Lan Haipeng, Wang Zhentao, Niu Hao, Zhang Hong, Zhang Yongcheng, Tang Yurong, Liu Yang
College of Mechanical Electrification Engineering Tarim University Alaer China.
Food Sci Nutr. 2020 Aug 12;8(9):5172-5181. doi: 10.1002/fsn3.1822. eCollection 2020 Sep.
The detection of soluble solid content in Korla fragrant pear is a destructive and time-consuming endeavor. In effort to remedy this, a nondestructive testing method based on electrical properties and artificial neural network was established in this study. Specifically, variations of electrical properties (e.g., equivalent parallel capacitance, quality factor, loss factor, equivalent parallel resistance, complex impedance, and equivalent parallel inductance) of Korla fragrant pears with accumulated temperature were tested using a workbench developed by ourselves. After that the characteristic variables of electrical properties were constructed by principal component analysis (PCA). In addition, three models were constructed to predict SSC in Korla fragrant pears based on the characteristic variables: general regression neural network (GRNN), back-propagation neural network (BPNN), and adaptive network fuzzy inference system (ANFIS). The results indicated that the GRNN model has the best prediction effects of SSC ( = 0.9743, RMSE = 0.2584), superior to that of the BPNN and ANFIS models. Results facilitate a successful, alternative application for rapid assessment of SSC of the maturation stage Korla fragrant pear.
库尔勒香梨可溶性固形物含量的检测是一项具有破坏性且耗时的工作。为了弥补这一不足，本研究建立了一种基于电学特性和人工神经网络的无损检测方法。具体来说，使用我们自行开发的工作台测试了库尔勒香梨电学特性（如等效并联电容、品质因数、损耗因数、等效并联电阻、复阻抗和等效并联电感）随积温的变化。之后，通过主成分分析（PCA）构建了电学特性的特征变量。此外，基于这些特征变量构建了三个模型来预测库尔勒香梨的可溶性固形物含量：广义回归神经网络（GRNN）、反向传播神经网络（BPNN）和自适应网络模糊推理系统（ANFIS）。结果表明，GRNN模型对可溶性固形物含量具有最佳的预测效果（ = 0.9743，RMSE = 0.2584），优于BPNN和ANFIS模型。研究结果有助于成功实现对成熟阶段库尔勒香梨可溶性固形物含量进行快速评估的替代应用。
