Advancements in machine learning, deep learning, and data fusion techniques for XRF spectrometry in heavy metal detection: a critical review | Synapse
March 12, 2026
Advancements in machine learning, deep learning, and data fusion techniques for XRF spectrometry in heavy metal detection: a critical review
Key Points
The aim is to evaluate the use of machine learning and data fusion in enhancing XRF spectroscopy for heavy metal detection.
Critical analysis of existing literature on XRF spectroscopy techniques.
Evaluation of machine learning and deep learning algorithms applied to XRF data.
Assessment of data fusion methods in improving detection accuracy.
Machine learning and deep learning techniques show significant enhancement in detection accuracy.
Data fusion techniques enable comprehensive elemental analysis.
Advancements reduce costs and increase the speed of heavy metal detection.
Abstract
X-ray fluorescence (XRF) spectroscopy is a widely used, non-destructive technique for rapid and cost-effective elemental analysis of soils, ores, and alloys.