Analyzing Near-Infrared Hyperspectral Imaging for Protein Content
  Regression and Grain Variety Classification Using Bulk References and Varying
  Grain-to-Background Ratios
Ole-Christian Galbo Engstrøm Erik Schou Dreier Birthe Møller Jespersen Kim Steenstrup Pedersen
Based on previous work, we assess the use of NIR-HSI images for calibrating
models on two datasets, focusing on protein content regression and grain
variety classification. Limited reference data for protein content is expanded
by subsampling and associating it with the bulk sample. However, this method
introduces significant biases due to skewed leptokurtic prediction
distributions, affecting both PLS-R and deep CNN models. We propose adjustments
to mitigate these biases, improving mean protein reference predictions.
Additionally, we investigate the impact of grain-to-background ratios on both
tasks. Higher ratios yield more accurate predictions, but including lower-ratio
images in calibration enhances model robustness for such scenarios.