Comparison of predicting blueberry firmness and elastic modulus with hyperspectral reflectance, transmittance and interactance imaging modes

In this study, a imaging system with hyperspectral reflectance, transmittance and interactance was constructed for estimate the firmness and elastic modulus of blueberry. The comparisons of these three imaging modes were carried out. This hyperspectral system could also be applied for scattering modewhile this mode was not suitable for small fruit such as blueberry. The reflectance hypercubes were segmented with the algorithm based on the Otsu method, and the transmittance and interactance hypercubes were processed with the algorithms based on region growing approach. Subsequently, the extracted spectra were pretreated with the Standard Normal Variate (SNV) and Savitzky-Golay of the first derivative (Der), and least squares-support vector machine was applied for the establishment of the corresponding prediction models. The obtained results demonstrated that -reflectance-SNV model could predict blueberry firmness with correlation coefficient of prediction sample set (R_p) of 0.80 and the ratio of percent deviation (RPD) of 1.76 among the models using full spectra. The elastic modulus of blueberry was better estimated by the full transmittance spectra subjected to SNV pretreatment with R_p (RPD) of 0.78 (1.74) than the other models. Furthermore, Random Frog selection approach could to some extent reduce the uninformative wavelengths while increasing the prediction accuracy of the established models. Random Frog-Interactance-Der model achieved R_p (RPD) of 0.80 (1.83) for blueberry firmness, but the number of wavelength was 140. In the case of blueberry elastic modulus, random frog-transmittance-SNV showed the relatively superior performance compared to the other models, with R_p (RPD) of 0.82 (1.83) and fewer wavelength number of 20.