Beyond preprocessing and directional bias: Transformer models for robust and efficient cross-instrument NIR calibration in wheat flour analysis
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Robust calibration achieved using transformer models, enhancing accuracy in near-infrared (NIR) analysis.
Key metric shows significant improvement in reliability across various instruments.
Analysis centered on cross-instrument calibration strategies for wheat flour using advanced machine learning.
Highlighting the need for efficient calibration techniques, enabling better consistency in analytical results.
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Beyond preprocessing and directional bias: Transformer models for robust and efficient cross-instrument NIR calibration in wheat flour analysis | Synapse