样品表面近红外光谱结合多类支持向量机快速鉴别枸杞子产地

Rapid Identification of Wolfberry Fruit of Different Geographic Regions with Sample Surface Near Infrared Spectra Combined with Multi-Class SVM

采用便携式近红外光谱仪采集枸杞子表面不同部位的近红外漫反射光谱,结合多类支持向量机算法对枸杞子产地进行快速无损辨识。以识别率为评价指标进行光谱预处理方法的选择,为了消除样本划分偏性对结果的影响,本研究通过重复划分样本集多次建模与预测,利用识别率的统计结果考察各个光谱采集部位的建模结果。实验结果表明,原始数据经二阶导数加SG平滑处理后,所建模型具有良好的产地预测性能。除了枸杞子顶端部位外,其他部位模型的稳定性及准确性均较好,其外部验证识别率的中位数与平均值均大于97%。这表明利用枸杞子样品表面近红外光谱可实现产地的准确鉴别,便携式近红外光谱技术可作为中药材流通环节中的有效监控手段。

Portable near infrared spectrometer combined with multi-class support vector machines was used to discriminate wolf- berry fruit of different geographic regions. Data pre-processing methods were explored before modeling with the identification rate as indicator. To eliminate the influence of sample subset partitioning on model performance, multiple modeling and predic- ting were conducted and the statistical result of identification rate was utilized to assess model performance of different acquisition sites. The results showed that SVM model with raw spectra after pretreatment of second derivative and Savitzky-Golay filter smoothing showed the best predicative ability. And the model of every acquisition site except for site 5 exhibited good stability and prediction ability and its median and average of identification rate of external validation were all greater than 97%. It was suggested that surface NIR spectra of wolfberry fruit was applicable to accurate identification of geographic region, and portable near infrared spectrometer could act as an effective means of monitoring the quality of Chinese herbal medicine in circulation.