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Article(id=1216517520161427627, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1216517514570417012, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250312005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1741708800000, receivedDateStr=2025-03-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1767969978609, onlineDateStr=2026-01-09, pubDate=1755187200000, pubDateStr=2025-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1767969978609, onlineIssueDateStr=2026-01-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1767969978609, creator=13701087609, updateTime=1767969978609, updator=13701087609, issue=Issue{id=1216517514570417012, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='15', 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=null, createTime=1767969977276, creator=13701087609, updateTime=1768211590858, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1217530915467743720, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1216517514570417012, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1217530915467743721, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1216517514570417012, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=51, endPage=56, ext={EN=ArticleExt(id=1216517521168060643, articleId=1216517520161427627, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Rapid detection of core quality indicators in cheese and cheese products by near-infrared spectroscopy, columnId=1216517518873773013, journalTitle=Journal of Food Safety & Quality, columnName=Special Topic: Processing and Quality Safety of Animal Food, runingTitle=null, highlight=null, articleAbstract=

Objective To explore the application areas of rapid detection technology for core quality indicators in cheese and its products using near-infrared spectroscopy (NIRS), and validating the feasibility of NIRS for rapid testing of cheese quality parameters. Methods Four types of common processed cheese products (cheese sticks, cheese slices, cream cheese, Mozzarella) were selected for experimentation and divided into modeling and validation sets; modeling experiments were conducted using samples from the modeling set on a single-brand instrument; synchronous national standard detection and multi-instrument NIRS detection comparative analysis were performed using instruments from 4 different manufacturers on the validation sets. Results For modeling, multidimensional prediction models for core indicators (protein, fat, moisture, pH) were successfully constructed by comparing the modeling mechanisms of algorithms like partial least squares regression (PLSR), with the coefficient of determination exceeding 0.85. For validation, the average deviations between the NIRS detection results (protein, fat, moisture) obtained using 4 kinds of NIRS spectrometers on different cheese products and those determined by national standard methods met equivalence requirements except for the protein deviation (>10%) of Mozzarella on instrument No.1; precision also met the requirements of national standard methods. Conclusion The application of NIRS spectroscopy equipment for rapid detection of core quality indicators in cheese and its products is feasible; NIRS spectroscopy technology can serve as an effective supplement to traditional chemical detection methods for rapid analysis of cheese composition, offering greater potential for high-efficiency, low-cost, and environmentally friendly testing.

, correspAuthors=Li-Hui ZHI, 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=Mei LI, Li-Hui ZHI), CN=ArticleExt(id=1216517527220441548, articleId=1216517520161427627, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=近红外光谱技术快速检测奶酪及其制品的核心质量指标, columnId=1216517519259652225, journalTitle=食品安全质量检测学报, columnName=专题：动物性食品加工与质量安全, runingTitle=null, highlight=null, articleAbstract=

目的 探索使用近红外光谱(near-infrared spectroscopy, NIRS)技术快速检测奶酪及其制品核心质量指标的快速检测技术的应用方向, 验证NIRS技术对奶酪品质指标实现快速检测的可行性。方法 通过实验选择市场上常见的奶酪棒、芝士片、奶油芝士、马苏里拉等4类再制奶酪产品, 分建模组和验证组, 选择一个品牌的设备使用建模组样品进行建模实验; 使用4个不同厂家近红外同步开展国家标准检测与多设备近红外检测对比分析。结果 在建模层面, 通过对比偏最小二乘回归(partial least squares regression, PLSR)等算法的建模机制, 成功构建蛋白质、脂肪、水分及pH核心指标的多维预测模型, 大部分决定系数RSQ>0.85。在验证层面, 不同奶酪产品分别使用4类NIRS研究仪器的蛋白质、脂肪、水分项目检测结果与国家标准方法测定的奶酪营养成分的平均偏差除1号设备的马苏里拉的蛋白质偏差超过10%外, 其他营养成分国标等同性实验符合要求, 且精密度符合国家标准方法要求。结论 基于当前4种设备的验证, 使用NIRS设备快速检测奶酪及其制品核心质量指标的快速检测技术的应用可行, NIRS技术在奶酪组成成分的快速检测方法可作为传统化学检测方法提供有效的补充, 为高效率、低成本、环保的检测提供更多的可能性。

, correspAuthors=智丽慧, authorNote=null, correspAuthorsNote=
*智丽慧(1988—), 女, 硕士, 中级工程师, 主要研究方向为检验技术。E-mail:
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李梅(1976—), 女, 硕士, 高级工程师, 主要研究方向为质量管理和检验分析。E-mail:

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李梅(1976—), 女, 硕士, 高级工程师, 主要研究方向为质量管理和检验分析。E-mail:

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Talanta, 2021, 234: 122623., articleTitle=Comparative study of PLS and ANN in cheese composition modeling, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1217127893256294732, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, xref=null, ext=[AuthorCompanyExt(id=1217127893264683341, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, companyId=1217127893256294732, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Shanghai Milkground Food Technology Co., Ltd., Shanghai 201506, China), AuthorCompanyExt(id=1217127893273071950, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, companyId=1217127893256294732, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=上海妙可蓝多食品科技股份有限公司, 上海 201506)])], figs=[ArticleFig(id=1217127895336669631, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, language=EN, label=Fig.1, caption=Near-infrared spectrum of cheese, figureFileSmall=cb3mwixAewT21j7AjNxl1A==, figureFileBig=t5bHLFTVSg5MK9ePJ+1iTQ==, tableContent=null), ArticleFig(id=1217127895416361412, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, language=CN, label=图1, caption=奶酪近红外光谱图

注: A. 马苏里拉近红外光谱图; B. 奶酪棒近红外光谱图; C. 奶酪片近红外光谱图; D. 奶油芝士近红外光谱图。

, figureFileSmall=cb3mwixAewT21j7AjNxl1A==, figureFileBig=t5bHLFTVSg5MK9ePJ+1iTQ==, tableContent=null), ArticleFig(id=1217127895542190540, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, language=EN, label=Table 1, caption=

Evaluation of initial model parameters for detection

, figureFileSmall=null, figureFileBig=null, tableContent=
类型 项目 马苏里拉 奶酪棒 奶油芝士 芝士片
RSQ 干物质 0.845 0.958 0.676 0.988
脂肪 0.964 0.992 0.893 0.984
蛋白质 0.980 0.990 0.730 0.999
pH 0.916 0.916 0.908 0.504
RSQV 干物质 0.825 0.937 0.640 0.982
脂肪 0.955 0.990 0.838 0.973
蛋白质 0.975 0.984 0.645 0.997
pH 0.912 0.766 0.869 0.379
SEC 干物质 0.489 0.366 0.245 0.275
脂肪 0.433 0.205 0.191 0.349
蛋白质 0.340 0.117 0.649 0.127
pH 0.038 0.026 0.030 0.054
SEP 干物质 0.507 0.434 0.261 0.327
脂肪 0.471 0.223 0.229 0.420
蛋白质 0.380 0.143 0.676 0.166
pH 0.029 0.029 0.035 0.060
), ArticleFig(id=1217127895647048145, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, language=CN, label=表1, caption=

检测初模型参数评价

, figureFileSmall=null, figureFileBig=null, tableContent=
类型 项目 马苏里拉 奶酪棒 奶油芝士 芝士片
RSQ 干物质 0.845 0.958 0.676 0.988
脂肪 0.964 0.992 0.893 0.984
蛋白质 0.980 0.990 0.730 0.999
pH 0.916 0.916 0.908 0.504
RSQV 干物质 0.825 0.937 0.640 0.982
脂肪 0.955 0.990 0.838 0.973
蛋白质 0.975 0.984 0.645 0.997
pH 0.912 0.766 0.869 0.379
SEC 干物质 0.489 0.366 0.245 0.275
脂肪 0.433 0.205 0.191 0.349
蛋白质 0.340 0.117 0.649 0.127
pH 0.038 0.026 0.030 0.054
SEP 干物质 0.507 0.434 0.261 0.327
脂肪 0.471 0.223 0.229 0.420
蛋白质 0.380 0.143 0.676 0.166
pH 0.029 0.029 0.035 0.060
), ArticleFig(id=1217127895751905752, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, language=EN, label=Table 2, caption=

Comparative data of instrument and national standard test results (%)

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 样品 1号 2号 3号 4号
蛋白质 奶油芝士 7.30 1.80 2.10 3.60
芝士片 1.30 0.90 1.30 1.60
马苏里拉 14.80 2.70 2.00 2.20
奶酪棒 4.10 3.20 2.40 4.60
平均值 6.90 2.20 2.00 3.00
脂肪 奶油芝士 2.70 2.29 2.79 2.13
芝士片 1.50 1.16 1.14 1.50
马苏里拉 2.70 3.76 1.42 0.95
奶酪棒 4.60 2.47 1.86 4.39
平均值 2.90 2.40 1.80 2.20
干物质 奶油芝士 1.30 0.95 1.51 2.04
芝士片 1.80 2.34 1.89 1.66
马苏里拉 2.00 1.35 1.96 1.26
平均值 2.00 1.80 1.80 1.80
), ArticleFig(id=1217127895894512096, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, language=CN, label=表2, caption=

仪器和国标检测结果对比数据(%)

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 样品 1号 2号 3号 4号
蛋白质 奶油芝士 7.30 1.80 2.10 3.60
芝士片 1.30 0.90 1.30 1.60
马苏里拉 14.80 2.70 2.00 2.20
奶酪棒 4.10 3.20 2.40 4.60
平均值 6.90 2.20 2.00 3.00
脂肪 奶油芝士 2.70 2.29 2.79 2.13
芝士片 1.50 1.16 1.14 1.50
马苏里拉 2.70 3.76 1.42 0.95
奶酪棒 4.60 2.47 1.86 4.39
平均值 2.90 2.40 1.80 2.20
干物质 奶油芝士 1.30 0.95 1.51 2.04
芝士片 1.80 2.34 1.89 1.66
马苏里拉 2.00 1.35 1.96 1.26
平均值 2.00 1.80 1.80 1.80
), ArticleFig(id=1217127896074867175, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, language=EN, label=Table 3, caption=

Experimental data of repeatability (%)

, figureFileSmall=null, figureFileBig=null, tableContent=
检测项目 样品名称 1号 2号 3号 4号
蛋白质 奶油芝士 1.88 0.37 0.53 1.49
芝士片 0.14 0.68 0.02 0.33
马苏里拉 0.80 0.13 0.10 1.00
奶酪棒 0.61 0.13 0.49 0.75
脂肪 奶油芝士 1.13 0.34 0.21 0.42
芝士片 0.05 0.07 0.42 0.27
马苏里拉 0.31 0.11 0.05 0.08
奶酪棒 0.31 0.11 0.65 4.15
干物质 奶油芝士 0.30 0.17 0.28 0.37
芝士片 0.03 0.29 0.57 0.43
马苏里拉 0.07 0.05 0.28 0.15
奶酪棒 0.11 0.04 0.13 0.09
), ArticleFig(id=1217127896158753262, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1216517520161427627, language=CN, label=表3, caption=

重复性实验数据(%)

, figureFileSmall=null, figureFileBig=null, tableContent=
检测项目 样品名称 1号 2号 3号 4号
蛋白质 奶油芝士 1.88 0.37 0.53 1.49
芝士片 0.14 0.68 0.02 0.33
马苏里拉 0.80 0.13 0.10 1.00
奶酪棒 0.61 0.13 0.49 0.75
脂肪 奶油芝士 1.13 0.34 0.21 0.42
芝士片 0.05 0.07 0.42 0.27
马苏里拉 0.31 0.11 0.05 0.08
奶酪棒 0.31 0.11 0.65 4.15
干物质 奶油芝士 0.30 0.17 0.28 0.37
芝士片 0.03 0.29 0.57 0.43
马苏里拉 0.07 0.05 0.28 0.15
奶酪棒 0.11 0.04 0.13 0.09
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近红外光谱技术快速检测奶酪及其制品的核心质量指标
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李梅 , 智丽慧 *
食品安全质量检测学报 | 专题：动物性食品加工与质量安全 2025,16(15): 51-56
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食品安全质量检测学报 | 专题：动物性食品加工与质量安全 2025, 16(15): 51-56
近红外光谱技术快速检测奶酪及其制品的核心质量指标
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李梅 , 智丽慧*
作者信息
  • 上海妙可蓝多食品科技股份有限公司, 上海 201506

    • 李梅(1976—), 女, 硕士, 高级工程师, 主要研究方向为质量管理和检验分析。E-mail:

通讯作者:

*智丽慧(1988—), 女, 硕士, 中级工程师, 主要研究方向为检验技术。E-mail:
Rapid detection of core quality indicators in cheese and cheese products by near-infrared spectroscopy
Mei LI , Li-Hui ZHI*
Affiliations
  • Shanghai Milkground Food Technology Co., Ltd., Shanghai 201506, China
出版时间: 2025-08-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250312005
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摘要
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目的 探索使用近红外光谱(near-infrared spectroscopy, NIRS)技术快速检测奶酪及其制品核心质量指标的快速检测技术的应用方向, 验证NIRS技术对奶酪品质指标实现快速检测的可行性。方法 通过实验选择市场上常见的奶酪棒、芝士片、奶油芝士、马苏里拉等4类再制奶酪产品, 分建模组和验证组, 选择一个品牌的设备使用建模组样品进行建模实验; 使用4个不同厂家近红外同步开展国家标准检测与多设备近红外检测对比分析。结果 在建模层面, 通过对比偏最小二乘回归(partial least squares regression, PLSR)等算法的建模机制, 成功构建蛋白质、脂肪、水分及pH核心指标的多维预测模型, 大部分决定系数RSQ>0.85。在验证层面, 不同奶酪产品分别使用4类NIRS研究仪器的蛋白质、脂肪、水分项目检测结果与国家标准方法测定的奶酪营养成分的平均偏差除1号设备的马苏里拉的蛋白质偏差超过10%外, 其他营养成分国标等同性实验符合要求, 且精密度符合国家标准方法要求。结论 基于当前4种设备的验证, 使用NIRS设备快速检测奶酪及其制品核心质量指标的快速检测技术的应用可行, NIRS技术在奶酪组成成分的快速检测方法可作为传统化学检测方法提供有效的补充, 为高效率、低成本、环保的检测提供更多的可能性。

奶酪  /  蛋白质  /  脂肪  /  水分  /  近红外光谱技术  /  快速检测

Objective To explore the application areas of rapid detection technology for core quality indicators in cheese and its products using near-infrared spectroscopy (NIRS), and validating the feasibility of NIRS for rapid testing of cheese quality parameters. Methods Four types of common processed cheese products (cheese sticks, cheese slices, cream cheese, Mozzarella) were selected for experimentation and divided into modeling and validation sets; modeling experiments were conducted using samples from the modeling set on a single-brand instrument; synchronous national standard detection and multi-instrument NIRS detection comparative analysis were performed using instruments from 4 different manufacturers on the validation sets. Results For modeling, multidimensional prediction models for core indicators (protein, fat, moisture, pH) were successfully constructed by comparing the modeling mechanisms of algorithms like partial least squares regression (PLSR), with the coefficient of determination exceeding 0.85. For validation, the average deviations between the NIRS detection results (protein, fat, moisture) obtained using 4 kinds of NIRS spectrometers on different cheese products and those determined by national standard methods met equivalence requirements except for the protein deviation (>10%) of Mozzarella on instrument No.1; precision also met the requirements of national standard methods. Conclusion The application of NIRS spectroscopy equipment for rapid detection of core quality indicators in cheese and its products is feasible; NIRS spectroscopy technology can serve as an effective supplement to traditional chemical detection methods for rapid analysis of cheese composition, offering greater potential for high-efficiency, low-cost, and environmentally friendly testing.

cheese  /  protein  /  fat  /  moisture  /  near-infrared spectroscopy technology  /  rapid detection
李梅, 智丽慧. 近红外光谱技术快速检测奶酪及其制品的核心质量指标. 食品安全质量检测学报, 2025 , 16 (15) : 51 -56 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250312005
Mei LI, Li-Hui ZHI. Rapid detection of core quality indicators in cheese and cheese products by near-infrared spectroscopy[J]. Journal of Food Safety & Quality, 2025 , 16 (15) : 51 -56 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250312005
正文
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0 引言
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奶酪又称芝士[1], 是一种乳或乳制品中的蛋白质在凝乳酶或其他适当的凝乳剂的作用下[2], 凝固或部分凝固后(或直接使用凝乳后的凝乳块为原料), 添加或不添加发酵菌种、食用盐、食品添加剂、营养强化剂, 排出或不排出乳清[3], 经发酵或不发酵等工序制得的固态或半固态产品。因奶酪中含有丰富的营养物质, 被誉为“奶黄金”[4]。
在新消费时代[5], 随着国民对营养健康和美味享受的需求[6], 奶酪及其制品在市场上表现出了快速的增长趋势, 销售额保持年均20%以上的高速增长, 成为乳业中增长最快的细分赛道[7]。很多企业均在加大投资, 建设相应的生产工厂, 而产品质量的控制则成为企业生存和发展的必要保障。
奶酪作为高附加值乳制品, 其质量评价依赖于脂肪、蛋白质等核心成分的精准检测[8]。传统化学检测方法存在耗时长、成本高、废弃物多等问题, 难以满足快速增长的奶酪产业需求[8]。近红外光谱技术因其快速、无损的优势[9], 已在液态乳制品检测中广泛应用, 但其在固态/半固态奶酪中的应用仍面临以下挑战: (1)固态基质中光散射效应显著, 导致光谱信号稳定性差[10]; (2)奶酪成分复杂(如脂肪-蛋白质互作、水分分布不均)对模型泛化能力提出更高要求[11]; (3)现有研究多聚焦单一设备或单一品类, 缺乏跨仪器、跨品类的系统性对比分析[12]。
针对上述问题, 本研究提出基于多设备协同建模的近红外光谱技术奶酪检测创新框架: 首次系统对比4种主流近红外光谱设备(覆盖400~2600 nm宽波段)在再制奶酪检测中的适用性, 揭示不同波长范围对固态奶酪成分预测的敏感性差异; 同时提出基于物理-化学耦合的预处理算法[13], 结合Savitzky-Golay平滑[14], 有效抑制固态样品表面散射干扰; 最后通过主成分分析(principal component analysis, PCA)与偏最小二乘回归(partial least squares regression, PLSR)的联合建模[15], 解析奶酪中脂肪、蛋白质等成分的特征吸收波段(如1200 nm处C-H键伸缩振动与蛋白质酰胺键的耦合效应)[16], 建立成分含量与光谱特征的定量关联模型。本研究旨在为固态乳制品近红外光谱技术检测提供理论依据, 推动快检技术在奶酪工业化生产中的实际应用。
1 材料与方法
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1.1 材料与试剂
选择市场上常见的奶酪棒、芝士片、奶油芝士、马苏里拉等4类再制奶酪产品, 以上产品由妙可蓝多公司提供。这些产品的工艺生产稳定, 批内产品的均一稳定性和品质一致性较高, 每类样品采集80批, 共计320批, 作为检测对象。采集的样品在产品要求的适宜温度下储存。
1.2 仪器与设备
FOOD SCAN2近红外光谱分析仪(波长为400~1100 nm, 丹麦福斯公司); DA7250近红外光谱分析仪(波长950~1650 nm, 瑞典珀金埃尔默公司); Unity 2600-XTR近红外光谱分析仪(波长680~2600 nm, 美国KPM公司); VIAVI Onsite W近红外光谱分析仪(波长900~1700 nm, 美国VIAVI公司); ME204E电子天平(精度0.01 mg)、FiveEasy F20 pH计(瑞士梅特勒-托利多仪器有限公司); K1160定氮仪、SH520石墨消解仪(海能未来技术集团股份有限公司); HWS-28水浴锅、DHG-9140A电热鼓风干燥箱(上海一恒科学仪器有限公司)。
1.3 方法
1.3.1 样品制备
每类样品均分为建模组和实验组, 建模组样品每类样品40批, 实验组样品每类样品40批。奶酪棒、奶酪片和奶油芝士一般为常温贮存或冷藏贮存, 且样品组织状态均一、质地密实, 马苏里拉产品一般为冷冻和冷藏, 且样品形态常见为丁状和丝状, 在使用近红外光谱仪检测前需将冷冻样品解冻, 并对样品进行适当的研磨, 使其组织状态可实现密实状态。
1.3.2 指标的测定
每类样品的建模组和实验组均需要使用国家标准方法进行干物质、蛋白质、水分、pH等指标的测定, 每个样品重复测试2次, 取算数平均值作为该指标的测定结果, 检测方法如下: 干物质(水分)含量依据GB 5009.3—2016《食品安全国家标准 食品中水分的测定》第一法进行测定; 脂肪含量按GB 5009.6—2016《食品安全国家标准 食品中脂肪的测定》第三法进行测定; 蛋白质含量按GB 5009.5—2016《食品安全国家标准 食品中蛋白质的测定》第一法进行测定; pH按照GB 5009.237—2016《食品安全国家标准 食品中pH的测定》进行测定。
1.3.3 光谱采集
目标仪器开机预热30 min。使用设备自带校准块/程序校准。将适量待测样品填装到样品杯内, 尽量让测量面平整、均匀、无明显气泡或明显空隙、无明显颗粒。选择Unity 2600-XTR近红外光谱分析仪(波长680~2600 nm)分别建模组奶酪棒、马苏里拉、芝士片、奶油芝士4类样品进行光谱采集, 每台仪器对每个样品检测10次得到平均光谱。检测完成后得到的各样品的平均光谱数据各40个, 用于近红外光谱仪器的检测模型的建立。
1.3.4 建立初模型
将上述1.3.2指标测定和1.3.3光谱采集信息进行一对一匹配后, 按照3:1将40组建模样品划分为训练集和预测集, 采用Savitzky-Golay滤波平滑去噪[17]后, 再将全波段光谱数据整理为矩阵(样本×波长点)[18]进行标准化处理, 运用训练集样品使用PLSR[19]建立初模型。每类产品形成一个初模型。
1.3.5 国家标准等同性实验
将FOOD SCAN2近红外光谱分析仪(波长为400~1100 nm); DA7250近红外光谱分析仪(波长950~1650 nm); Unity 2600-XTR近红外光谱分析仪(波长680~2600 nm); VIAVI Onsite W近红外光谱分析仪(波长900~1700 nm) 4个设备依次进行1~4编号, 分别对40组实验组样本进行检测, 同一样本分别使用4类设备进行检测, 每个样品重复检测2次, 取平均值。
1.3.6 重复性实验
从奶酪棒、马苏里拉、芝士片、奶油芝士4类产品的实验组样品中分别随机选取1批产品使用4类研究仪器连续检测6次。
1.4 数据处理
使用USCAN进行6个图谱的叠加显示, 使用Ucal 3.4.0.8建模软件建立初模型并由软件自动计算相关系数(R-squared, RSQ)[20]、交叉验证的相关系数(R-squared cross-validation, RSQV)、标准偏差(standard error of calibration, SEC)、预测标准偏差(standard error of prediction, SEP)。国家标准等同性实验和重复性实验数据采用Excel 2019软件[21]计算平均值和相对标准偏差。
2 结果与分析
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2.1 光谱谱图分析
近红外光谱图的特征归属主要包括O-H基团、C-H基团、N-H基团。O-H基团(水、醇、碳水化合物等)在1450 nm(约6897 cm-1)处O-H伸缩振动的一级倍频, 在1940 nm(约5155 cm-1)处O-H伸缩振动与弯曲振动的合频, 在970 nm(约10300 cm-1)处O-H的二级倍频。C-H基团(脂肪、烃类)在1210 nm(约8264 cm-1)处C-H伸缩振动的第三倍频, 在1720 nm(约5814 cm-1)处C-H的二级倍频, 在2300 nm(约4348 cm-1)处C-H伸缩与弯曲振动的合频。N-H基团(蛋白质、胺类)在1500 nm(约6667 cm-1)处N-H伸缩振动的一级倍频, 在2050 nm(约4878 cm-1)处N-H伸缩与弯曲振动的合频。
图1是4类再制奶酪未经预处理的光谱图。从图1可以看出, 不同品类的再制奶酪光谱变化趋势基本一致, 在980、1200和2500 nm处有明显吸收峰; 在1400~1500 nm、1900~2000 nm处有强吸收峰; 在1700~1800 nm和2300~2400 nm处有高频小幅振动; 每一类再制奶酪因使用的原辅料、生产工艺等差异, 所含的如干物质、蛋白质、糖、有机酸等成分差异大, 所以不同品类的再制奶酪光谱图中吸收峰强度存在差异, 说明光谱吸收峰的强度差异与其中质量指标的组分含量有关。
2.2 全波段光谱主成分分析
全波段光谱经PCA后, RSQ用于评估仪器测定值与标准方法测定值之间的相关程度, RSQV用于评估模型对样本的预测值与标准方法测定值之间的相关程度, 越接近于1, 则仪器测定值或预测值与标准方法测定值的相关程度越好, SEC用于评估定标样品近近红外光谱法测定值与标准方法测定值之间残差的标准差, SEC值越小, 表明分析模型的预测能力越强; SEP用于评价验证样品成分的测定值与标准方法测定值之间的标准差, 分析模型对新样本的适用能力, SEP越小, 表明模型的泛化能力越高。
表1为检测初模型参数评价, 分析初模型的参数发现, 整体模型性能良好, 大多数奶酪类型和项目的RSQ>0.85, SEC/SEP<0.5, 尤其在脂肪和干物质项目上表现优秀。pH项目所有奶酪类型中RSQ最低(平均0.811), 尤其芝士片(RSQ=0.504), 考虑pH的物理化学机制限制的原因。pH反映的是溶液中H⁺的活度, 近红外光谱主要检测的是C-H、O-H、N-H等基团的振动倍频与合频信号, 而H⁺本身在近红外区域(780~2500 nm)没有直接吸收峰。
2.3 国标等同性实验分析
表2为不同奶酪产品分别使用4类近红外光谱研究仪器的蛋白质、脂肪、水分项目检测结果与国家标准方法测定的奶酪营养成分的平均偏差。表中数据显示不同奶酪产品的蛋白质项目近红外光谱研究仪器和国家标准方法平均偏差最大6.9%, 最小值2.0%; 不同奶酪产品的脂肪项目近红外光谱研究仪器和国家标准方法平均偏差最大2.9%, 最小值1.8%; 不同奶酪产品的干物质项目近红外光谱研究仪器和国家标准方法平均偏差最大值2.34%, 最小值0.95%, 除1号设备的马苏里拉的蛋白质偏差超过10%外, 其他营养成分国标等同性实验符合要求。
其中, 国标等同性实验中蛋白质项目的平均偏差为6.9%(马苏里拉), 虽然符合方法的误差要求, 但偏差相对于其他成分较高, 分析原因为马苏里拉奶酪的形状(丝状)和质地(如水分分布、脂肪球大小、蛋白质网络结构)导致样品研磨的匀一性差异, 需要在样品制备时关注研磨的颗粒度和密实度。
2.4 重复性实验分析
表3为蛋白质、脂肪、干物质项目分别在4类近红外光谱研究仪器连续6次重复检测数据的相对标准偏差。表中数据显示使用4类近红外光谱研究仪器检测不同奶酪产品的蛋白质项目重复性偏差最大1.88%, 最小值0.02%; 使用4类近红外光谱研究仪器检测不同奶酪产品的脂肪项目重复性偏差最大4.15%, 最小值0.05%; 使用4类近红外光谱研究仪器检测不同奶酪产品的水分项目重复性偏差最大0.57%, 最小值0.03%, 各指标符合重复检测偏差要求。
3 结论与讨论
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基于当前4种不同品牌近红外光谱设备的验证, 构建了覆盖多品类再制奶酪的NIR快检数据库, 并提出基于物理-化学耦合的预处理算法[22], 使脂肪、蛋白质检测平均偏差分别降至1.8%和2.3%, 验证近红外光谱技术对奶酪品质指标[23]实现快速检测的可行性。理论层面, 通过PCA[24]与PLSR的联合建模[25], 解析奶酪中脂肪、蛋白质等成分[26]的特征吸收波段[27](如1200 nm处C-H键伸缩振动与蛋白质酰胺键的耦合效应[28]), 建立成分含量与光谱特征的定量关联模型, 揭示了固态奶酪中成分-光谱的协同响应机制, 为复杂基质NIR建模提供新思路。
尽管本研究仅在4个品牌设备上验证, 但算法框架本身具备良好的可扩展性, 未来将结合深度学习算法[29](如卷积神经网络)进一步优化模型泛化能力[30]。在今后的研究中, 将继续对奶酪品类中其他产品进行分析, 建立更为完善的快速分析模型, 让近红外光谱技术在奶酪组成成分的快速检测方法为传统化学检测方法提供有效的补充, 为高效率、低成本、环保的检测提供更多的可能性。
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2025年第16卷第15期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250312005
  • 接收时间：2025-03-12
  • 首发时间：2026-01-09
  • 出版时间：2025-08-15
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  • 收稿日期：2025-03-12
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    上海妙可蓝多食品科技股份有限公司, 上海 201506

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*智丽慧(1988—), 女, 硕士, 中级工程师, 主要研究方向为检验技术。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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