Laser-Based Gas Sensing and Hyperspectral Imaging for Environmental and Food Analysis
This research community develops optical instruments that measure the chemical composition of gases, soils, and food products, primarily using infrared and near-infrared light.
The work centers on high-sensitivity detection of trace gases using laser absorption, photoacoustic spectroscopy, and light-induced thermoelastic spectroscopy. A significant portion of the research applies near-infrared and hyperspectral imaging to determine the content of nutrients, moisture, and contaminants in agricultural products and soil. These optical measurements are frequently paired with machine learning and deep learning algorithms to process spectral data, enabling rapid, non-destructive analysis of complex samples ranging from atmospheric pollutants to food safety markers.
The community’s output is distributed across fundamental elements, with the largest share of research appearing in carbon, hydrogen, and nitrogen studies. These elements account for the highest paper counts within the group, reflecting the focus on organic compounds and atmospheric gases.
The community comprises 11,479 papers, published most frequently in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Applied Spectroscopy, and Vibrational Spectroscopy.
Recent work continues to refine laser-based gas sensors for high-sensitivity detection of specific molecules like hydrogen fluoride and methane, alongside the application of machine learning to improve the accuracy of nutrient prediction in soil and food samples.