Article(id=1320881062440457151, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320880625310085394, articleNumber=null, orderNo=null, doi=10.13386/j.issn1002-0306.2025080046, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1754841600000, receivedDateStr=2025-08-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792852186069, onlineDateStr=2026-10-24, pubDate=1788192000000, pubDateStr=2026-09-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792852186069, onlineIssueDateStr=2026-10-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792852186069, creator=13701087609, updateTime=1792852186069, updator=13701087609, issue=Issue{id=1320880625310085394, tenantId=1146029695717560320, journalId=1260987677001138203, year='2026', volume='47', issue='17', pageStart='1', pageEnd='596', issueExtLink='null', onlineDate='null', pubDate='1788192000000', pubDateStr='2026-09-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792852081848, creator='13701087609', updateTime=1792852081848, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=260, endPage=272, ext={EN=ArticleExt(id=1320881062666949568, articleId=1320881062440457151, tenantId=1146029695717560320, journalId=1260987677001138203, language=EN, title=Development of Artemia Biscuits and Analysis of Their Nutritional and Flavor Characteristics, columnId=1261343843090313538, journalTitle=Science and Technology of Food Industry, columnName=Processing Technology, runingTitle=null, highlight=null, articleAbstract=

To explore the potential of Artemia to be developed as a food resource, promote the diversified development of the Artemia industry, and expand the biscuit food market. Biscuits containing Artemia flavor were developed. Single-factor experiments were performed to evaluate the effects of the Artemia addition, quinoa flour supplementation, and baking time and temperature on the sensory scores of the biscuits. Subsequently, the optimal processing parameters were determined using both response surface methodology (RSM) and artificial neural network (ANN) modeling. Furthermore, the nutritional and flavor profiles of the Artemia biscuits were characterized by means of nutritional composition and electronic tongue analyses as well as headspace solid-phase microextraction-coupled gas chromatography-mass spectrometry (SPME-GC-MS). Compared with the RSM model, the ANN model exhibited superior predictive and optimization capabilities, as demonstrated by its lower root mean square error (RMSE) of 1.8614, higher correlation coefficient (r) of 0.9602, and smaller errors between the measured and predicted values. The ANN-optimized biscuit formulation was as follows: Artemia (13.56 g), quinoa flour (47.26 g), low-gluten wheat flour (100 g), milk (40 g), corn oil (30 g), butter (60 g), salt (2 g), and xylitol (25 g), baked at 150 °C for 20.70 min. This optimized process yielded the highest sensory score (73.00). Additionally, the biscuits contained 6.46 g/100 g protein and 30.30 g/100 g fat. Electronic tongue analysis indicated that the taste profile was dominated by umami and saltiness. Moreover, GC-MS identified 48 volatile compounds, primarily aldehydes, acids, esters, furans, and pyrazines, with (E,E)-2,4-decadienal exhibiting the highest content (103.7 µg/kg) and odor activity value (3840.74). The development of Artemia biscuits enriches the variety of products in the biscuit market and offers a novel approach and theoretical foundation for utilizing Artemia resources in the snack-food industry.

, authors=Xiaodong JIANG1, 2, Shuaizhong ZHANG1, 2, Bo PENG3, Ying WANG1, 2, *, Yuanqin SUN1, 2, Xiao LI1, 2, Lei JI1, 2, authorsList=Xiaodong JIANG, Shuaizhong ZHANG, Bo PENG, Ying WANG, Yuanqin SUN, Xiao LI, Lei JI, authorCompany=null, correspAuthors=Ying 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=1320881066580235225, articleId=1320881062440457151, tenantId=1146029695717560320, journalId=1260987677001138203, language=CN, title=卤虫饼干研制及其营养与风味分析, columnId=1261343845481066826, journalTitle=食品工业科技, columnName=工艺技术, runingTitle=null, highlight=null, articleAbstract=

为挖掘卤虫作为食品资源的开发潜力,推进卤虫产业多元化发展并开拓饼干食品市场。本研究以卤虫为原料,采用单因素实验考察卤虫添加量、藜麦粉添加量、烘焙时间和温度对饼干感官评分的影响,结合响应面试验和人工神经网络确定最佳工艺,并通过营养成分、电子舌、顶空固相微萃取气质联用等试验分析卤虫饼干营养和风味特性。结果表明:相较于响应面模型,人工神经网络具有更小的均方根误差1.8614和更大的相关系数0.9602,实测值与预测值之间误差更小,具有更强更稳定的预测和优化能力。采用人工神经网络模型优化得卤虫饼干工艺参数:添加卤虫13.56 g,藜麦粉47.26 g、低筋小麦粉100 g、牛奶40 g、玉米油30 g、黄油60 g、盐2 g、木糖醇25 g,于150 ℃上烘烤20.70 min,此条件下卤虫饼干感官评分最高,为73.00。卤虫饼干蛋白质含量为6.46 g/100 g、脂肪含量为30.30 g/100 g。电子舌分析表明,卤虫饼干滋味以鲜咸为主。气相色谱-质谱结果表明,卤虫饼干含挥发性化合物48种,主要为醛类、酸类、酯类和呋喃吡喃类,其中含量与气味活性值最高的化合物均为(E,E)-2,4-癸二烯醛,分别为103.7 µg/kg和3840.74。卤虫饼干的研制丰富了饼干市场产品种类,为卤虫资源在休闲食品领域的开发应用提供了新的渠道和理论参考。

, authors=姜晓东1, 2, 张帅中1, 2, 彭博3, 王颖1, 2, *, 孙元芹1, 2, 李晓1, 2, 纪蕾1, 2, authorsList=姜晓东, 张帅中, 彭博, 王颖, 孙元芹, 李晓, 纪蕾, authorCompany=null, correspAuthors=王颖, authorNote=

姜晓东(1988−),男,硕士,助理研究员,研究方向:食品生物技术,E-mail:

, correspAuthorsNote=
王颖(1971−),女,硕士,研究员,研究方向:海洋生物资源利用,E-mail:。
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注:小写字母不同表示差异显著(P<0.05);图3~图5同。

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tableContent=null), ArticleFig(id=1320881445309100791, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=图4, caption=温度对饼干感官评分的影响, figureFileSmall=KXYUOUtIUY+6gV7SNK8L6A==, figureFileBig=8AxTcBJv4mcRsccBotnbHg==, tableContent=null), ArticleFig(id=1320881445380403960, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Fig.5, caption=Effect of baking time on biscuit sensory score, figureFileSmall=p9y3aEoUzQQG9/mYkJbtIA==, figureFileBig=GpEnpPzY4RdBXWtQhtUCwQ==, tableContent=null), ArticleFig(id=1320881445451707129, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=图5, caption=时间对饼干感官评分的影响, figureFileSmall=p9y3aEoUzQQG9/mYkJbtIA==, figureFileBig=GpEnpPzY4RdBXWtQhtUCwQ==, tableContent=null), ArticleFig(id=1320881445527204602, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Fig.6, caption=Influence of factor interaction on sensory evaluation of Artemia biscuits, figureFileSmall=pELX/kxl1Sp+IVO5H3mjUQ==, figureFileBig=Q+xGI3ZNmxEY9COZ5UIfgg==, tableContent=null), ArticleFig(id=1320881445615284987, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=图6, caption=因素交互作用对卤虫饼干感官评分的影响, figureFileSmall=pELX/kxl1Sp+IVO5H3mjUQ==, figureFileBig=Q+xGI3ZNmxEY9COZ5UIfgg==, tableContent=null), ArticleFig(id=1320881445707559676, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Fig.7, caption=Effects of the number of hidden layers on the performance of ANN model, figureFileSmall=iD/uFNpDQ1AlBa/MZHI5Jg==, figureFileBig=pOfb8ZClijoeLe1xzPiFlg==, tableContent=null), ArticleFig(id=1320881445791445757, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, 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figureFileBig=Gyh1GyGqBRRg6ADpmCjJgg==, tableContent=null), ArticleFig(id=1320881446080852737, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=图9, caption=ANN模型训练适配曲线, figureFileSmall=myF4fow7SRCoTydmRdlzWQ==, figureFileBig=Gyh1GyGqBRRg6ADpmCjJgg==, tableContent=null), ArticleFig(id=1320881446147961602, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Fig.10, caption=Fitness curve, figureFileSmall=sfpyCohOgW0DsRnPrP5/tw==, figureFileBig=UpcIi4TLdGyJba/qj6yFtw==, tableContent=null), ArticleFig(id=1320881446206681859, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=图10, caption=适应度曲线, figureFileSmall=sfpyCohOgW0DsRnPrP5/tw==, figureFileBig=UpcIi4TLdGyJba/qj6yFtw==, tableContent=null), ArticleFig(id=1320881446294762244, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Fig.11, caption=Taylor diagram of RSM and ANN models, figureFileSmall=ViYCBXGn3cpkRxKhdRiJKA==, figureFileBig=QD1c0b15vrPC7DWY93s4MQ==, tableContent=null), ArticleFig(id=1320881446361871109, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=图11, caption=RSM和ANN模型的泰勒图, figureFileSmall=ViYCBXGn3cpkRxKhdRiJKA==, figureFileBig=QD1c0b15vrPC7DWY93s4MQ==, tableContent=null), ArticleFig(id=1320881446424785670, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Fig.12, caption=Artemia biscuit electronic tongue radar image, figureFileSmall=hlBZG0/fRgmVZzbd14o4hg==, figureFileBig=+IS9nQexKeynI+w+vQNX2w==, tableContent=null), ArticleFig(id=1320881446483505927, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=图12, caption=卤虫饼干电子舌雷达图, 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Response surface test factors and levels

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水平A卤虫添加量(g)B藜麦粉添加量(g)C时间(min)D温度(℃)
−1124419145
01547.520150
1185121155
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响应面试验因素及水平

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水平A卤虫添加量(g)B藜麦粉添加量(g)C时间(min)D温度(℃)
−1124419145
01547.520150
1185121155
), ArticleFig(id=1320881446718386954, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Table 2, caption=

Sensory evaluation criteria for Artemia biscuit

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项目评分标准分值
色泽
(20分)
色泽浅淡、颜色不均匀、边缘焦糊发暗0~6
色泽浅黄色、颜色整体均匀、无焦糊过白现象7~14
色泽金黄、颜色均匀、整体无焦糊现象15~20
形态
(15分)
外形不完整、薄厚不均匀、严重收缩变形、表面裂纹较多、底部凹陷0~5
外形基本完整、薄厚均匀、略有变形、表面略有裂纹、底部凹陷较浅6~10
外形完整、薄厚均匀、无变形、表面无裂纹、底部无凹陷11~15
香气
(20分)
有糊味或腥味,苦味严重0~6
鲜香味略淡、无焦糊异味7~14
香味浓、无异味、具有卤虫特有的风味15~20
滋味
(25分)
滋味偏咸或偏甜,有异味0~9
滋味一般,比较酥松10~17
入口味道浓郁,滋味微甜或微咸,酥松可口18~25
口感
(20分)
饼干口感过硬或松软、粘牙、过于油腻0~8
较为松软、略为粘牙、油腻适中9~15
口感较为酥脆、不粘牙、口感细腻16~20
), ArticleFig(id=1320881446798078731, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=表2, caption=

卤虫饼干感官评分标准

, figureFileSmall=null, figureFileBig=null, tableContent=
项目评分标准分值
色泽
(20分)
色泽浅淡、颜色不均匀、边缘焦糊发暗0~6
色泽浅黄色、颜色整体均匀、无焦糊过白现象7~14
色泽金黄、颜色均匀、整体无焦糊现象15~20
形态
(15分)
外形不完整、薄厚不均匀、严重收缩变形、表面裂纹较多、底部凹陷0~5
外形基本完整、薄厚均匀、略有变形、表面略有裂纹、底部凹陷较浅6~10
外形完整、薄厚均匀、无变形、表面无裂纹、底部无凹陷11~15
香气
(20分)
有糊味或腥味,苦味严重0~6
鲜香味略淡、无焦糊异味7~14
香味浓、无异味、具有卤虫特有的风味15~20
滋味
(25分)
滋味偏咸或偏甜,有异味0~9
滋味一般,比较酥松10~17
入口味道浓郁,滋味微甜或微咸,酥松可口18~25
口感
(20分)
饼干口感过硬或松软、粘牙、过于油腻0~8
较为松软、略为粘牙、油腻适中9~15
口感较为酥脆、不粘牙、口感细腻16~20
), ArticleFig(id=1320881446865187596, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Table 3, caption=

Program and results of Box-Benhnken design

, figureFileSmall=null, figureFileBig=null, tableContent=
试验号ABCDY感官评分
100−1−161.70
2−100−150.30
30−10−164.90
4010−152.00
5100−162.50
6001−167.80
7−10−1062.70
80−1−1060.70
901−1056.50
1010−1067.80
11−1−10053.80
12−110050.00
13000063.80
14000063.90
15000067.80
16000064.80
17000068.60
181−10056.80
19110053.80
20−101068.80
210−11062.80
22011061.10
23101058.80
2400−1168.30
25−100169.70
260−10150.80
27010151.60
28100150.30
29001160.20
), ArticleFig(id=1320881446923907853, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=表3, caption=

Box-Benhnken试验设计方案与结果

, figureFileSmall=null, figureFileBig=null, tableContent=
试验号ABCDY感官评分
100−1−161.70
2−100−150.30
30−10−164.90
4010−152.00
5100−162.50
6001−167.80
7−10−1062.70
80−1−1060.70
901−1056.50
1010−1067.80
11−1−10053.80
12−110050.00
13000063.80
14000063.90
15000067.80
16000064.80
17000068.60
181−10056.80
19110053.80
20−101068.80
210−11062.80
22011061.10
23101058.80
2400−1168.30
25−100169.70
260−10150.80
27010151.60
28100150.30
29001160.20
), ArticleFig(id=1320881446991016718, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Table 4, caption=

Variance analysis of the regression model for sensory score

, figureFileSmall=null, figureFileBig=null, tableContent=
来源平方和自由度均方 F值P值
模型1072.351476.608.250.0002**
A2.3412.340.250.6234
B51.25151.255.520.0340*
C0.2710.270.0290.8670
D5.7415.740.620.4448
AB0.1610.160.0170.8974
AC57.00157.006.140.0266*
AD249.641249.6426.890.0001**
BC1.5611.560.170.6878
BD46.92146.925.050.0412*
CD50.41150.415.430.0569
A265.36165.368.090.0353*
B2400.821400.8243.17<0.0001**
C238.59138.594.160.0608
D277.13177.138.310.0121*
残差129.97149.28
失拟项109.531010.952.140.2408
纯误差20.4545.11
总值1202.3328
R2=0.8919R2adj=0.7838RSN=9.087C.V.%=5.04
), ArticleFig(id=1320881447058125583, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=表4, caption=

感官评分回归模型方差分析

, figureFileSmall=null, figureFileBig=null, tableContent=
来源平方和自由度均方 F值P值
模型1072.351476.608.250.0002**
A2.3412.340.250.6234
B51.25151.255.520.0340*
C0.2710.270.0290.8670
D5.7415.740.620.4448
AB0.1610.160.0170.8974
AC57.00157.006.140.0266*
AD249.641249.6426.890.0001**
BC1.5611.560.170.6878
BD46.92146.925.050.0412*
CD50.41150.415.430.0569
A265.36165.368.090.0353*
B2400.821400.8243.17<0.0001**
C238.59138.594.160.0608
D277.13177.138.310.0121*
残差129.97149.28
失拟项109.531010.952.140.2408
纯误差20.4545.11
总值1202.3328
R2=0.8919R2adj=0.7838RSN=9.087C.V.%=5.04
), ArticleFig(id=1320881447125234448, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Table 5, caption=

Acid value and peroxide value of Artemia biscuit

, figureFileSmall=null, figureFileBig=null, tableContent=
 项目酸价(mg/g)过氧化值(g/100 g)
卤虫饼干2.40±0.300.097±0.004
国家标准要求≤5≤0.25
), ArticleFig(id=1320881447209120529, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=表5, caption=

卤虫饼干酸价和过氧化值

, figureFileSmall=null, figureFileBig=null, tableContent=
 项目酸价(mg/g)过氧化值(g/100 g)
卤虫饼干2.40±0.300.097±0.004
国家标准要求≤5≤0.25
), ArticleFig(id=1320881447272035090, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Table 6, caption=

Nutrients of Artemia biscuits (wet weight)

, figureFileSmall=null, figureFileBig=null, tableContent=
项目能量
(kJ/100 g)
蛋白质
(g/100 g)
脂肪
(g/100 g)
碳水化合物
(g/100 g)
钠
(mg/100 g)
卤虫饼干21926.46±0.4830.30±1.2556.50±1.15283±26
市售黄油饼干23134.4034.2057.20346
石斑鱼骨饼干[30]−6.1326.31−−
油橄榄果渣
曲奇饼干[31]
−−33.29−−
方竹笋曲
奇饼干[32]
−4.9927.60−−
), ArticleFig(id=1320881447339143955, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=表6, caption=

卤虫饼干营养成分(湿重)

, figureFileSmall=null, figureFileBig=null, tableContent=
项目能量
(kJ/100 g)
蛋白质
(g/100 g)
脂肪
(g/100 g)
碳水化合物
(g/100 g)
钠
(mg/100 g)
卤虫饼干21926.46±0.4830.30±1.2556.50±1.15283±26
市售黄油饼干23134.4034.2057.20346
石斑鱼骨饼干[30]−6.1326.31−−
油橄榄果渣
曲奇饼干[31]
−−33.29−−
方竹笋曲
奇饼干[32]
−4.9927.60−−
), ArticleFig(id=1320881447423030036, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=EN, label=Table 7, caption=

Analysis of volatile flavor components in Artemia biscuits

, figureFileSmall=null, figureFileBig=null, tableContent=
类别物质名称阈值[33]
(mg/kg)
卤虫饼干市售黄油饼干
含量(µg/kg)OAV含量(µg/kg)OAV
醇类乙醇95014.4±0.80.00002ND/
1-戊醇0.15023.6±0.20.02ND/
1-庚醇0.00541.5±0.10.28ND/
1-辛烯-3-醇0.00156.0±0.34ND/
4-甲基2-庚醇nND/8.1±0.4/
3,4,4-三甲基-1-戊-3-醇nND/6.9±0.4/
(S)-(+) -5-甲基-1-庚醇n1.5±0.1b/2.7±0.2a/
合计27.017.7
醛类戊醛0.01243.5±2.33.63ND/
(E)-2-庚醛0.048.7±0.40.22ND/
壬醛0.001110.6±0.69.64ND/
(E)-2-辛烯醛0.0039.7±0.33.23ND/
(E)-2-壬烯醛0.000195.5±0.228.95ND/
(E,E)-2,4-癸二烯醛0.000027103.7±5.83840.74ND/
苯甲醛0.750899.7±0.7a0.018.0±0.3b0.01
苯乙醛0.006329.9±1.64.75ND/
2,5-二甲基苯甲醛0.2ND/2.9±0.10.01
合计221.310.9
酮类2-庚酮0.14ND/12.6±0.40.09
2-壬酮0.04126.4±1.7b0.6452.0±3.7a1.27
2-十一烷酮0.005526.7±0.9b4.8552.0±2.9a9.45
2-十三烷酮n10.2±0.6b/25.7±1.4a/
2,2,5-三甲基-3,4-己二酮n0.7±0.1b/2.4±0.2a/
羟基丙酮102.4±0.2a0.00022.3±0.3a0.0002
环丁酮0.92.1±0.2a0.0021.7±0.2a0.002
合计68.5148.7
酸类乙酸9920.3±0.7b0.000229.2±2.9a0.0003
丙酸2.191.2±0.10.001ND/
丁酸2.412.5±0.4a0.0110.6±1.1a0.004
戊酸111.8±0.10.0002ND/
己酸0.8955.9±3.6b0.182.6±4.7a0.09
辛酸338.0±2.7b0.0179.6±3.4a0.03
壬酸4.630.2±1.8b0.0148.0±2.4a0.01
癸酸1019.5±0.4b0.00228.0±1.6a0.003
丙酸酐3.4*3.8±0.20.001ND/
合计183.2278
酯类辛酸甲酯0.26.8±0.3a0.034.3±0.7b0.02
癸酸甲酯0.00435.9±0.41.37ND/
壬酸乙酯0.377ND/1.8±0.20.005
癸酸乙酯0.005ND/32.9±3.16.58
乙酸丙酯2ND/14.4±0.80.01
丙酮酸丙酯n14.3±0.6a/14.0±0.4a/
2-丙烯酸丁酯0.00280.6±4.540.30ND/
乳酸叶醇酯nND/14.7±0.9/
对叔丁基苯乙酸甲酯nND/6.6±0.2/
2 - 乙基己基亚硫酸酯nND/6.2±0.6/
S-甲基硫代丁酸酯0.14*ND/1.9±0.20.01
2-氧代丙酸甲酯n1.2±0.2/ND/
合计108.896.8
呋喃吡喃类2-戊基呋喃0.00585.1±0.3a0.881.9±0.1b0.33
2-呋喃甲醇4.500528.8±1.1a0.0132.3±2.4a0.007
糠醛9.562ND/10.8±1.50.001
2-乙酰基呋喃15.0252ND/1.8±0.20.0001
2,3-二氢-3,5-二羟基-6-甲基-4(4H)-吡喃酮3528.2±2.3b0.00164.3±4.8a0.002
4-羟基-2,5-二甲基-3(2H)-呋喃酮0.02235.7±0.60.26ND/
2H-吡喃-2,6 (3H)-二酮nND/1.7±0.2/
麦芽酚1.2436.4±3.4b0.03166.0±12.2a0.13
异麦芽酚n0.8±0.1b/1.9±0.2a/
乙基麦芽酚nND/67.6±5.3/
γ-丁内酯13.4±0.2a0.0031.7±0.5b0.002
δ-辛内酯0.424.8±0.50.01ND/
γ-壬内酯0.0097ND/209.6±17.821.61
δ-壬内酯2.6#12.6±1.10.005ND/
γ-癸内酯0.0011ND/50.9±2.446.27
δ-癸内酯0.066ND/382.7±19.75.80
δ-十二内酯0.053ND/45.3±3.10.85
四氢糠醇丙酸酯n2.3±0.3/ND/
合计128.11038.5
其它2-(5-甲基噻唑-4-基)乙基醋酸酯nND/113.5±9.4/
5-(2-羟乙基)-4-甲基噻唑0.1#ND/48.8±2.70.49
丁基醚0.4*5.5±0.50.01ND/
1-(己氧基)-5-甲基-己烷n10.6±0.7/ND/
(d)-柠檬烯0.0347.5±0.40.22ND/
4-(1-甲基丙基)苯酚n9.5±0.5/ND/
2-甲氧基-5-乙烯基苯酚0.000151.1±0.17.33ND/
合计34.2162.3
), ArticleFig(id=1320881447506916117, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881062440457151, language=CN, label=表7, caption=

卤虫饼干挥发性风味成分分析

, figureFileSmall=null, figureFileBig=null, tableContent=
类别物质名称阈值[33]
(mg/kg)
卤虫饼干市售黄油饼干
含量(µg/kg)OAV含量(µg/kg)OAV
醇类乙醇95014.4±0.80.00002ND/
1-戊醇0.15023.6±0.20.02ND/
1-庚醇0.00541.5±0.10.28ND/
1-辛烯-3-醇0.00156.0±0.34ND/
4-甲基2-庚醇nND/8.1±0.4/
3,4,4-三甲基-1-戊-3-醇nND/6.9±0.4/
(S)-(+) -5-甲基-1-庚醇n1.5±0.1b/2.7±0.2a/
合计27.017.7
醛类戊醛0.01243.5±2.33.63ND/
(E)-2-庚醛0.048.7±0.40.22ND/
壬醛0.001110.6±0.69.64ND/
(E)-2-辛烯醛0.0039.7±0.33.23ND/
(E)-2-壬烯醛0.000195.5±0.228.95ND/
(E,E)-2,4-癸二烯醛0.000027103.7±5.83840.74ND/
苯甲醛0.750899.7±0.7a0.018.0±0.3b0.01
苯乙醛0.006329.9±1.64.75ND/
2,5-二甲基苯甲醛0.2ND/2.9±0.10.01
合计221.310.9
酮类2-庚酮0.14ND/12.6±0.40.09
2-壬酮0.04126.4±1.7b0.6452.0±3.7a1.27
2-十一烷酮0.005526.7±0.9b4.8552.0±2.9a9.45
2-十三烷酮n10.2±0.6b/25.7±1.4a/
2,2,5-三甲基-3,4-己二酮n0.7±0.1b/2.4±0.2a/
羟基丙酮102.4±0.2a0.00022.3±0.3a0.0002
环丁酮0.92.1±0.2a0.0021.7±0.2a0.002
合计68.5148.7
酸类乙酸9920.3±0.7b0.000229.2±2.9a0.0003
丙酸2.191.2±0.10.001ND/
丁酸2.412.5±0.4a0.0110.6±1.1a0.004
戊酸111.8±0.10.0002ND/
己酸0.8955.9±3.6b0.182.6±4.7a0.09
辛酸338.0±2.7b0.0179.6±3.4a0.03
壬酸4.630.2±1.8b0.0148.0±2.4a0.01
癸酸1019.5±0.4b0.00228.0±1.6a0.003
丙酸酐3.4*3.8±0.20.001ND/
合计183.2278
酯类辛酸甲酯0.26.8±0.3a0.034.3±0.7b0.02
癸酸甲酯0.00435.9±0.41.37ND/
壬酸乙酯0.377ND/1.8±0.20.005
癸酸乙酯0.005ND/32.9±3.16.58
乙酸丙酯2ND/14.4±0.80.01
丙酮酸丙酯n14.3±0.6a/14.0±0.4a/
2-丙烯酸丁酯0.00280.6±4.540.30ND/
乳酸叶醇酯nND/14.7±0.9/
对叔丁基苯乙酸甲酯nND/6.6±0.2/
2 - 乙基己基亚硫酸酯nND/6.2±0.6/
S-甲基硫代丁酸酯0.14*ND/1.9±0.20.01
2-氧代丙酸甲酯n1.2±0.2/ND/
合计108.896.8
呋喃吡喃类2-戊基呋喃0.00585.1±0.3a0.881.9±0.1b0.33
2-呋喃甲醇4.500528.8±1.1a0.0132.3±2.4a0.007
糠醛9.562ND/10.8±1.50.001
2-乙酰基呋喃15.0252ND/1.8±0.20.0001
2,3-二氢-3,5-二羟基-6-甲基-4(4H)-吡喃酮3528.2±2.3b0.00164.3±4.8a0.002
4-羟基-2,5-二甲基-3(2H)-呋喃酮0.02235.7±0.60.26ND/
2H-吡喃-2,6 (3H)-二酮nND/1.7±0.2/
麦芽酚1.2436.4±3.4b0.03166.0±12.2a0.13
异麦芽酚n0.8±0.1b/1.9±0.2a/
乙基麦芽酚nND/67.6±5.3/
γ-丁内酯13.4±0.2a0.0031.7±0.5b0.002
δ-辛内酯0.424.8±0.50.01ND/
γ-壬内酯0.0097ND/209.6±17.821.61
δ-壬内酯2.6#12.6±1.10.005ND/
γ-癸内酯0.0011ND/50.9±2.446.27
δ-癸内酯0.066ND/382.7±19.75.80
δ-十二内酯0.053ND/45.3±3.10.85
四氢糠醇丙酸酯n2.3±0.3/ND/
合计128.11038.5
其它2-(5-甲基噻唑-4-基)乙基醋酸酯nND/113.5±9.4/
5-(2-羟乙基)-4-甲基噻唑0.1#ND/48.8±2.70.49
丁基醚0.4*5.5±0.50.01ND/
1-(己氧基)-5-甲基-己烷n10.6±0.7/ND/
(d)-柠檬烯0.0347.5±0.40.22ND/
4-(1-甲基丙基)苯酚n9.5±0.5/ND/
2-甲氧基-5-乙烯基苯酚0.000151.1±0.17.33ND/
合计34.2162.3
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卤虫饼干研制及其营养与风味分析
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姜晓东 1, 2 , 张帅中 1, 2 , 彭博 3 , 王颖 1, 2, * , 孙元芹 1, 2 , 李晓 1, 2 , 纪蕾 1, 2
食品工业科技 | 工艺技术 2026,47(17): 260-272
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食品工业科技 |工艺技术 2026 , 47 (17) : 260 -272
卤虫饼干研制及其营养与风味分析
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姜晓东1, 2 , 张帅中1, 2, 彭博3, 王颖1, 2, * , 孙元芹1, 2, 李晓1, 2, 纪蕾1, 2
作者信息
  • 1山东省海洋科学研究院,山东青岛 266104
  • 2青岛市水产生物品质评价与利用工程研究中心,山东青岛 266104
  • 3中国海洋大学食品科学与工程学院,山东青岛 266100
通讯作者:
王颖(1971−),女,硕士,研究员,研究方向:海洋生物资源利用,E-mail:。
作者简介:

姜晓东(1988−),男,硕士,助理研究员,研究方向:食品生物技术,E-mail:

Development of Artemia Biscuits and Analysis of Their Nutritional and Flavor Characteristics
Xiaodong JIANG1, 2 , Shuaizhong ZHANG1, 2, Bo PENG3, Ying WANG1, 2, * , Yuanqin SUN1, 2, Xiao LI1, 2, Lei JI1, 2
Affiliations
  • 1Marine Science Research Institute of Shandong Province, Qingdao 266104, China
  • 2Municipal Engineering Research Center of Aquatic Biological Quality Evaluation and Application, Qingdao 266104, China
  • 3College of Food Science and Engineering, Ocean University of China, Qingdao 266100, China
出版时间: 2026-09-01 doi: 10.13386/j.issn1002-0306.2025080046
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为挖掘卤虫作为食品资源的开发潜力,推进卤虫产业多元化发展并开拓饼干食品市场。本研究以卤虫为原料,采用单因素实验考察卤虫添加量、藜麦粉添加量、烘焙时间和温度对饼干感官评分的影响,结合响应面试验和人工神经网络确定最佳工艺,并通过营养成分、电子舌、顶空固相微萃取气质联用等试验分析卤虫饼干营养和风味特性。结果表明:相较于响应面模型,人工神经网络具有更小的均方根误差1.8614和更大的相关系数0.9602,实测值与预测值之间误差更小,具有更强更稳定的预测和优化能力。采用人工神经网络模型优化得卤虫饼干工艺参数:添加卤虫13.56 g,藜麦粉47.26 g、低筋小麦粉100 g、牛奶40 g、玉米油30 g、黄油60 g、盐2 g、木糖醇25 g,于150 ℃上烘烤20.70 min,此条件下卤虫饼干感官评分最高,为73.00。卤虫饼干蛋白质含量为6.46 g/100 g、脂肪含量为30.30 g/100 g。电子舌分析表明,卤虫饼干滋味以鲜咸为主。气相色谱-质谱结果表明,卤虫饼干含挥发性化合物48种,主要为醛类、酸类、酯类和呋喃吡喃类,其中含量与气味活性值最高的化合物均为(E,E)-2,4-癸二烯醛,分别为103.7 µg/kg和3840.74。卤虫饼干的研制丰富了饼干市场产品种类,为卤虫资源在休闲食品领域的开发应用提供了新的渠道和理论参考。

卤虫  /  饼干  /  人工神经网络模型  /  工艺  /  风味表征  /  感官评分

To explore the potential of Artemia to be developed as a food resource, promote the diversified development of the Artemia industry, and expand the biscuit food market. Biscuits containing Artemia flavor were developed. Single-factor experiments were performed to evaluate the effects of the Artemia addition, quinoa flour supplementation, and baking time and temperature on the sensory scores of the biscuits. Subsequently, the optimal processing parameters were determined using both response surface methodology (RSM) and artificial neural network (ANN) modeling. Furthermore, the nutritional and flavor profiles of the Artemia biscuits were characterized by means of nutritional composition and electronic tongue analyses as well as headspace solid-phase microextraction-coupled gas chromatography-mass spectrometry (SPME-GC-MS). Compared with the RSM model, the ANN model exhibited superior predictive and optimization capabilities, as demonstrated by its lower root mean square error (RMSE) of 1.8614, higher correlation coefficient (r) of 0.9602, and smaller errors between the measured and predicted values. The ANN-optimized biscuit formulation was as follows: Artemia (13.56 g), quinoa flour (47.26 g), low-gluten wheat flour (100 g), milk (40 g), corn oil (30 g), butter (60 g), salt (2 g), and xylitol (25 g), baked at 150 °C for 20.70 min. This optimized process yielded the highest sensory score (73.00). Additionally, the biscuits contained 6.46 g/100 g protein and 30.30 g/100 g fat. Electronic tongue analysis indicated that the taste profile was dominated by umami and saltiness. Moreover, GC-MS identified 48 volatile compounds, primarily aldehydes, acids, esters, furans, and pyrazines, with (E,E)-2,4-decadienal exhibiting the highest content (103.7 µg/kg) and odor activity value (3840.74). The development of Artemia biscuits enriches the variety of products in the biscuit market and offers a novel approach and theoretical foundation for utilizing Artemia resources in the snack-food industry.

Artemia  /  biscuit  /  artificial neural network model  /  technique  /  flavor characteristics  /  sensory scores
姜晓东, 张帅中, 彭博, 王颖, 孙元芹, 李晓, 纪蕾. 卤虫饼干研制及其营养与风味分析. 食品工业科技, 2026 , 47 (17) : 260 -272 . DOI: 10.13386/j.issn1002-0306.2025080046
Xiaodong JIANG, Shuaizhong ZHANG, Bo PENG, Ying WANG, Yuanqin SUN, Xiao LI, Lei JI. Development of Artemia Biscuits and Analysis of Their Nutritional and Flavor Characteristics[J]. Science and Technology of Food Industry, 2026 , 47 (17) : 260 -272 . DOI: 10.13386/j.issn1002-0306.2025080046
卤虫(Artemia),又称丰年虾、盐虫子、盐水丰年虫,是一类广泛分布于全球高盐度水域的小型甲壳类生物[1]。其休眠卵具有易于储存及孵化等特性,被广泛应用于水产养殖业中鱼虾幼苗的活体饵料,对全球水产经济具有重要价值[2]。卤虫营养价值较高,蛋白质含量占干基质量的50%以上,且富含不饱和脂肪酸、二核苷多磷酸盐等多种活性功能物质[3],研究发现卤虫具有促进皮肤细胞的新陈代谢、抗衰老等功效[4−5]。因其营养丰富、味道鲜美,卤虫被广泛应用于食品加工领域。如在新西兰、孟加拉国等地,卤虫被添加至蛋卷、油炸馅饼、烤肉等食品中以增加营养和口感[6],韩玉谦等[7]利用卤虫卵研制出一款营养丰富的氨基酸饮料,姜晓东等[8]利用卤虫研发了一款海鲜风味酱,卤虫已展现出成为新食品原料的巨大潜力[9]。
饼干是以谷物粉(如小麦粉、燕麦粉)为主要原料,添加糖类、油脂、乳制品等辅料,经混合、成型、烘烤或挤压等工艺制成的含水量低、耐储性强的焙烤食品,其营养与风味受原料配比、加工参数及淀粉糊化-老化行为等因素调控[10]。作为全球广泛消费的方便食品,饼干因种类多样及便携性,成为能量补充、休闲消费及应急食品的重要载体。近年来,随着消费者对健康与功能化食品需求的增长,低盐低糖、富含蛋白的饼干创新开发成为研究热点[11]。
神经网络模型(Neural Network Model)是一种受生物神经系统启发的机器学习架构,通过模拟神经元间的连接与信号传递机制,构建多层非线性计算单元以实现复杂数据的特征提取与模式识别,其核心优势在于通过反向传播算法优化权重参数,自动学习数据中的高阶抽象表征,并展现出强大的非线性映射能力,尤其在图像分类、自然语言处理、时序预测等任务中表现卓越[12−13]。其中,人工神经网络(Artificial Neural Network,ANN)作为机器学习领域的经典模型,通过模拟生物神经元间的信息传递机制,构建由输入层、隐藏层与输出层组成的多层前馈结构,利用加权求和与非线性激活函数实现数据的内在模式挖掘,在解决线性不可分问题、处理中小规模结构化数据(如回归预测、模式识别)及可解释性分析方面具有独特优势[14]。近年来,人工神经网络模型已在食品加工领域广泛应用,Kaddou等[15]利用ANN结合荧光光谱法对热处理牛奶进行鉴别,结果发现模型在区分物种和热处理方式时的准确率为100%;Meserye等[16]研究了基于ANN的红外对流干燥对大蒜品质的影响,结果发现ANN建模揭示了干燥参数与大蒜质量之间的复杂关系,实现了出色的拟合。
目前,国内外以卤虫为原料开发食品的相关研究较少,而基于人工神经网络开发卤虫饼干的研究尚未见报道。因此,为挖掘卤虫作为食品资源的开发潜力,本研究以卤虫为原料,以感官评分为指标,结合响应面试验(Response Surface Methodology,RSM)和人工神经网络确定卤虫饼干制备工艺,研制一款营养丰富、风味独特的卤虫饼干,并对其营养和风味特征进行分析,以期为卤虫资源在休闲食品领域的开发应用提供科学参考。
卤虫 滨州铭硕生物科技有限公司;藜麦粉 朝阳泰然科技食品有限公司;低筋小麦粉、牛奶、玉米油、黄油、木糖醇、盐 市售;对照用黄油饼干 福建省星玛食品有限公司;氘代-1,2-二氯代苯 上海麦克林生化科技股份有限公司;所有检测用有机溶剂均为国产分析纯。
CRTF38烤箱 广东佛山市伟仕达电器实业有限公司;AR323CN电子分析天平 奥豪斯仪器有限公司;JYL-C91T厨房料理机 九阳股份有限公司;KT8400全自动蛋白测定仪 慈溪市华徐衡器实业有限公司;BPG-9156A鼓风干燥箱 上海一恒科技有限公司;5110电感耦合等离子体光谱仪、7890B-5977B GC-MS气质联用仪 安捷伦科技有限公司;TS-5000Z电子舌系统 日本insent公司。
原辅料准备→黄油打发→添加卤虫→面粉过筛→搅拌→裱花→烘烤→冷却→成品。
卤虫:清洗、沥干后,利用厨房料理机将其匀浆至糊状,置60 ℃烘箱内烘干200 min,备用。
牛奶、玉米油:将牛奶与玉米油混合后,充分搅拌为乳白色,备用。
将提前软化的黄油与木糖醇、盐搅拌均匀,用打蛋器高速打发至颜色发白,体积膨胀。随后加入牛奶和玉米油的混合物,一边加入一边打发,待每次充分打发均匀再加入下一次,直至混合物呈乳膏状。
将烘干后的卤虫少量多次添加至打发后的混合物中,翻拌均匀。
将藜麦粉和低筋小麦粉过60目筛添加至上述混合物中。
用刮刀采用压拌,翻拌,切拌的方法,将原辅料混合均匀,至搅拌均匀没有干粉和颗粒物。
在烤盘中铺好锡纸,裱花袋垂直于烤盘,挤出直径约为2 cm的圆形。
提前打开烤箱预热,根据实验设计设置烘烤温度和烘烤时间,烘烤完成后取出,冷却至室温后即为成品。
根据国内外研究[17−18]和预实验结果,饼干基础配方与工艺为:卤虫20 g、藜麦粉40 g、低筋小麦粉100 g、牛奶40 g、玉米油30 g、黄油60 g、盐2 g、木糖醇25 g,在160 ℃下烘烤20 min。固定基础配方与工艺中其他条件不变,分别改变卤虫添加量(10、15、20、25、30 g)、藜麦粉添加量(25.0、32.5、40.0、47.5、55 g)、烘烤温度(140、150、160、170、180 ℃)、烘烤时间(16、18、20、22、24 min),进行单因素实验。
在单因素实验基础上,选择卤虫添加量、藜麦粉添加量、烘烤时间、烘烤温度为自变量,以感官评分为响应值,设计4因素3水平的Box-Benhnken中心组合试验。因素水平表见表1。
参照Zhang等[19]的方法并略作修改,创建3层(输入层、隐含层、输出层)的误差反向传播ANN模型优化卤虫饼干加工工艺,模型网络结构如图1所示。以卤虫添加量、藜麦粉添加量、烘烤时间、烘烤温度作为网络4个输入神经元,以感官评分作为网络输出神经元,隐含层为单层,选取S型函数为激活函数。隐含层神经元范围参考经验公式并结合实际实验确定。为提高模型精度并避免过拟合,将响应面试验所获的29组数据随机排列后,按70%:15%:15%的比例划分为训练集、验证集和测试集。使用遗传算法(Genetic Algorithm,GA)进行全局寻优,寻找使感官评分最高的最优工艺参数组合[20]。以均方根误差(Root Mean Square Error,RMSE)和相关系数(r) [21]大小作为模型优劣的评价标准。RMSE越低,r越接近1,则表明所建立的ANN模型越稳健。根据计算得到的统计学指标,比较RSM法和ANN法的预测响应值。
隐含层神经元个数计算公式:
$\rm P=\sqrt{{\mathrm{N+Q}}}+{\mathrm{Z}} $
式中,P为隐含层神经元个数;N为输入层神经元个数;Q为输出层神经元个数;Z为经验值。
饼干的感官评价方法参考《饼干质量通则》(GB/T 20980-2021),分别对饼干的色泽、形态、香气、滋味、口感等进行评分。组织10名经过感官评定训练的食品相关专业人员(男:女比例为1:1,年龄在20至40岁之间),如表2所示,从色泽、形态、香气、滋味、口感方面进行感官评价,总分为100分,结果取平均值。
酸价:参照《食品安全国家标准食品中酸价的测定》(GB 5009.229-2016)中冷溶剂指示剂滴定法测定;过氧化值:参照《食品安全国家标准食品中过氧化值的测定》(GB 5009.227-2023)中指示剂滴定法测定。
能量:参照《食品安全国家标准预包装食品营养标签通则》(GB 28050-2011)计算;水分:参照《食品安全国家标准食品中水分的测定》(GB 5009.3-2016)中直接干燥法测定;粗蛋白:参照《食品安全国家标准食品中蛋白质的测定》(GB 5009.5-2016)中凯氏定氮法测定;脂肪:参照《食品安全国家标准食品中脂肪的测定》(GB 5009.6-2016)中酸水解法测定;灰分:参照《食品安全国家标准食品中灰分的测定》(GB 5009.4-2016)第一法测定;碳水化合物:参照《食品安全国家标准预包装食品营养标签通则》(GB 28050-201)测定;钠:参照《食品安全国家标准食品中钾、钠的测定》(GB5009.91-2017)中电感耦合等离子体发射光谱法测定。
将市售黄油饼干作为对照品。将卤虫饼干与市售黄油饼干在研钵中捣碎,称取30 g样品于烧杯中,添加120 mL 40 ℃的纯净水,置于超声波清洗机中超声10 min,倒入离心机中4000 r/min离心10 min,取上清液用于测试。参照Liu等[22]的方法,利用TS-5000Z电子舌系统测定样品酸味、甜味、苦味、咸味、鲜味和涩味及回味。
样品准备:分别称取卤虫饼干和市售黄油饼干各2 g,溶于100 mL纯净水,备用。
萃取方法[23]:在20 mL的顶空进样瓶中装入1.00 g待测样品,随即加入10 μL浓度为100 μg/mL的内标物氘代-1,2-二氯代苯,旋紧瓶盖后将萃取针于250 ℃老化20 min,插入固相萃取瓶中,平稳放置在65 ℃水浴锅中吸附40 min,结束后立即将萃取针插入气相进样口,250 ℃温度下解吸10 min。
GC条件:色谱柱为HP-INNOWax(60 m×250 μm×0.5 μm);载气为氦气(纯度≥99.999%),流速为1 mL/min;不分流进样;升温程序:40 ℃保持5 min,5 ℃/min的速率升至180 ℃后保持3 min,10 ℃/min的速率升至230 ℃后保持12 min。
MS条件:离子源为电子轰击离子源;电子能量70 eV;离子源温度为230 ℃,四级杆温度为150 ℃;电子倍增器工作电压为1985 V;质量扫描范围为35~350 m/z。
定性:基于NIST谱库物质进行匹配,选取匹配度大于80%的物质作为定性结果。
定量:采用内标半定量法计算样品中挥发性化合物的含量,即根据化合物峰面积及内标峰面积比值计算。
$\rm \text{OAV}=\frac{C}{T} $
式中,C为挥发性气味化合物的含量,µg/kg;T为该化合物在水中的阈值,µg/kg。
每组实验重复3次,结果以平均值±标准差表示,采用SPSS 25.0进行单因素方差分析,采用Matlab R2015b、Design expert 8.0.6和Excel 2007对实验数据进行处理和分析。
卤虫添加量对卤虫饼干感官评分的影响见图2。
卤虫的添加有助于提高饼干的营养价值并赋予其独特的风味。由图2可知,随着卤虫添加量的增加,卤虫饼干的感官评分呈先上升后下降的趋势,当卤虫原料添加量为15 g时,感官评分显著高于其他实验组(P<0.05)。此时产品具有较显著且适宜的卤虫特殊风味。当添加量过大时,卤虫中的鲜味物质会掩盖饼干原有的香甜气味,饼干色泽也变得更加黝黑、表面容易开裂。因此,选择卤虫添加量15 g进行后续实验。
藜麦粉添加量对卤虫饼干感官评分的影响见图3。
藜麦富含维生素、多酚、黄酮类、皂苷和植物甾醇类物质,具有多种健康功能[24],将藜麦粉添加到饼干中,不仅可以提高饼干的营养价值,还能赋予饼干独特的风味和口感。由图3可知,随着藜麦粉添加量不断增加,饼干感官评分呈先上升后下降的趋势。当添加量为47.5 g时,感官评分显著高于其他实验组(P<0.05)。此时饼干不仅保留了自身风味,还融入了藜麦的独特风味,口感丰富且有层次感。当添加量过高时,藜麦粉的风味过于突出,掩盖了饼干原有的风味,同时会使饼干质地变得粗糙,影响食用口感。因此,综合考虑饼干风味和口感,选择藜麦粉添加量47.5 g进行后续实验。
付丽红等[25]研究了紫薯藜麦饼干的配方,结果发现当低筋粉与藜麦粉比例为16:7时,饼干感官评分最高,本研究中低筋小麦粉与藜麦粉的比例约为2:1,与该结论相近;王颖等[26]研究发现藜麦粉添加量占小麦粉20%以上时,饼干的风味会产生显著变化,本研究与该结论一致。本研究中藜麦粉添加量的不同显著影响卤虫饼干色泽、口感等感官指标,添加量过高或过低时都会导致感官评分下降,这是因为藜麦中多酚、黄酮等色素含量较高,从而在加工过程中影响饼干色泽;此外藜麦中不含面筋蛋白,其添加量改变时会导致面团中面筋蛋白含量变化,从而影响面筋蛋白网络结构连续性,改变饼干的质地与口感。
温度对卤虫饼干感官评分的影响见图4。
温度会影响饼干在烘焙过程中美拉德反应、淀粉糊化等化学反应的速度和程度,从而对饼干的色泽、香气和口感产生重要影响。由图4可知,随着烘焙温度的上升,饼干的感官评分呈先上升后下降的趋势。当温度为150 ℃时,感官评分显著高于其他实验组(P<0.05)。此温度下,饼干表面能够形成良好的色泽和香气,内部组织结构也较为理想。当温度继续升高时,感官评分逐渐下降。这是由于高温会使饼干表面迅速焦糖化,产生焦糊味,同时内部水分快速蒸发,导致饼干质地干硬,口感变差。因此,选择温度150 ℃进行后续实验。
时间对卤虫饼干感官评分的影响见图5。
烘烤时间的长短直接关系到饼干内部水分的蒸发程度、表面色泽的形成以及风味物质的产生等,不同的烘烤时间会使饼干呈现出不同的品质特征。由图5可知,随着时间的延长,饼干的感官评分呈先上升后下降的趋势。当烘烤时间为20 min时,感官评分显著高于其他实验组(P<0.05)。此条件下饼干的色泽、口感和风味都达到了最佳状态。烘烤时间过短,饼干内部水分蒸发不充分,口感发软,缺乏酥脆感;而烘烤时间过长,饼干表面会过度焦糖化,产生焦糊味,同时内部质地变得干硬,影响食用品质。因此,选择时间20 min进行后续实验。
以卤虫添加量、藜麦粉添加量、时间和温度为因素,采用4因素3水平Box-Benhnken中心组合试验,以Y感官评分为响应值,试验结果和方差分析见表3和表4。
对表3中数据拟合分析,得到回归方程:Y=65.78−0.44A−2.07B+0.15C−0.69D+0.20AB−3.77AC−7.90AD+0.63BC+3.43BD−3.55CD−4.05A2−7.86B2+2.44C2−3.45D2。
由表4可知,模型P<0.01,表明所得方程极显著。失拟项P=0.2408>0.05,表明该模型拟合效果较好,不存在显著误差,具有高度可靠性。校正决定系数R2adj=0.7838,表明无明显过拟合现象;信噪比RSN=9.087>4,表明试验信号受噪声干扰少,数据可靠性高;变异系数5.04%<10%,说明结果稳定,数据分布均匀。方程一次项B为显著因素(P<0.05),A、C和D不显著(P>0.05);二次项中AD 为极显著(P<0.01),AC、BD为显著(P<0.05),而AB、BC和CD不显著(P>0.05);平方项中B2为极显著(P<0.01),A2、D2为显著(P<0.05),而C2不显著(P>0.05)。各因素对响应值的影响大小排序为B藜麦粉添加量>D温度>A卤虫添加量>C时间。最佳理论条件为:卤虫添加量16.35 g,藜麦粉添加量46.44 g,时间 20.99 min,温度144.99 ℃,感官评分最大预测值为70.73。
响应面图能反映各因素间的交互作用,AC、AD、BD交互作用对感官评分影响的响应面图见图6。
响应面坡度的陡峭程度反映了响应值对于操作条件的敏感程度。由图6可知,图6(a)(b)(c)中各因素交互作用的曲面较为陡峭,表明它们对感官评分有明显影响,这与表4中方差分析得出的AD交互作用极显著,AC、BD交互作用显著的结论相吻合。图6(c)中,沿藜麦粉添加量方向较烘烤温度方向的曲面陡,说明藜麦粉添加量对感官评分的影响较烘烤温度显著。
隐含层神经元个数与ANN模型的预测结果密切相关,隐含层神经元数量对ANN模型性能的影响见图7。
参考隐含层神经元个数经验公式并结合实际实验,选择3~20个隐含层神经元个数进行实验。由图7可知,当隐含层神经元个数为15时,ANN模型预测RMSE最小,为1.8617,准确性最高,因此选择隐含层神经元个数为15进行后续实验。
将隐含层神经元个数设定为15,设定训练目标误差为0.0001,学习率为0.01,最大训练步数为5000,开始训练网络[27]。感官评分网络训练曲线见图8、ANN模型训练适配曲线见图9。
由图8、图9可知,训练样本、测试样本以及验证样本与其对应预测值高度吻合,全部数据拟合结果的相关系数为0.96132,说明所建立的ANN模型预测性好,准确度高,可以将其应用到卤虫饼干加工工艺优化中。
GA是一种模拟自然选择过程的优化算法,其核心操作包括选择、交叉和变异。本研究中,设定最大进化迭代数为100,种群规模为20,交叉概率为0.4,变异概率为0.05。适应度曲线见图10。
以ANN构建的模型$ \text{Y}=\text{net}(\text{x}) $为适应度函数,以感官评分为适应度。利用GA进行全局寻优,以寻找使感官评分最高的最优工艺参数组合。经过62代后达到最佳适应度,其感官评分达到最大值,随后基本不变。此时感官评分最大预测值为73.65,其适应的条件为:卤虫原料13.56 g,藜麦粉47.26 g,烘烤温度149.95 ℃,烘烤时间20.68 min。
按RSM和ANN模型给出的理论条件进行验证实验。为便于实际操作,将工艺条件修正为:RSM:卤虫添加量16.35 g,藜麦粉添加量46.44 g,时间 21.00 min,温度145 ℃;ANN:卤虫添加量13.56 g,藜麦粉47.26 g,烘烤温度150 ℃,烘烤时间20.70 min。按上述工艺进行验证,RSM和ANN模型制作的卤虫饼干感官评分实际值分别为69.90±0.20和73.00±0.20,实际值与各自预测值相近且ANN模型感官评分更高。
对比RSM和ANN两种模型对卤虫饼干感官评分的优化和预测能力,即感官评分的预测值和实测值,计算r和RMSE值,结果见图11。
由图11可知,与RSM相比,ANN模型有更大的r值0.9602和更小的RMSE值1.8614,说明ANN模型对感官评分的优化能力更强,预测误差更小,具有更高的拟合效果和更优良的预测性能。因此,选择人工神经网络优化工艺进行后续实验。
食品加工通常涉及的参数体系属于非线性或不稳定的系统,而ANN技术作为预测复杂系统输出响应的方法在食品工业中发挥着重要的作用[28]。Zhang等[19]将Box-Behnken试验获取的29组数据随机排列,采用交叉验证训练网络确定ANN模型用于优化螃蟹内脏匀浆液中镉离子脱除工艺,结果发现ANN模型具有稳定的预测和优化能力;王仪等[29]以Box-Behnken试验得到的17组数据为训练样本,利用ANN模型优化肉葡萄球菌高密度培养基配方,结果发现ANN模型能够精确地预测培养基配方对肉葡萄球菌活菌数的影响。上述研究表明利用RSM数据建立的ANN模型,具有较高的稳定预测和优化能力。本研究利用29组RSM试验数据训练和建立ANN模型,在节省试验成本与时间的同时,实现了预测卤虫饼干的最佳工艺的目的。
卤虫饼干酸价与过氧化值见表5。
酸价和过氧化值是评价脂质氧化酸败的关键指标,可用于判断饼干在加工过程中脂质的氧化程度。由表5可知,卤虫饼干酸价为 2.40 mg/g,过氧化值为0.097 g/100 g,符合《食品安全国家标准饼干》(GB 7100-2015)中酸价、过氧化值的限量要求,表明本产品脂质氧化程度低,新鲜度和品质优良,饼干的烘焙工艺有利于延长货架期,保障消费者食用时的口感与品质。
卤虫饼干营养成分见表6。
由表6可知,卤虫饼干蛋白质含量为 6.46 g/100 g,高于市售饼干4.40 g/100 g。蛋白质是人体重要的营养物质,参与人体众多生理过程,卤虫饼干蛋白质含量较高能提供更充足的优质蛋白来源,满足机体生长、修复等需求。卤虫饼干脂肪含量为 30.30 g/100 g,低于市售饼干的 34.20 g/100 g。研究表明杂粮富含的膳食纤维具有吸水性和持油性,可降低饼干的脂肪含量[11],本研究在卤虫饼干中添加了一定量的藜麦粉,有助于降低饼干中脂肪含量。随着人们健康意识提升,对高脂肪食品的摄入愈发谨慎,卤虫饼干较低的脂肪含量符合健康饮食趋势。卤虫饼干钠含量为283 mg/100 g,较市售饼干更低。高钠饮食与高血压等疾病相关,卤虫饼干的低钠特点使其更适宜追求低盐饮食以及患有高血压等慢性疾病的人群。在能量供应方面,卤虫饼干能量为 2192 kJ/100 g,低于市售饼干的 2313 kJ/100 g,在满足饱腹感的同时,可减少消费者能量摄入,有助于维持能量平衡。
与其他研究相比,卤虫饼干的蛋白质含量高于石斑鱼骨饼干、方竹笋曲奇饼干,而脂肪含量低于油橄榄果渣曲奇饼干,高于石斑鱼骨饼干、方竹笋曲奇饼干,营养组成的差异可能与不同饼干所用原料、配方、制备工艺等有关。本研究发现,卤虫饼干具有蛋白质含量高、低盐低脂等特点,在营养与健康层面表现较好,在休闲食品市场中具备较大的发展潜力。
卤虫饼干电子舌结果见图12。
由图12可知,卤虫饼干未呈现出酸味特征,其涩味、苦味回味以及涩味回味的强度均处于较低水平,接近或低于无味点,其鲜味、咸味、苦味、甜味、丰富度(鲜味回味)等味觉指标均表现出显著的响应。与市售黄油饼干相比,本产品的鲜味、咸味、苦味和丰富度较高,而甜味则略低于对照样,说明本产品味道整体以鲜咸为主,在风味层次上较对照样展现出更复杂多样的味觉体验,滋味更为丰富。若需在后续开发中降低产品苦味,可添加葡萄糖、蔗糖等进行美拉德反应,从而提高饼干鲜香感。
卤虫饼干挥发性风味物质结果见表7。
由表7可知,卤虫饼干共检测出挥发性化合物48种,其中醇类5种、醛类8种、酮类6种、酸类9种、酯类5种、呋喃吡喃类10种和其他5种,较对照品市售黄油饼干(43种)、布拉迪酵母发酵麸皮饼干(35种)[34]、香菇豆沙曲奇饼干(28种)[35]和松茸曲奇饼干(25种)[36]更多,略低于薏仁运动营养饼干(51种)[37],表明卤虫的添加极大地丰富了饼干中挥发性化合物的种类,使得卤虫饼干风味复杂多样、富有层次。
卤虫饼干中主要的挥发性化合物为醛类、酸类、呋喃吡喃类和酯类,其含量分别为221.3、183.2、128.1和108.8 µg/kg,是形成卤虫饼干风味的重要组分。与对照品市售黄油饼干相比,卤虫饼干与黄油饼干共有的挥发性风味物质共22种,包括醇类1种、醛类1种、酮类6种、酸类6种、酯类2种和呋喃吡喃类6种,其中羟基丙酮、环丁酮、丁酸、丙酮酸丙酯、2-呋喃甲醇5种物质的含量在两类饼干之间无显著差异(P>0.05),其余17种物质的含量均存在显著差异(P<0.05)。卤虫饼干特有的挥发性风味物质有26种,黄油饼干特有的挥发性风味物质21种,说明卤虫的添加对饼干的风味物质组成产生了极大影响,使得卤虫饼干形成了区别传统黄油饼干的独特风味特征。
醛类化合物大多具有令人愉悦的清新青草味和淡水果香味,是美拉德反应的主要产物。卤虫饼干中醛类含量占比最高,为28.69%,这与猴头菇酥性饼干[23]、香菇豆沙曲奇饼干[35]和薏仁饼干[37]等研究中醛类含量最高的结果一致。卤虫饼干中醛类物质主要包括(E,E)-2,4-癸二烯醛(103.7 µg/kg)、戊醛(43.5 µg/kg)、苯乙醛(29.9 µg/kg)等,醛类物质主要是不饱和脂肪氧化、氨基酸降解和基于斯特雷克氨基酸合成反应的美拉德初级产物降解还原等反应形成[38]。卤虫不饱和脂肪酸和氨基酸含量丰富,推测可能是在高温烘烤过程中,卤虫等原料中的不饱和脂肪酸发生了氧化、氨基酸发生了降解,因此释放了较多的醛类物质。酸类物质是由氨基酸降解后经氧化还原作用产生。卤虫饼干中酸类含量占比为23.76%,主要包括己酸(55.9 µg/kg)、辛酸(38.0 µg/kg)、壬酸(30.2 µg/kg)等。酸类阈值较大,其对产品整体气味的贡献较小,但酸类物质可与其他风味物质产生协同效应,从而使卤虫饼干气味更加丰富和强烈。呋喃类化合物通常在焦糖化和糖降解中产生,对食品的整体香味有较好的修饰作用。卤虫饼干中呋喃类化合物含量占比为16.61%,主要包括麦芽酚(36.4 µg/kg)、2-呋喃甲醇(28.8 µg/kg)、2,3-二氢-3,5-二羟基-6-甲基-4(4H)-吡喃酮(28.2 µg/kg)等,这些呋喃类化合物可强化卤虫饼干的焦糖香气特征,对其整体风味品质起到优化与改善作用。酯类物质在饼干中主要提供不同花香、果香,是饼干整体香味形成的重要组成。卤虫饼干中酯类含量占比为14.11%,其中2-丙烯酸丁酯具有明显的水果香味,其含量最高,为80.6 µg/kg,占酯类物质总量的74.08%,是影响卤虫饼干酯类香气特征的主要因素。
不同的挥发性化合物具有不同阈值,可利用OAV评价样品中挥发性化合物对整体风味的贡献,当OAV>1时,表明其对样品的整体风味有显著贡献。卤虫饼干中OAV>1的挥发性化合物共有11种,包括醛类6种、酯类2种、醇类1种、酮类1种、甲氧基苯酚类1种,由高到低依次为(E,E)-2,4-癸二烯醛(脂肪香、鸡肉香)、2-丙烯酸丁酯(水果香味)、 (E)-2-壬烯醛(清香味)、壬醛(脂肪味、柑橘味、青草味、玫瑰味)、2-甲氧基-5-乙烯基苯酚(坚果香、丁香)、2-十一烷酮(油脂香、芸香、桃香)、苯乙醛(浆果香、坚果香、蜂蜜香)、1-辛烯-3-醇(土壤香、油脂香、蘑菇香)、戊醛(高浓度时有辛辣、刺激气味,低浓度时有面包味、果香)、(E)-2-辛烯醛(青草香、果香、脂肪香)和癸酸甲酯(花香、果香)[39−40],其中(E,E)-2,4-癸二烯醛OAV最高,为3840.74,占卤虫饼干挥发性化合物OAV贡献的97.19%,2-丙烯酸丁酯和(E)-2-壬烯醛OAV次之,分别为40.30和28.95,其余8种物质的OAV在1至10之间。(E,E)-2,4-癸二烯醛天然存在于橙皮、柠檬、草莓、烤鸡等食物的挥发性成分当中,其阈值很低,具有油味和鸡肉香味,本研究发现(E,E)-2,4-癸二烯醛对卤虫饼干的肉香和脂肪气息具有显著贡献。市售黄油饼干OAV最高的挥发性化合物为具有水果味、桃子味的γ-癸内酯(46.27)和具有椰子味的γ-壬内酯(21.61),可见相较于市售黄油饼干,卤虫饼干肉香、油香明显。
比较响应面和人工神经网络模型,人工神经网络模型具有更强更稳定的预测和优化能力。采用人工神经网络模型优化卤虫饼干工艺参数为:添加卤虫13.56 g,藜麦粉47.26 g、低筋小麦粉100 g、牛奶40 g、玉米油30 g、黄油60 g、盐2 g、木糖醇25 g,于150 ℃上烘烤20.70 min,此条件下卤虫饼干感官评分最高,为73.00。卤虫饼干蛋白质含量为6.46 g/100 g、脂肪含量为30.30 g/100 g,蛋白质含量较高。电子舌分析表明卤虫饼干滋味以鲜咸为主。挥发性风味成分分析表明,卤虫饼干含挥发性化合物48种,主要为醛类、酸类、酯类和呋喃吡喃类,其中含量与气味活性值最高的化合物均为(E,E)-2,4-癸二烯醛。卤虫饼干的研制丰富了饼干市场产品种类,为卤虫资源在休闲食品领域的开发应用提供了新的渠道和理论参考。

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2026年第47卷第17期
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doi: 10.13386/j.issn1002-0306.2025080046
  • 接收时间:2025-08-11
  • 首发时间:2026-10-24
  • 出版时间:2026-09-01
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  • 收稿日期:2025-08-11
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    1山东省海洋科学研究院,山东青岛 266104
    2青岛市水产生物品质评价与利用工程研究中心,山东青岛 266104
    3中国海洋大学食品科学与工程学院,山东青岛 266100

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王颖(1971−),女,硕士,研究员,研究方向:海洋生物资源利用,E-mail:。
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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