Nondestructive detection on firmness of peaches based on hyperspectral imaging and artificial neural networks

To explore a nondestructive method to measure peach internal quality, a hyperspectral imaging technology combined with Artificial Neural Networks (ANN) was applied to evaluate the firmness of intact peaches. The hyperspectral images of 140 peaches during 12 day storage were acquired from 900 nm to 1700 nm, and the average reflective spectrum of interest region of 40 pixel×40 pixel in each image was calculated and was used as the original spectra. The spectra were preprocessed by Savitzky-Golay smoothing and the standard normal variate. The sample set was partitioned based on joint x-y into calibration sets (105) and prediction sets (35). Then the successive projection algorithm, uninformative variable elimination method and competitive adaptive reweighted sampling method were used to select characteristic wavelengths by 12, 103 and 22 from 216 wavelengths, respectively. A support vector machine and an error back propagation (BP) network model were established based on full spectra and selected characteristic wavelengths for predicting the firmness of intact peaches. The result shows that BP model based on full spectra has the best prediction performance with a correlation coefficient and a root-mean-square error of 0.856 and 0.931, respectively. This study offers the base for identifying internal qualities of peaches in industry. ©, 2015, Chinese Academy of Sciences. All right reserved.