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Article(id=1217529307610661415, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217529305693864468, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20241209002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1733673600000, receivedDateStr=2024-12-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1768211207535, onlineDateStr=2026-01-12, pubDate=1752508800000, pubDateStr=2025-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768211207535, onlineIssueDateStr=2026-01-12, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768211207535, creator=13701087609, updateTime=1768211207535, updator=13701087609, issue=Issue{id=1217529305693864468, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='13', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1768211207077, creator=13701087609, updateTime=1768212057891, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1217532874337730593, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217529305693864468, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1217532874337730594, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217529305693864468, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=282, endPage=290, ext={EN=ArticleExt(id=1217529309623927381, articleId=1217529307610661415, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Optimization of extraction process and anti-fatigue activity from Pollen pini polysaccharide based on genetic algorithm-neural network algorithm, columnId=1151923894560060071, journalTitle=Journal of Food Safety & Quality, columnName=Food Nutrition and Functional Foods, runingTitle=null, highlight=null, articleAbstract=

Objective To optimize the subcritical water extraction process of Pollen pini polysaccharides based on genetic algorithm-neural network (GA-NN) algorithm and further explore its anti-fatigue activity. Methods Using broken shell Pollen pini as raw material, a response surface was designed through Box-Behnken test on a single factor basis, and a network neural model was constructed and optimized using GA-NN algorithm. Under the optimal process conditions, the anti-fatigue effect of polysaccharides was evaluated through mouse weight-bearing swimming test. Results The relative error and coefficient of determination (R2) of the constructed neural network model were 0.03267 and 0.98476, respectively. The genetic algorithm was iterated 60 times for subcritical water extraction of polysaccharides, and the optimal parameters were obtained as follows: Temperature 148 ℃, time 28 min, liquid to material ratio 40:1 (mL/g), pressure 5 MPa, and polysaccharide yield of 23.7893 mg/g. After verification, there was no significant difference between the actual value and the predicted value, indicating good accuracy of the model. The study on anti fatigue activity showed that compared with the blank group, the high-dose group of mice had a certain degree of increase in body weight, while there was no significant difference in other groups (P>0.05). Compared with the blank group, the swimming time of mice in the polysaccharide group was prolonged, the levels of blood lactate and urea nitrogen were reduced, and the reserves of muscle glycogen and liver glycogen were significantly increased (P<0.05). Conclusion GA-NN can effectively optimize the subcritical water extraction process of Pollen pini polysaccharide, and the polysaccharide has a certain anti fatigue effect.

, correspAuthors=Zhan-Ao LIU, 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=Zhan-Ao LIU, Yan-Qin PEI, Hua YE), CN=ArticleExt(id=1217529311981126350, articleId=1217529307610661415, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=基于神经网络-遗传算法优化松花粉多糖提取工艺及其抗疲劳活性研究, columnId=1151923894698472105, journalTitle=食品安全质量检测学报, columnName=食品营养及功能性食品, runingTitle=null, highlight=null, articleAbstract=

目的 探究神经网络-遗传算法(genetic algorithm-neural network, GA-NN)优化松花粉多糖的亚临界水提取工艺, 并进一步探究其抗疲劳活性。方法 以破壳松花粉为原料, 在单因素基础上, 通过Box-Behnken试验设计响应面试验, 并利用GA-NN算法构建网络神经模型及优化工艺, 在最佳工艺条件下, 通过小鼠负重游泳试验评价多糖的抗疲劳作用。结果 所构建的神经网络模型相对误差和决定系数(R2)为0.03267和0.98476, 遗传算法对亚临界水提取多糖工艺迭代60次后, 得到最佳参数为: 温度148 ℃, 时间28 min, 液料比40:1 (mL:g), 压力5 MPa, 多糖得率为23.7893 mg/g, 经验证, 实际值与预测值不存在显著性差异, 模型准确度良好。抗疲劳活性研究表明与空白组相比, 高剂量组小鼠体重有一定程度的增加, 其他组无显著性差异(P>0.05)。相比空白组, 多糖组小鼠游泳时间延长(P<0.05), 血乳酸、尿素氮含量降低, 肌糖原、肝糖原储备量均显著增加(P<0.05)。结论 GA-NN能够有效优化松花粉多糖的亚临界水提取工艺, 且该多糖具有一定的抗疲劳作用。

, correspAuthors=刘占鳌, authorNote=null, correspAuthorsNote=
*刘占鳌(1983—), 男, 硕士, 副教授, 主要研究方向为全科医学和高等职业教育。E-mail:
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keyword=工艺优化), Keyword(id=1217901259303076481, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, orderNo=4, keyword=抗疲劳)], refs=[Reference(id=1217901264759866264, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, doi=null, pmid=null, pmcid=null, year=2024, volume=40, issue=10, pageStart=105, pageEnd=112, url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=宋世浩, 尹利端, 吕素媛, journalName=现代食品科技, refType=null, unstructuredReference=宋世浩, 尹利端, 吕素媛, 等. 松花粉超临界CO2萃取物抑制小鼠良性前列腺增生[J]. 现代食品科技, 2024, 40(10): 105-112., articleTitle=松花粉超临界CO2萃取物抑制小鼠良性前列腺增生, refAbstract=null), Reference(id=1217901264873112479, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, doi=null, pmid=null, pmcid=null, year=2024, volume=40, issue=10, pageStart=105, pageEnd=112, url=null, language=null, rfNumber=[1], rfOrder=1, authorNames=SONG SH, YIN LD, LV SY, journalName=Modern Food 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Journal of Jilin University (Medical Edition), 2024, 5(3): 689-696., articleTitle=Anti-fatigue effect of Wujia Shengmai Yin in mice and its mechanism, refAbstract=null)], funds=[Fund(id=1217901264499819400, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, awardId=19Z01, language=CN, fundingSource=西安医学高等专科学校2019年度校级科研项目(19Z01), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1217901255381402004, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, xref=null, ext=[AuthorCompanyExt(id=1217901255385596309, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, companyId=1217901255381402004, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Xi’an Medical College, Xi’an 710309, China), AuthorCompanyExt(id=1217901255393984919, tenantId=1146029695717560320, journalId=1149652044408987649, 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caption=Fitness curve, figureFileSmall=kOrYJ0+S3W5qqoblY/AdbA==, figureFileBig=nxFdB4wXXHicv3m8N5P4/A==, tableContent=null), ArticleFig(id=1217901261261816581, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, label=图7, caption=适应度曲线, figureFileSmall=kOrYJ0+S3W5qqoblY/AdbA==, figureFileBig=nxFdB4wXXHicv3m8N5P4/A==, tableContent=null), ArticleFig(id=1217901261362479884, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=EN, label=Fig.8, caption=Effects of Pollen pini polysaccharides on mouse body weight (n=10), figureFileSmall=Ra69QcbEp/h9Ez4hrZFQZg==, figureFileBig=OL+7BVp/AWxcpEfQozYxOA==, tableContent=null), ArticleFig(id=1217901261492503316, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, label=图8, caption=松花粉多糖对小鼠体重的影响(n=10), figureFileSmall=Ra69QcbEp/h9Ez4hrZFQZg==, figureFileBig=OL+7BVp/AWxcpEfQozYxOA==, tableContent=null), ArticleFig(id=1217901262822097690, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=EN, label=Fig.9, caption=Effects of Pollen pini polysaccharides on the weight-bearing swimming time of mice (n=10), figureFileSmall=y9U72Mn4u8OkH0Dd56lUFg==, figureFileBig=vfLAS3IU+8ueheaiv9TRPQ==, tableContent=null), ArticleFig(id=1217901262947926818, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, label=图9, caption=松花粉多糖对小鼠负重游泳时间的影响(n=10), figureFileSmall=y9U72Mn4u8OkH0Dd56lUFg==, figureFileBig=vfLAS3IU+8ueheaiv9TRPQ==, tableContent=null), ArticleFig(id=1217901263098921774, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=EN, label=Fig.10, caption=Effects of Pollen pini polysaccharides on serum lactic acid and urea nitrogen content in mice after exhaustive exercise (n=10), figureFileSmall=Iw4cHrBsDhAvf7W3MDNzRw==, figureFileBig=kgfA8KmDClPzjoIURWFLHQ==, tableContent=null), ArticleFig(id=1217901263212167990, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, label=图10, caption=松花粉多糖对小鼠力竭运动后血清乳酸、尿素氮含量的影响(n=10)

注: 图中不同小写字母表示组间差异显著(P<0.05)。下同。

, figureFileSmall=Iw4cHrBsDhAvf7W3MDNzRw==, figureFileBig=kgfA8KmDClPzjoIURWFLHQ==, tableContent=null), ArticleFig(id=1217901263346385725, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=EN, label=Fig.11, caption=Effects of Pollen pini polysaccharides on muscle glycogen and liver glycogen content in mice after exhaustive exercise (n=10), figureFileSmall=Iq7XKYw7cSkclutXpg6yxQ==, figureFileBig=cMcyCySeulgRu49yiSJD0w==, tableContent=null), ArticleFig(id=1217901263497380680, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, label=图11, caption=松花粉多糖对小鼠力竭运动后肌糖原、肝糖原含量的影响(n=10), figureFileSmall=Iq7XKYw7cSkclutXpg6yxQ==, figureFileBig=cMcyCySeulgRu49yiSJD0w==, tableContent=null), ArticleFig(id=1217901263614821197, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=EN, label=Table 1, caption=

Factors and levels

, figureFileSmall=null, figureFileBig=null, tableContent=
水平 因素
温度/℃ 时间/min 液料比(mL:g) 压力/MPa
-1 130 20 30:1 3
0 140 25 40:1 5
1 150 30 50:1 7
), ArticleFig(id=1217901263757427541, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, label=表1, caption=

因素及水平

, figureFileSmall=null, figureFileBig=null, tableContent=
水平 因素
温度/℃ 时间/min 液料比(mL:g) 压力/MPa
-1 130 20 30:1 3
0 140 25 40:1 5
1 150 30 50:1 7
), ArticleFig(id=1217901263900033888, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=EN, label=Table 2, caption=

Design and results of response surface

, figureFileSmall=null, figureFileBig=null, tableContent=
序列 温度
/℃		 时间
/min		 液料比(mL:g) 压力
/MPa		 实测得率
/(mg/g)
1 130 20 40:1 5 22.005
2 140 25 50:1 7 22.530
3 130 25 30:1 5 19.870
4 140 25 50:1 3 21.545
5 140 20 50:1 5 22.665
6 140 25 40:1 5 22.230
7 150 30 40:1 5 20.380
8 140 20 30:1 5 22.440
9 130 25 40:1 7 21.635
10 150 25 40:1 3 22.365
11 140 25 30:1 3 22.305
12 140 25 40:1 5 20.510
13 140 30 30:1 5 19.610
14 140 30 50:1 5 19.120
15 150 25 50:1 5 21.205
16 140 25 30:1 7 20.120
17 140 20 40:1 3 21.815
18 140 30 40:1 7 21.705
19 140 20 40:1 7 20.685
20 150 20 40:1 5 20.245
21 130 25 50:1 5 19.405
22 140 25 40:1 5 22.490
23 150 25 40:1 7 21.025
24 140 25 40:1 5 22.585
25 150 25 30:1 5 21.445
26 130 30 40:1 5 21.925
27 140 30 40:1 3 21.335
28 130 25 40:1 3 22.145
29 140 25 40:1 5 20.830
), ArticleFig(id=1217901264025863017, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, label=表2, caption=

响应面设计及结果

, figureFileSmall=null, figureFileBig=null, tableContent=
序列 温度
/℃		 时间
/min		 液料比(mL:g) 压力
/MPa		 实测得率
/(mg/g)
1 130 20 40:1 5 22.005
2 140 25 50:1 7 22.530
3 130 25 30:1 5 19.870
4 140 25 50:1 3 21.545
5 140 20 50:1 5 22.665
6 140 25 40:1 5 22.230
7 150 30 40:1 5 20.380
8 140 20 30:1 5 22.440
9 130 25 40:1 7 21.635
10 150 25 40:1 3 22.365
11 140 25 30:1 3 22.305
12 140 25 40:1 5 20.510
13 140 30 30:1 5 19.610
14 140 30 50:1 5 19.120
15 150 25 50:1 5 21.205
16 140 25 30:1 7 20.120
17 140 20 40:1 3 21.815
18 140 30 40:1 7 21.705
19 140 20 40:1 7 20.685
20 150 20 40:1 5 20.245
21 130 25 50:1 5 19.405
22 140 25 40:1 5 22.490
23 150 25 40:1 7 21.025
24 140 25 40:1 5 22.585
25 150 25 30:1 5 21.445
26 130 30 40:1 5 21.925
27 140 30 40:1 3 21.335
28 130 25 40:1 3 22.145
29 140 25 40:1 5 20.830
), ArticleFig(id=1217901264155886452, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=EN, label=Table 3, caption=

Results of analysis of variance

, figureFileSmall=null, figureFileBig=null, tableContent=
来源 平方和 df 平均方差 F P 显著性
模型 29.5 14 2.11 21.45 <0.0001 **
A-温度 2.19 1 2.19 22.28 0.0003 **
B-时间 12.6 1 12.6 128.25 <0.0001 **
C-料液比 0.6984 1 0.6984 7.11 0.0184 **
D-压力 3.56 1 3.56 36.23 <0.0001 **
AB 0.0004 1 0.0004 0.0041 0.9500
AC 0.9216 1 0.9216 9.38 0.0084 **
AD 0.6521 1 0.6521 6.64 0.0022 **
BC 0.1849 1 0.1849 1.88 0.1916
BD 0.0248 1 0.0248 0.2525 0.6231
CD 0.2233 1 0.2233 2.27 0.1539
A² 3.61 1 3.61 36.76 <0.0001 **
B² 3.84 1 3.84 39.14 <0.0001 **
C² 1.72 1 1.72 17.5 0.0009 **
D² 3.79 1 3.79 38.63 <0.0001 **
残差 1.38 14 0.0982
失拟项 1.21 10 0.1214 3.01 0.1499 不显著
纯误差 0.1613 4 0.0403
总和 30.87 28
), ArticleFig(id=1217901264273326970, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307610661415, language=CN, label=表3, caption=

方差分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
来源 平方和 df 平均方差 F P 显著性
模型 29.5 14 2.11 21.45 <0.0001 **
A-温度 2.19 1 2.19 22.28 0.0003 **
B-时间 12.6 1 12.6 128.25 <0.0001 **
C-料液比 0.6984 1 0.6984 7.11 0.0184 **
D-压力 3.56 1 3.56 36.23 <0.0001 **
AB 0.0004 1 0.0004 0.0041 0.9500
AC 0.9216 1 0.9216 9.38 0.0084 **
AD 0.6521 1 0.6521 6.64 0.0022 **
BC 0.1849 1 0.1849 1.88 0.1916
BD 0.0248 1 0.0248 0.2525 0.6231
CD 0.2233 1 0.2233 2.27 0.1539
A² 3.61 1 3.61 36.76 <0.0001 **
B² 3.84 1 3.84 39.14 <0.0001 **
C² 1.72 1 1.72 17.5 0.0009 **
D² 3.79 1 3.79 38.63 <0.0001 **
残差 1.38 14 0.0982
失拟项 1.21 10 0.1214 3.01 0.1499 不显著
纯误差 0.1613 4 0.0403
总和 30.87 28
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基于神经网络-遗传算法优化松花粉多糖提取工艺及其抗疲劳活性研究
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刘占鳌 * , 裴艳琴 , 叶华
食品安全质量检测学报 | 食品营养及功能性食品 2025,16(13): 282-290
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食品安全质量检测学报 | 食品营养及功能性食品 2025, 16(13): 282-290
基于神经网络-遗传算法优化松花粉多糖提取工艺及其抗疲劳活性研究
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刘占鳌* , 裴艳琴, 叶华
作者信息
  • 西安医学高等专科学校, 西安 710309

通讯作者:

*刘占鳌(1983—), 男, 硕士, 副教授, 主要研究方向为全科医学和高等职业教育。E-mail:
Optimization of extraction process and anti-fatigue activity from Pollen pini polysaccharide based on genetic algorithm-neural network algorithm
Zhan-Ao LIU* , Yan-Qin PEI, Hua YE
Affiliations
  • Xi’an Medical College, Xi’an 710309, China
出版时间: 2025-07-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241209002
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摘要
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目的 探究神经网络-遗传算法(genetic algorithm-neural network, GA-NN)优化松花粉多糖的亚临界水提取工艺, 并进一步探究其抗疲劳活性。方法 以破壳松花粉为原料, 在单因素基础上, 通过Box-Behnken试验设计响应面试验, 并利用GA-NN算法构建网络神经模型及优化工艺, 在最佳工艺条件下, 通过小鼠负重游泳试验评价多糖的抗疲劳作用。结果 所构建的神经网络模型相对误差和决定系数(R2)为0.03267和0.98476, 遗传算法对亚临界水提取多糖工艺迭代60次后, 得到最佳参数为: 温度148 ℃, 时间28 min, 液料比40:1 (mL:g), 压力5 MPa, 多糖得率为23.7893 mg/g, 经验证, 实际值与预测值不存在显著性差异, 模型准确度良好。抗疲劳活性研究表明与空白组相比, 高剂量组小鼠体重有一定程度的增加, 其他组无显著性差异(P>0.05)。相比空白组, 多糖组小鼠游泳时间延长(P<0.05), 血乳酸、尿素氮含量降低, 肌糖原、肝糖原储备量均显著增加(P<0.05)。结论 GA-NN能够有效优化松花粉多糖的亚临界水提取工艺, 且该多糖具有一定的抗疲劳作用。

亚临界水  /  神经网络  /  工艺优化  /  抗疲劳

Objective To optimize the subcritical water extraction process of Pollen pini polysaccharides based on genetic algorithm-neural network (GA-NN) algorithm and further explore its anti-fatigue activity. Methods Using broken shell Pollen pini as raw material, a response surface was designed through Box-Behnken test on a single factor basis, and a network neural model was constructed and optimized using GA-NN algorithm. Under the optimal process conditions, the anti-fatigue effect of polysaccharides was evaluated through mouse weight-bearing swimming test. Results The relative error and coefficient of determination (R2) of the constructed neural network model were 0.03267 and 0.98476, respectively. The genetic algorithm was iterated 60 times for subcritical water extraction of polysaccharides, and the optimal parameters were obtained as follows: Temperature 148 ℃, time 28 min, liquid to material ratio 40:1 (mL/g), pressure 5 MPa, and polysaccharide yield of 23.7893 mg/g. After verification, there was no significant difference between the actual value and the predicted value, indicating good accuracy of the model. The study on anti fatigue activity showed that compared with the blank group, the high-dose group of mice had a certain degree of increase in body weight, while there was no significant difference in other groups (P>0.05). Compared with the blank group, the swimming time of mice in the polysaccharide group was prolonged, the levels of blood lactate and urea nitrogen were reduced, and the reserves of muscle glycogen and liver glycogen were significantly increased (P<0.05). Conclusion GA-NN can effectively optimize the subcritical water extraction process of Pollen pini polysaccharide, and the polysaccharide has a certain anti fatigue effect.

subcritical water  /  neural network  /  process optimization  /  resist fatigue
刘占鳌, 裴艳琴, 叶华. 基于神经网络-遗传算法优化松花粉多糖提取工艺及其抗疲劳活性研究. 食品安全质量检测学报, 2025 , 16 (13) : 282 -290 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241209002
Zhan-Ao LIU, Yan-Qin PEI, Hua YE. Optimization of extraction process and anti-fatigue activity from Pollen pini polysaccharide based on genetic algorithm-neural network algorithm[J]. Journal of Food Safety & Quality, 2025 , 16 (13) : 282 -290 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241209002
正文
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0 引言
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松花粉是松科植物马尾松、油松等的花粉, 兼具药食两用特性, 富含蛋白质、不饱和脂肪酸、维生素、矿物质等营养成分及黄酮、植物甾醇等功能活性因子, 被誉为“世界上唯一的全营养食品”[1-2]。近年来, 松花粉的研究主要集中在活性成分提取、功能及健康产品开发等方面[3], 松花粉多糖是其发挥作用的主要活性物质之一, CHU等[4]研究表明松花粉多糖对HepG2细胞增殖具有抑制作用; LI等[5]发现松花粉多糖能够提高免疫器官体重指数, 淋巴细胞转移率等免疫指标, 有助于提升免疫力; 王寒月[6]发现松花粉多糖能够通过调节糖代谢、氨基酸代谢减缓氧化应激反应及炎症发生; 此外, 松花粉多糖在降血糖、延缓衰老等方面同样疗效显著[7]。但鲜少涉及松花粉的抗疲劳功效研究。
植物多糖属于生物大分子, 来源广泛, 其活性与其单糖组成、分子量、糖苷键类型等密切相关[8], 而不同的提取方法原理不同常导致多糖分子量、单糖组成差异较大, 因此选择合适的提取方法对于保留多糖生物活性十分必要。目前松花粉多糖的提取方法包括: 水煮法、酶解法、超声波法、超临界CO2法、微波法等[9], 但这些提取方法一般存在效率低、能耗高等缺点。亚临界水是在一定压力下, 将水加热至100~374 ℃范围, 水仍保持液体状态, 继续升温, 水分子逐步由强极性变为非极性, 从而选择性地提取不同极性成分的方法, 该法能够促使多糖分子从基质中加速溶出, 具有绿色、高效、操作简单等优点[10]。
人工神经网络(neural network, NN)是一种新兴的建模方法, 由输入层、隐藏层和输出层构成。通过学习算法训练, NN不断的调整自己的权重和阈值, 使输出值接近真实值。传统的NN权重和阈值初值随机给定, 初值的确定直接影响NN的预测精度。遗传算法(genetic algorithm, GA)是一种模拟自然界生物进化过程的全局寻优方法, 通过自然选择、交叉、变异寻求最优解。为了提升NN的预测性能, 可以采用GA对NN权重和阈值初始值优化, 获得最优的NN预测模型, 从而提升寻优速度和稳定性[11]。
鉴于此, 本研究拟以松花粉为原料, 采用亚临界水法提取多糖, 并利用GA-NN对工艺优化, 在最优工艺的基础上, 对多糖的抗疲劳作用进一步探讨, 旨在为松花粉资源的高质化利用提供理论依据。
1 材料与方法
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1.1 材料与试剂
SPF级雄性小鼠50只[上海懿尚生物科技有限公司, 体重(20±2) g, 许可证号: SYXK(沪)2022-0029]。
破壁松花粉(破壁率>98%, 朝阳松海生物科技有限公司); 葡萄糖标准品(纯度99%, 上海源叶生物科技有限公司); 苯酚、浓硫酸(分析纯, 国药集团化学试剂有限公司); 乙醇(分析纯, 天津科密欧化学试剂有限公司); 蒸馏水(实验室自制); 血乳酸检测试剂盒(货号: BC2235)、尿素氮检测试剂盒(货号: BC1535)、肝糖原检测试剂盒(货号: BC0340)、肌糖原检测试剂盒(货号: BC0345)(北京索莱宝科技有限公司)。
1.2 仪器与设备
UV759CRT紫外分光光度计(佛山南北潮电子商务有限公司); HH-1恒温水浴锅(常州市亿能实验仪器厂); EP-100X60药材万能破碎机(鹤壁市先烽仪器仪表有限公司); LanJ-316J30超声波清洗仪(广东蓝鲸智能超声波洗净设备有限公司); FA1004N电子分析天平(感量0.1 mg, 苏州顺强机电设备有限公司); RE-52CS旋转蒸发仪(上海秉越电子仪器有限公司); M1416R低温高速离心机(深圳市瑞沃德生命科技股份有限公司)。
1.3 试验方法
1.3.1 松花粉多糖制备
参考王娜等[12]方法, 略有修改。将松花粉在50 ℃下低温烘干8 h, 粉碎, 过80目筛。以液料比40:1 (mL:g)加入高压反应釜, 利用氮气瓶加压, 设置压力为5 MPa, 在温度140 ℃下, 萃取25 min, 结束后冷却至室温, 12000 r/min离心10 min, 收集上清液, 采用Sevage法(正丁醇:氯仿=4:1, V:V)脱蛋白, 分离出水相, 浓缩后, 加入无水乙醇至浓度95%, 4 ℃下过夜, 6000 r/min离心10 min, 收集沉淀, 加入适量蒸馏水复溶1 h, 过3500 Da透析袋, 透析48 h, 收集滤液, 冻干成粉末, 即得松花粉多糖。
1.3.2 多糖的测定
采用硫酸-苯酚法[13]测定多糖含量。准确称取一定量的葡萄糖标准品, 配制成1 mg/mL的标准溶液, 分别吸取0、0.2、0.4、0.8、1.0、1.2 mL标准溶液加入25 mL具塞试管, 以蒸馏水补充至2 mL, 摇匀。加入1 mL浓度5%的苯酚溶液, 然后迅速加入5 mL浓硫酸, 摇匀, 沸水浴15 min, 静置, 冷却至室温, 以蒸馏水补充至刻度线, 于490 nm波长下测定吸光值, 绘制标准曲线: Y=0.7894X-0.0002, r2=0.9978。依据标准曲线计算松花粉多糖的含量。
1.3.3 单因素试验
按照1.3.1方法分别考察不同温度(120、130、140、150、160 ℃)、不同液料比(10:1、20:1、30:1、40:1、50:1, mL:g)、不同提取时间(15、20、25、30、35 min)、不同压力(1、3、5、7、9 MPa)对松花粉多糖得率的影响。
1.3.4 响应面试验设计
在单因素试验基础上, 以合适的温度、液料比、时间、压力为考察变量, 以多糖得率为评价指标, 设计4因素3水平响应面试验, 试验设计因素和水平见表1。
1.3.5 神经网络-遗传算法优化
参考尹乐斌等[14]方法, 略有修改。利用Matlab软件进行GA全局多点寻优, 以温度、时间、料液比、压力为输入层, 多糖得率为输出层, 示意图见图1(a), 采用“traingdm”训练函数, 以测试集均方误差(mean squared error, MSE)为评价模型准确的指标, 建立基于响应面设计试验的参数优化, 以此模型的输出值构建个体适应度函数进行整体寻优, 对响应面结果和虚拟样本进行仿真试验, 拟合获得多糖最优目标, 并反馈其提取条件。根据实际情况设定种群大小、最大迭代数、交叉、变异、代构参数, 运行程序步骤[11]见图1(b)。
1.3.6 抗疲劳试验
(1)分组与给药
参考周玉蕾[15]方法。适应性喂养小鼠1周, 之后7 d内, 每天进行约30 min的游泳训练, 评估其游泳能力, 将小鼠随机分为5组, 空白对照组(CON组)、阳性对照组(positive control, PC组)、低剂量组(low pine pollen polysaccharides, L-PP组)、中剂量组(medium pine pollen polysaccharides, M-PP组)、高剂量组(high pine pollen polysaccharides, H-PP组), 确保各组游泳能力类似。其中CON组不作任何处理正常喂养, PC组(红景天苷提取物120 mg/(kg·d), 低中高剂量组分别为0.05、0.25、0.50 g/(kg·d), 连续灌胃28 d, 每2 d称量体重并记录。
(2)负重游泳时间
对各试验组开展负重游泳试验, 将一块重量为小鼠5%的铅皮固定在小鼠尾部, 将其放置于游泳箱中, 水深30 cm, 水温(25±1) ℃, 以小鼠头部全部没入水中, 8 s不浮出水面为终点, 记录游泳时间。
(3)血清生化指标测定
将小鼠取出, 进行眼球取血, 7000 r/min离心10 min, 分离血清, 分别按照试剂盒方法测定血乳酸、尿素氮含量。将取血后的小鼠颈部脱臼处死, 解剖取肝脏和后肢肌肉, 使用0.9%生理盐水清洗3遍, 拭干表面水分, 称取500 mg标本, 加入5 mL生理盐水, 匀浆机中低温制备匀浆液, 3500 r/min离心15 min, 取上清液, 按照试剂盒中的方法测定肌糖原和肝糖原含量。
1.4 数据处理
采用SPSS 22.0软件进行组间差异分析, 结果以平均值±标偏偏差表示, 以P<0.05为显著性差异, 采用Design-expert软件设计响应面, 利用Matlab R2016a软件运行程序构建NN模型, 利用Orign 2019b软件作图。
2 结果与分析
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2.1 单因素试验结果
由图2可知, 提取温度对松花粉多糖得率的影响较明显, 随着温度升高, 多糖得率呈先增后降的趋势, 在140 ℃时得率最高达(23.8±0.7) mg/g, 适当的升温能够促使多糖分子快速从松花粉中溶出, 这是由于温度升高使水的表面张力和黏度降低, 更易渗透入细胞内, 达到增加多糖扩散速度的目的[16]。当温度过高则导致多糖降解, 刁山山等[17]采用亚临界水提取南瓜多糖, 在温度高于150 ℃时, 多糖得率降低, 这与本研究结论较类似, 因此, 选择140 ℃作为最佳的提取温度。由图2可知松花粉多糖随着提取时间的延长, 多糖得率逐渐升高, 当超过25 min后, 得率显著下降, 这是由于提取刚开始时溶剂传质效果较好, 多糖大量溶出, 但时间过长, 多糖处于高温下, 容易破坏分解[18]。随着液料比的增加, 多糖得率在40:1 (mL:g)达到最高, 为(21.9±0.6) mg/g, 当松花粉比例较低时, 溶剂与松花粉接触面积增大, 更容易溶剂进入, 加速多糖溶出, 但此时多糖受原料限制浓度较低, 当松花粉过量时, 固液分配不均匀, 导致固液不易接触, 得率也较低[19], 因此40:1 (mL:g)是最佳的液料比。研究表明升高压力能够迫使溶剂进入细胞, 加速多糖扩散溶出[20], 由图2可知, 当压力为5 MPa时多糖得率最高为(23.2±0.7) mg/g, 当压力进一步增大, 导致多糖链断裂, 发生降解[21], 因此选择5 MPa为最佳的提取压力。
2.2 响应面试验设计及GA-NN优化结果
2.2.1 响应面试验结果
在单因素基础上, 以温度、时间、液料比、压力为考察因素, 以多糖得率为响应值设计四因素三水平试验方案, 结果见表2, 不同试验组的实测得率分布范围为19.120~22.665 mg/g, 差异明显, 表明不同因素对多糖得率的影响较大, 对表2数据拟合可得回归方程为:
Y=22.47+0.427A+1.02B+0.241C+0.545D-0.01AB+0.48AC-0.40AD+0.215BC-0.05BD-0.23CD-0.75A2-0.77B2-0.51C2- 0.76D2
2.2.2 响应面方差分析及交互作用
方差分析结果见表3, 由表3可知, 模型的显著性为极显著(P<0.001), 失拟项P>0.05不显著, 表明该模型可行性良好。一次项A、B、D均为极显著(P<0.01), 二项式中AC、AD均为极显著(P<0.01), A2、B2、C2、D2均为极显著(P<0.01), 由F大小可知4个因素对多糖得率的影响强度分别为时间(B)>压力(D)>温度(A)>料液比(C)。
由表3可知仅AC、AD两者对多糖得率的影响较显著, 因此对其进行交互作用分析, 结果见图3。图3(a)和3(b)分别为温度、料液比交互作用的等高线图和3D曲面图, 等高线图呈椭圆, 表明交互作用明显, 在温度方向的等高线较密集, 表明温度对多糖得率的影响更大; 图3(c)和3(d)分别为温度、压力的交互作用, 等高线呈椭圆, 交互作用显著, 由3D曲面图可知, 压力方向的变化较陡峭, 表明压力对多糖得率的影响更大, 综上A、C、D三者对多糖得率的影响强度依次为D>A>C, 这与方差分析结果较一致。
2.2.3 神经网络模型构建
(1)神经网络模型均方误差分析
通过建模确定网络拓扑结构为4-10-1型, 利用Matlab软件工具箱对试验数据训练, 训练集、测试集、验证集按照70%、15%、15%随机分组并进行迭代训练[22-23]。NN预测值与期望值之间的误差采用均方误差表示(mean-square error, MSE)[24], MSE越小代表预测值与期望值越接近, 模型性能越好, 精确度越高[25]。松花粉多糖提取NN模型的MSE值见图4, 由图4可知, 随着训练步数增加, MSE接近最佳值, 对应的误差为0.03267, 当NN迭代至第5次时, 训练趋于平稳, 表示该模型达到了拟合要求。
(2)相关系数的评估
通过训练集、验证集、测试集数据的目标值和输出值, 计算相关性系数, 拟合结果见图5, 相关系数达0.98476, 表明建立的NN模型性能较好, 准确度较高。
(3)人工神经网络模型性能测试
对NN模型中11组数据进行验证集和期望值的随机验证[26], 见图6。由图6可知预测值与期望值的变化趋势较一致, 表明所建模型输出的预测值具有较高的可行性。
(4)利用遗传算法优化及工艺参数验证
将训练好的NN与GA结合对提取工艺全局多点寻优, 程序运算后所得结果见图7, 由图7可知, 迭代至60代后达到最佳适应度, 此时松花粉多糖得率为23.9522 mg/g, 对应的工艺参数为温度147.85 ℃, 时间27.99 min, 液料比39.86:1 mL/g, 压力4.97 MPa, 经修正, 取温度148 ℃, 时间28 min, 液料比40:1 mL/g, 压力5 MPa, 验证试验结果(n=3)为23.7893 mg/g, 真实值较预测值低了0.68%, 与预测值不存在显著性差异(P>0.05)。
2.3 抗疲劳活性研究
2.3.1 松花粉多糖对小鼠体重的影响
由图8可知, 与CON组相比, PC组、L-PP组、M-PP组之间不存在显著性差异(P>0.05), H-PP组在14 d之前, 体重略低于其他组, 之后的增长速度较快, 崔鹤馨等[27]研究发现在饲料中添加2%的松花粉多糖能够促进小猪体重增加。张任帅[28]研究发现松花粉多糖能够促进肠道益生菌增殖, 加速腐败菌随粪便排出, 可能松花粉通过促进肠道益生菌, 提高小鼠的生长性能, 导致体重有一定程度的增长。
2.3.2 松花粉多糖对小鼠力竭游泳时间的影响
负重游泳时间能够客观反映小鼠的疲劳状况[29], 如图9所示, 与CON组相比, 其他试验组的游泳时间均有延长, 其中H-PP组时间最长, 较CON组延长了91.9%, 与PC组相比, 其运动持久能力也显示出一定的优势。
2.3.3 松花粉多糖对小鼠运动后血清乳酸和尿素氮含量的影响
当运动时间过长时, 机体的乳酸、尿素氮等代谢废弃物会过度积累导致疲劳产生, 乳酸是细胞无氧呼吸下发生糖酵解的产物, 短时间内提供能量来源, 但产量过大就会引起疲劳, 尿素氮是蛋白质代谢的最终产物, 易导致肌肉收缩, 产生疲劳[30]。由图10可知, 相比CON组, H-PP组的血乳酸含量明显降低(P<0.05), 表明松花粉多糖能够一定程度减少乳酸的产生; 此外各试验组的尿素氮均明显相对减少(P<0.05), 其中H-PP组降低最多, 且与L-PP、M-PP组之间存在一定的剂量依赖关系。因此, 松花粉多糖能够通过调节糖、蛋白代谢等过程降低乳酸、尿素氮水平, 具有一定的抗疲劳作用。
2.3.4 松花粉多糖对小鼠运动后肝糖原和肌糖原含量的影响
糖原是能量的主要来源, 运动过程中, 肌糖原和肝糖原被分解利用并提供能量, 因此糖原含量对机体的运动能量有直接的影响[31]。由图11可知, 与CON组相比, 其他试验组的肌糖原、肝糖原储备量均明显增加(P<0.05), 其中低、中、高剂量组的肌糖原含量分别增加了9%、19.7%、26.6%, 肝糖原含量分别增加了19.1%、32.7%、37.4%。H-PP组与PC组之间的肝糖原、肌糖原含量不存在显著性差异(P>0.05), 表明与红景天苷提取物相比, 松花粉多糖在提高小鼠肝糖原、肌糖原储备量方面具有同等的能力。
3 结论
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本研究以破壳松花粉为原料, 采用亚临界水法提取多糖, 分别考察提取温度、时间、液料比、压力对多糖得率的影响, 通过响应面试验设计与GA-NN算法结合, 将4个因素与多糖得率之间通过Matlab软件构建了非线性关系, 模型具有良好的预测性能和准确性。将NN模型与GA结合, 以均方误差函数为适应度函数, 确定亚临界水萃取松花粉多糖的最佳工艺参数为: 温度148 ℃, 时间28 min, 液料比40:1 (mL:g), 压力5 MPa, 对应的最佳多糖得率为23.7893 mg/g, 经验证真实值与预测值之间不存在显著性差异。
抗疲劳活性试验表明松花粉多糖能够明显延长小鼠游泳时间, 降低血清乳酸、尿素氮含量, 提高肌糖原、肝糖原储备量, 表明松花粉多糖能够提高运动持久性、耐力, 具有一定的抗疲劳作用。NN耦合GA具有较强的数据拟合能力和自我学习能力, 随着样本数据量的增加模型的准确度不断提升, 对于工业化生产的指导作用更科学精准。本研究表明松花粉多糖具有一定的抗疲劳作用, 但其作用机制尚不清晰, 未来将结合细胞组学、基因组学等手段进一步开展机理方面的探讨与研究, 为松花粉高值化产品开发提供一定的理论依据。
基金
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  • 西安医学高等专科学校2019年度校级科研项目(19Z01)
文献
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2025年第16卷第13期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20241209002
  • 接收时间：2024-12-09
  • 首发时间：2026-01-12
  • 出版时间：2025-07-15
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  • 收稿日期：2024-12-09
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西安医学高等专科学校2019年度校级科研项目(19Z01)
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    西安医学高等专科学校, 西安 710309

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*刘占鳌(1983—), 男, 硕士, 副教授, 主要研究方向为全科医学和高等职业教育。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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