Handling batch-to-batch variability in portable spectroscopy of fresh fruit with minimal parameter adjustment

Near-infrared (NIR) spectroscopy models for fresh fruit quality prediction often fail when used on a new batch or scenario having new variability which was absent in the primary calibration. To handle the new variability often model updating is required. In this study, to solve the challenge of updating NIR models related to fresh fruit quality properties, the use of a semi-supervised parameter-free calibration enhancement (PFCE) approach was proposed. Model updating with PFCE was shown in two ways: first where the model on the primary batch was updated individually for each new fruit batch, and second where the model was sequentially updated for the next batches. Furthermore, for the first time, a case of updating an instrument transferred model was also presented. The PFCE approach was shown in two real cases related to moisture and total soluble solids prediction in pear and kiwi fruit. In the case of pear, the model was later updated for 3 new measurement batches, while, for kiwi, a commercial model was updated to incorporate the variability of a new experiment carried out with a new instrument in the laboratory environment. For each modelling demonstration, the performance was benchmarked with the partial least-square (PLS) regression analysis on the primary batch. The results showed that the models updated with a semi-supervised approach kept a high predictive performance on new measurement batches, without any extra parameter optimization. An instrument transferred model was also updated to maintain its performance on different batches. Further, the sequential updating approach was found to be performing better than the update for individual batches, as the models were able to learn from multiple batches. Model updating with a semi-supervised approach can allow the NIR spectroscopy of fresh fruit to be scalable, where models can be shared between scientific or application community.