函数型数据异常检测在光谱数据中的应用

Functional Data Outlier Detection for Spectral Data

光谱分析技术是分析化学中常用的方法之一,也广泛应用于中药分析等领域.本文用函数型数据分析的方法分析光谱数据,研究针对光谱数据的异常检测方法,挑选出异常(离群)的样本.基于现有方法,我们提出“欧加深度检测方法”.数值模拟结果显示,欧加深度检测方法能较好地挑选出异常情况的样本.本文将欧加深度检测方法和三种已有方法应用于分析73剂中药六混液光谱数据,结果显示,欧加深度方法能够检测出全部6剂不合格样本,有较好的应用前景.

Currently,spectroscopy technology is widely used in traditional Chinese medicine analysis.In this paper,from the functional data analysis perspective,we study outlier detection methods for spectral data,detect outliers,and propose the“Oja depth detection method”.Sim-ulation studies demonstrate the advantages of the Oja depth detection method.We compare the Oja depth detection method with three existing methods on a Chinese medicine spectral data of 73-dose six-mixture liquid.The results show the proposed Oja depth method is able to detect all six unqualified samples and has the highest accuracy.