Background: The manual identification of Fructus Crataegi processed products is inefficient and unreliable. Therefore, efficient identification of the Fructus Crataegis’ processed products is important.

Objective: In order to efficiently identify Fructus Crataegis processed products with different odor characteristics, a new method based on an electronic nose and convolutional neural network is proposed.

Methods: First, the original smell of Fructus Crataegis processed products is obtained by using the electronic nose and then preprocessed. Next, feature extraction is carried out on the preprocessed data through convolution pooling layer L_CP1, convolution pooling layer L_CP2 and a full connection layer L_FC. Thus, the feature vector of the processed products can be obtained. Then, the recognition model for Fructus Grataegis processed products is constructed, and the model is trained to obtain the optimized parameters: filters F1 and F2, bias vectors B1, B2, B3, and B4, matrices M1 and M2. Finally, the features of the target processed products are extracted through the trained parameters to achieve accurate prediction.

Results: The experimental results show that the proposed method has higher accuracy for the identification of Fructus Crataegis processed products, and is competitive with other machine learning based methods.

Conclusion: The method proposed in this paper is effective for the identification of Fructus Crataegi processed products.

背景：人工鉴定山楂加工产品效率低下且不可靠。因此，有效鉴别山楂的加工产品很重要。

目的：为了高效识别不同气味特征的山楂加工产品，提出一种基于电子鼻和卷积神经网络的新方法。

方法：首先利用电子鼻获得山楂加工品的原味，然后进行预处理。接下来，通过卷积池化层L _CP1、卷积池化层L _CP2和全连接层L _FC对预处理后的数据进行特征提取。这样就可以得到加工产品的特征向量。然后，构建了枸杞加工品的识别模型，对模型进行训练，得到优化后的参数：滤波器F1和F2，偏置向量B1、B2、B3和B4，矩阵M1和M2。最后通过训练后的参数提取目标加工产品的特征，实现准确预测。

结果：实验结果表明，该方法对山楂加工品的识别具有更高的准确率，与其他基于机器学习的方法具有竞争优势。

结论：本文提出的方法对山楂加工品的鉴别是有效的。