Near Infrared (NIR) Spectroscopy

Researchers from Tianjin Agricultural University, Nankai University, and Zhejiang A&F University have developed a highly accurate method using near-infrared spectroscopy and machine learning to rapidly detect and classify microplastics in chicken feed.

Researchers at Wroclaw University of Science and Technology and Université catholique de Louvain have demonstrated how diffuse reflectance spectroscopy (DRS) in the 900 nm to 1100 nm range can non-destructively assess powder blend homogeneity in metal additive manufacturing. Their findings suggest that DRS offers a fast, reliable method for ensuring uniformity in aluminum alloy powders used in powder bed fusion 3D printing.

Shanghai researchers develop high-accuracy machine learning system to identify colorless microplastics across varied environments.

Researchers from Northwestern University, University of Cádiz, and University of Arizona have developed new formulae for analyzing optical thin films that outperform traditional models by accounting for complex geometries and absorbing substrates. These advances offer more precise ultraviolet-visible-near-infrared (UV-vis-NIR) spectroscopic analysis of film materials used in critical modern technologies.

A recent study explored new rapid screening alternatives to traditional methods for detecting pork adulteration in meatballs, aiding halal food authentication efforts.

In this Icons of Spectroscopy article, Executive Editor Jerome Workman Jr. delves into the life and impact of Bruce Kowalski, an analytical chemist whose major contributions to chemometrics helped establish the field of applying advanced quantitative and qualitative mathematics to extract meaningful chemical information from complex datasets. Kowalski’s visionary approach to chemical data analysis, education, and software development has transformed the landscape of modern analytical chemistry for academia and industry.

Researchers in China have pioneered a rapid, green, and non-destructive detection system using NIR spectroscopy and machine learning to ensure yak milk powder quality.

Researchers from Tohoku University, Shibaura Institute of Technology, and Shizuoka University unveil advanced sorting system using NIR, THz, and machine learning for improved recycling outcomes.

Researchers from Tsinghua and Hainan Universities have developed a portable, non-destructive method using NIR spectroscopy, hyperspectral imaging, and machine learning to accurately assess the quality and detect adulteration in whey protein supplements.

Researchers from Jiangnan University introduced a sensitive, selective, and highly adaptable new probe for detecting hydrazine.

New predictive models promise to revolutionize livestock feeding strategies in one of China’s most important pastoral regions.

Researchers have developed a wireless, wearable brain-monitoring device using functional near-infrared spectroscopy (fNIRS) to detect cognitive fatigue in real time. The miniaturized system enables mobile brain activity tracking, with potential applications in driving, military, and high-stress work environments.

A recent review article highlights the promise of near-infrared (NIR) spectroscopy for on-farm analysis of liquid organic manure.

A new study published in Geoderma Regional by J. A. Arias-Rios and colleagues at IFAB demonstrates that near-infrared (NIR) spectroscopy is a rapid, cost-effective tool for assessing soil and tree traits critical to forest ecosystem monitoring and management.

Macarena Garcia Marin, an astrophysicist and instrument scientist for the European Space Agency, highlights the role spectroscopy techniques have played in the pivotal research done on the James Webb Telescope since its launch in 2021.

A study from Chinese researchers demonstrates how combining satellite imagery, land use data, and machine learning can improve pollution monitoring in fast-changing urban rivers. The study focuses on non-optically active pollutants in the Weihe River Basin and showcases promising results for remote, data-driven water quality assessments.

New research highlights how remote satellite sensing technologies are changing the way scientists monitor inland water quality, offering powerful tools for tracking pollutants, analyzing ecological health, and supporting environmental policies across the globe.

Modern remote sensing technologies have evolved from coarse-resolution multispectral sensors like MODIS and MERIS to high-resolution, multi-band systems such as Sentinel-2 MSI, Landsat OLI, and UAV-mounted spectrometers. These advancements provide greater spectral and spatial detail, enabling precise monitoring of environmental, agricultural, and land-use dynamics.

A new study published in Applied Food Research demonstrates that near-infrared spectroscopy (NIRS) can effectively detect subclinical bovine mastitis in milk, offering a fast, non-invasive method to guide targeted antibiotic treatment and support sustainable dairy practices.

A global research team has detailed how smart sensors, artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies are transforming the detection and management of environmental pollutants. Their comprehensive review highlights how spectroscopy and sensor networks are now key tools in real-time pollution tracking.

Jiangxi Agricultural University researchers use AI and vis-NIRS to predict meat quality and freezing duration with high accuracy.

Researchers from Jiangsu University and Zhejiang University of Water Resources and Electric Power have developed a transfer learning approach that significantly enhances the accuracy and adaptability of NIR spectroscopy models for detecting mycotoxins in cereals.

In this two-part "Icons of Spectroscopy" column, executive editor Jerome Workman Jr. details how Karl H. Norris has impacted the analysis of food, agricultural products, and pharmaceuticals over six decades. His pioneering work in optical analysis methods including his development and refinement of near-infrared spectroscopy, has transformed analysis technology. In this Part II article of a two-part series, we summarize Norris’ foundational publications in NIR, his patents, achievements, and legacy.

Researchers from Gifu Pharmaceutical University and Gifu University Hospital unveil a novel polaprezinc (PLZ) mucoadhesive film designed to replace painful lozenges for cancer patients.

In this "Icons of Spectroscopy" column, executive editor Jerome Workman Jr. details how Karl H. Norris has impacted the analysis of food, agricultural products, and pharmaceuticals over six decades. His pioneering work in optical analysis methods including his development and refinement of near-infrared (NIR) spectroscopy has transformed analysis technology. This Part I article of a two-part series introduces Norris’ contributions to NIR.

A new study by researchers from Palo Alto Research Center (PARC, a Xerox Company) and LG Chem Power presents a novel method for real-time battery monitoring using embedded fiber-optic sensors. This approach enhances state-of-charge (SOC) and state-of-health (SOH) estimations, potentially improving the efficiency and lifespan of lithium-ion batteries in electric vehicles (xEVs).

A recent study from Chinese researchers sheds light on protein unfolding and hydration structure dynamics in hydrogels, with implications for drug delivery and biomedical applications.

Researchers in China have developed a lightweight deep learning system for rapid, non-destructive analysis of wheat flour composition.

A study published in Sustainability by Giuseppe Bonifazi and his team at Sapienza University of Rome demonstrates how short-wave infrared (SWIR) spectroscopy combined with machine learning offers a noninvasive, accurate, and sustainable method for detecting asbestos in various materials.

This study compares the sensitivity of CIE Lab values, peak area, and yellowness index for the determination of color attributes among a set of white and stained seashells exposed to tea tannins.