Detection of Eggplant External Defects Using Hyperspectral Technology
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Abstract:
Hyperspectral technology (in the range of 900~1700 nm) was employed to distinguish sound, suberized and rotten eggplants. A total of 252 eggplant samples were collected, including 170 sound eggplants, 60 suberized eggplants and 22 rotten eggplants. The hyperspectral imaging system was used to acquire hyperspectral images of 252 eggplant samples in the three types of areas (sound, suberized and rotten fruit regions), and a reasonable region of interest (ROI) was extracted to obtain the spectral data. A variety of preprocessing methods were used for spectral pretreatment and a partial least squares (PLS) discriminant analysis model was established. The results show that the prediction was the best after the model was subjected to normalization pretreatment. Therefore, normalization was selected as the pretreatment method, based on the pre-treated spectral data, the characteristic wavelengths were extracted by successive projections algorithm (SPA), regression coefficient (RC) and competitive adaptive reweighted sampling (CRAS) methods, and partial least squares (PLS) and multiple linear regression (MLR) discriminant models were established for analysis. The results showed that the CRAS-MLR model discriminate most effectively the 3 types of samples, with the coefficient of determination for the calibration set (Rc2) as 0.944, the coefficient of determination for prediction set (Rp2) as 0.901, and the RMSEC and RMSEP as 0.199 and 0.213, respectively. The discriminant accuracy of the prediction set was 96.8%. In this study, hyperspectral technology can be used to distinguish effectively the sound, suberized and rotten eggplants, which provides a theoretical reference for the nondestructive detection of eggplant defects.
