At this point in our series dealing with linearity, we have determined that the data under investigation do indeed show a statistically significant amount of nonlinearity, and we have developed a way of characterizing that nonlinearity. Our task now is to come up with a way to quantify the amount of nonlinearity, independent of the scale of either variable, and even independent of the data itself.
LIBS Illuminates the Hidden Health Risks of Indoor Welding and Soldering
April 23rd 2025A 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.
Smarter Sensors, Cleaner Earth Using AI and IoT for Pollution Monitoring
April 22nd 2025A 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.
New AI Strategy for Mycotoxin Detection in Cereal Grains
April 21st 2025Researchers 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.