Near Infrared (NIR) Spectroscopy

This curated collection of recent Spectroscopy magazine mini-tutorials highlights the latest analytical and data-driven innovations in vibrational spectroscopy. Covering NIR, Raman, O-PTIR, and related optical methods, the series emphasizes practical workflows, emerging machine learning integrations, and advanced chemometric techniques for real-world laboratory applications—from food and environmental monitoring to biomedical analysis and nanoscale imaging.

A new perspective from researchers at the Karlsruhe Institute of Technology explores the evolving relationship between human expertise and artificial intelligence in polymer chemistry.

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.

Astronomers have captured the first detailed optical spectrum of 3I/ATLAS, the third known interstellar object to visit our Solar System. Using the VLT’s MUSE instrument, the team finds a red, dust-dominated coma with no detectable gas emissions, offering a rare glimpse into the composition of alien comets.

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.

New observations of interstellar comet 3I/ATLAS, the third known interstellar object ever to visit our solar system, reveal unexpected activity and composition, challenging many previous assumptions about interstellar objects.

Newly captured spectroscopic data of the third-ever known interstellar object, 3I/ATLAS, reveals a red, organic-rich surface and an enigmatic early dust coma, providing unprecedented insight into materials from beyond our solar system.

Astronomers have conducted detailed spectrophotometric observations of the mysterious interstellar comet 3I/ATLAS using the Palomar 200-inch and Apache Point telescopes. The findings reveal unexpected activity and unique spectral features, enhancing our understanding of this cosmic visitor.

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.

A recent study examined how portable instrumentation could be applied in the detection of diabetes.

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.

A recent study proposed and tested a new approach for monitoring the nutritional quality of orange juice.

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