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.
Researchers explore soft optical waveguides as a breakthrough technology for biocompatible sensing and robotics.
A recent review published in Sensors explores the dynamic field of continuum robotics, with a particular focus on the advances in optical sensing technologies. The study, led by researchers from the Technical University of Košice and the University of Texas at Austin, highlights the dominance of optical fiber sensors in tracking robotic shape perception and environmental interactions, demonstrating spectroscopic applications and future potential.
Researchers at Oregon State University explore how machine learning, optical sensors, and robotics are transforming food quality assessment and processing, improving efficiency and reducing waste.
A research team from Nanjing University of Finance and Economics has developed a new analytical model using fluorescence spectroscopy and neural networks to improve the detection of aflatoxin B1 (AFB1) in vegetable oils. The model effectively restores AFB1’s intrinsic fluorescence by accounting for absorption and scattering interferences from oil matrices, enhancing the accuracy and efficiency for food safety testing.
University of Granada researchers compared different methods and their effectiveness in classifying ink found in historical documents.
Researchers have developed a high-sensitivity optical fiber vibration sensor based on Fabry-Perot (F-P) interference, designed to improve wind turbine tower monitoring. This innovation addresses issues with traditional electrical sensors and has strong potential for integration into the Internet of Things (IoT) for real-time structural health monitoring.
A study by researchers at Universidad de Talca in Chile explores the integration of artificial intelligence (AI), the Internet of Things (IoT), and remote sensing to modernize modern farming. The research highlights how these technologies optimize resource use, improve crop yields, and promote sustainable agricultural practices.
A recent review by researchers at Nagpur University and Seth Kesarimal Porwal College explores the ever advancing landscape of the Internet of Things (IoT) and its essential components—sensors and actuators. The review paper classifies various IoT sensors and examines their role in integrating the physical and digital worlds to enable smarter devices and enhanced automation.
