Near Infrared (NIR) Spectroscopy

A new deep-learning framework that was recently developed improves the accuracy of near-infrared spectroscopy for non-destructively measuring internal quality traits.

The bulky bench-top NIR spectrometer is quietly being dismantled and rebuilt as a wafer-scale photonic chip, a self-calibrating algorithm, and a sensor small enough to ride in a shirt pocket. What once demanded a grating, a moving mirror, and a climate-controlled lab now fits inside a handheld module, a bioreactor probe, or a drone payload, and it increasingly figures out what it is looking at on its own.

Peter R. Griffiths, a pioneering figure in Fourier transform infrared (FT-IR) spectroscopy who authored or coauthored more than 300 scientific papers and numerous books over five decades, died August 29, 2026, at age 84. This tribute traces his career from Oxford and Digilab through Ohio University and the University of Idaho, his landmark books and the Handbook of Vibrational Spectroscopy, and his many honors, including the 2023 Ellis R. Lippincott Award and 2024 Optica Fellowship.

This is Chemometrics in Spectroscopy Column Number 251. In Column Number 250, we left you with a puzzle. We had just shown that a little-known piece of two-hundred-year-old mathematics, Lagrange's method of undetermined multipliers, could force an MLR calibration to ignore one particular, carefully chosen direction of repack-induced spectral change. It worked, but it only handled one direction at a time, and we admitted we had no idea whether the trick could be extended to PCR or PLS. That bothered us. So in this installment we go looking for the more general version of the idea, and we find it sitting in some very good recent work by Neal Gallagher and Nathanial Watson on what they call clutter suppression (a framework built for a completely different problem, target detection in hyperspectral imaging, that turns out to fit ours almost perfectly). Put the two together and you get an algorithm that whitens the calibration spectra with a clutter covariance matrix estimated from repack replicates, then hands the whitened data to the same Lagrangian machine we built last time. The payoff: the correction now generalizes to several, non-proportional directions of diffuse-reflection variability at once, and, because it is just a preprocessing step, it works ahead of PLS and PCR as well as MLR, which finally answers the question we could not answer before. This approach may very well be an answer to repack variation in repeated solid or slurry sample measurements using diffuse reflection. In this column we change gears with our writing tone and format by taking a "chemometry" approach, rather than a strict tutorial one. In future columns we hope to "unpack" this information in our more typical extended and tutorial manner. Let's explore solving this repack variation problem together.

Sapienza Università di Roma team reports near-infrared (NIR) techniques that identify and quantify cocaine and cutting agents through sealed police evidence bags, addressing safety and chain-of-custody concerns in forensic laboratories.

An upcoming poster presentation set to take place at the American Chemical Society (ACS) Fall 2026 Meeting will explore how spectroscopy is being used to distinguish different roasted coffee types.

Nearly every diabetic on earth still stabs a finger to measure their blood sugar, but a small army of lasers, infrared beams, and machine-learning algorithms is quietly plotting to end that ritual for good. This review tracks the photons chasing glucose through skin, from near-infrared reflectance to Raman fingerprints, and asks why the “needle-free” promise keeps slipping just out of reach.

Spectrometers that once filled an entire lab bench now fit in a coat pocket, and they are quietly reshaping how fentanyl gets identified on a roadside, how counterfeit cashmere gets caught on a loading dock, and how a 500-year-old painting gets authenticated without ever leaving the wall. This review tracks five years of coverage from Spectroscopy alongside the broader literature to show how handheld Raman, FT-IR, NIR, and XRF instruments moved from laboratory novelty to field necessity.

A plain-language glossary of 40 spectroscopic and elemental analytical methods—FTIR, Raman, ICP-MS, XRF, atomic absorption, and more—with verified references for lab professionals, students, and instrument buyers.

A rule proposed by the White House Office of Management and Budget (OMB) would give political appointees final authority over discretionary grant decisions ahead of peer reviewers, allow agencies to terminate active grants without a formal right of appeal, and restrict international collaboration and publication funding across federal science agencies. For the optical, molecular, vibrational, and atomic spectroscopy community, the proposal could affect the grants, journal support, and student and postdoctoral positions that sustain the field.

A European study of nearly 9,000 cropland soil samples found that combining visible–near-infrared (vis-NIR) spectroscopy with climate and topographic data only improves soil carbon and nitrogen prediction accuracy at spectral resolutions of 10 nanometers or finer.

This review highlights major advances in infrared (IR) and near-infrared (NIR) spectroscopy from 2022–2026, including instrument miniaturization, hyperspectral imaging, machine learning, and artificial intelligence. These developments are expanding the use of vibrational spectroscopy beyond the laboratory into portable and field-deployable analytical systems. Environmental applications such as microplastic detection, biosolids analysis, soil characterization, and contaminant monitoring are emerging as key growth areas.

A comprehensive introduction to LIBs coverage published in Spectroscopy Online from 2022–2025.

Over the past two years, Spectroscopy magazine has extensively documented and analyzed the growing role of artificial intelligence in spectroscopy through articles, interviews, podcasts, and technical features, highlighting both its hype and its potential as a transformative advancement in data processing and analytical science.

This new feature in Spectroscopy introduces a structured, application-focused series that curates and examines the most influential research papers in molecular and atomic spectroscopy. Each installment presents a focused “Top 10” collection of seminal publications within a specific analytical domain, spanning techniques such as ultraviolet–visible, infrared, Raman, near-infrared, and atomic spectroscopy. Across biomedical, biopharmaceutical, environmental, and forensic applications, the selected papers illustrate how spectroscopic methods are applied to real-world analytical challenges. Emphasis is placed on the integration of spectral data with chemometric approaches to enable robust calibration, accurate prediction, and meaningful interpretation. Together, these curated collections provide practitioners with a concise, application-oriented perspective on impactful developments in spectroscopy. This article brings together the first nine “Top 10” collections in the series, offering a cross-disciplinary view of influential work shaping the field.

In this brief Q&A, Christina Ryder, who is a postdoctoral researcher at Texas A&M University and the lead author of this study, explains the remaining limitations in using NIR spectroscopy for collagen prediction.

In this brief Q&A, Christina Ryder, who is a postdoctoral researcher at Texas A&M University and the lead author of this study, explains why restricting sampling to the 2030–2060 nm spectral region was key in avoiding interference from consolidants.

In this brief Q&A interview, Christina Ryder, who is a postdoctoral researcher at Texas A&M University and the lead author of this study, discusses her team’s findings.

Over the past two years, molecular spectroscopy has undergone a marked transformation from a predominantly laboratory-based analytical approach into a field-deployable, data-rich forensic toolkit. This evolution has been driven by three converging trends: (i) advances in vibrational spectroscopic instrumentation (Fourier transform infrared [FT-IR], Raman, and near-infrared [NIR], (ii) the integration of chemometrics and machine learning for extracting actionable information from complex spectra, and (iii) the emergence of portable and miniaturized devices suitable for in situ analysis. The ten papers reviewed here collectively demonstrate how spectroscopy is now addressing some of the most persistent challenges in forensic science—such as time since deposition (TSD), post-mortem interval (PMI), trace evidence discrimination, and rapid drug identification—while maintaining evidentiary integrity through non-destructive analysis. Importantly, these works also reflect a shift toward interpretability, validation, and legal defensibility, which are essential for courtroom acceptance.

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.

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.

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.

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.

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.