基于卷积神经网络与投票机制的蒲黄炮制品近红外判别方法

蒲黄炭是由香蒲花粉炮制而成，具有止血、化瘀、通淋等多种功效，被广泛应用于临床抗血栓，创面和出血。然而蒲黄炭在炒炭过程中，常常会出现炭化过轻或者炭化过重的现象，从而出现不同炭化程度的蒲黄炭药品，主要为轻度炭化、标准炭化与重度炭化三种不同的蒲黄炭药品。由于炭化程度不同，蒲黄炭的凝血效果优劣不等，其中标准炭化的蒲黄炭药品药效最优。目前，鉴别蒲黄炭药品的方法多为人工凭借肉眼与经验进行判别。基于人工的蒲黄炭药品判别方法判别效率低，受主观因素影响大，判别结果不稳定，难以区分出标准炭化的蒲黄炭。为有效地对不同炭化程度的蒲黄炭进行识别，提出一种基于卷积神经网络与投票机制的蒲黄炮制品近红外判别方法。该方法创新性地结合深度学习与机器学习算法，有效利用卷积神经网络强大表征提取能力的同时通过投票决策提升算法模型的泛化能力与鲁棒性。首先通过近红外光谱技术获取蒲黄炭的近红外光谱，并通过卷积神经网络分别提取样本经过四种预处理方法所得到光谱图的高阶特征，并计算预测结果。按照样本准确率与损失值为四种预处理方法分配相应权重得到蒲黄炮制品预测模型。该模型将所得到的四种预测结果结合权重共同投票出样本的最终结果，从而鉴别出蒲黄炭的炭化程度。实验结果表明所提方法可以有效判别蒲黄炮制品的炭化程度。当训练集所占样本比例为80%时，预测准确率达到95.4%。所提方法与传统卷积神经网络方法、线性判别分析方法以及标准正太变量变换-线性判别分析方法相比预测准确率分别提高8.6%，4.3%和2.6%。同时，所提方法具有一定的稳定性，当训练集所占样本比例大于70%时，测试准确率高于90%；当训练集比例仅占10%时，预测准确性仍然能够达到约80%。

Identification Method of Pollen Typhae Processed Products Based on Convolutional Neural Network and Voting Mechanism

Carbonized Typhae Pollen (CTP) is processed by Pollen Typhae. It has various effects such as hemostasis, removing blood stasis and treating stranguria. It has been widely used in clinical anti-thrombosis, wounds and bleeding. However, in the process of CTP, the carbonization is often too light or too heavy, resulting in different degrees of CTP, mainly for light carbonization, standard carbonization and heavy carbonization CTP. Due to the different degrees of carbonization, the coagulation effect of CTP is different. The standard CTP has the best effect. At present, the identification of CTP mainly relies on eyes and experience. The manual method is challenging to distinguish the standard CTP because it is inefficient, volitional, and unstable. Therefore, to effectively identify CTP with different degrees of carbonization, a near-infrared identification method based on Convolutional Neural Network (CNN) and the voting mechanism is proposed. This method innovatively combines deep learning and machine learning algorithms, effectively utilizes the powerful representation extraction ability of CNN, and applies voting decisions to improve the generalization ability and robustness of the prediction model. The near-infrared spectrum of CTP is firstly obtained. Then the high-order features of the spectrum processed by four different pre-processing methods are extracted by CNN. Next, the prediction results are calculated. The weights of four pre-processing methods are allocated according to the accuracy and loss to get the prediction model. Finally, the model combines the four prediction results with the weights to identify the CTP with different degrees of carbonization. The experimental results show that the proposed method can effectively distinguish the CTP with different degrees of carbonization. When the training set occupies 80%, the test accuracy is up to 95.4%. Compared with CNN, Linear Discriminant Analysis (LDA) and Standard Normal Variable (SNV)-LDA, the proposed method improves the prediction accuracy by 8.6%, 4.3% and 2.6%, respectively. At the same time, the proposed method is robust. When the proportion of the training set occupies more than 70%, the test accuracy is higher than 90%. When the proportion of the training set only occupies 10%, the prediction accuracy can still reach about 80%.