Forecasting Apple Sugar Content Based on Leaf Characteristic Spectra in Different Phenological Phases

作者：Zhang Yao; Zheng Li Hua^*; Li Min Zan; Xiao Chang Yi

来源：Chinese Journal of Analytical Chemistry, 2015, 43(6): 862-870.

• Apple sugar content
• Spectroscopy
• Phenological phases
• Apple leaf characteristic spectra
• Support vector machine

Apple sugar content is one of the important indicators in evaluating fruit quality. The apple leaf spectra characteristics were detected separately in six important phenological periods including flowering stage, shoot-growing stage, fruit setting stage, branch shooting stage, bud differentiation stage, and defoliation stage. 2-D correlation operation between the leaf spectra and apple sugar contents was done to explore the sensitive spectral bands reflecting sugar content. After calculating, sugar contribution value of different phenological phases was obtained, which was used to construct the weighted sensitive spectra to forecast fruit sugar content. Firstly, the visible and near infrared spectral reflectance of apple leaf samples from different phenological phases were measured using the spectrophotometric method. And the sugar content of the fruit sample growing near each leaf samples was collected and measured using laboratory method. By introducing 2-D correlation analysis technology, the fruit sugar content sensitive spectra (530-570 nm and 700-720 nm) were achieved. Then the principal component analysis was conducted among each sensitive spectrum and the principal components were obtained in different phenological phases. The principal components were used to perform sugar content regression analysis, which quantified the contribution proportion to fruit sugar accumulation in each certain phase. And the other important information such as intensity change of photosynthesis in different physiological phases was obtained as well. By using of the sugar contribution weight in each different phenological phase, the original sensitive spectra were transformed and the weighted leaf characteristic spectra were achieved. Based on the characteristic spectra, two models were established, multiple linear regression model based on the principal component analysis and the model based on SVM (Support Vector Machine) with parametric optimal solution. The SVM regression model showed good accuracy. The calibration Rc(2) reached 0.9202 with the RMSEC ( Root Mean Square Error of Calibration) of 0.3892 Brix, and the validation Rv(2) reached to 0.9443 with the RMSEP ( Root Mean Square Error of Prediction) of 0.5246 Brix. This research indicated that it was possible to forecast apple sugar content by using apple leaf spectra information in different phenological phases and meanwhile the fruit sugar accumulation process was revealed.

• 出版日期2015-6
• 单位中国农业大学