CN109507138A - 一种基于紫外可见光谱及极限学习机的二元葡萄籽油掺伪定量分析方法 - Google Patents
一种基于紫外可见光谱及极限学习机的二元葡萄籽油掺伪定量分析方法 Download PDFInfo
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Abstract
本发明属于食品分析领域，涉及一种基于紫外可见光谱及极限学***均值与方差比值确定极限学习机的激励函数和隐含层节点数，利用训练集建立极限学习机模型，对未知样品进行预测。本发明的优势在于采集样品的光谱，速度快；采用紫外可见分光光度计仪器，价格低廉；采用极限学习机建模，预测准确。本发明适用于二元掺伪葡萄籽油中各组分油的准确定量。
Description
技术领域
本发明属于食品分析领域，涉及一种基于紫外可见光谱及极限学习机的二元葡萄籽油掺伪定量分析方法。
背景技术
葡萄籽油不仅营养价值高，风味颇佳，而且还具有降低胆固醇，扩张血管，并防止血栓等医用功效。因此，葡萄籽油的价格比普通食用植物油的价格高。为获取高额利润，不法商家将劣质食用植物油掺杂在葡萄籽油中以谋取巨额利润。因此寻找合适的葡萄籽油鉴别方法，对确保葡萄籽油质量具有重要意义。
目前，对食用植物油掺伪的研究主要方法有化学方法、物理方法、气相色谱法(武彦文，刘玲玲，李冰宁，欧阳杰，姜树，气相色谱质谱联用检测食用油中芳烃矿物油的方法，中国发明专利，2018，CN201711372650.9)、核磁共振法(刘芸，张睿，丁涛，张健，费晓庆，陈磊，张晓燕，吴斌，一种基于核磁共振技术用于荆条蜜、油菜蜜和洋槐蜜的鉴别方法，中国发明专利，2018，CN201710742918.7)和近红外光谱法(柴琴琴，林双杰，王武，蔡逢煌，林琼斌，林伟群，赖添悦，一种基于近红外光谱的葡萄籽油掺假鉴别方法，中国发明专利，2017，CN201710373663.1)等。气相色谱法不仅检测时间长且需要样品预处理；核磁共振法技术仪器设备还十分昂贵；近红外光谱无破坏性且检测精度高，但是光谱信号较弱，谱峰重叠严重。
紫外可见光谱是物质吸收紫外可见光后，其价电子从低能级向高能级跃迁，产生吸收峰形成的。由于紫外光谱技术具有分析速度快、仪器价格低廉、操作简单等特点，并且绝大部分的有机化合物都有吸收光谱的特性如吸收光谱形状、各吸收峰的波长位置等特点，所以紫外光谱法广泛的应用于有机化合物和无机化合物的定量和定性分析以及其它化合物的测定。
但是掺伪葡萄籽油样品成分复杂，很难通过单一峰的高度对不同食用油组分含量进行分析，需要借助多元校正技术。常用的多元校正方法主要有主成分回归法、偏最小二乘回归法、人工神经网络法、支持向量回归法等。其中偏最小二乘回归因其简单、快速得到了广泛应用。但是非线性现象严重，偏最小二乘回归的建模结果就会受到影响。人工神经网络和支持向量回归虽然可以解决建模中遇到的非线性问题，但也存在学习速度慢、需设置大量参数、易陷入局部极小、算法复杂，难以改进扩展等问题。极限学习机法(Guang-BinHuang，Lei Chen，and Chee-Kheong Siew，Universal approximation using incrementalconstructive feedforward networks with random hidden nodes，IEEE Transactionson Neural Networks，2016，17(4)：879-892)无需人为设置大量网络参数，且产生唯一最优解，学习速度快，泛化性能好，具有线性与非线性建模方法的优势。
发明内容
本发明的目是针对上述问题，以紫外可见光谱技术作为技术手段，建立极限学习机模型，对未知样品进行含量预测。
为实现本发明所提供的技术方案包括以下步骤：
1)购买葡萄籽油样品及其它食用油，按0-100％配置成二元掺伪葡萄籽油样品，样品数目至少50个。样品手动震荡10次以上、超声20-30分钟，静置1天以后测量。
2)设置紫外可见分光光度计的参数，波长范围为350-1000nm，间隔1nm，使用光程为1cm的比色皿盛放样品。用紫外可见分光光度计仪器检测样品，每个实验样品测三次。将三次光谱做平均处理，并其作为最终光谱数据进行处理。
3)对样品数据进行KS分组，将样品总数的2/3作为训练集，1/3作为预测集。
4)确定极限学习机模型的激励函数和隐含层节点数
激励函数分别采用S型函数、正弦函数、硬阈值函数、三角函数和径向基函数，隐含层节点数从1-1000，间隔为1进行改变，在每个激励函数及隐含层节点数下建立极限学***均值与方差的比值。相关系数平均值与方差比值的最大值对应的激励函数和隐含层节点数为极限学习机模型的最佳激励函数和隐含层节点数。
5)建立极限学习机模型并对预测集中样品组分的含量进行预测。
采用最佳的最佳激励函数和隐含层节点数，建立极限学习机模型，将预测集中样品的光谱代入到模型中，预测二元掺伪葡萄籽油中各葡萄籽油和大豆油组分的比例。
本发明将紫外可见光谱与极限学习机方法结合对二元葡萄籽油进行定量分析，具有操作简单、快速、准确、经济的优点。
附图说明
图1是51个二元掺伪葡萄籽油样品的紫外光谱图。
图2是二元掺伪葡萄籽油数据中葡萄籽油组分极限学***均值与方差比值随激励函数及隐含层节点数的变化图。
图3是二元掺伪葡萄籽油数据中大豆油组分极限学***均值与方差比值随激励函数及隐含层节点数的变化图。
图4是二元掺伪葡萄籽油数据中葡萄籽油(a)和大豆油(b)组分预测值与真实值的相关图。
具体实施方式
为更好理解本发明，下面结合实施例对本发明做进一步地详细说明，但是本发明要求保护的范围并不局限于实施例所表示的范围。
实施例
本实施例应用于葡萄籽油和大豆油二元掺伪组分的检测。具体的步骤如下：
1)采用8种品牌的葡萄籽油和大豆油配制二元掺伪葡萄籽油，葡萄籽油浓度在100-0％，间隔2％，大豆油浓度0-100％，间隔2％。配置共51个样品。样品手动震荡10 次以上，超声20分钟，静置1天以后测量。
2)采用Evolution 300紫外可见分光光度计(赛默飞世尔科技公司，美国)采集样品的紫外可见光谱。实验前，打开仪器设置紫外可见分光光度计的参数，采样的波长范围为350-1000nm，间隔1nm，使用光程为1cm的比色皿盛放样品。预热30分钟，以空气为参比，每个实验样品测三次，将三次光谱的平均值作为最终光谱数据进行处理。如图1显示了实验测得的51个二元掺伪葡萄籽油样品的紫外可见光谱。
3)对样品数据进行KS分组，将样品总数的2/3作为训练集，1/3作为预测集。
4)确定极限学习机模型的隐含层节点数和激励函数
激励函数分别采用S型函数、正弦函数、硬阈值函数、三角函数和径向基函数，隐含层节点数从1-1000，间隔为1进行改变，在每个激励函数及隐含层节点数下建立极限学***均值与方差的比值。图2显示了二元掺伪葡萄籽油数据中葡萄籽油组分相关系数平均值与方差比值随激励函数和隐含层节点数的变化。从图中可以看出，相关系数平均值与方差比值最大值对应的最佳激励函数和隐含层节点数为S型函数和841。同样图3显示了大豆油组分的参数优化结果，可以得到大豆油组分最佳激励函数和隐含层节点数为S型函数和952。
5)建立极限学习机模型并对预测集中样品分组的含量进行预测。
对葡萄籽油和大豆油组分，激励函数和隐含层节点数分别取S型函数和841，S型函数和952，分别建立极限学习机模型，将预测集的光谱代入到模型中，分别预测两个组分的含量。图4显示了葡萄籽油(a)和大豆油(b)组分预测值与真实值的关系图。可以看出，两组分的线性关系非常好，相关系数都为0.9998，预测准确度非常高。
Claims (4)
1.一种基于紫外可见光谱及极限学习机的二元葡萄籽油掺伪定量分析方法，其特征在于：配置二元葡萄籽油样品，并采集样品的紫外可见光谱，将数据集划分为训练集和预测集，确定极限学习机的激励函数和隐含层节点数，最后建立最优的极限学习机模型，将未知样品带入模型中，对二元葡萄籽油进行定量分析。
2.根据权利要求1所述的一种基于紫外可见光谱及极限学习机的二元葡萄籽油掺伪定量分析方法，其特征在于：所述的采集紫外可见光谱的波长范围为350-1000nm，间隔1nm，使用光程为1cm的比色皿盛放样品。
3.根据权利要求1所述的一种基于紫外可见光谱及极限学习机的二元葡萄籽油掺伪定量分析方法，其特征在于：所述的样品配制方式为：按0-100％配置成二元掺伪葡萄籽油样品，样品数目至少50个。样品手动震荡10次以上，超声20-30分钟，静置1天以后测量。
4.根据权利要求1所述的一种基于紫外可见光谱及极限学习机的二元葡萄籽油掺伪定量分析方法，其特征在于：对掺伪葡萄籽油的其它油的种类没有限制，大豆油，玉米油，稻米油，棕果油，棉籽油其中的任意一种都可以。
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