基于氢核磁共振技术和化学计量学方法鉴别蜂蜜品种
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摘要
利用氢核磁共振(~1H NMR)技术结合化学计量学方法对不同品种的蜂蜜进行鉴别。采集33个洋槐蜜、48个油菜蜜、63个荔枝蜜的核磁指纹图谱,对数据进行不同方式的预处理后,采用有监督的偏最小二乘判别分析(PLS-DA)和正交偏最小二乘判别分析(OPLS-DA)建立判别模型。结果表明,不同的数据预处理方式对模型解释能力和预测能力的影响较大,自标度化(UV)模式更适于蜂蜜核磁数据的分析。建立的OPLS-DA模型可有效地分离判别3种蜂蜜,所建模型对3种蜂蜜的判别解释能力达95.8%,对未知样本的预测能力为90.5%。因此,利用~1H NMR结合OPLS-DA方法可有效地实现不同品种蜂蜜的快速鉴别。
~1H nuclear magnetic resonance(~1H NMR) spectroscopy combined with chemometrics was developed for the identification and classification of Chinese honey of different floral origins.33 acacia honeys,48 rape honeys and 63 lychee honeys were analyzed by NMR,and the NMR data were preprocessed in different modes.Supervised partial least squares discriminant analysis(PLS-DA) and orthogonal partial least squares discriminant analysis(OPLS-DA) were both used to set up discriminant models by extracting the useful information from NMR signals.Results showed that different data preprocessing methods have significant influences on the interpretation and prediction abilities of the models,and the unit variance(UV) scaling was more suitable for the analysis of honey NMR data than the others.The OPLS-DA model could effectively discriminate three kinds of honey of different floral origins.The explanation and prediction abilities based on the OPLS-DA model were 95.8% and 90.5%,respectively.Therefore,~1H NMR spectroscopy combined with chemometric could effectively realize the rapid and accurate identification of Chinese honeys of different floral origins.
~1H nuclear magnetic resonance(~1H NMR) spectroscopy combined with chemometrics was developed for the identification and classification of Chinese honey of different floral origins.33 acacia honeys,48 rape honeys and 63 lychee honeys were analyzed by NMR,and the NMR data were preprocessed in different modes.Supervised partial least squares discriminant analysis(PLS-DA) and orthogonal partial least squares discriminant analysis(OPLS-DA) were both used to set up discriminant models by extracting the useful information from NMR signals.Results showed that different data preprocessing methods have significant influences on the interpretation and prediction abilities of the models,and the unit variance(UV) scaling was more suitable for the analysis of honey NMR data than the others.The OPLS-DA model could effectively discriminate three kinds of honey of different floral origins.The explanation and prediction abilities based on the OPLS-DA model were 95.8% and 90.5%,respectively.Therefore,~1H NMR spectroscopy combined with chemometric could effectively realize the rapid and accurate identification of Chinese honeys of different floral origins.
引文
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[3]Chen L Z,Ye Z H,Zhao J.Food Sci.(陈兰珍,叶志华,赵静.食品科学),2008,29(3):494-498.
[4]Shen Y B,Tian H X,Chen C.Food Ind.(申永波,田怀香,陈臣.食品工业),2016,37(4):251-254.
[5]Yang Y,Battesti M J,Paolini J,Muselli A,Tomi P,Costa J.Food Chem.,2013,134(1):37-47.
[6]Conte L S,Miorini M,Giomo A,Bertacco G,Zironi R.J.Agric.Food Chem.,1998,46(5):1844-1849.
[7]Cavazza A,Corradini C,Musci M,Salvadeo P.J.Sci.Food Agric.,2012,93(5):1169-1175.
[8]Keckes S,Gasic U,Velickovic T C,Milojkovic-Opsenica D,Natic M,Teˇsi.Food Chem.,2013,138(1):32-40.
[9]Seisonen S,Kivima E,Vene K.Food Chem.,2015,169:34-40.
[10]Herrero L C,Pe1a-Crecente R M,García M S,Barciela G J.Food Chem.,2013,141(4):3559-3565.
[11]Gok S,Severcan M,Goormaghtigh E,Kandemir I,Severcan F.Food Chem.,2015,170:234-240.
[12]Corvucci F,Nobili L,Melucci D,Grillenzoni F V.Food Chem.,2015,169:297-304.
[13]Lenhardt L,Zekovic I,Dramicanin T,Dramicanin M D,Bro R.Appl.Spectrosc.,2014,68(5):557-563.
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[15]Gao H B,Zhang Z F.Principles and Experimental Methods of Nuclear Magnetic Resonance.Wuhan:Wuhan University Press(高汉宾,张振芳.核磁共振原理与实验方法.武汉:武汉大学出版社),2008.
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[17]Brescia M A,Mazzilli V,Sgaramella A,Ghelli S,Fanizzi F P,Sacco A.J.Am.Oil Chem.Soc.,2004,81(5):431-436.
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[19]Arana V A,Medina J,Alarcon R,Moreno E,Heintz L,Schafer H,Wist J.Food Chem.,2015,175:500-506.
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[21]Boffo E F,Tavares L A,Tobias C A T,Ferreira M M C,Ferreira A G.LWT-Food Sci.Technol.,2009,42(9):1455-1460.
[22]Zheng X,Zhao Y R,Wu H F,Dong J Y,Feng J H.Food Anal.Methods,2016,9(6):1470-1479.
[23]Lolli M,Bertelli D,Plessi M,Sabatini A G,Restani C.J.Agric.Food Chem.,2008,56(4):1298-1304.
[24]Craig A,Cloareo O,Holmes E,Nicholson J K,Lindon J C.Anal.Chem.,2006,78(7):2262-2267.
[25]Saccenti E,Smilde A K,Westerhuis J A,Hendriks M M.J.Chemom.,2011,25(12):644-652.
[26]Deng L L,Cheng K K,Shen G P,Zhou L,Liu X Z,Dong J Y,Chen Z.Spectrosc.Spectral Anal.(邓伶莉,Cheng Kian-Kai,沈桂平,周玲,刘新卓,董继扬,陈忠.光谱学与光谱分析),2014,34(10):2868-2872.
[27]Fang M L,Xing J,Li Z Y,Qin X M.Chin.Tradit.Herbal Drugs(范玛莉,邢婕,李震宇,秦雪梅.中草药),2014,45(22):3230-3237.
[28]Bylesj9 M,Rantalainen M,Cloarec O,Nicholson J K,Holmes E,Trygg J.J.Chemom.,2006,20(8/10):341-351.