Beyond preprocessing and directional bias: Transformer models for robust and efficient cross-instrument NIR calibration in wheat flour analysis

Key Points

• 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.