Atomic Spectroscopy

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.

In Part III of our conversation with David Clases, we discuss how optical trapping better enables polymer identification via Raman spectroscopy, and where ICP-MS research is heading in the future.

In this video segment, Sarah Theiner explains why using LA-ICP-TOF-MS was the ideal technique for this research compared to more conventional methods. She also touches upon how the findings from her study could inform the design of future chemotherapies and combination treatments.

In this interview clip, David Clasas of the University of Graz discusses the trapping mechanism of the OF2i and how it improves downstream Raman and ICP-TOFMS measurements and also highlights the multimodal approach his team used to improve microplastic analysis.

In the final part of our conversation with Ken Marcus, he discusses how external acquisition systems enhance isotopic precision and what spectroscopic trends experts should be paying attention to in 2026.

In this video segment, Sarah Theiner dives into the research that she conducted at the University of Vienna, which focused on how chemoresistance affects drug distribution in the tumor microenvironment.

In this interview clip, we turn the focus to the advent of artificial intelligence (AI) and machine learning (ML) in spectroscopy, and Alex Scheeline discusses how AI and ML will enhance SIBS and LIBS calibration.

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.

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.

This tutorial explains how baseline drift and multiplicative scatter distort spectroscopic data, reviews correction techniques such as MSC, SNV, EMSC, wavelet-based detrending, and AsLS baseline estimation with matrix-based derivations, and explores emerging data-driven scatter modeling strategies and future research directions.

A recent review by Jhonatan Contreras and Thomas Bocklitz from Friedrich Schiller University Jena and the Leibniz Institute of Photonic Technology delves into the emerging field of explainable artificial intelligence (XAI) in spectroscopy.

This tutorial examines the development of universal spectral libraries, reviewing standardization efforts, mathematical frameworks, and practical examples across multiple spectroscopies, while emphasizing metadata harmonization, FAIR principles, and the emerging role of AI in building interoperable, machine-readable repositories. This remains an unsolved problem in spectroscopy.