Article(id=1153433637652517623, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433633999282214, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20241016002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1729008000000, receivedDateStr=2024-10-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1752929608977, onlineDateStr=2025-07-19, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752929608977, onlineIssueDateStr=2025-07-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752929608977, creator=13701087609, updateTime=1752929608977, updator=13701087609, issue=Issue{id=1153433633999282214, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='6', pageStart='1', pageEnd='322', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752929608105, creator=13701087609, updateTime=1758086445549, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1175062977960096080, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433633999282214, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1175062977960096081, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433633999282214, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=290, endPage=297, ext={EN=ArticleExt(id=1153433638084530951, articleId=1153433637652517623, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Quantitative determination of chlorophyll content in vegetables by convergence chromatography, columnId=1151895321388347923, journalTitle=Journal of Food Safety & Quality, columnName=Food Analysis and Detection, runingTitle=null, highlight=null, articleAbstract=

Objective To establish a method for the determination of chlorophyll a and chlorophyll b content in vegetables by convergence chromatography (CC). Methods The Soxhlet extraction method was used for efficient extraction of chlorophyll from vegetables and their products, followed by separation and detection using an ultra-performance convergence chromatography system coupled with a ultraviolet-visible detector. Results Through comparison of the stability of different solvents, petroleum ether was selected as the extraction solvent, and the extraction conditions were optimized. The method was validated with a limit of detection of 0.02 mg/kg, a limit of quantification of 0.06 mg/kg, a linear range of 0.06-800.00 mg/kg, a spike recovery rate of 99%-102%, and a relative standard deviation of 2.2%-14.0% for real samples. The reliability and accuracy of the method were confirmed through precision testing of real samples, including kale, fresh mustard greens, pickled mustard greens, dried pickled mustard greens, steamed dried pickled mustard greens, and boiled dried pickled mustard greens, as well as cross-validation with existing standard methods. Conclusion This study establishes a CC-based method for detecting chlorophyll a and chlorophyll b in vegetables, demonstrating high sensitivity, a wide linear range, good specificity, and precision. The method is not only suitable for laboratory research but also has practical application potential.

, correspAuthors=Zhi-Dong SUN, 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=Shan-Qiao CHEN, Chao-Yan LU, Hai-Tao SHANG, Hao ZHANG, Yin-Liang WU, Chun-Bo FAN, Zhi-Dong SUN), CN=ArticleExt(id=1153433654358429968, articleId=1153433637652517623, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=合相色谱法定量检测蔬菜中的叶绿素含量, columnId=1151895321958773274, journalTitle=食品安全质量检测学报, columnName=食品分析与检测, runingTitle=null, highlight=null, articleAbstract=

目的 建立合相色谱法(convergence chromatography, CC)测定蔬菜中叶绿素a和叶绿素b的含量。方法 采用索氏抽提法对蔬菜及其制品中的叶绿素进行高效提取, 并使用超高效合相色谱仪结合紫外-可见检测器进行分离与检测。结果 通过对不同溶剂稳定性的对比, 选择了石油醚作为提取溶剂, 并优化了提取条件。经验证, 检出限为0.02 mg/kg, 定量限为0.06 mg/kg, 线性范围为0.06~800.00 mg/kg, 加标回收率为99%~102%, 真实样品相对标准偏差2.2%~14.0%。通过对真实样品, 包括羽衣甘蓝、鲜芥菜、雪菜、梅干菜、蒸煮梅干菜、水煮梅干菜的检测精密度考察并与现行标准方法的交叉验证, 证明了该方法的可靠性和准确性。结论 本研究建立了一种基于CC的检测蔬菜中叶绿素a和叶绿素b的方法, 具有较高的灵敏度、较宽的线性范围和良好的特异性和精密度。该方法不仅适用于实验室研究, 也具备实际应用潜力。

, correspAuthors=孙志栋, authorNote=null, correspAuthorsNote=
* 孙志栋(1963—), 男, 教授级高级工程师, 主要研究方向为食品加工与安全。E-mail:
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陈山乔(1987—), 男, 工程师, 主要研究方向为食品科学与工程。E-mail:

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注: A. 空白基质; B. 定量限水平加标样品; C. 实测样品(鲜芥菜)。

, figureFileSmall=pxlDOuPMcotMGIy/5Sm65w==, figureFileBig=4QmJ7ibtjOLKopUkWBg8Ww==, tableContent=null), ArticleFig(id=1175086796493959921, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=EN, label=Fig.3, caption=Stability of chlorophyll a and chlorophyll b in methanol, ethanol, and petroleum ether under different solvent extractions (n=6), figureFileSmall=pRB9dLFP3qFBei9h3ufmNA==, figureFileBig=1hEcuvQ3/Kug2ZpH+Th3oA==, tableContent=null), ArticleFig(id=1175086796548485874, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=CN, label=图3, caption=叶绿素a和叶绿素b在不同溶剂(甲醇、乙醇、石油醚)萃取中的稳定性(n=6), figureFileSmall=pRB9dLFP3qFBei9h3ufmNA==, figureFileBig=1hEcuvQ3/Kug2ZpH+Th3oA==, tableContent=null), ArticleFig(id=1175086796632371955, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=EN, label=Table 1, caption=

Determination of chlorophyll a and chlorophyll b in matrix samples spiked at LOD-3SD, LOD, and LOD+3SD levels (n=20)

, figureFileSmall=null, figureFileBig=null, tableContent=
目标物 浓度水平/
(μg/kg)
检测结果
/(μg/kg)
检出概率
/%
叶绿素a 20.0-3×5.7 3.84±4.0 65
20.0 18.0±6.4 95
20.0+3×5.7 39.3±5.3 100
叶绿素b 20.0-3×6.4 2.20±2.8 45
20.0 20.2±5.5 95
20.0+3×6.4 38.1±4.3 100
), ArticleFig(id=1175086796682703604, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=CN, label=表1, caption=

LOD-3SD、LOD、LOD+3SD水平加标的基质样品叶绿素a和叶绿素b的测定结果(n=20)

, figureFileSmall=null, figureFileBig=null, tableContent=
目标物 浓度水平/
(μg/kg)
检测结果
/(μg/kg)
检出概率
/%
叶绿素a 20.0-3×5.7 3.84±4.0 65
20.0 18.0±6.4 95
20.0+3×5.7 39.3±5.3 100
叶绿素b 20.0-3×6.4 2.20±2.8 45
20.0 20.2±5.5 95
20.0+3×6.4 38.1±4.3 100
), ArticleFig(id=1175086796774978293, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=EN, label=Table 2, caption=

Spectrophotometric chlorophyll results of real samples and relative differences with CC results (n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
样品 叶绿素a 叶绿素b
分光光度计测得含量/(mg/kg) CC测得含量
/(mg/kg)
相对
相差/%
分光光度计测得含量/
(mg/kg)
CC测得含量
/(mg/kg)
相对
相差/%
羽衣甘蓝1 (2.09±0.13)×102 (2.14±0.06)×102 1.90 (3.04±0.22)×102 (3.19±0.12)×102 1.30
羽衣甘蓝2 (1.92±0.13)×102 (1.89±0.05)×102 1.60 (2.67±0.24)×102 (2.71±0.08)×102 1.50
鲜芥菜 (5.05±0.36)×10 (5.01±0.24)×10 0.65 (6.10±0.53)×102 (6.09±0.13)×102 0.09
), ArticleFig(id=1175086796829504246, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=CN, label=表2, caption=

真实样品的分光光度计检测叶绿素结果及与CC检测结果的相对相差(n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
样品 叶绿素a 叶绿素b
分光光度计测得含量/(mg/kg) CC测得含量
/(mg/kg)
相对
相差/%
分光光度计测得含量/
(mg/kg)
CC测得含量
/(mg/kg)
相对
相差/%
羽衣甘蓝1 (2.09±0.13)×102 (2.14±0.06)×102 1.90 (3.04±0.22)×102 (3.19±0.12)×102 1.30
羽衣甘蓝2 (1.92±0.13)×102 (1.89±0.05)×102 1.60 (2.67±0.24)×102 (2.71±0.08)×102 1.50
鲜芥菜 (5.05±0.36)×10 (5.01±0.24)×10 0.65 (6.10±0.53)×102 (6.09±0.13)×102 0.09
), ArticleFig(id=1175086796879835895, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=EN, label=Table 3, caption=

Results and recoveries of matrix spiked samples (n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
样品 叶绿素a 叶绿素b
含量/(mg/kg) 回收率/% 回收率要求/% 含量/(mg/kg) 回收率/% 回收率要求/%
LOQ (6.04±0.40)×10-2 101 70~110 (5.98±0.30)×10-2 100 70~110
2 LOQ 0.119±0.003 99 80~110 0.120±0.006 100 80~110
10 LOQ 0.604±0.012 101 80~110 0.610±0.022 102 80~110
低水平 8.09±0.16 101 80~110 7.95±0.21 99 80~110
中水平 80.7±1.6 101 80~110 80.5±1.0 100 80~110
高水平 (7.97±0.06)×102 100 90~100 (8.00±0.08)×102 100 90~100
), ArticleFig(id=1175086796955333368, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=CN, label=表3, caption=

基质加标样品的检测结果和回收率(n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
样品 叶绿素a 叶绿素b
含量/(mg/kg) 回收率/% 回收率要求/% 含量/(mg/kg) 回收率/% 回收率要求/%
LOQ (6.04±0.40)×10-2 101 70~110 (5.98±0.30)×10-2 100 70~110
2 LOQ 0.119±0.003 99 80~110 0.120±0.006 100 80~110
10 LOQ 0.604±0.012 101 80~110 0.610±0.022 102 80~110
低水平 8.09±0.16 101 80~110 7.95±0.21 99 80~110
中水平 80.7±1.6 101 80~110 80.5±1.0 100 80~110
高水平 (7.97±0.06)×102 100 90~100 (8.00±0.08)×102 100 90~100
), ArticleFig(id=1175086797022442233, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=EN, label=Table 4, caption=

CC test results of real samples (n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
样品 叶绿素a 叶绿素b
含量/(mg/kg) RSDs/% RSD要求/% 含量/(mg/kg) RSDs/% RSD要求/%
羽衣甘蓝1 (2.14±0.06)×102 2.6 11 (3.19±0.12)×102 3.7 10
羽衣甘蓝2 (1.89±0.05)×102 2.8 11 (2.71±0.08)×102 3.1 10
鲜芥菜 50.1±2.4 4.7 13 (6.09±0.13)×102 2.2 9.1
雪菜 17.3±0.8 4.4 16 25.7±0.9 3.4 15
梅干菜 2.69±0.14 5.0 21 11.6±0.7 5.7 17
蒸煮梅干菜 0.389±0.036 9.4 28 1.13±0.08 7.0 24
水煮梅干菜 (6.53±0.70)×10-2 10.7 36 0.187±0.026 14.0 31
), ArticleFig(id=1175086797139882746, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637652517623, language=CN, label=表4, caption=

真实样品的CC检测结果(n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
样品 叶绿素a 叶绿素b
含量/(mg/kg) RSDs/% RSD要求/% 含量/(mg/kg) RSDs/% RSD要求/%
羽衣甘蓝1 (2.14±0.06)×102 2.6 11 (3.19±0.12)×102 3.7 10
羽衣甘蓝2 (1.89±0.05)×102 2.8 11 (2.71±0.08)×102 3.1 10
鲜芥菜 50.1±2.4 4.7 13 (6.09±0.13)×102 2.2 9.1
雪菜 17.3±0.8 4.4 16 25.7±0.9 3.4 15
梅干菜 2.69±0.14 5.0 21 11.6±0.7 5.7 17
蒸煮梅干菜 0.389±0.036 9.4 28 1.13±0.08 7.0 24
水煮梅干菜 (6.53±0.70)×10-2 10.7 36 0.187±0.026 14.0 31
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合相色谱法定量检测蔬菜中的叶绿素含量
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陈山乔 1, 2 , 卢超艳 3 , 尚海涛 1 , 章豪 1, 2 , 吴银良 1, 2 , 范春波 3 , 孙志栋 1, *
食品安全质量检测学报 | 食品分析与检测 2025,16(6): 290-297
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食品安全质量检测学报 | 食品分析与检测 2025, 16(6): 290-297
合相色谱法定量检测蔬菜中的叶绿素含量
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陈山乔1, 2 , 卢超艳3, 尚海涛1, 章豪1, 2, 吴银良1, 2, 范春波3, 孙志栋1, *
作者信息
  • 1.宁波市农业科学研究院, 宁波 315040
  • 2.宁波市特色农产品质量安全检测与控制重点实验室, 宁波 315040
  • 3.宁波新紫云堂水产食品有限公司, 宁波 315040
  • 陈山乔(1987—), 男, 工程师, 主要研究方向为食品科学与工程。E-mail:

通讯作者:

* 孙志栋(1963—), 男, 教授级高级工程师, 主要研究方向为食品加工与安全。E-mail:
Quantitative determination of chlorophyll content in vegetables by convergence chromatography
Shan-Qiao CHEN1, 2 , Chao-Yan LU3, Hai-Tao SHANG1, Hao ZHANG1, 2, Yin-Liang WU1, 2, Chun-Bo FAN3, Zhi-Dong SUN1, *
Affiliations
  • 1. Ningbo Academy of Agricultural Sciences, Ningbo 315040, China
  • 2. Ningbo Key Laboratory of Testing and Control for Characteristic Agro-product Quality and Safety, Ningbo 315040, China
  • 3. Ningbo New Ziyuntang Aquatic Products Co., Ltd., Ningbo 315040, China
出版时间: 2025-03-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241016002
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目的 建立合相色谱法(convergence chromatography, CC)测定蔬菜中叶绿素a和叶绿素b的含量。方法 采用索氏抽提法对蔬菜及其制品中的叶绿素进行高效提取, 并使用超高效合相色谱仪结合紫外-可见检测器进行分离与检测。结果 通过对不同溶剂稳定性的对比, 选择了石油醚作为提取溶剂, 并优化了提取条件。经验证, 检出限为0.02 mg/kg, 定量限为0.06 mg/kg, 线性范围为0.06~800.00 mg/kg, 加标回收率为99%~102%, 真实样品相对标准偏差2.2%~14.0%。通过对真实样品, 包括羽衣甘蓝、鲜芥菜、雪菜、梅干菜、蒸煮梅干菜、水煮梅干菜的检测精密度考察并与现行标准方法的交叉验证, 证明了该方法的可靠性和准确性。结论 本研究建立了一种基于CC的检测蔬菜中叶绿素a和叶绿素b的方法, 具有较高的灵敏度、较宽的线性范围和良好的特异性和精密度。该方法不仅适用于实验室研究, 也具备实际应用潜力。

合相色谱法  /  叶绿素  /  羽衣甘蓝  /  芥菜  /  雪菜  /  梅干菜

Objective To establish a method for the determination of chlorophyll a and chlorophyll b content in vegetables by convergence chromatography (CC). Methods The Soxhlet extraction method was used for efficient extraction of chlorophyll from vegetables and their products, followed by separation and detection using an ultra-performance convergence chromatography system coupled with a ultraviolet-visible detector. Results Through comparison of the stability of different solvents, petroleum ether was selected as the extraction solvent, and the extraction conditions were optimized. The method was validated with a limit of detection of 0.02 mg/kg, a limit of quantification of 0.06 mg/kg, a linear range of 0.06-800.00 mg/kg, a spike recovery rate of 99%-102%, and a relative standard deviation of 2.2%-14.0% for real samples. The reliability and accuracy of the method were confirmed through precision testing of real samples, including kale, fresh mustard greens, pickled mustard greens, dried pickled mustard greens, steamed dried pickled mustard greens, and boiled dried pickled mustard greens, as well as cross-validation with existing standard methods. Conclusion This study establishes a CC-based method for detecting chlorophyll a and chlorophyll b in vegetables, demonstrating high sensitivity, a wide linear range, good specificity, and precision. The method is not only suitable for laboratory research but also has practical application potential.

convergence chromatography  /  chlorophyll  /  kale  /  fresh mustard greens  /  pickled mustard  /  preserved pickled mustard
陈山乔, 卢超艳, 尚海涛, 章豪, 吴银良, 范春波, 孙志栋. 合相色谱法定量检测蔬菜中的叶绿素含量. 食品安全质量检测学报, 2025 , 16 (6) : 290 -297 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241016002
Shan-Qiao CHEN, Chao-Yan LU, Hai-Tao SHANG, Hao ZHANG, Yin-Liang WU, Chun-Bo FAN, Zhi-Dong SUN. Quantitative determination of chlorophyll content in vegetables by convergence chromatography[J]. Journal of Food Safety & Quality, 2025 , 16 (6) : 290 -297 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241016002
叶绿素是自然界中最常见的色素之一, 它吸收光线中的蓝光和红光, 通过光合作用维持植物的生长[1]。此外叶绿素还广泛存在于加工食品中[2], 尤其是腌渍蔬菜[3]和果蔬汁[4]等。叶绿素作为功能性成分, 具有抗氧化[5]、抗炎[6]和降低糖尿病风险[7]等潜力。而且叶绿素的含量, 往往和食品的新鲜度存在相关性[8]。因为无论对于农业还是食品领域的研究, 叶绿素的高效、准确测定都有着巨大的需求。然而, 由于叶绿素在光和氧的存在下容易降解[9], 这种不稳定性会影响测定结果的准确性; 叶绿素测定常依赖有机溶剂提取, 其效率受提取时间、溶剂种类和样品状态的影响[10], 如果提取不充分, 测定结果会受到影响; 此外, 植物组织中除了叶绿素, 还有类胡萝卜素、酚类化合物等, 可能与叶绿素的吸收峰重叠或在测定过程中相互干扰[11]。因此叶绿素的测定方法开发, 一直是此领域中的难点。
当前已经有不同原理的检测方法被应用于叶绿素的分析, 其中主要包括分光光度法[12]、荧光光度法[13]、色谱法[14]、原位快速测定法[15]等。然而, 不同溶剂及其配比对叶绿素的光谱特性影响很大, 因此先前摸索出的计算公式对提取不同的提取溶剂并不适用[16], 甚至溶剂配制时的配比波动也会影响检测结果。除了前述的其他色素外, 脱镁叶绿素等叶绿素降解产物由于存在相似化学结构, 会干扰基于荧光光谱的叶绿素检测[17]。使用高效液相色谱(high performance liquid chromatography, HPLC)技术对化合物进行预分离, 是解决其他化合物干扰的可行方法, 其中又以反相HPLC为最常见的固定相类型, 例如利用C18填料的HPLC分析橄榄油中的水溶性叶绿素铜着色剂过程中, 分离到了叶绿素a和叶绿素b[18]; 对胡萝卜、番茄、菠菜中不同色素抗氧化能力的研究中, 使用了C30填料的HPLC分析了叶绿素a和叶绿素b[19]。然而叶绿素分子具有强疏水性[20], 这使其在传统反向相色谱柱上的分离效果不佳, 容易导致拖尾和峰形不理想, 影响检测灵敏度[21], 且依然容易受到其他脂溶性色素的干扰。为了解决这一问题, 通常HPLC会与质谱检测器联用, 通过对特征性离子的二次分离来提高定性和定量精度。例如大气压化学电离源的三重四极杆质谱法被用于检测茶叶中的叶绿素a和叶绿素b[22]; 电喷雾离子源结合离子阱质谱法被用以分析叶绿素及其降解产物[23]。然而质谱检测器会带来高硬件成本和更高的运行维护成本, 使得分析方法欠缺经济性。并且叶绿素在质谱的离子源内会发生复杂的结构转变[24], 也会对定量带来影响。
合相色谱(convergence chromatography, CC)技术是一种结合了超临界流体色谱(supercritical fluid chromatography, SFC)和HPLC优势的色谱技术。CC的关键是使用超临界CO₂作与有机溶剂组成流动相。这种技术的核心特征是CO₂在超临界状态时具有较低的黏度和较高的扩散系数, 因此能够实现更高的分离效率和更快的分离速度[25] 。合相色谱相比传统的反向HPLC, 由于CO₂的低黏度和高扩散性, CC通常比传统液相色谱在更短时间内提供更好的分离效率和分辨率, 以及更广泛的化合物极性适用性。
本研究拟利用索氏抽提法对蔬菜极其制品中的叶绿素进行高效率提取, 采用超高效CC仪结合紫外-可见检测器, 进行分离与检测, 并对方法进行了优化和验证。为叶绿素的精确定量提出了一种有潜力的分析方法。
2种羽衣甘蓝(Brassica oleracea var. sabellica)来自浙江湖州, 人工光照栽培; 鲜芥菜(Brassica juncea var. crispifolia)采收自浙江宁波, 品种为甬雪5号; 雪菜由前述同品种芥菜腌制, 15%盐料比腌制时间6个月; 梅干菜由前述雪菜晒干制得, 水分含量34.7%; 豆芽为实验室在严格避光环境下利用自动豆芽机萌发本地市场购得绿豆(Vigna radiata)而成[26], 萌发5 d。
乙醇、石油醚(沸程30~60 ℃)、无水硫酸钠、丙酮(分析纯, 国药集团化学试剂有限公司); 甲醇(色谱纯, 美国Supelco Analytical Products公司); CO₂ (4N级, 宁波市方辛气体有限公司); 叶绿素a对照品、叶绿素b对照品(纯度90%, 上海源叶生物科技有限公司); 所用水均为实验室自制去离子水。
Soxtec2500自动索氏抽提仪(丹麦FOSS公司), 配备有NDC型循环水冷却器(浙江优纳特科学仪器有限公司); Milli-Q去离子水发生器(美国Millipore公司); ACQUITY UPC2 PDACC系统、Torus DEA色谱柱(3.0 mm×100 mm, 1.7 μm)(美国Waters公司); BlueStar B型紫外可见分光光度计(北京莱伯泰科仪器有限公司); KS4000ic型控温摇床(德国IKA公司); R-210型旋转蒸发仪(瑞士BUCHI公司); N-EVAP 112型氮吹仪(美国Organomation公司); Quintix124电子天平(精度0.1 mg, 德国Sartorius公司)。
植物样品取叶片由高速粉碎机匀浆后, 进行后续测试, 现用现制备。豆芽收获后切取根部, 匀浆并检测确认无叶绿素后, 作为植物基质空白, -80 ℃下储存待用。
基于分光光度计的叶绿素含量依照NY/T 3082—2017《水果、蔬菜及其制品中叶绿素含量的测定 分光光度法》进行测定。
分别准确称取适量的叶绿素a和叶绿素b固体对照品分别用乙醇溶解, 配制成1 mg/mL的单标和1 mg/mL的混标储备液, 在-20 ℃下避光暂存, 配制后当日用。后续的标准曲线由甲醇逐级稀释为40.0、20.0、10.0、5.0、2.0、1.0、0.5 μg/mL。提取溶剂优化实验中直接精确吸取储备液加入。植物基质加标样品由称取解冻后的空白基质, 精确吸取并添加计算后目标量的储备液后0~2 ℃下搅拌15 min制成。标准曲线样品和加标样品现用现配。
Torus DEA色谱柱(3.0 mm×100 mm, 1.7 μm), 流动相: A为CO2, B为甲醇, 体积比为A:B=92:8, 等度洗脱; 流速: 1.5 mL/min; 背压: 1800 psi; 柱温: 40 ℃; 样品室温度: 4 ℃; 进样量: 1 μL; 分析时间5 min。
样品提取以索氏抽提法进行。在烧杯中精确秤取5~10 g样品匀浆, 并加入大致等量的无水硫酸钠, 使用玻璃棒充分搅拌均匀后, 放入索氏抽提仪滤纸杯中, 并用10 mL提取溶剂将烧杯和玻璃棒中的样品洗入滤纸杯(使用萃取铝杯接收滴出的溶剂)。将滤纸杯和萃取杯加载入抽提仪后, 加入70 mL萃取溶剂, 开始萃取。抽提完成后不进行溶剂回收, 不进行溶剂回收和干燥, 冷却至室温, 将抽提液旋转蒸发浓缩, 并转移至氮吹仪吹干, 最终以甲醇复溶并定溶至约1 mL, 若浓度过大可适当稀释, 过0.45 μm滤膜后上机待测。结果由标准曲线外标法定量。
提取溶剂的选择, 通过考察将1.3.3种所描述的加标提取溶剂种叶绿素的稳定性进行, 具体为甲醇、乙醇、石油醚各80 mL分别加入萃取铝杯中, 不加载样品在索氏抽提仪中进行回流, 调整加热板温度至回流液体速率约为2滴/s, 到达回流时间后冷却至室温, 直接吸取铝杯中的溶剂上机检测。
参考国家认证认可监督管理委员会颁布的《食品化妆品专业化学分析方法验证程序》, 根据预实验中了解到的方法的初步灵敏度情况, 以该浓度水平作为参考检出限(limit of detection, LOD)独立检测6个标准添加样品, 统计检测结果, 计算出标准偏差(standard deviation, SD)。以LOD-3SD、LOD、LOD+3SD 3个浓度水平分别检测20个标准添加样品, 有19个样品能被有效检出的浓度即为方法的检出限。定量限(limit of quantitation, LOQ)则定为3倍LOD, 待后续方法学验证。
实验中相对偏差、相对标准偏差等计算和数据处理使用R-Studio (Version 1.3.1093)[27]软件, 基于R (version 3.6.1)[28]进行。制表、绘图基于ggplot2软件包(version 3.3.3)[29]。除LOD设定实验重复次数为20外, 其他实验重复次数为6。
叶绿素a和叶绿素b在全波长扫描下其在流动相溶剂中的紫外-可见光谱如图1所示。如图1所示, 叶绿素a和叶绿素b分别在665.5 nm和655.6 nm处有特征性的吸收, 且两者的特征吸收波长差异仅为9.9 nm, 为了使检测方法能够适配仅配备单波长紫外-可见检测器的色谱仪, 故将检测波长设置为两者的平均数, 既为660.5 nm。叶绿素a和叶绿素b的特征吸收波长分别为665.5 nm和655.6 nm, 正好与它们在植物体中典型的吸收行为相似[30], 但这与单纯甲醇中它们的的吸收波长(叶绿素a: 665 nm; 叶绿素b: 650 nm)[31]略有差异, 这可能与CC流动相中的超临界二氧化碳(supercritical carbon dioxide, scCO2)有关, scCO2的低极性可能导致叶绿素分子周围的溶剂化壳发生变化, 导致电子跃迁的能量略微不同[32], 此外高压力下溶剂密度的变化会进一步影响叶绿素的吸收特性[33]。在660.5 nm下的典型色谱图如图2所示, 从图2上可以看出叶绿素a和叶绿素b已经达到基线分离, 因此使用等度流动相即可满足分离需求, 同时能够缩短批量分析时间。
经调整, 甲醇、乙醇和石油醚在索氏抽提仪中的回流速率约为2滴/s时, 对应加热板温度分别为70、135和175 ℃。而相对的, 其在萃取杯中的降解曲线如图3所示。如图3所示, 甲醇和乙醇中, 无论叶绿素a还是叶绿素b都无法维持稳定。这主要是由于溶剂的沸点要求了更高的溶剂温度, 而高温导致了叶绿素的分解[34], 此外溶剂本身的性质也有可能影响了叶绿素的热稳定性[35]。而从所获得的降解曲线可得, 石油醚中叶绿素含量能够在12 h内维持稳定, 没有降解, 因此适合于叶绿素的萃取。NY/T 3082—2017中所述叶绿素提取溶剂为乙醇-丙酮溶液, 其中丙酮以其更强的渗透性能够有助于叶绿素从植物细胞中溶出, 但是由于丙酮在当前作为易制毒试剂进行管理, 在可得性和便利性上相比本方法的提取溶剂, 存在劣势。考虑到冗余, 本研究设定样品提取条件设定为以下。提取溶剂: 石油醚(沸程30~60 ℃); 提取时间: 8 h; 加热板温度: 70 ℃。
参考GB/T 27417—2017《合格评定 化学分析方法确认和验证指南》要求验证了6组平行的标准曲线样品的相关系数(r2)均大于0.99。其中典型叶绿素a标准曲线公式为Y=2.565×104X+1.005×103, r2=0.9999; 叶绿素b标准曲线公式为Y=2.480×104X-1.992×103, r2=0.9993。式中Y为上样液质量浓度, μg/mL, X为目标分析物的峰面积, 无量纲。
经过6平行LOD为0.02 mg/kg水平加标样品检测, 计算得到叶绿素a和叶绿素b对应的SD分别为5.7 μg/kg和6.4 μg/kg。LOD-3SD、LOD、LOD+3SD 3个水平的加标样品检测结果如表1所示, 经过计算, 叶绿素a和叶绿素b的LOD均为0.02 mg/kg。LOQ则定为3倍LOD, 并且在标准曲线的范围内, 为0.06 mg/kg。有研究使用高效液相色谱-串联质谱法检测叶绿素, 叶绿素a的LOD可以达到0.24 ng/mL[36], 以水的密度进行换算, 比本研究更灵敏, 但这更可能归因于质谱检测器相比紫外-可见更加灵敏。但是本研究的LOD和LOQ相较NY/T 4357—2023《植物源性食品中叶绿素的测定 高效液相色谱法》和NY/T 3082—2017《水果、蔬菜及其制品中叶绿素含量的测定 分光光度法》灵敏度更高。因此虽然LOD和LOQ不如某些高灵敏度技术低, 但在常规检测中已足够有效, 适用于较为常见的检测场景, 可以进行后续的方法学验证。
正确度通过测定空白样品基质中加入已知量分析物的回收率获得。可接受的回收率范围与分析目的和分析物在样品中的浓度有关, 参考《食品化妆品专业化学分析方法验证程序》中表C.3。
分别以本研究所开发的CC方法和基于分光光度法两种分析原理不同的方法, 检测真实新鲜蔬菜样品的叶绿素含量, 并额外对腌制后的雪菜和晒干后的梅干菜中叶绿素进行测定, 其结果如表2所示。对比两种方法的结果, 可见其相对相差在3种样品中均小于10%(标准要求的的相对相差), 可见本研究所述方法在多种可能存在的共存物质干扰下, 依然保持与标准方法相似的检测结果, 这表明其对叶绿素的特异性较高。
参考《食品化妆品专业化学分析方法验证程序》, 由于没有有证标准物质(certified reference material, CRM), 正确度通过测定空白样品基质中加入已知量分析物的回收率获得。设置了LOQ、2倍LOQ、10倍LOQ的加标样品进行了正确度的验证, 此外还设置了低水平(8 mg/kg)、中水平(80 mg/kg)以及线性范围内的最高检测含量800 mg/kg含量水平的基质加标样品并标记为高水平, 其检测结果如表3所示。对比验证程序中的回收率要求, 本研究所述方法均在可接受范围内, 并且在较高浓度下其回收率能够非常接近100%, 说明本方法结果的偏倚度符合分析方法开发的要求。其中LOQ水平下, 回收率符合要求, 证明本方法对于LOQ水平的样品能够准确定量。同样, 对于线性范围内最高浓度水平(HQC)的正确性考察表明, 本方法在线性范围为0.06~800.00 mg/kg下, 能够准确定量。虽然回收率接近100%是正面的, 但是由于加标样品并不能完全代表真实样品的性质, 会使验证结果存在一定局限性。特别是使用固液提取方法进行叶绿素提取时, 往往由于溶剂穿透性差异而导致提取效率相对加标样品存在差异[37]。因此, 必须综合考虑准确性和利用其他不同原理检测方法进行交叉验证。而对比和表2来看, 本方法的正确性能够满足实际检测的需求。
本研究中, 对于相对标准偏差(relative standard deviation, RSD)的要求受到其具体含量的影响, 可接受RSD (RSDr)与样品中含量有关, 通过Horwitz方程[38]的2/3水平进行计算, 计算方法见公式(1)。Horwitz方程以一种简单的数学表达式呈现, 便于快速估算测量不确定性和再现性标准差, 特别是跨实验室研究中, 能够快速评估结果的可靠性和一致性, 并且适用于广泛的浓度范围, 从百分级到痕量级, 为不同浓度下的分析方法提供了可靠的精度评估[39]。根据计算结果(表4), 在5种真实样品中, 本研究所述方法的精密度均符合方法开发程序要求。此结果的RSD即为重复性精密度, 是指在相同的条件下, 对同一样本进行多次测量或实验时, 所得到结果的一致性或接近程度, 是误差的一种度量方式, 用于评价多次测量之间的变异性。重复性精密度越高, 说明多次测量结果之间的差异越小, 测量结果越稳定。此外通过对梅干菜、蒸煮梅干菜、水煮梅干菜中叶绿素的检测结果可见, 如文献报道所述, 叶绿素在高温烹调的情况下会逐渐降解[40]。并且在长时间加热下本方法依然能检测到残留的叶绿素, 表明本方法的低检出限对于食品加工领域研究的价值。
RSDr=$\frac{2}{3}$×2(1–0.5)log10C2
式中: RSDr为可接受RSD, %; C为试样中叶绿素a或叶绿素b的质量分数, 无量纲。
本研究建立了一种基于CC检测蔬菜中叶绿素a和叶绿素b的方法。其优化后的前处理方法为利用石油醚(沸程30~60 ℃)在加热板温度70 ℃下对匀浆后并用无水硫酸钠脱水的样品进行8 h的索氏抽提, 抽提液浓缩吹干后以甲醇复溶上机CC检测。色谱柱为Torus DEA色谱柱(3.0 mm× 100 mm, 1.7 μm), 流动相: A: CO2, B: 甲醇, A:B=92:8 (V:V); 流速: 1.5 mL/min; 背压: 1800 psi; 柱温: 40 ℃; 样品室温度: 4 ℃; 进样量: 1 μL; 分析时间5 min; 检测波长660.5 nm。经过验证, 本研究的LOD为0.02 mg/kg, LOQ为0.06 mg/kg, 线性范围为0.06~800.00 mg/kg, 经过真实样品和加标基质样品考察结合与基于现行标准分光光度法的交叉验证, 其特异性、精密度、正确度均满足实际应用要求。但是由于条件所限, 本研究仅进行了重复性精密度考察, 后续研究中需要进行在不同的实验室, 由不同的操作人员使用不同设备, 按相同的测试方法, 对同一被测对象相互独立进行的测试的再现性精密度考察。
  • 宁波市公益类项目(2022S140)
  • 绿色食品(绿色优质农产品)高质量创新发展技术研究项目(GFJY20240010)
  • “科创甬江2035”关键技术突破计划项目(20241ZDYF020210)
  • 宁波市食品安全与营养健康重大专项(2022Z176)
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20241016002
  • 接收时间:2024-10-16
  • 首发时间:2025-07-19
  • 出版时间:2025-03-25
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  • 收稿日期:2024-10-16
基金
宁波市公益类项目(2022S140)
绿色食品(绿色优质农产品)高质量创新发展技术研究项目(GFJY20240010)
“科创甬江2035”关键技术突破计划项目(20241ZDYF020210)
宁波市食品安全与营养健康重大专项(2022Z176)
作者信息
    1.宁波市农业科学研究院, 宁波 315040
    2.宁波市特色农产品质量安全检测与控制重点实验室, 宁波 315040
    3.宁波新紫云堂水产食品有限公司, 宁波 315040

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* 孙志栋(1963—), 男, 教授级高级工程师, 主要研究方向为食品加工与安全。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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