In order to obtain a robust near infrared spectral model, a method based on variate selection and model updating was adopted. 240 Red Fuji apples were used to obtain near infrared diffuse transmission spectra and soluble solids content data, and a partial least squares regression model was developed to predict apple soluble solids content. The modelling variates were selected by using backward interval partial least squares and competitive adaptive reweighting algorithms. The model was updated by adding some samples from the new batch to the old batch and recalibrating. The results indicate that the model performance can be improved by variable selection, with the prediction coefficient of determination increasing to 0.7915, the root mean square error of prediction decreasing to 0.5810 and the prediction bias decreasing to 0.2627. Combining the model update strategy, the root mean square error of prediction and the prediction bias were further reduced. Model updating using only 20 samples has already led to a significant improvement in model performance, with the prediction coefficient of determination improving to 0.8506, the root mean square error of prediction decreasing to 0.4358 and the prediction bias decreasing to 0.1045, the result that is useful for robust near infrared spectroscopy modelling of a wide range of fruits.