Article(id=1320880482347241922, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320880262012056083, articleNumber=null, orderNo=null, doi=10.13386/j.issn1002-0306.2025070036, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751558400000, receivedDateStr=2025-07-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792852047764, onlineDateStr=2026-10-24, pubDate=1789401600000, pubDateStr=2026-09-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792852047764, onlineIssueDateStr=2026-10-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792852047764, creator=13701087609, updateTime=1792852047764, updator=13701087609, issue=Issue{id=1320880262012056083, tenantId=1146029695717560320, journalId=1260987677001138203, year='2026', volume='47', issue='18', pageStart='1', pageEnd='613', issueExtLink='null', onlineDate='null', pubDate='1789401600000', pubDateStr='2026-09-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1792851995232, creator='13701087609', updateTime=1792921655980, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321172440479290195, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320880262012056083, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321172440479290196, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320880262012056083, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=295, endPage=305, ext={EN=ArticleExt(id=1320880483114799555, articleId=1320880482347241922, tenantId=1146029695717560320, journalId=1260987677001138203, language=EN, title=Research on the Functional Components of Non-centrifugal Cane Sugar of Sugarcane and Comprehensive Evaluation of Its Efficacy Using Near-infrared Spectroscopy, columnId=1320880262846722580, journalTitle=Science and Technology of Food Industry, columnName=Modern Analysis and Intelligent Detection, runingTitle=null, highlight=null, articleAbstract=

Objective: The study aimed to systematically profile the functional components of non-centrifugal cane sugar of sugarcane (NCS) using a chemomics-based strategy, and to establish a near-infrared (NIR) rapid detection technique for comprehensive quality grading of NCS. Methods: The potential functional components in NCS were identified and analyzed by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS). The total phenolic acid content was determined by ultraviolet-visible spectrophotometry. Key quality indicators, including sucrose, reducing sugar, total sugar, and loss on drying, were quantified according to industry and national standards. A comprehensive evaluation index (Fq) was introduced, in which weight coefficients for each indicator were optimally assigned based on their deviation from the mean of a large sample set, enabling integrated quality assessment of NCS. Results: A total of 21 major chemical components were identified in NCS, among which 16 were phenolic acids, primarily including citric acid, vanillic acid, syringic acid, p-hydroxybenzoic acid, and chlorogenic acid. Key indicators of 76 batches of samples were determined, and calibration models between these indicators and corresponding near-infrared spectral data were established. The determination coefficients (R2cal) of the prediction models for sucrose, reducing sugars, total phenolic acids, and loss on drying were 0.867, 0.900, 0.938, and 0.981, respectively, all exceeding 0.85. The root mean square errors of cross-validation (RMSECV) were 1.06, 1.01, 0.0245, and 0.342, respectively, indicating satisfactory model fitting. Quality classification of the samples was performed based on the normal distribution of the Fq value. Experimental validation demonstrated that high-quality samples exhibited superior antioxidant activity compared to low-quality samples, confirming the rationality of the established quality evaluation system. Conclusion: The proposed method incorporates functional component analysis into the current regulatory framework and achieves comprehensive quality evaluation through NIR rapid detection, providing a practical approach for rapid efficacy assessment of NCS.

, authors=Jiangtao WANG1, Yutong GUI1, Kaixin LIU1, Guoqing LUAN1, Xiaotao HOU2, Yuanyuan HOU1, *, Gang BAI1, 2, authorsList=Jiangtao WANG, Yutong GUI, Kaixin LIU, Guoqing LUAN, Xiaotao HOU, Yuanyuan HOU, Gang BAI, authorCompany=null, correspAuthors=Yuanyuan HOU, 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=1320880486193418708, articleId=1320880482347241922, tenantId=1146029695717560320, journalId=1260987677001138203, language=CN, title=甘蔗原生态糖功效成分解析及近红外综合评价方法, columnId=1320880262997717526, journalTitle=食品工业科技, columnName=现代分析与智能检测, runingTitle=null, highlight=null, articleAbstract=

目的:通过化学物质组学策略对甘蔗原生态糖(non-centrifugal cane sugar of sugarcane,NCS)的功效成分进行系统解析,建立一种适用于原生态糖等级综合评价的近红外快速检测技术。方法:通过超高效液相色谱-四极杆飞行时间质谱(Ultra Performance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry,UPLC/Q-TOF-MS)技术分析鉴定原生态红糖中的潜在功效成分,结合紫外可见分光光度法对其中抗氧化功能的总酚酸含量进行测定。同时依据行业标准QB/T 2343.2-2013和国家标准GB 5009.3-2016,对蔗糖、还原糖、总糖及干燥失重等关键指标进行定量分析,并引入综合评价指数(Fq)的概念,基于各检测指标相对大样本均值的偏离度优化分配各指标的权重系数,实现对甘蔗原生态糖品质的综合评估。结果:从甘蔗原生态糖中共鉴定出21种主要化学成分,其中16种为酚酸类化合物,主要包括柠檬酸、香草酸、丁香酸、p-羟基苯甲酸、绿原酸等。针对76批样品的关键指标进行检测,结合对应的近红外光谱构建了关键指标含量与光谱数据之间的拟合模型,蔗糖、还原糖、总酚酸、干燥失重等4项指标预测模型的R2cal分别为0.867、0.900、0.938、0.981,均大于0.85;其RMSECV分别为1.06、1.01、0.0245、0.342,预测结果显示模型拟合良好。依据Fq值的正态分布情况对样品进行品质划分,实验验证结果表明高品质样品抗氧化能力优于低品质样品,证明了等级评价体系的合理性。结论:该方法在现行的监管体系基础上引入功效成分检测,结合近红外快检技术实现了品质等级的综合评价,为甘蔗原生态糖的快速功效评价提供了参考的方法。

, authors=王江涛1, 桂宇彤1, 刘凯昕1, 栾国庆1, 侯小涛2, 侯媛媛1, *, 白钢1, 2, authorsList=王江涛, 桂宇彤, 刘凯昕, 栾国庆, 侯小涛, 侯媛媛, 白钢, authorCompany=null, correspAuthors=侯媛媛, authorNote=

王江涛(2000−),男,硕士研究生,研究方向:中药质量控制,E-mail:

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侯媛媛(1977−),女,博士,教授,研究方向:中药系统生物学与化学生物学,E-mail:。
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China Condiment, 2022, 47(9): 153−156162., articleTitle=null, refAbstract=null), Reference(id=1320880497337684580, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=35, rfOrder=58, authorNames=null, journalName=null, refType=null, unstructuredReference=刘昌孝. 中药质量标志物(Q-Marker)研究发展的5年回顾[J]. 中草药, 2021, 52(9): 2511−2518. [LIU C X. A 5-year review of the research and development of quality markers in traditional Chinese medicine (Q-Marker)[J]. Chinese Herbal Medicine, 2021, 52(9): 2511−2518.], articleTitle=null, refAbstract=null), Reference(id=1320880497564176997, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=35, rfOrder=59, authorNames=null, journalName=null, refType=null, unstructuredReference=LIU C X. A 5-year review of the research and development of quality markers in traditional Chinese medicine (Q-Marker)[J]. Chinese Herbal Medicine, 2021, 52(9): 2511−2518., articleTitle=null, refAbstract=null), Reference(id=1320880497861972582, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=36, rfOrder=60, authorNames=null, journalName=null, refType=null, unstructuredReference=刘昌孝, 陈士林, 肖小河, 等. 中药质量标志物 (Q-Marker): 中药产品质量控制的新概念[J]. 中草药, 2016, 47(9): 1443−1457. [LIU C X, CHEN S L, XIAO X H, et al. Quality markers in traditional Chinese medicine (Q-Marker): A new concept of quality control of traditional Chinese medicine products[J]. Chinese Herbal Medicine, 2016, 47(9): 1443−1457.], articleTitle=null, refAbstract=null), Reference(id=1320880497950052967, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=36, rfOrder=61, authorNames=null, journalName=null, refType=null, unstructuredReference=LIU C X, CHEN S L, XIAO X H, et al. Quality markers in traditional Chinese medicine (Q-Marker): A new concept of quality control of traditional Chinese medicine products[J]. Chinese Herbal Medicine, 2016, 47(9): 1443−1457., articleTitle=null, refAbstract=null), Reference(id=1320880498038133352, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=37, rfOrder=62, authorNames=null, journalName=null, refType=null, unstructuredReference=刘昌孝. 中药质量标志物(Q-marker): 提高中药质量标准及质量控制理论和促进中药产业科学发展[J]. 中草药, 2019, 50(19): 4517−4518. [LIU C X. Quality marker of traditional Chinese medicine (Q-marker): To improve the quality standards and quality control theory of traditional Chinese medicine and promote the scientific development of traditional Chinese medicine industry[J]. Chinese Herbal Medicine, 2019, 50(19): 4517−4518.], articleTitle=null, refAbstract=null), Reference(id=1320880498293985897, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=37, rfOrder=63, authorNames=null, journalName=null, refType=null, unstructuredReference=LIU C X. Quality marker of traditional Chinese medicine (Q-marker): To improve the quality standards and quality control theory of traditional Chinese medicine and promote the scientific development of traditional Chinese medicine industry[J]. Chinese Herbal Medicine, 2019, 50(19): 4517−4518., articleTitle=null, refAbstract=null), Reference(id=1320880498629530218, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=38, rfOrder=64, authorNames=null, journalName=null, refType=null, unstructuredReference=白钢, 刘昌孝, 张铁军, 等. 基于质量综合评价指数的药材品质快速评价[J]. 中草药, 2021, 52(2): 313−320. [BAI G, LIU C X, ZHANG T J, et al. Rapid evaluation of the quality of medicinal materials based on comprehensive quality evaluation index[J]. Chinese Herbal Medicine, 2021, 52(2): 313−320.], articleTitle=null, refAbstract=null), Reference(id=1320880498767942251, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=38, rfOrder=65, authorNames=null, journalName=null, refType=null, unstructuredReference=BAI G, LIU C X, ZHANG T J, et al. Rapid evaluation of the quality of medicinal materials based on comprehensive quality evaluation index[J]. Chinese Herbal Medicine, 2021, 52(2): 313−320., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1320880486428299733, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, xref=1, ext=[AuthorCompanyExt(id=1320880486440882646, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, companyId=1320880486428299733, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1State Key Laboratory of Medicinal Chemical Biology, School of Pharmacy, Nankai University, Tianjin 300353, China), AuthorCompanyExt(id=1320880486449271255, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, companyId=1320880486428299733, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1南开大学药学院,药物化学生物学全国重点实验室,天津 300353)]), AuthorCompany(id=1320880486516380120, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, xref=2, ext=[AuthorCompanyExt(id=1320880486524768729, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, companyId=1320880486516380120, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning 530200, China), AuthorCompanyExt(id=1320880486533157338, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, companyId=1320880486516380120, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2广西中医药大学,广西中药药效研究重点实验室,广西南宁 530200)])], figs=[ArticleFig(id=1320880489137820171, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Fig.1, caption=HPLC chromatography diagram of NCS and the main phenolic acid standards, figureFileSmall=Q5LopTbyTSb8zZeA76l24A==, figureFileBig=qH2zo0FEGmNtL+8nC05Rbw==, tableContent=null), ArticleFig(id=1320880489213317644, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=图1, caption=甘蔗原生态糖与主要酚酸类成分的高效液相色谱图

注:a为甘蔗原生态糖;b为酚酸;1. 柠檬酸;2. 香草酸;3. 丁香酸;4. p-羟基苯甲酸;5. 绿原酸。

, figureFileSmall=Q5LopTbyTSb8zZeA76l24A==, figureFileBig=qH2zo0FEGmNtL+8nC05Rbw==, tableContent=null), ArticleFig(id=1320880489523696141, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Fig.2, caption=Analysis of the wavenumber range of NIRS for raw cane sugar, figureFileSmall=BuJF2F3m6foyQnWS/BNqow==, figureFileBig=7eohccDgC449QfS5IgkSTw==, tableContent=null), ArticleFig(id=1320880489607582222, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=图2, caption=原生态糖NIRS的波数范围分析, figureFileSmall=BuJF2F3m6foyQnWS/BNqow==, figureFileBig=7eohccDgC449QfS5IgkSTw==, tableContent=null), ArticleFig(id=1320880489691468303, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Fig.3, caption=Preprocessing methods and wavenumber range selection for NIRS modeling, figureFileSmall=HXHB22JPbSrijIU8Z7CX1w==, figureFileBig=R5EHQdNQPEODlgXkzfucIw==, tableContent=null), ArticleFig(id=1320880489838268946, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=图3, caption=NIRS模型的预处理方法及波数范围的选择, figureFileSmall=HXHB22JPbSrijIU8Z7CX1w==, figureFileBig=R5EHQdNQPEODlgXkzfucIw==, tableContent=null), ArticleFig(id=1320880489922155028, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Fig.4, caption=Determination of sucrose, total phenolic acids, reducing sugar content and dry weight of multiple batches of raw sugar, figureFileSmall=cVy0wy9q9l9BrxAfgUBJKw==, figureFileBig=gQYSVCp2xUHkSfMaFZinPw==, tableContent=null), ArticleFig(id=1320880490018624022, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=图4, caption=多批次原生态糖的蔗糖、总酚酸、还原糖含量及干燥失重的测定, figureFileSmall=cVy0wy9q9l9BrxAfgUBJKw==, figureFileBig=gQYSVCp2xUHkSfMaFZinPw==, tableContent=null), ArticleFig(id=1320880490098315799, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Fig.5, caption=Near-infrared prediction results of sucrose, total phenolic acids, reducing sugar content and dry weight of the original sugar, figureFileSmall=M2wBSrxYm4Oy7CtBF4t/5g==, figureFileBig=8AxVYuAxeXyu67iQpm/JqQ==, tableContent=null), ArticleFig(id=1320880490182201880, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=图5, caption=原生态糖的蔗糖、总酚酸、还原糖含量及干燥失重的近红外预测结果, figureFileSmall=M2wBSrxYm4Oy7CtBF4t/5g==, figureFileBig=8AxVYuAxeXyu67iQpm/JqQ==, tableContent=null), ArticleFig(id=1320880490266087961, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Fig.6, caption=Optimization of the comprehensive evaluation index Fq for raw sugar from sugarcane, sample distribution, and OPLS-DA hierarchical clustering, figureFileSmall=5Ga3LYDkPWdm8mU1anoXXA==, figureFileBig=M+IiQed2rTB00fkpK5FRQQ==, tableContent=null), ArticleFig(id=1320880490370945562, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=图6, caption=甘蔗原生态糖综合评价指数Fq的优化、样本分布及OPLS-DA等级聚类, figureFileSmall=5Ga3LYDkPWdm8mU1anoXXA==, figureFileBig=M+IiQed2rTB00fkpK5FRQQ==, tableContent=null), ArticleFig(id=1320880490517746203, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Fig.7, caption=Investigation on the antioxidant activity of raw cane sugar based on the comprehensive evaluation index (Fq), figureFileSmall=GQhZpDZxpOdv/8t+u0YzIw==, figureFileBig=MyomIcvWcoy9vkdt3yx82w==, tableContent=null), ArticleFig(id=1320880490630992412, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=图7, caption=基于综合评价指数(Fq)的原生态糖抗氧化活性考察

注:与四等组比较,****表示P<0.0001。

, figureFileSmall=GQhZpDZxpOdv/8t+u0YzIw==, figureFileBig=MyomIcvWcoy9vkdt3yx82w==, tableContent=null), ArticleFig(id=1320880490689712669, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Fig.8, caption=Clustering analysis of raw brown sugar based on NIRS information, figureFileSmall=pVTdl5ju0NESLuAj5t7Xtw==, figureFileBig=biHniJcgNWs9vN3HoXffKg==, tableContent=null), ArticleFig(id=1320880490781987358, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=图8, caption=基于NIRS信息的原生态红糖聚类分析, figureFileSmall=pVTdl5ju0NESLuAj5t7Xtw==, figureFileBig=biHniJcgNWs9vN3HoXffKg==, tableContent=null), ArticleFig(id=1320880490853290527, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Table 1, caption=

Brown sugar sample information

, figureFileSmall=null, figureFileBig=null, tableContent=
样品编号产地生产公司样品编号产地生产公司
S1四川成都月月红实业有限公司S39天津天津市维利可食品有限公司
S2天津天津茂佳食品有限公司S40天津天津茂佳食品有限公司
S3云南云南原品阁食品有限公司S41天津天津市维利可食品有限公司
S4天津天津什福来食品科技有限公司S42广西潮州市潮安区绿雅红糖厂
S5上海上海谷欣食品有限公司S43广西潮州市潮安区绿雅红糖厂
S6河北河北古松农副产品有限公司S44广西潮州市潮安区绿雅红糖厂
S7天津天津市维利可食品有限公司S45广西潮州市潮安区绿雅红糖厂
S8江苏南京甘汁园股份有限公司S46广西潮州市潮安区绿雅红糖厂
S9天津天津市聚荣华商贸有限公司S47广西潮州市潮安区绿雅红糖厂
S10广西广西凤糖生化股份有限公司S48广西潮州市潮安区绿雅红糖厂
S11上海上海谷欣食品有限公司S49广西潮州市潮安区绿雅红糖厂
S12江苏南京甘汁园股份有限公司S50广西潮州市潮安区绿雅红糖厂
S13浙江嘉兴市诚浩佳浓食品有限公司S51广西潮州市潮安区绿雅红糖厂
S14云南云南王锦记大健康发展有限公司S52广西潮州市潮安区绿雅红糖厂
S15河北河北古松农副产品有限公司S53广西广西凤糖生化股份有限公司
S16广东潮州市潮安区绿雅红糖厂S54广西广西凤糖生化股份有限公司
S17广西潮州市潮安区绿雅红糖厂S55广西广西凤糖生化股份有限公司
S18广西潮州市潮安区绿雅红糖厂S56广西广西凤糖生化股份有限公司
S19安徽安徽华耀堂健康产业有限公司S57广西广西凤糖生化股份有限公司
S20安徽安徽华耀堂健康产业有限公司S58广西广西凤糖生化股份有限公司
S21广西潮州市潮安区绿雅红糖厂S59广西广西凤糖生化股份有限公司
S22广西潮州市潮安区绿雅红糖厂S60广西广西凤糖生化股份有限公司
S23广西潮州市潮安区绿雅红糖厂S61安徽安徽祈善堂生物科技有限公司
S24广西潮州市潮安区绿雅红糖厂S62安徽安徽祈善堂生物科技有限公司
S25广西潮州市潮安区绿雅红糖厂S63安徽安徽祈善堂生物科技有限公司
S26广西潮州市潮安区绿雅红糖厂S64广东广东省丰收糖业发展有限公司
S27广西潮州市潮安区绿雅红糖厂S65海南益民富硒古法红糖有限公司
S28广东潮州市潮安区绿雅红糖厂S66海南益民富硒古法红糖有限公司
S29广西潮州市潮安区绿雅红糖厂S67海南益民富硒古法红糖有限公司
S30广西潮州市潮安区绿雅红糖厂S68云南云南百川食品有限公司
S31广西潮州市潮安区绿雅红糖厂S69云南云南百川食品有限公司
S32广西潮州市潮安区绿雅红糖厂S70云南云南百川食品有限公司
S33广东潮州市潮安区绿雅红糖厂S71云南景洪工业园区老彭红糖加工坊
S34广东潮州市潮安区绿雅红糖厂S72云南景洪工业园区老彭红糖加工坊
S35广西潮州市潮安区绿雅红糖厂S73云南景洪工业园区老彭红糖加工坊
S36广东潮州市潮安区绿雅红糖厂S74海南海南儋州长坡南华糖业有限公司
S37广西潮州市潮安区绿雅红糖厂S75海南海南儋州长坡南华糖业有限公司
S38广西潮州市潮安区绿雅红糖厂S76海南海南儋州长坡南华糖业有限公司
), ArticleFig(id=1320880490953953824, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=表1, caption=

红糖样品信息

, figureFileSmall=null, figureFileBig=null, tableContent=
样品编号产地生产公司样品编号产地生产公司
S1四川成都月月红实业有限公司S39天津天津市维利可食品有限公司
S2天津天津茂佳食品有限公司S40天津天津茂佳食品有限公司
S3云南云南原品阁食品有限公司S41天津天津市维利可食品有限公司
S4天津天津什福来食品科技有限公司S42广西潮州市潮安区绿雅红糖厂
S5上海上海谷欣食品有限公司S43广西潮州市潮安区绿雅红糖厂
S6河北河北古松农副产品有限公司S44广西潮州市潮安区绿雅红糖厂
S7天津天津市维利可食品有限公司S45广西潮州市潮安区绿雅红糖厂
S8江苏南京甘汁园股份有限公司S46广西潮州市潮安区绿雅红糖厂
S9天津天津市聚荣华商贸有限公司S47广西潮州市潮安区绿雅红糖厂
S10广西广西凤糖生化股份有限公司S48广西潮州市潮安区绿雅红糖厂
S11上海上海谷欣食品有限公司S49广西潮州市潮安区绿雅红糖厂
S12江苏南京甘汁园股份有限公司S50广西潮州市潮安区绿雅红糖厂
S13浙江嘉兴市诚浩佳浓食品有限公司S51广西潮州市潮安区绿雅红糖厂
S14云南云南王锦记大健康发展有限公司S52广西潮州市潮安区绿雅红糖厂
S15河北河北古松农副产品有限公司S53广西广西凤糖生化股份有限公司
S16广东潮州市潮安区绿雅红糖厂S54广西广西凤糖生化股份有限公司
S17广西潮州市潮安区绿雅红糖厂S55广西广西凤糖生化股份有限公司
S18广西潮州市潮安区绿雅红糖厂S56广西广西凤糖生化股份有限公司
S19安徽安徽华耀堂健康产业有限公司S57广西广西凤糖生化股份有限公司
S20安徽安徽华耀堂健康产业有限公司S58广西广西凤糖生化股份有限公司
S21广西潮州市潮安区绿雅红糖厂S59广西广西凤糖生化股份有限公司
S22广西潮州市潮安区绿雅红糖厂S60广西广西凤糖生化股份有限公司
S23广西潮州市潮安区绿雅红糖厂S61安徽安徽祈善堂生物科技有限公司
S24广西潮州市潮安区绿雅红糖厂S62安徽安徽祈善堂生物科技有限公司
S25广西潮州市潮安区绿雅红糖厂S63安徽安徽祈善堂生物科技有限公司
S26广西潮州市潮安区绿雅红糖厂S64广东广东省丰收糖业发展有限公司
S27广西潮州市潮安区绿雅红糖厂S65海南益民富硒古法红糖有限公司
S28广东潮州市潮安区绿雅红糖厂S66海南益民富硒古法红糖有限公司
S29广西潮州市潮安区绿雅红糖厂S67海南益民富硒古法红糖有限公司
S30广西潮州市潮安区绿雅红糖厂S68云南云南百川食品有限公司
S31广西潮州市潮安区绿雅红糖厂S69云南云南百川食品有限公司
S32广西潮州市潮安区绿雅红糖厂S70云南云南百川食品有限公司
S33广东潮州市潮安区绿雅红糖厂S71云南景洪工业园区老彭红糖加工坊
S34广东潮州市潮安区绿雅红糖厂S72云南景洪工业园区老彭红糖加工坊
S35广西潮州市潮安区绿雅红糖厂S73云南景洪工业园区老彭红糖加工坊
S36广东潮州市潮安区绿雅红糖厂S74海南海南儋州长坡南华糖业有限公司
S37广西潮州市潮安区绿雅红糖厂S75海南海南儋州长坡南华糖业有限公司
S38广西潮州市潮安区绿雅红糖厂S76海南海南儋州长坡南华糖业有限公司
), ArticleFig(id=1320880491083977249, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Table 2, caption=

Identification of raw brown sugar components of sugarcane

, figureFileSmall=null, figureFileBig=null, tableContent=
峰号保留时间(min)分子式质荷比理论值误差(ppm)碎片离子鉴定结果
11.070C7H6O5169.0177169.0182−2.96127.05,105.02,89.00没食子酸
(Gallic Acid)
21.326C7H12O6191.0582191.055613.61173.05,127.04奎尼酸
(Quinic acid)
32.047C6H8O7191.0214191.019211.52111.01柠檬酸
(Citric acid)
49.308C13H16O8299.0762299.0767−1.67137.024-羟基苯甲酸葡糖苷
(4-Hydroxybenzoic acid glucoside)
510.354C13H16O9315.0728315.07163.81153.02,152.01,109.03,108.02龙胆酸-2/5-O-β-葡糖苷
(Gentisic acid-2/5-O-β-glucoside)
611.565C14H18O9329.0893329.08736.08167.03,152.01香草酸葡糖苷
(Vanillic acid glucoside)
711.572C8H8O4167.0346167.03441.20152.01,108.02香草酸
(Vanillic acid)
812.171C13H16O9315.0728315.07163.81153.02,109.03原儿茶酸-4-β-葡糖苷
(Protocatechuic acid-4-β-glucoside)
913.036C9H10O5197.0459197.0454.57123.01丁香酸
(Syringic acid)
1013.086C7H6O3137.0235137.0239−2.92122.02,104.02p-羟基苯甲酸[25]
(p-hydroxybenzoic acid)
1114.960C7H6O4153.0177153.0188−7.19109.03原儿茶酸(Protocatechuic acid)
1216.998C16H18O9353.0877353.08731.13191.03,179.01,161.00绿原酸
(Chlorogenic Acid)
1316.448C17H20O9367.1033367.10291.09193.05,191.06,173.04,134.043-O-阿魏酰奎宁酸
(3-O-feruloyquinic acid)
1418.019C15H16O11371.0611371.0614−0.81173.01,111.01咖啡酰葡糖二酸(Caffeoylglucaric acid)
1518.276C17H20O9367.1033367.10291.09193.05,191.06,173.05,134.045-O-阿魏酰奎宁酸
(5-O-feruloyquinic acid)
1618.333C26H28O14563.14563.1401−0.18473.11,443.10夏佛塔苷
(Schaftoside)
1718.411C9H8O3163.0411163.03959.81119.05对香豆酸
(p-Coumaric acid)
1818.469C22H22O11461.1119461.10847.59443.10,341.07,311.08香叶木素-8-C-葡糖苷[26]
(Diosmetin-8-C-glucoside)
1918.875C22H22O10445.1143445.11351.80327.08,297.04当药黄素
(Sweitisin)
2019.161C29H32O17651.1605651.15616.76329.07,314.04小麦黄素-7-O-α-L-鼠李糖葡糖苷酸
(Tricin-7-O-α-L-rhamnosyl-glucuronide)
2119.225C29H34O16637.1792637.17693.61329.07小麦黄素-7-O-新橙皮苷
(Tricin-7-O-neohesperidoside)
), ArticleFig(id=1320880491155280418, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=表2, caption=

甘蔗原生态糖成分鉴定

, figureFileSmall=null, figureFileBig=null, tableContent=
峰号保留时间(min)分子式质荷比理论值误差(ppm)碎片离子鉴定结果
11.070C7H6O5169.0177169.0182−2.96127.05,105.02,89.00没食子酸
(Gallic Acid)
21.326C7H12O6191.0582191.055613.61173.05,127.04奎尼酸
(Quinic acid)
32.047C6H8O7191.0214191.019211.52111.01柠檬酸
(Citric acid)
49.308C13H16O8299.0762299.0767−1.67137.024-羟基苯甲酸葡糖苷
(4-Hydroxybenzoic acid glucoside)
510.354C13H16O9315.0728315.07163.81153.02,152.01,109.03,108.02龙胆酸-2/5-O-β-葡糖苷
(Gentisic acid-2/5-O-β-glucoside)
611.565C14H18O9329.0893329.08736.08167.03,152.01香草酸葡糖苷
(Vanillic acid glucoside)
711.572C8H8O4167.0346167.03441.20152.01,108.02香草酸
(Vanillic acid)
812.171C13H16O9315.0728315.07163.81153.02,109.03原儿茶酸-4-β-葡糖苷
(Protocatechuic acid-4-β-glucoside)
913.036C9H10O5197.0459197.0454.57123.01丁香酸
(Syringic acid)
1013.086C7H6O3137.0235137.0239−2.92122.02,104.02p-羟基苯甲酸[25]
(p-hydroxybenzoic acid)
1114.960C7H6O4153.0177153.0188−7.19109.03原儿茶酸(Protocatechuic acid)
1216.998C16H18O9353.0877353.08731.13191.03,179.01,161.00绿原酸
(Chlorogenic Acid)
1316.448C17H20O9367.1033367.10291.09193.05,191.06,173.04,134.043-O-阿魏酰奎宁酸
(3-O-feruloyquinic acid)
1418.019C15H16O11371.0611371.0614−0.81173.01,111.01咖啡酰葡糖二酸(Caffeoylglucaric acid)
1518.276C17H20O9367.1033367.10291.09193.05,191.06,173.05,134.045-O-阿魏酰奎宁酸
(5-O-feruloyquinic acid)
1618.333C26H28O14563.14563.1401−0.18473.11,443.10夏佛塔苷
(Schaftoside)
1718.411C9H8O3163.0411163.03959.81119.05对香豆酸
(p-Coumaric acid)
1818.469C22H22O11461.1119461.10847.59443.10,341.07,311.08香叶木素-8-C-葡糖苷[26]
(Diosmetin-8-C-glucoside)
1918.875C22H22O10445.1143445.11351.80327.08,297.04当药黄素
(Sweitisin)
2019.161C29H32O17651.1605651.15616.76329.07,314.04小麦黄素-7-O-α-L-鼠李糖葡糖苷酸
(Tricin-7-O-α-L-rhamnosyl-glucuronide)
2119.225C29H34O16637.1792637.17693.61329.07小麦黄素-7-O-新橙皮苷
(Tricin-7-O-neohesperidoside)
), ArticleFig(id=1320880491230777891, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Table 3, caption=

Content distribution of four key components in calibration and validation set samples (%)

, figureFileSmall=null, figureFileBig=null, tableContent=
指标含量范围
校正集(N=66)验证集(N=10)
蔗糖73.27~88.1175.14~85.96
还原糖2.82~16.087.53~12.42
总酚酸0.11~0.550.18~0.51
干燥失重0.15~10.180.91~9.79
), ArticleFig(id=1320880491327246884, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=表3, caption=

校正集与验证集样品中4项指标性成分的含量分布(%)

, figureFileSmall=null, figureFileBig=null, tableContent=
指标含量范围
校正集(N=66)验证集(N=10)
蔗糖73.27~88.1175.14~85.96
还原糖2.82~16.087.53~12.42
总酚酸0.11~0.550.18~0.51
干燥失重0.15~10.180.91~9.79
), ArticleFig(id=1320880491394355749, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Table 4, caption=

Effects of different spectra pretreatments, spectral section on the models

, figureFileSmall=null, figureFileBig=null, tableContent=
指标预处理方法波数范围(cm−1)R2calRMSECVRPD
蔗糖FD+SNV6102.6~5447.80.8771.062.70
ECO6102.6~5447.80.7831.362.15
FD+MSC6102.6~5447.80.8161.252.33
FD6102.6~5447.80.7551.442.02
还原糖SD7033.97~6880.3,5343.3~5008.2,4273.58~4264.3580.9001.013.16
SAL7034~6880.3,5343.3~5008.2,4273.58~4258.210.8761.122.84
ECO7034~6880.3,5343.3~5008.20.8251.332.39
FD+SAL7034~6880.3,5343.3~5008.2,4273.58~4258.210.8211.352.36
总酚酸SAL7501.2~6099.5,5450.9~4599.40.9380.02454.04
ECO6102.6~4599.40.9310.0263.81
SD6102.6~4599.40.9350.02513.94
SD+SAL7501.2~4599.40.9310.0263.81
干燥失重MMN9400.9~7498.1,6102.6~4012.30.9810.3427.30
MSC9400.9~7498.1,6102.6~4599.40.9800.3507.15
ECO9400.9~5447.8,4602.5~4012.30.9780.3746.68
SNV9400.9~7498.1,6102.6~4599.40.9800.3497.13
), ArticleFig(id=1320880491482436134, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=表4, caption=

不同预处理方法、波数范围对模型的影响

, figureFileSmall=null, figureFileBig=null, tableContent=
指标预处理方法波数范围(cm−1)R2calRMSECVRPD
蔗糖FD+SNV6102.6~5447.80.8771.062.70
ECO6102.6~5447.80.7831.362.15
FD+MSC6102.6~5447.80.8161.252.33
FD6102.6~5447.80.7551.442.02
还原糖SD7033.97~6880.3,5343.3~5008.2,4273.58~4264.3580.9001.013.16
SAL7034~6880.3,5343.3~5008.2,4273.58~4258.210.8761.122.84
ECO7034~6880.3,5343.3~5008.20.8251.332.39
FD+SAL7034~6880.3,5343.3~5008.2,4273.58~4258.210.8211.352.36
总酚酸SAL7501.2~6099.5,5450.9~4599.40.9380.02454.04
ECO6102.6~4599.40.9310.0263.81
SD6102.6~4599.40.9350.02513.94
SD+SAL7501.2~4599.40.9310.0263.81
干燥失重MMN9400.9~7498.1,6102.6~4012.30.9810.3427.30
MSC9400.9~7498.1,6102.6~4599.40.9800.3507.15
ECO9400.9~5447.8,4602.5~4012.30.9780.3746.68
SNV9400.9~7498.1,6102.6~4599.40.9800.3497.13
), ArticleFig(id=1320880491574710823, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=EN, label=Table 5, caption=

Final predictive model of four indicators

, figureFileSmall=null, figureFileBig=null, tableContent=
指标R2calRMSECVR2testRMSEP
蔗糖0.8681.060.8701.13
还原糖0.9001.010.9450.416
总酚酸0.9380.02450.9550.0307
干燥失重0.9810.3420.9770.463
), ArticleFig(id=1320880491658596904, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320880482347241922, language=CN, label=表5, caption=

4项指标的最终预测模型

, figureFileSmall=null, figureFileBig=null, tableContent=
指标R2calRMSECVR2testRMSEP
蔗糖0.8681.060.8701.13
还原糖0.9001.010.9450.416
总酚酸0.9380.02450.9550.0307
干燥失重0.9810.3420.9770.463
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甘蔗原生态糖功效成分解析及近红外综合评价方法
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王江涛 1 , 桂宇彤 1 , 刘凯昕 1 , 栾国庆 1 , 侯小涛 2 , 侯媛媛 1, * , 白钢 1, 2
食品工业科技 | 现代分析与智能检测 2026,47(18): 295-305
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食品工业科技 |现代分析与智能检测 2026 , 47 (18) : 295 -305
甘蔗原生态糖功效成分解析及近红外综合评价方法
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王江涛1 , 桂宇彤1, 刘凯昕1, 栾国庆1, 侯小涛2, 侯媛媛1, * , 白钢1, 2
作者信息
  • 1南开大学药学院,药物化学生物学全国重点实验室,天津 300353
  • 2广西中医药大学,广西中药药效研究重点实验室,广西南宁 530200
通讯作者:
侯媛媛(1977−),女,博士,教授,研究方向:中药系统生物学与化学生物学,E-mail:。
作者简介:

王江涛(2000−),男,硕士研究生,研究方向:中药质量控制,E-mail:

Research on the Functional Components of Non-centrifugal Cane Sugar of Sugarcane and Comprehensive Evaluation of Its Efficacy Using Near-infrared Spectroscopy
Jiangtao WANG1 , Yutong GUI1, Kaixin LIU1, Guoqing LUAN1, Xiaotao HOU2, Yuanyuan HOU1, * , Gang BAI1, 2
Affiliations
  • 1State Key Laboratory of Medicinal Chemical Biology, School of Pharmacy, Nankai University, Tianjin 300353, China
  • 2Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning 530200, China
出版时间: 2026-09-15 doi: 10.13386/j.issn1002-0306.2025070036
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目的:通过化学物质组学策略对甘蔗原生态糖(non-centrifugal cane sugar of sugarcane,NCS)的功效成分进行系统解析,建立一种适用于原生态糖等级综合评价的近红外快速检测技术。方法:通过超高效液相色谱-四极杆飞行时间质谱(Ultra Performance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry,UPLC/Q-TOF-MS)技术分析鉴定原生态红糖中的潜在功效成分,结合紫外可见分光光度法对其中抗氧化功能的总酚酸含量进行测定。同时依据行业标准QB/T 2343.2-2013和国家标准GB 5009.3-2016,对蔗糖、还原糖、总糖及干燥失重等关键指标进行定量分析,并引入综合评价指数(Fq)的概念,基于各检测指标相对大样本均值的偏离度优化分配各指标的权重系数,实现对甘蔗原生态糖品质的综合评估。结果:从甘蔗原生态糖中共鉴定出21种主要化学成分,其中16种为酚酸类化合物,主要包括柠檬酸、香草酸、丁香酸、p-羟基苯甲酸、绿原酸等。针对76批样品的关键指标进行检测,结合对应的近红外光谱构建了关键指标含量与光谱数据之间的拟合模型,蔗糖、还原糖、总酚酸、干燥失重等4项指标预测模型的R2cal分别为0.867、0.900、0.938、0.981,均大于0.85;其RMSECV分别为1.06、1.01、0.0245、0.342,预测结果显示模型拟合良好。依据Fq值的正态分布情况对样品进行品质划分,实验验证结果表明高品质样品抗氧化能力优于低品质样品,证明了等级评价体系的合理性。结论:该方法在现行的监管体系基础上引入功效成分检测,结合近红外快检技术实现了品质等级的综合评价,为甘蔗原生态糖的快速功效评价提供了参考的方法。

甘蔗原生态糖  /  近红外光谱分析技术  /  综合评价  /  等级鉴定  /  功效因子

Objective: The study aimed to systematically profile the functional components of non-centrifugal cane sugar of sugarcane (NCS) using a chemomics-based strategy, and to establish a near-infrared (NIR) rapid detection technique for comprehensive quality grading of NCS. Methods: The potential functional components in NCS were identified and analyzed by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS). The total phenolic acid content was determined by ultraviolet-visible spectrophotometry. Key quality indicators, including sucrose, reducing sugar, total sugar, and loss on drying, were quantified according to industry and national standards. A comprehensive evaluation index (Fq) was introduced, in which weight coefficients for each indicator were optimally assigned based on their deviation from the mean of a large sample set, enabling integrated quality assessment of NCS. Results: A total of 21 major chemical components were identified in NCS, among which 16 were phenolic acids, primarily including citric acid, vanillic acid, syringic acid, p-hydroxybenzoic acid, and chlorogenic acid. Key indicators of 76 batches of samples were determined, and calibration models between these indicators and corresponding near-infrared spectral data were established. The determination coefficients (R2cal) of the prediction models for sucrose, reducing sugars, total phenolic acids, and loss on drying were 0.867, 0.900, 0.938, and 0.981, respectively, all exceeding 0.85. The root mean square errors of cross-validation (RMSECV) were 1.06, 1.01, 0.0245, and 0.342, respectively, indicating satisfactory model fitting. Quality classification of the samples was performed based on the normal distribution of the Fq value. Experimental validation demonstrated that high-quality samples exhibited superior antioxidant activity compared to low-quality samples, confirming the rationality of the established quality evaluation system. Conclusion: The proposed method incorporates functional component analysis into the current regulatory framework and achieves comprehensive quality evaluation through NIR rapid detection, providing a practical approach for rapid efficacy assessment of NCS.

non-centrifugal cane sugar of sugarcane  /  near-infrared spectroscopy  /  comprehensive evaluation  /  grade assessment  /  functional ingredients
王江涛, 桂宇彤, 刘凯昕, 栾国庆, 侯小涛, 侯媛媛, 白钢. 甘蔗原生态糖功效成分解析及近红外综合评价方法. 食品工业科技, 2026 , 47 (18) : 295 -305 . DOI: 10.13386/j.issn1002-0306.2025070036
Jiangtao WANG, Yutong GUI, Kaixin LIU, Guoqing LUAN, Xiaotao HOU, Yuanyuan HOU, Gang BAI. Research on the Functional Components of Non-centrifugal Cane Sugar of Sugarcane and Comprehensive Evaluation of Its Efficacy Using Near-infrared Spectroscopy[J]. Science and Technology of Food Industry, 2026 , 47 (18) : 295 -305 . DOI: 10.13386/j.issn1002-0306.2025070036
甘蔗原生态糖是通过甘蔗破碎、提汁、澄清等物理处理后,未经化学助剂添加,经浓缩、结晶等工艺制得的非分蜜红糖(non-centrifugal sugar,NCS)。由于未经历离心分蜜工序,相较于白糖,较为完整地保留了甘蔗中的营养成分[1−2]。中医理论认为,红糖性温、味甘,归脾经,具有益气养血、活血化瘀、健脾暖胃及温宫养颜的功效[3−4]。甘蔗原生态糖的成分除了包含蔗糖和还原糖外,还完整保留了甘蔗中的酚酸类化合物、黄酮类物质、矿物质、维生素、蛋白质、微量元素、氨基酸等多种活性成分[5−7]。甘蔗汁中保留的功效成分具有促进生长、细胞保护、抗氧化和抗肿瘤等多种活性,尤其是酚酸类和黄酮类成分在抗氧化方面作用显著[8]。因此,甘蔗原生态糖除了营养功效外,还具有一定药用价值[9−11]。随着消费者对天然健康食品的需求增加,甘蔗原生态糖的市场需求也在增长。原生态糖采用较为原始的生产方法,且不同厂家采用的生产工艺并不统一,存在如产品澄清不彻底、糖汁纯度控制不准确、生产流程机械化不完善等许多问题,从而导致产品品质存在很大差异[12]。另外,王智能等[1]、杨婷等[13]、赵春杰等[14]研究表明,以不同品种的甘蔗作为原料对红糖品质及抗氧化活性也具有显著影响。因此,原生态红糖需要更加全面、系统的评价方法来保证其品质,建立一种简便、快速、综合的质量评价方法来控制原生态糖品质具有重要意义。
目前,甘蔗原生态糖的指标成分定量分析主要依赖高效液相色谱法、气相色谱法[15]、红外光谱法[16−17]、旋光法和比色法[18]等。然而,这些方法普遍存在成本高、效率低、前处理复杂等问题,且检测指标单一,难以全面把控原生态红糖的质量。近红外光谱技术(Near-Infrared Spectroscopy,NIRS)因其测定速度快、样品无损、操作便捷、成本低廉且环保无污染等优势[19−20],作为一种能够实现全过程自动化检测的集成分析技术,近年来在制药、农业、食品以及化工等诸多领域得到了广泛应用[21−22]。本研究利用UPLC/Q-TOF-MS技术对甘蔗原生态糖中的潜在药理活性化学成分进行系统鉴定,并建立相应的分析方法。同时,通过整合蔗糖、还原糖及干燥失重等关键指标的行业标准检测结果,构建基于综合评价指数(Fq)的整体评价体系,并进一步结合近红外快检技术希望实现对原生态糖品质的快速综合评估,为甘蔗原生态糖品质的监管提供新的思路。
红糖产品51批(S1~S9、S11~S52) 市售;原生态红糖产品9批(S10,S53~S60)、安徽3批(S61~S63)、广东产地1批(S64)、云南6批(S68~S73)、海南6批(S65~S67,S74~S76) 广西凤糖有限公司,产品信息见表1;1,1-二苯基-2-三硝基苯肼(DPPH)(货号D807297,纯度≥99.0%)、2,2'-联氮-双-3-乙基苯并噻唑啉-6-磺酸(ABTS)(货号D800764,纯度≥98.0%) 上海麦克林生化科技股份有限公司;甲醇、乙腈 色谱级,天津康科德科技有限公司;其他均采用分析纯试剂。
TENSOR 37型近红外光谱仪 德国Bruker Corporation公司;Master10D型傅里叶变换近红外光谱仪 中国荧飒光学公司;Q-TOFG2-XS液质联用仪 美国Waters Corporation公司;XS105DU微量天平 美国Mettler-Toledo International Inc.公司;Spark多功能酶标仪 瑞士Tecan Group Ltd.公司;Mikro 220R台式高速冷冻离心机 德国Hettich GmbH & Co. KG公司。
取原生态糖样品约1 g,置具塞锥形瓶中,加水20 mL溶解,离心(转速10000 r/min,离心半径10 cm)10 min,取上清即得。
Q-TOFG2-XS液质联用仪,ACQUITY UPLC HSS(2.1×100 nm,1.7 µm)色谱柱,柱温30 ℃,流动相A为0.1%甲酸-水,流动相B为乙腈,梯度洗脱(0~3 min,0% B;3~15 min,0%~10% B;15~25 min,10%~80% B;25~26 min,80%~0% B:26~30 min,0% B),流速0.3 mL/min,进样量2 μL。
采用负离子扫描模式,扫描范围m/z 100~1500,离子源为电喷雾离子源(ESI),离子源温度100 ℃,电压为3.0 kV;气帘气为50 L/h,脱溶剂气温度250 ℃,脱溶剂气流速600 L/h,雾化器压力为45 psig,源偏移电压为80 V,锥孔电压为30 V,毛细管电压为2.5 kV,补偿电压为80 V。
对所获得的质谱数据根据GNPS数据库匹配,结合化合物精确分子量、碎片离子峰、色谱保留时间等进行解析,鉴定原生态糖的非糖成分。
为了进一步确证原生态糖中酚酸类成分,参照潘振辉等[23]方法,测定原生态糖与酚酸对照品(柠檬酸、香草酸、丁香酸、p-羟基苯甲酸、绿原酸)谱图信息。色谱条件:LC-20AT型高效液相色谱仪,色谱柱为ZORBAX SB-C18(4.6 mm×250 mm,5 μm),进样量为10 μL,柱温为35 ℃,流动相A为0.4%甲酸水,流动相B为乙腈,流速为1.0 mL/min,洗脱程序:0~5 min,8% B;5~10 min,8%~15% B;10~25 min,15%~35% B;25~30 min,35%~50% B;30~31 min,50%~100% B;31~36 min,100%~5% B。检测波长设定为325 nm。所有对照品用75%甲醇溶解,配制成0.01 mg/mL的对照品溶液。样品配制成1.0 mg/mL的供试品溶液,经0.22 μm滤膜过滤后分析。
蔗糖、还原糖含量按照轻工行业标准QB/T 2343.2-2013《赤砂糖试验方法》进行测定;干燥失重按照国家标准GB 5009.3-2016《食品安全国家标准 食品中水分的测定》第二法减压干燥法进行测定;甘蔗原生态糖中的总酚酸含量参照文献[24]的方法,采用紫外可见分光光度法测定。取适量样品,依次加入无水乙醇、SDS溶液及三氯化铁-铁氰化钾显色液,避光反应后,加入稀盐酸继续避光静置显色,于720 nm波长处测定吸光度。以没食子酸为对照品,结果以没食子酸当量计。
甘蔗原生态糖样品光谱的采集:Master10D傅里叶变换近红外光谱仪开机稳定30 min后采集背景光谱,背景采集完毕后,将约1 g的供试品均匀置于直径2 cm的石英杯中,铺平,扫描获取近红外光谱,扫描范围11500~4000 cm−1。
对照品特征光谱的采集:取丁香酸与香草酸对照品分别配制成10 mg/mL的氘代DMSO溶液。使用布鲁克Tensor37型号傅里叶变换近红外光谱仪,采用2 mm光程的玻璃杯,应用透射检测元件、铟镓砷检测器对氘代DMSO溶解的对照品溶液进行光谱采集,用于酚酸类成分特征波段的筛查。
分别采用一阶导数(First derivative,FD)、二阶导数(Second derivative,SD)、消除常数偏移量(Elimination of constant offset,ECO)、多元散射校正(Multiple scattering correction,MSC)、最大最小归一化(Minimum maximum normalization,MMN)、正态变量校正(Standard normal variate,SNV)、减去一条直线(Straight line subtraction, SAL)等方法,及FD+MSC和FD+SNV等组合方法对光谱进行预处理,结合波数范围选取最佳预处理方法。
利用NIRS技术以偏最小二乘法(PLS)构建甘蔗原生态糖的质控成分与光谱之间的拟合模型,并进行成分定量。选取最佳主因子数,对光谱的不同预处理方法及建模波段进行筛选,以校正集决定系数(R2cal)、验证集决定系数(R2pre)、校正均方根偏差(RMSEC)、预测均方根偏差(RMSEP)、交叉验证均方根误差(RMSECV)、性能偏差比(RPD)、预测相对偏差(RSEP)为指标评价模型的稳健性[25−28]。其中,R2cal和R2pre用于表示样品实测值与模型预测值之间的拟合程度,数值越高表明模型的准确性越强;而RMSECV、RMSEC、RMSEP以及RSEP则常被用来评估模型的预测误差,数值越低说明模型的稳定性和预测性能越好。
取2 mL浓度为0.05 mg/mL的甘蔗原生态糖水溶液,加入0.05 mg/mL DPPH乙醇溶液2 mL充分混匀,室温避光静置30 min后于517 nm处测定吸光度。以维生素C作为阳性对照,每组实验重复3次。DPPH自由基清除率计算公式:
$ \mathrm{DPPH自由基清除率(\text{%})=[1-(A}_{ \mathrm{样品}} \mathrm{-A}_{ \mathrm{对照}} \mathrm{)/A}_{ \mathrm{空白}} \mathrm{]\times 100} $
式中:A样品为实验组吸光度;A对照为用蒸馏水代替DPPH溶液,按上述方法测定的吸光度;A空白为用无水乙醇代替甘蔗原生态糖溶液,按上述方法测定的吸光度。
配制7 mmol /L ABTS水溶液和4.9 mmol/L过硫酸钾溶液各10 mL,等体积混合,于室温条件下暗处反应16 h,用磷酸盐缓冲液稀释至A734 nm在0.7左右。取0.5 mL浓度为0.05 mg/mL的甘蔗原生态糖水溶液,加入3.0 mL制得的ABTS稀释溶液,混匀,静置6 min,在734 nm处测定吸光度,以维生素C作为阳性对照,每组实验重复3次。ABTS+自由基清除率计算公式:
$ \mathrm{ABTS}^{ \mathrm+} \mathrm{自由基清除率(\text{%})=(1-A}_{ \mathrm{1}} \mathrm{/A}_{ \mathrm{0}} \mathrm{)\times 100} $
式中:A1为实验组吸光度;A0为用蒸馏水代替甘蔗原生态糖的水溶液,按上述方法测定的吸光度。
所有实验均独立重复3次,数据采用GraphPad Prism 9进行统计学处理,选择均值±标准差(Mean±SD)来表示。使用t检验对单次比较数据进行统计,P<0.05认为具有显著性差异。
采用UPLC/Q-TOF-MS对甘蔗原生态糖样品中的关键化学成分进行鉴定分析。结果如表2所示,共鉴定出21种化学成分,其主要成分为有机酸、酚苷、黄酮及其衍生物等化合物。其中酚酸类成分最为丰富,包括没食子酸、绿原酸、柠檬酸、奎尼酸、3/5-O-阿魏酰奎宁酸、对香豆酸、4-羟基苯甲酸葡糖苷、原儿茶酸、龙胆酸-2/5-O-β-葡糖苷、p-羟基苯甲酸、香草酸、香草酸葡糖苷、原儿茶酸-4-β-葡糖苷、丁香酸、咖啡酰葡糖二酸等。
通过对比原生态糖中酚酸类成分与柠檬酸、香草酸、丁香酸、p-羟基苯甲酸、绿原酸的保留时间(图1),进一步确证了原生态糖中的主要酚酸类成分。
运用Matlab的randperm函数,从甘蔗原生态糖样品中随机选取66个样本作为校正集、10个样本作为验证集。依据“1.2.3.1”的方法,对样品中的4项指标进行评估。结果见表3,校正集中各质控成分的含量范围均大于验证集,说明校正集与验证集的分布合理且具有代表性。
为提升模型预测的准确性和专属性,本研究通过对比原生态糖样品与蔗糖、香草酸及丁香酸标准品光谱中的特征吸收峰,筛选出建模最优波段。如图2A所示,原生态糖与蔗糖的近红外光谱对比显示,二者在4470~4820 cm−1、4900~5000 cm−1、5364~5940 cm−1以及6824~7326 cm−1范围内均表现出相似的吸收峰。此外,通过对比原生态糖与丁香酸及香草酸的光谱(图2B与图2C)可以发现,在4306~5074 cm−1、5242~6030 cm−1以及6824~7326 cm−1范围内,三者呈现出类似的吸收特征。对上述波数范围分析可知,4306~5074 cm−1为芳环C-H伸缩振动,弯曲振动和变形振动的组合频;5242~6030 cm−1主要为O-H伸缩振动,弯曲振动的组合频;6824~7326 cm−1为O-H伸缩振动基频的一级倍频谱带[29]。据Cayuela等[30]报道水分和糖的吸收峰分别在10416 cm−1及6877 cm−1处,而程琳等[31]的研究发现,总酚酸含量测定模型的光谱波段为9403.9~7498.4、6102.1~5446.4和4605.5~4242.9 cm−1,可知上述范围对于定量模型的建立具备一定的参考价值。
主因子数的合理选择对提升模型性能和预测精度具有决定性作用。若因子数量不足,可能造成关键信息的遗漏,从而引发“欠拟合”问题,削弱模型的泛化能力;反之,若因子数量过多,则可能引入噪声,导致“过拟合”现象,进而增大预测误差[32]。本研究采用交叉验证法以R2cal、RMSECV、RPD作为核心指标对其进行优化。
甘蔗原生态糖样品的颗粒度、颜色以及仪器响应等因素会对光谱的采集产生影响,为排除噪声干扰,需对原始光谱信号进行预处理。结果如表4及图3所示,本研究选取R2cal大于0.85且RMSECV最小的模型的预处理方法为最佳预处理方法,分别为FD+SNV、SD、SAL、MMN。
按照“1.2.3.1”的方法对76批原生态糖样品的4项指标进行测定,依据GB/T 35885-2018《红糖》中理化性质规定,其中不合格品共有10个,占比为13.15%(见图4),可能因其购置后放置时间较长,红糖的吸水性导致干燥失重超标,或者在生产过程中糖分浓缩不足等原因导致。选取上述最佳光谱预处理方法和波数区间建立NIRS定量分析模型(见图5),蔗糖、还原糖、总酚酸、干燥失重等4项指标预测模型的R2cal分别为0.867、0.900、0.938、0.981,均大于0.85;其RMSECV分别为1.06、1.01、0.0245、0.342(见表5),均为各自模型随变量变化的最小值。利用上述模型对10批验证集样品进行预测,结果显示其4项指标的预测值与实测值之间的R2test分别为0.870、0.945、0.955和0.977(见表5),表明模型预测结果具有较好的准确性。
考虑到检测指标的量纲以及正负差异与品质内涵相关性的不同,将正相关离散度(Kin)按照式(1)计算,如原生态糖中的总酚酸、蔗糖、还原糖;负相关离散度($\text{K}_{\text{in}}^{\text{'}} $)依据式(2)进行处理,如干燥失重。
$ {\text{K}}_{\text{in}}=\frac{{\text{X}}_{\text{in}}}{\overline{{\text{X}}_{\text{i}}}} $
$ \text{K}_{\text{in}}^{\text{'}}=\frac{\overline{{\text{X}}_{\text{i}}}}{{\text{X}}_{\text{in}}} $
式中,Xin指第i个指标的第n个样品含量测定结果;$ \overline{{\mathrm{X}}_{\mathrm{i}}} $指第i个指标含量的平均值;Kin和$\text{K}_{\text{in}}^{\text{'}} $分别表示第n个样品中第i个指标的含量与其均值的比例。
采用响应面法对甘蔗原生态糖中4个检测指标的权重系数W1~W4(蔗糖、还原糖、总酚酸、干燥失重)进行优化。基于Design-Expert 10.0软件进行试验设计,以W1~W4为自变量,Fq不合格率(不合格品Fq值分布占比,以国家标准GB/T 35885-2018为准,低于二级样品为不合格品)为响应值,通过响应面分析确定最佳权重分配。最终得到W1~W4的优化值分别为0.21、0.202、0.511、0.15。其中两因素交互作用对Fq不合格率的响应面图见图6A(以蔗糖、总酚酸为例),从而确定Fq值的计算公式:
$ \rm{\text{F}}_{\text{q}}=\left(0.21\frac{x_1}{\overline{x_1}}+0.202\frac{x_2}{\overline{x_2}}+0.511\frac{x_3}{\overline{x_3}}+0.15\frac{\overline{x_4}}{x_4}\right)\times100 $
根据上述公式对每个样品得分进行计算,结果显示不合格样本的数量低于13.15%,与实测结果基本相近。
将76批样本的上述4项检测指标输入Fq公式,得到样本Fq值的分布图(图6B),呈现正态分布特征(sig=0.23>0.05)。依据Fq值正态分布特性,将待测样品均分为四个等级:一等:101及以上;二等:94(含)至101(不含)之间;三等:80(含)至94(不含)之间;四等:80以下。基于NIRS光谱的OPLS-DA聚类分析显示,各等级的样品分布均存在交集,结果证明在小样本量下的纯光谱聚类不能实现样品等级区分(图6C)。
随机选取原生态糖样品中一等、二等及四等的样品各两组,对其抗氧化活性进行考察。结果如图7所示,相对于蔗糖样品,原生态糖表现出良好的抗氧化活性,且一等或二等样品的抗氧化能力明显高于四等品,而蔗糖则未能体现明显的抗氧化活性,证明该评价体系对原生态糖品质的综合评价具有较好的可行性。
利用SIMCA软件对不同来源的样品进行判别分析。将不同来源的原生态红糖样品的NIRS数据导出,整合成光谱矩阵,选取4000~8000 cm−1之间的吸光度作为因变量,产地作为自变量,应用OPLS-DA分析方法对样品进行聚类分析。结果表明(图8),尽管所选样品均为桂产原生态糖,但在自然归类分析中并未完全聚为一类(红圈内样品),且同等级原生态糖样品也并未完全归为一类,说明其产品性质可能存在显著差异,这种差异很可能源于原料品种、生产工艺或关键操作参数(如澄清阶段的pH与温度、蒸发温度及熟化时间等)的不同。具体而言,较高的澄清温度会促进美拉德反应和热降解反应,导致多酚类物质减少;相反,若采用较为温和的加热条件,则有助于保留更多的天然酚酸与矿物质成分。上述工艺差异可进一步体现在蔗糖含量、水分、多酚含量等多个品质指标上[33]。因此上述结果进一步说明本研究评价体系的科学性。
本研究首先采用UPLC/Q-TOF-MS技术从甘蔗原生态糖中鉴定出21种化学成分,其中16种为酚酸类化合物,提示酚酸类物质是其主要功效物质基础。在此基础上,建立了蔗糖、还原糖、总酚酸及干燥失重4项指标的近红外定量预测模型,各模型决定系数(R2cal)均大于0.85,RMSECV较小,表明模型预测性能良好。进一步引入样本相对离散度与响应面优化权重,构建了综合品质评价指数Fq,并将76批样品按Fq值的正态分布特征划分为四个等级。抗氧化活性验证结果显示,高等级样品的抗氧化能力显著优于低等级样品,初步证明了该等级评价体系的合理性。
现有关于红糖品质的研究多侧重于单一或少数指标。杨婷等[13]通过DPPH与ABTS法测定了红糖的总多酚与黄酮含量及抗氧化能力,邢美婵等[34]基于角度转换算法建立了蔗糖、果糖和葡萄糖的NIR定量模型,但这些方法均未能实现多指标的综合评价。本研究在常规理化指标基础上,新增了总酚酸这一关键功效成分的检测,并将其纳入综合评价体系。与上述研究相比,本文所构建的方法不仅保留了糖分等基础质量指标的监控,更通过抗氧化物质基础评价强化了“功能性”内涵,使得品质评价更为系统。
本研究借鉴了质量标志物(Q-Marker)与质量综合评价指数(Fq)的理念[35−38],将多指标离散度整合为单一指数,并利用响应面法优化权重分配,使评价结果能够客观反映各指标对整体品质的贡献差异。优化后权重显示,总酚酸的权重最高(0.511),蔗糖、还原糖与干燥失重的权重依次降低,这与酚酸类化合物作为原生态糖主要抗氧化功效物质的鉴定结果相吻合,从统计分析角度佐证了化学物质组学发现的合理性。Fq值的正态分布特性也为等级划分提供了统计学依据,避免了主观阈值的随意性。
近红外光谱技术的引入使该评价方法具备了快速、无损、可现场应用的优势,4项指标均可通过一次光谱采集实现同步预测,结合Fq公式即可直接输出品质等级,为大批量原生态糖的在线质控提供了可能。然而,本研究仍存在一些局限:功效成分鉴定主要聚焦于酚酸,对黄酮及其他类成分的覆盖尚不全面;抗氧化活性验证仅基于体外化学实验,与体内补血养颜等传统功效的关联仍需深入研究;NIR模型与权重优化均基于现有76批样品,样本来源和批次数目可能限制模型的普适性和稳健性。此外,Fq指数中各项指标的权重分配虽经响应面优化,但其是否普遍适用于不同品种、产地或工艺的原生态糖,还需多中心、大样本的验证。
综上所述,本研究将功效成分评价与近红外快速检测技术相融合,首次建立了一种兼具化学物质基础和统计优化特质的原生态糖品质综合评价方法,为功能性红糖的科学监管和产业升级提供了新的技术支撑。未来工作将着重于扩大样本多样性、纳入更多功效指标(如黄酮、多糖及体内活性评价),并探索模型传递策略以提升方法的适用性。
本研究基于化学物质组学明确了甘蔗原生态糖中酚酸类化合物为其主要功效物质基础;结合近红外光谱技术,成功建立了蔗糖、还原糖、总酚酸和干燥失重的快速预测模型,并在此基础上,通过相对离散度处理与响应面权重优化,构建了综合品质评价指数Fq。应用该指数对76批原生态糖进行等级划分,高品质样品的抗氧化活性更强,验证了评价体系的有效性。该方法在现有标准基础上引入功效成分检测,利用近红外快检技术实现了原生态糖品质的综合、快速、客观评价,为功能性红糖的质量控制与真伪优劣判别提供了科学手段。当前方法对黄酮等其他功效成分的考量有限,且适用性尚需在多产地、大样本中进一步验证;后续可围绕更全面的功效成分谱、体内活性评价及模型转移技术展开深入研究,以推动原生态糖品质评价体系的持续完善。

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2026年第47卷第18期
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doi: 10.13386/j.issn1002-0306.2025070036
  • 接收时间:2025-07-04
  • 首发时间:2026-10-24
  • 出版时间:2026-09-15
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    1南开大学药学院,药物化学生物学全国重点实验室,天津 300353
    2广西中医药大学,广西中药药效研究重点实验室,广西南宁 530200

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侯媛媛(1977−),女,博士,教授,研究方向:中药系统生物学与化学生物学,E-mail:。
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2种不同金属材料的力学参数
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genus
种数
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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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