Article(id=1320880262666367507, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320880262012056083, articleNumber=null, orderNo=null, doi=10.13386/j.issn1002-0306.2025070092, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751904000000, receivedDateStr=2025-07-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792851995387, onlineDateStr=2026-10-24, pubDate=1789401600000, pubDateStr=2026-09-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792851995387, onlineIssueDateStr=2026-10-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792851995387, creator=13701087609, updateTime=1792851995387, updator=13701087609, issue=Issue{id=1320880262012056083, tenantId=1146029695717560320, journalId=1260987677001138203, year='2026', volume='47', issue='18', pageStart='1', pageEnd='613', issueExtLink='null', onlineDate='null', pubDate='1789401600000', pubDateStr='2026-09-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1792851995232, creator='13701087609', updateTime=1792921655980, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321172440479290195, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320880262012056083, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321172440479290196, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320880262012056083, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=306, endPage=315, ext={EN=ArticleExt(id=1320880262922220053, articleId=1320880262666367507, tenantId=1146029695717560320, journalId=1260987677001138203, language=EN, title=Changes of Volatile Flavor Compounds in Chicken Soup during Stewing Process of Yellow-feathered Chicken Using Multiple Technologies Combined with PCA-BP Neural Network, columnId=1320880262846722580, journalTitle=Science and Technology of Food Industry, columnName=Modern Analysis and Intelligent Detection, runingTitle=null, highlight=null, articleAbstract=

To investigate the changes in volatile flavor compounds in chicken soup during the stewing process of yellow-feathered chicken, headspace solid phase microextraction (HS-SPME)-gas chromatography-mass spectrometry (GC-MS), electronic nose, and electronic tongue combined with PCA-BP neural network were used to study chicken soup samples at different cooking times (0.5, 1.0, 1.5, 2.0 and 2.5 h). The results showed that as the stewing time increased, the color of the chicken soup became darker. A total of 35 volatile compounds were identified through GC-MS, primarily consisting of aldehydes and alcohols. The total content of volatile flavor compounds increased first and then decreased, and reached a peak after stewing for 2.0 h. GC-MS combined with OAVs value method identified 13 key volatile compounds, including 1-hexanol, 1-octene-3-ol, (E,E)-2,4-decadiena,l (E,E)-2,4-nonadienal, (Z)-2-heptenal, (E)-2-nonenal, (E)-2-octenal, (E)-2-decenal, 2-pentylfuran, heptanal, hexanal, nonanal and octanal. Based on VIP values, 19 characteristic volatile compounds were screened, which could be used to distinguish 5 chicken soup samples. Significant differences in the electronic nose and electronic tongue of chicken soup samples with different stewing times, and these samples could be effectively distinguished by PCA and cluster analysis. The electronic tongue PCA-BP neural network model could accurately predict the stewing time of chicken soup samples and the linear relationship between the true value and the predicted value was good (R2=0.9998). The chicken soup stewed for 2.0 h showed significantly high scores for flavor, color, appearance, and overall acceptability. In conclusion, the optimal stewing time for chicken soup was 2.0 h. The results can be used as the basis for the flavor of the original chicken soup and pave the way for subsequent high-quality Chinese herbal chicken soup research.

, authors=Hongxia LÜ1, Yongrui WANG2, *, authorsList=Hongxia LÜ, Yongrui WANG, authorCompany=null, correspAuthors=Yongrui WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2026 Science and Technology of Food Industry. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320880265837261349, articleId=1320880262666367507, tenantId=1146029695717560320, journalId=1260987677001138203, language=CN, title=多技术结合PCA-BP神经网络研究黄羽鸡炖煮过程中鸡汤挥发性风味物质变化, columnId=1320880262997717526, journalTitle=食品工业科技, columnName=现代分析与智能检测, runingTitle=null, highlight=null, articleAbstract=

为研究黄羽鸡炖煮过程中鸡汤挥发性风味物质的变化,采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)结合气相色谱-质谱联用(gas-chromatographic mass-spectrometric,GC-MS)、电子鼻、电子舌结合PCA-BP神经网络对不同炖煮时间(0.5、1.0、1.5、2.0和2.5 h)鸡汤样品进行研究。结果表明,随着炖煮时间增加,鸡汤颜色变浓。通过GC-MS共鉴定出35种挥发性化合物,主要为醛类和醇类化合物。挥发性风味物质的总含量随炖煮时间先增加后降低,其中在炖煮2.0 h时达到了最高。GC-MS结合OAVs值法鉴定出关键挥发性物质13种,分别为1-己醇、1-辛烯-3-醇、(E,E)-2,4-癸二烯醛、(E,E)-2,4-壬二烯醛、(Z)-2-庚烯醛、(E)-2-壬烯醛、(E)-2-辛烯醛、(E)-2-十一烯醛、2-戊基呋喃、庚醛、己醛、壬醛和辛醛。基于VIP值筛选出19种特征挥发性化合物,可用来区分5种鸡汤样品。不同炖煮时间鸡汤样品的电子鼻和电子舌数据存在显著差异,并能通过PCA及聚类分析有效区分。电子舌数据PCA-BP神经网络模型可准确预测鸡汤样品炖煮时间,真实值与预测值线性关系良好(R2=0.9998)。感官评价表明,炖煮2.0 h的鸡汤在风味、色泽、外观和总体可接受性方面得分很高。综上,鸡汤的最佳炖煮时间为2.0 h,研究结果可作为原始鸡汤的风味依据,为后续高质量的中药鸡汤研究作为铺垫。

, authors=吕虹霞1, 王永瑞2, *, authorsList=吕虹霞, 王永瑞, authorCompany=null, correspAuthors=王永瑞, authorNote=

吕虹霞(1984−),女,硕士,副教授,研究方向:有机合成、中药材有效成分提取分离鉴定,E-mail:

, correspAuthorsNote=
王永瑞(1991−),男,博士,研究方向:农产品加工,E-mail:。
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吕虹霞(1984−),女,硕士,副教授,研究方向:有机合成、中药材有效成分提取分离鉴定,E-mail:

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吕虹霞(1984−),女,硕士,副教授,研究方向:有机合成、中药材有效成分提取分离鉴定,E-mail:

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Transactions of the Chinese Society of Agricultural Engineering, 2007, 23(7): 166−169., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1320880266059559462, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, xref=1, ext=[AuthorCompanyExt(id=1320880266072142375, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, companyId=1320880266059559462, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Medical College, Xi'an International University, Xi'an 710077, China), AuthorCompanyExt(id=1320880266080530984, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, companyId=1320880266059559462, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1西安外事学院医学院,陕西西安 710077)]), AuthorCompany(id=1320880266151834153, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, xref=2, ext=[AuthorCompanyExt(id=1320880266164417066, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, companyId=1320880266151834153, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China), AuthorCompanyExt(id=1320880266168611371, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, companyId=1320880266151834153, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2宁夏大学动物科技学院,宁夏银川 750021)])], figs=[ArticleFig(id=1320880268081214023, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=EN, label=Fig.1, caption=Chicken soup samples with different stewing times, figureFileSmall=lAV3WE4EgZUCNTLBLhK6+A==, figureFileBig=LrZ/Wcqur6lq93Astcu+8Q==, tableContent=null), ArticleFig(id=1320880268165100104, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=CN, label=图1, caption=不同炖煮时间鸡汤样品, figureFileSmall=lAV3WE4EgZUCNTLBLhK6+A==, figureFileBig=LrZ/Wcqur6lq93Astcu+8Q==, tableContent=null), ArticleFig(id=1320880269960262218, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=EN, label=Fig.2, caption=Cluster heatmap of volatile flavor compounds in chicken soup samples with different stewing times, figureFileSmall=7owiBYPKE5DRJ6cnjqlyTg==, figureFileBig=LlHLOCaGvVfTmJkUKXGCww==, tableContent=null), ArticleFig(id=1320880270031565387, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=CN, label=图2, caption=不同炖煮时间鸡汤样品挥发性风味物质聚类热图

注:图中括号数字为挥发性化合物编号。

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注:图(b)、图(d)中数字为挥发性化合物编号。

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注:*代表显著相关(P<0.05)。

, figureFileSmall=l2dCB/aLDX4C7UiuQFZPwg==, figureFileBig=+6BLax5O62uRQZJSYjhkag==, tableContent=null), ArticleFig(id=1320880270849454678, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=EN, label=Table 1, caption=

Sensory evaluation criteria of chicken soup samples

, figureFileSmall=null, figureFileBig=null, tableContent=
指标0~3分4~6分7~9分10分
外观清水样/浑浊、浮沫
残留
浑浊略微浑浊均匀不浑浊
色泽无色/灰暗汤色发灰不
透亮
奶白浓稠鲜亮、金黄
风味无味/异味(腥味、腐败味、酸臭味等)略带腥味、脂
肪味缺乏肉香
浓郁肉香、脂肪味厚重浓郁鲜香、肉香、
总体可
接受性
外观、色泽、风味
整体较差
外观、色泽、风味整体一般外观、色泽、风味整体良好外观、色泽、风味整体极佳
), ArticleFig(id=1320880270912369239, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=CN, label=表1, caption=

鸡汤样品感官评价标准

, figureFileSmall=null, figureFileBig=null, tableContent=
指标0~3分4~6分7~9分10分
外观清水样/浑浊、浮沫
残留
浑浊略微浑浊均匀不浑浊
色泽无色/灰暗汤色发灰不
透亮
奶白浓稠鲜亮、金黄
风味无味/异味(腥味、腐败味、酸臭味等)略带腥味、脂
肪味缺乏肉香
浓郁肉香、脂肪味厚重浓郁鲜香、肉香、
总体可
接受性
外观、色泽、风味
整体较差
外观、色泽、风味整体一般外观、色泽、风味整体良好外观、色泽、风味整体极佳
), ArticleFig(id=1320880270992061016, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=EN, label=Table 2, caption=

OAVs and contribution rate of key volatile substance of chicken soup

, figureFileSmall=null, figureFileBig=null, tableContent=
关键挥发性
化合物
阈值
(μg/kg)
0.5 h1.0 h1.5 h2.0 h2.5 h
OAVs贡献率(%)OAVs贡献率(%)OAVs贡献率(%)OAVs贡献率(%)OAVs贡献率(%)
1-己醇5.60.22±0.03b0.11±0.02a0.35±0.06b0.09±0.07ab−−1.04±0.31a0.03±0.01b0.14±0.04b0.03±0.01b
1-辛烯-3-醇1.54.60±1.28b2.26±0.71bc23.92±8.84a4.71±0.84a6.95±1.73b2.00±0.29c22.07±4.1a0.70±0.08d17.43±2.03a3.24±0.31b
(E,E)-2,4-癸二烯醛0.027183.12±9.50b89.43±2.42bc470.91±232.61b86.64±2.37c320.58±85.49b91.09±1.41b3074.86±233.61a97.78±0.21a482.65±41.66b89.60±1.02bc
(E,E)-2,4-壬二烯醛11.66±0.28c0.81±0.16bc3.29±1.37b0.63±0.06c3.68±0.79ab1.07±0.16a4.14±0.25ab0.13±0.01d5.15±0.43a0.96±0.04ab
(Z)-2-庚烯醛100.16±0.02b0.08±0.01cd1.82±0.89a0.34±0.01a0.37±0.05b0.11±0.03c1.95±0.45a0.06±0.01d1.28±0.15a0.24±0.02b
(E)-2-壬烯醛0.193.16±0.99c1.54±0.46c13.37±6.39ab2.49±0.06ab7.07±1.56bc2.05±0.34bc14.35±2.00a0.45±0.03d14.08±1.26a2.62±0.17a
(E)-2-辛烯醛30.42±0.05c0.20±0.03c1.73±0.84ab0.32±0.01ab0.98±0.21bc0.29±0.08b2.09±0.27a0.07±0.00d2.02±0.13a0.38±0.02a
(E)-2-十一烯醛5−−−−0.44±0.11c0.13±0.01b1.29±0.14a0.04±0.00c0.89±0.09b0.17±0.00a
2-戊基呋喃5.80.12±0.02c0.06±0.01c0.59±0.25b0.11±0.01b−−1.55±0.41a0.05±0.01c0.84±0.12b0.16±0.02a
庚醛2.80.61±0.22b0.30±0.10a1.17±0.29a0.25±0.09ab−−0.99±0.27ab0.03±0.01c0.79±0.11ab0.15±0.02bc
己醛55.31±1.77ba2.59±0.88a12.00±3.50a2.48±0.82ab4.41±0.97c1.29±0.29bc9.23±2.20ab0.29±0.05d9.19±2.60ab1.71±0.48ab
壬醛1.12.70±0.04c1.32±0.04a4.99±2.02ab0.97±0.14b3.25±0.89bc0.96±0.31b6.53±0.92a0.21±0.01c4.15±0.30bc0.77±0.10b
辛醛0.592.65±0.60b1.29±0.29a4.76±1.23a0.99±0.34a3.47±1.08ab1.00±0.21a4.94±0.91a0.16±0.02b−−
), ArticleFig(id=1320880271071752793, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=CN, label=表2, caption=

鸡汤关键挥发性物质OAVs及OAVs贡献率

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关键挥发性
化合物
阈值
(μg/kg)
0.5 h1.0 h1.5 h2.0 h2.5 h
OAVs贡献率(%)OAVs贡献率(%)OAVs贡献率(%)OAVs贡献率(%)OAVs贡献率(%)
1-己醇5.60.22±0.03b0.11±0.02a0.35±0.06b0.09±0.07ab−−1.04±0.31a0.03±0.01b0.14±0.04b0.03±0.01b
1-辛烯-3-醇1.54.60±1.28b2.26±0.71bc23.92±8.84a4.71±0.84a6.95±1.73b2.00±0.29c22.07±4.1a0.70±0.08d17.43±2.03a3.24±0.31b
(E,E)-2,4-癸二烯醛0.027183.12±9.50b89.43±2.42bc470.91±232.61b86.64±2.37c320.58±85.49b91.09±1.41b3074.86±233.61a97.78±0.21a482.65±41.66b89.60±1.02bc
(E,E)-2,4-壬二烯醛11.66±0.28c0.81±0.16bc3.29±1.37b0.63±0.06c3.68±0.79ab1.07±0.16a4.14±0.25ab0.13±0.01d5.15±0.43a0.96±0.04ab
(Z)-2-庚烯醛100.16±0.02b0.08±0.01cd1.82±0.89a0.34±0.01a0.37±0.05b0.11±0.03c1.95±0.45a0.06±0.01d1.28±0.15a0.24±0.02b
(E)-2-壬烯醛0.193.16±0.99c1.54±0.46c13.37±6.39ab2.49±0.06ab7.07±1.56bc2.05±0.34bc14.35±2.00a0.45±0.03d14.08±1.26a2.62±0.17a
(E)-2-辛烯醛30.42±0.05c0.20±0.03c1.73±0.84ab0.32±0.01ab0.98±0.21bc0.29±0.08b2.09±0.27a0.07±0.00d2.02±0.13a0.38±0.02a
(E)-2-十一烯醛5−−−−0.44±0.11c0.13±0.01b1.29±0.14a0.04±0.00c0.89±0.09b0.17±0.00a
2-戊基呋喃5.80.12±0.02c0.06±0.01c0.59±0.25b0.11±0.01b−−1.55±0.41a0.05±0.01c0.84±0.12b0.16±0.02a
庚醛2.80.61±0.22b0.30±0.10a1.17±0.29a0.25±0.09ab−−0.99±0.27ab0.03±0.01c0.79±0.11ab0.15±0.02bc
己醛55.31±1.77ba2.59±0.88a12.00±3.50a2.48±0.82ab4.41±0.97c1.29±0.29bc9.23±2.20ab0.29±0.05d9.19±2.60ab1.71±0.48ab
壬醛1.12.70±0.04c1.32±0.04a4.99±2.02ab0.97±0.14b3.25±0.89bc0.96±0.31b6.53±0.92a0.21±0.01c4.15±0.30bc0.77±0.10b
辛醛0.592.65±0.60b1.29±0.29a4.76±1.23a0.99±0.34a3.47±1.08ab1.00±0.21a4.94±0.91a0.16±0.02b−−
), ArticleFig(id=1320880271151444570, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=EN, label=Table 3, caption=

Sensory evaluation of chicken soup samples with different stewing times

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指标0.5 h1.0 h1.5 h2.0 h2.5 h
外观5.38±0.14e6.27±0.12d6.55±0.11c8.77±0.13a8.22±0.14b
色泽5.37±0.12d6.27±0.17c6.53±0.05c8.49±0.26a7.52±0.14b
风味4.16±0.84c4.73±0.08bc5.26±0.20b7.44±0.25a7.21±0.15a
总体可接受性5.35±0.08e5.77±0.04d6.17±0.07c8.22±0.13a7.60±0.07b
), ArticleFig(id=1320880271226942043, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880262666367507, language=CN, label=表3, caption=

不同炖煮时间鸡汤样品的感官评价

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指标0.5 h1.0 h1.5 h2.0 h2.5 h
外观5.38±0.14e6.27±0.12d6.55±0.11c8.77±0.13a8.22±0.14b
色泽5.37±0.12d6.27±0.17c6.53±0.05c8.49±0.26a7.52±0.14b
风味4.16±0.84c4.73±0.08bc5.26±0.20b7.44±0.25a7.21±0.15a
总体可接受性5.35±0.08e5.77±0.04d6.17±0.07c8.22±0.13a7.60±0.07b
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多技术结合PCA-BP神经网络研究黄羽鸡炖煮过程中鸡汤挥发性风味物质变化
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吕虹霞 1 , 王永瑞 2, *
食品工业科技 | 现代分析与智能检测 2026,47(18): 306-315
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食品工业科技 |现代分析与智能检测 2026 , 47 (18) : 306 -315
多技术结合PCA-BP神经网络研究黄羽鸡炖煮过程中鸡汤挥发性风味物质变化
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吕虹霞(1984−),女,硕士,副教授,研究方向:有机合成、中药材有效成分提取分离鉴定,E-mail:

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吕虹霞(1984−),女,硕士,副教授,研究方向:有机合成、中药材有效成分提取分离鉴定,E-mail:

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吕虹霞1 , 王永瑞2, *
作者信息
  • 1西安外事学院医学院,陕西西安 710077
  • 2宁夏大学动物科技学院,宁夏银川 750021
通讯作者:
王永瑞(1991−),男,博士,研究方向:农产品加工,E-mail:。
作者简介:

吕虹霞(1984−),女,硕士,副教授,研究方向:有机合成、中药材有效成分提取分离鉴定,E-mail:

Changes of Volatile Flavor Compounds in Chicken Soup during Stewing Process of Yellow-feathered Chicken Using Multiple Technologies Combined with PCA-BP Neural Network
Hongxia LÜ1 , Yongrui WANG2, *
Affiliations
  • 1Medical College, Xi'an International University, Xi'an 710077, China
  • 2College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China
出版时间: 2026-09-15 doi: 10.13386/j.issn1002-0306.2025070092
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为研究黄羽鸡炖煮过程中鸡汤挥发性风味物质的变化,采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)结合气相色谱-质谱联用(gas-chromatographic mass-spectrometric,GC-MS)、电子鼻、电子舌结合PCA-BP神经网络对不同炖煮时间(0.5、1.0、1.5、2.0和2.5 h)鸡汤样品进行研究。结果表明,随着炖煮时间增加,鸡汤颜色变浓。通过GC-MS共鉴定出35种挥发性化合物,主要为醛类和醇类化合物。挥发性风味物质的总含量随炖煮时间先增加后降低,其中在炖煮2.0 h时达到了最高。GC-MS结合OAVs值法鉴定出关键挥发性物质13种,分别为1-己醇、1-辛烯-3-醇、(E,E)-2,4-癸二烯醛、(E,E)-2,4-壬二烯醛、(Z)-2-庚烯醛、(E)-2-壬烯醛、(E)-2-辛烯醛、(E)-2-十一烯醛、2-戊基呋喃、庚醛、己醛、壬醛和辛醛。基于VIP值筛选出19种特征挥发性化合物,可用来区分5种鸡汤样品。不同炖煮时间鸡汤样品的电子鼻和电子舌数据存在显著差异,并能通过PCA及聚类分析有效区分。电子舌数据PCA-BP神经网络模型可准确预测鸡汤样品炖煮时间,真实值与预测值线性关系良好(R2=0.9998)。感官评价表明,炖煮2.0 h的鸡汤在风味、色泽、外观和总体可接受性方面得分很高。综上,鸡汤的最佳炖煮时间为2.0 h,研究结果可作为原始鸡汤的风味依据,为后续高质量的中药鸡汤研究作为铺垫。

鸡汤  /  炖煮过程  /  气相色谱-质谱  /  电子舌  /  电子鼻  /  PCA-BP神经网络  /  挥发性化合物  /  感官分析

To investigate the changes in volatile flavor compounds in chicken soup during the stewing process of yellow-feathered chicken, headspace solid phase microextraction (HS-SPME)-gas chromatography-mass spectrometry (GC-MS), electronic nose, and electronic tongue combined with PCA-BP neural network were used to study chicken soup samples at different cooking times (0.5, 1.0, 1.5, 2.0 and 2.5 h). The results showed that as the stewing time increased, the color of the chicken soup became darker. A total of 35 volatile compounds were identified through GC-MS, primarily consisting of aldehydes and alcohols. The total content of volatile flavor compounds increased first and then decreased, and reached a peak after stewing for 2.0 h. GC-MS combined with OAVs value method identified 13 key volatile compounds, including 1-hexanol, 1-octene-3-ol, (E,E)-2,4-decadiena,l (E,E)-2,4-nonadienal, (Z)-2-heptenal, (E)-2-nonenal, (E)-2-octenal, (E)-2-decenal, 2-pentylfuran, heptanal, hexanal, nonanal and octanal. Based on VIP values, 19 characteristic volatile compounds were screened, which could be used to distinguish 5 chicken soup samples. Significant differences in the electronic nose and electronic tongue of chicken soup samples with different stewing times, and these samples could be effectively distinguished by PCA and cluster analysis. The electronic tongue PCA-BP neural network model could accurately predict the stewing time of chicken soup samples and the linear relationship between the true value and the predicted value was good (R2=0.9998). The chicken soup stewed for 2.0 h showed significantly high scores for flavor, color, appearance, and overall acceptability. In conclusion, the optimal stewing time for chicken soup was 2.0 h. The results can be used as the basis for the flavor of the original chicken soup and pave the way for subsequent high-quality Chinese herbal chicken soup research.

chicken soup  /  stewing process  /  GC-MS  /  E-tongue  /  E-nose  /  PCA-BP neural network  /  volatile flavor compounds  /  sensory analysis
吕虹霞, 王永瑞. 多技术结合PCA-BP神经网络研究黄羽鸡炖煮过程中鸡汤挥发性风味物质变化. 食品工业科技, 2026 , 47 (18) : 306 -315 . DOI: 10.13386/j.issn1002-0306.2025070092
Hongxia LÜ, Yongrui WANG. Changes of Volatile Flavor Compounds in Chicken Soup during Stewing Process of Yellow-feathered Chicken Using Multiple Technologies Combined with PCA-BP Neural Network[J]. Science and Technology of Food Industry, 2026 , 47 (18) : 306 -315 . DOI: 10.13386/j.issn1002-0306.2025070092
在中国,鸡肉被认为是一种营养丰富且美味的肉类产品。与其他肉类相比,鸡肉具有低脂肪、低胆固醇、低热量和高蛋白质的优势[1−3]。全球对鸡肉需求的增长速度超过了猪肉和牛肉,其中中国消耗了全球约12%的鸡肉产量[4]。鸡肉的烹饪方式有很多种,如煎、炸、烤、煮、蒸等,其中炖煮成鸡汤在中国是较为常见的一种烹饪方式[5]。鸡汤富含多肽、氨基酸等多种营养成分,具有缓解感冒症状、消除疲劳、促进机体代谢、增强免疫力等作用,特别适合儿童、老年人、体弱者以及手术后的患者饮用[6−8]。在炖煮过程中,来自鸡肉中的部分蛋白质、碳水化合物、维生素、矿物质、多肽和氨基酸等水溶性成分会溶解到鸡汤中,随着炖煮时间的延长,汤中的这些成分含量会逐渐增加[9−10]。鸡汤中还含有大量源自肉类的风味化合物。这些化合物在炖煮过程中逐渐积累,传统上认为这种积累是炖汤独特风味的重要来源[11]。 黄羽鸡饲养周期较长,超过90 d,主要加工成煮鸡肉或鸡汤。基于鲜美肉质和特殊香气,这种鸡肉制成的鸡汤深受中国消费者的喜爱[12]。
风味是影响消费者对鸡汤接受度和偏好的重要因素。鸡汤的风味是由烹饪过程中碳水化合物、蛋白质和脂质的热反应产生的,主要包括美拉德反应、脂质氧化以及反应产物之间的相互作用[1,13−14]。醛、酯、醇、酸、内酯、烃、醚、杂环化合物和硫化合物是最常见的风味物质。许多研究比较了鸡汤(包括鸡肉)成分中的挥发性化合物,到目前为止在鸡肉和汤中发现了数百种挥发性化合物[11,15]。Jia等[16]研究了云南武定鸡与狄高肉鸡杂交F1代鸡在炖煮过程中的挥发性风味物质变化,在炖煮1、1.5、2、2.5和3 h的5个鸡汤样品共检测出86种挥发性风味化合物,包括20种醛类、18种醇类、2种呋喃、16种烯烃、4种酯类、8种酮类、4种芳香烃以及14种其他化合物。Qi等[17]采用了GC-MS结合OAV研究了不同炖煮次数黄羽鸡鸡汤挥发性风味化合物,发现戊醛、己醛、庚醛、辛醛、(E)-2-辛烯醛、壬醛、(Z)-4-癸烯醛、癸醛、(E,E)-2,4-癸二烯醛、1-戊醇和1-辛烯-3-醇是黄羽鸡鸡汤中的主要挥发性化合物。并且Qi等[17]还发现随着炖煮时间的延长,鸡汤中醛类显著减少,醇类则显著增加,2 h炖煮后产生的强烈香气主要归因于1-戊醇和1-辛烯-3-醇。Guan等[18]采用GC-MS研究中国大沽鸡汤发现鸡汤中挥发性化合物主要是己醛、辛醛、庚醛、(E,E)-2,4-癸二烯醛、壬醛和1-辛烯-3-醇。Liao等[19]研究确定(E,E)-2,4-癸二烯为鸡汤中最重要的气味成分,其权重重要性排序(Variable Importance in Projection,VIP)和气味活性值(Odor Activity Value,OAV)最高。醛、烯、酮、杂环化合物和硫化合物在鸡肉煮制过程中对鸡肉风味有重要影响[20]。然而目前的研究主要是对鸡汤风味物质含量以及贡献度的研究,对于鸡汤品质控制的预测模型研究较少。
鉴于此,本研究旨在通过GC-MS鉴定鸡汤炖煮过程中的挥发性化合物,基于OAV和偏最小二乘判别分析(Partial Least Squares Discriminant Analysis,PLS-DA)确定鸡汤中的关键挥发性化合物和关键标志化合物。然后采用电子鼻、电子舌对不同鸡汤样品进行区分并建立鸡汤电子舌PCA-BP神经网络预测模型,旨在对炖煮过程中鸡汤样品的品质控制提供一定的理论基础。
鸡肉 150日龄,雌性,当天现宰,宁夏晓鸣农牧股份有限公司;邻二氯苯(99.78%,g/g)、辛醛(99.0%)、1-己醇(98.0%)、己醛(95.0%)、(E)-2-辛烯醛(97.0%)、(E)-2-壬烯醛(95.0%)、壬醛(99.0%)、(E)-2-十一烯醛(96%)、庚醛(97%)、正构烷烃(C7~C40)标准品 美国Sigma-Aldrich公司;1-辛烯-3-醇(98.0%)、(E,E)-2,4-癸二烯醛(94.0%)、(E,E)-2,4-壬二烯醛(95.8%)、2-戊基呋喃(98.0%) 梯希爱(上海)化成工业发展有限公司;甲醇(色谱纯) 赛默飞世尔科技有限公司。
Qp2010ultra气相色谱-质谱仪 日本Shimadzu公司;PEN 3.5电子鼻 德国Airsence公司;cTongue 型电子舌 保圣科技有限公司;ST22J1型不锈钢汤锅 苏泊尔股份有限责任公司;C21-SC011型九阳触控式电磁炉 九阳股份有限公司;WNB22型精密数显恒温水浴槽 上海树立仪器仪表有限公司;LT202E型电子天平 常熟市天量仪器有限责任公司。
取20块新鲜带皮鸡胸肉(黄羽鸡,重量为290~320 g)洗净平均分为5组。称取4块鸡胸肉,加入2.5倍于鸡胸肉的纯净水于4 L不锈钢锅中,将电磁炉设置为300 W,待水煮沸时加入鸡肉。分别炖0.5、1.0、1.5、2.0和2.5 h。使用分液漏斗分离出汤面油层后,对鸡汤样品进行检测。
准确称取(2.00±0.01)g鸡汤样品于15 mL顶空瓶,使用10 μL微型注射器加入4 μL浓度为6.42 μg/mL的邻二氯苯/甲醇溶液,旋盖并用聚四氟乙烯隔膜密封,涡旋混匀。于55 ℃的恒温水浴中平衡20 min后使用50/30 μm DVB/CAR/PDMS纤维萃取头吸附挥发性化合物,吸附时间为30 min。结束后立即将萃取头转移到GC进样口,250 ℃解吸7 min。每个样品平行测定3次。
GC:DB-WAX色谱柱(30 m×0.25 mm×0.25 μm);程序升温:柱温40 ℃保持3 min,5 ℃/min升至90 ℃,8 ℃/min升至230 ℃保持10 min。载气为He,流速为1.8 mL/min。进样口温度250 ℃;不分流模式,进样量1 μL。
MS:电子电离源,温度230 ℃,能量70 eV;四极杆温度150 ℃;全扫描,质量扫描范围m/z 20~350。
挥发性化合物定性方式为NIST质谱库(MS)、保留指数(RI)以及标准品(S)。定量计算公式如下:
$\rm {M}_{x}=\frac{4\times 1000\times {A}_{x}}{2\times {A}_{i}\times 1000}\times {C}_{i} $
式中:Mx为目标挥发性化合物的含量(μg/kg);Ax为目标挥发性化合物峰面积;Ai为邻二氯苯的峰面积;Ci为邻二氯苯质量浓度(μg/kg)。
浓度以及感觉阈值共同决定了挥发性物质对于样品总体气味的贡献大小,因而本实验采用气味活度值(odor activity values,OAVs)法[21]对鸡汤样品中挥发性物质的香气贡献进行分析。OAVs按式(2)计算:
$ \mathrm{OAV}_{ \mathrm{x}} \mathrm{=M}_{ \mathrm{x}} \mathrm{/T}_{ \mathrm{i}} $
式中:Mx和Ti分别为各挥发性物质的含量(μg/kg)和感觉阈值(μg/kg)。
OAVs贡献率为鸡汤样品中某一关键挥发性物质的OAVs与该样品中所有关键挥发性物质总OAVs的百分比。
准确称取(2.00±0.01)g鸡汤样品置于电子鼻进样瓶内,旋盖并用聚四氟乙烯隔膜密封。50 ℃恒温水浴中放置30 min,取出至室温静置平衡30 min。电子鼻参数与王永瑞等[22]一致,每个样品平行测定3次。
根据Zhu等[23]的方法对样品进行预处理,并作修改。称取10 g样品加入100.0 mL超纯水进行均质化;然后将溶液在4.0 ℃下以3000 r/min离心15 min。使用12.5 cm滤纸过滤稀释的样品上机进行检测分析。电子舌由6个传感器(铂S1、金S2、钯S3、钨S4、钛S5、银S6)组成,每个传感器取三个特征数值,总共有18个特定值,即P-1~P-18。传感器的信号放大倍数为100倍,检测时间为120 s。
感官评价由宁夏大学18名研究生(9名女性和9名男性,年龄22~25岁)进行。在评估之前,所有学生都接受了专业培训。选择四个感官特征来评估鸡汤的质量,包括外观、色泽、风味和总体可接受性[24]。感官评估小组对鸡汤的感官属性进行0到10的评分,评价标准见表1。
每个样品重复测定三次,数据的平均值和标准差采用Excel软件进行;采用SPSS 19.0软件进行单因素方差分析,并结合Duncan法进行多重比较检验组间差异性,设定P<0.05为显著性差异。PLS-DA分析由SIMCA 14.1软件完成;聚类热图、相关性分析热图、OAVs贡献率图和PCA分析采用在线网站http://www.bioinformatics.com.cn/绘制。其他雷达图、OAVs值热图、柱状图由Origin 18C软件绘制。
将鸡胸肉分别炖煮0.5、1.0、1.5、2.0和2.5 h后得到鸡汤样品如图1所示。由图1可知随着炖煮时间的延长,鸡汤颜色加深,浓稠度升高。在鸡肉炖煮的过程中,鸡肉中的营养物质如蛋白质、碳水化合物、维生素、矿物质、多肽和氨基酸等水溶性成分会溶解到鸡汤中[9−10]。随着加热时间的延长,汤中的这些成分含量会逐渐增加,导致了鸡汤的颜色加深、浓稠度升高。
采用HS-SPME结合GC-MS对鸡汤样品中的挥发性化合物进行萃取分析,5份样品中共鉴定出35种挥发性化合物,包括15种醛类、8种醇类、8种酸类、2种酮类、1种酯类和1种杂环类化合物,这些物质的含量变化如图2所示。这一结果与Jia等[16]和Qi等[17]的研究结果差异明显,这主要归因于鸡汤炖煮工艺、检测仪器、萃取头型号、升温程序的差异。共有14种挥发性化合物在5个炖煮样品中均被检测出,分别为1-庚醇、1-辛醇、1-辛烯-3-醇、己醛、壬醛、(E,E)-2,4-癸二烯醛、(E,E)-2,4-庚二烯醛、(E,E)-2,4-壬二烯醛、(Z)-2-庚烯醛、(E)-2-壬烯醛、(E)-2-辛烯醛、2,4-二甲基苯甲醛、2,3-辛二酮和壬酸。有研究表明己醛、1-辛烯-3-醇、壬醛、(E,E)-2,4-癸二烯醛和(E)-2-壬烯醛在煮熟的鸡肉或肉汤中具有强烈的气味效力[12,25]。这些物质主要来源于脂质氧化,表明鸡汤在炖煮过程中的风味物质的形成主要来自肌内脂肪中各不饱和脂肪酸的氧化裂解。在炖煮0.5、1.0、1.5、2.0和2.5 h样品中,分别鉴定出26、22、26、27和26种化合物,各类别化合物种类如图3(a)所示。醛类、醇类和酸类化合物是所有样品中种类最多的3类化合物,各类化合物数目随着炖煮时间的增加变化不大(图3(b))。鸡汤中鉴定出的醇、醛类物质主要为C6~C10类化合物。醇类物质除1-辛烯-3-醇和2-乙基-1-己醇外均为直链1号位醇。醛类物质为2种直链醛、5种直链烯醛、3种直链二烯醛和2种芳香醛。有研究表明,己醛、辛醛、壬醛、庚醛、(E)-2-辛烯醛和(E)-2-己烯醛等醛类为鸡汤的关键风味物质[26-27]。酸类物质均为直链饱和脂肪酸,是肉中长链脂肪酸氧化代谢的产物。2,3-辛二酮和2-戊基呋喃是肉中较常见的挥发性风味物质,在鲜鸡肉[24]、鲜羊肉[28]、鲜牛肉[29]、烤羊肉[28,30]、烤鸡肉[31]、炸鸡肉[24]样品中均有存在。在所有样本中,挥发性化合物的总含量范围在67.49~270.45 μg/kg。随着炖煮时间的增加,挥发性化合物的总量先增加后降低,其中在炖煮2.0 h时达到了最高。Qi等[17]研究表明炖煮3 h鸡汤样品挥发性化合物总含量较炖煮2 h鸡汤样品明显降低,这可能是因为在鸡汤炖煮2 h后部分低沸点、不稳定的挥发性香气成分挥发或者分解导致总香气强度减弱。醛类和醇类化合物是鸡汤样品中含量较高的两类化合物,其总含量与所有挥发性化合物总含量的变化一致,而酯类物质只在炖煮0.5 h样品中检测出。酸类化合物和杂环类化合物总含量在炖煮2.0 h时最高,而酮类在炖煮0.5 h的鸡汤样品中含量最高。在这些化合物中,己醛在不同炖煮时间样品中含量普遍较高,含量范围在22.04~59.99 μg/kg,且在1.5 h达到最高。1-辛烯-3-醇(6.90~35.88 μg/kg)和(E,E)-2,4-癸二烯醛(4.94~83.02 μg/kg)也是鸡汤样品中含量较高的化合物,它们分别在1.5 h和2.0 h样品中达到最高。此外,(Z)-2-庚烯醛的含量在个别样品中也超过了10 μg/kg,范围在1.63~19.51 μg/kg之间,炖煮2.0 h后最高。其他的化合物的含量在所有样品中均小于10 μg/kg,这表明相对于炖煮鸡肉的挥发性物质,鸡汤中的风味物质含量较低[17]。鸡汤中风味物质主要来源于鸡肉中风味前体物质溶解、反应,并且随着炖煮的进行,部分挥发性风味化合物随水分的蒸发被带出。
根据Guadagni气味活度值理论,食品中香气浓度高而阈值低的成分很可能是食品的特征香气,因此结合化合物浓度以及阈值研究鸡汤炖煮过程中的关键挥发性化合物[32]。结合《化合物香味阈值汇编(原书第二版)》[33]、《食用调香术》[32]以及文献[34]中的香气阈值计算出鸡汤样品中各挥发性化合物的OAVs。通过筛选最终得到13种化合物的OAVs>1,分别为1-己醇、1-辛烯-3-醇、(E,E)-2,4-癸二烯醛、(E,E)-2,4-壬二烯醛、(Z)-2-庚烯醛、(E)-2-壬烯醛、(E)-2-辛烯醛、(E)-2-十一烯醛、2-戊基呋喃、庚醛、己醛、壬醛和辛醛,这与Qi等[17]研究结果相近。这13种化合物为鸡汤的关键风味物质,其OAVs在5种鸡汤样品中的变化如表2所示。由于(E,E)-2,4-癸二烯醛的香气阈值较低,因此其在所有鸡汤样品中的OAVs均最高。脂质氧化是肉类风味物质产生的重要原因,辛醛、壬醛和(E)-2-十一烯醛主要由油酸氧化生成。己醛、1-己醇、(E)-2-壬烯醛、(E,E)-2,4-癸二烯醛和2-戊基呋喃主要由亚油酸氧化产生[34−35]。亚油酸的9-氢过氧化物和13-氢过氧化物的降解分别导致(E,E)-2,4-癸二烯醛和己醛生成[36]。(E,E)-2,4-癸二烯醛的反式醇化反应生成(E)-2-辛烯醛[37]。庚醛在肉中的形成主要由亚油酸降解主导、氨基酸与美拉德反应协同[38]。具有蘑菇香气的1-辛烯-3-醇主要由花生四烯酸氧化生成[39]。(E,E)-2,4-壬二烯醛来源于α-亚麻酸,是ω-3油脂氧化程度的标志物[40]。(Z)-2-庚烯醛的前体物质为ω-6多不饱和脂肪酸,如亚油酸或花生四烯酸。其中亚油酸经热氧化生成13-氢过氧化物,进一步裂解产生庚醛,庚醛再脱水或异构化形成(Z)-2-庚烯醛。花生四烯酸氧化生成12-氢过氧化物,裂解后可直接产生(Z)-2-庚烯醛,同时生成其他烯醛(如(E)-2-辛烯醛)。此外,庚醛经Strecker降解或与含硫化合物结合,可能异构化为(Z)-2-庚烯醛。这13种关键挥发性化合物对鸡汤的整体风味起重要的贡献作用。而其余OAV<1的化合物为鸡汤样品的整体香气起到重要修饰作用[41]。
为进一步了解这13种化合物对于鸡汤风味的贡献,计算出了各物质OAVs贡献率,结果如表2所示。在5种鸡汤样品中(E,E)-2,4-癸二烯醛的OAVs贡献率均在85%以上,并且在炖煮2.0 h时(E,E)-2,4-癸二烯醛的OAVs贡献率达到了97.78%,表明(E,E)-2,4-癸二烯醛是鸡汤风味最主要的贡献者,这与Liao等[19]的研究结果一致。有研究表明(E,E)-2,4-癸二烯醛有助于提高鸡汤的浓郁度[19]。相较于其他关键挥发性醛类化合物,(E,E)-2,4-癸二烯醛碳链较长。随着醛类链长的增加,鸡汤中的肉基质对醛类物质的吸附能力也随之增强[42]。其余12种关键挥发性化合物的OAVs贡献率则全部小于5%。
采用SIMCA 14.0软件对GC-MS数据进行PLS-DA,以寻找鸡汤样品特征挥发性成分。由图4(a)可知,炖煮0.5 h鸡汤位于第1象限,炖煮1.5 h和2.0 h鸡汤样品位于第2象限,炖煮1.0 h鸡汤样品位于第4象限,炖煮2.5 h鸡汤样品位于第3象限。5个鸡汤样品差异显著,可以明显区分。挥发性化合物对已构建的分析模型的贡献大小可由PLS载荷图反映。挥发性化合物离中心圆点和主要化合物团越远,说明其对已构建的模型贡献越大[43]。从图4(b)可以看出,在鸡汤样品中戊酸(33)、1-壬醇(4)、十三醛(35)、乙酸己酯(20)、(Z)-2-癸烯醛(12)等化合物在载荷图中的位置与炖煮0.5 h鸡汤样品在得分图中的位置相近,表明这些化合物为炖煮0.5 h鸡汤的主要挥发性物质。1-十二醇(1)、苯甲醛(21)、2,4-二甲基苯甲醛(22)等挥发性物质为炖煮1.0 h鸡汤样品主要挥发性物质。6,10-二甲基-5,9-十一烯-2-酮(18)、十四碳酸(34)为炖煮2.5 h鸡汤样品的主要挥发性物质。2,3-辛二酮(8)、辛醛(31)为炖煮1.5 h鸡汤样品的主要挥发性物质。1-己醇(3)、1-辛醇(5)、庚醛(24)、己醛(26)、癸酸(28)、2-乙基-1-己醇(13)、(E,E)-2,4-癸二烯醛(9)、1-庚醇(2)、壬醛(29)、壬酸(30)、(Z)-2-庚烯醛(14)、1-辛烯-3-醇(6)、己醛(27)、庚酸(25)等为炖煮2.0 h鸡汤样品的主要挥发性物质。
采用有监督的PLS-DA进行分析时易产生过拟合现象,因此通过200次响应的置换检验验证PLS-DA模型是否存在过拟合现象[22]。如图4(c)所示,置换检验得到R2为0.451,Q2为−0.742,Q2与Y轴的截距为负,表明该模型不存在过拟合现象,模型的预测能力好,能够胜任后续特征挥发性物质的筛选。VIP展示每个变量对样品分类的贡献大小,其中VIP值大于1的变量通常被认为是模型的重要标志化合物。VIP值越大的变量在判别过程中贡献越大,在样品之间的差异也越显著[44]。从图4(d)可以看出,有19个香气组分VIP值大于1.0,可作为区分5个鸡汤样品的重要挥发性化合物。分别为十四碳酸(34)、6,10-二甲基-5,9-十一烯-2-酮(18)、辛醛(31)、苯甲醛(21)、2,4-二甲基苯甲醛(22)、庚醛(24)、乙酸己酯(20)、1-十二醇(1)、(E)-2-十一烯醛(17)、戊酸(33)、十三醛(35)、(Z)-2-癸烯醛(12)、癸酸(28)、(E,E)-2,4-壬二烯醛(11)、己醛(27)、己醛(26)、2,3-辛二酮(8)、1-壬醇(4)和(E,E)-2,4-癸二烯醛(9)。以上挥发性物质能作为5个鸡汤样品的关键变量,可用于区分不同炖煮时间鸡汤样品。
电子鼻是通过其内部的金属传感器对挥发性化合物的响应来辨别气味的电子系统,在PEN3.5型电子鼻中共含有10个金属传感器,分别为:W1C(响应特征:芳香类物质)、W5S(响应特征:氮氧化合物)、W3C(响应特征:氨,芳香类化合物)、W6S(响应特征:氢气)、W5C(响应特征:烷烃类,芳香类化合物)、W1S(响应特征:甲烷,与W2S传感器类似)、W1W(响应特征:萜烯类)、W2S(响应特征:醇类,部分芳香化合物)、W2W(响应特征:芳香类化合物,有机硫化物)、W3S(响应特征:烷烃类)[45]。采用PEN3.5电子鼻对不同炖煮时间鸡汤样品进行检测分析,各传感器对样品的响应值结果如图5(a)所示。金属传感器W1C、W3C、W6S和W5C对鸡汤样品的响应曲线始终在1附近,表明氨、烷烃类以及部分芳香族化合物在鸡汤样品中的含量较低且变化较小。传感器W5S、W2W、W2S、W1W以及W1S对鸡汤样品的响应明显,其中W5S的响应值与炖煮时间呈正相关,这表明氮氧类化合物随着炖煮时间增加含量上升。W2W、W1W、W2S和W1S传感器的响应值在炖煮2.0 h时最大,表明鸡肉炖煮2.0 h时鸡汤中的芳香类化合物、有机硫化物、萜烯类、醇类和部分芳香化合物含量较高。
PCA是一种多元统计分析方法, 它利用多元线性变换选择几个显著变量,通常用于分析观测变量之间的关系,已在肉制品风味鉴定方面广泛应用[46]。电子鼻数据PCA结果如图5(b)所示,鸡汤样品在PC1和PC2维度上有较好区分,PC1和PC2的贡献度分别为74.0%和13.4%。PC1+PC2为87.4%,说明前2个PC已将鸡汤样品中绝大多数挥发性化合物信息涵盖。结合雷达图和PCA得分图看,炖煮2.5 h的鸡汤样品与其他4个样品差异显著。雷达图中炖煮2.5 h鸡汤样品的10个传感器的轮廓与其他4个样品差异明显,因而在PCA得分图中炖煮2.5 h的鸡汤样品位于第一象限、第二象限的边缘处,独立于其他4个鸡汤样品。炖煮0.5 h和炖煮1.0 h的鸡汤样品完全坐落于第三象限,平行样之间相互略有交集。炖煮1.5 h鸡汤样品横跨第三象限、第四象限和第一象限。炖煮2.0 h位于第四和第一象限,与炖煮1.5 h 鸡汤样品互相略有交集。5个鸡汤样品在PCA得分图中呈现逆时针的过渡,表明鸡汤样品中的挥发性化合物随着炖煮时间的增加呈现规律性的变化。
电子舌是一种被广泛采用、客观且高效的工具,用于评估食物的味道特征[47−48]。不同炖煮时间的鸡汤样本的电子舌检测结果如图6(a)所示。可以看出,5个不同时间鸡汤的电子舌传感器特征数据存在差异。其中特征值P-1、P-2、P-7、P-8、P-13和P-14随着炖煮时间增加明显上升。18个特征值在炖煮2.5 h达到最大值。这说明炖煮2.5 h后,鸡汤内滋味物质丰富。为了进一步了解5种不同炖煮时间的鸡汤样品在味道特征上的差异,使用PCA和聚类分析对它们的味道成分进行了定性比较分析(图6(b)和图6(c))。由图6(b)可知,PC1方差贡献率为84.80%,PC2方差贡献率11.20%,累计方差贡献率96.00%;5个鸡汤样品按味道大致可分为3类,即炖煮0.5、1.0和1.5 h为一类,炖煮2.0 h为一类,炖煮2.5 h为一类。图6(c)显示了以树状图形式呈现的合并数据,其中水平轴表示组之间的欧几里德距离,垂直轴表示鸡汤滋味相似性。人工神经网络是模仿人脑神经细胞的结构和功能的系统,是一种大规模并行的非线性动力系统,能够实现输入与输出之间的高度非线性映射,在信号和模式识别中的应用非常广泛[49−51]。采用主成分分析(PCA)降维:将18个电子舌特征值压缩成了两个主成分(图6(d))。横轴:主成分1,占84.7835%的信息量。纵轴:主成分2,占11.1425%的信息量。总共解释了95.9%的数据变异性,已经覆盖了大部分有效信息。垂直轴是BP神经网络的输出值(炖煮时间),呈现一个明显的单峰分布,说明模型在主成分空间中有一个清晰的最优响应区域。采用主成分分析结合BP人工神经网络技术建立鸡汤样品鉴别模型。在最优条件下模型定标集的相关系数为0.996,验证集的相关系数为 0.9998。对预测模型进行验证,R2=0.9998表明模型训练的决定系数非常高,接近1,说明预测值和真实值拟合度极高(图6(e))。模型拟合的线性方程为:预测值=0.9998×真实值+0.0002。
感官评价用于表征样品的各种视觉、味觉、嗅觉的特征。如表3所示,炖煮2.0 h的鸡汤在风味、色泽、外观和总体可接受性方面得分最高,这表明此时会产生很多气味化合物,更具吸引力。并且GC-MS数据得出挥发性风味物质的总含量在炖煮2.0 h时达到了最高。鉴于此,鸡汤的最佳炖煮时间为2.0 h。
在炖煮过程中,鸡肉中的营养物质逐渐渗透至汤中,发生各类化学反应形成挥发性风味物质。这些物质的含量与炖煮时间存在着一定的关系。将鸡汤中关键挥发性物质的含量与电子鼻数据、电子舌数据、感官评价数据进行相关性分析,结果如图7所示。1-己醇与电子鼻W2S和W2W传感器呈显著正相关(P<0.05),与电子鼻W1C、W3C和W5C传感器呈显著负相关(P<0.05)。辛醛与电子鼻W1S和W3S传感器呈显著正相关(P<0.05)。壬醛与电子鼻W3C和W5C传感器呈显著负相关(P<0.05),与电子鼻W2S和W2W传感器呈显著正相关(P<0.05)。(E,E)-2,4-癸二烯醛与电子鼻W2W传感器呈显著正相关(P<0.05)。(E,E)-2,4-壬二烯醛与电子舌的P-1、P-2、P-7、P-8、P-9、P-13和P-14特征值呈显著正相关(P<0.05)。(E)-2-壬烯醛与电子鼻W5S传感器、电子舌的P-7、P-8、P-9、P-13和P-14特征值呈显著正相关(P<0.05)。(E)-2-辛烯醛与电子鼻W5S传感器、电子舌的P-7、P-8、P-9、P-13和P-14特征值、感官评价的外观、色泽、风味和总体可接受性呈显著正相关(P<0.05)。(E)-2-十一烯醛与电子鼻W1W传感器、感官评价的外观、色泽、风味和总体可接受性显著正相关(P<0.05)。2-戊基呋喃与感官评价的外观、色泽、风味和总体可接受性显著正相关(P<0.05)。
本文研究了黄羽鸡炖煮过程中鸡汤挥发性风味物质的变化。鸡汤颜色随着炖煮时间增加逐渐变浓。在所有鸡汤样品中共鉴定出35种挥发性化合物,醛类、醇类和酸类化合物种类最多。鸡汤中的关键挥发性化合物主要来源于脂质氧化,(E,E)-2,4-癸二烯醛在其中起到极其重要的作用。不同炖煮时间的鸡汤样品可通过电子鼻和电子舌进行有效区分。电子舌PCA-BP神经网络模型可准确预测鸡汤样品炖煮时间,模型拟合的线性方程为:预测值=0.9998×真实值+0.0002。综合感官评价和GC-MS数据得出鸡汤的最佳炖煮时间为2.0 h。论文研究结果可初步作为原始鸡汤风味物质的依据,下一步就中药对鸡汤的风味影响以及鸡汤滋味物质变化进行研究,有助于生产高质量的药食同源的鸡汤产品。

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2026年第47卷第18期
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doi: 10.13386/j.issn1002-0306.2025070092
  • 接收时间:2025-07-08
  • 首发时间:2026-10-24
  • 出版时间:2026-09-15
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    1西安外事学院医学院,陕西西安 710077
    2宁夏大学动物科技学院,宁夏银川 750021

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王永瑞(1991−),男,博士,研究方向:农产品加工,E-mail:。
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2种不同金属材料的力学参数
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种数
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species
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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