Abstract
This study compares three transfer learning strategies for adapting convolutional neural network (CNN) models in near-infrared spectroscopy to new instruments. Strategy 1 freezes pre-trained weights except for the output layer; Strategy 2 fine-tunes all weights with new data; Strategy 3 adds new fully connected layers to the pre-trained model. Using tablet spectra to predict active pharmaceutical ingredient content, results show that fine-tuning all network weights (Strategy 2) yields the best performance. The study also finds that moderate training sample sizes balance model accuracy and resource expenditure. Transfer learning is effective for near-infrared model transfer.
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