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.