|
|
|
|
|
|
| Geographical Origin Identification of Lycium Barbarum Using Near-Infrared Hyperspectral Imaging |
| WANG Lei1, 2, QIN Hong1,2*, LI Jing3, ZHANG Xiao-bo3, YU Li-na1, 2, LI Wei-jun1, 2, HUANG Lu-qi4* |
1. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
2. Center of Materials Science and Optoelectronics Engineering & School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
3. State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
4. State Key Laboratory Breeding Base of Dao-di Herbs, China Academy of Chinese Medical Sciences, Beijing 100700, China |
|
|
|
|
Abstract Lycium barbarum produced in Ningxia belongs to the genuine regional drugs contained in the Pharmacopoeia of the People's Republic of China. Due to the small planting area, low yield, high medicinal value and high consumer preference, the market is filled with chaos, and the phenomenon of passing others origins off as Ningxia happens occasionally. Therefore, it is of considerable significance to establish a rapid and effective geographical origin identification model of Lycium barbarum to supervise the market. In the process of market transactions, discriminating origin of Lycium barbarum is often based on experience, which has much error and low credibility. The traditional physical and chemical experiment has a long identification cycle and can't be operated by non-professionals. In recent years, some scholars have found that the content of Lycium barbarum in different producing areas is different. However, because of the small sample size, irregular shape and uneven distribution of components, the near-infrared spectroscopy identification technique often needed to smash Lycium barbarum to collect spectral information. Near-infrared hyperspectral image technology combined with near-infrared spectroscopy and image technology, which contains rich spatial information and spectral information, can achieve non-destructive acquisition of spectral information. In this research, near-infrared hyperspectral image technology was used to discriminate the geographical origin of Lycium barbarum samples, which were gathered from Gansu, Qinghai, Xinjiang, Ningxia and Inner Mongolia in China. After collecting the hyperspectral information of 1 650 samples by hyperspectral image system, the region of interest (ROI) was effectively extracted by threshold image segmentation and denoising. During the pretreatment process, the comparison between zero-phase component analysis (ZCA) whitening results and normalization results indicated that ZCA whitening was an effective spectral preprocessing method to remove correlation between features and improve the accuracy of the model. Partial least squares based dimension reduction (PLSDR) was used to reduce the complexity of the model for the preprocessed data. The experimental results indicated that the data after ZCA whitening pretreatment could be reduced from 288-dimensional features to 4 principal components, which made the correlation-removed features can be represented by fewer hidden features. Finally, the dimensionality-reduced features were fed to different classifiers to train model, including support vector machine (SVM), linear discriminant analysis (LDA) and softmax regression. Among those models, the average recognition accuracy based on ZCA whitening, PLSDR and softmax regression was 94.06% on the test set. The results demonstrated that the proposed method could effectively discriminate the origin of Lycium barbarum samples.
|
|
Received: 2019-03-18
Accepted: 2019-07-20
|
|
|
|
Corresponding Authors:
QIN Hong, HUANG Lu-qi
E-mail: qinh@semi.ac.cn; huangluqi01@126.com
|
|
[1] Gong G, Dang T, Deng Y, et al. International Journal of Biological Macromolecules, 2018, 109: 611.
[2] Forino M, Tartaglione L, Carmela Dell’Aversano, et al. Food Chemistry, 2016, 194：1254.
[3] Yang R F, Zhao C, Chen X, et al. Journal of Functional Foods, 2015, 17：903.
[4] Lu W, Jiang Q, Shi H, et al. Journal of Agricultural and Food Chemistry, 2014, 62(37)：9073.
[5] Li Q, Yu X, Xu L, et al. Food Chemistry, 2017, 221：1113.
[6] Yin W, Zhang C, Zhu H, et al. PLoS One, 2017, 12(7)：e0180534.
[7] WANG Huan, JIN Zhe-xiong, WEN Mei-jia, et al(王 欢, 金哲雄, 温美佳，等). Research and Practice on Chinese Medicines(现代中药研究与实践), 2014, 28(6): 21.
[8] QU Yun-qing, ZHANG Tong-gang, LIU Dun-hua(曲云卿, 张同刚, 刘敦华). Food & Machinery(食品与机械), 2015, 31(2): 76.
[9] Tang J L, Wang D, Zhang Z G, et al. Computers and Electronics in Agriculture, 2017, 135: 63.
[10] Chen H, Tan C, Lin Z, et al. Spectrochimica Acta Part A, 2018, 189: 183.
[11] Dang T, Sethu V, Ambikairajah E. Compensation Techniques for Speaker Variability in Continuous Emotion Prediction. IEEE Transactions on Affective Computing, 2018.
[12] Lee J, Chang K, Jun C H, et al. Chemometrics and Intelligent Laboratory Systems, 2015, 147: 139.
[13] Naseer N, Noori F M, Qureshi N K, et al. Fronties in Human Neuroscience, 2016, 10: 237.
[14] Dai X, Duan Y, Hu J, et al. Infrared Physics & Technology, 2018, 97: 25. |
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
| [3] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [4] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [5] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [6] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [7] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [8] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [9] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [10] |
HE Qing-yuan1, 2, REN Yi1, 2, LIU Jing-hua1, 2, LIU Li1, 2, YANG Hao1, 2, LI Zheng-peng1, 2, ZHAN Qiu-wen1, 2*. Study on Rapid Determination of Qualities of Alfalfa Hay Based on NIRS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3753-3757. |
| [11] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [12] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [13] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [14] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [15] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
|
|
|
|
