Hyperspectral Detection of Soluble Solids Content on Apple Based on GA-SVR

doi:10.3969/j.issn.1673-1689.2019.09.018

Home > Archive>Volume 38, Issue 9, 2019 >125-132. DOI:10.3969/j.issn.1673-1689.2019.09.018

Hyperspectral Detection of Soluble Solids Content on Apple Based on GA-SVR
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                        10.3969/j.issn.1673-1689.2019.09.018
                    
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In order to established a high precision method for the determination of soluble solids content in apple. In this study，the average spectral reflectance in the region of interest（ROI） of 150 pixels in the hyperspectral image is extracted. To reduce the noise in spectral，the extracted reflectance spectra were preprocessed by Savtitzky-Golay smoothing（S-G），Standard Normal Variable Transform（SNV），and Wavelet Transform（WT） methods. The preprocessed spectra were then used to select sensitive wavelengths by Successive Projections Algorithm（SPA） method. Back-propagation neural network（BPNN） and genetic support vector machine（GA-SVR） were applied to build discriminant models with the selected wavelength variables. In the process of establishing GA-SVR model，GA method was used to select the optimal parameters of SVR automatically. The results indicated that the GA-SVR model preprocessed by S-G method was the best model. The prediction correlation coefficient of the model was 0.850 5，and the root mean square error of prediction was 0.303 1. The results show that the GA-SVR model based on the data extracted from this interest region was feasible to improve the performance of the model.

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ZHA Qiming, GU Baoxing, JI Changying. Hyperspectral Detection of Soluble Solids Content on Apple Based on GA-SVR[J]. Journal of Food Science and Biotechnology,2019,38(9):125-132.

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Online: March 31,2020
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