基于电子鼻及GC-MS技术的啤酒识别研究

为区分不同品牌啤酒并分析样品间挥发性风味物质种类及含量的差异,采用PEN3电子鼻和气相色谱-质谱联用(GC-MS)技术从宏观和微观上对5种品牌啤酒的挥发性成分进行检测。利用主成分分析(PCA)、Fisher判别分析(FDA)对电子鼻响应值进行数据处理并建立分类判别模型,并与GC-MS数据进行了相关性分析。结果表明,电子鼻PCA分析与FDA分析都能对样品进行区分,R6和R9传感器起主要区分作用。GC-MS共鉴定出24种酯类、16种醇类、13种酸类、10种醛类、12种烷类、7种酮类以及13种芳香族化合物等挥发性物质。5种啤酒的主要挥发性成分的种类大体相同,但是各组分含量有所区别。根据偏最小二乘回归模型,电子鼻与挥发性风味物质表现出良好的相关性。这两种方法在分类识别啤酒方面有很好的应用前景。     

基于电子鼻技术的糙米黄曲霉毒素污染快速检测方法研究

为验证电子鼻技术用于粮食真菌毒素污染快速检测的可行性,本研究利用Fox3000型电子鼻对受黄曲霉毒素侵染的糙米样品的挥发性物质进行了检测分析,建立了电子鼻响应信号与黄曲霉毒素水平的相关关系模型。结果显示,偏最小二乘判别分析(PLS-DA)法可较好区分不同黄曲霉毒素含量水平的糙米样品,模型的留一交互验证正确率高于80%。PLS回归分析显示电子鼻响应信号与糙米中黄曲霉毒素B_1、B_2、G_1、G_2及总量之间呈现较高相关性,其中对黄曲霉毒素B1的预测精度最高,预测相关系数和均方根误差分别达到0.808和127.3μg/kg。进一步,通过对电子鼻各气体传感器响应信号的载荷分析确定了各传感器贡献率的差异,结合气相色谱-质谱联用(GC-MS)技术揭示了受黄曲霉毒素污染糙米样品的挥发性组分的变化主要体现在酮醛类、醇类、芳香烃类和烷烃类上。结果表明,利用电子鼻对糙米的黄曲霉毒素污染的快速检测具有一定可行性,为粮食真菌毒素污染的早期预警提供一种新思路和新方法。     

基于电子鼻与GC-MS分析精氨酸-葡萄糖美拉德反应体系挥发性风味成分的差异性

为研究不同反应时间对精氨酸-葡萄糖美拉德反应体系挥发性风味物质的影响,利用电子鼻与气相色谱-质谱联用(gas chromatography-mass spectrometry,GC-MS),结合主成分分析(principal component analysis,PCA)和线性判别分析(linear discriminant analysis,LDA),对不同反应时间精氨酸-葡萄糖美拉德反应体系的挥发性风味成分进行差异性分析。结果显示:PCA和LDA均能够较好地区分30 d和40 d的样品,但对于其它样品PCA区分效果要比LDA好。GC-MS从精氨酸-葡萄糖美拉德反应体系中共检测出50种挥发性物质,可分为醇类、醛类、酮类、酯类、烷烃类和其它类化合物等6类物质,其中酯类物质为10、20 d样品中的主要挥发性物质,醇类物质是20 d和30 d样品中的主要挥发性成分,不同反应时间主要挥发性成分差异显著。对不同精氨酸-葡萄糖美拉德反应体系样品挥发性物质进行主成分分析,建立其品质评价模型,得出不同时间的综合得分顺序依次为20、10、60、40、30 d和50 d。     

砂蜜豆樱桃货架期品质及挥发性物质的分析

以砂蜜豆樱桃为材料,采用气相色谱质谱联用(Gas Chromatograph-Mass Spectrometer,GC-MS)及电子鼻两种技术,并结合感官评价、品质指标,对货架期5 d内(13℃、20℃、经13℃中过渡12 h再置于20℃贮藏)樱桃挥发性物质的变化进行检测,进而对其货架期品质进行分析。电子鼻结果表明,线性判别分析、主成分分析方法可对不同货架天数的樱桃有效区分;GC-MS峰面积归一法分析结果表明,3组处理下的樱桃在货架期间峰面积总和下降,不同处理下挥发性物质种类存在一定差异。其主要挥发性物质是醛类、醇类,与电子鼻检测结果吻合。4 d时,20℃组中检出大量乙醇,13℃组中乙醇含量最少。实验结果表明,在13℃货架3 d时的樱桃品质最佳;变温可能是导致樱桃品质下降的主要原因之一。GC-MS结合电子鼻检测结果与樱桃内部品质指标变化和感官评价结果大体一致。因此,这两种技术相结合的方式对樱桃货架期香气品质的评价具有可行性。     

基于电子鼻及气相色谱-质谱联用技术对川西高原4种蜂蜜挥发性成分分析

为分析川西高原不同蜜源蜂蜜挥发性物质种类及成分的差异,采用电子鼻、顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)与气相色谱-质谱联用(gas chromatography-mass spectrometry,GC-MS)技术对川西高原油菜蜂蜜、当归药花蜜、白刺花蜜和山花蜜4种蜂蜜挥发性成分的响应值进行主成分分析、线性判别分析以及成分定性定量分析。结果表明:电子鼻检测到油菜蜂蜜、当归药花蜜、白刺花蜜、山花蜜的挥发性成分差异明显;HS-SPME-GC-MS分别检测出40、51、39种和46种挥发性成分,主要为醇类、酯类、酮类、醛类、烃类等化合物。4种蜂蜜中共有挥发性成分有7种,特有挥发性成分分别有22、27、26种和19种。电子鼻联合GC-MS分析可以成功区分不同来源蜂蜜样品的挥发性成分。     

不同颜色蒜泥的风味及挥发性物质分析

以打破休眠期的大蒜为原料,制备白色、蓝色、绿色和黄色4种颜色蒜泥,采用电子鼻和气相色谱-质谱(gas chromatography-mass spectrometry,GC-MS)联用技术揭示其风味及挥发性物质的变化。电子鼻检测中,不同颜色蒜泥对传感器的响应值雷达图和主成分分析(principal component analysis,PCA)结果显示各个样品间的风味存在显著差异。GC-MS结果显示4种颜色蒜泥共检测出83种挥发性物质,其中蒜泥的主要挥发性物质是硫醚类和醛类化合物,此为蒜泥的主要风味物质。使用PCA实现对蒜泥样品挥发性物质的区分,明确4种蒜泥风味差异的挥发性物质,为研究不同颜色蒜泥的风味差异提供理论依据。     

电子鼻结合GC-MS检测玫瑰香葡萄贮后货架期内挥发性物质的变化

应用电子鼻、顶空固相微萃取/气相色谱质谱联用(HS-SPME/GC-MS)两种技术,检测玫瑰香葡萄贮后货架期内挥发性物质的变化,从香气成分的角度评价葡萄货架期品质。玫瑰香葡萄0℃冷藏20 d后出库,设置18℃~20℃、8℃~10℃两种货架温度,模拟常温销售和超市货柜销售,测定5 d货架期内的理化、感官、营养指标,结合电子鼻、GC-MS分析。电子鼻检测结果表明,应用主成分分析、线性判别分析方法可以对不同货架时间的样品进行有效区分。GC-MS峰面积归一法分析结果表明,玫瑰香葡萄挥发性物质组成及其相对含量在货架期内发生变化,主要特征香气成分(E)-2-己烯醛、香叶醇、香茅醇、橙花醇等含量下降,乙醇、正己醇、乙酸等含量增加,峰面积总和下降,其变化规律与理化指标及电子鼻分析结果大致一致。因此,电子鼻结合GC-MS方法对玫瑰香葡萄货架期香气品质的判别具有可行性。     

基于电子鼻和气质联用技术分析不同品牌道口烧鸡的香气差异性

为比较不同品牌道口烧鸡的香气差异,利用电子鼻和气质联用技术(gas chromatography-mass spectrometer,GC-MS)对其香气成分进行分析。结果表明,电子鼻结合线性判别分析(linear discriminant analysis,LDA)可实现对不同品牌道口烧鸡香气的快速区分;采用气质联用技术共检出道口烧鸡中的51种挥发性物质,其在各品牌样品中的组成和比例不同,分别形成了各品牌烧鸡的独特风味;采用相对气味活度值法(relative odor activity value,ROAV)分析得到13种关键香气化合物,包括2种醇类物质、6种醛类物质、4种萜烯类物质和2-戊基呋喃,它们是样品香气差异的主要物质。通过对关键香气化合物的主成分分析(principal component analysis,PCA),这13种关键香气化合物可以区分不同品牌道口烧鸡样品。     

基于电子鼻和HS-SPME-GC-MS检测并分析籼稻谷储藏期间挥发性物质的研究

利用电子鼻对不同储藏时间(0、30、60、90、120、150、180 d)、储藏温度35℃、储藏水分15.5%的籼稻进行检测,并对籼稻的整体挥发性物质进行主成分分析,在此基础上利用顶空固相微萃取结合气质联用法(HS-SPME-GC-MS)对籼稻储藏期间挥发性物质的相对含量进行测定,最后采用主成分分析法对挥发性物质进行分析。结果表明:电子鼻检测第一、二主成分的累计贡献率达到99%,区分指数为81,主成分图显示初始和储藏6个月的数据无重叠,区分效果较好;GC-MS结果显示,样品中的挥发性物质共102种,烃类、醇类、醛类、酮类、杂环类挥发性物质的相对含量随储藏时间延长而呈现增大的趋势,主成分分析结果显示,醇、醛、酮对样品整体挥发性物质的贡献最大,然后依次为杂环类、烃类、酸酯类,通过计算主成分得分和观察挥发性物质相对含量的变化,推断出籼稻谷储藏期间导致其品质劣变的特征性物质为2-己基-1-癸醇、苯甲醇、己醛、癸醛、顺-2-癸烯醛、2-十二烯醛、2-十一酮、5-十三酮,电子鼻和GC-MS联用能较好地从宏观和微观2个方面对样品挥发性物质进行检测和分析,同时主成分分析能有效地推断出籼稻谷品质劣变的特征性挥发物。     

电子鼻应用于猪肉丸子香味预测

为应用电子鼻技术快速、客观地评价猪肉丸子风味,实验设计了4种肥肉、瘦肉配比(100%瘦、90%瘦、80%瘦和70%瘦)的猪肉丸子,使用电子鼻和顶空固相微萃取和气相色谱-质谱(gas chromatography-mass spectrometry,GC-MS)联用对猪肉丸子的各种挥发性物质进行检测。同时对其进行香味指标感官评定。采用线性判别和神经网络方法对不同猪肉丸子的电子鼻数据进行识别分类;并利用偏最小二乘回归方法对电子鼻传感器和挥发性物质的相关性进行分析。结果表明,4类丸子在香味评价上差异极显著(P0.01),肥肉比例高的丸子获得了较高的评分。线性判别分析和神经网络方法的分类效果显示,电子鼻对4类猪肉丸子具有良好的分类能力。GC-MS共检测出了67种风味化合物,其中主要是醛类、醇类、酮类等物质,挥发性风味物质的差异是造成各类丸子感官评分差异的根本原因。偏最小二乘回归模型显示电子鼻传感器数据与主要挥发性化合物相对含量具有良好的相关性。使用逐步回归建立电子鼻与评价指标数据之间的分值预测回归模型(R20.9,P0.01),表明丸子香味可以使用电子鼻进行预测。     

Variety‐based discrimination of apple juices by an electronic nose and gas chromatography–mass spectrometry

The variety of raw material plays a crucial role in the quality and authenticity of fruit juices and juice products. To characterise and classify apple juices according to variety on the basis of their volatile compounds, electronic nose (EN) and gas chromatography–mass spectrometry (GC‐MS) were applied to detect the apple juices prepared by eight different varieties. The EN was used to analyse the mixture of volatile compounds as a whole and enabled rapid classification of juice samples when coupled with linear discriminant analysis (LDA). LDA showed a perfect discrimination of apple juices based on varieties. GC‐MS was utilised to illustrate the differences of volatile compounds among juice samples. Identification of volatile compositions and their contents provides useful access to differentiate juices from different varieties.     

Geographical origin identification of propolis using GC–MS and electronic nose combined with principal component analysis

Dynamic headspace sampling (DHS) coupled with gas chromatography–mass spectrometry and olfactometry (GC–MS–O) analysis have been applied for the determination of the characteristic volatile profile of propolis with the aim to differentiate the propolis from different regions of China. Acids, esters, alcohols, terpenes, aromatics represented the most abundant compounds in propolis among the ninety-nine volatile components identified by comparing with mass spectra and retention indices (RI) or from literature. In addition, principal component analysis (PCA) based on the data of DHS–GC–MS and electronic nose was used to study and obtain the important volatile compounds contributed to the differentiation of the propolis samples from different regions. Furthermore, a total of 28 odor-active compounds were detected and characterized by the trained panel of judges in the sniffing port of GC–O by using detection frequency analysis (DFA). In conclusion, GC–MS analysis and electronic nose combining with PCA could successfully distinguish the twelve representative raw propolis samples from 4 different geographical regions of China. The samples have been assigned to four large groups in accordance with their vegetal sampling location and we also have observed the volatile compounds resulting in the odor differentiation.     

电子鼻同步检测花生霉菌及霉菌毒素

利用电子鼻技术,建立了花生受不同霉菌感染后的霉变程度及毒素含量的同步快速检测方法。花生经辐照灭菌后接种5 种常见霉菌（3 种产毒菌株、2 种非产毒菌株）,于培养箱（26 ℃、相对湿度80%）中培养6 d。每天取出不同霉菌污染的样品采集电子鼻信号,同时测定其菌落总数和黄曲霉毒素B1（aflatoxin B1,AFB1）含量,建立不同霉菌感染下霉变程度及毒素含量定性判别模型。主成分分析结果显示不同霉菌污染下有一定的聚类趋势,且污染样品位于可接受样品的上方;利用线性判别分析和偏最小二乘判别分析整体准确率达到80%以上,其中根据产毒菌株和非产毒菌株分类正确率高于95.7%,根据AFB1含量分类正确率90%以上,根据菌落总数分类正确率较低。所有模型中假阴性均低于17%。因此,电子鼻技术对不同霉菌感染下的霉变程度及毒素含量的测定具有可行性。未来研究应继续扩大样品数量,补充受其他更多霉菌侵染及不同品种的花生样品,同时考虑实际情况,以提高模型的准确性和稳定性。     

电子鼻技术在区分酸羊奶发酵菌种中的应用

利用电子鼻技术快速区分酸羊奶的发酵菌种。通过电子鼻采集不同酸羊奶挥发成分的响应值,然后利用主成分分析(principal component analysis,PCA)、Fisher线性判别分析(fisher linear discriminant analysis,FLDA)以及BP神经网络(back propagation neural network,BP-NN)分析进行判别,建立基于电子鼻技术区分酸羊奶发酵菌种的方法。结果表明,FLDA及PCA都能够区分出不同菌种发酵的酸羊奶,FLDA区分效果优于PCA。利用FLDA和BP-NN分析预测酸羊奶发酵菌种类别的正确率分别为100.0%和98.4%。因此,利用电子鼻快速区分酸羊奶的发酵菌种是可行的。     

Application of Sensory Evaluation,HS‐SPME GC‐MS,E‐Nose,and E‐Tongue for Quality Detection in Citrus Fruits

In this study, electronic tongue (E‐tongue), headspace solid‐phase microextraction gas chromatography‐mass spectrometer (GC‐MS), electronic nose (E‐nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E‐tongue and E‐nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E‐tongue system and E‐nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC‐MS) and sensory attributes (QDA). The simultaneous utilization of E‐tongue and E‐nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R² > 0.994 for training sets and R² > 0.983 for testing sets) and for the volatile components (R² > 0.992 for training sets and R² > 0.990 for testing sets) based on random forest. Being easy‐to‐use, cost‐effective, robust, and capable of providing a fast analysis procedure, E‐nose and E‐tongue could be used as an alternative detection system to traditional analysis methods, such as GC‐MS and sensory evaluation by human panel in the fruit industry.     

Content of trichodiene and analysis of fungal volatiles (electronic nose) in wheat and triticale grain naturally infected and inoculated with Fusarium culmorum

Four groups of cereal kernels were analyzed in terms of their volatile metabolite contents using GC/MS and the electronic nose. Analyses were conducted on 36 triticale breeding lines and 22 wheat breeding lines. Grain came from field samples inoculated with Fusarium culmorum and simultaneous non-inoculated samples-controls. All sample groups contained significantly varied levels of trichodiene (TRICH), a precursor for the formation of fusarium metabolites, with approx. two times higher concentration recorded in triticale. In inoculated samples TRICH concentration for wheat was on average six times higher and for triticale eight times higher than in non-inoculated samples. In the course of analysis using the electronic nose in tested groups of grain differences were observed in the profiles of detected volatile compounds. This resulted in a statistically significant distribution of investigated samples into four objects.     

The potential of different techniques for volatile compounds analysis coupled with PCA for the detection of the adulteration of olive oil with hazelnut oil

This paper investigates the effectiveness of three rapid methods of volatile compounds analysis with subsequent principal component analysis (PCA) treatment of data for differentiation between virgin olive oil samples adulterated with hazelnut oil. Tested methods included comparison of chromatograms of volatiles obtained using SPME-fast GC-FID, volatiles analysis by electronic nose based on MOS sensors (HS-Enose), and by direct coupling of SPME to MS (SPME-MS). Volatile compounds were analyzed also by SPME-GC/MS technique. Data obtained as a result of SPME-GC/MS was subjected to PCA. SPME-GC–MS analysis with subsequent PCA yielded good results, however being time consuming. The three methods of analysis of volatiles, with subsequent PCA treatment of data, allowed detection of olive oil adulteration with different contents of hazelnut oil ranging from 5 to 50% (v/v).     

Electronic Nose Technology in Quality Assessment: Predicting Volatile Composition of Danish Blue Cheese During Ripening

This work describes for the 1st time the use of an electronic nose (e‐nose) for the determination of changes of blue cheeses flavor during maturation. Headspace analysis of Danish blue cheeses was made for 2 dairy units of the same producer. An e‐nose registered changes in cheeses flavor 5, 8, 12, and 20 wk after brining. Volatiles were collected from the headspace and analyzed by gas chromatography‐mass spectrometry (GC‐MS). Features from the chemical sensors of the e‐nose were used to model the volatile changes by multivariate methods. Differences registered during ripening of the cheeses as well as between producing units are described and discussed for both methods. Cheeses from different units showed significant differences in their e‐nose flavor profiles at early ripening stages but with ripening became more and more alike. Prediction of the concentration of 25 identified aroma compounds by e‐nose features was possible by partial least square regression (PLS‐R). It was not possible to create a reliable predictive model for both units because cheeses from 1 unit were contaminated by Geotrichum candidum, leading to unstable ripening patterns. Correction of the e‐nose features by multiple scatter correction (MSC) and mean normalization (MN) of the integrated GC areas made correlation of the volatile concentration to the e‐nose signal features possible. Prediction models were created, evaluated, and used to reconstruct the headspace of unknown cheese samples by e‐nose measurements. Classification of predicted volatile compositions of unknown samples by their ripening stage was successful at a 78% and 54% overall correct classification for dairy units 1 and 2, respectively. Compared with GC‐MS, the application of the rapid and less demanding e‐nose seems an attractive alternative for this type of investigation.     

基于电子鼻与HS-SPME/GC-MS技术的香芋南瓜果实香气物质解析

为了解香芋南瓜果实香气挥发性成分,以及香味主要贡献物在不同发育时期的变化特征,本研究采用电子鼻方法和顶空固相微萃取结合气相色谱-质谱联用(HS-SPME/GC-MS)技术对香芋南瓜和非香芋南瓜资源进行分析。电子鼻检测结果显示,香芋南瓜和非香芋南瓜整体风味差异明显。采用HS-SPME/GC-MS技术对香芋南瓜成熟果实挥发性成分进行鉴定,共定性挥发性物质31种。利用mass profiler professional软件鉴定香芋南瓜与2类非香芋南瓜成熟果实之间的挥发性成分差异,结果表明不同资源间挥发性成分差异显著,这一结果与电子鼻分析相吻合。并且从2组比较中获得共有差异化合物2-乙酰基-1-吡咯啉(2-AP)。最后,以香芋南瓜未授粉、授粉后25 d、授粉后55 d果实为研究对象,比较了2-AP在不同发育时期的变化趋势。2-AP在未授粉及授粉25 d的果实中含量相似,而在成熟后期果实中呈现显著下降。本研究结果将为后续香芋南瓜香气性状的研究提供参考。     

基于多元统计学分析红曲米醋醋酸发酵阶段挥发性风味特征组分

为探究红曲米醋酿造过程中挥发性风味及特性组分变化规律,以红曲米醋醋酸发酵阶段为研究对象,采用电子鼻、气相色谱-质谱（gas chromatography-mass spectrometry,GC-MS）联用和气相色谱-嗅闻（gas chromatography-olfactometry,GC-O）联用技术对挥发性风味物质进行分析,并结合聚类分析、主成分分析和偏最小二乘判别法的多元统计方法进行不同发酵阶段的风味物质差异性分析,最终筛选出特征性组分。电子鼻分析可用于区分不同发酵时期的红曲米醋。通过GC-MS和GC-O识别出发酵过程中共有54 种挥发性风味化合物。经多元统计学方法筛选得到挥发性风味特征组分,醋酸发酵早期为正辛醇、异丁醇和戊酸乙酯;中期为苯甲酸、棕榈酸乙酯、正己醇、2,4-二叔丁基苯酚和乳酸乙酯;中后期为乙酸丙酯、乳酸乙酯和乙酸异丁酯;末期为L(＋)-2,3-丁二醇和庚酸乙酯。本研究结果为红曲米醋香气的调控和风味改善提供重要理论依据。