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Development Trend in Non-Destructive Techniques for Cultural Heritage: From Material Characterization to AI-Driven Diagnosis
Development Trend in Non-Destructive Techniques for Cultural Heritage: From Material Characterization to AI-Driven Diagnosis
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Development Trend in Non-Destructive Techniques for Cultural Heritage: From Material Characterization to AI-Driven Diagnosis
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Development Trend in Non-Destructive Techniques for Cultural Heritage: From Material Characterization to AI-Driven Diagnosis
Development Trend in Non-Destructive Techniques for Cultural Heritage: From Material Characterization to AI-Driven Diagnosis

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Development Trend in Non-Destructive Techniques for Cultural Heritage: From Material Characterization to AI-Driven Diagnosis
Development Trend in Non-Destructive Techniques for Cultural Heritage: From Material Characterization to AI-Driven Diagnosis
Journal Article

Development Trend in Non-Destructive Techniques for Cultural Heritage: From Material Characterization to AI-Driven Diagnosis

2025
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Overview
Cultural heritage (CH) relics are irreplaceable records of human civilization, encompassing diverse historical, technological, and artistic achievements. Extracting their structural and compositional information without affecting their physical integrity is a critical challenge. This review summarizes recent advances in non-destructive techniques (NDTs) for CH analysis and emphasizes the balance between the depth of analysis and conservation ethics. Techniques are broadly categorized into spectrum-based, X-ray-based, and digital-based methods. Spectroscopic techniques such as Fourier transform infrared (FTIR), Raman, and nuclear magnetic resonance (NMR) spectroscopy provide molecular-level insights into organic and inorganic components, often requiring minimal or no sampling. X-ray-based techniques, including conventional and spatially resolved XRD/XRF and total reflection XRF (TRXRF), provide powerful means for crystal and elemental analysis, including in situ pigment identification and trace material analysis. Digital-based methods include high-resolution imaging, three-dimensional modeling, data fusion, and AI-driven diagnosis to achieve the non-invasive visualization, monitoring, and virtual restoration of CH assets. This review highlights a methodology shift from traditional molecular-level detection to data-centric and AI-assisted diagnosis, reflecting the paradigm shift in heritage science.