Article(id=1217845643012395646, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217845635080962613, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250519004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1747584000000, receivedDateStr=2025-05-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1768286627771, onlineDateStr=2026-01-13, pubDate=1756051200000, pubDateStr=2025-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768286627771, onlineIssueDateStr=2026-01-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768286627771, creator=13701087609, updateTime=1768286627771, updator=13701087609, issue=Issue{id=1217845635080962613, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='16', pageStart='1', pageEnd='324', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1768286625881, creator=13701087609, updateTime=1768287480278, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1217849218753024879, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217845635080962613, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1217849218753024880, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217845635080962613, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=288, endPage=296, ext={EN=ArticleExt(id=1217845643524100764, articleId=1217845643012395646, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Optimization of coffee peel beverage formula by response surface methodology, columnId=1151895321719223288, journalTitle=Journal of Food Safety & Quality, columnName=Food Processing and Technology, runingTitle=null, highlight=null, articleAbstract=

Objective To optimize coffee peel beverage formula by response surface methodology with coffee peel and black tea as the main ingredients. Methods First, the ripe coffee fresh fruits were peeled. After homogenization and filtration, coffee peel liquid was obtained. This liquid was then mixed with black tea liquid, fructose and citric acid for formulation. Single-factor experiments and the response surface methodology were employed to investigate the effects of the addition amounts of coffee peel juice, black tea liquid, fructose, and citric acid on the coffee peel beverage, thereby determining the optimal formulation for it. Results The optimal addition amounts were 28% coffee peel juice, 40% black tea liquid, 5% fructose and 0.05% citric acid. This formulation yielded a product with protein content (5.13±0.34)%, fat content (4.94±0.78)%, dietary fiber (18.43±1.06)%, ash (1.00±0.01)%. The coffee peel juice beverage processed according to this process had a strong aroma, a deep amber color, and a delicious sour and sweet taste. Conclusion The application of response surface methodology to optimize the formula of coffee peel beverage provides another feasible way for the high value utilization of coffee peel and the development of functional products.

, correspAuthors=Li-Sha FENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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, authorCompany=null, fund=null, authors=null, authorsList=Li-Sha FENG, Qiu CHEN), CN=ArticleExt(id=1217845648448213992, articleId=1217845643012395646, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=响应面法优化咖啡果皮饮料配方, columnId=1151895322281734685, journalTitle=食品安全质量检测学报, columnName=食品加工与工艺, runingTitle=null, highlight=null, articleAbstract=

目的 以咖啡果皮和红茶为主要原料, 采用响应面法来优化咖啡果皮饮料配方。方法 首先对成熟的咖啡鲜果进行剥皮, 匀浆过滤后得到咖啡果皮汁, 加入红茶液、果糖和柠檬酸进行调配, 通过单因素实验和响应面法实验分析了咖啡果皮汁添加量、红茶液添加量、果糖添加量和柠檬酸添加量对咖啡果皮饮料的影响, 确定了咖啡果皮饮料的最佳配方。结果 咖啡果皮汁添加量为28%、红茶液添加量为40%、果糖添加量为5%、柠檬酸添加量为0.05%。此配方产品蛋白质含量(5.13±0.34)%、脂肪含量(4.94±0.78)%、膳食纤维(18.43±1.06)%、灰分(1.00±0.01)%。按此工艺加工的咖啡果皮汁饮料香气浓郁、色泽呈深琥珀色、酸甜可口。结论 应用响应面法优化咖啡果皮饮料配方, 为咖啡果皮的高值化利用和功能产品研发提供了另一条可行路径。

, correspAuthors=冯黎莎, authorNote=null, correspAuthorsNote=
* 冯黎莎(1981—), 女, 硕士, 副教授, 主要研究方向为资源植物。E-mail:
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冯黎莎(1981—), 女, 硕士, 副教授, 主要研究方向为资源植物。E-mail:

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Chemical Engineer, 2024, 38(6): 90-95, 54., articleTitle=Optimization of preparation technology of ginseng selenium polysaccharideby star-point design-response surface methodology, refAbstract=null)], funds=[Fund(id=1217864274307891237, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, awardId=2018JS367, language=CN, fundingSource=云南省教育厅科学研究基金项目(2018JS367), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1217864265424355926, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, xref=null, ext=[AuthorCompanyExt(id=1217864265445327449, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, companyId=1217864265424355926, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Yunnan Open University, School of Chemical Engineering, Yunnan Polytechnic of National Defense Industry, Wenshan 650504, China), AuthorCompanyExt(id=1217864265462104668, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, companyId=1217864265424355926, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=云南开放大学, 云南国防工业职业技术学院化学工程学院, 文山 650504)])], figs=[ArticleFig(id=1217864267861246714, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.1, caption=Single-factor test results of coffee peel juice addition level, figureFileSmall=UcT2GVft6vtHw4XMm+7+gQ==, figureFileBig=4PpzjYWXHpPYzGzFkroBUQ==, tableContent=null), ArticleFig(id=1217864269274727168, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图1, caption=咖啡果皮汁添加量单因素实验结果

注: 不同小写字母表示差异显著(P<0.05), 图2~4同。

, figureFileSmall=UcT2GVft6vtHw4XMm+7+gQ==, figureFileBig=4PpzjYWXHpPYzGzFkroBUQ==, tableContent=null), ArticleFig(id=1217864269446693643, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.2, caption=Single-factor test results of black tea addition level, figureFileSmall=yhOGqxlnnGnmKOBESZFhwA==, figureFileBig=WMA4QITEBmksvPLFZUoYAA==, tableContent=null), ArticleFig(id=1217864269585105688, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图2, caption=红茶液添加量单因素实验结果, figureFileSmall=yhOGqxlnnGnmKOBESZFhwA==, figureFileBig=WMA4QITEBmksvPLFZUoYAA==, tableContent=null), ArticleFig(id=1217864269828375331, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.3, caption=Single-factor test results of fructose addition level, figureFileSmall=etrg/KV6VAqwTqZu3L9YAA==, figureFileBig=vKG0lk1wgx71ZeSAKav3CQ==, tableContent=null), ArticleFig(id=1217864269979370287, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图3, caption=果糖添加量单因素实验结果, figureFileSmall=etrg/KV6VAqwTqZu3L9YAA==, figureFileBig=vKG0lk1wgx71ZeSAKav3CQ==, tableContent=null), ArticleFig(id=1217864270113588028, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.4, caption=Single-factor test results of citric acid addition level, figureFileSmall=eB3n8DMpwpmIivCcO2narQ==, figureFileBig=EvnXs/1lvWH/xDzS5tMiwg==, tableContent=null), ArticleFig(id=1217864270218445633, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图4, caption=柠檬酸加量单因素实验结果, figureFileSmall=eB3n8DMpwpmIivCcO2narQ==, figureFileBig=EvnXs/1lvWH/xDzS5tMiwg==, tableContent=null), ArticleFig(id=1217864270331691851, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.5, caption=Response surface graph and contour plot of the influence of interaction between coffee peel juice and black tea addition, figureFileSmall=bmcTMF4BUfcjyEo8bCQIlQ==, figureFileBig=cdlVkhF+YhCerreON52jjA==, tableContent=null), ArticleFig(id=1217864270432355154, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图5, caption=咖啡果皮汁和红茶液添加量对感官评分的响应曲面图和等高线图, figureFileSmall=bmcTMF4BUfcjyEo8bCQIlQ==, figureFileBig=cdlVkhF+YhCerreON52jjA==, tableContent=null), ArticleFig(id=1217864270587544413, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.6, caption=Response surface graph and contour plot of the influence of interaction between coffee peel juice and fructose addition, figureFileSmall=uy/S7yMzRkhkJ3USpkUL6w==, figureFileBig=Gq10wxc5DgjXPUzh2n0+aA==, tableContent=null), ArticleFig(id=1217864270692402024, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图6, caption=咖啡果皮汁与果糖添加量对感官评分的响应曲面图和等高线图, figureFileSmall=uy/S7yMzRkhkJ3USpkUL6w==, figureFileBig=Gq10wxc5DgjXPUzh2n0+aA==, tableContent=null), ArticleFig(id=1217864270805648239, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.7, caption=Response surface graph and contour plot of the influence of interaction between coffee peel juice and citric acid addition, figureFileSmall=Ase5/rEtIvcTEomxTYx+Ig==, figureFileBig=hUph/lcOMdWosF+lFHx4uw==, tableContent=null), ArticleFig(id=1217864270897922934, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图7, caption=咖啡果皮汁与柠檬酸添加量对感官评分的响应曲面图和等高线图, figureFileSmall=Ase5/rEtIvcTEomxTYx+Ig==, figureFileBig=hUph/lcOMdWosF+lFHx4uw==, tableContent=null), ArticleFig(id=1217864271011169151, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.8, caption=Response surface graph and contour plot of the influence of interaction between black tea and fructose addition, figureFileSmall=2yu9MfBQHeSpCeHKNaUm3g==, figureFileBig=pq/VnYz+GzURjZMpJu2xmg==, tableContent=null), ArticleFig(id=1217864271199912844, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图8, caption=红茶液和果糖添加量对感官评分的响应曲面图和等高线图, figureFileSmall=2yu9MfBQHeSpCeHKNaUm3g==, figureFileBig=pq/VnYz+GzURjZMpJu2xmg==, tableContent=null), ArticleFig(id=1217864271334130581, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.9, caption=Response surface graph and contour plot of the influence of interaction between black tea addition and citric acid addition, figureFileSmall=fiPGGf+/W9TX5bUrRmX1ZQ==, figureFileBig=YL7olcP74IO3XpNXpu5ZJg==, tableContent=null), ArticleFig(id=1217864271418016669, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图9, caption=红茶液和柠檬酸添加量对感官评分的响应曲面图和等高线图, figureFileSmall=fiPGGf+/W9TX5bUrRmX1ZQ==, figureFileBig=YL7olcP74IO3XpNXpu5ZJg==, tableContent=null), ArticleFig(id=1217864271501902757, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Fig.10, caption=Response surface graph and contour plot of the influence of interaction between fructose addition and citric acid addition, figureFileSmall=ju9LC4qe5W9xdn4WBqKIiQ==, figureFileBig=fN7E7Hab6XBsoOE5GaaRLw==, tableContent=null), ArticleFig(id=1217864271619343277, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=图10, caption=果糖和柠檬酸添加量对感官评分的响应曲面图和等高线图, figureFileSmall=ju9LC4qe5W9xdn4WBqKIiQ==, figureFileBig=fN7E7Hab6XBsoOE5GaaRLw==, tableContent=null), ArticleFig(id=1217864271720006583, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Table 1, caption=

Sensory evaluation criteria for coffee peel beverage

, figureFileSmall=null, figureFileBig=null, tableContent=
感官指标 评分标准 分数/分
色泽 呈现深琥珀色、透明 16~25
呈现红棕色、透明度不够 6~15
色泽暗淡或无色 1~5
组织与形态 澄清透明、无沉淀 21~35
略有浑浊、有少量悬浮物 11~20
色泽浑浊、有大量沉淀 1~10
香气及滋味 香气浓郁、滋味协调、无异味 31~40
香味不明显, 异味稍明显 21~30
无香味, 异味十分明显 1~20
), ArticleFig(id=1217864271837447105, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=表1, caption=

咖啡果皮饮料的感官评价标准

, figureFileSmall=null, figureFileBig=null, tableContent=
感官指标 评分标准 分数/分
色泽 呈现深琥珀色、透明 16~25
呈现红棕色、透明度不够 6~15
色泽暗淡或无色 1~5
组织与形态 澄清透明、无沉淀 21~35
略有浑浊、有少量悬浮物 11~20
色泽浑浊、有大量沉淀 1~10
香气及滋味 香气浓郁、滋味协调、无异味 31~40
香味不明显, 异味稍明显 21~30
无香味, 异味十分明显 1~20
), ArticleFig(id=1217864271984247758, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Table 2, caption=

Factor levels of coffee peel beverage formulation

, figureFileSmall=null, figureFileBig=null, tableContent=
水平 因素
A(咖啡果皮汁
添加量)/%
B(红茶液
添加量)/%
C(果糖
添加量)/%
D(柠檬酸
添加量)/%
-1 10 30 4 0.04
0 20 40 6 0.05
1 30 50 8 0.06
), ArticleFig(id=1217864272101688281, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=表2, caption=

咖啡果皮饮料配方的因素水平

, figureFileSmall=null, figureFileBig=null, tableContent=
水平 因素
A(咖啡果皮汁
添加量)/%
B(红茶液
添加量)/%
C(果糖
添加量)/%
D(柠檬酸
添加量)/%
-1 10 30 4 0.04
0 20 40 6 0.05
1 30 50 8 0.06
), ArticleFig(id=1217864272185574373, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Table 3, caption=

Experimental design and results of response surface methodology for coffee peel beverage formula

, figureFileSmall=null, figureFileBig=null, tableContent=
实验号 A(咖啡
果皮汁
添加量)/%
B(红茶液
添加量)/%
C(果糖
添加量)/%
D(柠檬酸
添加量)/%
感官评分
/分
1 30 50 6 0.05 83
2 20 30 6 0.04 78
3 20 30 6 0.06 77
4 20 50 6 0.06 79
5 20 40 8 0.06 80
6 20 40 6 0.05 93
7 20 40 6 0.05 92
8 20 40 6 0.05 93
9 20 30 8 0.05 76
10 30 30 6 0.05 78
11 10 30 6 0.05 73
12 20 50 6 0.04 83
13 30 40 4 0.05 84
14 10 50 6 0.05 77
15 10 40 6 0.04 78
16 20 40 4 0.04 87
17 20 30 4 0.05 80
18 20 50 8 0.05 77
19 10 40 6 0.06 74
20 20 40 6 0.05 91
21 20 40 8 0.04 74
22 20 40 6 0.05 92
23 20 50 4 0.05 79
24 10 40 8 0.05 79
25 10 40 4 0.05 74
26 30 40 6 0.04 79
27 30 40 8 0.05 77
28 20 40 4 0.06 74
29 30 40 6 0.06 82
), ArticleFig(id=1217864272323986416, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=表3, caption=

咖啡果皮饮料配方响应面实验设计与结果

, figureFileSmall=null, figureFileBig=null, tableContent=
实验号 A(咖啡
果皮汁
添加量)/%
B(红茶液
添加量)/%
C(果糖
添加量)/%
D(柠檬酸
添加量)/%
感官评分
/分
1 30 50 6 0.05 83
2 20 30 6 0.04 78
3 20 30 6 0.06 77
4 20 50 6 0.06 79
5 20 40 8 0.06 80
6 20 40 6 0.05 93
7 20 40 6 0.05 92
8 20 40 6 0.05 93
9 20 30 8 0.05 76
10 30 30 6 0.05 78
11 10 30 6 0.05 73
12 20 50 6 0.04 83
13 30 40 4 0.05 84
14 10 50 6 0.05 77
15 10 40 6 0.04 78
16 20 40 4 0.04 87
17 20 30 4 0.05 80
18 20 50 8 0.05 77
19 10 40 6 0.06 74
20 20 40 6 0.05 91
21 20 40 8 0.04 74
22 20 40 6 0.05 92
23 20 50 4 0.05 79
24 10 40 8 0.05 79
25 10 40 4 0.05 74
26 30 40 6 0.04 79
27 30 40 8 0.05 77
28 20 40 4 0.06 74
29 30 40 6 0.06 82
), ArticleFig(id=1217864272454009848, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Table 4, caption=

Regression analysis results of sensory rating model and regression coefficient

, figureFileSmall=null, figureFileBig=null, tableContent=
来源 离差平方和 自由度 均方 F P 显著性
模型 1050.8753 14 75.06 40.60 0.0000 **
A(咖啡果皮汁添加量) 65.3333 1 65.33 35.34 0.0000 **
B(红茶液
添加量)
21.3333 1 21.33 11.54 0.0043 **
C(果糖
添加量)
18.7500 1 18.75 10.14 0.0066 **
D(柠檬酸
添加量)
14.0833 1 14.08 7.62 0.0153 *
AB 0.2500 1 0.25 0.14 0.7186
AC 36.0000 1 36.00 19.47 0.0006 **
AD 12.2500 1 12.25 6.63 0.0221 *
BC 1.0000 1 1.00 0.54 0.4742
BD 2.2500 1 2.25 1.22 0.2886
CD 90.2500 1 90.25 48.82 0.0000 **
A2 342.5153 1 342.52 185.26 0.0000 **
B2 319.3532 1 319.35 172.73 0.0000 **
C2 308.0761 1 308.08 166.63 0.0000 **
D2 264.9950 1 265.00 143.33 0.0000 **
残差 25.8833 14 1.85
失拟项 23.0833 10 2.31 3.30 0.1307 ns
纯误差 2.8000 4 0.70
总和 1076.7586 28
R2=0.9760; Adj R2=0.9519; Pre R2=0.8725
), ArticleFig(id=1217864273821352958, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=表4, caption=

感官评分模型及回归系数的回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
来源 离差平方和 自由度 均方 F P 显著性
模型 1050.8753 14 75.06 40.60 0.0000 **
A(咖啡果皮汁添加量) 65.3333 1 65.33 35.34 0.0000 **
B(红茶液
添加量)
21.3333 1 21.33 11.54 0.0043 **
C(果糖
添加量)
18.7500 1 18.75 10.14 0.0066 **
D(柠檬酸
添加量)
14.0833 1 14.08 7.62 0.0153 *
AB 0.2500 1 0.25 0.14 0.7186
AC 36.0000 1 36.00 19.47 0.0006 **
AD 12.2500 1 12.25 6.63 0.0221 *
BC 1.0000 1 1.00 0.54 0.4742
BD 2.2500 1 2.25 1.22 0.2886
CD 90.2500 1 90.25 48.82 0.0000 **
A2 342.5153 1 342.52 185.26 0.0000 **
B2 319.3532 1 319.35 172.73 0.0000 **
C2 308.0761 1 308.08 166.63 0.0000 **
D2 264.9950 1 265.00 143.33 0.0000 **
残差 25.8833 14 1.85
失拟项 23.0833 10 2.31 3.30 0.1307 ns
纯误差 2.8000 4 0.70
总和 1076.7586 28
R2=0.9760; Adj R2=0.9519; Pre R2=0.8725
), ArticleFig(id=1217864273930403848, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=EN, label=Table 5, caption=

Analysis results of product physical and chemical indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 检测值
蛋白质/% 5.13±0.34
脂肪/% 4.94±0.78
膳食纤维/% 18.43±1.06
灰分/% 1.00±0.01
), ArticleFig(id=1217864274039455758, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217845643012395646, language=CN, label=表5, caption=

产品理化指标分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 检测值
蛋白质/% 5.13±0.34
脂肪/% 4.94±0.78
膳食纤维/% 18.43±1.06
灰分/% 1.00±0.01
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响应面法优化咖啡果皮饮料配方
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冯黎莎 * , 陈秋
食品安全质量检测学报 | 食品加工与工艺 2025,16(16): 288-296
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食品安全质量检测学报 | 食品加工与工艺 2025, 16(16): 288-296
响应面法优化咖啡果皮饮料配方
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冯黎莎* , 陈秋
作者信息
  • 云南开放大学, 云南国防工业职业技术学院化学工程学院, 文山 650504
  • 冯黎莎(1981—), 女, 硕士, 副教授, 主要研究方向为资源植物。E-mail:

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* 冯黎莎(1981—), 女, 硕士, 副教授, 主要研究方向为资源植物。E-mail:
Optimization of coffee peel beverage formula by response surface methodology
Li-Sha FENG* , Qiu CHEN
Affiliations
  • Yunnan Open University, School of Chemical Engineering, Yunnan Polytechnic of National Defense Industry, Wenshan 650504, China
出版时间: 2025-08-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250519004
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目的 以咖啡果皮和红茶为主要原料, 采用响应面法来优化咖啡果皮饮料配方。方法 首先对成熟的咖啡鲜果进行剥皮, 匀浆过滤后得到咖啡果皮汁, 加入红茶液、果糖和柠檬酸进行调配, 通过单因素实验和响应面法实验分析了咖啡果皮汁添加量、红茶液添加量、果糖添加量和柠檬酸添加量对咖啡果皮饮料的影响, 确定了咖啡果皮饮料的最佳配方。结果 咖啡果皮汁添加量为28%、红茶液添加量为40%、果糖添加量为5%、柠檬酸添加量为0.05%。此配方产品蛋白质含量(5.13±0.34)%、脂肪含量(4.94±0.78)%、膳食纤维(18.43±1.06)%、灰分(1.00±0.01)%。按此工艺加工的咖啡果皮汁饮料香气浓郁、色泽呈深琥珀色、酸甜可口。结论 应用响应面法优化咖啡果皮饮料配方, 为咖啡果皮的高值化利用和功能产品研发提供了另一条可行路径。

云南小粒咖啡  /  咖啡果皮  /  红茶  /  饮料  /  响应面法

Objective To optimize coffee peel beverage formula by response surface methodology with coffee peel and black tea as the main ingredients. Methods First, the ripe coffee fresh fruits were peeled. After homogenization and filtration, coffee peel liquid was obtained. This liquid was then mixed with black tea liquid, fructose and citric acid for formulation. Single-factor experiments and the response surface methodology were employed to investigate the effects of the addition amounts of coffee peel juice, black tea liquid, fructose, and citric acid on the coffee peel beverage, thereby determining the optimal formulation for it. Results The optimal addition amounts were 28% coffee peel juice, 40% black tea liquid, 5% fructose and 0.05% citric acid. This formulation yielded a product with protein content (5.13±0.34)%, fat content (4.94±0.78)%, dietary fiber (18.43±1.06)%, ash (1.00±0.01)%. The coffee peel juice beverage processed according to this process had a strong aroma, a deep amber color, and a delicious sour and sweet taste. Conclusion The application of response surface methodology to optimize the formula of coffee peel beverage provides another feasible way for the high value utilization of coffee peel and the development of functional products.

Yunnan arabica coffee  /  coffee peel  /  black tea  /  beverage  /  response surface methodology
冯黎莎, 陈秋. 响应面法优化咖啡果皮饮料配方. 食品安全质量检测学报, 2025 , 16 (16) : 288 -296 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250519004
Li-Sha FENG, Qiu CHEN. Optimization of coffee peel beverage formula by response surface methodology[J]. Journal of Food Safety & Quality, 2025 , 16 (16) : 288 -296 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250519004
云南小粒种咖啡种植区主要分布在云南普洱、临沧、保山、德宏、西双版纳、大理、怒江等地州, 优越的地理条件使得云南的咖啡产业发展迅速[1-4]。据统计, 2010年云南省咖啡种植面积183.15万亩。之后随着咖啡价格持续走低而有所减少。截至2022年, 种植面积达到127.3万亩, 总产量11.36万t, 农业产值34.5亿元, 种植面积、产量、农业产值均占全国的98%以上[5-7]
咖啡豆的初级加工普遍都采用湿法加工技术[8-10]。据调查, 一般加工1 t咖啡鲜果产生0.5 t皮渣(包括果皮、果肉), 云南咖啡植区每年咖啡鲜果处理加工产生皮渣约39.68万t[11]。陈旋等[12]利用咖啡果皮制备乙醇。李雄[13]和王丹丹等[14]以咖啡果皮为原料提取可溶性膳食纤维。徐紫琪等[15]探究从咖啡果皮中提取色素并研究果皮色素的抗氧化活性。赵青云等[16]建议将咖啡果皮添加纤维素酶和果胶酶堆沤, 这样制作的有机肥有利于作物生长, 否则咖啡果皮直接还田会抑制咖啡幼苗生长。郎心茹[17]用不同的方式提取咖啡果皮果胶。张云鹤[18]从咖啡果皮中提取花青素。这些研究都为咖啡果皮的高值化利用提供了理论支撑。
胡荣锁等[19]通过对5个产区咖啡果皮的基本成分、脂肪酸和氨基酸组成测定, 结果表明纤维素、蛋白质和总糖是咖啡果皮主要成分, 咖啡果皮营养价值丰富。因此, 本研究通过开发咖啡果皮饮料, 将加工废弃物转化为功能性饮品, 减少环境污染, 助力绿色农业发展。
本研究选用产自云南的小粒咖啡鲜果作为实验对象。该咖啡鲜果采摘于云南省普洱市, 生长环境的绝对海拔约为1300 m。
凤庆红茶、果糖、柠檬酸: 市售, 均为食品级。
LC-FA1004型电子天平(精度0.0001 g, 力辰科学仪器有限公司); IKA T10型分散机(艾卡仪器设备有限公司); SPX501S型搅拌机(浙江苏泊尔股份有限公司)。
制备咖啡果皮饮料的生产工艺:
①咖啡鲜果→剥皮→搅拌→分散匀浆→过滤→咖啡果皮汁;
②红茶→冲泡→过滤→红茶液;
③辅料→溶解→过滤→辅料溶液;
①+②+③搅拌混合均匀→成品。
选取咖啡鲜果时应注意其外观完整, 圆润饱满, 颜色呈深红色或红紫色。挑除有破皮、腐烂、病虫害的果实。挑取新鲜的咖啡果, 通过剥皮得到咖啡果皮, 按照咖啡果皮与水质量比1:4进行搅拌, 浆液采用分散机进行均质, 均质后的浆液采用120目滤袋进行过滤, 得到咖啡果皮汁。
称取一定量的红茶, 以纯净水作为溶剂, 90 ℃水浴浸提20 min, 过滤后加沸水定容至1 L备用。
在1 L的红茶液中加入适量的蔗糖、柠檬酸, 进行搅拌调配均匀。
咖啡果皮饮料感官评价由食品专业人员20人组成评定小组, 针对产品的色泽(0~25分)、组织与形态(0~35分)、香气及滋味(0~40分)进行感官分析及评价[20-21], 感官评价标准见表1
以感官评分为指标, 对咖啡果皮汁添加量(10%、20%、30%、40%、50%), 红茶液添加量(10%、20%、30%、40%、50%), 果糖添加量(2%、4%、6%、8%、10%), 柠檬酸添加量(0.03%、0.04%、0.05%、0.06%、0.07%)进行单因素实验[22-26]
按照GB 5009.5—2016《食品安全国家标准 食品中蛋白质的测定》、GB 5009.6—2016《食品安全国家标准 食品中脂肪的测定》、GB 5009.88—2023《食品安全国家标准 食品中膳食纤维的测定》、GB 5009.4—2016《食品安全国家标准 食品中灰分的测定》分别测定咖啡果皮饮料中蛋白质、脂肪、膳食纤维和灰分含量。
采用Excel 2019数据统计软件分析处理所得实验数据。通过Design expert 10.0.3进行响应面实验方案的设计与分析、响应曲面图的绘制。每组实验均重复3次, 实验结果求平均值, 并且以均值±标准偏差表示。
本研究选取5份咖啡果皮汁含量分别为10%、20%、30%、40%、50%, 与50%红茶液、6%果糖、0.05%柠檬酸进行咖啡果皮汁添加量的单因素实验, 实验结果见图1
图1可知, 当咖啡果皮汁添加量为20%时, 感官评价分值最高。随着咖啡果皮汁添加量的增加, 感官评价分值呈现先上升后降低的趋势。因此, 选择10%、20%和30%的咖啡果皮汁进行响应面实验, 以优化咖啡果皮汁添加量。
本研究选取5份红茶液含量分别为10%、20%、30%、40%、50%, 与20%咖啡果皮汁、6%果糖、0.05%柠檬酸进行红茶液添加量的单因素实验, 实验结果见图2
图2可知, 当红茶液添加量为40%时, 感官评价分值最高。随着红茶液添加量的增加, 感官评价分值呈现逐渐先上升后降低的趋势。因此, 选30%、40%和50%的红茶液进行响应面实验, 以优化红茶液添加量。
本研究选取5份果糖添加量分别为2%、4%、6%、8%、10%, 与20%咖啡果皮汁、50%红茶液、0.05%柠檬酸进行蔗糖添加量的单因素实验, 实验结果见图3
图3可见, 当果糖添加量为6%时, 感官评分最高, 所制备饮料酸甜适口。因此选用4%、6%、8%的果糖添加量, 进行响应面实验, 以优化出最佳添加量。
本研究选取5份柠檬酸添加量0.03%、0.04%、0.05%、0.06%、0.07%, 与20%咖啡果皮汁、50%红茶液、6%果糖进行柠檬酸添加量的单因素实验, 实验结果见图4
图4可知, 当柠檬酸添加量为0.05%时, 感官评价分值最高。但从感官评价的总体影响来看, 与其他因素相比, 柠檬酸添加量对感官评价的影响最低。因此, 选用0.04%、0.05%、0.06%%的柠檬酸添加量, 进行响应面实验, 以优化出最佳添加量。
结合单因素实验结果, 以咖啡果皮汁添加量(A)、红茶液添加量(B)、果糖添加量(C)、柠檬酸添加量(D)为自变量, 响应变量Y为感官评价分值。各取3个水平标记为-1、0、1, 采用响应面软件Design Expert 10.0.3进行因素水平设计, 进而得到感官评分的条件, 咖啡果皮饮料配方的因素水平见表2, 响应面实验设计与结果见表3
根据响应曲面法的实验设计及结果, 实验采用Design expert 10.0.3数据分析软件对实验数据进行多元回归拟合[27-31], 设咖啡果皮汁添加量、红茶液添加量、果糖添加量、柠檬酸添加量分别为ABCD, 以感官评分为响应值进行多元回归拟合, 回归模型系数及显著性检验结果见表4, 得到二次多项回归模型。
Y感官评分=92.20+2.33A+1.33B-1.25C-1.08D+0.25AB-3.00AC+1.75AD+0.50BC-0.75BD+4.75CD-7.27A2-7.02B2-6.89C2-6.39D2
表4进行模型整体评价, 回归模型F=40.60, P<0.0001, R2=0.9760, 表明回归模型高度显著且拟合度优异, 能解释97.60%的感官评分变异; 失拟项P=0.1307 (>0.05), 说明失拟项无显著失拟, 模型形式合理; 回归决定系数R2=0.9760, 校正决定系数R2=0.9519, 预测系数R2=0.8725, 说明模型稳定性好。因此可以利用方程模型对感官评分的工艺参数进行优化。
表4进行显著性因素分析, 主效应中A(咖啡果皮汁添加量)、B(红茶液添加量)、C(果糖添加量)、D(柠檬酸添加量)均显著(P<0.05, P<0.01), 其中AB正向影响评分, CD负向影响, 以此表明咖啡果皮汁、红茶液、果糖和柠檬酸的添加量对感官评分均有显著影响。4个因素对感官评分影响的重要程度为: 咖啡果皮汁添加量(A)>红茶液添加量(B)>果糖添加量(C)>柠檬酸添加量(D)。
主效应分析, A(咖啡果皮汁添加量)、B(红茶液添加量)具有正向线性影响, 表明存在最优添加量, C(果糖添加量)、D(柠檬酸添加量)应尽量低剂量添加。
交互作用分析, AC协同抑制感官评分, 需避免同时高剂量添加, AD协同提升感官评分, 可适度搭配, CD协同提升感官评分, 需平衡两者添加量。ABBCBD无显著交互作用, 可独立调整。
二次项分析, 所有因素的二次项(A2B2C2D2)均极显著(P<0.01), 表明各因素与感官评分呈抛物线关系, 存在最优添加量; 过量添加任何成分均会降低评分。
等高线与曲面图能反映因素之间的交互作用, 图形表现得越陡峭表明两者之间的交互作用越显著。咖啡果皮汁添加量(A)、红茶液添加量(B)、果糖添加量(C)、柠檬酸添加量(D)对感官评分的影响见图5~10
图5所示, AB交互曲面中, 曲面呈“山峰状”, AB均存在最佳添加量, 超过后评分下降, 呼应回归模型中二次项(A2, B2)的极显著性(P<0.01)。左上角(A=10%、B=50%): 评分仅50分, 提示低AB导致风味失衡。右下角(A=30%、B=30%): 评分约75分, 过量A稀释茶香, 需规避。最优区(红色中心): A=20%~25%、B=40%~45%时, 评分高达90分。热力图颜色梯度显示AB无强协同或拮抗, 与回归分析中AB交互项不显著(P=0.7186)一致。高分区沿对角线延伸, 表明AB需按比例同步增加, 以维持风味平衡。咖啡果皮汁添加量模型预测极值为28.381%, 红茶液添加量模型预测极值41.042%。
图6所示, AC交互曲面中, 曲面顶部呈“山脊状”, 表明AC需协同控制, 过量或不足均导致评分下降。AC极端值(如A=10%或30%、C=4%或8%)时, 评分降至70~75分, 验证了过量添加的负面影响。曲面呈现先升后降的抛物线趋势, 呼应回归模型中A2C2的极显著性(P<0.01), 说明两者存在明确最优添加阈值。当AC同时增加(如A>25%、C>6%), 曲面快速下降, 直观体现AC交互项的显著负向效应(P=0.0006)。等高线红色区域呈“狭长椭圆形”, 表明AC需严格按比例调整。等高线绿色边缘区域密集, 表明超出阈值后评分急剧下降。咖啡果皮汁添加量模型预测极值为28.381%、果糖添加量模型预测极值为5.399%。
图7所示, AD交互曲面中, 曲面顶部呈“山脊状”, 表明AD需协同控制, 过量或不足均导致评分下降。曲面呈现先升后降的抛物线趋势, 呼应回归模型中A2D2的极显著性(P<0.01), 说明两者存在明确最优添加阈值。当AD同时增加(如A>25%、D>0.055%), 曲面快速下降, 直观体现AD交互项的显著负向效应(P=0.0221)。等高线红色区域呈“狭长椭圆形”, 表明AD需严格按比例调整。等高线绿色边缘区域密集, 表明超出阈值后评分急剧下降。若需提高咖啡果皮风味, 需同步减少柠檬酸添加量。模型预测咖啡果皮汁添加量的最优值为28.381%、柠檬酸添加量为0.049%。
图8所示, BC交互曲面中, 曲面呈“山脊状”, 峰值区域沿对角线延伸, 表明BC需协同调整。左下角(B=30%、C=8%), 评分75分, 红茶液不足导致茶香寡淡, 果糖过量引发甜腻。右上角(B=50%、C=4%), 评分80分, 红茶液过量稀释果糖甜味, 风味失衡。等高线红色区域呈“椭圆形”, 长轴沿B方向延伸, 表明B的调整范围比C更宽容。等值线呈对称放射状, 无显著倾斜, 与回归模型中BC交互项不显著(P=0.4742)一致, 表明BC无强协同或拮抗效应。模型预测极值B=41.042%、C=5.399%。
图9所示, BD交互曲面中, 曲面呈“山峰状”, 峰值区域集中于中央, 表明BD均需控制在适中范围。左下角(B=30%, D=0.06%), 评分70分, 红茶液不足导致茶香缺失, 柠檬酸过量引发尖锐酸味。右上角(B=50%, D=0.04%), 评分80分, 红茶液过量稀释柠檬酸, 酸味不足导致甜腻失衡。曲面呈现先升后降趋势, 验证回归模型中B2D2的极显著性(P<0.01), 两者均存在明确最优阈值。等高线同心圆呈对称放射状, 表明BD的交互作用不显著, 与回归模型中BD交互项P=0.2886一致。等高线无倾斜, 说明BD对评分的影响独立, 调整时无需严格协同比例。模型预测极值B=41.042%、D=0.049%。
图10所示, CD交互曲面中, 曲面呈“山峰状”, 验证回归模型中C2D2的极显著性(P<0.01), 两者均存在最优添加阈值。曲面顶部3个红色标记点显示最优组合范围, 验证了甜酸平衡的黄金配比。等高线椭圆长轴沿C方向延伸, 表明果糖调整宽容度高于柠檬酸。
根据回归方程模型, 以感官评分最大值为优化目标, 得到预测的最优参数为: 咖啡果皮汁添加量为28.381%、红茶液添加量为41.042%、果糖添加量为5.399%、柠檬酸添加量为0.049%, 根据实验实际条件, 将条件修正为: 咖啡果皮汁添加量为28%、红茶液添加量为40%、果糖添加量为5%、柠檬酸添加量为0.05%, 与预测值总感官评分相差在5%范围内, 证实了预测值和实验值之间的良好相关性, 说明用响应面法优化得到的最佳加工工艺是合理的。
为评价咖啡果皮饮料的品质, 对其理化指标进行检测分析, 分析结果如表5所示, 可为咖啡果皮作为功能产品的研发提供依据。
以咖啡果皮汁和红茶液为主要原料, 通过响应面实验设计得到咖啡果皮汁饮料的最佳配方为: 咖啡果皮汁添加量为28%、红茶液添加量为40%、果糖添加量为5%、柠檬酸添加量为0.05%。此配方得到的产品蛋白质含量(5.13±0.34)%、脂肪含量(4.94±0.78)%、膳食纤维含量(18.43±1.06)%、灰分(1.00±0.01)%。按此工艺加工的咖啡果皮汁饮料香气浓郁、色泽呈深琥珀色、酸甜可口, 具有广阔的市场开发前景。
  • 云南省教育厅科学研究基金项目(2018JS367)
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2025年第16卷第16期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250519004
  • 接收时间:2025-05-19
  • 首发时间:2026-01-13
  • 出版时间:2025-08-25
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  • 收稿日期:2025-05-19
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云南省教育厅科学研究基金项目(2018JS367)
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    云南开放大学, 云南国防工业职业技术学院化学工程学院, 文山 650504

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* 冯黎莎(1981—), 女, 硕士, 副教授, 主要研究方向为资源植物。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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