Infrared (IR) Spectroscopy

In this interview clip, Lenka Halámková, an assistant professor of high-dimensional data analysis at Texas Tech University, explains the next steps needed in order for attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and machine learning to become a part of routine forensic and clinical toxicology workflows for opioid exposure.

In this interview clip, Lenka Halámková, an assistant professor of high-dimensional data analysis at Texas Tech University, explains her team’s data-driven approach when detecting fentanyl in human nails.

This article is derived from an invited talk given at the Pittcon Conference and Expo in San Antonio, Texas on Monday, March 9, exploring how generative artificial intelligence may transform the daily practice of analytical chemistry. It was presented in The James L. Waters Symposium.

In this Q&A overview, we explore how these scientific advancements are reshaping our understanding of Ancient Egyptian history and culture.

The Pittcon (Pittsburgh) Conference and Expo in San Antonio featured a forward-looking symposium exploring how generative artificial intelligence (AI) may transform the daily practice of analytical chemistry. The James L. Waters Symposium, “Generative AI in the Analytical Chemist’s Toolbox for Chemical Measurements”, took place on Monday, March 9, 2026 (2:30–4:40 p.m.) in Room 221A. The session was presided over by Daniel W. Armstrong of The University of Texas at Arlington, who introduced the topic by emphasizing the rapidly expanding knowledge base required of modern analytical chemists. In addition to chemistry, today’s analytical scientist must command elements of physics, advanced mathematics, data science, and, increasingly, AI. The symposium focused on the practical integration of generative AI tools into chemical measurement science. Speakers discussed how AI can assist analytical chemists with tasks such as algorithm generation, signal processing, literature synthesis, and data interpretation. Importantly, the session emphasized responsible implementation, highlighting the need for rigorous validation, high-quality data sets, and integration into existing laboratory workflows.

This overview of nearfield spectroscopy highlights how this technique operates and when it should be used.

At the Pittcon Conference and Expo in Saan Antonio, Texas, on Monday, March 9, 2026 (8:30–11:00 AM, Room 304C), the session “Spectroscopy and Sustainability: A Perfect Match” explored how modern spectroscopic technologies are helping laboratories and industries operate more efficiently while reducing environmental impact. Chaired by John Wasylyk and sponsored by the Society for Applied Spectroscopy, the session brought together 6 presentations covering applications from pharmaceutical process monitoring and biomedical diagnostics to chemical manufacturing, defense, and remote sensing. Throughout the morning, a consistent theme emerged: spectroscopy’s speed, nondestructive nature, and rich chemical information make it inherently aligned with the goals of sustainability.

As part of our coverage of the Pittcon 2026 conference, Spectroscopy sat down with Seth Kenkel to talk about his work using nearfield infrared (IR) spectroscopy.

A recent study used minimally invasive synchrotron and spectroscopic techniques to fully characterize the materials, degradation processes, and conservation needs of a 26th Dynasty Ptah–Sokar–Osiris wooden statuette from Giza, establishing technical baselines for Saite workshop practices and future preservation of polychrome artifacts.

Top articles published this week include a couple previews of upcoming “Pathways in Spectroscopy” episodes and an inside look at the role spectroscopy is playing in modern agriculture.

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

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.

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.

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.

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

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.

HÜBNER Photonics has announced the launch of the C-WAVE BTS, which is a continuous-wave (CW), single-frequency titanium:sapphire laser.

In this tutorial article, we review the latest in microplastic analysis, highlighting the techniques that have been frequently used in this space and where they have been most effective.

At Pittcon, generative artificial intelligence will be presented at the James L Waters Symposium on Monday, March 9, 2:30 PM to 4:40 PM in Room 221A. Generative artificial intelligence has transitioned from a conceptual novelty to a practical approach for innovation in spectroscopic data analysis. During 2025, a small set of highly influential publications crystallized this transformation by demonstrating how generative models can synthesize realistic spectra, solve inverse spectral problems, accelerate materials discovery, and automate molecular structural elucidation. This article reviews six pivotal contributions published in 2025 that collectively define the state of generative artificial intelligence in spectroscopy. These works establish theoretical foundations, survey emerging methods, introduce physics-informed generative architectures, and demonstrate transformative applications across vibrational, electronic, and magnetic resonance spectroscopies.

In this article, we highlight some of the important talks on atomic spectroscopy that will take place at Pittcon on Sunday March 8th.

A study in the Journal of Environmental Management shows that wastewater treatment plants in coastal Catalonia remove most microplastics but still release tens of billions of particles annually, with emissions peaking during warmer, tourism-heavy months due to environmental and population-driven pressures.

Fourier transform infrared (FT-IR) spectroscopy has undergone a notable evolution in biopharmaceutical analysis over the past three years. Advances in crystal engineering, process analytical technology (PAT), chemometrics, machine learning (ML), and hyphenated analytical platforms have significantly expanded FT-IR’s analytical scope. This article reviews ten of the most influential publications from 2023–2026 that exemplify FT-IR’s growing role across the biopharmaceutical lifecycle, from drug substance design and formulation to manufacturing, quality control, and clinical bioanalysis.

In our ongoing review of infrared spectra, we will study organic nitrogen containing compounds including amides and amines. Amides contain both nitrogen and a C=O group and are found in proteins and polymers. Amines contain carbon, nitrogen, and hydrogen, and are ubiquitous in medicines. As always, concepts will be illustrated with reference spectra.

Over the past three to four years, Fourier Transform Infrared (FT-IR) spectroscopy has emerged as one of the most rapidly expanding vibrational techniques in biomedical research. Driven by advances in attenuated total reflectance (ATR), live-cell measurements, chemometrics, and machine learning (ML), FT-IR has moved beyond descriptive biochemical profiling toward predictive diagnostics and translational clinical science. This article highlights and critically summarizes the top 10 most influential peer-reviewed articles published recently on FT-IR applications in tissues, cells, hair, blood, saliva, urine, and exercise physiology, emphasizing analytical innovation, clinical relevance, and future impact.