Abstract
The purpose of this paper was to apply terahertz time-domain spectroscopy (THz-TDS) combined with clustering by fast search and find of density peaks (CFSFDP) for pesticide detection. To solve similar distance problem in THz spectra data, we proposed a method PCA-CFSFDP. Principal component analysis (PCA) was used to reduce dimensions of THz spectrum data, and then used the result of PCA as the input of CFSFDP. The performance of PCA-CFSFDP was investigated, and the experimental results showed that PCA-CFSFDP approach can get a satisfactory detection result. In addition, the proposed method was compared with K-means. The comparison results demonstrated that PCA-CFSFD outperforms K-means. Therefore, THz-TDS combined with PCA-CFSFDP is a potential identification tool for pesticide detection.
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Acknowledgements
The authors gratefully acknowledge the support for this research by Yulin Normal University Research Grant (Grant No. 2016YJKY06), Nature Science Foundation of China (Grant No. 21565028), Guangxi Colleges and Universities Program of Innovative Research Team and Outstanding Talent.
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Qin, B., Li, Z., Luo, Z. et al. Terahertz time-domain spectroscopy combined with PCA-CFSFDP applied for pesticide detection. Opt Quant Electron 49, 244 (2017). https://doi.org/10.1007/s11082-017-1080-x
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DOI: https://doi.org/10.1007/s11082-017-1080-x
Keywords
- Terahertz time-domain spectroscopy
- PCA-CFSFDP
- Unsupervised learning
- Pesticide detection