Using Calibration Transfer Strategy to Update Hyperspectral Model for Quantitating Soluble Solid Content of Blueberry Across Different Batches

Model updating is a challenging task with regard to maintaining the performance of non-destructive detection models while using hyperspectral imaging techniques for detecting the internal quality of fresh fruits like blueberries. Different sample batches and differences in hyperspectral image acquisition environments may lead to a significant decline in the performance of hyperspectral detection models. This study investigated the transferability of a hyperspectral model for the quantitating soluble solid content of blueberries across different batches for two harvest years. Hyperspectral images and SSC values of blueberries were collected from two batches, including 364 samples from 2024 and 175 samples from 2025. The differences between SSC measurements and spectral data across these two batches were analyzed. Based on the sample dataset of the year 2024, a high-performance quantitative model for detecting SSC values was established by combining it with partial least squares regression (PLSR) and competitive adaptive reweighted sampling (CARS). This high-performance model could achieve a high determination coefficient (RP2) of 0.8965 and a low root mean square error of prediction (RMSEP) of 0.3707 °Brix. Using the sample dataset for the year 2025, the hyperspectral model was updated by the semi-supervised parameter-free calibration enhancement (SS-PFCE) algorithm. The updated model performed better than those established using individual datasets from 2024 and 2025, and obtained an RP2 of 0.8347 and an RMSEP of 0.4930 °Brix. This indicates that the calibration transfer strategy is superior in improving hyperspectral model performance. This study demonstrated that the SS-PFCE algorithm, as a calibration transfer strategy, could effectively improve the transferability of the established model for detecting the SSC of blueberries across different sample batches.