Data Analytics, Statistics, Chemometrics, and Artificial Intelligence

Measuring neonicotinoids in drinking water is not heavily regulated in the United States, but low-cost, rapid analytical tools are changing the conversation.

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.

Can spectroscopy help cities manage water quality during monsoons?

A new review in Food Physics finds that combining near-infrared spectroscopy with machine learning is transforming food quality testing across sectors like meat, dairy, and produce.

The following articles are the 10 most accessed digital object identifier (DOI) manuscripts for Spectroscopy and LCGC International during the month of June 2026.

Spectroscopy’s “What’s Nu” newsletter in May highlights the development of lasers in spectroscopy, compensating for repack variation in near-infrared (NIR) spectroscopy, and validity by design.

What were attendees talking about the most at Spring SciX?

In their milestone 250th column, Howard Mark and Jerome Workman, Jr. describe a mathematically rigorous algorithm that minimizes or eliminates sampling repack variation in near-infrared spectroscopy. The method separates systematic spectral changes caused by sample rearrangement from true compositional information, enabling more robust calibration models and significantly improving analytical repeatability for powdered and heterogeneous solid samples.

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.

Spectroscopy is playing an increasingly important role in detecting adulteration in food products. We highlight some of the recent research on this topic in this Q&A.

Researchers at the National University of Singapore have demonstrated that widely used spectroscopic methods for detecting adulteration in edible bird's nest products are based on a false assumption — that genuine product has a uniform chemical signature.

Spectroscopy is telling us the extraterrestrial history of space objects, helping us learn about planetary origins.

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.

The following articles are the 10 most accessed digital object identifier (DOI) manuscripts for Spectroscopy and LCGC International during March, 2026.

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.

At Pittcon 2026 in San Antonio, Texas, the LCGC International Awards Session was held on Tuesday, March 10, from 1:30 PM to 4:40 PM. This session, presided by Jerome Workman, Jr., celebrated two distinguished scientists whose work has significantly influenced modern separation science. This annual session honors both a lifetime of achievement and the promise of emerging leadership in chromatography. In its nineteenth year, the program recognized Jack Henion with the LCGC Lifetime Achievement Award and Bob W. J. Pirok with the LCGC Emerging Leader in Chromatography Award.

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.

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.

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

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.