期刊文献+

近红外光谱和特征光谱的山茶油掺假鉴别方法研究 被引量:25

Detection of Camellia Oleifera Oil Adulterated with Sunflower Oil with Near Infrared(NIR)Spectroscopy and Characteristic Spectra
下载PDF
导出
摘要 山茶油素有"东方橄榄油"美誉,实现掺假山茶油的鉴别具有重要实用价值,采用近红外光谱技术对掺有葵花油的山茶油进行检测。分别以1%,5%,10%为梯度制备掺假比例不同的山茶油样品,并根据掺假比例将其分为A组(0%~5%)和B组(6%~10%)共11个样品,C组(15%~40%)6个和D组(50%~100%)6个样品。将每个掺假样品充分混匀后再分为9份,依次采集其1 000~2 500nm范围的吸收光谱,共获得207条光谱曲线。每组样品的光谱数据按2∶1随机分为训练集与验证集。经去除首尾噪声后,通过主成分分析法(principal component analysis,PCA)降维,并利用前四个主成分建立了鉴别山茶油不同掺假等级的主成分-支持向量机判别模型,训练集与验证集的总体判别准确率分别达96.38%和94.20%;进一步,通过对前四个主成分的载荷系数的分析,并结合原始光谱,提取建模过程中权重较大的波长并解析其化学含义,最终确定出五个特征波长:1 212,1 705,1 826,1 905及2 148nm,以此波长重新建立近红外特征光谱山茶油掺假等级判别模型,对训练集与验证集的总体判别准确率也达到了94.20%和92.75%。研究结果表明,利用近红外光谱和特征光谱均能够较好实现山茶油掺假等级的鉴别,同时所建立的近红外特征光谱模型也为设计相应的掺假山茶油实用便携式检测仪器提供了理论基础。 Camellia oleifera oil has the reputation of"oriental olive oil";it is important to detect the adulterated camellia oleifera oil.In this paper,NIR spectra were used to detect camellia oleifera oil adulterated with sunflower oil.Camellia oleifera oil adulterated with varying mass fraction of sunflower oil were prepared,i.e.,11 samples in 0%~10% with the gradient of 1%,6samples in 15%~40% with the gradient of 5%,6samples in 50%~100% with the gradient of 10%,and all the samples were divided into four groups such as A(0%~5%),B(6%~10%),C(15%~40%)and D(50% ~100%).A total of 207 absorbance spectra(1 000~2 500nm)were acquired by sampling 9times in each adulteration.Calibration set was consist of twothirds of the spectra data in each group selected randomly,and the validation set was made up of the last spectral data.After removing the noise in both ends of the spectra,principal component analysis(PCA)was used to reduce the dimensionality,then the first four PCs were used to build the support vector machine(SVM)identification model,and the identification accuracies of96.38% and 94.20% in calibration and validation set were obtained.Furthermore,five characteristic wavelengths(1 212,1 705,1 826,1 905 and 2 148nm)were selected based on the loading of the PCs,the peaks or troughs of the original spectra and the chemical functional groups they were corresponding to.A NIR simplified SVM identification model was built by them,and the identification accuracies were 94.20% and 92.75%.Overall,both NIR spectroscopy and NIR characteristic spectra can realize the identification of camellia oleifera oil adulterated with sunflower oil,and the characteristic wavelengths,selected in this study,provide a basis for the design of corresponding instrument.
作者 褚璇 王伟 赵昕 姜洪喆 王伟 刘声泉 CHU Xuan WANG Wei ZHAO Xin JIANG Hong-zhe WANG Wei LIU Sheng-quan(Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China College of Mechanical and Electronic Engineering, Tarim University, Alar 843300, China)
出处 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2017年第1期75-79,共5页 Spectroscopy and Spectral Analysis
基金 国家科技支撑计划项目(2012BAK08B04)资助
关键词 山茶油 掺假检测 近红外光谱技术 特征光谱 支持向量机 Camellia oleifera oil Detection of adulterations NIR spectroscopy Characteristic wavelengths SVM
  • 相关文献

参考文献4

二级参考文献35

  • 1褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展,2004,16(4):528-542. 被引量:565
  • 2顾伟珠,汪延祥.多元线性回归法分析油菜籽含油量的近红外光谱数据[J].中国粮油学报,1995,10(2):57-64. 被引量:22
  • 3邹小波,赵杰文,夏蓉,孙乐六.苹果糖度近红外光谱小波去噪和iPLS建模[J].农业机械学报,2006,37(6):79-82. 被引量:42
  • 4王江蓉,周建平,张令夫,刘荣,邓志坚.植物油掺伪检测方法的应用与研究进展[J].中国油脂,2007,32(6):78-81. 被引量:42
  • 5Sato Tetsuo, Uezono Ichiro. Nondestructive estlm ate on of fatty acid composition in seeds of Brassica napus L. by near-infrared spectroscopy[J]. Journal of the American Oil Chemists' Society, 1998,75(12).
  • 6王惠文.偏最小二乘回归方法及其应用[M].北京:国防工业出版社,2001.
  • 7Baibing Li. Model selection for partial least squares regression [J]. Chemometrics and Intelligent Laboratory Systems, 2002,64:79-89.
  • 8Huang J. A comparison of calibration methods based on calibration datasize and robustness[J]. Chemometrics and Intelligent Laboratory Systems, 2002,62 : 25- 35.
  • 9Dick Kleinknecht. A statistician's view of the single-Y PLS problem [Z]. Homepage of Chemometrics, editorial June 2002.
  • 10António S. Barros, Douglas N Rutledge. Principal components transform-partial least squares: a novel method to accelerate cross-validation in PLS regression[J]. Chemometrics and Intelligent Laboratory Systems, 2004,73 : 245-255.

共引文献60

同被引文献368

引证文献25

二级引证文献120

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部