Predicting Soluble Solids Concentration of ‘Geneva 3’ Kiwiberries Using Near Infrared Spectroscopy

Abstract

Near infrared (NIR) spectroscopy can be applied to nondestructively assess soluble solids concentration (SSC) of ripening, physiologically mature ‘Geneva 3’ kiwiberries (Actinidia arguta). Spectrographic signatures were captured using a handheld NIR produce quality meter to build predictive models of internal fruit quality for ‘Geneva 3’ kiwiberries that had been held under cold storage (CS) conditions (0 to 1 °C, >90% relative humidity) as well as those not subjected to CS. The CS model, constructed using scans of 133 berries following 4 to 6 weeks in CS, predicts SSC using NIR wavelengths in the range of 729 to 975 nm. A total of 507 berries fresh from the vine were used to construct a predictive model for SSC of non-CS fruit using the same wavelength range. In each case, model predictive performance was investigated using split-half cross-validation, resulting in mean absolute error (MAE) values of 1.2% and 0.8% SSC for the CS and non-CS model, respectively. Each full model was then used to predict SSC of kiwiberries subjected to the alternative CS condition. The non-CS model maintained a low MAE (1.6% SSC) when applied to CS fruit, but the MAE of the CS model applied to non-CS fruit rose considerably (4.5% SSC). The performance of a combined model was tested against both CS and non-CS models, and a benefit to using tailored, CS-specific models was found, particularly in light of cross-seasonal results. As it has proven in many crops, NIR spectroscopy appears to be a promising tool for nondestructively assessing SSC in ‘Geneva 3’ kiwiberry fruit, with accuracy being enhanced by training models specific to postharvest regimes and/or defined ranges of SSC.