Total soluble solids concentration (TSS) and flesh firmness (FF) are two important quality attributes indicating the eating quality and postharvest storability of kiwifruit. Prediction of TSS and FF using non-destructive techniques would allow strategic marketing of fruit. This paper investigates the ability of visible-near-infrared (Vis-NIR) spectroscopy utilised as a sole input at harvest, to quantitatively predict both TSS and FF after cool storage using a blackbox model. Four at-harvest Vis-NIR spectral and post storage fruit quality data sets were collected during 2012-2013, in order to develop regression models using partial least squares (PLS) and support vector machines (SVM). The SVM models performed better than PLS. Predictive accuracy was fair to good for TSS (R-2 = 0.58-0.83; RMSE = 0.66-1.02 degrees Brix) and was poor to moderate for FF (R-2 = 030-0.60; RMSE = 2.65-4.32 N). The ratio of prediction deviation, SDR values (1.5-2.3 for TSS; 1.4-1.7 for FF) suggest the developed regression models are not as yet useful for online sorting purposes. (C) 2017 Elsevier Ltd. All rights reserved.