Researchers at the Swiss Federal Institute of Technology in Zurich have developed a new QTOF method for detecting pesticides on produce.
Researchers at the Swiss Federal Institute of Technology in Zurich have developed a new method for detecting pesticides on produce. The new method uses plasma to collect molecules from the surface of fruits or vegetables and push them into a quadrupole time-of-flight (QTOF) mass spectrometer for analysis.
This new method uses an atmospheric pressure glow-discharge (APGD) source developed by members of the research team to generate the plasma. By eliminating the time-intensive step of extracting physical samples from the produce for analysis, this method makes the detection process considerably faster than traditional detection methods.
According to the scientists, however, the process is still in the early stages of development. The system can detect the presence of pesticides on the produce, but it cannot determine the amount of those chemicals present. Future applications for the method include detection of explosives or drugs on surfaces, inspection of meat for spoilage or harmful bacteria, and detection of doping and other banned substances in athletes.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.