Barlow Twins for semi-supervised learning in NIR spectroscopy

Abstract

Near-infrared (NIR) spectroscopy is a widely used technology in the horticulture industry for non-destructive fruit grading. Partial Least Squares (PLS) regression is the dominant method for producing fruit quality predictions from measured spectra. Alternative deep learning methods have shown promise, but often require large amounts of labelled data to train. This study proposes a semi-supervised method based on Barlow Twins to include unlabelled data in the training process. We adopt the Barlow Twins method by using repeated measurements on the same fruit from different devices as different “views” to encode into the same latent space and combine the encoder network with a regression head for prediction. Our approach demonstrates improved performance over PLS with up to 17% lower RMSE, especially when the labelled data is limited. The Barlow loss function also improves calibration transfer results.