柚子皮果胶快速测定的高光谱成像系统
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摘要
利用高光谱成像技术测定了柚子皮果胶含量,研究高光谱定量分析中不同系统参数对校正模型的影响。利用推扫式图像扫描部件采集柚子果皮切片样本的近红外高光谱数据,使用多变量计量方法建立定量模型并评估预测效果。针对在不同光源强度、不同镜片焦距、有无测量背景的情况下获取数据完成建模分析,比较不同系统参数对建模效果的影响。建立多变量高光谱成像分析的定量模型,融合不同的预处理方法实现模型优化,能够得到良好的预测结果,验证了高光谱成像分析技术在农作物质量检测中的应用能力。所做的系统参数性能测试对比方案有望推广应用于其他光谱分析过程中,便于为不同的待测对象选择优化的建模参数,对于在线无人机高光谱遥感数据采集的系统设计具有一定参考价值。
Hyperspectral imaging( HSI) technique was applied to quantitatively determine the content of pectin in shaddock peel samples. The influence was studied based on different systematic parameters in the built-up of the HSI instrumental system. Slices of shaddock peels were measured and analyzed using the HSI data acquisition system with the built-up of a prevalent pushbroom scanner in the near infrared region. The calibration models were established by discussing some multivariate data analysis methods,and the model predictive effects were evaluated. The multivariate analytical models were optimized in comparison about some measurement parameters,under the discussion of using different watts for light intensity,different lenses when data were caught,and different materials as the scanning backgrounds.The multivariate analytical methods performed well in the calibration and prediction processes,going smooth with some different tunable pretreatment methods. It was verified that the HSI technology was capable to be applied on the analysis of agricultural crop quality. The test for HSI system optimization by comparing the measurement properties and parameters has prospective potential application on other spectral analysis,and provides referential contributions to the design of the online hyperspectral data acquisition systems using unmanned aerial vehicles in the field of remote sensing.
Hyperspectral imaging( HSI) technique was applied to quantitatively determine the content of pectin in shaddock peel samples. The influence was studied based on different systematic parameters in the built-up of the HSI instrumental system. Slices of shaddock peels were measured and analyzed using the HSI data acquisition system with the built-up of a prevalent pushbroom scanner in the near infrared region. The calibration models were established by discussing some multivariate data analysis methods,and the model predictive effects were evaluated. The multivariate analytical models were optimized in comparison about some measurement parameters,under the discussion of using different watts for light intensity,different lenses when data were caught,and different materials as the scanning backgrounds.The multivariate analytical methods performed well in the calibration and prediction processes,going smooth with some different tunable pretreatment methods. It was verified that the HSI technology was capable to be applied on the analysis of agricultural crop quality. The test for HSI system optimization by comparing the measurement properties and parameters has prospective potential application on other spectral analysis,and provides referential contributions to the design of the online hyperspectral data acquisition systems using unmanned aerial vehicles in the field of remote sensing.
引文
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[4] Elmasry G,Wang N,Vigneault C. Postharv. Biol. Technol.,2009,52(1):1
[5] Zou Y Q,Liu W D,Jin S Z. Chin. J. Anal. Lab.,2015,34(5):562邹艳秋,刘卫东,金尚忠.分析试验室,2015,34(5):562
[6] Wang W,Paliwal J. Sen. Instrum. Food Qual. Saf.,2007,1(4):193
[7] Dooley K,Lomax S,Zeibel J,et al. Analyst,2013,138(17):4838
[8] Lei T,Zhao G X,Zhu X C,et al. Acta Ecol. Sin.,2010,30(9):2276雷彤,赵庚星,朱西存,等.生态学报,2010,30(9):2276
[9] Cluff K,Naganathan G K,Subbiah J,et al. Sen. Instrum. Food Qual. Saf.,2008,2(3):189
[10] Zhou Q,Zhu D Z,Wang C,et al. Food Sci.,2010,31(23):423周全,朱大洲,王成,等.食品科学,2010,31(23):423
[11] Niu Z Y,Li X J,Gao H L. Transactions of the CSAE,2014,30(22):181牛智有,李晓金,高海龙.农业工程学报,2014,30(22):181
[12] Sun D W(ed.). Hyperspectralimaging for food quality analysis and control. Elsevier,2010
[13] Chen H Z,Song Q Q,Tang G Q,et al. J. Cereal Sci.,2014,60(3):595
[14] Chen H Z,Shi K,Cai K,et al. RSC Adv.,2015,5(98):80612
[15] Liu S J,Tian Y W,Zhang F,et al. Food Sci.,2017,38(8):277刘思伽,田有文,张芳,等.食品科学,2017,38(8):277
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[17] Yang X K,Zhong M L,Jing X J,et al. Acta Optica Sin.,2012,32(7):0711001杨秀坤,钟明亮,景晓军,等.光学学报,2012,32(7):0711001
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[19] Zhang R,Sun M R. Sci. Technol. Food Ind.,2006,9(5):120张锐,孙美榕.食品工业科技,2006,9(5):120
[20] Gourdin W H,Datte P,Jensen W,et al. Fusion Eng. Des.,2016,112:343
[21] Li F T,Li Z G,Wang X X,et al. Opt. Precis. Eng.,2017,25(8):2004李福田,李志刚,王晓旭,等.光学精密工程,2017,25(8):2004
[22] Manley M. Chem. Soc. Rev.,2014,43(24):8200
[23] Miller J,Miller J C. Statistics and chemometrics for analytical chemistry(6th ed.),Prentice Hall,2010
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[25] Zhu R G,Duan H W,Yao X D,et al. Spectrosc. Spect. Anal.,2016,36(9):2925朱荣光,段宏伟,姚雪东,等.光谱学与光谱分析,2016,36(9):2925