Eva Bei, Dimitris Argiropoulos, Jo Van Herwegen, Oriana Incognito, Laura Menichetti, Christian Tarchi, Chiara Pecini
Neuromyths: Misconceptions about neurodevelopment by Italian teachers
Trends in neuroscience and education
arsenic contamination complicates mineral processing also poses environmental health risks address challenges research investigates feasibility utilizing hyperspectral imaging combined machine learning techniques identification arseniccontaining minerals copper ore samples focus practical application sorting processing operations experimentation various copper sulfide ores neighborhood component analysis nca employed select essential wavelength bands hyperspectral data subsequently used inputs machine learning algorithms identify arsenic concentrations results demonstrate selecting subset informative bands using nca accurate mineral identification achieved significantly reduced size dataset enabling efficient processing analysis comparison wavelength selection methods highlights superiority nca optimizing classification accuracy specifically identification accuracy showed 919 utilizing 8 bands selected nca comparable hyperspectral data analysis 204 bands findings suggest potential costeffective implementation multispectral cameras mineral processing operations future research directions include refining machine learning algorithms exploring broader applications across diverse ore types integrating hyperspectral imaging emerging sensor technologies enhanced mineral processing capabilities