|
|
|
|
|
|
Progress in Research on Rapid and Non-Destructive Detection of Seed Quality Based on Spectroscopy and Imaging Technology |
WANG Dong1,3, WANG Kun2, WU Jing-zhu2*, HAN Ping1,3* |
1. Beijing Research Center for Agricultural Standards and Testing(BRCAST), Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
2. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
3. Laboratory of Quality & Safety Risk Assessment for Agro-products (Beijing), Ministry of Agriculture and Rural Affairs, Beijing 100097, China |
|
|
Abstract Seed is an important means of production in the process of agricultural production. The quality evaluation, vigor and aging detection, purity and authenticity identification, classification and traceability are common problems in seed quality detection. Seed quality mainly includes the content of moisture, protein, fatty acid, starch, and so forth, which is the important indices of seed quality classification. Moreover, seed quality is related to the safety of storage. Seed vigor is the sum of seed germination and emergence rate, seedling growth potential, plant stress resistance and production potential. High vigor seeds are of obvious growth advantage and production potential. Seed aging refers to the natural decline of seed vigor, which is manifested by seed discoloration, low germination rate, poor growth potential and crop yield reduction. The purity and authenticity of seeds will affect crop yield and agricultural product quality. Seed classification and traceability is an important method to ensure the purity and identify the authenticity of seeds, by which, crop yield and product quality will be guaranteed. For seed quality analysis, it usually need to do irreversible destructive analysis on samples according to the traditional methods, which is time-consuming with complex procedures. It is obvious that traditional methods are difficult to meet the needs of modern agriculture for seed production. Therefore, it is an urgent problem to carry out the research on non-destructive and rapid detection technology of seed quality. In recent years, with the development of chemometrics and the progress of computer technology, near-infrared spectroscopy, with the advantages of fast, non-destructive and high efficiency, has been widely applied in the non-destructive and rapid analysis of agricultural products, food, agricultural inputs, and so on. In addition, combined with spectral and imaging technology, hyperspectral imaging technology is rising in recent years. Compared with the traditional spectral technology, hyperspectral imaging technology can acquire not only the spectral information of the sample but also the spatial distribution information and image characteristics of it. In this paper, based on the near-infrared spectroscopy and hyperspectral imaging technology, the literature of seed quality non-destructive detection from the aspects of seed quality evaluation, vigor and aging detection, purity and authenticity identification, classification and traceability research were reviewed. Based on the analysis of the characteristics of different detection technologies, the problems of seed quality detection are sorted out, respectively. Furthermore, the technical characteristics of non-destructive and rapid detection of seed quality are summarized and prospected.
|
Received: 2019-12-10
Accepted: 2020-04-12
|
|
Corresponding Authors:
WU Jing-zhu, HAN Ping
E-mail: hanp@brcast.org.cn;pubwu@163.com
|
|
[1] WEI Li-feng, JI Jian-wei(魏利峰, 纪建伟). Journal of Chinese Agricultural Mechanization(中国农机化学报), 2016, 37(7): 80.
[2] LU Bing, SUN Jun, YANG Ning, et al(芦 兵, 孙 俊, 杨 宁, 等). Journal of Southern Agriculture(南方农业学报), 2018, 49(11): 2342.
[3] Zhang Y M, Guo W C. International Journal of Food Science & Technology, 2020,55:631.
[4] KANG Yue-qiong, HAO Feng(康月琼, 郝 风). Seed(种子), 2004, 23(7): 10, 16.
[5] LIU Ting, WANG Chuan-tang, TANG Yue-yi, et al(刘 婷, 王传堂, 唐月异, 等). Shandong Agricultural Sciences(山东农业科学), 2018, 50(6): 163.
[6] TANG Yue-yi, WANG Xiu-zhen, LIU Ting, et al(唐月异, 王秀贞, 刘 婷, 等). Shandong Agricultural Sciences(山东农业科学), 2018, 50(6): 159.
[7] ZHANG Xin, TANG Yue-yi, HU Dong-qing, et al(张 欣, 唐月异, 胡东青, 等). Shandong Agricultural Sciences(山东农业科学), 2018, 50(10): 138.
[8] LIU Pan, ZHANG Yan-xin, LI Dong-hua, et al(刘 盼, 张艳欣, 黎冬华, 等). Chinese Journal of Oil Crop Sciences(中国油料作物学报), 2016, 38(6): 722.
[9] WANG Chun-yang, MA Yu-han, LIU Bin-mei, et al(王纯阳, 马玉涵, 刘斌美, 等). Journal of Nuclear Agricultural Sciences(核农学报), 2019, 33(10): 2003.
[10] HUI Wen-kai, WANG Yi, CHEN Xiao-yang(惠文凯, 王 益, 陈晓阳). Journal of Beijing Forestry University(北京林业大学学报), 2018, 40(1): 1 .
[11] CHEN Jun-kun, HE Ping, XU Chun, et al(陈俊锟, 何 萍, 徐 春, 等). Anhui Agricultural Sciences(安徽农业科学), 2013, 41(3): 985.
[12] Velasco L, Becker H C. Euphytica, 1998, 101: 221.
[13] Wang J J, Liu H, Ren G X. The Crop Journal, 2014, 2(1): 28.
[14] Zhang K, Tan Z L, Chen C C, et al. Energy & Fuels, 2017, 7: 1.
[15] WU Jing-zhu, LIU Qian, CHEN Yan, et al(吴静珠, 刘 倩, 陈 岩, 等). Transducer and Microsystem Technologies(传感器与微系统), 2016, 35(7): 42.
[16] Rodríguez-Pulido F J, Hernández-Hierro J M, Nogales-Bueno J, et al. Talanta, 2014, 122: 145.
[17] Zhang X L, He Y. Industrial Crops and Products, 2013, 42: 416.
[18] Wang L, Pu H B, Sun D W, et al. Food Analytical Methods, 2015, 8(6): 1535.
[19] LI Xiao-fan, WANG Cheng, SONG Peng, et al(李孝凡, 王 成, 宋 鹏, 等). Seed(种子), 2019, 38(6): 61.
[20] FAN Xue-ting, ZHU Ming-dong, YANG Chen-guang, et al(范雪婷, 朱明东, 杨晨光, 等). Hybrid Rice(杂交水稻), 2019, 34(4): 62.
[21] Wu J Z, Dong W F, Liu Q, et al. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(Supp. 2): 272.
[22] Chen J Y, Chen H H, Wang X D, et al. The Characteristic of Hyperspectral Image of Wheat Seeds During Sprouting. 7th International Conference on Computer and Computing Technologies in Agriculture (CCTA) 2013, pp 408-421 (10.1007/978-3-642-54344-9-47). (hai-01220944).
[23] PENG Yan-kun, ZHAO Fang, BAI Jing, et al(彭彦昆, 赵 芳, 白 京, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(2): 327.
[24] ZHANG Ting-ting, XIANG Ying-ying, YANG Li-ming, et al(张婷婷, 向莹莹, 杨丽明, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(5): 1556.
[25] Matzrafi M, Herrmann I, Nansen C, et al. Frontiers in Plant Science, 2017, 8: 474.
[26] Ambrose A, Lohumi S, Lee W H, et al. Sensors and Actuators B: Chemical, 2016, 224: 500.
[27] WU Xiao-fen, ZHAO Guang-wu, QI Heng-nian(吴小芬, 赵光武, 祁亨年). Journal of Anhui Agricultural Sciences(安徽农业科学), 2017, 45(29): 12.
[28] LI Mei-ling, DENG Fei, LIU Ying, et al(李美凌, 邓 飞, 刘 颖, 等). Acta Agriculturae Zhejiangensis(浙江农业学报), 2015, 27(1): 1.
[29] Nansen C, Zhao G P, Dakin N, et al. Journal of Photochemistry and Photobiology B: Biology, 2015, 145: 19.
[30] WU Xiu-ting(吴秀婷). Agriculture Engineering Technology(农业工程技术), 2017, 10: 81.
[31] WEI Li-feng, JI Jian-wei(魏利峰, 纪建伟). Hubei Agricultural Sciences(湖北农业科学), 2016, 55(21): 5445, 5478.
[32] WANG Li-ping, ZHAO Xing-zhong, CHEN Wen-jie, et al(王丽萍, 赵兴忠, 陈文杰, 等). Chemical Analysis and Meterage(化学分析计量), 2017, 26(5): 43.
[33] XU Zhuo-pin, FAN Shuang, CHENG Wei-min, et al(徐涿频, 范 爽, 程维民, 等). Chinese Agricuntural Science Bulletin(中国农学通报), 2017, 33(2): 142.
[34] RAN Hang, CUI Yong-jin, JIN Zhao-xi, et al(冉 航, 崔永进, 靳召晰, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(9): 2743.
[35] Wilkes T, Nixon G, Bushell C, et al. Food and Nutrition Sciences, 2016, 7: 355.
[36] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al(钱丽丽, 宋雪健, 张东杰, 等). Food Science(食品科学), 2018, 39(16): 321.
[37] QIAN Li-li, SONG Xue-jian, ZHANG Dong-jie, et al(钱丽丽, 宋雪健, 张东杰, 等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2017, 32(10): 185, 196.
[38] SONG Xue-jian, QIAN Li-li, ZHOU Yi, et al(宋雪健, 钱丽丽, 周 义, 等). Food Research and Development(食品研究与开发), 2017, 38(11): 134.
[39] SONG Xue-jian, QIAN Li-li, ZHOU Yi, et al(宋雪健, 钱丽丽, 周 义, 等). Farm Products Processing(农产品加工), 2017, (5): 13.
[40] ZHOU Zi-li, ZHANG Yu, HE Yong, et al(周子立, 张 瑜, 何 勇, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(8): 131.
[41] Kong W W, Zhang C, Liu F, et al. Sensors, 2013, 13: 8916.
[42] Rodríguez-Pulido F J, Barbin D F, Sun D W, et al. Postharvest Biology and Technology, 2013, 76: 74.
[43] ZHANG Hang, YAO Chuan-an, JIANG Meng-meng, et al(张 航, 姚传安, 蒋梦梦, 等). Journal of Triticeae Crops(麦类作物学报), 2019, 39(1): 96.
[44] LIU Xiao-dan, FENG Xu-ping, LIU Fei, et al(刘小丹, 冯旭萍, 刘 飞, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(22): 189.
[45] Dong G, Guo J, Wang C, et al. International Journal of Agricultural and Biological Engineering, 2017, 10(2): 251.
[46] ZHANG Chu, LIU Fei, KONG Wen-wen, et al(张 初, 刘 飞, 孔汶汶, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(20): 270.
[47] Gao J F, Li X L, Zhu F L, et al. Computers and Electronics in Agriculture, 2013, 99: 186.
[48] Qiu Z J, Chen J, Zhao Y Y, et al. Applied Sciences, 2018, 8: 212.
[49] Zhao Y Y, Zhu S S, Zhang C, et al. RSC Advances, 2018, 8: 1337.
[50] Zhu S S, Zhou L, Gao P, et al. Molecules, 2019, 24: 3268.
[51] Zhao Y Y, Zhang C, Zhu S S, et al. Molecules, 2013, 23: 1352.
[52] Feng X P, Peng C, Chen Y, et al. Scientific Reports, 2017, 7: 15934.
[53] Feng X P, Zhao Y Y, Zhang C, et al. Sensors, 2017, 17: 1894.
[54] Yang S, Zhu Q B, Huang M, et al. Food Anal. Methods, 2017, 10: 424.
[55] Yang X L, Hong H M, You Z H, et al. Sensors, 2015, 15: 15578.
[56] Zhang X L, Liu F, He Y, et al. Sensors, 2012, 12(12): 17234.
[57] Huang M, He C J, Zhu Q B, et al. Applied Sciences, 2016, 6: 183.
[58] Wang R T, Tan K Z, Li M Y, et al. Journal of Computational Methods in Sciences and Engineering, 2019, 19: 1001(doi: 10.3233/JCM-193562).
[59] Huang M, Tang J Y, Yang B, et al. Computers and Electronics in Agriculture, 2016, 122: 139.
[60] He C, Zhu Q, Huang M, et al. American Society of Agricultural and Biological Engineers, 2016, 59(6): 1529.
[61] Wang Q G, Huang M, Zhu Q B. Characteristics of Maize Endosperm and Germ in the Geographical Origins and Years Identification Using Hyperspectral Imaging. American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE, 2014, 6: 4420. |
[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] |
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. |
[3] |
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. |
[4] |
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. |
[5] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[11] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[12] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[13] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[14] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[15] |
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. |
|
|
|
|