Infrared (IR) Spectroscopy

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.

Drawing from Brian C. Smith’s “IR Spectral Interpretation” column, this Q&A article explores the unique characteristics, definitions, and spectral signatures of inorganic compounds.

For Pittcon 2026, the James L. Waters Symposium, scheduled for Monday, March 9, from 2:30 to 4:40 p.m. in Room 221A, turns its focus on Generative artificial intelligence (AI) systems in analytical chemistry, which are increasingly being used for analytical data interpretation, algorithm development, experimental planning, and scientific communication. This article introduces the general concepts of generative AI and its use in spectroscopy.

The 2026 James L. Waters Annual Symposium at Pittcon will focus on the integration of generative AI into analytical chemistry, examining how large language models and AI tools can support method development, data analysis, and chemical measurement while maintaining scientific rigor, validation, and interpretability. Continuing its decades-long tradition of connecting historical perspective with emerging technologies, the symposium will feature presentations from leading chemists and spectroscopists, highlighting both the opportunities and challenges of responsibly incorporating AI into chemical measurement science.

Pittcon 2026 will take place March 9–11, 2026, in San Antonio, Texas, building on the 2025 conference’s emphasis on eco-friendly practices and serving as the premier global event in analytical research, scientific instrumentation, and applied spectroscopy. An opening plenary session, “Spectroscopy and Sustainability: A Perfect Match,” will highlight how advances in spectroscopic technologies—from biomedical and pharmaceutical applications to UAV-based hyperspectral imaging, CBRNE detection, hazardous materials monitoring, and even extraterrestrial studies—are driving sustainable solutions across diverse fields.

A recent study demonstrated that hyperspectral imaging offers a highly accurate, dramatically faster alternative to traditional FT-IR methods for identifying microplastics in the Po River.

Researchers at Washington State University Tri-Cities demonstrate that combining Raman and infrared spectroscopy with convolutional neural networks enables highly accurate, low-cost, and field-ready automated plastic identification.

A recent review article explored methods that are used in agriculture to detect ammonia in pig housing. In the second part of our discussion of this topic, we focus on the protocols and frameworks and their importance in measuring ammonia in pig production.

A new review article systematically compared ammonia monitoring technologies and measurement protocols in pig production, offering a practical decision-support framework to guide researchers, farmers, and policymakers toward reliable, regulation-ready emission monitoring.

Artificial intelligence is transforming vibrational spectroscopy by automating calibration, feature extraction, and interpretation across Raman, infrared, near-infrared (NIR), and hyperspectral imaging (HSI) systems. This review of articles highlighted in Spectroscopy during 2025 captures several major developments, spanning data fusion, spectral imaging, and industrial and biomedical applications.

ABB announced that they will develop a lunar soil analysis instrument for Canada's Lunar Utility Rover, enhancing lunar exploration and resource utilization through advanced infrared spectroscopy.