Atomic Spectroscopy

How can micro-particle induced X-ray emission (µ-PIXE) and micro-ion beam induced luminescence (µ-IBIL) spectroscopy improve conservation practices?

What does the aluminosilicate and carbonate particles on bitumen-coated bandages of mummies tell us about the burial environment?

Abstract submissions are open through April 15 as conference expands scope to include molecular methods.

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.

The 2024-2026 period has been marked by rapid methodological innovation and critical reassessment of established atomic spectrometric techniques in environmental analysis. Advances in inductively coupled plasma–tandem mass spectrometry (ICP-MS/MS) reaction-cell chemistry, matrix-effect correction in X-ray fluorescence (XRF), microwave-sustained plasma sources, and green preconcentration strategies have expanded analytical capabilities for soils, waters, sediments, plants, and atmospheric particulates. Simultaneously, comparative evaluations of inductively coupled plasma–mass spectrometry (ICP-MS), inductively coupled plasma–optical emission spectrometry (ICP-OES), and XRF have sharpened our understanding of detection limits, bias, and field applicability. This brief review highlights 10 of the most influential publications shaping environmental applications of XRF, ICP-MS, and ICP-OES during 2024–2026. Each paper is discussed with emphasis on its technical contributions and broader impact on environmental monitoring, regulatory science, and instrumental development.

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.

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.

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.

How are ICP-MS and ICP-OES revealing heavy-metal trends in pet food? In this short tutorial, we explain how these techniques have been tracking pet food trends, and what owners can do in response to these trends.

This year’s Emerging Leader in Atomic Spectroscopy Award recipient is Sarah Theiner, whose research is focused on the application of atomic spectroscopy techniques—laser ablation inductively coupled plasma–mass spectrometry (LA-ICP-MS) and single-cell ICP-MS—to expand these analytical techniques as tools for biological and clinical imaging and drug-distribution studies.

The primary goal of this study was to evaluate two microwave digestion systems for the acid decomposition of biological tissues: (1) a single reaction chamber (SRC) system and (2) a rotor-based, closed vessel microwave (RBCVM) system.

The 12th Nordic Conference on Plasma Spectrochemistry and Ionization Principles in Mass Spectrometry will take place from June 14–18, 2026, in Loen, Norway. We preview the conference here.

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.

In this edition of “Inside the Laboratory,” Martin Resano, a Coordinator of the Rapid Analysis Methods with Spectroscopic Techniques (MARTE) group and as part of the Aragon Institute for Engineering Research (I3A) at the University of Zaragoza, discusses how compressed sensing spectroscopic techniques are used in his laboratory.

Gerardo Gamez, a Professor and Graduate Advisor at Texas Tech University, previews his upcoming talk at the Winter Conference on Plasma Spectrochemistry.

At the Winter Conference on Plasma Spectrochemistry, Hunter Andrews, an R&D Staff Scientist at Oak Ridge National Laboratory, will be giving a talk about using laser-induced breakdown spectroscopy (LIBS) for molten salt reactor monitoring. Andrews provides a preview of his upcoming talk here.

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.

Discover a cost-effective method for analyzing electrolyte elements in sports drinks using microwave plasma atomic emission spectroscopy, enhancing quality control and accuracy.

A recent study demonstrated that deep learning combined with ICP spectroscopy can accurately and affordably predict multiple soil properties.

The article highlights how rapidly advancing, non-destructive spectroscopic and imaging technologies, supported by AI, multi-modal data integration, and increasing instrument portability.

This first article in a two-part series introduces the foundations and terminology of AI as applied to chemometrics, defines key algorithmic approaches, and explores their growing role in spectral data analysis, model quantitative calibration, classification, and interpretability.

Here are ten main unsolved problems in vibrational and atomic spectroscopy, each accompanied by a tutorial-style synopsis suitable for advanced practitioners or graduate-level students. Each of these tutorials, spanning advanced spectroscopy modeling, chemometrics, machine learning (ML) interpretability, and standardization, consists of a descriptive article. Each piece is well-referenced (with detailed matrix equations, radiative transfer models, chemometric derivations, and so forth), and includes the following. • Special focus on each topic—including mathematical derivations in matrix notation. • Conservative, verifiable content anchored to established reference sources. • Appropriate tutorial article structure: Title, Summary, Abstract, Introduction, Theory with equations, Examples, Discussion & Future Research, and References.

James Dudley Winefordner was a preeminent figure in analytical chemistry whose contributions to spectrochemical method development, instrumentation, and mentoring shaped generations of scientists.