[Feature extraction of hyperspectral scattering image for apple mealiness based on singular value decomposition]

Apple mealiness is an important sensory parameter for classification of apple quality. Hyperspectral scattering technique was investigated for noninvasive detection of apple mealiness. A singular value decomposition (SVD) method was proposed to extract the feature/ or singular values of the hyperspectral scattering images between 600 and 1000 nm for 20 mm distance including 81 wavelengths. As characteristic parameters of apple mealiness, singular values were applied to develop the classification model coupled with partial least squares discriminant analysis (PLSDA) using the samples from different origin and different storage conditions. The classification accuracies for the two-class ("mealy" and "non-mealy") model were between 76.1% and 80.6% better than mean method (75.3%-76.5%). The results indicated that SVD method was potentially useful for the feature extraction of hyperspectral scattering images and the model developed with these features can detect the mealy and non-mealy apple, but the classification accuracies need to be improved.