Mid-infrared spectroscopy has been increasingly used as a nondestructive analytical technique in Chinese herbal medicine identification in recent years. In this study, a new chemometric model named as PLS–NN model was proposed based on the mid-infrared spectral data of Cornus officinalis samples from 11 origins. It was realized by combining the partial least squares and neural networks for the identification of the origin of Chinese herbal medicines. First, we extracted features from the spectral data in 3448 bands using the partial least squares method, and extracted 122 components that contained more than 95% of the information. Then, we trained the PLS–NN model by neural network using the extracted components as inputs and the corresponding origin classes as outputs. Finally, based on an external test set, we evaluated the generalization ability of the PLS–NN model using metrics such as accuracy, F₁-Score and Kappa coefficient. The results show that the PLS–NN model performs well in all three metrics when compared to models such as Decision trees, Support vector machine, Partial least squares Discriminant analysis, and Naive bayes. The model not only realizes the dimensionality reduction of full-spectrum data and improves the training efficiency of the model, but also has higher accuracy compared with the full-spectrum data model. The PLS–NN model was applied to identify the origin of Cornus officinalis with an accuracy of 91.9%.

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基于中红外光谱的PLS-NN模型在山茱萸产地鉴定中的应用

近年来，中红外光谱作为无损分析技术在中药材鉴定中得到越来越多的应用。本研究基于11个产地山茱萸样品的中红外光谱数据，提出了一种新的化学计量学模型，称为PLS-NN模型。结合偏最小二乘法和神经网络实现中药材产地鉴定。首先，我们利用偏最小二乘法对3448个波段的光谱数据进行特征提取，提取出122个成分，包含了95%以上的信息。然后，我们使用提取的组件作为输入，并将相应的原始类作为输出，通过神经网络训练 PLS-NN 模型。最后，基于外部测试集，我们使用准确度、F ₁ -Score 和 Kappa 系数等指标评估了 PLS-NN 模型的泛化能力。结果表明，与决策树、支持向量机、偏最小二乘判别分析和朴素贝叶斯等模型相比，PLS-NN 模型在所有三个指标上都表现良好。该模型不仅实现了全谱数据的降维，提高了模型的训练效率，而且相比全谱数据模型具有更高的准确率。应用PLS-NN模型对山茱萸产地进行鉴定，准确率达到91.9%。