Si Lu, Li Xiaopeng, Zhu Yuanhuan, Sheng Yong, Ma Hui
Opt Express. 2020 Mar 30;28(7):10456-10466. doi: 10.1364/OE.389181.
The surface morphology of electrospun fibers largely determines their application scenarios. Conventional scanning electron microscopy is usually used to observe the microstructure of polymer electrospun fibers, which is time consuming and will cause damage to the samples. In this paper, we use backscattering Mueller polarimetry to classify the microstructural features of materials by statistical learning methods. Before feeding the Mueller matrix (MM) data into the classifier, we use a two-stage feature extraction method to find out representative polarization parameters. First, we filter out the irrelevant MM elements according to their characteristic powers measured by mutual information. Then we use Correlation Explanation (CorEx) method to group interdependent elements and extract parameters that represent their relationships in each group. The extracted parameters are evaluated by the random forest classifier in a wrapper forward feature selection way and the results show the effectiveness in classification performance, which also shows the possibility to detect nonporous electrospun fibers automatically in real time.
电纺纤维的表面形态在很大程度上决定了它们的应用场景。传统的扫描电子显微镜通常用于观察聚合物电纺纤维的微观结构，这既耗时又会对样品造成损伤。在本文中，我们使用背散射穆勒偏振测量法通过统计学习方法对材料的微观结构特征进行分类。在将穆勒矩阵（MM）数据输入分类器之前，我们使用两阶段特征提取方法来找出具有代表性的偏振参数。首先，我们根据通过互信息测量的特征功率滤除不相关的MM元素。然后我们使用相关性解释（CorEx）方法对相互依赖的元素进行分组，并提取代表每组中它们关系的参数。提取的参数通过随机森林分类器以包装器前向特征选择方式进行评估，结果表明在分类性能方面是有效的，这也表明了实时自动检测无孔电纺纤维的可能性。
