光谱学与光谱分析 |
|
|
|
|
|
Advances of NIR Spectroscopy Technology Applied in Seed Quality Detection |
ZHU Li-wei1, MA Wen-guang2, HU Jin1*, ZHENG Yun-ye2, TIAN Yi-xin2,GUAN Ya-jing1, HU Wei-min1 |
1. Seed Science Center, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China 2. Tobacco Agricultural Science Research Academy of Yunan Province, Yuxi 653100, China |
|
|
Abstract Near infrared spectroscopy (NIRS) technology developed fast in recent years, due to its rapid speed, less pollution, high-efficiency and other advantages. It has been widely used in many fields such as food, chemical industry, pharmacy, agriculture and so on. The seed is the most basic and important agricultural capital goods, and seed quality is important for agricultural production. Most methods presently used for seed quality detecting were destructive, slow and needed pretreatment, therefore, developing one kind of method that is simple and rapid has great significance for seed quality testing. This article reviewed the application and trends of NIRS technology in testing of seed constituents, vigor, disease and insect pests etc. For moisture, starch, protein, fatty acid and carotene content, the model identification rates were high as their relative contents were high; for trace organic, the identification rates were low as their relative content were low. The heat-damaged seeds with low vigor were discriminated by NIRS, the seeds stored for different time could also been identified. The discrimination of frost-damaged seeds was impossible. The NIRS could be used to identify health and infected disease seeds, and did the classification for the health degree; it could identify parts of the fungal pathogens. The NIRS could identify worm-eaten and health seeds, and further distinguished the insect species, however the identification effects for small larval and low injury level of insect pests was not good enough. Finally, in present paper existing problems and development trends for NIRS in seed quality detection was discussed, especially the single seed detecting technology which was characteristic of the seed industry, the standardization of its spectral acquisition accessories will greatly improve its applicability.
|
Received: 2013-12-27
Accepted: 2014-03-14
|
|
Corresponding Authors:
HU Jin
E-mail: jhu@zju.edu.cn
|
|
[1] Norris K H. Agricultural Engineering, 1964, 45(7): 370. [2] YANG Chuan-de, YU Hong-tao, GUAN Shu-yan, et al(杨传得, 于洪涛, 关淑艳, 等). Journal of Peanut Science(花生学报), 2012, 41(1): 6, 20. [3] SHEN Lin-feng, SHEN Zhang-quan(沈林峰,沈掌泉). Chemical Analysis and Meterage(化学分析计量), 2008,(06): 26. [4] Shao Y N, Cen Y L, He Y, et al. Food Chemistry, 2011, 126(4): 1856. [5] Wu J G, Shi C H, Zhang X. Field Crops Research, 2002, 75(1): 1. [6] Kumar S, Andy A. International Food Research Journal, 2013, 20(2): 759. [7] Berardo N, Brenna O V, Amato A, et al. Innovative Food Science and Emerging Technologies, 2004, 5(3): 393. [8] Xue J T, Wu C J, Wang L L, et al. Food Chemistry, 2011, 126(2): 725. [9] Li X, He Y, Wu C. Journal of Stored Products Research, 2008, 44(3): 264. [10] Esteve A L, Ellis D D, Duvick S, et al. Journal of Cereal Science, 2012, 55(2): 160. [11] Xie L J, Ying Y B, Ying T J, et al. Analytical Climica Acta, 2007, 584(2): 379. [12] LIANG Liang, LIU Zhi-xiao, YANG Min-hua, et al(梁 亮, 刘志霄, 杨敏华, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2009, 28(5): 353. [13] Peiris K H S, Pumphrey M O, Dong Y H, et al. Cereal Chemistry, 2010, 87(6): 511. [14] FENG Lei, CHEN Shuang-shuang, FENG Bin, et al(冯 雷, 陈双双, 冯 斌, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(1): 139. [15] Ridgway C, Chambers J. Journal of Near Infrared Spectroscopy, 1998, 6: 115. [16] Baker J E, Dowell F E, Throne J E. Biological Control, 1999, 16(1): 88. [17] Maghirang E B, Dowell F E, Baker J E, et al. Proceedings of ASAE, Chicago, IL, Paper, 2002, (023067). [18] ZHU Li-wei, HUANG Yan-yan, WANG Qing, et al(朱丽伟, 黄艳艳, 王 庆, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(SUPPL2): 237. [19] Gustin J L, Jackson S, Williams C, et al. Journal of Agricultural and Food Chemistry, 2013, 61(46): 10872. |
[1] |
ZHENG Hong-quan, DAI Jing-min*. Research Development of the Application of Photoacoustic Spectroscopy in Measurement of Trace Gas Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 1-14. |
[2] |
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. |
[3] |
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. |
[4] |
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. |
[5] |
YANG Guang1, JIN Chun-bai1, REN Chun-ying2*, LIU Wen-jing1, CHEN Qiang1. Research on Band Selection of Visual Attention Mechanism for Object
Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 266-274. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
[11] |
YI Min-na1, 2, 3, CAO Hui-min1, 2, 3*, LI Shuang-na-si1, 2, 3, ZHANG Zhu-shan-ying1, 2, 3, ZHU Chun-nan1, 2, 3. A Novel Dual Emission Carbon Point Ratio Fluorescent Probe for Rapid Detection of Lead Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3788-3793. |
[12] |
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. |
[13] |
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. |
[14] |
LU Wen-jing, FANG Ya-ping, LIN Tai-feng, WANG Hui-qin, ZHENG Da-wei, ZHANG Ping*. Rapid Identification of the Raman Phenotypes of Breast Cancer Cell
Derived Exosomes and the Relationship With Maternal Cells[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3840-3846. |
[15] |
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. |
|
|
|
|