Estimation of Apple Mealiness by means of Laser Scattering Measurement

Abstract

Abstract Mealiness is a phenomenon in which intercellular adhesions in apples loosen during storage, causing soft and floury texture at the time of eating, and leading to lower consumer preference. Although apples can be stored and commercially sold throughout the year, the occurrence of mealiness is not monitored during storage. Therefore, the objective of this research was to non-destructively estimate the mealiness of apple fruit by means of laser scattering measurement. This method is based on laser light backscattering imaging but can quantify a wider range of backscattered light than the conventional method. Lasers with wavelengths of 633 nm and 850 nm were used as a light source, and after acquiring backscattered images, profiles and images were obtained. Profile features such as curve fitting coefficients and profile gradients, and image features such as statistical image features and texture features were extracted from the profiles and images, respectively. PLS, SVM, and ANN models were used for the estimation of mealiness. The results of the estimation based on these features showed that the ANN model combining both wavelengths had a higher performance (R = 0.634, RMSE = 7.621) than the models constructed from features calculated from the data obtained by a single wavelength. In order to improve the performance of model, we applied various ensemble learning. As a result, the ensemble model showed the highest performance (R = 0.682, RMSE = 7.281). These results suggest that laser scattering measurement is a promising method for estimating the apple fruit mealiness.