基于近红外网络的小麦品质监测

doi:10.3864/j.issn.0578-1752.2011.09.006

摘要/Abstract

摘要： 【目的】利用自行建立的中国谷物主产区的近红外分析网络，研究小麦的品质分布监测、品种鉴别及种植省份的区域划分。【方法】应用FOSS公司的Infratec 1241型近红外分析仪构建谷物品质近红外分析网络，该网络主要由网络主机、参比实验室、网络管理中心和分布于中国粮食主产省区的网络子机构成。利用该近红外分析网络并结合GPS定位采样技术和GIS技术，对2009年的冬小麦品质分布进行监测；采用软独立建模分类法（SIMCA）对获取的近红外光谱进行分析，建立小麦品种、所在省份的识别模型。【结果】通过网络中心建立和管理模型，可以有效节约成本并具有统一的准确度和精度，该网络的一致性较好，子机与主机的相关系数高于0.92。基于该网络可构建不同尺度区域下的小麦品质分布图，从而获得不同地区小麦品质的分布信息。根据近红外网络提供的光谱数据，可进一步对小麦的品种和所在省份进行区分，对山东省5个小麦品种的识别正确率＞80%，对北京、山东、江苏三省所种植的小麦的区分准确率＞90%。【结论】近红外网络在小麦的品质区划分布、品种鉴别及种植省份识别方面具有较大应用潜力，可提供大范围的小麦品质数据支撑，从而为优质优价收购和政府决策提供技术支撑。

关键词: 近红外网络 , 近红外光谱 , 小麦 , 谷物

Abstract: 【Objective】 Based on the NIR network that constructed in main grain producing areas in China, the quality distribution monitoring of wheat, the variety discrimination, and producing area classification were studied.【Method】The Infratec 1241 NIR analyzer from FOSS Company was used to construct the grain NIR network. This network consisted of the main instrument, reference library, network administration center and several satellite instruments that distributed in the main grain producing areas of China. Combined with GPS and GIS technology, the network was used to analyze the quality distribution of winter wheat in 2009. The collected NIR spectra were analyzed by Soft Independent Modeling of Class Analogy (SIMCA) , and the classification models for variety, and producing provinces of wheat were constructed. 【Result】The results showed that it could save cost and keep unitive accuracy and precise by using network center to construct and manage the calibration models. All the satellite instruments had good constancy with the main instrument, and their correlation coefficients were over 0.92. Based on the NIR network, the wheat quality distribution map in different scales could be obtained, thus obtained the quality distribution information of wheat. With the spectra collected from NIR network, the wheat variety and producing area could be discriminated. The classification accuracy for five varieties of wheat in Shandong province was over 80%, and for discriminating wheat that produced in Beijing, Shandong and Jiangsu, the classification accuracy was over 90%. 【Conclusion】 The results showed that NIR network had a potential in analyzing the quality distribution, variety and producing areas discrimination for wheat. It could provide large scale data support and thus be used to guide the wheat purchase, quality management and decision-making.

Key words: near infrared network , near infrared spectroscopy , wheat , grain

朱大洲1;2;黄文江1;马智宏2;赵柳2;杨小冬1;王纪华1;2. 基于近红外网络的小麦品质监测[J]. 中国农业科学, 2011, 44(9): 1806-1814.

ZHU Da-zhou; HUANG Wen-jiang; MA Zhi-hong; ZHAO Liu ; YANG Xiao-dong; WANG Ji-hua; . Wheat Quality Monitoring by NIR Network[J]. Scientia Agricultura Sinica, 2011, 44(9): 1806-1814.

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参考文献

[1]严衍禄, 赵龙莲, 韩东海, 杨曙明. 近红外光谱分析基础与应用. 北京: 中国轻工业出版社, 2005.
Yan Y L, Zhao L L, Han D H, Yang S M. Elements and Application of Near-Infrared Spectra Analysis. Beijing: China Light Industry Press, 2005. (in Chinese)
[2]陆婉珍. 现代近红外光谱分析技术. 第二版. 北京: 中国石化出版社, 2007.
Lu W Z. Modern Near Infrared Spectroscopy Analytical Technology. 2nd ed. Beijing: China Petrochemical Press, 2007. (in Chinese)
[3]Büchmann N B, Runfors S. The standardisation of Infratec 1221 near infrared transmission instruments in the Danish network used for the determination of protein and moisture in grains. Journal of Near Infrared Spectroscopy, 1995, 3(1): 35-42.
[4]Büchmann N B, Josefsson H, Cowe I A. Performance of European artificial neural network (ANN) calibrations for moisture and protein in cereals using the Danish near-infrared transmission (NIT) network. Cereal Chemistry, 2001, 78: 572-577.
[5]http://www.provimi.nl/searchnews_en.html?id=91&page=
[6]Tillmann P, Paul C. Networking of near infrared spectroscopy instruments for rapeseed analysis: a comparison of different procedures. Journal of Near Infrared Spectroscopy, 2000, 8(2): 101-107.
[7]Tseng C H, Ma K M, Wang N. Internet-enabled near-infrared analysis of oilseeds//Luthria D L. Oil Extraction and Analysis: Critical Issues and Comparative Studies. Urbana: AOCS Press, 2004.
[8]Williams P. Near-infrared Instrument Networking: Design and Implementation. Thailand: NIR-2009 Bangkok Thailand, 2009.
[9]李军会. 近红外光谱分析图谱库、网络共享、与模型的优化研究[D]. 北京: 中国农业大学, 2001.
Li J H. Study on the NIR spectra database, sharing through network and the optimizing of model[D]. Beijing: China Agricultural University, 2001. (in Chinese)
[10]肖颖, 陈斌, 张索非. 近红外光谱仪器远程监控平台的开发. 现代科学仪器, 2008(5):10-13.
Xiao Y, Chen B, Zhang S F. The research of integrated monitoring system of near infrared instrument. Modern Scientific Instruments, 2008(5): 10-13. (in Chinese)
[11]王艳斌, 袁洪福, 陆婉珍. 一种基于目标因子分析的模型传递方法. 光谱学与光谱分析, 2005, 25(3): 398-401.
Wang Y B, Yuan H F, Lu W Z. A new calibration transfer method based on target factor analysis. Spectroscopy and Spectral Analysis, 2005,25(3):398-401. (in Chinese)
[12]Haaland D M, Melgaard D K. New augmented classical least squares methods for improved quantitative spectral analyses. Vibrational Spectroscopy, 2002, 29(1-2): 171-175.
[13]Sulub Y, Small G W. Spectral simulation methodology for calibration transfer of near-infrared spectra. Applied Spectroscopy, 2007, 61(4): 406-413.
[14]Yusuf S, Rosario L, Richard V, Busolo W. Near-infrared multivariate calibration updating using placebo: A content uniformity determination of pharmaceutical tablets. Vibrational Spectroscopy, 2008, 46(2): 128-134.
[15]Alamar M C, Bobelyn J, Lammertyn J. Calibration transfer between NIR diode array and FT-NIR spectrophotometers for measuring the soluble solids contents of apple. Postharvest Biology Technology, 2007, 45(1): 38-45.
[16]Pereira C F, Pimentel M F, Galvão R K. A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers. Analytica Chimica Acta, 2008, 611(1): 41-47.
[17]Yoon J, Lee S, Han C. Calibration transfer of near-infrared spectra based on compression of wavelet coefficients. Chemometrics and Intelligent Laboratory Systems, 2002, 64(1): 1-14.
[18]Wang Y, Veltkamp D J, Kowalski B R. Multivariate instrument standardization. Analytical Chemistry, 1991, 63: 2750-2756.
[19]Osborne B G, Fearn T. Collaborative evaluation of universal calibrations for the measurement of protein and moisture in flour by near infrared reflectance. Journal of Food Technology, 1983, 18: 453-460.
[20]刘爱霞, 王长耀, 刘正军, 牛铮. 基于RS与GIS的干旱区棉花信息提取及长势监测. 地理与地理信息科学, 2003, 19(4): 101-104
Liu A X, Wang C Y, Liu Z J, Niu Z, Cotton information extraction and growth monitoring in arid area based on RS and GIS. Geography and Geo-Information Science, 2003, 19(4): 101-104. (in Chinese)
[21]黄敬峰, 王秀珍, 王人潮. 地理信息系统技术在水稻产量时空变化研究中的应用. 中国水稻科学,2000,14(4):213-218.
Huang J F, Wang X Z, Wang R C. GIS application in analyzing the time and spatial variation of rice production. Chinese Journal of Rice Science, 2000,14(4):213-218. (in Chinese)
[22]黄芬, 朱艳, 姜东, 荆奇, 曹卫星. 基于模型与GIS 的小麦籽粒品质空间差异分析. 中国农业科学,2009,42(9):3087-3095.
Huang F, Zhu Y, Jiang D, Jing Q, Cao W X. Spatial variation analysis of wheat grain quality based on model and GIS. Scientia Agricultura Sinica, 2009,42(9):3087-3095. (in Chinese)
[23]罗菊花, 黄文江, 韦朝领, 王纪华, 潘瑜春. 基于GIS的农作物病虫害预警系统的初步建立. 农业工程学报,2008,24(12): 127-131.
Luo J H, Huang W J, Wei C L, Wang J H, Pan Y C. Establishment of prewarning system for crop diseases and insect pests. Transactions of the CSAE, 2008,24(12): 127-131. (in Chinese)
[24]Davis J C. Statistics and Date Analysis in Geology. 3rd ed. New York: John Wiley & Sons Inc, 2002: 525-529.
[25]Wold S. Pattern recognition by means of disjoint principal components models. Pattern Recognition, 1976, 8: 127-139.
[26]王纪华, 李存军, 刘良云, 黄文江, 赵春江. 作物品质遥感监测预报研究进展. 中国农业科学, 2008, 41(9): 2633-2640.
Wang J H, Li C J, Liu L Y, Huang W J, Zhao C J. Progress of remote sensing monitoring and forecasting crop quality. Scientia Agricultura Sinica, 2008, 41(9): 2633-2640. (in Chinese)
[27]Blank T B, Sum S T, Brown S D, Monfre S L. Transfer of near-infrared multivariate calibrations without standards. Analytical Chemistry, 1996, 68: 2987-2995.