Jerome Workman, Jr.

The interstellar object 3I/ATLAS, discovered in July, has captivated astronomers with its unusual characteristics. While some scientists attribute its behaviors to natural cometary processes, others propose more speculative theories, including the possibility of it being an artificial probe. This article examines both mainstream and speculative interpretations of 3I/ATLAS's anomalous features. Other news articles this week will look specifically at the spectroscopic results of telescopes recently analyzing this mysterious object.

This tutorial explores the challenges posed by nonlinearities in spectroscopic calibration models, including physical origins, detection strategies, and correction approaches. Linear regression methods such as partial least squares (PLS) dominate chemometrics, but real-world data often violate linear assumptions due to Beer–Lambert law deviations, scattering, and instrumental artifacts. We examine extensions beyond linearity, including polynomial regression, kernel partial least squares (K-PLS), Gaussian process regression (GPR), and artificial neural networks (ANNs). Equations are provided in full matrix notation for clarity. Practical applications across near-infrared (NIR), mid-infrared (MIR), Raman, and atomic spectroscopies are discussed, and future research directions are outlined with emphasis on hybrid models that integrate physical and statistical knowledge.

Despite decades of major monetary investment for applied research in multiple spectroscopic sensing technologies, achieving an accurate, portable, and painless noninvasive glucose monitor remains a major unmet goal in diabetes care. This goal is extremely difficult due to persistent challenges with sensitivity, analyte specificity, accuracy, calibration stability, and biological interference.

This Chemometrics in Spectroscopy column traces the historical and technical development of these methods, emphasizing their application in calibrating spectrophotometers for prediction of measured sample chemical or physical properties and explores how AI and deep learning are reshaping the spectroscopic landscape.

A new study demonstrates that combining multi-pass FT-IR with a quantum cascade laser, two-dimensional correlation spectroscopy, and machine learning reportedly boosts the accuracy of non-invasive blood-glucose testing. The approach reports a 98.8% classification accuracy, suggesting potential for clinically viable, needle-free diabetes monitoring.

This tutorial explores the motivation, mathematical underpinnings, and practical approaches to fusing spectral data, with emphasis on early, intermediate, and late fusion strategies.

This Icons of Spectroscopy Series article features William George “Bill” Fateley, who shaped modern vibrational spectroscopy through landmark reference books and research papers, pioneering instrumentation, decades of editorial leadership, and deep commitments to students and colleagues. This article reviews his career arc, scientific contributions, and enduring legacy.

A research team in Japan has proposed a new principle, called the emission integral effect, to explain how mid-infrared passive spectroscopic imaging can detect blood glucose levels without invasive methods. Their findings suggest that dilute components like glucose may be more identifiable than concentrated ones when using this technique.

Researchers have developed a miniature non-invasive blood glucose monitoring system using near-infrared (NIR) technology. The compact, low-cost device uses infrared light to measure sugar levels through the fingertip, offering a painless alternative to traditional finger-prick tests.

Researchers from Sharif University of Technology, Tehran, present an approach using near-infrared absorbance and molar absorptivity to estimate blood glucose with a drawn blood sample—showing comparable performance to methods that apply principal components regression (PCR).

Near-infrared (NIR) spectroscopy combined with aquaphotomics shows potential for a rapid, non-invasive approach to detect subtle biochemical changes in biofluids and agricultural products. By monitoring water molecular structures through water matrix coordinates (WAMACs) and visualizing water absorption spectrum patterns (WASPs) via aquagrams, researchers can identify disease biomarkers, food contaminants, and other analytes with high accuracy. This tutorial introduces the principles, practical workflow, and applications of NIR aquaphotomics for everyday laboratory use.

This tutorial provides an in-depth discussion of methods to make machine learning (ML) models interpretable in the context of spectroscopic data analysis. As atomic and molecular spectroscopy increasingly incorporates advanced ML techniques, the black-box nature of these models can limit their utility in scientific research and practical applications. We present explainable artificial intelligence (XAI) approaches such as SHAP, LIME, and saliency maps, demonstrating how they can help identify chemically meaningful spectral features. This tutorial also explores the trade-off between model complexity and interpretability.

Researchers have demonstrated a non-invasive method using milk and near-infrared spectroscopy combined with Aquaphotomics to accurately detect Paratuberculosis in dairy cattle. The technique offers faster, more sensitive diagnosis than traditional methods.

Researchers have demonstrated that visible and near-infrared spectroscopy, combined with chemometric and aquaphotomic analysis, can accurately classify and quantify aflatoxin contamination in white and yellow maize, offering a faster, non-destructive alternative to traditional methods.

Researchers have developed a rapid, non-invasive screening method for esophageal squamous cell carcinoma (ESCC) using near-infrared spectroscopy and aquaphotomics. The approach analyzes plasma water patterns, achieving over 95% accuracy in distinguishing patients from healthy controls

This Icons of Spectroscopy article profiles Lester W. Strock, whose innovative research in crystal chemistry and spectroscopy bridged fundamental science and applied geochemistry.

A new study demonstrates how a machine learning technique, quantile regression forest, can provide both accurate predictions and sample-specific uncertainty estimates from infrared spectroscopic data. The work was applied to soil and agricultural samples, highlighting its value for chemometric modeling.

This Icons of Spectroscopy Series article features Infrared pioneer Earle Keith Plyler (1897–1976), who transformed molecular spectroscopy—building precision techniques, reference data, and instruments that set enduring methods and standards at the National Bureau of Standards (NBS, now NIST). As a teacher and mentor, he established a generation of leaders in molecular spectroscopy.

The Icons of Spectroscopy series shines a spotlight on the pioneers whose work laid the foundation for modern analytical science.

This tutorial contrasts classical analytical error propagation with modern Bayesian and resampling approaches, including bootstrapping and jackknifing. Uncertainty estimation in multivariate calibration remains an unsolved problem in spectroscopy, as traditional, Bayesian, and resampling approaches yield differing error bars for chemometric models like PLS and PCR, highlighting the need for deeper theoretical and practical solutions.

Researchers from Brazil have developed an improved method combining infrared and Raman spectroscopic techniques to better identify and characterize microplastics. This integrated approach enhances accuracy in distinguishing various polymer types and provides refined spectral analysis crucial for environmental studies.

A new study investigates how colorants embedded in microplastics (MPs) interfere with Raman spectroscopy, one of the key tools used to identify microplastic particles. The research details how fluorescence from these additives complicates spectral analysis, underscoring challenges in environmental microplastic detection.

Researchers have developed a novel approach to quantify microplastics in water environments by combining Raman spectroscopy with convolutional neural networks (CNN). This integrated method enhances the accuracy and speed of microplastic identification, offering a promising tool for environmental monitoring.

This tutorial investigates the persistent issue of sample heterogeneity—chemical and physical—during spectroscopic analysis. Focus will be placed on understanding how spatial variation, surface texture, and particle interactions influence spectral features. Imaging spectroscopy, localized sampling strategies, and adaptive averaging algorithms will be reviewed as tools to manage this problem, as one of the remaining unsolved problems in spectroscopy.

This tutorial guides spectroscopy practitioners through the integration of Raman spectroscopy and machine learning (ML) techniques for detecting microplastics (MPs) in aquatic and environmental samples.

This Icons of Spectroscopy Series article features E. Bright Wilson, a pioneer of chemical physics. Wilson’s contributions to infrared, Raman, and microwave spectroscopy provided the theoretical and practical foundation for analyzing molecular structure and dynamics. As a revered professor at Harvard and coauthor of landmark texts, he mentored nearly 150 students and researchers, leaving a lasting legacy of scientific excellence and integrity.

ATR FT-IR shows polyethylene and polypropylene particles were common in facial scrubs and creams.

A new study uses spectroscopic tools to analyze the spread and transformation of microplastics across water, soil, and air systems. Researchers also examined the limitations of global policies in addressing this multidimensional pollutant.

The findings suggest that bottling processes and PET containers are major contributors to beverage contamination, raising concerns about food safety and environmental pollution.

A new study using infrared spectroscopy reveals that commercial beet sugar contains microplastic particles, raising concerns over food processing and packaging practices. Scientists identified various plastic types in sugar samples, including polyethylene and PET.