Work

Barlow Twins for semi-supervised learning in NIR spectroscopy

Year: 2026

Type: article

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.

Source: Chemometrics and Intelligent Laboratory Systems

DOI: 10.1016/j.chemolab.2026.105664

Authors: Mark Wohlers, Andrew McGlone, Eibe Frank, Geoffrey Holmes

Institutions: Bioeconomy Science Institute, University of Waikato

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,970