近红外漫反射光谱云芝提取物快速掺假识别研究

doi:10.16178/j.issn.0528-9017.20170731

浙江农业科学 ›› 2017, Vol. 58 ›› Issue (7): 1192-1195.DOI: 10.16178/j.issn.0528-9017.20170731

近红外漫反射光谱云芝提取物快速掺假识别研究

涂红艳¹, 唐敏^{2, *}, 何晋浙³, 邵平³

1.湖州出入境检验检疫局综合技术服务中心,浙江湖州 313000;
2.杭州食品药品检测研究院,浙江杭州 3230001;
3.浙江工业大学食品科学系,浙江杭州 310014

收稿日期:2017-02-15 出版日期:2017-07-15
通讯作者: 唐敏,硕士,从事食品检测技术应用研究工作,E-mail:41700770@qq.com。
作者简介:涂红艳,硕士,从事食品检测技术应用研究工作,E-mail:19808164@qq.com。
基金资助:
浙江省公益技术应用研究项目(2016C32SA501036); 湖州市公益技术应用研究项目(2016GY02); 杭州市科技项目20170432B20)

Received:2017-02-15 Online:2017-07-15

摘要/Abstract

摘要： 为探索准确识别云芝提取物掺假(糊精)的方法,采用近红外漫反射光谱技术,对不同产地云芝提取物及掺假样品进行定性识别研究。在12 500~4 000 cm^-1波段范围,采集样品的光谱,应用偏最小二乘判别法(PLS-DA)建立云芝提取物掺假识别模型。通过Kolmogorov-Smirnov法检验模型中云芝提取物分类变量值的分布规律,从而建立识别云芝提取物的最佳阈值区间,以实现对云芝提取物的掺假研究。结果表明,使用一阶导数(FD)和多元散射校正(MSC)的预处理方法,在主因子数为2,以及分别设置云芝提取物及掺假样品的分类变量为1、0时,云芝提取物的分类变量值服从正态分布,其最佳阈值区间为0.54~1.28(包含0.54和1.28)。建立偏最小二乘判别(PLS-DA)模型,对验证集样品进行识别,正确率可达100%。因此,该方法可较好地对云芝提取物进行掺假识别,同时为云芝提取物掺假识别模型的界限值确立、建立最佳阈值区间提供理论依据和方法。

关键词: 云芝提取物, 掺假, 近红外, 阈值区间

中图分类号:

O657
S567

涂红艳, 唐敏, 何晋浙, 邵平. 近红外漫反射光谱云芝提取物快速掺假识别研究[J]. 浙江农业科学, 2017, 58(7): 1192-1195.

参考文献

[1] SUN X,SUN Y,ZHANG Q,et al. Screening and comparison of antioxidant activities of polysaccharides from coriolus versicolor [J]. International Journal of Biological Macromolecules,2014,69(8):12-19.
[2] JIAN C,CHISTI Y. Polysaccharopeptides of coriolus versicolor:Physiological activity,uses,and production [M]. John Wiley & Sons,Inc,2012.
[3] YI LZ,HE J,LIANG YZ,et al. Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on gc/ms and pls-lda [J]. FEBS Letters,2006,580(30):6837-6845.
[4] 柏婷婷. 真菌多糖掺假检测方法的研究[M]. 扬州:扬州大学,2008.
[5] 王灵灵,黄亚伟,戚淑叶,等. 基于主根横断面近红外光谱的西洋参和人参鉴别研究 [J]. 光谱学与光谱分析,2012,32(4):925-929.
[6] DONG J,ZHANG K,HUANG Y,et al. Adaptive total pls based quality-relevant process monitoring with application to the tennessee eastman process [J]. Neurocomputing,2015,154(1):77-85.
[7] MUSUMARRA G,BARRESI V,CONDORELLI D F,et al. Genome-based identification of diagnostic molecular markers for human lung carcinomas by pls-da [J]. Computational Biology and Chemistry,2005,29(3):183-195.
[8] WANG M,YAN G,FEI Z. Kernel pls based prediction model construction and simulation on theoretical cases [J]. Neurocomputing,2015,165(1):389-394.
[9] WANG A,AN N,CHEN G,et al. Improving pls-rfe based gene selection for microarray data classification [J]. Computers in Biology and Medicine,2015,62(1):14-24.
[10] RUTH S M V,VILLEGAS B,AKKERMANS W,et al. Prediction of the identity of fats and oils by their fatty acid,triacylglycerol and volatile compositions using pls-da [J]. Food Chemistry,2010,118(4):948-955.
[11] VERDUN S,CARIOU V,QANNARI E M. Weighted pls-discriminant analysis with application to conventional sensory profiling [J]. Food Quality and Preference,2012,23(1):25-29.
[12] 邵平,王钧,王星丽,等. 近红外漫反射光谱技术快速无损识别灵芝和云芝提取物研究 [J]. 核农学报,2015,29(3):499-505.
[13] 任绍钢. 近红外光谱技术在药用真菌云芝活性成分快速无损分析中的应用[J].长春:吉林大学,2008.
[14] ROSSINI K,VERDUN S,CARIOU V,et al. Pls discriminant analysis applied to conventional sensory profiling data [J]. Food Quality and Preference,2012,23(1):18-24.
[15] RICHTER N F,CEPEDA G,ROLDÁN J L,et al. European management research using partial least squares structural equation modeling (pls-sem) [J]. European Management Journal,2015,33(1):1-3.
[16] REZAEI S. Segmenting consumer decision-making styles (cdms) toward marketing practice:A partial least squares (pls) path modeling approach [J]. Journal of Retailing and Consumer Services,2015,22(1):11-15.
[17] RAHMAN IA,NAGAPAN S,ASMI A. Initial pls model of construction waste factors [J]. Procedia-Social and Behavioral Sciences,2014,129(1):469-474.
[18] MORA-LÓPEZ L,MORA J. An adaptive algorithm for clustering cumulative probability distribution functions using the kolmogorov-smirnov two-sample test [J]. Expert Systems with Applications,2015,42(8):4016-4021.
[19] 从飞云,陈进,董广明. 基于ar模型的kolmogorov-smirnov检验性能退化及预测研究 [J]. 振动与冲击,2012,31(10):79-82.
[20] 张瑜,谈黎虹,曹芳,等. 可见-近红外光谱分析技术对鱼油掺假定量快速无损检测方法研究 [J]. 光谱学与光谱分析,2013,33(6):1532-1536.
[21] OUERTANI S S,MAZEROLLES G,BOCCARD J,et al. Multi-way pls for discrimination:Compact form equivalent to the tri-linear pls2 procedure and its monotony convergence [J]. Chemometrics and Intelligent Laboratory Systems,2014,133(1):25-32.
[22] OLIVERI P,LOPEZ M I,CASOLINO M C,et al. Partial least squares density modeling (pls-dm)-a new class-modeling strategy applied to the authentication of olives in brine by near-infrared spectroscopy [J]. Analytica chimica acta,2014,851(2):30-36.
[23] TIAN Z,GU B,YANG L,et al. Hybrid ann-pls approach to scroll compressor thermodynamic performance prediction [J]. Applied Thermal Engineering,2015,77(1):113-120.
[24] KAYNAK R,SERT T,SERT G,et al. Supply chain unethical behaviors and continuity of relationship:Using the pls approach for testing moderation effects of inter-organizational justice [J]. International Journal of Production Economics,2015,162(2):83-91.
[25] BEVILACQUA M,MARINI F. Local classification:Locally weighted-partial least squares-discriminant analysis (lw-pls-da) [J]. Analytica chimica acta,2014,838(1):20-30.
[26] KRISHNAN A,WILLIAMS L J,MCINTOSH A R,et al. Partial least squares (pls) methods for neuroimaging:A tutorial and review [J]. NeuroImage,2011,56(2):455-475.
[27] LENHARDT L,BRO R,ZEKOVIC I,et al. Fluorescence spectroscopy coupled with parafac and pls da for characterization and classification of honey [J]. Food chemistry,2015,175(2):284-291.
[28] CHI Q,LIANG J. A multiple model predictive control strategy in the pls framework [J]. Journal of Process Control,2015,25(1):129-141.

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