基于多尺度卷积网络的药材质量鉴别OA

Objective To address issues in terahertz technology for qualitative and quantitative analysis of medicinal herbs,such as difficulties in standardizing experimental data,indistinct characteristic absorption peaks of medicinal herbs,and the inability of pattern recognition methods to effectively extract spectral features,a quality identification method for medicinal herbs based on a multi-scale convolutional network is proposed.Method Using the terahertz time-domain spectral data of Angelica dahurica,the frequency-domain absorption coefficients are first obtained through fast Fourier transform(FFT)and converted into two-dimensional images.The InceptionTime network is then employed to extract multi-scale features from the time-domain waveforms,while the ConvNeXt network extracts texture features from the frequency-domain images.The outputs from the two branches are fixed-length feature vectors,which are concatenated at the channel level and fed into a fully connected fusion network.Finally,the Softmax function is applied to generate the probability distribution of quality categories,forming the QID-MCN model for comprehensive analysis of medicinal herbs.Results The QID-MCN model achieved an identification accuracy of 98.6%for Angelica dahurica,significantly improving the accuracy and speed of medicinal herb quality identification.Conclusion The QID-MCN model effectively solves the challenge of extracting terahertz spectral features by parallel extraction and integration of time-domain and frequency-domain features,providing an efficient and precise method for medicinal herb quality analysis.