Ouyang Hongkun, Tang Lingling, Ma Jinglong, Pang Tao
College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya'an 625000, China.
Plants (Basel). 2024 Apr 22;13(8):1163. doi: 10.3390/plants13081163.
Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar content in crisp pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and convolution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed with convolution smoothing (SG) was the most accurate, with a correlation coefficient 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algorithm (SPA) to select key representative wavelengths from the spectral data. Finally, we used the retrieved characteristic wavelength data to create a support vector machine model (GASVR) that was genetically tuned. The correlation coefficient of the SG-GASVR model in the prediction set was higher by 0.0321 and the root mean square prediction error (RMSEP) was lower by 0.0267 compared with those of the SG-SVR model. The SG-CARS-GASVR model had the highest correlation coefficient, at 0.8992. In conclusion, the developed SG-CARS-GASVR model provides a reliable method for detecting the sugar content in crisp pear using hyperspectral technology, thereby increasing the accuracy and efficiency of the quality assessment of crisp pear.
糖含量是评估酥梨品质和分类的重要指标，用于水果品质鉴定和市场销售预测。在本研究中，我们将支持向量机（SVM）算法与遗传算法优化相结合，以可靠地估计酥梨中的糖含量。我们评估了酥梨的光谱数据和实际糖含量，然后对光谱数据应用了三种预处理方法：标准正态变量变换（SNV）、多元散射校正（MSC）和卷积平滑（SG）。使用这些处理方法建立了支持向量回归（SVR）模型。根据研究结果，经卷积平滑（SG）预处理的SVM模型最为准确，其相关系数比原始光谱数据高0.0742。基于这一发现，我们使用竞争性自适应重加权（CARS）和连续投影算法（SPA）从光谱数据中选择关键代表性波长。最后，我们使用检索到的特征波长数据创建了一个经过遗传调整的支持向量机模型（GASVR）。与SG-SVR模型相比，预测集中SG-GASVR模型的相关系数高0.0321，均方根预测误差（RMSEP）低0.0267。SG-CARS-GASVR模型的相关系数最高，为0.8992。总之，所开发的SG-CARS-GASVR模型为利用高光谱技术检测酥梨中的糖含量提供了一种可靠的方法，从而提高了酥梨品质评估的准确性和效率。
