CN107314988A - 一种油菜籽粒油酸含量近红外分析方法 - Google Patents
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Abstract
本发明属于油菜籽粒品质分析检测方法领域,具体涉及一种油菜籽粒油酸含量近红外分析方法。本发明的主要步骤包括:样品油酸含量的化学测定;采集样品籽粒的近红外光谱;每个样品的化学值和近红外光谱的一一对应;对光谱进行预处理,剔除异常值,并用化学计量学方法分析数据,建立近红外模型;利用验证集的光谱和化学值对应数据对模型进行验证,以检验近红外模型的效果。本发明具有高效、快速、无损、操作简便的特点,省去了对材料的化学预处理,对材料的选择也更加灵活。
Description
技术领域
本发明属于油菜籽粒品质检测方法领域,具体涉及油菜籽粒油酸含量的检测方法,是一种油酸油菜籽粒油酸含量的近红外快速无损测定方法。
背景技术
高油酸作为甘蓝型油菜的重要性状,一直是品质育种的主要研究方向之一。油酸是一种十八碳单不饱和脂肪酸,高油酸菜籽油在提高食用油的食用、贮藏品质方面及工业上也有很高的应用价值。在煎炸食物时,高油酸食用油高温不起烟,能够缩短煎炸时间,减少油吸收过量(Miller等,Genetic control of high oleic acid content in sunfloweroil.CropSciences,1987,27:923‐926.);在日常饮食中,高油酸可降低血液中的低密度脂蛋白胆固醇的含量,有利于防止动脉血管硬化(Chang等,Effects of the ratio ofpolyunsaturated and monounsaturated fatty acid onrat plasma and liver lipidconcentration.Lipids,1998,33:481‐487)。高油酸菜籽油在加工、储运过程中不易氧化,热稳定性较好,可以延长商品的货架期。
经典的脂肪酸分析采用气相色谱法。需要破坏性地处理种子,同时效率也较低。早在1990年以前,近红外技术就已经在石油化工、制药、军工、食品和农业等多个领域迅速发展(G.T.Xu等,Study of quantitative calibration model suitability innear‐infrared spectroscopy analysis,Spectrosc.Spectr.Anal.21(2001)459–463)。利用近红外光谱分析技术来获得油酸指标是从大量预选材料中筛选高油酸材料的重要途径。近红外光谱分析技术(NIRS)具有高效、快速、无损、操作简便的特点,省去了对材料的化学预处理,对材料的选择也更加灵活。油菜中已有种子近红外分析方法,但这些均是基于油酸含量在15‐60%样品制备的分析曲线,不能准确分析高油酸含量(>70%)材料。
发明内容
本发明的目的在于克服现有技术的缺陷,提供一种高油酸材料(60%~80%)油菜籽粒油酸含量近红外分析方法,本发明步骤包括:样品油酸含量的化学测定;采集样品籽粒的近红外光谱;每个样品的化学值和近红外光谱的一一对应;对光谱进行预处理,剔除异常值,并用化学计量学方法分析数据,建立近红外模型;利用验证集的光谱和化学值对应数据对模型进行验证,以检验近红外模型的效果。
本发明利用近红外光谱分析方法可以准确测定高油酸材料(例如油酸含量为59%~80%的待测材料),为油菜种质资源筛选提供新的测试方法。同时本发明具有高效、快速、无损、操作简便的特点,省去了对材料的化学预处理,对材料的选择也更加灵活。
本发明的技术方案如下:
一种油菜籽粒油酸含量近红外分析方法,包括如下步骤:
(1)利用气相色谱法测定油菜样品籽粒的油酸含量。先用乙醚‐石油醚(体积比1:1)将脂肪酸从种子中萃取出来,气相色谱仪测得各种脂肪酸含量,作为样品化学值;
(2)利用NIRSystems 5000系统采集样品1100‐2500nm范围内近红外漫反射光谱,每隔2nm采集一次,如图2所述;
(3)将步骤(1)得到的样品化学值与步骤(2)得到的近红外光谱信息一一对应,对光谱数据进行预处理剔除异常值,从处理后的波谱数据中筛选出特征波长,用化学计量法对光谱数据进行的散射校正和数学处理,优选方法为利用多元散射校正法(MSC)效果较好;
(4)最后用改进最小二乘法(MPLS,即在最小二乘法的基础上,在每一次循环运算后对数据进行归一化处理)建立回归方程。
最小二乘法公式:
所建立的近红外预测模型:
Cnirs=B0+B1*A1+B2*A2+............+Bk*Ak
其中Bi为回归系数,Ai为第i个特征波长是的吸光度;
(5)从同一批样品中随机分出122个样品作为验证集,将验证集的光谱预测值与化学测量值对应数据对模型进行验证,以检验近红外模型的效果,将验证集的近红外预测值与化学值代入回归系数,得到验证集的光谱与化学值的相关系数R2=0.929(见图1);
(6)利用不同年份的高油酸(60%~80%)种子材料的近红外光谱油酸预测值与GC测得的油酸含量化学值对应数据对步骤4得到的高油酸近红外模型进行验证,以检验模型对不同年份材料的适用性,得到验证材料光谱预测值与化学测量值的相关系数R2=0.861(见图2);
作为优选技术方案,上述步骤(3)中剔除异常值的方法为剔除距离总体样品中心点马氏距离大于3.0的样品,采用的定标方法为改进最小二乘法。
本发明的优点在于:
1、本发明弥补了以往油菜模型对高油酸材料近红外预测存在较大偏差。
2、本发明实现了油菜油酸含量的快速检测。相比于化学方法较长的测定周期,近红外检测只需要3‐4秒。
3、本发明实现了对油菜油酸含量的无损检测。近红外检测可在不破坏种子的情况下进行,对种子量很少的珍贵育种材料来说有很重要的意义。
附图说明
图1:将验证集的近红外预测值与化学值代入回归系数,得到验证集预测值与化学值之间的相关系数。
图2:194份油菜籽粒定标样品近红外光谱图。
图3:194份油菜籽粒定标样品油酸含量分布图。
图4:86份不同年份的油菜籽粒材料对模型进行验证,近红外预测值与化学值的回归线和报警界限图。
图5:86份油菜籽粒材料的验证样品油酸含量分布图。
具体实施方式
实施例1(试验材料与方法)
本发明模型建立前先将原始样品随机分为两份,校正集(194份油菜样品,用于模型建立)和验证集(122份油菜样品,用于模型验证)(见表1)。
表1 模型校正集和验证集参数
样本量 | 最小值 | 最大值 | 平均值 | 方差 | |
校正集 | 194 | 59.443 | 78.195 | 71.6704 | 4.8883 |
验证集 | 122 | 58.838 | 77.845 | 68.309 | 5.2297 |
利用程序将校正集随机分成四份,用其中3份样品定标,剩余一份做验证,重复4次得到校正集内部交叉验证结果,利用验证集对已建好的模型进行验证得到外部验证结果,见表2。
表2 模型内部验证、外部验证结果
表2的说明:MSC,多元离散校正
近红外光谱采集
将没有杂质的油菜籽粒放入样品槽,并完全覆盖样品槽底部石英玻璃,用近红外光谱仪采集光谱数据。
扫描参数如下:扫描间隔:2nm,扫描次数:3次,测量方式:漫反射,波长范围:1100‐2500nm,数据点数:700。
样品油酸含量化学值检测方法
将30粒油菜籽粒磨碎后加无水乙醚与石油醚(1:1)来萃取油菜籽中的脂肪酸,然后用Agilent 7890A GC System测定油酸含量。
模型评估指标
用RSQ(决定系数,即R2)和RMSEC(预测标准差)来评估模型预测的准确性。
式中,n为验证样品数,yi为第i个样品的油酸含量GC化学检测值,为第i个样品的油酸含量预测值,R2越接近1说明模型预测准确性越好,RMSEC越接近0,说明模型稳定性越好。
实施例2:不同年份的高油酸(油酸含量>60%)油菜籽粒对高油酸近红外模型进行检验
(1)采集高油酸油菜籽粒样品,得到86份待测样品。
(2)将没有杂质的油菜籽粒放入样品槽,并完全覆盖样品槽底部石英玻璃,用近红外光谱仪采集光谱数据。
(3)用已建立的油菜籽粒高油酸近红外模型处理光谱数据得到样品油酸含量的近红外预测值。
(4)将30粒油菜籽粒磨碎后加无水乙醚与石油醚(1:1)来萃取油菜籽中的脂肪酸,然后用Agilent 7890A GC System测定油酸的含量。
实验材料相关信息见表3。
表3 86份油菜籽粒GC测得的化学值与近红外预测值对照表
(5)检验结果见表4表4 86份甘蓝型油菜种子油酸含量化学值与近红外预测值统计结果及相关系数
最小值 | 最大值 | 平均值 | 标准差 | 标准误 | 相关系数 |
60.222 | 81.625 | 73.416 | 5.452 | 0.588 | 0.861 |
参考文献:
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[2]Liang C,Yuan H,Zhao Z,et al.A new multivariate calibration modeltransfer method of near-infrared spectral analysis[J].Chemometrics andIntelligent Laboratory Systems,2016,153:51-57.
[3]Liu Y,Delhom C,Campbell B T,et al.Application of near infraredspectroscopy in cotton fiber micronaire measurement[J].Information Processingin Agriculture,2016,3(1):30-35.
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Claims (3)
1.一种油菜籽粒油酸含量近红外分析方法,其特征在于,所述方法包括如下步骤:
(1)利用气相色谱法测定样品籽粒的油酸含量,先用体积比为的1:1的乙醚:石油醚将脂肪酸从种子中萃取出来,以气相色谱仪测得各种脂肪酸相对含量,作为样品化学值;
(2)利用NIRSystems 5000系统采集样品1100‐2500nm范围内近红外漫反射光谱,每隔2nm采集一次;
(3)将步骤(1)得到的样品化学值与步骤(2)得到的近红外光谱信息一一对应,对光谱数据进行预处理,剔除异常值,用化学计量法对光谱数据进行的散射校正和数学处理,优选为多元散射校正,即MSC法;
(4)最后用改进最小二乘法即MPLS,建立回归方程:
最小二乘法公式:
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<mo>-</mo>
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<mn>1</mn>
<mi>N</mi>
</mfrac>
<mi>&Sigma;</mi>
<mi>x</mi>
<mi>&Sigma;</mi>
<mi>y</mi>
</mrow>
<mrow>
<msup>
<mi>&Sigma;x</mi>
<mn>2</mn>
</msup>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<msup>
<mrow>
<mo>(</mo>
<mi>&Sigma;</mi>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mfrac>
</mrow>
<mrow>
<mi>b</mi>
<mo>=</mo>
<mover>
<mi>y</mi>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<mi>a</mi>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
</mrow>
建立近红外预测模型:
Cnirs=B0+B1*A1+B2*A2+............+Bk*Ak
其中:Bi为回归系数,Ai为第i个特征波长是吸光度;
(5)用油酸含量为75%以上材料对定标方程进行扩展,使模型对高油酸材料预测能力更强,并提高模型效率。
2.根据权利要求1所述的油菜籽粒油酸含量近红外分析方法,其特征在于,步骤(4)中改进最小二乘法是主成份回归的改进形式,即每次主成份分析的残余变量经平均残余变量标准化后再进行下一次主成份分析,即主成份+湿化学数据,在此基础上进行定标建模。
3.权利要求1或2所述的油菜籽油酸含量的近红外分析方法在选育高油酸甘蓝型油菜中的应用。
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CN113484270A (zh) * | 2021-06-04 | 2021-10-08 | 中国科学院合肥物质科学研究院 | 一种单粒水稻脂肪含量定量分析模型的构建及检测方法 |
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