• EI
  • Scopus
  • 中国科技期刊卓越行动计划项目资助期刊
  • 北大核心期刊
  • DOAJ
  • EBSCO
  • 中国核心学术期刊RCCSE A+
  • 中国精品科技期刊
  • JST China
  • FSTA
  • 中国农林核心期刊
  • 中国科技核心期刊CSTPCD
  • CA
  • WJCI
  • 食品科学与工程领域高质量科技期刊分级目录第一方阵T1
中国精品科技期刊2020

牛肉掺假鉴别技术研究进展

刘敏, 李升升, 张艳, 刘成录

刘敏,李升升,张艳,等. 牛肉掺假鉴别技术研究进展[J]. 食品工业科技,2023,44(7):477−489. doi: 10.13386/j.issn1002-0306.2022060044.
引用本文: 刘敏,李升升,张艳,等. 牛肉掺假鉴别技术研究进展[J]. 食品工业科技,2023,44(7):477−489. doi: 10.13386/j.issn1002-0306.2022060044.
LIU Min, LI Shengsheng, ZHANG Yan, et al. Research Progress of Beef Adulteration Identification Technology[J]. Science and Technology of Food Industry, 2023, 44(7): 477−489. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022060044.
Citation: LIU Min, LI Shengsheng, ZHANG Yan, et al. Research Progress of Beef Adulteration Identification Technology[J]. Science and Technology of Food Industry, 2023, 44(7): 477−489. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2022060044.

牛肉掺假鉴别技术研究进展

基金项目: 青海省自然科学基金(2020-ZJ-961Q)。
详细信息
    作者简介:

    刘敏(1996−),女,硕士研究生,研究方向:畜产品加工,E-mail:1300903618@qq.com

    通讯作者:

    李升升(1984−),男,博士,副研究员,研究方向:畜产品科学与工程,E-mail:lishsh123@163.com

  • 中图分类号: TS251.7

Research Progress of Beef Adulteration Identification Technology

  • 摘要: 牛肉因其营养价值高而成为消费者饮食中重要的组成之一,近年来,牛肉掺假的问题纷至沓来。随着分析技术的不断发展,鉴别牛肉掺假的质谱、酶联免疫、基因、传感器、光谱等技术也得到发展。质谱技术准确性高,适用于鉴别经过深加工后的牛肉或物种相近的牛肉,但设备昂贵;酶联免疫吸附技术成本低、特异性强,适用于鉴别肉类种类来源,但鉴别相近物种时结果易发生假阳性;基因技术中基因芯片技术检测速度快可大规模地检测牛肉样品,但存在芯片标准化等问题;传感器技术操作简单适用于牛肉品种判别但准确度低需与其他技术相结合提高其准确度;光谱技术检测快速,但需要复杂的数据作为支撑。综上,本文分析了目前牛肉掺假鉴别技术的原理及优缺点,旨在为牛肉掺假鉴别技术的发展提供参考。
    Abstract: Beef has become one of the important components of consumers' diets due to its high nutritional value. In recent years, the problem of beef adulteration has been coming. With the development of analytical techniques, mass spectrometry, enzyme-linked immunoassay, genetic, sensor and spectroscopic techniques has been used to identify adulterated beef. Mass spectrometry has highly accurate and is used to identify beef after deep processing or similar species beef, but the equipment is expensive. Enzyme-linked immunosorbent assays has low cost and high specific and are used to identify the meat species, however, the results are prone to false positive when identifying similar species. Gene chip technology can be used to test beef samples fast and large-scale, but there has problems with standardization of gene chip. Sensor technology has been used to beef identification, and needs to be combined with other technologies because the detection precision is low. The spectroscopy technique is fast to detect, but requires complex data support. In conclusion, this paper analyzes the principles, advantages and disadvantages of beef adulteration identification technology, which is expected to provide reference for the development of beef adulteration identification technology.
  • 牛肉的营养价值较高,蛋白质含量在20%左右,高于羊肉和猪肉,且脂肪含量较低,在10%左右[1]。其含有丰富的B族维生素、蛋白质、氨基酸以及矿物质[2],具有有效提高机体抗病能力、增强记忆力、促进碳水化合物及脂肪代谢、补气养血等多重功效[3],是中国居民主要肉类食品原料之一,深受人们的喜爱。2021 年全球牛肉产量为 5777.7 万t,消费量为 5599.4 万t,其中中国牛肉产量为 683.0 万t,消费量为981.0 万t,进口量为300.0 万t [4],我国已成为牛肉进口大国,消费需求呈上涨趋势,价格也逐渐攀升,不法分子在利益的诱导下,对牛肉进行掺假,导致牛肉出现安全问题。

    牛肉食品掺假类型包括:牛肉地理原产地标签不符、出售早期经过冷冻的牛肉[5];牛肉及其制品中掺入价格低廉的鸡肉、猪肉[6]和老鼠肉[7];发现销售的牛肉制品与产品标签不符并使用未进行申报的肉类代替牛肉[8];发现使用粘合肉糜或切片肉制品的粘合剂[9],严重影响了消费者的健康、破坏了市场秩序、影响社会和谐与安定[10-11]

    化学技术可以根据不同物种肉类间的差异性进行鉴别[12],但牛肉中掺入肉制品的种类繁多,其理化性质和外观相似,传统鉴别方法已无法满足上述牛肉掺假检测[13]。因此,采用简单、无损、快速、灵敏的鉴别牛肉掺假技术,对保障牛肉食品的安全,维护消费者自身利益有重要意义。

    本文综述了国内外掺假牛肉鉴别的质谱技术、酶联免疫试验、基因技术、传感器技术及光谱技术的原理(见图1)、应用进展,并对未来掺假牛肉的鉴别技术提出了展望,旨在为牛肉食品安全检测及掺假鉴别技术的应用前景提供参考。

    图  1  牛肉掺假鉴别技术原理图
    Figure  1.  Schematic diagram of beef adulteration identification technology

    质谱技术可以对不同物种的肉类、血液中所存在的不同蛋白种类、多肽、氨基酸序列进行特异性检测,检测速度快,准确度高[14]。本文中的质谱技术主要包括液相色谱-串联质谱技术(liquid chromatography-tandem mass spectrometry,LC-MS/MS)和表面解吸常压化学电离(desorption atmospheric pressure chemical ionization,DAPCI)质谱技术(DAPCI-MS)。

    LC-MS/MS是蛋白质组学与代谢组学相结合的技术[15],具有高特异性和高灵敏度,主要利用物种特异性多肽鉴别物质掺假[16],采用三重串联四级杆液质进行定量和定性分析可以避免检出限高、产生假阳性、加工过程中样品易分解等问题[17],且多肽的稳定性高[18]于脱氧核糖核酸(deoxyribonucleic acid,DNA)链[19],已应用于经过深加工的肉制品鉴别[20],为牛肉的掺假鉴别提供新方法。通过图2可看出基于蛋白质组学的物种真实性鉴别流程[21]

    图  2  基于蛋白质组学的物种真实性鉴别流程图[21]
    Figure  2.  Flow chart of species authenticity identification based on proteomics[21]

    康超娣等[22]利用LC-MS/MS技术实现了对牛肉制品中牛肉与猪肉、鸡肉的定量分析,构建了基于物种特异性多肽的相对定量方法,线性关系达0.99以上,最低可检测0.5%比例的牛肉、猪肉和鸡肉掺假。Kim等[23]运用了一维凝胶电泳法对牛肉、鸡肉、猪肉、鸭肉的肌浆蛋白和肌原纤维进行分析,发现肌钙蛋白I、烯醇化酶3、L-乳酸脱氢酶和磷酸三糖异构酶是区分哺乳动物和家禽类动物的主要物质,并对部分蛋白质进行LC-MS/MS分析,可以从新鲜牛肉和牛肉混合物中鉴别出牛肉,说明此技术可以进行特异性检测。张颖颖等[24]利用LC-MS/MS技术找到牛肉、鸡肉和猪肉的物种特异性多肽,并对牛肉中掺入的猪肉和鸡肉进行定性和定量分析,成功定量检测出0.5%的掺假牛肉,证明此方法的检测限低、灵敏度较高。张颖颖等[21]利用LC-MS/MS和聚合酶链式反应(polymerase chain reaction,PCR)技术对市售牛肉串真假进行比较,实验结果表明两者检测方法的检测结果一致,均检测出14%的牛肉串掺假,但LC-MS/MS技术准确性较高,实验操作更为简便。烤制、煮沸、烘焙等加工方式会导致肉制品蛋白变性,因此鉴定热稳定性肽具有重要意义。Wang等[25]运用超高效液相色谱-串联三重四级杆飞行时间质谱分析鉴定了猪肉、鸡肉、鸭肉、牛肉、羊肉生肉中的全蛋白和多肽,并结合多反应监测(multiple reaction monitoring,MRM)确认了34 个肽生物标记物,20 个热稳定肽标记物,以高度的特异性和选择性鉴别出熟牛肉。Zhang等[26]通过优化还原、烷基化、消解、纯化步骤将LC-MS/MS的前处理时间缩短至2.5 h,创建了方便、省时的样品制备方法。通过经过此样品制备的LC-MS/MS技术,可以筛选出牛、猪、羊等7种动物的35 种热稳定性肽标记物,鉴别出浓度为1.0%的牛肉掺假。

    DAPCI-MS技术是可以通过电晕放电的形式产生初级试剂离子,作用在待测肉样品中,使肉样品中的待测分子解吸、离子化的一种方法[27],仪器装置示意图见图3[28]。此方法无需使用解吸溶剂,因此可以达到对样品无污染、无破坏处理的目的,且还具有操作时间短、分析速度快等优点[29]

    图  3  表面解吸常压化学电离源仪器装置图[28]
    Figure  3.  Surface desorption atmospheric pressure chemical ionization source instrumentation diagram[28]

    李倩等[30]建立了100 ms的LTQ-XL型线性离子阱质谱仪全谱扫描时间,30~50 ms的碰撞解离时间,以期快速获取牛肉、猪肉、鸭肉、羊肉以及自制假羊肉指纹图谱的DAPCI-MS技术,并通过主成分分析(principal component analysis,PCA)发现五种肉类的指纹图谱之间均存在区别,可以将牛肉和其他肉类进行区分,为后续掺假牛肉鉴别提供新方法和思路。

    质谱技术鉴别牛肉掺假,准确度高、不受外界因素干扰,尤其对经过深加工的牛肉制品以及物种相近的品种有独特优势[19],但仪器成本高、DAPCI-MS技术电离源难以普及、技术人员要求高,不适用于基层检测。

    酶联免疫吸附试验(enzyme linked immuno-sorbent assay,ELISA)技术最早出现于20世纪70年代,具有操作方法简单、适用范围广、成本低等优点[31],且可以检测复杂混合物。ELISA有两种检测形式,一种简单的检测形式,间接ELISA技术,将抗原固定到固体表面,酶结合抗原后仍保持酶活性和免疫活性,可以通过肉眼看出溶液颜色的变化从而判断免疫反应是否进行[32],颜色的深浅与待测抗体的量成正比,原理示意图见图4[33];另外一种检测形式是双抗体夹心ELISA技术,将特异性抗体和固相载体结合形成固相抗体,随后和待检血清中相应抗原结合形成免疫复合物,洗涤后加入酶标记抗体,与免疫复合物中的抗原结合形成酶标抗体-抗原-固相抗体复合物,加底物显现颜色,判断抗原含量[12],原理示意图见图5[33]。可以通过物种的蛋白质定量和定性分析物种特异性抗原[34]鉴别肉类的品种,因此在动物源性食品成分鉴别方面得到了广泛的应用[35]

    图  4  间接ELISA技术原理图[33]
    Figure  4.  Schematic diagram of indirect ELISA technology[33]
    图  5  双抗体夹心ELISA技术原理图[33]
    Figure  5.  Schematic diagram of double antibody sandwich ELISA technology[33]

    Kang’ethe等[36]运用间接ELISA技术检测出牛肉和马肉的种属特异性抗体,鉴别出牛肉中所掺入的马肉。Dincer等[37]使用间接ELISA技术检测出牛肉等生肉样品中特定的白蛋白,可鉴别牛肉中掺杂5%的猪肉或绵羊肉,但检测经过腌制和烹饪后的肉样时,由于部分蛋白质失活,导致检测信号值降低70%~74%,降低了检测肉类物种来源的能力。因此,对于商业化的试剂盒应制备针对热稳定蛋白的抗体,来实现对经过热加工牛肉制品的掺假检测。Kreuz等[38]利用夹心ELISA技术,将牛的骨钙素(组织特异性靶蛋白)作为识别抗原,对加热到145 ℃的样品进行检测,发现掺入0.1%(W/W)的牛肉时,就可被检测出。

    此技术灵敏度高、成本低、能提供定性结果[39],适用于一般企业进行牛肉鉴别检测,但抗原抗体识别过程中特异性较强,如果蛋白质失活变性,会导致抗原-抗体无法有效识别[40-41]

    DNA是物种的主要遗传物质,存在于绝大多数动物的细胞中,其含量和蛋白质相比更为丰富[42],可以用做物种鉴别方法。

    PCR技术,是在靶基因设计特异性引物,用引物扩增出目的片段的技术[43],是我国肉骨粉中牛源性成分的出入境检验的标准方法[44]。在鉴别牛肉时,PCR将线粒体DNA作为鉴别时的目标基因[12],通常一个个体上只存在一个等位基因[45]

    Rajapaksha等[46]以水牛肉线粒体细胞色素b(cytochrome b,cytb)基因为模板,建立了鉴别水牛肉、牛肉及其他肉类的PCR检测方法,发现水牛DNA中有一条242 bp的条带,在指定的反应条件下,水牛肉DNA不与牛肉DNA发生交叉反应,且此技术较为灵敏,可鉴别屠宰10 d、干的或经过热加工的水牛肉。Piknova等[47]采用cytb的线粒体基因序列为导向进行PCR试验,检测出牛肉中掺入的猪肉,检测限为0.05%。侯东军等[48]以cytb基因组为模板,建立了检测牛肉中掺入猪肉的PCR检测方法,成功鉴别出牛肉中所掺入的2%、4%和6%的猪肉。Ulca等[49]利用PCR试剂盒可成功鉴定出生或经过热加工后的牛肉,对于牛肉混合制品最低可检测出低于0.1%的猪肉添加量。吴周林等[50]建立了以牦牛线粒体12S rRNA基因为特异性引物鉴别牦牛肉干的PCR方法,对四川地区所销售的7种牛肉干进行检测,成功检测出牦牛肉干,检出率为100%。但Musto等[51]发现,PCR技术对于经过微波加热的肉类易发生DNA降解,此技术不适用于微波加热后的掺假牛肉鉴别。

    多重PCR技术(multiplex polymerase chain reaction,mPCR)将多种引物同时在同一个PCR体系中进行扩增,最终根据有无产物进行相关检测[52]

    李杰等[53]以牛线粒体基因为模板设计特异性引物,建立了快速鉴定牛肉掺假的mPCR方法,检测限为9.8×101 fg/μL,可对市场所销售的牛肉制品进行掺假鉴定。Qin等[54]利用mPCR技术鉴别出牛肉中掺入的羊肉和鸭肉,灵敏度为0.005 ng,并检测出生牛肉和经过热加工处理后的牛肉混合物,检测限为1%。刘婉婉[55]利用mPCR技术,构建了牛及其他物种的8重检测体系,检测限值达到0.05 ng/μL,检测限值较低可用于市场商业掺假牛肉制品的鉴定。Lee 等[56]应用 mPCR 的方法在牦牛 12S rRNA 区域发现特定位点,由于黄牛、水牛和牦牛同属于牛科,很难从基因上将其分别,但利用此技术即使反应物中混合了少量的线粒体DNA,也可将牦牛与黄牛、水牛进行区分,且使用真实肉类样本的特异性达99%。

    PCR-限制性片段长度多态性方法(polymerase chain reaction-restrition fragment length polymophism,PCR-RFLP)利用基因组非编码重复序列而设计的品种特异性的杂交探针,对于分析线粒体DNA具有特别的优势[57]。通过使用一对通用引物并且和几种限制性酶进行相应的结合可以更好地鉴别出不同肉类品种[58]

    高琳等[59]建立了线粒体DNA中cytb区段的保守序列为目的基因的PCR-RFLP检测方法,鉴别出生牛肉、生猪肉、生羊肉及其肉制品中牛源性成分,采用扩增子Alu限制性酶切产生的片段差异可区分牛肉和猪肉及其肉制品。Girish等[60]建立了以线粒体12S rRNA基因为目的基因的PCR-RFLP方法鉴别水牛肉和牛肉,成功将水牛肉和牛肉进行区分,但扩增PCR产物数量和目标DNA数量存在差异,此方法在同种属掺假肉样中并不太适用。Kumar等[61]以线粒体cytb为目的基因,采用PCR-RFLP技术,通过Alu1和Taq1限制性内切酶对609 bp PCR扩增子进行限制性内切酶消化,成功鉴别出牛肉和水牛肉。

    实时荧光PCR技术(real-time fluorescent quantitative PCR,Real-Time PCR)在不需要借助电泳的条件下可以实现高灵敏度的检测[62-63],根据荧光模式的不同,分为荧光探针法和荧光染料法,其中应用最多的荧光探针法原理示意图见图6[64]。Real-Time PCR技术已成为我国GB/T 38164-2019《常见畜禽动物源性成分检测方法实时荧光PCR法》[65]及地方DB15/T 2025-2020《牛和猪源性成分同步检测方法实时荧光PCR法》[34]肉类检测的标准检测方法。

    图  6  基于TaqMan荧光探针法PCR技术原理图[64]
    Figure  6.  Schematic diagram of PCR technology based on TaqMan fluorescent probe method[64]

    Dooley等[66]通过实时TaqMan荧光探针PCR检测技术,检测出牛肉、猪肉、羊肉的混合样品,检测灵敏度低于0.1%。Lubis等[67]建立了一种基于ZEMTM探针的新型实时荧光PCR技术,可检测1 pg/μL的猪肉DNA,且理论上可鉴别牛肉中0.001%的猪肉掺假。曾少灵等[68]根据线粒体cytb为基因序列,采用多重实时荧光PCR技术,检测出100%的牛源性成分,适用于牛羊源性成分检测,且检测效率相对于国标提高了3倍,灵敏度提高了10倍。刘睿茜等[69]筛选出当雄高山牦牛的单核苷酸多态性(single nucleotide polymorphisms,SNP)位点,建立基于SNP位点的多重实时荧光PCR技术,成功在5个不同地区的牦牛肉中鉴别出当雄高山牦牛肉。陈晓宇等[70]运用Real-Time PCR技术对市售的牛肉及其制品进行掺假检测,发现餐饮店样品中掺入非牛肉成分最多,超市产品较少,因此,此技术可为相关基层检测机构提供便利。

    基因芯片是指将探针DNA按照特定的顺序固定于玻璃载体,将待测样品的核酸分子经过标记与载体上DNA探针按照碱基配对的原理同时进行杂交,再通过激光共聚焦荧光检测系统对芯片进行扫描,检测信号强度通过数据分析获得分子数量[71],技术流程示意图见图7[71]

    图  7  基因芯片技术流程图[71]
    Figure  7.  Flow chart of gene chip technology[71]

    石丰运[72]将线粒体DNA 16S rRNA基因作为目标基因,设计了4条特异性基因芯片检测探针及2条质控探针,成功鉴别出牛源性成分,灵敏度达10 pg。朱业培等[73]选择线粒体DNA基因和cytb基因作为目标基因,设计了6条鉴别探针,可快速准确地鉴别牛源性成分,灵敏度为0.5 pg。赵睿骁等[74]利用PCR-膜芯片技术,提取纯化黄牛、牦牛、水牛、真犏牛及假犏牛DNA,利用多重PCR技术测试膜芯片的准确度,成功鉴别出牦牛肉和普通牛肉,灵敏度为0.1 ng,膜芯片特异性强无交叉反应。PCR-膜芯片技术可以对多种动物源性成分进行鉴定,检测结果快速且准确[72],但现阶段部分芯片还处于研发阶段、价格较贵、标准化还没进行统一[71],对牛肉掺假鉴别有所限制。

    基因技术具有较好的灵敏性和特异性,其中基因芯片技术适合大量样品的高通量检测,准确性高、自动化程度高,适合检测机构用于掺假牛肉检测,但基因技术还存在一定的问题,如分析时间长,试验结果重复困难,样品易受DNA污染而发生降解从而干扰检测结果[75],芯片标准化问题,检测成本高,需要具有牢固知识的专业型人才进行操作[71],PCR-RFLP技术不能进行定量检测,因此未来应大力开发基因芯片技术,为牛肉掺假鉴别提供快速、简便的检测方法。

    传感器技术是将需要检测的肉类样本的特异性成分变成电子设备,可以识别物理和化学的信号,通过信号来鉴别牛肉样品[76],原理示意图见图8。对于牛肉的掺假鉴别,本文中的传感器技术主要讲述电子鼻技术和电子舌技术。

    图  8  电子鼻、电子舌原理图
    Figure  8.  Schematic diagram of electronic nose and electronic tongue

    电子鼻指将生物的嗅觉模式转移到电子设备系统,通过对牛肉所散发的气味进行分析和检测[77],主要由气敏传感器阵列、信号处理系统和模式识别系统组成[78]

    董福凯等[79]建立了一种利用PEN3型电子鼻鉴别假牛肉卷的方法,结合PCA和线性判别分析(linear discriminantanalysis,LDA)建立识别模型,发现电子鼻反映出假牛肉卷中挥发性物质,从而成功鉴别出假牛肉卷中的猪肉和鸭肉。周秀丽等[80]运用PEN3型便携式电子鼻结合PCA和LDA,可以区分加入不同比例的鸡肉、鸡皮及猪肉的牛胸肉馅,且LDA图中的数据分布呈现出线性规律,效果好于PCA。Han等[81]将PEN3电子鼻、LDA和极限学习机算法(extreme learning machine,ELM)相结合,鉴别出纯牛肉和不同比例猪肉混合所形成的掺假牛肉,ELM识别率优于LDA,达91.27%。在此基础上建立了反向传播神经网络(back propagation-artificial neural network,BP-ANN)模型预测牛肉中混合猪肉的含量,所建模型的相关系数为0.85。贾洪峰等[82]建立了一种FOX4000电子鼻鉴别牦牛肉和牛肉的方法,采用PCA、判别因子分析(discriminant factor analysis,DFA)、偏最小二乘分析(partial least squares,PLS)方法建立识别模型,对数据进行分析,发现利用此模型可以鉴别不同部位的牦牛肉和牛肉。许文娟等[83]利用PEN3型电子鼻结合PCA方法,发现不同掺假比例的牛肉在传感器上响应规律不同,成功鉴别出牛肉中掺入5%~80%的猪肉。

    电子舌和电子鼻相似,通过模仿哺乳动物的味觉反应相关信息。主要由进样器、味敏传感器阵列、信号处理仪器以及模式识别仪器组成[84]

    黄珏等[85]建立了一种α-ASTREE系统的电子舌对进口牛肉进行分析的方法,发现在酸味(SRS)、咸味(STS)、甜味(SWS)和复合味(GPS 2)传感器上存在区别,通过近红外光谱处理后的数据进行PCA和典则判别分析(canonical discriminant analysis,CDA),可以对不同产地的牛肉进行区分,区分率为100%,有利于对我国进口牛肉的质量安全进行检测。Zhang等[86]利用TS-5000Z电子舌结合PCA分析方法,测定不同样品中的粗蛋白、脂肪等化学组成及酸味、咸味等风味组成,可以快速鉴别出和牛、安格斯牛和西门塔尔牛。

    传感器技术可以对牛肉进行快速、简单的鉴别[87],对操作者的要求较低,适合用于大量样品检测,但实验结果的重复性不高、需要大量数据做支撑、若牛肉制品中成分复杂,还需与其他技术相结合从而提高其准确率。

    光谱技术是通过分析肉样品在不同的波长下所产生的光谱差异进行掺假鉴别[88]。本文中的光谱技术主要介绍近红外光谱技术、拉曼光谱技术、高光谱成像技术。

    近红外光谱技术(near-infrared spectroscopy,NIR)原理是指在波长750~2500 nm之间,波数范围在12000~4000 cm−1之间,存在于可见光和中红外区之间的电磁波,其记录的为分子中单个化学键的基础频率振动中的倍频和合频吸收,主要是含有氢基团X-H(=C、N、O、S)[89]。光谱对不同的物种成分(如水、蛋白质、脂类)具有特异性[88],结合化学计量学方法从复杂数据中提取特征信息,从而进行定性分析,且检测速度快、样品制备过程简单[90],因此可以通过此方法鉴别掺假牛肉,近红外采集系统的基本组成见图9[91]

    图  9  近红外光谱技术采集系统的基本组成[91]
    Figure  9.  Basic composition of near-infrared spectroscopy acquisition system[91]

    白京等[92]建立了NIR结合化学计量学法鉴别不同肥肉占比的解冻牛肉汉堡中掺入猪肉的定性模型及定量模型,结果表明,牛肉汉堡中掺假猪肉试验中偏最小二乘判别分析(partial least squares discrimination analysis,PLS-DA)适用于定性检测,正确率达100%,偏最小二乘回归(partial least squares regression,PLSR)算法适用于定量检测。Rady等[93]建立了牛肉沫中掺入植物源性蛋白的可见NIR结合PLSR模型,对牛肉沫真假鉴别准确率达100%,为NIR技术鉴别加工牛肉制品提供方法。冷拓[94]通过NIR技术、核磁共振(nuclear magnetic resonance,NMR)技术结合判别分析(discriminant analysis,DA)、PLS和支持向量机回归(support vector machine regression,SVR)化学计量学方法,建立了基于NIR和NMR技术的牛肉掺假模型,成功鉴别出纯牛肉和掺假牛肉,且掺假牛肉的三元体系准确率达90.2%,二元体系鉴别达100%。张丽华等[95]通过NIR、多元散射校正(Multiplicative scatter correction,MSC)、标准正态变量变换(Standard normal variate transformation,SNV)、5点平滑处理和一阶导数(first derivative,1 stder)处理等方法对牛肉中掺入的鸭肉进行预处理后,建立支持向量机(nu-SVM)模型,可成功鉴别出牛里脊肉中掺入的鸭肉,正确判别率达94.00%。通过检测不同动物肌肉中的脂肪和水分含量的红外反射光谱可鉴别不同的牛肉样品[96]。Morsy等[97]建立了一种可见-NIR鉴别新鲜和冷冻牛肉掺假方法,所建PLSR模型鉴别出新鲜牛肉中掺入猪肉。

    拉曼光谱技术是基于印度物理学家Raman发现的拉曼散射效应[98],是以分子转动和振动信息为基础,可以用来鉴别不同物种结构的分析方法[99],具有检测快速、良好的信噪比不重叠、远距离在线分析检测、实时检测和无损检测[91]等优点,产生过程示意图见图10[99]

    图  10  拉曼光谱技术产生过程[99]
    Figure  10.  Raman spectroscopic technique generation process[99]

    Zhao等[100]利用拉曼光谱检测牛肉汉堡中牛肉掺杂内脏,在900~1800 cm−1处产生指纹图谱,利用PCA分析牛肉汉堡中的特殊蛋白质和脂质化学结构有关的特性波长从而鉴别真实和掺假牛肉汉堡样品,并建立PLS-DA和软独立建模分析(soft independent modeling of class analogy,SIMCA)模型,发现掺假牛肉的判别准确率高于90%。红肉指在烹饪前色泽为红色的肉,主要为牛肉、羊肉等哺乳动物的肉。消费者购买红肉时主要通过色泽等感官评价进行分析,但哺乳动物肉色泽都呈现红色,很难将其区分,因此构建标准化的质量无损检测方法具有重要意义。Robert等[101]使用拉曼光谱仪鉴别牛肉、鹿肉和羊肉,并建立PLS-DA和SVM模型结合PCA进行分析,发现线性和非线性SVM模型灵敏度分别达到87%和90%以上,特异性在88%以上,PLS-DA法准确率大于80%,为鉴别红肉提供新方法。Biasio等[102]运用显微拉曼光谱技术结合PCA、LDA分析,可以有效地鉴别出牛肉、羊肉、马肉。Zając等[103]利用傅里叶变换拉曼光谱技术依据氨基酸特征光谱之间良好拟合性鉴别牛肉中掺入不同含量的马肉,在480、829、856、879和937 cm−1氨基酸谱带鉴别出牛肉中掺入的马肉。Boyacı等[104]利用拉曼光谱技术结合PCA建立了一种快速鉴别牛肉中掺入马肉的模型,在波数为200~2000 cm−1区分牛肉和马肉纯脂肪,并对49份牛肉和马肉样品进行来源分析,利用模型30 s鉴别出牛肉中掺入不同含量的马肉。

    高光谱成像技术(hyperspectral imaging,HSI)是将光谱和成像技术相结合所得到的一种新型技术,可以同时取得所测样品的光谱信息和图像[105],包括外部属性(如样品形状和大小)和内部属性(如化学组成)[106],高光谱成像技术系统示意图见图11[91]。此方法具有快速检测、样品前处理简单、成本较低、应用广[107]、无损检测[108]等优点。

    图  11  高光谱成像技术系统示意图[91]
    注:a:反射率,b:散射。
    Figure  11.  Schematic diagram of hyperspectral imaging technology system[91]

    Kamruzzaman等[109]利用波长在400~1000 nm的近红外高光谱成像技术建立PLSR模型,通过图像处理技术可以定量、可视化地鉴别出碎牛肉中掺入的鸡肉。Ropodi等[110]通过多光谱成像结合数据分析的方法,鉴别出牛肉中掺假的马肉,判别正确率达90%。Liu等[111]利用波长为405~970 nm的多谱线成像技术建立PLSR预测模型,将预测方程转化为可直观看到的水分分布图,由此鉴别出注水牛肉。王彩霞等[112]利用可见/近红外高光谱成像技术结合全波段和运用竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)、连续投影算法(successive projections algorithm,SPA)和无信息变量消除算法(uninformative variable elimination,UVE)得到的特征波长进行PLS-DA、K最近邻法(k-nearest neighbor,KNN)和径向基函数(radial basis function,RBF)-支持向量机(RBF-SVM)分析,最终得到14 个特征波长,其中基于CARS法提取的特征波长所建立的RBF-SVM模型校正集正确率达100%,可以对荷斯坦奶牛、秦川牛和西门塔尔牛的品种进行无损鉴别。

    光谱技术虽然能快速、无损地鉴别掺假牛肉样品,但是需要结合准确的数据模型为依托,耗费大量资金;模型的稳定性也容易受到外界环境和仪器因素的影响[113]

    质谱技术、酶联免疫吸附试验、基因技术、传感器技术及光谱技术在牛肉食品中的应用广泛,但均各有两面性(表1),因此根据不同样品选择合适的鉴别技术尤为重要。

    表  1  牛肉掺假鉴别技术优缺点及应用
    Table  1.  Advantages and disadvantages of beef adulteration identification technology and its application
    检测技术优点缺点应用
    表面解吸常压化学电离质谱无需前处理、检测快速、无化学污染电离源难获取鉴别肉类样品[27]
    液相色谱-质谱联用操作简单、准确性高、多肽稳定性强设备昂贵、数据分析复杂、需要高水平人员鉴别深加工肉制品[114]
    酶联免疫吸附灵敏度高、特异性强、成本低蛋白质易发生变性,相近物种易产生假阳性肉类种类来源鉴定[115]
    PCR灵敏度高、特异性强操作繁琐、微波加热肉制品容易造成DNA降解加工肉品和产品的鉴别[116]
    多重PCR高灵敏度、特异性强、可同时分析多种样品对样品DNA的纯度要求较高熟肉和加工肉类[12]
    PCR-RFLP灵敏度高、特异性强分析时间长、定量检测有限制品种鉴定[71]
    实时荧光PCR灵敏度高、特异性强、可定量分析专业的操作人员新鲜肉样品、肉加工品[116]及熟肉鉴定[12]
    基因芯片操作简单、检测速度快、大规模分析检测样品部分芯片仍在研发、成本高、芯片标准化鉴别肉类种属[73]
    电子鼻检测快速、简单、操作要求低重复性低、需要与其他方法结合提高其准确度肉制品产地判别、品种判别[117]
    电子舌
    近红外光谱检测快速、非接触式复杂的数据作为支撑、模型的稳定性易受外界因素干扰肉中掺伪比例、来源[78]
    拉曼光谱小样品量、非接触的、检测快速有潜力根据肌原纤维和结缔组织蛋白
    确定肉类和肉制品的游离锌[8]
    高光谱成像前处理简单、无损、成本低鉴别牛肉品种[112]
    下载: 导出CSV 
    | 显示表格

    由于牛肉掺假的复杂性,产地来源的多变性以及可掺入种类的多样性,使牛肉的掺假鉴别具有一定挑战性。当前鉴别牛肉掺假技术具有双面性,基于蛋白质组学的质谱技术具有高灵敏度和准确度,适用于鉴别经过深加工后的掺假牛肉,但仪器价格高,电离源难以普及。当检测机构需要进行现场检测牛源性成分时,基于特异性抗原的ELISA技术更合适,但其蛋白质易发生变性,导致抗原-抗体无法有效识别,影响检测结果。由于每个物种都具有独特的DNA序列,因此基于DNA的基因技术具有高特异性和敏感性,适合鉴别新鲜或经过深加工的掺假牛肉,但常规PCR技术鉴别经过微波或加工后的牛肉,可能会发生样品降解,从而改变PCR扩增的结果。基因芯片技术检测速度快、可大规模地分析检测样品。与其他检测技术相比,基于气味和味觉的传感器技术可快速、低成本检测,适合判别牛肉品种和产地,但电子舌技术检测准确度较低。基于不同波长的光谱技术,可对牛肉样品进行无损检测,适合鉴别牛肉来源和掺假比例,但需要有准确的数据模型做支撑且模型通用性不强,数据稳定性易受外界环境影响。

    综上所述,鉴别基于蛋白质、特异性抗原、DNA的质谱、ELISA和基因技术经过高温加工后的牛肉及其制品或区分类似物种如牦牛和黄牛时,识别标记物尤为重要,直接决定了方法的准确性,对此未来可选用具有高度物种特异性的N-糖基化作为标记物,可将此标记物应用到基因芯片中,为牛肉的掺假鉴别提供快速、高特异性、能区分相似物种的方法;同时还需建立牛肉各产品模型数据库并开发有效算法,便于快速在线分析,高效提取有效数据,优化传感器和光谱处理技术;并通过优化各技术条件,研发小型、快速、准确的鉴别牛肉掺假技术和便携设备,是食品安全检测领域的共同追求。

  • 图  1   牛肉掺假鉴别技术原理图

    Figure  1.   Schematic diagram of beef adulteration identification technology

    图  2   基于蛋白质组学的物种真实性鉴别流程图[21]

    Figure  2.   Flow chart of species authenticity identification based on proteomics[21]

    图  3   表面解吸常压化学电离源仪器装置图[28]

    Figure  3.   Surface desorption atmospheric pressure chemical ionization source instrumentation diagram[28]

    图  4   间接ELISA技术原理图[33]

    Figure  4.   Schematic diagram of indirect ELISA technology[33]

    图  5   双抗体夹心ELISA技术原理图[33]

    Figure  5.   Schematic diagram of double antibody sandwich ELISA technology[33]

    图  6   基于TaqMan荧光探针法PCR技术原理图[64]

    Figure  6.   Schematic diagram of PCR technology based on TaqMan fluorescent probe method[64]

    图  7   基因芯片技术流程图[71]

    Figure  7.   Flow chart of gene chip technology[71]

    图  8   电子鼻、电子舌原理图

    Figure  8.   Schematic diagram of electronic nose and electronic tongue

    图  9   近红外光谱技术采集系统的基本组成[91]

    Figure  9.   Basic composition of near-infrared spectroscopy acquisition system[91]

    图  10   拉曼光谱技术产生过程[99]

    Figure  10.   Raman spectroscopic technique generation process[99]

    图  11   高光谱成像技术系统示意图[91]

    注:a:反射率,b:散射。

    Figure  11.   Schematic diagram of hyperspectral imaging technology system[91]

    表  1   牛肉掺假鉴别技术优缺点及应用

    Table  1   Advantages and disadvantages of beef adulteration identification technology and its application

    检测技术优点缺点应用
    表面解吸常压化学电离质谱无需前处理、检测快速、无化学污染电离源难获取鉴别肉类样品[27]
    液相色谱-质谱联用操作简单、准确性高、多肽稳定性强设备昂贵、数据分析复杂、需要高水平人员鉴别深加工肉制品[114]
    酶联免疫吸附灵敏度高、特异性强、成本低蛋白质易发生变性,相近物种易产生假阳性肉类种类来源鉴定[115]
    PCR灵敏度高、特异性强操作繁琐、微波加热肉制品容易造成DNA降解加工肉品和产品的鉴别[116]
    多重PCR高灵敏度、特异性强、可同时分析多种样品对样品DNA的纯度要求较高熟肉和加工肉类[12]
    PCR-RFLP灵敏度高、特异性强分析时间长、定量检测有限制品种鉴定[71]
    实时荧光PCR灵敏度高、特异性强、可定量分析专业的操作人员新鲜肉样品、肉加工品[116]及熟肉鉴定[12]
    基因芯片操作简单、检测速度快、大规模分析检测样品部分芯片仍在研发、成本高、芯片标准化鉴别肉类种属[73]
    电子鼻检测快速、简单、操作要求低重复性低、需要与其他方法结合提高其准确度肉制品产地判别、品种判别[117]
    电子舌
    近红外光谱检测快速、非接触式复杂的数据作为支撑、模型的稳定性易受外界因素干扰肉中掺伪比例、来源[78]
    拉曼光谱小样品量、非接触的、检测快速有潜力根据肌原纤维和结缔组织蛋白
    确定肉类和肉制品的游离锌[8]
    高光谱成像前处理简单、无损、成本低鉴别牛肉品种[112]
    下载: 导出CSV
  • [1] 王佳佳, 邓源喜, 王丹丹, 等. 牛肉的营养价值及牛肉嫩化技术的研究进展[J]. 肉类工业,2019(9):55−58. [WANG Jiajia, DENG Yuanxi, WANG Dandan, et al. Nutritional value of beef and research progress of beef tenderization technology[J]. Meat Industry,2019(9):55−58. doi: 10.3969/j.issn.1008-5467.2019.09.013
    [2] 陈珍, 刘涛, 顾千辉, 等. 奶公犊牛肉营养成分的分析[J]. 肉类研究,2016,30(4):21−24. [CHEN Zhen, LIU Tao, GU Qianhui, et al. Analysis of nutritional components of milk calf beef[J]. Meat Research,2016,30(4):21−24.
    [3] 吴端钦, 贺志雄, 乔君毅, 等. 牛肉品质影响因素及改善技术的研究进展[J]. 肉类研究,2012,26(10):41−44. [WU Duanqin, HE Zhixiong, QIAO Junyi, et al. Research progress on influencing factors and improvement techniques of beef quality[J]. Meat Research,2012,26(10):41−44.
    [4] 曹兵海, 张越杰, 李俊雅, 等. 2021年肉牛牦牛产业技术发展报告[J]. 中国畜牧杂志,2022,58(3):245−250. [CAO Binghai, ZHANG Yuejie, LI Junya, et al. Report on technological development of beef and yak industry in 2021[J]. Chinese Journal of Animal Husbandry,2022,58(3):245−250.
    [5]

    MONTOWSKA M, POSPIECH E. Authenticity determination of meat and meat products on the protein and DNA basis[J]. Food Reviews International,2010,27(1):84−100. doi: 10.1080/87559129.2010.518297

    [6] 唐穗平, 张燕, 黄景辉. 广东省牛羊肉及其制品中掺杂掺假情况的调查分析[J]. 食品安全质量检测学报,2016,7(5):1882−1886. [TANG Suiping, ZHANG Yan, HUANG Jinghui. Investigation and analysis on adulteration of beef and mutton and their products in Guangdong Province[J]. Journal of Food Safety and Quality Inspection,2016,7(5):1882−1886.
    [7]

    FANG X, ZHANG C. Detection of adulterated murine components in meat products by TaqMan© real-time PCR[J]. Food Chemistry,2016,192:485−490. doi: 10.1016/j.foodchem.2015.07.020

    [8]

    O'MAHONY P J. Finding horse meat in beef products-a global problem[J]. QJM Monthly Journal of the Association of Physicians,2013,106(6):595−597. doi: 10.1093/qjmed/hct087

    [9]

    OUISSAM A, MANUELA Z, VINCENT B, et al. Analytical methods used for the authentication of food of animal origin[J]. Food Chemistry,2018,246:6−17. doi: 10.1016/j.foodchem.2017.11.007

    [10]

    MOYER D C, DEVRIES J W, SPINK J. The economics of a food fraud incident-Case studies and examples including melamine in wheat gluten[J]. Food Control,2017,71:358−364. doi: 10.1016/j.foodcont.2016.07.015

    [11]

    ZHANG W, XUE J. Economically motivated food fraud and adulteration in China: An analysis based on 1553 media reports[J]. Food Control,2016,67:192−198. doi: 10.1016/j.foodcont.2016.03.004

    [12] 朱雨薇. 肉类掺假检测鉴定技术的研究进展[J]. 食品工业,2014,35(11):242−248. [ZHU Yuwei. Research progress on detection and identification technology of meat adulteration[J]. Food Industry,2014,35(11):242−248.
    [13] 孙莹, 刘延平, 赵燕华. 探析食品检验对肉制品安全的重要性[J]. 食品安全导刊,2019(28):74. [SUN Ying, LIU Yanping, ZHAO Yanhua. Analysis on the importance of food inspection to meat product safety[J]. Food Safety Guide,2019(28):74.
    [14] 王守云, 袁明美, 封聪, 等. 肉类掺假鉴别技术研究进展[J]. 肉类研究,2017,31(4):56−61. [WANG Shouyun, YUAN Mingmei, FENG Cong, et al. Research progress of meat adulteration identification technology[J]. Meat Research,2017,31(4):56−61.
    [15] 郑越男, 郭亚辉, 曹进, 等. 液相色谱质谱技术在食品掺假中的应用[J]. 食品安全质量检测学报,2019,10(23):7953−7958. [ZHENG Yuenan, GUO Yahui, CAO Jin, et al. Application of liquid chromatography mass spectrometry in food adulteration[J]. Journal of Food Safety and Quality Inspection,2019,10(23):7953−7958.
    [16]

    NAVEENA B M, JAGADEESH D S, JAGADEESH BABU A, et al. OFFGEL electrophoresis and tandem mass spectrometry approach compared with DNA-based PCR method for authentication of meat species from raw and cooked ground meat mixtures containing cattle meat, water buffalo meat and sheep meat[J]. Food Chemistry,2017,233:311−320. doi: 10.1016/j.foodchem.2017.04.116

    [17]

    FORNAL E, MONTOWSKA M. Species-specific peptide-based liquid chromatography-mass spectrometry monitoring of three poultry species in processed meat products[J]. Food Chemistry,2019,283:489−498. doi: 10.1016/j.foodchem.2019.01.074

    [18]

    STELLA R, SETTE G, MORESSA A, et al. LC-HRMS/MS for the simultaneous determination of four allergens in fish and swine food products[J]. Food Chemistry,2020,331:127276. doi: 10.1016/j.foodchem.2020.127276

    [19]

    PRANDI B, LAMBERTINI F, FACCINI A, et al. Mass spectrometry quantification of beef and pork meat in highly processed food: Application on Bolognese sauce[J]. Food Control,2017,74:61−69. doi: 10.1016/j.foodcont.2016.11.032

    [20]

    MONTOWSKA M. Using peptidomics to determine the authenticity of processed meat[M]. Proteomics in Food Science, 2017, Chapter 14: 225−240.

    [21] 张颖颖, 李莹莹, 范维, 等. 测定肉串源性成分的液相色谱-串联质谱与聚合酶链式反应方法对比研究[J]. 肉类研究,2020,34(7):70−77. [ZHANG Yingying, LI Yingying, FAN Wei, et al. Comparative study of liquid chromatography tandem mass spectrometry and polymerase chain reaction for the determination of meat kebab derived components[J]. Meat Research,2020,34(7):70−77.
    [22] 康超娣, 王守伟, 张颖颖, 等. 液相色谱-串联质谱法对牛肉中掺假成分的相对定量分析[J]. 食品科学,2022,43(4):270−276. [KANG Chaodi, WANG Shouwei, ZHANG Yingying, et al. Relative quantitative analysis of adulterated components in beef by liquid chromatography tandem mass spectrometry[J]. Food Science,2022,43(4):270−276.
    [23]

    KIM G D, SEO J K, YUM H W, et al. Protein markers for discrimination of meat species in raw beef, pork and poultry and their mixtures[J]. Food Chemistry,2017,217:163−170. doi: 10.1016/j.foodchem.2016.08.100

    [24] 张颖颖, 赵文涛, 李慧晨, 等. 液相色谱串联质谱对掺假牛肉的鉴别及定量研究[J]. 现代食品科技,2017,33(2):230−237. [ZHANG Yingying, ZHAO Wentao, LI Huichen, et al. Identification and quantitative study on adulterated beef by liquid chromatography tandem mass spectrometry[J]. Modern Food Technology,2017,33(2):230−237.
    [25]

    WANG G J, ZHOU G Y, REN H W, et al. Peptide biomarkers identified by LC-MS in processed meats of five animal species[J]. Journal of Food Composition and Analysis,2018,73:47−54. doi: 10.1016/j.jfca.2018.07.004

    [26]

    ZHANG M, LI Y, ZHANG Y, et al. Rapid LC-MS/MS method for the detection of seven animal species in meat products[J]. Food Chemistry,2022,371:131075. doi: 10.1016/j.foodchem.2021.131075

    [27] 李倩. 表面解吸常压化学电离质谱法在肉品质与龋齿分析中的应用研究[D]. 南昌: 东华理工大学, 2015

    LI Qian. Application of surface desorption atmospheric pressure chemical ionization mass spectrometry in the analysis of meat quality and dental caries[D]. Nanchang: Donghua University of Technology, 2015.

    [28] 张逸寒, 马涛涛, 王荣浩, 等. 新型常压离子化技术研究进展[J]. 科学技术与工程,2019,19(28):1−15. [ZHANG Yihan, MA Taotao, WANG Ronghao, et al. Research progress of new atmospheric ionization technology[J]. Science, Technology and Engineering,2019,19(28):1−15. doi: 10.3969/j.issn.1671-1815.2019.28.001
    [29]

    ZHU L, YAN J P, ZHU Z Q, et al. Differential analysis of camphor wood products by desorption atmospheric pressure chemical ionization mass spectrometry[J]. Journal of Agricultural and Food Chemistry,2013,61(3):547−552. doi: 10.1021/jf303793t

    [30] 李倩, 王姜, 陈焕文, 等. 表面解吸常压化学电离质谱法快速鉴别羊肉真伪[J]. 质谱学报,2014,35(6):502−508. [LI Qian, WANG Jiang, CHEN Huanwen, et al. Rapid identification of mutton by surface desorption atmospheric pressure chemical ionization mass spectrometry[J]. Journal of Mass Spectrometry,2014,35(6):502−508.
    [31] 李婷婷, 张桂兰, 赵杰, 等. 肉及肉制品掺假鉴别技术研究进展[J]. 食品安全质量检测学报,2018,9(2):409−415. [LI Tingting, ZHANG Guilan, Zhao Jie, et al. Research progress of adulteration identification technology of meat and meat products[J]. Journal of Food Safety and Quality Inspection,2018,9(2):409−415.
    [32]

    BONWICK G A, SMITH C J. Immunoassays: Their history, development and current place in food science and technology[J]. International Journal of Food Science and Technology,2004,39(8):817−827. doi: 10.1111/j.1365-2621.2004.00855.x

    [33] 马永征, 马冬, 白娣斯, 等. 免疫学检测肉类制品掺假研究进展[J]. 肉类研究,2012,26(9):26−29. [MA Yongzheng, MA Dong, BAI Disi, et al. Advances in immunological detection of adulteration in meat products[J]. Meat Research,2012,26(9):26−29.
    [34] 许文娟, 赵晗, 孔彩霞, 等. 肉及肉制品掺假鉴别技术研究进展[J]. 肉类工业,2021(7):44−49. [XU Wenjuan, ZHAO Han, KONG Caixia, et al. Research progress on adulteration identification technology of meat and meat products[J]. Meat Industry,2021(7):44−49.
    [35] 李珍妮, 曾文祥. 酶联免疫吸附法在食品检验中的应用研究[J]. 现代食品,2020(22):190−191, 196. [LI Zhenni, ZENG Wenxiang. Application of enzyme linked immunosorbent assay in food inspection[J]. Modern Food,2020(22):190−191, 196.
    [36]

    KANG’ETHE E K, JONES S J, PATTERSON R L S. Identification of the species origin of fresh meat using an enzyme-linked immunosorbent assay procedure[J]. Meat Science,1982,7(3):229−240. doi: 10.1016/0309-1740(82)90088-2

    [37]

    DINCER B, SPEAROW J L, CASSENS R G, et al. The effects of curing and cooking on the detection of species origin of meat products by competitive and indirect ELISA techniques[J]. Meat Science,1987,20(4):253−265. doi: 10.1016/0309-1740(87)90081-7

    [38]

    KREUZ G, ZAGON J, BROLL H, et al. Immunological detection of osteocalcin in meat and bone meal: A novel heat stable marker for the investigation of illegal feed adulteration[J]. Food Addit Contam,2012,29(5):716−726. doi: 10.1080/19440049.2011.645219

    [39]

    ASENSIO L, GONZÁLEZ I, GARCÍA T, et al. Determination of food authenticity by enzyme-linked immunosorbent assay (ELISA)[J]. Food Control,2008,19(1):1−8. doi: 10.1016/j.foodcont.2007.02.010

    [40]

    MANDLI J, EL FATIMI I, SEDDAOUI N, et al. Enzyme immunoassay (ELISA/immunosensor) for a sensitive detection of pork adulteration in meat[J]. Food Chemistry,2018,255:380−389. doi: 10.1016/j.foodchem.2018.01.184

    [41]

    PERESTAM A T, FUJISAKI K K, NAVA O, et al. Comparison of real-time PCR and ELISA-based methods for the detection of beef and pork in processed meat products[J]. Food Control,2017,71:346−352. doi: 10.1016/j.foodcont.2016.07.017

    [42]

    KUMAR A, KUMAR R R, SHARMA B D, et al. Identification of species origin of meat and meat products on the DNA basis: A review[J]. Critical Reviews in Food Science and Nutrition,2015,55(10):1340−1351. doi: 10.1080/10408398.2012.693978

    [43] 徐瑗聪, 董凯, 黄昆仑, 等. 猪肉、牛肉和绵羊肉掺伪PCR的检测技术[J]. 农业生物技术学报,2013,21(12):1504−1508. [XU Yuancong, DONG Kai, HUANG Kunlun, et al. Detection of adulteration of pork, beef and sheep by PCR[J]. Journal of Agricultural Biotechnology,2013,21(12):1504−1508.
    [44] 全国文献工作标准化技术委员会. SN/T 1119-2002 《进口动物源性饲料中牛羊源性成分检测方法PCR方法》[S]. 北京: 中国标准出版社, 2002

    National Technical Committee for Standardization of Documentation. SN/T 1119-2002 Methods for the detection of bovine and sheep derived components in imported animal derived feeds PCR method[S]. Beijing: China Standards Press, 2002.

    [45]

    UNSELD M, BEYERMANN B, BRANDT P, et al. Identification of the species origin of highly processed meat products by mitochondrial DNA sequences[J]. PCR Methods Appl,1995(4):241−243.

    [46]

    RAJAPAKSHA W, THILAKARATNE I, CHANDRASIRI A D N, et al. Development of PCR assayfor identification of buffalo meat[J]. Asian-australasian Journal of Animal Sciences,2003,16(7):1046−1048. doi: 10.5713/ajas.2003.1046

    [47]

    PIKNOVA L, KUCHTA T. Detection of the beef component in meat products using polymerase chain reaction[J]. Bull Potravinarskeho Vyskumu,2002,41(2):107−111.

    [48] 侯东军, 杨红菊, 姜艳彬, 等. PCR鉴定牛羊肉中搀杂猪肉的方法建立[J]. 食品工业科技,2009,30(3):328−330. [HOU Dongjun, YANG Hongju, JIANG Yanbin, et al. Establishment of PCR method for identification of mixed pork in beef and mutton[J]. Food Industry Science and Technology,2009,30(3):328−330.
    [49]

    ULCA P, BALTA H, ÇAĞIN İ, et al. Meat species identification and Halal authentication using PCR analysis of raw and cooked traditional Turkish foods[J]. Meat Science,2013,94(3):280−284. doi: 10.1016/j.meatsci.2013.03.008

    [50] 吴周林, 赵莉, 王健蓉, 等. 利用12S rRNA基因对四川地区7种牛肉干进行真伪鉴别[J]. 当代畜牧,2018(12):61−63. [WU Zhoulin, ZHAO Li, WANG Jianrong, et al. 12S rRNA gene was used to identify the authenticity of 7 kinds of dried beef in Sichuan[J]. Contemporary Animal Husbandry,2018(12):61−63.
    [51]

    MUSTO M, FARAONE D, CELLINI F, et al. Changes of DNA quality and meat physicochemical properties in bovine supraspinatus muscle during microwave heating[J]. Journal of the Science of Food and Agriculture,2014,94(4):785−791. doi: 10.1002/jsfa.6441

    [52] 巩红霞, 任永宏, 巩强. 用多重PCR方法鉴别生羊肉的真假[J]. 中国畜牧兽医,2006(8):38−39. [GONG Hongxia, REN Yonghong, GONG Qiang. Identification of raw mutton by multiplex PCR[J]. China Animal Husbandry and Veterinary,2006(8):38−39.
    [53] 李杰, 乔绪稳, 余兴龙, 等. 快速鉴定猪肉和牛肉多重PCR方法的建立及初步应用[J]. 湖南畜牧兽医,2011(2):13−15. [LI Jie, QIAO Xuwen, YU Xinglong, et al. Establishment and preliminary application of multiplex PCR method for rapid identification of pork and beef[J]. Hunan Animal Husbandry and Veterinary Medicine,2011(2):13−15.
    [54]

    QIN P, HONG Y, KIM H Y. Multiplex-PCR assay for simultaneous identification of lamb, beef and duck in raw and heat-treated meat mixtures[J]. Journal of Food Safety,2016,36(3):367−374. doi: 10.1111/jfs.12252

    [55] 刘婉婉. 一种用于肉源鉴定的多重PCR技术研究[D]. 苏州: 苏州大学, 2019

    LIU Wanwan. Study on a multiplex PCR technique for meat source identification[D]. Suzhou: Suzhou University, 2019.

    [56]

    LEE J N, JIANG M F, WEN Y L, et al. Multiplex assay for identifying animal species found in the tibetan area using the mitochondrial 12S rRNA gene[J]. Animal Biotechnology,2018,29(1):75−80. doi: 10.1080/10495398.2017.1350690

    [57]

    QUINTEIRO J, REHBEIN H, PRYDE SE, et al. Use of mtDNA direct polymerase chain reaction (PCR) sequencing and PCR-restriction fragment length polymorphism methodologies in species identification of canned tuna[J]. Journal of Agricultural and Food Chemistry,1998,46(4):1662−1669. doi: 10.1021/jf970552+

    [58]

    MENNAH-GOVELA Y A, BORNHORST G M. Food buffering capacity: Quantification methods and its importance in digestion and health[J]. Food & Function,2021,12(2):543−563.

    [59] 高琳, 徐幸莲, 周光宏. 应用PCR-RFLP法鉴别肉制品中的猪和牛源性成分[J]. 南京农业大学学报,2008(2):135−138. [GAO Lin, XU Xinglian, ZHOU Guanghong. Identification of porcine and bovine derived components in meat products by PCR-RFLP[J]. Journal of Nanjing Agricultural University,2008(2):135−138.
    [60]

    GIRISH P S, ANJANEYULU A S R, VISWAS K N, et al. Meat species identification by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) of mitochondrial 12S rRNA gene[J]. Meat Science,2005,70(1):107−112. doi: 10.1016/j.meatsci.2004.12.004

    [61]

    KUMAR D, SINGH S P, KARABASANAVAR N S, et al. Authentication of beef, carabeef, chevon, mutton and pork by a PCR-RFLP assay of mitochondrial cytb gene[J]. Journal of Food science and Technology,2014,51(11):3458−3463. doi: 10.1007/s13197-012-0864-z

    [62]

    RENÉ K, JÜRG R, JÜRG R. Multiplex real-time PCR for the detection and quantification of DNA from beef, pork, horse and sheep[J]. European Food Research and Technology,2011,232(1):151−155. doi: 10.1007/s00217-010-1371-y

    [63]

    SAKAI Y, KOTOURA S, YANO T, et al. Quantification of pork, chicken and beef by using a novel reference molecule[J]. Bioscience, Biotechnology, and Biochemistry,2011,75(9):1639−1643. doi: 10.1271/bbb.110024

    [64] 吴翠. 农产品病原体的核酸快速扩增和荧光检测系统研究[D]. 杭州: 浙江大学, 2021

    WU Cui. Study on rapid nucleic acid amplification and fluorescence detection system of agricultural pathogens[D]. Hangzhou: Zhejiang University, 2021.

    [65] 全国生化检测标准化技术委员会. GB/T 38164-2019 常见畜禽动物源性成分检测方法 实时荧光PCR法[S]. 北京: 中国国家标准化管理委员会, 2019

    National Technical Committee for Standardization of Biochemical Testing. GB/T 38164-2019 Methods for the detection of common livestock poultry and animal derived components real-time fluorescent PCR[S]. Beijing: China National Standardization Administration, 2019.

    [66]

    DOOLEY J J, PAINE K E, GARRETT S D, et al. Detection of meat species using TaqMan real-time PCR assays[J]. Meat Science,2004,68(3):431−438. doi: 10.1016/j.meatsci.2004.04.010

    [67]

    LUBIS H, SALIHAH N T, HOSSAIN M M, et al. Development of fast and sensitive real-time qPCR assay based on a novel probe for detection of porcine DNA in food sample[J]. LWT,2017,84:686−692. doi: 10.1016/j.lwt.2017.06.043

    [68] 曾少灵, 秦智锋, 阮周曦, 等. 多重实时荧光PCR检测牛、山羊和绵羊源性成分[J]. 生物工程学报,2009,25(1):139−146. [ZENG Shaoling, QIN Zhifeng, RUAN Zhouxi, et al. Detection of bovine, goat and sheep derived components by multiplex real-time fluorescent PCR[J]. Journal of Bioengineering,2009,25(1):139−146. doi: 10.3321/j.issn:1000-3061.2009.01.021
    [69] 刘睿茜, 其美次仁, 李家鹏, 等. 基于单核苷酸多态性位点的多重实时荧光定量聚合酶链式反应法鉴别当雄高山牦牛肉[J]. 肉类研究,2020,34(10):47−52. [LIU Ruiqian, QI Meiciren, LI Jiapeng, et al. Identification of Dangxiong mountain yak meat by multiplex real-time fluorescence quantitative polymerase chain reaction based on single nucleotide polymorphism[J]. Meat Research,2020,34(10):47−52. doi: 10.7506/rlyj1001-8123-20200917-228
    [70] 陈晓宇, 陆利霞, 熊雄, 等. 实时荧光PCR技术鉴别流通环节的掺假牛肉及其制品[J]. 生物加工过程,2021,19(2):214−226. [CHEN Xiaoyu, LU Lixia, XIONG Xiong, et al. Identification of adulterated beef and its products in circulation by real-time fluorescent PCR[J]. Bioprocessing Process,2021,19(2):214−226.
    [71] 石丰运. 应用基因芯片技术鉴别检测动物源性成分[D]. 兰州: 甘肃农业大学, 2010

    SHI Fengyun. Identification and detection of animal derived components by gene chip technology[D]. Lanzhou: Gansu Agricultural University, 2010.

    [72] 石丰运, 缪建锟, 张利平, 等. 运用基因芯片技术检测牛、山羊、猪和鸡源性成分[J]. 生物工程学报,2010,26(6):823−829. [SHI Fengyun, MIAO Jiankun, ZHANG Liping, et al. Detection of bovine, goat, pig and chicken derived components by gene chip technology[J]. Journal of Bioengineering,2010,26(6):823−829.
    [73] 朱业培, 王玮, 吕青骎, 等. 基于基因芯片技术检测6种动物源性成分[J]. 南京农业大学学报,2015,38(6):1003−1008. [ZHU Yepei, WANG Wei, LÜ Qingxiang, et al. Detection of 6 animal derived components based on gene chip technology[J]. Journal of Nanjing Agricultural University,2015,38(6):1003−1008.
    [74] 赵睿骁, 韩斐, 江明锋, 等. PCR-膜芯片技术在牦牛及犏牛肉制品的真假鉴定中的应用[J]. 生物技术通报,2020,36(1):202−208. [ZHAO Ruixiao, HAN Fei, JIANG Mingfeng, et al. Application of PCR membrane chip technology in the identification of yak and beef products[J]. Biotechnology Bulletin,2020,36(1):202−208.
    [75]

    BELLAGAMBA F, MORETTI V M, COMINCINI S, et al. Identification of species in animal feedstuffs by polymerase chain reaction-restriction fragment length polymorphism analysis of mitochondrial DNA[J]. Journal of Agricultural and Food Chemistry,2001,49(8):3775−3781. doi: 10.1021/jf0010329

    [76] 相婷婷. 基于核酸, 蛋白质和特异性风味物质的牛羊肉掺伪鉴定[D]. 扬州: 扬州大学, 2019

    XIANG Tingting. Identification of adulteration in beef and mutton based on nucleic acid, protein and specific flavor substances[D]. Yangzhou: Yangzhou University, 2019.

    [77] 刘洋, 贾文珅, 马洁, 等. 电子鼻技术在肉与肉制品检测中的研究进展和应用展望[J]. 智慧农业(中英文),2021,3(4):29−41. [LIU Yang, JIA Wenli, MA Jie, et al. Research progress and application prospect of electronic nose technology in meat and meat products detection[J]. Smart Agriculture (Chinese and English),2021,3(4):29−41.
    [78] 田晓静. 基于电子鼻和电子舌的羊肉品质检测[D]. 杭州: 浙江大学, 2014

    TIAN Xiaojing. Mutton quality detection based on electronic nose and electronic tongue[D]. Hangzhou: Zhejiang University, 2014.

    [79] 董福凯, 周秀丽, 查恩辉. 电子鼻在掺假牛肉卷识别中的应用[J]. 食品工业科技,2018,39(4):219−221, 227. [DONG Fukai, ZHOU Xiuli, CHA Enhui. Application of electronic nose in recognition of adulterated beef rolls[J]. Food Industry Technology,2018,39(4):219−221, 227.
    [80] 周秀丽, 刘全, 查恩辉. 电子鼻在掺假牛肉馅识别中的应用[J]. 食品工业科技,2017,38(4):73−76, 80. [ZHOU Xiuli, LIU Quan, CHA Enhui. Application of electronic nose in identification of adulterated beef stuffing[J]. Food Industry Technology,2017,38(4):73−76, 80.
    [81]

    HAN F, HUANG X, H. AHETO J, et al. Detection of beef adulterated with pork using a low-cost electronic nose based on colorimetric sensors[J]. Foods,2020,9(2):193. doi: 10.3390/foods9020193

    [82] 贾洪锋, 卢一, 何江红, 等. 电子鼻在牦牛肉和牛肉猪肉识别中的应用[J]. 农业工程学报,2011,27(5):358−363. [JIA Hongfeng, LU Yi, HE Jianghong, et al. Application of electronic nose in recognition of yak meat, beef and pork[J]. Journal of Agricultural Engineering,2011,27(5):358−363.
    [83] 许文娟, 韩芳, 赵晗, 等. 电子鼻结合主成分分析法快速鉴别掺假牛肉[J]. 肉类工业,2021(10):33−35. [XU Wenjuan, HAN Fang, ZHAO Han, et al. Rapid identification of adulterated beef by electronic nose combined with principal component analysis[J]. Meat Industry,2021(10):33−35. doi: 10.3969/j.issn.1008-5467.2021.10.007
    [84] 黄嘉丽, 黄宝华, 卢宇靖, 等. 电子舌检测技术及其在食品领域的应用研究进展[J]. 中国调味品,2019,44(5):189−193, 196. [HUANG Jiali, HUANG Baohua, LU Yujing, et al. Research progress of electronic tongue detection technology and its application in food field[J]. Chinese Condiments,2019,44(5):189−193, 196.
    [85] 黄珏, 王正亮, 李慕雨, 等. 基于电子舌和近红外光谱技术的进口牛肉产地溯源[J]. 中国食品学报,2021,21(12):254−260. [HUANG Jue, WANG Zhengliang, LI Muyu, et al. Origin traceability of imported beef based on electronic tongue and near infrared spectroscopy[J]. Chinese Journal of Food,2021,21(12):254−260.
    [86]

    ZHANG X, ZHANG Y, MENG Q, et al. Evaluation of beef by electronic tongue system TS-5000Z: Flavor assessment, recognition and chemical compositions according to its correlation with flavor[J]. PloS One,2015,10(9):e0137807. doi: 10.1371/journal.pone.0137807

    [87] 汪敏, 赵晔. 电子鼻和电子舌在鱼肉鲜度评价中的应用研究[J]. 肉类研究,2009(6):63−65. [WANG Min, ZHAO Ye. Study on the application of electronic nose and electronic tongue in the evaluation of fish freshness[J]. Meat Research,2009(6):63−65. doi: 10.3969/j.issn.1001-8123.2009.06.017
    [88] 秦盼柱. 基于核酸扩增原理的常见肉源性成分快速鉴定研究[D]. 合肥: 合肥工业大学, 2021

    QIN Panzhu. Rapid identification of common meat derived components based on the principle of nucleic acid amplification[D]. Hefei: Hefei University of Technology, 2021.

    [89] 杨志敏. 应用近红外光谱技术快速检测原料肉新鲜度及掺假的研究[D]. 咸阳: 西北农林科技大学, 2011

    YANG Zhimin. Study on rapid detection of freshness and adulteration of raw meat by near infrared spectroscopy[D]. Xianyang: Northwest A & F University, 2011.

    [90] 冯永巍, 王琴. 肉类掺假检验技术研究进展[J]. 食品与机械,2013,29(4):237−240. [FENG Yongwei, WANG Qin. Research progress of meat adulteration inspection technology[J]. Food and Machinery,2013,29(4):237−240.
    [91]

    WANG W, PENG Y, SUN H, et al. Spectral detection techniques for non-destructively monitoring the quality, safety, and classification of fresh red meat[J]. Food Analytical Methods,2018,11(10):2707−2730. doi: 10.1007/s12161-018-1256-4

    [92] 白京, 李家鹏, 邹昊, 等. 近红外光谱定性定量检测牛肉汉堡饼中猪肉掺假[J]. 食品科学,2019,40(8):287−292. [BAI Jing, LI Jiapeng, ZOU Hao, et al. Qualitative and quantitative detection of pork adulteration in beef burger cake by near infrared spectroscopy[J]. Food Science,2019,40(8):287−292.
    [93]

    RADY A, ADEDEJI A. Assessing different processed meats for adulterants using visible-near-infrared spectroscopy[J]. Meat Sicence,2018,136:59−67.

    [94] 冷拓. 基于近红外和核磁共振技术的牛肉肉糜掺假和品质指标预测[D]. 南昌: 南昌大学, 2020

    LENG Tuo. Adulteration and quality index prediction of beef minced meat based on near infrared and nuclear magnetic resonance technology[D]. Nanchang: Nanchang University, 2020.

    [95] 张丽华, 相启森, 李顺峰, 等. 基于支持向量机的近红外光谱技术鉴别掺假牛肉[J]. 西北农林科技大学学报(自然科学版),2016,44(12):201−205. [ZHANG Lihua, XIANG Qisen, LI Shunfeng, et al. Identification of adulterated beef by near infrared spectroscopy based on support vector machine[J]. Journal of Northwest University of Agriculture and Forestry Science and Technology (Natural Science Edition),2016,44(12):201−205.
    [96]

    ALOMAR D, GALLO C, CASTANEDA M, et al. Chemical and discriminant analy-sis of bovine meat by near infrared reflectance spectroscopy (NIRS)[J]. Meat Sci,2003,63(4):441−450. doi: 10.1016/S0309-1740(02)00101-8

    [97]

    MORSY N, SUN D W. Robust linear and non-linear models of NIR spectros-copy for detection and quantification of adulterants in fresh and fro-zen-thawed minced beef[J]. Meat Sci,2012,93(2):292−302.

    [98] 刘晨, 陈复生, 夏义苗, 等. 拉曼光谱技术在食品分析中的应用[J]. 食品工业,2020,41(4):267−271. [LIU Chen, CHEN Fusheng, XIA Yimiao, et al. Application of Raman spectroscopy in food analysis[J]. Food Industry,2020,41(4):267−271.
    [99] 周秀军. 基于拉曼光谱的食用植物油定性鉴别与定量分析[D]. 杭州: 浙江大学, 2013

    ZHOU Xiujun. Qualitative identification and quantitative analysis of edible vegetable oil based on Raman spectroscopy[D]. Hangzhou: Zhejiang University, 2013.

    [100]

    ZHAO M, DOWNEY G, O’DONNELL C P. Dispersive Raman spectroscopy and multivariate data analysis to detect offal adulteration of thawed beefburgers[J]. Journal of Agricultural and Food Chemistry,2015,63(5):1433−1441. doi: 10.1021/jf5041959

    [101]

    ROBERT C, FRASER-MILLER S J, JESSEP W T, et al. Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy[J]. Food Chemistry,2021,343:128441. doi: 10.1016/j.foodchem.2020.128441

    [102]

    BIASIO M D, STAMPFER P, LEITNER R, et al. Micro-Raman spectroscopy for meat type detection[C]//Next-Generation Spectroscopic Technologies VIII: volume 9482. SPIE, 2015: 377−382.

    [103]

    ZAJĄC A, HANUZA J, DYMIŃSKA L. Raman spectroscopy in determination of horse meat content in the mixture with other meats[J]. Food Chemistry,2014,156:333−338. doi: 10.1016/j.foodchem.2014.02.002

    [104]

    BOYACI İ H, TEMIZ H T, UYSAL R S, et al. A novel method for discrimination of beef and horsemeat using Raman spectroscopy[J]. Food Chemistry,2014,148:37−41. doi: 10.1016/j.foodchem.2013.10.006

    [105]

    AMIGO J M, MARTÍ I, GOWEN A. Chapter 9-hyperspectral imaging and chemometrics: A perfect combination for the analysis of food structure, composition and quality[M/OL]//MARINI F. Data Handling in Science and Technology, 28. Elsevier, 2013: 343-370[2022-05-18]. https://www.sciencedirect.com/science/article/pii/B9780444595287000090.

    [106]

    HITCHCOCK C H S, CRIMES A A. methodology for meat species identification: A review[J]. Meat Science,1985,15(4):215−224. doi: 10.1016/0309-1740(85)90077-4

    [107]

    KAMRUZZAMAN M, BARBIN D, ELMASRY G, et al. Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat[J]. Innovative Food Science & Emerging Technologies,2012,16:316−325.

    [108] 郎玉苗, 杨春柳, 李翠, 等. 光谱技术在肉品掺杂掺假鉴别中的应用研究进展[J]. 肉类研究,2019,33(2):72−77. [LANG Yumiao, YANG Chunliu, LI Chui, et al. Research progress on the application of spectral technology in the identification of adulterated meat[J]. Meat Research,2019,33(2):72−77. doi: 10.7506/rlyj1001-8123-20181217-231
    [109]

    KAMRUZZAMAN M, MAKINO Y, OSHITA S. Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning[J]. Journal of Food Engineering,2016,170:8−15. doi: 10.1016/j.jfoodeng.2015.08.023

    [110]

    ROPODI A I, PANAGOU E Z, NYCHAS G J E. Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat[J]. Food Control,2017,73:57−63. doi: 10.1016/j.foodcont.2016.05.048

    [111]

    LIU J, CAO Y, WANG Q, et al. Rapid and non-destructive identification of water-injected beef samples using multispectral imaging analysis[J]. Food Chemistry,2016,190:938−943. doi: 10.1016/j.foodchem.2015.06.056

    [112] 王彩霞, 王松磊, 贺晓光, 等. 基于可见/近红外高光谱成像技术的牛肉品种鉴别[J]. 食品工业科技,2019,40(12):241−247. [WANG Caixia, WANG Songlei, HE Xiaoguang, et al. Beef variety identification based on visible/near infrared hyperspectral imaging technology[J]. Food Industry Science and Technology,2019,40(12):241−247.
    [113] 王勇峰, 郎玉苗, 黄必志, 等. 光谱技术鉴别冷鲜肉和冻融肉的研究进展[J]. 黑龙江畜牧兽医,2017(11):68−71. [WANG Yongfeng, LANG Yumiao, HUANG Bizhi, et al. Research progress in the identification of cold and frozen meat by spectroscopy[J]. Heilongjiang Animal Husbandry and Veterinary,2017(11):68−71.
    [114]

    PENG J, ZHANG H, NIU H, et al. Peptidomic analyses: The progress in enrichment and identification of endogenous peptides[J]. TrAC Trends in Analytical Chemistry,2020,125:115835. doi: 10.1016/j.trac.2020.115835

    [115]

    JONES S J, PATTERSON R L S. A modified indirect ELISA procedure for raw meat speciation using crude anti-species antisera and stabilised immunoreagents[J]. Journal of the Science of Food and Agriculture,1986,37(8):767−775. doi: 10.1002/jsfa.2740370809

    [116] 任君安. 羊肉及其制品中掺假动物源性成分数字PCR技术精准定量研究[D]. 北京: 中国农业大学, 2017

    REN Junan. Accurate quantitative study on adulterated animal derived components in mutton and its products by digital PCR[D]. Beijing: China Agricultural University, 2017.

    [117] 田晓静, 王俊, 裘姗姗, 等. 电子鼻和电子舌信号联用方法分析及其在食品品质检测中的应用[J]. 食品工业科技,2015,36(1):386−389. [TIAN Xiaojing, WANG Jun, QIU Shanshan, et al. Analysis of electronic nose and electronic tongue signal combined method and its application in food quality detection[J]. Food Industry Science and Technology,2015,36(1):386−389.
  • 期刊类型引用(2)

    1. 安容慧,陈兴开,常子安,任紫烟,张婕,连欢,贾连文,杨相政. 采后不同时间压差预冷对水蜜桃货架品质和香气成分的影响. 食品工业科技. 2024(09): 317-324 . 本站查看
    2. 隋海涛,陈东杰,王凤丽,邹泽宇,郭风军,马倩倩,隋青,张长峰,孙崇德. 桃果实采后品质变化机制及调控技术研究进展. 中国果菜. 2024(12): 1-8+19 . 百度学术

    其他类型引用(2)

图(11)  /  表(1)
计量
  • 文章访问数:  372
  • HTML全文浏览量:  130
  • PDF下载量:  41
  • 被引次数: 4
出版历程
  • 收稿日期:  2022-06-05
  • 网络出版日期:  2023-02-03
  • 刊出日期:  2023-03-31

目录

/

返回文章
返回
x 关闭 永久关闭