气相离子迁移谱对山茶油掺假的检测
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摘要
以掺假山茶油样为气相离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)检测对象,利用多维主成分分析(multi-way principal component analysis,MPCA)法和偏最小二乘(partial least squares,PLS)回归分析处理二维谱图数据,探索并建立一种山茶油纯度检测方法。对配制的不同比例3种食用植物油的掺假油样进行GC-IMS检测,采用MPCA压缩并提取矩阵中的得分矩阵进行主成分分析,将提取的得分矩阵进行PLS分析,建立掺假量的定量预测模型。结果表明,MPCA处理后的主成分图可以明显区分山茶油样和掺入不同种类食用油的掺假山茶油样,且不同掺入比例组有其明显的归属区域;采用PLS对MPCA的得分矩阵进行回归分析,可实现对山茶油掺假比例的准确定量测定。该方法具有快速、准确、无损的特点,可应用推广到其他联用仪器的数据分析处理中,在食用油品质控制与评价方法中具有很大的应用前景。
This study aimed to establish a new method for quick detection of camellia oil purity. Adulterated camellia oil samples were detected by gas chromatography-ion mobility spectrometry(GC-IMS) and the two-dimensional spectral data obtained were analyzed by multi-way principal component analysis(MPCA) and partial least squares(PLS) regression.Mixtures of camellia oil with three other vegetable oils at different concentrations respectively were analyzed by GC-IMS.MPCA was used to reduce the dimensionality of the data and extract a score matrix from the three-dimensional matrix for principal component analysis(PCA), and quantitative predictive models for adulterated oils were established by PLS analysis of the score matrix. The experimental results showed that pure and adulterated samples were clearly distinguished in the PCA plots with MPCA processing and that each adulteration ratio exhibited an obvious region in the plots. PLS regression analysis of the scores matrix extracted by MPCA enabled accurate quantitative analysis of camellia oil adulteration levels.The presented method proved to be a fast, accurate and non-destructive method for the detection of camellia oil adulteration.This method can also be applied to data analysis in other combined instruments, and has a great potential for applications in the quality control and evaluation of edible oils.
This study aimed to establish a new method for quick detection of camellia oil purity. Adulterated camellia oil samples were detected by gas chromatography-ion mobility spectrometry(GC-IMS) and the two-dimensional spectral data obtained were analyzed by multi-way principal component analysis(MPCA) and partial least squares(PLS) regression.Mixtures of camellia oil with three other vegetable oils at different concentrations respectively were analyzed by GC-IMS.MPCA was used to reduce the dimensionality of the data and extract a score matrix from the three-dimensional matrix for principal component analysis(PCA), and quantitative predictive models for adulterated oils were established by PLS analysis of the score matrix. The experimental results showed that pure and adulterated samples were clearly distinguished in the PCA plots with MPCA processing and that each adulteration ratio exhibited an obvious region in the plots. PLS regression analysis of the scores matrix extracted by MPCA enabled accurate quantitative analysis of camellia oil adulteration levels.The presented method proved to be a fast, accurate and non-destructive method for the detection of camellia oil adulteration.This method can also be applied to data analysis in other combined instruments, and has a great potential for applications in the quality control and evaluation of edible oils.
引文
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[17]GARRIDO-DELGADO R,MU袿Z-PéREZ M E,ARCE L.Detection of adulteration in extra virgin olive oils by using UVIMSand chemometric analysis[J].Food Control,2018,85:292-295.DOI:10.1016/j.foodcont.2017.10.012.
[18]GARRIDODELGADO R,ARCE L,VALCáRCEL M.Multi-capillary column-ion mobility spectrometry:a potential screening system to differentiate virgin olive oils[J].Analytical&Bioanalytical Chemistry,2012,402(1):489-498.DOI:10.1007/s00216-011-5328-1.
[19]HAJIALIGOL S,GHORASHI S A,ALINOORI A H,et al.Thermal solid sample introduction-fast gas chromatography-low flow ion mobility spectrometry as a field screening detection system[J].Journal of Chromatography A,2012,1268(4):123-129.DOI:10.1016/j.chroma.2012.10.010.
[20]胡国梁,徐立荣,许生陆,等.基于电子鼻的食用油氧化判别分析[J].食品科学,2016,37(20):141-145.DOI:10.7506/spkx1002-6630-201620023.
[21]李红,赵博,冉晓鸿,等.基于傅里叶变换中红外光谱识别正常食用植物油和精炼潲水油模型分析[J].食品科学,2014,35(6):121-124.DOI:10.7506/spkx1002-6630-201406025.
[22]PACELLA M,COLOSIMO B M.Multilinear principal component analysis for statistical modeling of cylindrical surface:a case study[J].Quality Technology&Quantitative Management,2016,15(4):1-19.DOI:10.1080/16843703.2016.1226710.
[23]杨仁杰,刘蓉,杨延荣,等.用二维相关近红外谱和多维主成分分析判别掺杂牛奶[J].光学精密工程,2014,22(9):2352-2358.DOI:10.3788/OPE.20142209.2352.
[24]HOLLINGSWORTH B V,REICHENBACH S E,TAO Q,et al.Comparative visualization for comprehensive two-dimensional gas chromatography[J].Journal of Chromatography A,2006,1105(1/2):51-58.DOI:10.1016/j.chroma.2005.11.074.
[25]SHENG Z,JIANG X Y,ZHEN W.Pseudo-color coding with phasemodulated image density[J].Advanced Materials Research,2012,403:1618-1621.DOI:10.1016/j.infrared.2013.01.006.
[26]张方圆,吴凌涛,林晨,等.气相色谱结合化学计量学用于6种食用植物油的分类[J].分析试验室,2016(11):1254-1258.DOI:10.13595/j.cnki.issn1000-0720.2016.0283.
[27]DOU X,MAO J,ZHANG L,et al.Multispecies adulteration detection of camellia oil by chemical markers[J].Molecules,2018,23(2):241-251.DOI:10.3390/molecules23020241.
[28]余佳荣,晁燕,谭丽娟,等.基于气相色谱法的湖南油茶籽油掺杂判定[J].中南林业科技大学学报,2014,34(5):88-91.DOI:10.14067/j.cnki.1673-923x.2014.05.004.
[29]冯棋琴,吴满梅,汪有存,等.不同产地压榨山茶油理化性质及组分比较分析[J].粮食与油脂,2017,30(12):39-42.
[30]田硕,刘建学,韩四海,等.基于特征脂肪酸的掺伪芝麻油快速鉴别模型建立[J].食品科学,2014,35(22):207-210.DOI:10.7506/spkx1002-6630-201422040.
[2]毛方华,王鸿飞,周明亮.山茶油的功能特性[J].食品科技,2010(1):181-185.
[3]毛宏敏,曾爱民,周毅,等.化学分析技术在植物食用油掺假分析技术中的应用[J].粮食流通技术,2016,10(19):75-79.DOI:10.16736/j.cnki.cn41-1434/ts.2016.19.024.
[4]LIU Y D,XIE Q H,WANG H Y,et al.Comparative study on camellia oil&olive oil quality and quantitative analysis of adulteration[J].2016,116(4):380-387.DOI:10.3788/OPE.20162413.0600.
[5]XIN-XIN M O,YING Z,TONG S,et al.Model optimization of ternary system adulteration detection in camellia oil based on visible/near infrared spectroscopy[J].Spectroscopy&Spectral Analysis,2016,119:15-22.DOI:10.3964/j.issn.1000-0593(2016)12-3881-04.
[6]王挥,宋菲,曹飞宇,等.基于荧光光谱的初榨椰子油掺假检测技术研究[J].食品工业,2017(5):293-296.DOI:10.16736/j.cnki.cn41-1434/ts.2016.19.024.
[7]ZOU M Q,ZHANG X F,QI X H,et al.Rapid authentication of olive oil adulteration by Raman spectrometry[J].Journal of Agricultural&Food Chemistry,2009,57(14):6001-6006.DOI:10.1021/jf900217s.
[8]YAN X L.A qualitative&quantitative study of distinguishing adulteration in camellia oil by gas chromatography[J].Food Engineering,2011,137:63-71.DOI:10.3788/OPE.20162413.0600.
[9]HAI Z,WANG J.Detection of adulteration in camellia seed oil and sesame oil using an electronic nose[J].Journal of the Chinese Cereals&Oils Association,2006,108(2):116-124.DOI:10.1002/ejlt.200501224.
[10]王晓玲,吴晶,谭明乾.低场核磁共振结合化学计量学方法快速检测掺假核桃油[J].分析测试学报,2015,34(7):789-794.DOI:10.3969/j.issn.1004-4957.2015.07.006.
[11]赵海香,任荷玲,许秀丽,等.应用脂肪酸甲酯快速筛选植物油中掺假地沟油[J].食品科学,2014,35(16):148-152.DOI:10.7506/spkx1002-6630-201416029.
[12]刘伟,张敏,张然,等.植物油掺伪检验技术研究进展[J].食品安全质量检测学报,2017,8(5):1533-1538.DOI:10.3969/j.issn.2095-0381.2017.05.003.
[13]EICEMAN G A,KARPAS Z,Jr.HILL H H.Ion mobility spectrometry[M].Florida:CRC Press,2013.
[14]WORTELMANN T,SIELEMANN S,MEDINA L M,et al.Ion mobility spectrometry a versatile analytical tool for metabolomics application in food science[J].Current Metabolomics,2014,2(4):264-271.DOI:10.2174/2213235X03999150212102944.
[15]BUNKOWSKI A,MADDULA S,DAVIES A N,et al.One-year time series of investigation of analytes within human breath using ion mobility spectrometry[J].International Journal for Ion Mobility Spectrometry,2010,13(3/4):141-148.DOI:10.1007/s12127-010-0052-7.
[16]ZHANG L X,SHUAI Q,LI P W,et al.Ion mobility spectrometry fingerprints:a rapid detection technology for adulteration of sesame oil[J].Food Chemistry,2016,192:60-66.DOI:10.1016/j.foodchem.2015.06.096.
[17]GARRIDO-DELGADO R,MU袿Z-PéREZ M E,ARCE L.Detection of adulteration in extra virgin olive oils by using UVIMSand chemometric analysis[J].Food Control,2018,85:292-295.DOI:10.1016/j.foodcont.2017.10.012.
[18]GARRIDODELGADO R,ARCE L,VALCáRCEL M.Multi-capillary column-ion mobility spectrometry:a potential screening system to differentiate virgin olive oils[J].Analytical&Bioanalytical Chemistry,2012,402(1):489-498.DOI:10.1007/s00216-011-5328-1.
[19]HAJIALIGOL S,GHORASHI S A,ALINOORI A H,et al.Thermal solid sample introduction-fast gas chromatography-low flow ion mobility spectrometry as a field screening detection system[J].Journal of Chromatography A,2012,1268(4):123-129.DOI:10.1016/j.chroma.2012.10.010.
[20]胡国梁,徐立荣,许生陆,等.基于电子鼻的食用油氧化判别分析[J].食品科学,2016,37(20):141-145.DOI:10.7506/spkx1002-6630-201620023.
[21]李红,赵博,冉晓鸿,等.基于傅里叶变换中红外光谱识别正常食用植物油和精炼潲水油模型分析[J].食品科学,2014,35(6):121-124.DOI:10.7506/spkx1002-6630-201406025.
[22]PACELLA M,COLOSIMO B M.Multilinear principal component analysis for statistical modeling of cylindrical surface:a case study[J].Quality Technology&Quantitative Management,2016,15(4):1-19.DOI:10.1080/16843703.2016.1226710.
[23]杨仁杰,刘蓉,杨延荣,等.用二维相关近红外谱和多维主成分分析判别掺杂牛奶[J].光学精密工程,2014,22(9):2352-2358.DOI:10.3788/OPE.20142209.2352.
[24]HOLLINGSWORTH B V,REICHENBACH S E,TAO Q,et al.Comparative visualization for comprehensive two-dimensional gas chromatography[J].Journal of Chromatography A,2006,1105(1/2):51-58.DOI:10.1016/j.chroma.2005.11.074.
[25]SHENG Z,JIANG X Y,ZHEN W.Pseudo-color coding with phasemodulated image density[J].Advanced Materials Research,2012,403:1618-1621.DOI:10.1016/j.infrared.2013.01.006.
[26]张方圆,吴凌涛,林晨,等.气相色谱结合化学计量学用于6种食用植物油的分类[J].分析试验室,2016(11):1254-1258.DOI:10.13595/j.cnki.issn1000-0720.2016.0283.
[27]DOU X,MAO J,ZHANG L,et al.Multispecies adulteration detection of camellia oil by chemical markers[J].Molecules,2018,23(2):241-251.DOI:10.3390/molecules23020241.
[28]余佳荣,晁燕,谭丽娟,等.基于气相色谱法的湖南油茶籽油掺杂判定[J].中南林业科技大学学报,2014,34(5):88-91.DOI:10.14067/j.cnki.1673-923x.2014.05.004.
[29]冯棋琴,吴满梅,汪有存,等.不同产地压榨山茶油理化性质及组分比较分析[J].粮食与油脂,2017,30(12):39-42.
[30]田硕,刘建学,韩四海,等.基于特征脂肪酸的掺伪芝麻油快速鉴别模型建立[J].食品科学,2014,35(22):207-210.DOI:10.7506/spkx1002-6630-201422040.
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