In this overview, we explore how spectroscopy is advancing the agriculture industry.

A continuation of our recap of a recent study published in Microchemical Journal highlights the implications of how Raman spectroscopy can help analyze ancient DNA remains.

Space-based monitoring platforms are transitioning from development to early operations. This article highlights a latest announcement from EarthDaily that spotlights a recent success on this front.

In an upcoming multipart interview on "Pathways in Spectroscopy," Ayush Agarwal will discuss his transition from chemical engineering to analytical chemistry.

A recent study shows that non-destructive Raman spectroscopy measurements of protein-to-mineral ratios in ancient teeth can accurately predict endogenous DNA preservation, enabling archaeologists to pre-screen specimens and avoid unnecessary destructive sampling.

Between 2024 and 2026, environmental applications of vibrational spectroscopy advanced rapidly through innovations in multimodal instrumentation (combining 2 or more distinct measurement techniques), spectral data fusion, portable sensing technologies, and the integration of chemometrics and machine learning (ML). Near-infrared (NIR), Fourier transform infrared (FTIR), and Raman spectroscopy were increasingly deployed to address pressing environmental challenges such as microplastics contamination, soil organic matter quantification, indoor air quality monitoring, and pesticide residue detection in food and ecological systems. This article reviews 10 influential peer-reviewed papers published during this period, providing expanded narrative discussions of their technical contributions and explaining why each paper represents a significant impact on the field.

In an upcoming multipart interview on "Pathways in Spectroscopy," Saikat Banerjee will discuss his background in spectroscopy and how it led him to his current position as an analytical laboratory manager at DuPont.

Previous “Focus on Quality” columns focused on analytical instrument qualification (AIQ) and computerized system validation (CSV). In this tutorial, we explore the critical frameworks of AIQ and CSV.

In this episode, John Margeson, who is a Product Manager at Thermo Fisher Scientific in their Tewkesbury headquarters, discusses the skills and qualifications necessary in order to be a successful product manager for an instrument manufacturer.

Top articles published this week include a couple new “Pathways in Spectroscopy” episodes, an interview with JAAS Prize and Nu Emerging Pioneer award winner David Clases, and a blog post that explores how early scientific ambitions translate to building a career in spectroscopy.

Researchers at Jiangsu University of Science and Technology showed that FT-IR spectroscopy combined with optimized chemometric modeling can rapidly and accurately detect and stage Bombyx mori nucleopolyhedrovirus infection in silkworms.

In a recent press release, CEA-Leti announced that a team of researchers from their institution and CEA-IRIG validated what they describe as the first chip-scale, battery-operated electron paramagnetic resonance (EPR) spectrometer.

A recent review article explores how machine learning (ML)-assisted Raman spectral classification is being used in applications such as biomedicine and material analysis.

Artificial intelligence and machine learning are rapidly reshaping how analytical data are modeled, interpreted, and deployed, but the conceptual foundation is already familiar to practitioners of chemometrics. Latent variables, calibration models, variance–bias tradeoffs, and multivariate optimization did not originate with neural networks; they have been central to spectroscopic data analysis for decades. This expanded glossary provides a rigorous, side-by-side translation between modern artificial intelligence (AI) terminology and established chemometric concepts. This glossary is intended to demystify AI terminology, while preserving statistical clarity. It is designed to help analytical scientists, spectroscopists, and chemometricians engage with modern data-driven methods without abandoning physical interpretability or statistical discipline.

A recent study presented an approach combining Fourier transform infrared (FT-IR) imaging spectroscopy, histology, and statistical analysis that can identify biochemical spectral markers and distinguish benign from malignant uterine smooth muscle tumors.

Spectroscopy is seeing several important changes in the industry. In this feature, we focus on four topics that have emerged.

In this tutorial, we break down these vibrational spectroscopy advancements and what we can expect in the months and years ahead.

In this blog post, Alexis Weber, a Field Applications Scientist at PerkinElmer, describes how early forensic-science ambitions inspired by NCIS and Bones evolved through education at University of Central Florida, University of New Haven, and University at Albany, SUNY into a PhD-level spectroscopy career and ultimately a dynamic Field Applications Scientist role at PerkinElmer, highlighting the value of exploring nontraditional science careers.

In this episode of “Pathways in Spectroscopy,” episode, Mercedes Bertotto provides insight into the technical, financial, and strategic decisions that new business owners should make in order to ensure the long-term success of their business venture.

In this episode of “Pathways in Spectroscopy,” Mercedes Bertotto, who is the Founder of Vibralytics, discusses translating her scientific and spectroscopic knowledge into a marketable business plan and some of the surprising realities she faced when starting her own venture.

A study conducted at Lawrence Berkeley National Laboratory (Berkeley, California), with collaboration from the University of Michigan (Ann Arbor, Michigan), presented a comprehensive characterization of the gaseous UF6 LIBS plasma behavior, examining the effects of laser pulse width and wavelength on spectral characteristics and fundamental plasma properties through temporally resolved analysis, Boltzmann-plot temperature determination, and electron number density evaluation. Spectroscopy spoke to George Chan of the Lawrence Berkeley National Laboratory and corresponding author for the paper resulting from this work.

In the final part of our conversation with David Clases at the Winter Conference on Plasma Spectrochemistry, he reacts to winning both awards and offers his perspective on the key spectroscopic trends this year.

Top articles published this week include the latest “Focus on Quality” column, an interview about handheld X-ray fluorescence (XRF) instrumentation, and the latest episode in “Pathways in Spectroscopy.”

A recent study demonstrates that updated predictive models based on NIR spectra can outperform traditional nitrogen-based prescreening methods in identifying samples suitable for radiocarbon dating.

From a chemometric standpoint, artificial intelligence (AI) in spectroscopy is best understood as an extension of established multivariate methods rather than as a replacement. Most AI approaches closely parallel familiar tools such as regression, classification, and principal component analysis, but offer greater flexibility to handle nonlinear behavior, interacting physical and chemical effects, and large, heterogeneous datasets. By learning directly from raw spectra, AI methods can reduce reliance on manual preprocessing while still indicating which spectral regions influence predictions. In this sense, AI represents a developmental layer of chemometrics that enables classical concepts to operate effectively in modern spectroscopic systems. Overall, AI is best viewed as the next developmental layer of chemometrics, not as a competing discipline. As with all current AI programs, domain knowledge of analytical chemistry is essential for AI’s effective application. Knowing the boundaries of what is plausible in any chemical or modeling system allows fine-tuning of the models towards useful and reliable analytical results.

In this “Pathways in Spectroscopy” episode, Mercedes Bertotto, who is the Founder of Vibralytics, talks about the necessary skills spectroscopists need when starting their own business, and what challenges they will need to face head-on during this process.

In this video segment, John Margeson, a Product Manager at Thermo Fisher Scientific’s Tewskbury headquarters, discusses the updated software capabilities in the company’s new line of handheld XRF spectrometers.

In this Pittcon preview, we highlight some important talks, workshops, and symposiums happening during the conference.