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A bi-layer model for nondestructive prediction of soluble solids content in apple based on reflectance spectra and
Xi Tian1, Jiangbo Li1, Qingyan Wang1
1Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China.
Abstract:
Hyperspectral imaging technology was used to investigate the effect of various peel colors on soluble solids content (SSC) prediction model and build a SSC model insensitive to the color distribution of apple peel. The SSC and peel pigments were measured, effective wavelengths (EWs) of SSC and pigments were selected from the acquired hyperspectral images of the intact and peeled apple samples, respectively. The effect of pigments on the SSC prediction was studied and optimal SSC EWs were selected from the peel-flesh layers spectra after removing the chlorophyll and anthocyanin EWs. Then, the optimal bi-layer model for SSC prediction was built based on the finally selected optimal SSC EWs. Results showed that the correlation coefficient of prediction, root mean square error of prediction and selected bands of the bi-layer model were 0.9560, 0.2528 and 41, respectively, which will be more acceptable for future online SSC prediction of various colors of apple.