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麻疯树种子含油量近红外光谱定标模型的建立

惠文凯 王益 陈晓阳

惠文凯, 王益, 陈晓阳. 麻疯树种子含油量近红外光谱定标模型的建立[J]. 北京林业大学学报, 2018, 40(1): 1-7. doi: 10.13332/j.1000-1522.20170388
引用本文: 惠文凯, 王益, 陈晓阳. 麻疯树种子含油量近红外光谱定标模型的建立[J]. 北京林业大学学报, 2018, 40(1): 1-7. doi: 10.13332/j.1000-1522.20170388
Hui Wen-kai, Wang Yi, Chen Xiao-yang. Calibration model building for investigating seed oil content of Jatropha curcas using near infrared spectroscopy[J]. Journal of Beijing Forestry University, 2018, 40(1): 1-7. doi: 10.13332/j.1000-1522.20170388
Citation: Hui Wen-kai, Wang Yi, Chen Xiao-yang. Calibration model building for investigating seed oil content of Jatropha curcas using near infrared spectroscopy[J]. Journal of Beijing Forestry University, 2018, 40(1): 1-7. doi: 10.13332/j.1000-1522.20170388

麻疯树种子含油量近红外光谱定标模型的建立

doi: 10.13332/j.1000-1522.20170388
基金项目: 

国家自然科学基金项目 30972388

广东省自然科学基金团队项目 9351064201000002

详细信息
    作者简介:

    惠文凯,博士生。主要研究方向:林木遗传育种。Email: xwk168@126.com 地址:510642广东省广州市天河区五山路483号华南农业大学林学与风景园林学院

    责任作者:

    陈晓阳,博士,教授。主要研究方向:林木遗传育种。Email:xychen@scau.edu.cn 地址:同上

  • 中图分类号: S758.015

Calibration model building for investigating seed oil content of Jatropha curcas using near infrared spectroscopy

  • 摘要: 目的本研究旨在建立麻疯树种子含油量的近红外光谱定标模型, 为近红外光谱技术应用于麻疯树种子含油量的测定提供依据。方法利用瑞典波通DA7200型近红外光谱成分分析仪采集了125份麻疯树种子样品的光谱数据, 通过样品化学值测定、光谱数据预处理以及回归统计方法建立了麻疯树含油量的近红外光谱定标模型, 并利用10份未知含油量的种子样品对模型的准确性进行了检验。结果本研究建模所选125份样品的含油量范围为25.23%~39.73%, 平均值为33.91%±2.64%, 中位数为34.31%, 基本覆盖了当前麻疯树的主要品种, 在种子油含量上具有一定的代表性。二阶导数联合标准正态变量转换法为建立麻疯树种子含油量近红外光谱定标模型的最佳预处理方法, 偏最小二乘法为最佳的回归方法。模型验证结果显示, 样品的化学测定值与近红外光谱定标模型预测值极显著相关, 且相关系数达0.9556, 预测标准偏差为0.6536。结论检验结果说明该模型具有较高的可靠性, 可应用于后期麻疯树种子油分含量的大批量快速测定。

     

  • 图  1  125份建模样品的近红外吸收光谱图

    Figure  1.  NIRS of 125 modeling samples

    图  2  经二阶导数+SNV处理后的光谱图

    Figure  2.  Line plot of NIRS after 2nd-derivative + SNV pre-treatment

    图  3  NIRS定标模型线性回归图

    Y1是近红外模型内部验证集合的拟合结果;Y2是校正集合的拟合结果。

    Figure  3.  Linear regression plot of the NIRS calibration model

    Y1 was the fitting results of inner revised set of the calibration model using near infrared spectroscopy, Y2 was the fitting results of validation set.

    表  1  125份建模样品种子含油量化学值描述性统计表

    Table  1.   Descriptive statistics of investigated value of seed oil content among 125 samples

    分析项目
    Analysis item
    平均值±标准差
    Mean±SD/%
    最大值
    Max./%
    最小值
    Min./%
    中位数
    Median/%
    变异系数
    Variable coefficient/%
    方差
    Variance
    化学测定值Chemical determination value 33.91±2.64 39.73 25.23 34.31 7.79 6.97
    下载: 导出CSV

    表  2  不同光谱预处理方法的模型参数

    Table  2.   Model parameters of each pre-treatment method

    模型
    Model
    预处理方法
    Pre-treatment method
    删除点数
    Outlier number
    RC RMSEC RCV RMSECV
    1 SNV 7 0.718 1.392 0.632 1.612
    2 MSC 7 0.733 1.353 0.679 1.501
    3 标准化Normalization 9 0.749 1.325 0.688 1.505
    4 一阶导数1st-derivative 8 0.851 1.013 0.771 1.254
    5 二阶导数2nd-derivative 3 0.857 0.980 0.750 1.310
    6 1st-derivative +SNV 4 0.813 1.139 0.760 1.303
    7 1st-derivative +MSC 7 0.824 1.108 0.769 1.280
    8 1st-derivative +normalization 11 0.783 1.195 0.709 1.404
    9 2nd-derivative +SNV 9 0.910 0.784 0.833 1.076
    10 2nd-derivative +MSC 10 0.904 0.799 0.824 1.086
    11 2nd-derivative +normalization 7 0.818 1.075 0.729 1.328
    12 SNV+1st-derivative 9 0.782 1.237 0.726 1.404
    13 MSC+1st-derivative 9 0.784 1.184 0.730 1.341
    14 Normalization+1st-derivative 9 0.808 1.150 0.729 1.376
    15 SNV+2nd-derivative 9 0.859 0.919 0.767 1.201
    16 MSC+2nd-derivative 10 0.837 0.968 0.740 1.249
    17 Normalization+2nd-derivative 10 0.885 0.860 0.774 1.211
    注:RC为校正模型相关系数;RMSEC为校正模型标准偏差;RCV为交互验证相关系数;RMSECV为交互验证标准偏差;SNV为标准正态变量转换法;MSC为乘积分散校正法。下同。
    Notes: RC, calibration model correlation coefficient; RMSEC, calibration model standard deviation; RCV, cross-validation correlation coefficient; RMSECV, cross-validation standard deviation; SNV, standard normal variable; MSC, multiplicative scatter correction. The same below.
    下载: 导出CSV

    表  3  不同回归方法处理后的模型参数

    Table  3.   Model parameters with varied regression methods

    预处理方法
    Pre-treatment method
    回归方法
    Regression method
    删除点数
    Outlier number
    RC RMSEC RCV RMSECV
    2nd-derivative +SNV PLS 9 0.910 0.784 0.833 1.076
    2nd-derivative +SNV PCR 9 0.677 1.358 0.612 1.495
    2nd-derivative +MSC PLS 10 0.904 0.799 0.824 1.086
    2nd-derivative +MSC PCR 10 0.624 1.410 0.542 1.553
    下载: 导出CSV

    表  4  检验样品的化学测定值、模型预测值及相关参数

    Table  4.   Chemical determination values, model predictive values and their relative parameters of test samples

    检测值类型
    Detection value item
    平均值
    Mean/%
    最大值
    Max./%
    最小值
    Min./%
    相关系数
    Correlation coefficient
    预测标准偏差
    Predictive standard deviation
    化学测定值Chemical determination value 35.22 38.25 30.57 0.9556** 0.6536
    预测值Predicted value 35.51 37.56 31.04
    注:**表示相关性极显著,P<0.01。Notes: ** means the very significant level, P<0.01.
    下载: 导出CSV
  • [1] 惠文凯, 杨舒贻, 陈涵斌, 等.赤霉素诱导麻疯树雌雄花分化的研究[J].南京林业大学学报(自然科学版), 2016, 40(6): 174-180. http://d.old.wanfangdata.com.cn/Periodical/njlydxxb201606027

    Hui W K, Yang S Y, Chen H B, et al. Female and male flower bud differentiation of Jatropha curcas L. induced by gibberellins[J]. Journal of Nanjing Forestry University(Natural Science Edition), 2016, 40(6): 174-180. http://d.old.wanfangdata.com.cn/Periodical/njlydxxb201606027
    [2] Koh M Y, Ghazi T I M. A review of biodiesel production from Jatropha curcas L. oil[J]. Renewable and Sustainable Energy Reviews, 2011, 15(5): 2240-2251. doi: 10.1016/j.rser.2011.02.013
    [3] Baldini M, Bulfoni E, Ferfuia C. Seed processing and oil quality of Jatropha curcas L. on farm scale: a comparison with other energy crops[J]. Energy for Sustainable Development, 2014, 19: 7-14. doi: 10.1016/j.esd.2013.12.005
    [4] 惠文凯, 陈晓阳, 刘明骞, 等.麻风树种源间种实性状变异研究[J].北京林业大学学报, 2014, 36(3): 110-114. doi: 10.13332/j.cnki.jbfu.2014.03.012

    Hui W K, Chen X Y, Liu M Q, et al. Variations of fruit and seed traits of Jatropha curcas L. among provenances[J]. Journal of Beijing Forestry University, 2014, 36(3): 110-114. doi: 10.13332/j.cnki.jbfu.2014.03.012
    [5] 李维莉, 杨辉, 林南英, 等.可再生能源麻疯树种子油化学成分研究[J].云南大学学报(自然科学版), 2000, 22(5): 324. doi: 10.3321/j.issn:0258-7971.2000.05.022

    Li W L, Yang H, Lin N Y, et al. Study on the chemical composition of seed oil of Jatropha as renewable energy[J]. Journal of Yunnan University (Natural Sciences Edition), 2000, 22(5): 324. doi: 10.3321/j.issn:0258-7971.2000.05.022
    [6] 佘珠花, 刘大川, 刘金波, 等.麻疯树籽油理化特性和脂肪酸组成分析[J].中国油脂, 2005, 30(5): 30-31. doi: 10.3321/j.issn:1003-7969.2005.05.008

    She Z H, Liu D C, Liu J B, et al. Physicochemical properties and fatty acid composition of Jatropha curcas L. seed oil[J]. China Oils and Fats, 2005, 30(5): 30-31. doi: 10.3321/j.issn:1003-7969.2005.05.008
    [7] Kaushik N, Kumar K, Kumar S, et al. Genetic variability and divergence studies in seed traits and oil content of Jatropha (Jatropha curcas L.) accessions[J]. Biomass and Bioenergy, 2007, 31(7): 497-502. doi: 10.1016/j.biombioe.2007.01.021
    [8] Roggo Y, Chalus P, Maurer L, et al. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies[J]. Journal of Pharmaceutical and Biomedical Analysis, 2007, 44(3): 683-700. doi: 10.1016/j.jpba.2007.03.023
    [9] 方彦, 王汉宁.利用近红外光谱法测定玉米籽粒含油量的研究[J].西北农业学报, 2007, 16(1): 111-113. doi: 10.3969/j.issn.1004-1389.2007.01.026

    Fang Y, Wang H N. Studies on the determining of oil contents for maize kernels using near infrared spectroscopy[J]. Acta Agriculturae Boreali-Occidentalis Sinica, 2007, 16(1): 111-113. doi: 10.3969/j.issn.1004-1389.2007.01.026
    [10] 原姣姣, 王成章, 陈虹霞.近红外光谱技术及其在植物油品质分析中的应用[J].生物质化学工程, 2010, 44(6): 59-65. doi: 10.3969/j.issn.1673-5854.2010.06.013

    Yuan J J, Wang C Z, Chen H X. Near infrared spectroscopy technology and analysis in quality traits of vegetable oil[J]. Biomass Chemical Engineering, 2010, 44(6): 59-65. doi: 10.3969/j.issn.1673-5854.2010.06.013
    [11] 杨翠玲, 陈文杰, 赵兴忠, 等.近红外光谱法同时分析油菜9种品质参数的研究[J].西北农林科技大学学报(自然科学版), 2006, 34(3): 61-67. doi: 10.3321/j.issn:1671-9387.2006.03.013

    Yang C L, Chen W J, Zhao X Z, et al. Determination of quality factors in intact rapeseed by near infrared reflectance spectroscopy(NIRS)[J]. Journal of Northwest Sci-Tech University of Agricultural and Forestry(Natural Science Edition), 2006, 34(3): 61-67. doi: 10.3321/j.issn:1671-9387.2006.03.013
    [12] Jiang H Y, Zhu Y J, Wei L M, et al. Analysis of protein, starch and oil content of single intact kernels by near infrared reflectance spectroscopy (NIRS) in maize (Zea mays L.)[J]. Plant Breeding, 2007, 126(5): 492-497. doi: 10.1111/pbr.2007.126.issue-5
    [13] 杨小红, 郭玉秋, 傅旸, 等.利用近红外光谱法分析玉米籽粒脂肪酸含量的研究[J].光谱学与光谱分析, 2009, 29(1): 106-109. doi: 10.3964/j.issn.1000-0593(2009)01-0106-04

    Yang X H, Guo Y Q, Fu Y, et al. Measuring fatty acid concentration in maize grain by near-infrared reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis, 2009, 29(1): 106-109. doi: 10.3964/j.issn.1000-0593(2009)01-0106-04
    [14] 刘波, 张丽娟, 苗保河, 等.近红外光谱法与国标法测定大豆蛋白质和脂肪的比较[J].山东农业科学, 2007 (1): 109-111. doi: 10.3969/j.issn.1001-4942.2007.01.037

    Liu B, Zhang L J, Miao B H, et al. Comparison between near infrared spectroscopy and GB method used to determine protein and fat in soybean[J]. Shandong Agricultural Sciences, 2007 (1): 109-111. doi: 10.3969/j.issn.1001-4942.2007.01.037
    [15] 朱文秀, 赵继献, 张品, 等.近红外光谱分析与化学方法检测油菜种子品质参数的比较[J].安徽农学通报, 2010, 16(17): 182-183, 185. doi: 10.3969/j.issn.1007-7731.2010.17.091

    Zhu W X, Zhao J X, Zhang P, et al. The comparison study of NIR and chemical method for determining quality parameters in Rapeseed[J]. Anhui Agricultural Science Bulletin, 2010, 16(17): 182-183, 185. doi: 10.3969/j.issn.1007-7731.2010.17.091
    [16] Posom J, Sirisomboon P. Evaluation of the moisture content of Jatropha curcas kernels and the heating value of the oil-extracted residue using near-infrared spectroscopy[J]. Biosystems Engineering, 2015, 130: 52-59. doi: 10.1016/j.biosystemseng.2014.12.003
    [17] Vaknin Y, Ghanim M, Samra S, et al. Predicting Jatropha curcas seed-oil content, oil composition and protein content using near-infrared spectroscopy: a quick and non-destructive method[J]. Industrial Crops and Products, 2011, 34(1): 1029-1034. doi: 10.1016/j.indcrop.2011.03.011
    [18] Luque De Castro M D, Priego-capote F. Soxhlet extraction: past and present panacea[J]. Journal of chromatography A, 2010, 1217(16): 2383-2389. doi: 10.1016/j.chroma.2009.11.027
    [19] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会.粮油检验粮食中粗脂肪含量测定: GB/T5512—2008[S].北京: 中国标准出版社, 2008.

    General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. Inspect of grain and oilseeds: determination of crude fat content in grain: GB/T5512—2008[S]. Beijing: Standards Press of China, 2008.
    [20] 吴建国.作物种子品质研究中近红外光谱分析模型的创建和应用[D].杭州: 浙江大学, 2004.

    Wu J G. NIR spectroscopy model construction and application in the research of crop seed qualities[D]. Hangzhou: Zhejiang University, 2004.
    [21] 奚如春, 钟燕梅, 邓小梅, 等.基于近红外光谱的油茶种子含油量定标模型构建[J].林业科学, 2013, 49(4): 1-6. doi: 10.3969/j.issn.1672-8246.2013.04.001

    Xi R C, Zhong Y M, Deng X M, et al. Models for determining oil contents in Camellia oleifera seeds by using near infrared spectroscopy[J]. Scientia Silvae Sinicae, 2013, 49(4): 1-6. doi: 10.3969/j.issn.1672-8246.2013.04.001
    [22] 李勇, 魏益民, 王锋.影响近红外光谱分析结果准确性的因素[J].核农学报, 2005, 19(3): 236-240. doi: 10.3969/j.issn.1000-8551.2005.03.017

    Li Y, Wei Y M, Wang F. Affecting factors on the accuracy of near-infrared spectroscopy analysis[J]. Acta Agriculturae Nucleatae Sinica, 2005, 19(3): 236-240. doi: 10.3969/j.issn.1000-8551.2005.03.017
    [23] Shabanimofrad M, Rafii M Y, Wahab P E M, et al. Phenotypic, genotypic and genetic divergence found in 48 newly collected Malaysian accessions of Jatropha curcas L.[J]. Industrial Crops and Products, 2013, 42: 543-551. doi: 10.1016/j.indcrop.2012.06.023
    [24] 祝诗平, 王一鸣, 张小超.农产品近红外光谱品质检测软件系统的设计与实现[J].农业工程学报, 2003, 19(4): 175-179. doi: 10.3321/j.issn:1002-6819.2003.04.043

    Zhu S P, Wang Y M, Zhang X C. Design and implementation of near infrared spectroscopy analysis software system for agricultural product quality detection[J]. Transactions of the Chinese Society of Agricultural Engineering, 2003, 19(4): 175-179. doi: 10.3321/j.issn:1002-6819.2003.04.043
    [25] 王毅.近红外光谱分析技术在食用植物油品质检测中的应用[D].镇江: 江苏大学, 2010.

    Wang Y. Application of near-infrared spectroscopy analysis in quality detection of edible vegetable oil[D]. Zhenjiang: Jiangsu University, 2010.
    [26] 陈锋, 何中虎, 崔党群.利用近红外透射光谱技术测定小麦籽粒硬度的研究[J].作物学报, 2004, 30(5): 455-459. doi: 10.3321/j.issn:0496-3490.2004.05.009

    Chen F, He Z H, Cui D Q. Measurement of wheat hardness by near infrared transmittance spectroscopy[J]. Acta Agronomica Sinica, 2004, 30(5): 455-459. doi: 10.3321/j.issn:0496-3490.2004.05.009
    [27] 王刚.花椒挥发油含量近红外光谱无损检测研究[D].重庆: 西南大学, 2008.

    Wang G. Nondestructive detection of volatile oil content in Zanthoxylum bungeagum Maxim. by near infrared spectroscopy[D]. Chongqing: Southwest University, 2008.
    [28] 韩智彪.棉籽油份含量近红外测定技术研究[D].武汉: 华中农业大学, 2012.

    Han Z B. Studies on the determination of cotton seed oil by near infrared reflectance spectroscopy[D]. Wuhan: Huazhong Agricultural University, 2012.
    [29] 高建芹, 张洁夫, 浦惠明, 等.近红外光谱法在测定油菜籽含油量及脂肪酸组成中的应用[J].江苏农业学报, 2007, 23(3): 189-195. doi: 10.3969/j.issn.1000-4440.2007.03.006

    Gao J Q, Zhang J F, Pu H M, et al. Analysis of oil, oleic acid and erucic acid contents in rapeseed by near infrared reflectance spectroscopy(NIRS)[J]. Jiangsu Journal of Agricultural Sciences, 2007, 23(3): 189-195. doi: 10.3969/j.issn.1000-4440.2007.03.006
    [30] 李君霞, 张洪亮, 严衍禄, 等.水稻蛋白质近红外定量模型的创建及在育种中的应用[J].中国农业科学, 2006, 39(4): 836-841. doi: 10.3321/j.issn:0578-1752.2006.04.029

    Li J X, Zhang H L, Yan Y L, et al. Establishment of math models of NIRS analysis for protein contents in seed and its application in rice breeding[J]. Scientia Agricultura Sinica, 2006, 39(4): 836-841. doi: 10.3321/j.issn:0578-1752.2006.04.029
    [31] 张学锋, 周新奇, 陈智锋, 等.新型国产近红外分析仪的菜籽菜粕快速检测技术研究[J].计算机与应用化学, 2010, 27(12): 1625-1628. doi: 10.3969/j.issn.1001-4160.2010.12.008

    Zhang X F, Zhou X Q, Chen Z F, et al. The research of rapid analysis for rapeseed and rapeseed meal based on a new type domestic near infrared analyzer[J]. Computers and Applied Chemistry, 2010, 27(12): 1625-1628. doi: 10.3969/j.issn.1001-4160.2010.12.008
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  • 收稿日期:  2017-10-27
  • 修回日期:  2017-11-13
  • 刊出日期:  2018-01-01

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