基于气相色谱-质谱联用与偏最小二乘-判别分析的啤酒爽口性评价
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摘要
目的:建立一种偏最小二乘-判别分析(partial least squares-discrimination analysis,PLS-DA)评价啤酒爽口性的方法。方法:基于顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)结合气相色谱-质谱(gas chromatography-mass spectrometry,GC-MS)联用检测啤酒中爽口性相关指标转化为数据矩阵,利用SIMCA-P软件进行PLS-DA。结果:不同爽口性的啤酒在得分图中能够明显区分。根据载荷图显示爽口性较差的样品集DA(1)中敏感特征变量为辛酸乙酯、癸酸乙酯及乙酸异戊酯等酯类化合物。选取50个样本为校准集,建立PLS-DA模型,4个爽口性不同的啤酒样本模型的回归相关系数分别为0.861、0.798、0.765、0.812,样品辨别率为92%。利用建立的PLS-DA模型对27个未知样品进行预测,不同爽口性样品的预测均方根误差分别为0.183、0.321、0.523、0.323,准确识别率为74.07%。结论:HS-SPME-GC-MS结合PLS-DA法是一种简单、快速、有效评价啤酒爽口性的方法。
Objective: This study aimed to establish a new method for the evaluation of beer crispness using partial least squares-discriminant analysis(PLS-DA). Methods: The concentrations of the chemical constituents associated with beer crispness were determined by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry(HS-SPME-GC-MS), and the obtained data were transformed to a matrix and analyzed by PLS-DA method using SIMCA-P software. Results: Beers with different crispness showed clear discrimination in the score plot of PLS-DA. According to the loading plot, ethyl caprylate, ethyl caprate and isoamyl acetate were the sensitive characteristic variables for the DA(1)set of samples with poor crispness. Fifty samples were selected as a validation set to establish a PLS-DA discrimination model.The correlation coefficients of the model for four beer samples with different crispness were 0.861, 0.798, 0.765 and 0.812,respectively, with a recognition rate of 92%. A total of 27 unknown samples were predicted by the PLS-DA model with root mean square errors of prediction(RMSEP) of 0.183, 0.321, 0.523 and 0.323 and a correct recognition rate of 74.07%. Conclusion:HS-SPME-GC-MS combined with PLS-DA is a simple and useful method for the evaluation of beer crispness.
Objective: This study aimed to establish a new method for the evaluation of beer crispness using partial least squares-discriminant analysis(PLS-DA). Methods: The concentrations of the chemical constituents associated with beer crispness were determined by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry(HS-SPME-GC-MS), and the obtained data were transformed to a matrix and analyzed by PLS-DA method using SIMCA-P software. Results: Beers with different crispness showed clear discrimination in the score plot of PLS-DA. According to the loading plot, ethyl caprylate, ethyl caprate and isoamyl acetate were the sensitive characteristic variables for the DA(1)set of samples with poor crispness. Fifty samples were selected as a validation set to establish a PLS-DA discrimination model.The correlation coefficients of the model for four beer samples with different crispness were 0.861, 0.798, 0.765 and 0.812,respectively, with a recognition rate of 92%. A total of 27 unknown samples were predicted by the PLS-DA model with root mean square errors of prediction(RMSEP) of 0.183, 0.321, 0.523 and 0.323 and a correct recognition rate of 74.07%. Conclusion:HS-SPME-GC-MS combined with PLS-DA is a simple and useful method for the evaluation of beer crispness.
引文
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[8]许杨,王德良,李红,等.响应面优化HS-SPME-GC/MS检测啤酒微量酯类物质条件[J].中国酿造,2017,36(9):163-169.DOI:10.11882/j.issn.0254-5071.2017.09.035.
[9]陈华磊,董建军,尹花,等.固相微萃取三重四极杆串联质谱法检测IPA啤酒中的果香硫醇[J].分析试验室,2016(4):398-401.
[10]秦伟帅,董书甲,姜凯凯,等.采用顶空固相微萃取和气质联用法分析氮源浓度对酵母香气物质合成的影响[J].食品与发酵工业,2017,43(3):61-65.DOI:10.13995/j.cnki.11-1802/ts.201703011.
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[12]范文来,张艳红,徐岩.应用HS-SPME和GC-MS分析白酒大曲中微量挥发性成分[J].酿酒科技,2007(12):74-78.DOI:10.3969/j.issn.1001-9286.2007.12.019.
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[14]张翠华,张亚楠.固相微萃取在农药残留检测中的应用研究[J].沧州师范学院学报,2017,33(2):29-34.DOI:10.3969/j.issn.2095-2910.2017.02.008.
[15]郝勇,孙旭东,高荣杰,等.基于可见/近红外光谱与SIMCA和PLS-DA的脐橙品种识别[J].农业工程学报,2010,26(12):373-377.DOI:10.3969/j.issn.1002-6819.2010.12.063.
[16]GAGULA G,MAGDI D,HORVAT D.PLSR modelling of quality changes of lager and malt beer during storage[J].Journal of the Institute of Brewing,2016,122(1):116-125.DOI:10.1002/jib.294.
[17]ZHANG Y Q,JIA S R,ZHANG W J.Predicting acetic acid content in the final beer using neural networks and support vector machine[J].Journal of the Institute of Brewing,2012,118(1):361-367.DOI:10.1002/jib.50.
[18]钟成,刘伶普,李清亮,等.采用代谢组学分析技术分析工业啤酒发酵过程中风味物质生成规律[J].中国生物工程杂志,2016,36(12):49-58.DOI:10.13746/j.njkj.2014560.
[19]陈斌,王豪,林松,等.基于相关系数法与遗传算法的啤酒酒精度近红外光谱分析[J].农业工程学报,2005,21(7):99-102.DOI:10.3321/j.issn:1002-6819.2005.07.023.
[20]刘宏欣,张军,黄富荣,等.近红外光谱法快速测定啤酒的主要品质参数[J].光谱学与光谱分析,2008,28(2):313-316.DOI:10.3964/j.issn.1000-0593.2008.02.018.
[21]冯尚坤,徐海菊.正交信号校正方法在啤酒酒精度近红外光谱分析中的应用[J].酿酒科技,2008(2):119-124.
[22]陆道礼,林松,陈斌.近红外光谱法快速测定啤酒中乙醇的含量[J].酿酒科技,2005(4):87-89.DOI:10.3969/j.issn.1001-9286.2005.04.023.
[23]王莉,何勇,刘飞,等.应用光谱技术和支持向量机分析方法快速检测啤酒糖度和p H值[J].红外与毫米波学报,2008,27(1):51-55.DOI:10.3321/j.issn:1001-9014.2008.01.012.
[24]李代禧,吴智勇,徐端钧,等.啤酒主要成分的近红外光谱法测定[J].分析化学,2004,32(8):1070-1073.DOI:10.3321/j.issn:0253-3820.2004.08.023.
[25]国家质量监督检验检疫总局.啤酒分析方法:GB/T 4928-2008[S].北京:中国标准出版社,2008.
[26]蔡定教.置换检验及其应用[J].安阳师范学院学报,2010(2):20-22.DOI:10.3969/j.issn.1671-5330.2010.02.008.
[27]MAYU O,YASUO M,TETSUYA W.Genetic analysis of bottomfermenting brewer’s yeast imparting good KIRE(crispness)to beer taste[R].Ljubljana:Proceedings of European Brewery Convention Congress,2017.
[28]KATO M,FUKUSHIMA Y,IMAI T,et al.Influence of high molecular weight proteins and polypeptides on smoothness of beer[R].Denver:Proceedings of World Brewing Congress,2016.
[29]OOMURO M,MOTOYAMA Y,WATANABE T.A new taste sensor for evaluation of beer body and smoothness using a lipid-coated quartz crystal microbalance[J].Journal of American Society of Brewing Chemists,2002,60(2):71-76.DOI:10.1094/ASBCJ-60-0071.
[30]KISHIMOTO T,MIYASHITA S,ONAGAWA Y,et al.Contribution of compositional profile of iso-alpha acid to the quality of bitterness[R].Ljubljana:Proceedings of European Brewery Convention Congress,2017.
[31]MIYASHITA S,KOBAYASHI M,HARUNA K,et al.Effect of retronasal aroma on kire of beer[R].Ljubljana:Research Laboratories for Alcohol Beverages,2017.
[2]ALEX B.New methods of sensory evaluation,their implications and applications for drinkability assessment and beer-food pairing based upon statistical and consumer studies[R].La Quinta:ASBC,2015.
[3]MIYASHITA S,KOBAYASHI M,KIKUCHI K,et al.Sensory and instrumental analyses of compounds affecting the KIRE(crispness)of beer[R].Denver:Proceedings of World Brewing Congress,2016.
[4]于静,李景明.顶空固相微萃取法(SPME)在红葡萄酒香气成分测定中的应用研究[J].中外葡萄与葡萄酒,2006(3):4-9.DOI:10.3969/j.issn.1004-7360.2006.03.001.
[5]张莉,王瑞庆.搅拌棒吸附法与固相微萃取提取葡萄酒香气成分比较[J].酿酒科技,2018(1):33-37.DOI:10.13746/j.njkj.2017259.
[6]刘琨毅,王琪,郑佳,等.基于HS-SPME-GC-MS剖析三种柑橘-葡萄酒的香气成分[J].中国食品添加剂,2017(12):72-78.
[7]AUMATELL M,MIR P,SERRA CAYUELA A,et al.Assessment of the aroma profiles of low-alcohol beers using HS-SPME-GC-MS[J].Food Research International,2014,57:196-202.DOI:10.1016/j.foodres.2014.01.016.
[8]许杨,王德良,李红,等.响应面优化HS-SPME-GC/MS检测啤酒微量酯类物质条件[J].中国酿造,2017,36(9):163-169.DOI:10.11882/j.issn.0254-5071.2017.09.035.
[9]陈华磊,董建军,尹花,等.固相微萃取三重四极杆串联质谱法检测IPA啤酒中的果香硫醇[J].分析试验室,2016(4):398-401.
[10]秦伟帅,董书甲,姜凯凯,等.采用顶空固相微萃取和气质联用法分析氮源浓度对酵母香气物质合成的影响[J].食品与发酵工业,2017,43(3):61-65.DOI:10.13995/j.cnki.11-1802/ts.201703011.
[11]尹婉嫱,丽云.应用HS-SPME和GC-MS分析浓香型“山庄老酒”香气成分[J].酿酒科技,2015(7):94-97.DOI:10.13746/j.njkj.2015062.
[12]范文来,张艳红,徐岩.应用HS-SPME和GC-MS分析白酒大曲中微量挥发性成分[J].酿酒科技,2007(12):74-78.DOI:10.3969/j.issn.1001-9286.2007.12.019.
[13]范文来,徐岩.应用浸入式固相微萃取(DI-SPME)方法检测中国白酒的香味成分[J].酿酒,2007(4):18-21.DOI:10.3969/j.issn.1002-8110.2007.01.010.
[14]张翠华,张亚楠.固相微萃取在农药残留检测中的应用研究[J].沧州师范学院学报,2017,33(2):29-34.DOI:10.3969/j.issn.2095-2910.2017.02.008.
[15]郝勇,孙旭东,高荣杰,等.基于可见/近红外光谱与SIMCA和PLS-DA的脐橙品种识别[J].农业工程学报,2010,26(12):373-377.DOI:10.3969/j.issn.1002-6819.2010.12.063.
[16]GAGULA G,MAGDI D,HORVAT D.PLSR modelling of quality changes of lager and malt beer during storage[J].Journal of the Institute of Brewing,2016,122(1):116-125.DOI:10.1002/jib.294.
[17]ZHANG Y Q,JIA S R,ZHANG W J.Predicting acetic acid content in the final beer using neural networks and support vector machine[J].Journal of the Institute of Brewing,2012,118(1):361-367.DOI:10.1002/jib.50.
[18]钟成,刘伶普,李清亮,等.采用代谢组学分析技术分析工业啤酒发酵过程中风味物质生成规律[J].中国生物工程杂志,2016,36(12):49-58.DOI:10.13746/j.njkj.2014560.
[19]陈斌,王豪,林松,等.基于相关系数法与遗传算法的啤酒酒精度近红外光谱分析[J].农业工程学报,2005,21(7):99-102.DOI:10.3321/j.issn:1002-6819.2005.07.023.
[20]刘宏欣,张军,黄富荣,等.近红外光谱法快速测定啤酒的主要品质参数[J].光谱学与光谱分析,2008,28(2):313-316.DOI:10.3964/j.issn.1000-0593.2008.02.018.
[21]冯尚坤,徐海菊.正交信号校正方法在啤酒酒精度近红外光谱分析中的应用[J].酿酒科技,2008(2):119-124.
[22]陆道礼,林松,陈斌.近红外光谱法快速测定啤酒中乙醇的含量[J].酿酒科技,2005(4):87-89.DOI:10.3969/j.issn.1001-9286.2005.04.023.
[23]王莉,何勇,刘飞,等.应用光谱技术和支持向量机分析方法快速检测啤酒糖度和p H值[J].红外与毫米波学报,2008,27(1):51-55.DOI:10.3321/j.issn:1001-9014.2008.01.012.
[24]李代禧,吴智勇,徐端钧,等.啤酒主要成分的近红外光谱法测定[J].分析化学,2004,32(8):1070-1073.DOI:10.3321/j.issn:0253-3820.2004.08.023.
[25]国家质量监督检验检疫总局.啤酒分析方法:GB/T 4928-2008[S].北京:中国标准出版社,2008.
[26]蔡定教.置换检验及其应用[J].安阳师范学院学报,2010(2):20-22.DOI:10.3969/j.issn.1671-5330.2010.02.008.
[27]MAYU O,YASUO M,TETSUYA W.Genetic analysis of bottomfermenting brewer’s yeast imparting good KIRE(crispness)to beer taste[R].Ljubljana:Proceedings of European Brewery Convention Congress,2017.
[28]KATO M,FUKUSHIMA Y,IMAI T,et al.Influence of high molecular weight proteins and polypeptides on smoothness of beer[R].Denver:Proceedings of World Brewing Congress,2016.
[29]OOMURO M,MOTOYAMA Y,WATANABE T.A new taste sensor for evaluation of beer body and smoothness using a lipid-coated quartz crystal microbalance[J].Journal of American Society of Brewing Chemists,2002,60(2):71-76.DOI:10.1094/ASBCJ-60-0071.
[30]KISHIMOTO T,MIYASHITA S,ONAGAWA Y,et al.Contribution of compositional profile of iso-alpha acid to the quality of bitterness[R].Ljubljana:Proceedings of European Brewery Convention Congress,2017.
[31]MIYASHITA S,KOBAYASHI M,HARUNA K,et al.Effect of retronasal aroma on kire of beer[R].Ljubljana:Research Laboratories for Alcohol Beverages,2017.