光谱学与光谱分析 |
|
|
|
|
|
Study on the Identification of Geographical Indication Wuchang Rice Based on the Content of Inorganic Elements |
LI Yong-le, ZHENG Yan-jie*, TANG Lu, SU Zhi-yi, XIONG Cen |
Shenzhen Academy of Metrology and Quality Inspection, Shenzhen 518131, China |
|
|
Abstract Wuchang rice is a geographical indication product in China. Due to its high quality and low production, the phenomenon of fake is more and more serious. An effective identification method of Wuchang rice is urgent needed, for the maintenance of its brand image and interest of consumers. Base on the content of inorganic elements which are analyzed by ICP-AES and ICP-MS in rice, the identification model of Wuchang rice is studied combining with principal component analysis (PCA), Fisher discrimination and artificial neural network (ANN) in this paper. The effect on the identification of samples is poor through PCA, while the samples from Wuchang area and other areas can be identified accurately through Fisher discrimination and ANN. The average accurate identification ratio of training and verification set through Fisher discrimination is 93.5%, while the average accurate identification ratio through ANN is 96.4%. The ability to identify of ANN is better than Fisher discrimination. Wuchang rice can be identified accurately through the result of this research which provides a technology for the protection of geographical indications of this product.
|
Received: 2014-10-18
Accepted: 2015-02-04
|
|
Corresponding Authors:
ZHENG Yan-jie
E-mail: zhengyj@smq.com.cn
|
|
[1] CHANG Xiang-yang, ZHU Bing-quan, CHEN Nan, et al(常向阳,朱炳泉,陈 南, 等). Journal of Guangzhou University(Natural Science Edition)(广州大学学报·自然科学版), 2007,(06): 49. [2] Luo M, Zheng Y, Xiong C, et al. Journal of AOAC International, 2013, 96(5): 1048. [3] Zheng Y, Ruan G, Li B, et al. European Food Research and Technology, 2014, 238(2): 337. [4] ZHENG Yan-jie, CHEN Su-juan, LI Jin-cai, et al(郑彦婕,陈素娟,李锦才, 等). Food and Fermentation Industries(食品与发酵工业), 2010, (12): 173. [5] XIA Li-ya, SHEN Shi-gang, LIU Zheng-hao, et al(夏立娅,申世刚,刘峥颢, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(1): 102. [6] Ariyama K, Shinozaki M, Kawasaki A. Journal of Agricultural and Food Chemistry, 2012, 60(7): 1628. [7] HUANG Xiao-long, HE Xiao-qing, ZHANG Nian, et al(黄小龙,何小青,张 念, 等). Modern Scientific Instruments(现代科学仪器), 2010,(01): 80. [8] RUAN Gui-hua, DU Fu-you, HUANG Xiao-long, et al(阮贵华,杜甫佑,黄小龙, 等). Food Science(食品科学), 2012, (07): 41. [9] Ariyama K, Horita H, Yasui A. Journal of Agricultural and Food Chemistry, 2004, 52(19): 5803. [10] ZHENG Yan-jie, HU Shu-yu, LI Yong-le, et al(郑彦婕,胡书玉,黎永乐, 等). Food and Fermentation Industries(食品与发酵工业), 2012, 38(9): 167. [11] Fawcett T. Pattern Recognition Letters, 2006, 27(8): 861. [12] Cheng P, Fan W, Xu Y. Food Control, 2014, 35(1): 153. [13] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011. [14] Bergmeir C, Benítez J M. Journal of Statistical Software, 2012, 46(7): 1. [15] Sommella A, Deacon C, Norton G, et al. Environmental Pollution, 2013, 181: 38. |
[1] |
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. |
[2] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[3] |
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. |
[4] |
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. |
[5] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[6] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[7] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[8] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[9] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[10] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
[11] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[12] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[13] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[14] |
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1*. Maize Yield Forecasting and Associated Optimum Lead Time Research Based on Temporal Remote Sensing Data and Different Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2627-2637. |
[15] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
|
|
|
|