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Article Navigation >  Journal of Pharmaceutical Practice and Service  > 2019  >  37(4): 322-331

GUO Yunxiang, CHEN Long, LI Xiaojin, WANG Guoping, JIANG Yiping, XIN Hailiang, JIA Xiaoguang. Rapid identification of domestic and imported hops based on NIRS technology and PCA-SVM algorithm[J]. Journal of Pharmaceutical Practice and Service, 2019, 37(4): 322-331. doi: 10.3969/j.issn.1006-0111.2019.04.008
Citation: GUO Yunxiang, CHEN Long, LI Xiaojin, WANG Guoping, JIANG Yiping, XIN Hailiang, JIA Xiaoguang. Rapid identification of domestic and imported hops based on NIRS technology and PCA-SVM algorithm[J]. Journal of Pharmaceutical Practice and Service, 2019, 37(4): 322-331. doi: 10.3969/j.issn.1006-0111.2019.04.008
shu

Rapid identification of domestic and imported hops based on NIRS technology and PCA-SVM algorithm

doi: 10.3969/j.issn.1006-0111.2019.04.008
  • 1.

    Traditional Chinese Medicine College of Xinjiang Medical University, Urumqi 830011, China;Xinjiang Institute of Traditional Chinese Medicine and Ethic Medicine, Key Laboratory of Traditional Chinese Medicine and Ethnic Medicine Resources, State Administration of Traditional Chinese Medicine, Urumqi 830002, China;Department of Pharmacognosy, School of Pharmacy, Naval Medical University, Shanghai 200433, China

  • 2.

    Xiangyang Central Hospital, Affiliated to Hubei University of Arts and Science, Xiangyang 441021, China;Key Laboratory of Traditional Chinese Resource and Compound Prescription, Hubei University of China Medicine, Wuhan 430065, China

  • 3.

    Traditional Chinese Medicine College of Xinjiang Medical University, Urumqi 830011, China;Xinjiang Institute of Traditional Chinese Medicine and Ethic Medicine, Key Laboratory of Traditional Chinese Medicine and Ethnic Medicine Resources, State Administration of Traditional Chinese Medicine, Urumqi 830002, China

  • 4.

    Xinjiang Institute of Traditional Chinese Medicine and Ethic Medicine, Key Laboratory of Traditional Chinese Medicine and Ethnic Medicine Resources, State Administration of Traditional Chinese Medicine, Urumqi 830002, China

  • 5.

    Department of Pharmacognosy, School of Pharmacy, Naval Medical University, Shanghai 200433, China

  • Received Date: 2019-01-09
  • Rev Recd Date: 2019-05-14
  • Objective To develop a rapid identification method for domestic and imported hops by the establishment of PCA-SVM model using near-infrared reflectance spectroscopy (NIRS),combined with principal component analysis (PCA) and support vector machine (SVM) algorithm. Methods The hop samples from different sources were collected and ground into uniform powder.The NIR spectra of each powder sample were collected in the range of 4000~12500 cm-1.The characteristic spectrum segment was selected from 9000~4100 cm-1,which was pretreated by different spectral pretreatment methods and subjected to PCA dimensionality reduction.According to the 2-dimensional principal component plane scatter plot,the pretreatment method was optimized.The SVM pattern recognition model was established by using the best preprocessing method to process the PCA dimensionality reduction data of the post-spectrum.The SVM model parameters were searched by grid search method,genetic algorithm (GA) and particle swarm optimization (PSO).The prediction accuracy of the PCA-SVM models built by different principal component numbers were compared to determine the optimal principal component number.Finally,the rapid NIR identification model of PCA-SVM is established. Results In the 6500~5400 cm-1 spectral segment,the first derivative (FD) is the best spectral pretreatment method,and the first 8 principal components are the best principal components of the spectrum extracted by PCA.The optimal SVM internal parameters are determined by the grid search method:the penalty factor(c)=2,the kernel function parameter(g)=1.The prediction accuracy rate of this hop PCA-SVM identification model was 97.37% for the 5-fold cross validation,97.37% for the calibration set and 97.44% for test set samples. Conclusion This model has high accuracy and consistent performance.It can be used for rapid and non-destructive identification of hop samples.
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Rapid identification of domestic and imported hops based on NIRS technology and PCA-SVM algorithm

doi: 10.3969/j.issn.1006-0111.2019.04.008
  • 1. Traditional Chinese Medicine College of Xinjiang Medical University, Urumqi 830011, China;Xinjiang Institute of Traditional Chinese Medicine and Ethic Medicine, Key Laboratory of Traditional Chinese Medicine and Ethnic Medicine Resources, State Administration of Traditional Chinese Medicine, Urumqi 830002, China;Department of Pharmacognosy, School of Pharmacy, Naval Medical University, Shanghai 200433, China
  • 2. Xiangyang Central Hospital, Affiliated to Hubei University of Arts and Science, Xiangyang 441021, China;Key Laboratory of Traditional Chinese Resource and Compound Prescription, Hubei University of China Medicine, Wuhan 430065, China
  • 3. Traditional Chinese Medicine College of Xinjiang Medical University, Urumqi 830011, China;Xinjiang Institute of Traditional Chinese Medicine and Ethic Medicine, Key Laboratory of Traditional Chinese Medicine and Ethnic Medicine Resources, State Administration of Traditional Chinese Medicine, Urumqi 830002, China
  • 4. Xinjiang Institute of Traditional Chinese Medicine and Ethic Medicine, Key Laboratory of Traditional Chinese Medicine and Ethnic Medicine Resources, State Administration of Traditional Chinese Medicine, Urumqi 830002, China
  • 5. Department of Pharmacognosy, School of Pharmacy, Naval Medical University, Shanghai 200433, China
  • Received Date: 2019-01-09
  • Rev Recd Date: 2019-05-14

Abstract: Objective To develop a rapid identification method for domestic and imported hops by the establishment of PCA-SVM model using near-infrared reflectance spectroscopy (NIRS),combined with principal component analysis (PCA) and support vector machine (SVM) algorithm. Methods The hop samples from different sources were collected and ground into uniform powder.The NIR spectra of each powder sample were collected in the range of 4000~12500 cm-1.The characteristic spectrum segment was selected from 9000~4100 cm-1,which was pretreated by different spectral pretreatment methods and subjected to PCA dimensionality reduction.According to the 2-dimensional principal component plane scatter plot,the pretreatment method was optimized.The SVM pattern recognition model was established by using the best preprocessing method to process the PCA dimensionality reduction data of the post-spectrum.The SVM model parameters were searched by grid search method,genetic algorithm (GA) and particle swarm optimization (PSO).The prediction accuracy of the PCA-SVM models built by different principal component numbers were compared to determine the optimal principal component number.Finally,the rapid NIR identification model of PCA-SVM is established. Results In the 6500~5400 cm-1 spectral segment,the first derivative (FD) is the best spectral pretreatment method,and the first 8 principal components are the best principal components of the spectrum extracted by PCA.The optimal SVM internal parameters are determined by the grid search method:the penalty factor(c)=2,the kernel function parameter(g)=1.The prediction accuracy rate of this hop PCA-SVM identification model was 97.37% for the 5-fold cross validation,97.37% for the calibration set and 97.44% for test set samples. Conclusion This model has high accuracy and consistent performance.It can be used for rapid and non-destructive identification of hop samples.

GUO Yunxiang, CHEN Long, LI Xiaojin, WANG Guoping, JIANG Yiping, XIN Hailiang, JIA Xiaoguang. Rapid identification of domestic and imported hops based on NIRS technology and PCA-SVM algorithm[J]. Journal of Pharmaceutical Practice and Service, 2019, 37(4): 322-331. doi: 10.3969/j.issn.1006-0111.2019.04.008
Citation: GUO Yunxiang, CHEN Long, LI Xiaojin, WANG Guoping, JIANG Yiping, XIN Hailiang, JIA Xiaoguang. Rapid identification of domestic and imported hops based on NIRS technology and PCA-SVM algorithm[J]. Journal of Pharmaceutical Practice and Service, 2019, 37(4): 322-331. doi: 10.3969/j.issn.1006-0111.2019.04.008
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