About Spectroscopy
Founded in 1986, Spectroscopy provides peer-reviewed articles, trusted advice from expert columnists, and the latest breaking developments to facilitate the advance of analytical spectroscopy and its use as an essential tool across a variety of applications and fields.
Founded in 1986, Spectroscopy provides peer-reviewed articles, trusted advice from expert columnists, and the latest breaking developments to facilitate the advance of analytical spectroscopy and its use as an essential tool across a variety of applications and fields.
Through our monthly print publication, website, newsletters, ebooks, webcasts, interviews, and special issues, Spectroscopy provides academicians, laboratory scientists and managers, technicians, and those using analytical spectroscopy outside the laboratory with news, information about technical advances, best practices, and sage advice for improved proficiency and competitive advantage.
Spectroscopy has a circulation of more than 25,000 audited readers and a broad web reach through our online publishing. Spectroscopy is indexed in the Web of Science, Journal Citation Reports, and EBSCOhost. Articles in Spectroscopy reach a large and diverse scientific readership.
Please direct all press releases to Caroline Hroncich, associate editorial director, at chroncich@mjhlifesciences.com.
Spectroscopy Editorial Advisory Board
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High-Speed Laser MS for Precise, Prep-Free Environmental Particle Tracking
April 21st 2025Scientists at Oak Ridge National Laboratory have demonstrated that a fast, laser-based mass spectrometry method—LA-ICP-TOF-MS—can accurately detect and identify airborne environmental particles, including toxic metal particles like ruthenium, without the need for complex sample preparation. The work offers a breakthrough in rapid, high-resolution analysis of environmental pollutants.
New AI Strategy for Mycotoxin Detection in Cereal Grains
April 21st 2025Researchers from Jiangsu University and Zhejiang University of Water Resources and Electric Power have developed a transfer learning approach that significantly enhances the accuracy and adaptability of NIR spectroscopy models for detecting mycotoxins in cereals.
