Classification and characterization of blueberry mechanical damage with time evolution using reflectance, transmittance and interactance imaging spectroscopy

The aim of this work was to evaluate the performance of hyperspectral data coupled with chemometrics methods in characterizing and detecting the non-visible mechanical damage of blueberries with time evolution. Reflectance and transmittance as well as interactance hypercubes were automatically segmented by the region growing based algorithms. The maximum-normalized spectra were pretreated by the Standard Normal Variate algorithm, and subsequently the Competitive Adaptive Reweighted Sampling algorithm was applied to extract the damage-specific wavelengths. Based on confusion matrices and area under Receiver Operating Characteristics curves, transmittance showed relatively superior performance to reflectance and interactance. Application of new sample set subjected to impact tests with time evolution, results demonstrated that it was especially difficult to distinguish fresh damage in blueberry. At 2 days after impacted, several transmittance-based classifiers obtained satisfactory accuracies for classifying damaged (sound) blueberries: logistic regression 79.1% (85.7%), multilayer perceptron-back propagation 74.4% (92.1%) and logistic function tree 72.1% (95.2%). Furthermore, the physical property preliminarily proved to be more pronounced than the absorbed impact energy for damage incidence and severity of blueberry via the use of multiple comparison.