基于傅里叶变换中红外光谱的不同维度光谱图像结合残差神经网络鉴别黄精属物种

Identification of Species in the Polygonatum Genus Based on Different Dimensional Spectral Images Combined with Residual Neural Networks in Fourier Transform Infrared Spectroscopy

将不同维度光谱图像的概念首次应用于物种鉴别,建立了快速准确的黄精属鉴别方法。采集6种黄精属共计563批样品,基于傅里叶变换中红外光谱(FT-MIR)的一阶导数(1st)、二阶导数(2nd)、乘法散射校正(MSC)、标准正态变量变换(SNV)和Savitzky-Golay(SG)5种预处理方法,构建了决策树(DT)、随机森林(RF)和支持向量机(SVM)3种机器学习算法。同时构建了深度学习残差神经网络(ResNet)模型,绘制了不同维度的光谱图像,包括一维MIR,同步、异步和综合二维相关光谱、三维相关光谱、三维相关光谱投影图像的10个数据集,并将其与ResNet模型相结合进行分类。结果表明,不同预处理方法对模型结果的影响不同,MSC预处理方法可显著提高DT、RF和SVM 3种算法的准确率。基于同步二维相关光谱数据集的ResNet算法建模效果最好,准确率达到100%,损失值较小,不需要复杂的预处理,时间成本低,可以准确鉴别黄精属物种,为食品、中草药等其他领域的鉴别提供了参考。

This study proposed the concept of spectral images with different dimensions,which was first applied to the identification of species in the Polygonatum genus,and established a fast and accurate identification method.A total of 563 batches of samples from 6 species of Polygonatum were collected.Five preprocessing methods were used based on Fourier transform mid infrared spectroscopy(FT-MIR),including first derivative(1st),second derivative(2nd),multiplicative scattering correction(MSC),standard normal variable(SNV)and Savitzky-Golay(SG).Decision trees(DT),random forests(RF)and support vector machines(SVM)were constructed.In addition,to avoid complex preprocessing,a deep learning residual convolutional neural network(ResNet)model was constructed to draw spectral images of different dimensions,including 10 datasets of one-dimensional MIR,synchronous,asynchronous,and integrative two-dimensional correlated spectra,three-dimensional correlated spectra,and three-dimensional correlated spectral projection images,and they were combined with the ResNet model for classification.The results showed that different preprocessing methods have different impacts on the model results,and the MSC preprocessing method significantly improved the accuracy of the DT,RF,and SVM algorithms.The ResNet algorithm based on synchronous two-dimensional correlated spectral dataset had the best modeling effect,with an accuracy of 100%,small loss value,no need for complex preprocessing,low time cost,and could accurately identify species of Polygonatum genus,providing reference for identification in other fields such as food and traditional Chinese medicine.