A new review in TrAC Trends in Analytical Chemistry by Alfred Chin Yen Tay and Liang Wang highlights how machine learning (ML) is transforming surface-enhanced Raman spectroscopy (SERS) into a powerful, clinically viable tool for rapid and accurate medical diagnostics.
Researchers have developed a powerful deep learning model that automates the identification of minerals using Raman spectroscopy, offering faster, more accurate results even in complex geological samples. By integrating attention mechanisms and explainable AI tools, the system boosts trust and performance in field-based mineral analysis.
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.
A team of international researchers has developed a faster, more accurate method to analyze soil carbon fractions using mid-infrared spectroscopy and deep learning. Their approach preserves the chemical balance of soil organic carbon components, paving the way for improved climate models and sustainable land management.
In the first part of this three-part interview, Ayanjeet Ghosh of the University of Alabama and Rohit Bhargava of the University of Illinois Urbana-Champaign discuss their interest in using discrete frequency infrared (IR) imaging to analyze protein secondary structures.
Our “Chemometrics in Spectroscopy” column highlights the methodology that is used in order to apply chemometric methods to data. Integrating chemometrics with spectroscopy allows scientists to understand solutions to their problems when they encounter surprising results. Recently, columnists Howard Mark and Jerome Workman, Jr., wrote a series of articles about data transforms in chemometric calibrations. In this listicle, we profile all pieces in this series and invite you to learn more about applying chemometric models to continuous spectral data.
A new study in the Journal of Food Composition and Analysis showcases high-performance detection using artificial intelligence and spectroscopy.
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 newly published review in the journal Advanced Materials explores how intelligent wearable sensors, powered by smart materials and machine learning, are changing healthcare into a decentralized, personalized, and predictive modeling system. An international team of researchers highlights emerging technologies that promise earlier diagnosis, improved therapy, and continuous health monitoring—anytime, anywhere.
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 tutorial provides a step-by-step, hands-on guide to using multivariate data analysis tools like PCA and PLS to extract meaningful insights from complex pharmaceutical data sets.
Top articles published this week include a preview of our upcoming content series for National Space Day, a news story about air quality monitoring, and an announcement from Metrohm about their new Midwest office.
A new dual-spectroscopy approach reveals real-time pollution threats in indoor workspaces. Chinese researchers have pioneered the use of laser-induced breakdown spectroscopy (LIBS) and aerosol mass spectrometry to uncover and monitor harmful heavy metal and dust emissions from soldering and welding in real-time. These complementary tools offer a fast, accurate means to evaluate air quality threats in industrial and indoor environments—where people spend most of their time.
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.
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.
A recent study by Southeast University researchers presented a cost-effective, high-accuracy solution for pharmaceutical quality control.
A new study highlights the potential of Raman spectroscopy for distinguishing between bee and wasp venom-based therapies from different manufacturers.
Researchers in China have developed a lightweight deep learning system for rapid, non-destructive analysis of wheat flour composition.
Using LIBS, infrared, and Raman spectroscopic techniques scientists detect quartz and hydrated silica, hinting at past Martian water activity and potential biosignatures
Scientists demonstrate submicron detection of microbes and minerals in Mars-analogue basalt using O-PTIR spectroscopy
Scientists detail the first 1,000 sols of spectroscopic mineral detections on Mars
Tianjin University researchers develop an advanced AI model to enhance food safety.
