ATR-FTIR在小麦及其制品呕吐毒素污染水平快速测定中的应用
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摘要
为快速测定小麦及其制品呕吐毒素(deoxynivalenol,DON)污染情况,搜集小麦、面粉及面粉制品共98份,利用衰减全反射-傅里叶变换红外光谱(attenuated total reflectance-Fourier transform infrared spectroscopy,ATR-FTIR)获取样品在4 000～600 cm~(-1)的光谱信息,对样品中的DON含量建立了基于偏最小二乘回归(partial least squares regression,PLSR)分析和逐步多元性回归(stepwise multiple linear regression,SMLR)分析方法的定量分析模型。结果显示,不同DON含量样品在1 740、1 648、1 549 cm~(-1)和1 300～900 cm~(-1)等波段处的吸收值存在显著差异。PLSR和SMLR均能较好预测样品中的DON含量,其中PLSR模型的预测集决定系数R_P~2、预测均方根误差(root mean squared error of prediction,RMSEP)和相对分析偏差(residual predictive deviation,RPD)值分别为0.86、0.438 mg/kg和2.6。SMLR结合9个波长所建模型的R_P~2、RMSEP和RPD值分别为0.86、0.426 mg/kg和2.6。结果表明,ATR-FTIR用于小麦及其制品DON污染快速分析具有可行性。
For rapid detection of the contamination level of deoxynivalenol (DON) in wheat and its products, a total of 98 samples of wheat, flour and flour products were collected and subjected to attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) analysis in the wavenumber range of 4 000 to 600 cm~(-1). Quantitative models between ATR-FTIR spectra and DON concentration were established by partial least squares regression analysis (PLSR) or stepwise multiple linear regression (SMLR). The results showed that the absorption values of samples with various DON contents were markedly different at 1 740, 1 648, 1 549 and 900–1 300 cm~(-1). Both PLSR and SMLR models could effectively predict DON contamination in samples. The coefficient of determination for prediction (R_P~2), root mean square error for prediction (RMSEP) and residual predictive deviation (RPD) of the PLSR model were 0.86, 0.438 mg/kg and 2.6, respectively. The SMLR model built with 9 wavenumbers was found to be optimal, with R_P~2, RMSEP and RPD of 0.86, 0.426 mg/kg and 2.6, respectively. These results indicated that ATR-FTIR offers the feasibility for rapid determination of wheat samples contaminated by DON.
For rapid detection of the contamination level of deoxynivalenol (DON) in wheat and its products, a total of 98 samples of wheat, flour and flour products were collected and subjected to attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) analysis in the wavenumber range of 4 000 to 600 cm~(-1). Quantitative models between ATR-FTIR spectra and DON concentration were established by partial least squares regression analysis (PLSR) or stepwise multiple linear regression (SMLR). The results showed that the absorption values of samples with various DON contents were markedly different at 1 740, 1 648, 1 549 and 900–1 300 cm~(-1). Both PLSR and SMLR models could effectively predict DON contamination in samples. The coefficient of determination for prediction (R_P~2), root mean square error for prediction (RMSEP) and residual predictive deviation (RPD) of the PLSR model were 0.86, 0.438 mg/kg and 2.6, respectively. The SMLR model built with 9 wavenumbers was found to be optimal, with R_P~2, RMSEP and RPD of 0.86, 0.426 mg/kg and 2.6, respectively. These results indicated that ATR-FTIR offers the feasibility for rapid determination of wheat samples contaminated by DON.
引文
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[24]封薇.小麦中镰刀菌毒素污染和脱氧雪腐镰刀菌烯醇(DON)积累分析[D].雅安:四川农业大学,2010.
[25]CAMPO S T M D,BONNAIRE N,PICQUE D,et al.Initial studies into the characterisation of ripening stages of Emmental cheeses by mid-infrared spectroscopy[J].Dairy Science&Technology,2009,89(2):155-167.DOI:10.1051/dst/2008041.
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[2]吴小荣,殷蔚申.饲料中脱氧雪腐镰刀菌烯醇的薄层层析测定法[J].微生物学通报,1990,17(2):116-119.DOI:10.13344/j.microbiol.china.1990.02.019.
[3]罗颖鹏,陈正行,王韧,等.固相萃取柱净化-高效液相色谱法测定小麦中脱氧雪腐镰刀菌烯醇[J].食品科学,2015,36(20):222-225.DOI:10.7506/spkx1002-6630-201520043.
[4]王娅琴,赵谋明,赵海锋.固相萃取-气相色谱-质谱联用法测定酱油中脱氧雪腐镰刀菌烯醇[J].食品与发酵工业,2012,38(6):157-160.DOI:10.13995/j.cnki.11-1802/ts.2012.06.007.
[5]梁颖,刘邻渭,张春晖.液质联用同时检测小麦中三种镰刀菌毒素[J].中国粮油学报,2006,21(6):160-162.DOI:10.3321/j.issn:1003-0174.2006.06.038.
[6]罗瑞峰.酶联免疫吸附法快速检测谷物中的脱氧雪腐镰刀菌烯醇[J].农产品加工,2011(5):95-96;109.DOI:10.3969/j.issn.1671-9646(X).2011.05.028.
[7]MAHOUBIFAR M,YOUSEFINEJAD S,ALIZADEH M,et al.Prediction of the acid value,peroxide value and the percentage of some fatty acids in edible oils during long heating time by chemometrics analysis of FTIR-ATR spectra[J].Journal of the Iranian Chemical Society,2016,13(12):2291-2299.DOI:10.1007/s13738-016-0948-1.
[8]MENG X,PAN Q,DING Y,et al.Rapid determination of phospholipid content of vegetable oils by FTIR spectroscopy combined with partial least-square regression[J].Food Chemistry,2014,147:272-278.DOI:10.1016/j.talanta.2009.07.030.
[9]FERNANDEZ-GONZALEZ A,MONTEJO-BERNADO J M,RODRIUEZ-PRIETO H,et al.Easy-to-use analytical approach based on ATR-FTIR and chemometrics to identify apple varieties under protected designation of origin(PDO)[J].Computers and Electronics in Agriculture,2014,108:166-172.DOI:10.1016/j.compag.2014.07.009.
[10]BUREAU S,RUIZ D,REICH M,et al.Application of ATR-FTIRfor a rapid and simultaneous determination of sugars and organic acids in apricot fruit[J].Food Chemistry,2009,115(3):1133-1140.DOI:10.1016/j.foodchem.2008.12.100.
[11]YU X,GAO Y,ZHAO Z,et al.Rapid determination of amino acids in Chinese wolfberry (Lycium bararum L.) fruit by using Fourier transform infrared spectroscopy and partial least square regression[J].Food Analytical Methods,2017,10(7):2436-2443.DOI:10.1007/s12161-017-0802-9.
[12]NAIR R,VENKATESH S,ATHMASELVI K A,et al.Rapid estimation and quantification of sucrose content in fruit juices using Fourier transform infrared-attenuated total reflectance(FTIR-ATR)spectroscopy[J].Journal of Food Measurement and Characterization,2016,10(1):24-31.DOI:10.1007/s11694-015-9272-1.
[13]HU X,TOYODA K,YAMANOUE M,et al.Evaluation of fatty acid profile of Wagyu beef by ATR-FTIR spectroscopy[J].Food and Bioprocess Technology,2010,3(6):883-891.DOI:10.1007/s11947-010-0375-8.
[14]NUNES K M,ANDRADE M V O,SANTOS A M P,et al.Detection and characterisation of frauds in bovine meat in natura by nonmeat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy[J].Food Chemistry,2016,205:14-22.DOI:10.1016/j.foodchem.2016.02.158.
[15]GEDIKOGLU A,LIN M,CLARKE A D.Prediction of oxidation in ground beef meatballs made with citrus fiber by Fourier transform infrared spectroscopy[J].Meat Science,2016,112:151.DOI:10.1016/j.meatsci.2015.08.112.
[16]KOS G,LOHNINGER H,KRSKA R.Fourier transform mid-infrared spectroscopy with attenuated total reflection (FTIR/ATR) as a tool for the detection of Fusarium fungi on maize[J].Vibrational Spectroscopy,2002,29(1):115-119.DOI:10.1016/S0924-2031(01)00196-5.
[17]GIROLAMO D A,LIPPOLIS V,NORDKVIST E,et al.Rapid and non-invasive analysis of deoxynivalenol in durum and common wheat by Fourier-transform near infrared (FT-NIR) spectroscopy[J].Food Additives and Contaminants,2009,26(6):907-917.DOI:10.1080/02652030902788946.
[18]关二旗,崔贵金,卞科,等.基于近红外光谱特征的赤霉病小麦籽粒SIMCA识别模型构建研究[J].中国粮油学报,2016,31(11):124-129.DOI:10.3969/j.issn.1003-0174.2016.11.021.
[19]沈飞,吴启芳,魏颖琪,等.谷物霉菌挥发性物质的电子鼻与GC-MS检测研究[J].中国粮油学报,2016,31(7):148-152.DOI:10.3969/j.issn.1003-0174.2016.07.027.
[20]王丽娟,柯润辉,安红梅,等.固相萃取柱净化-液相色谱-串联质谱法测定糕点中脱氧雪腐镰刀菌烯醇及其衍生物和玉米赤霉烯酮[J].食品工业科技,2017,38(14):31-34.DOI:10.13386/j.issn1002-0306.2017.14.007.
[21]GALVAO R K H,ARAUJO M C U,SILVA E C,et al.Cross-validation for the selection of spectral variables using the successive projections algorithm[J].Journal of the Brazilian Chemical Society,2007,18(8):1580-1584.DOI:10.1590/S0103-50532007000800021.
[22]吴启芳.稻米黄曲霉毒素污染的电子鼻和红外光谱快速无损检测方法研究[D].南京:南京财经大学,2016.
[23]ADEDIPE O E,JOHANNINGSMEIER S D,TRUONG V D,et al.Development and validation of a near-infrared spectroscopy method for the prediction of acrylamide content in French-fried potato[J].Journal of Agricultural and Food Chemistry,2016,64(8):1850-1860.DOI:10.1021/acs.jafc.5b04733.
[24]封薇.小麦中镰刀菌毒素污染和脱氧雪腐镰刀菌烯醇(DON)积累分析[D].雅安:四川农业大学,2010.
[25]CAMPO S T M D,BONNAIRE N,PICQUE D,et al.Initial studies into the characterisation of ripening stages of Emmental cheeses by mid-infrared spectroscopy[J].Dairy Science&Technology,2009,89(2):155-167.DOI:10.1051/dst/2008041.
[26]杨晓蓉,周建新,姚明兰.不同储藏条件下稻谷脂肪酸值变化和霉变相关性研究[J].粮食储藏,2006,35(5):49-53.DOI:10.3969/j.issn.1000-6958.2006.05.013.
[27]WANG J,JIANG R.Eggshell crack detection by dynamic frequency analysis[J].European Food Research and Technology,2005,221(1/2):214-220.DOI:10.1007/s00217-005-1149-9.
[28]HELM D,LABISCHINSKI H,NAUMANN D.Elaboration of a procedure for identification of bacteria using Fourier-transform IRspectral libraries:a stepwise correlation approach[J].Journal of Microbiological Methods,1991,14(2):127-142.DOI:10.1016/0167-7012(91)90042-O.
[29]赵亚娟.赤霉病感染小麦品质特性的研究[D].郑州:河南工业大学,2013:31-45.
[30]NIE M,ZHANG W Q,XIAO M,et al.FT-IR spectroscopy and artificial neural network identification of Fusarium species[J].Journal of Phytopathology,2007,155(6):364-367.DOI:10.1111/j.1439-0434.2007.01245.x.
[31]HELL J,PRUCKLER M,DANNER L,et al.A comparison between near-infrared (NIR) and mid-infrared (ATR-FTIR) spectroscopy for the multivariate determination of compositional properties in wheat bran samples[J].Food Control,2016,60:365-369.DOI:10.1016/j.food cont.2015.08.003.
[32]HERRMANN S,MAYER J,MICHEL K,et al.Predictive capacity of visible-near infrared spectroscopy for quality parameter assessment of compost[J].Journal of Near Infrared Spectroscopy,2009,17(5):289-301.DOI:10.1255/jnirs.850.