Monte carlo simulation on gamma spectrometry and application to radioactivity measurement in honey samples

Abstract

By integrating computational modeling (MCNP5, ANGLE, MEFFTRAN) and experimental techniques (gamma spectrometry, AAS, CHARM II),this thesis develops and applies advanced methodologies for analyzing radioactivity and contaminants in honey, a dense and complex matrix with significant nutritional value. The study specifically addresses critical challenges in gamma spectrometry, including photon attenuation, self-absorption, and true coincidence summing (TCS) effects. The optimized Monte Carlo simulations demonstrated excellent agreement with the experimental data (deviations <5% for low-energy gamma rays). The validated model was then applied to quantify naturally occurring radionuclides (226Ra, 232Th, and 40K) in honeysamples from various geographical origins. Additional analyses using AAS and CHARM II were employed to evaluate trace levels of heavy metals (K, Zn, Cu, Al, As) and antibiotic residues (tetracyclines and chloramphenicol), revealing contamination trends linked to industrial zones and environmental or apicultural practices. This interdisciplinary approach offers a robust and transferable framework for contaminant analysis in dense matrices, with important implications for food safety regulations, environmental monitoring, and public health protection