PerkinElmer is a global leader focused on improving human and environmental health, for the better. We provide our customers with critical knowledge, expertise, and innovative detection, imaging, software, and service solutions so that they can make better decisions for better outcomes. At PerkinElmer, we make a difference every day, helping scientists, clinicians, and governments detect earlier and more accurately to improve the health and safety of people and the environment. Our solutions range from enabling the discovery of more effective diagnostics and therapies, to making sure that the food we eat, the water we drink, and our environment are safe from contaminants.
PerkinElmer is a leading provider of precision instrumentation, reagents and chemistries, software, and services for a wide range of scientific and industrial laboratory applications, including environmental monitoring, food and beverage quality/safety, and chemical analysis, as well as genetic screening, drug discovery, and development.
PerkinElmer, Inc. offers a wide breadth of instrumentation and solutions to meet your analytical measurement needs:
PerkinElmer, Inc. operates globally in 150 countries.
PerkinElmer, Inc.
940 Winter Street
Waltham, MA 02451
TELEPHONE
(781) 663-6900
FAX
(781) 663-6052
E-MAILas.info@perkinelmer.com
WEB SITEwww.perkinelmer.com
NUMBER OF EMPLOYEES
9000
YEAR FOUNDED
1937
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.