Model robustness in estimation of blueberry SSC using NIRS

In near-infrared (NIR) spectroscopy analysis of blueberry often the differences between biological variability factors (cultivar, season, etc.) lead to variations in light propagation properties and cell structure. Therefore, non-invasive detection models for blueberry developed based on NIR spectroscopy are often limited and unstable. To accommodate diverse prediction scenarios and improve the accuracy of NIR models in blueberry SSC estimation, a combined model calibration strategy that integrates Global-modeling method and calibration transfer method is proposed in this study. The Global-modeling method and calibration transfer methods (DOP and SBC) were applied for datasets containing only seasonal or cultivar differences and for those containing both seasonal and cultivar differences, respectively. Compared with the prediction model without correction, both Global-modeling and calibration transfer strategies improved model performance in the face of seasonal/cultivar variation challenges, with higher Rp and lower RMSEP values. After the application of Global-modeling, DOP and SBC methods, for the correction of seasonal variables (Bluecrop-2014), the RMSEP values of the model were reduced by 55.9%, 44.7% and 44.5%; for the correction of cultivar variables (M2-2015), the RMSEP values of the model were reduced by 45.8%, 22.8% and 37.9%; for the correction of cultivar variables (Duke-2015), the RMSEP values of the model were reduced by 9.3%, 5.9% and 2.8%. The experimental results indicated that combining Global-modeling and calibration transfer methods can weaken the influence of external conditions on blueberry NIR spectra to a certain extent, enhance the robustness of the model to biological variation, and improve the detection accuracy of blueberry SSC.