HORIBA Scientific offerings encompass Raman, fluorescence, elemental analysis, forensics, GDS, ICP, particle characterization, spectroscopic ellipsometry, sulfur-in-oil, water quality, XRF, and OEM spectrometers. We also provide components, custom and OEM solutions, and worldwide support.
Our global team is dedicated to providing researchers with the highest quality products and solutions by integrating and aligning HORIBA's core strengths of scientific research, development, applications, sales, service, and support.
Prominent acquired brands include Jobin Yvon, IBH, SPEX, Instruments S.A., ISA, Dilor, Sofie, SLM, Beta Scientific, Photon Technology, Inc. (PTI), and Optical Building Blocks (OBB).
HORIBA Scientific is part of the HORIBA Group, with manufacturing facilities in Edison, New Jersey, as well as in France and Japan. Sales, service, and applications facilities are located around the world.
HORIBA Scientific
3880 Park Avenue
Edison, NJ 08820
TELEPHONE
(732) 494-8660
FAX
(732) 549-5125
E-MAILinfo.sci@horiba.com
WEB SITEwww.horiba.com/scientific
NUMBER OF EMPLOYEES
700
Elsewhere: 5000
YEAR FOUNDED
1819
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.