Shelf-Life Prediction of ‘Gros Michel’ Bananas with Different Browning Levels Using Hyperspectral Reflectance Imaging

Abstract

A hyperspectral imaging system was developed in the spectral region between 400 and 1,100 nm to investigate its potential for predicting the shelf life of bananas with different browning levels. Principal component analysis (PCA) was conducted for reducing data dimensionality and selecting optimal wavelengths. Five optimal wavelengths (454, 486, 559, 686, and 728 nm) were found. Among all principal component (PC) images, PC-4 was selected to segregate browning area from normal surface using a simple threshold algorithm for extracting image features of browning area. The average spectra were obtained by calculating the mean of the spectra of all browning areas in the hyperspectral images of bananas. Then, image features and average spectra in five optimal wavelengths were used to develop classification models for predicting the shelf life of banana samples by determining their browning levels using back propagation (BP), radial basis function (RBF), and self-organizing feature maps (SOM) networks. Results indicated that the classification models using both image features and average spectra were obviously superior to those models using image features or average spectra alone. Among all classifiers, BP classifier had the best performance with the best classification rates of 95.6 % for training set and 90.5 % for testing set, respectively. The results demonstrated that hyperspectral imaging has great potential in predicting banana shelf life based on combination of image features and average spectra.