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Research on calibration transfer methods in coal quality spectral analysis across instruments and coal types.
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Background: In coal quality analysis, spectroscopic techniques such as near-infrared spectroscopy (NIRS) and X-ray fluorescence (XRF) offer rapid and non-destructive measurements, but differences between instruments and coal types may lead to the failure of established models. Given the complexity of coal, it is important to systematically evaluate the applicability of traditional calibration transfer methods, such as the Slope/Bias (S/B) method and Piecewise Direct Standardization (PDS). Meanwhile, machine learning algorithm such as Domain-Adversarial Neural Networks (DANN), have shown great potential in addressing data distribution differences between source and target domains, offering new possibilities for calibration transfer.
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