Combination of visible reflectance and acoustic response to improve non-destructive assessment of maturity and indirect prediction of internal quality of red-fleshed pomelo

Maturity and the internal quality of red-fleshed pomelo in terms of the sugar–acid ratio were studied by assessing whole fruit properties using acoustic response and local properties based on the visible reflectance of the fruit surface. Pomelo fruit samples were harvested at different maturity stages. Maturity classification and prediction models were built using discriminant analysis and partial least squares regression, respectively. Better classification was achieved by the model incorporating both surface visible reflectance and the resonant frequency compared with the model based on only surface visible reflectance. Similarly, the sugar–acid ratio was better predicted by the model using both acoustic response and surface visible reflectance. The accuracy of prediction was maintained when the data from two harvest seasons were combined to build the model for prediction.