Near Infrared (NIR) Spectroscopy

A new study reveals how changes in refractive index can skew transient absorption data in thin-film materials, leading to misinterpretations unless properly corrected.

Researchers in Brazil have developed new optical techniques—SLIM, IC-scan, and RICO-scan—to probe the complex nonlinear properties of scattering and disordered materials, expanding potential applications in photonics, biomedicine, and thermometry.

Researchers have introduced a simple yet powerful new rule based on Rayleigh scattering theory that accurately links the absorption behavior of composite media, like aerosols or colloids, to the properties of their nanoparticle constituents.

A Texas A&M AgriLife Research study published in Rangelands found that region-specific calibration significantly improves the accuracy of FNIRS-based nutritional monitoring for beef cattle grazing the Edwards Plateau.

In this tutorial, Thomas G. Mayerhöfer and Jürgen Popp introduce complex-valued chemometrics as a more physically grounded alternative to traditional intensity-based spectroscopy measurement methods. By incorporating both the real and imaginary parts of the complex refractive index of a sample, this approach preserves phase information and improves linearity with sample analyte concentration. The result is more robust and interpretable multivariate models, especially in systems affected by nonlinear effects or strong solvent and analyte interactions.

A new study demonstrates how handheld near-infrared spectroscopy can be a powerful, accurate tool to distinguish real cashmere from wool.

Chinese researchers have developed a powerful new method using near-infrared (NIR) hyperspectral imaging combined with a convolutional neural network (CNN) to identify hazardous explosive materials, like trinitrotoluene (TNT) and ammonium nitrate, from a distance, even when concealed by clothing or packaging.

Researchers have developed an analytical method combining remote near-infrared and Raman spectroscopy with machine learning to noninvasively map moisture and salt damage in historic buildings, offering critical insight into ongoing structural deterioration.

Researchers in China propose novel postharvest processing mode using vis-NIR spectroscopy and deep learning to accurately measure pomelo sweetness.

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.