【摘要】 目的 采用近红外光谱（near-infrared spectioscopy, NIRS）技术对黄芩的河北省道地性进行二分类实验，探究不同数据预处理算法及其组合、不同波段选择算法和不同分类方法对模型性能的影响。方法 研究采集138份黄芩样本，采用12 500～4 000 cm^-1波段对不同黄芩样品进行近红外光谱采集。首先，对比不同光谱预处理方法的单一性能与组合性能；其次，对比竞争性自适应重加权采样方法 （CARS）、无信息变量消除方法 （UVE）、连续投影方法 （SPA）和主成分分析（PCA）在红外光谱波段选择与特征提取方面的性能；最后，对比偏最小二乘判别分析（PLS-DA）、支持向量机（SVM）、人工神经网络（ANN）、随机森林（RF）、传统一维卷积神经网络（CNN）和堆叠自编码器（SAE）在建立中药属性分类模型中的性能差异。结果 最佳预处理算法是使用均值中心化（MC）和多元散射校正（MSC）,总体正确率可以达到92.9%;最佳波段选择算法是基于PCA选择出的25维变量，能将总正确率提升10.7%;最佳分类算法是经过MSC处理和PCA降维后建立的一维CNN模型，可以实现冀产黄芩100%正确率的道地性二分类。结论 通过近红外光谱技术的道地性分类算法研究为黄芩道地性鉴别提供了快速、无损的检测手段及可靠的数据分析方法，为中药材产地溯源提供新的方法参考。更多还原

【Abstract】 OBJECTIVE To explore the effects of different data preprocessing algorithms and their combinations, different band selection algorithms and different classification methods on the performance of the model by using near infrared spectroscopy to classify the genuineness of Scutellaria baicalensis Georgi in Hebei province. METHODS A total of 138 samples of Scutellaria baicalensis Georgi were collected, and the spectral acquisition of different Scutellaria baicalensis Georgi samples was carried out by using 12 500-4 000 cm^-1 band. Firstly, the single performance and combined performance of different spectral preprocessing methods are compared. Secondly, the performance of competitive adaptive reweighted sampling（CARS）, uninformative variable elimination（UVE）, successive projections algorithm（SPA） and principal component analysis（PCA） in infrared spectral band selection and feature extraction are compared. Finally, the performance differences of partial least squares discriminant analysis（PLS-DA）, support vector（SVM）, artificial neural network（ANN）, random forest（RF）, traditional one-dimensional convolutional neural network（CNN） and stacked autoencoder（SAE） in establishing the attribute classification model of traditional Chinese medicine were compared. RESULTS The best preprocessing algorithm is to use mean centralization（MC） and multiple scattering correction（MSC）, and the overall accuracy rate can reach 92.9 %. The optimal band selection algorithm is based on the 25-dimensional variables selected by PCA, which can increase the total accuracy by 10.7 %. The best classification algorithm is a one-dimensional CNN model established after MSC processing and PCA dimensionality reduction, which can achieve 100 % accuracy of geo-authentic binary classification. CONCLUSIONS The study of genuineness classification algorithm by infrared spectroscopy provides a fast and non-destructive detection method and reliable data analysis method for the genuineness classification of Scutellaria baicalensis, and provides a new method reference for the traceability of Chinese medicinal materials.更多还原