Functional Data Outlier Detection for Spectral Data

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''. Simulation 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.