CN111830181B - 冰鲜肉新鲜度标志物及其筛选和预测模型拟合方法和用途 - Google Patents
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Abstract
本发明公开了基于代谢组学的冰鲜肉新鲜度标志物及其筛选和预测模型拟合方法和用途。该标志物,包括吲哚‑3‑甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺、丝氨酸‑苯丙氨酸。预测模型为:Y=3.964+1.97E‑7X1‑4.22E‑7X2‑3.37E‑7X3+8.80E‑8X4+1.26E‑8X5‑5.57E‑7X6,该方法利用Agilent 1290 UHPLC串联Q Exactive Orbitrap高分辨率质谱,具有更高的分辨率,可以更准确的检测到更多的物质,能够更全面的从整体上阐明冰鲜鸡保存过程中的代谢产物,所得结果更加可靠。
Description
技术领域
本发明涉及食品检测技术领域,特别涉及冰鲜肉新鲜度标志物及其筛选和预测模型拟合方法和用途。
背景技术
冰鲜肉,又叫冷却肉、排酸肉、冰鲜肉。是指对屠宰后的畜胴体迅速进行冷却处理,使胴体温度(以后腿肉中心为测量点)在24小时内降为0-4℃,并在后续加工、流通和销售过程中始终保持0-4℃范围内的生鲜肉。
冰鲜鸡是指经检疫后的活鸡,屠宰后迅速冷却,使鸡胴体中心温度保持在0-4℃,然后进行分割、修整、包装,并在后续的贮存、运输和销售过程中始终保持在0-4℃范围内的新鲜鸡。与冰鲜鸡相关的安全问题主要是食物中存在的有毒物质和致病菌,这可能会影响公共健康。值得注意的事,欧洲议会和委员会条例178/2002,不仅将对消费者身体有害的食品视为不安全食品,而且将不适合人类食用的食品也视为不安全食品。从这个角度来说,外观、口感、风味较差的不新鲜甚至变质鸡肉同样为不安全食品。因此,保证冰鲜鸡新鲜度对于消费者食品安全来说至关重要。而开发检测冰鲜鸡新鲜度的精确方法是保证冰鲜鸡新鲜度,维护消费者安全的重要前提。
鸡肉新鲜度随时间的延长显著下降,特别是未加工处理的鲜鸡肉,而鸡肉新鲜度决定了消费者的购买选择。鸡肉新鲜度下降即腐败变质的过程,肉类变质是一个复杂的事件,包括生物和化学活动及两者可能的相互作用,最终导致产品无法被人类食用。除了脂质氧化和自溶酶促反应外,肉类变质被认为是多种微生物活动的结果,因为肉类的营养成分、pH(5.5-6.5)和高水分含量可以使多种微生物生长和存活。微生物活动导致的变质过程,会产生大量的小分子量代谢物,这些代谢物身份的分析表征可能为食品控制、分类、质量评估提供关键信息。
代谢组学可以定义为对生物细胞、组织、器官或生物体中低分子量(<1500Da)代谢产物(代谢组)的综合研究。作为主要的组学工具之一,代谢组学技术已用于鸡蛋、鱼肉、贝类、大豆等新鲜度的分析及相关生物标志物的筛选。但是,关于冷鲜鸡贮存过程中代谢组分析和新鲜度生物标志物的相关研究却鲜有报道。冰鲜鸡在保存过程中,由于微生物的作用,质量不断下降,危害消费者身体健康。因此,筛选可用于鉴别冰鲜鸡新鲜度的标志物,对于冰鲜鸡新鲜度检测新方法的开发具有重要意义。
发明内容
发明目的:本发明目的是提供一种检测更加精确、全面的基于代谢组学的冰鲜鸡新鲜度标志物。
本发明另目的是提供所述检测更加精确、全面的基于代谢组学的冰鲜鸡新鲜度标志物的筛选和预测模型拟合方法。
本发明最后一目的是提供所述检测更加精确、全面的基于代谢组学的冰鲜鸡新鲜度标志物的用途。
技术方案:本发明提供一种基于代谢组学的冰鲜鸡新鲜度标志物,包括吲哚-3-甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺、丝氨酸-苯丙氨酸。
所述的基于代谢组学的冰鲜鸡新鲜度标志物的筛选和预测模型拟合方法,包括如下步骤:
(1)取冰鲜肉样本添加乙腈、甲醇、水,涡旋、匀质,冰水浴中超声,静置孵育,离心,取上清液保存备用;
(2)对上清液进行超高效液相色谱串联质谱分析;
(3)通过冰鲜鸡肌肉代谢组学轮廓分析、数据的前处理、基于二级质谱数据定性定量代谢物,将代谢物的精确分子量与数据库进行比最终获得冰鲜鸡代谢物图谱;
(4)然后通过Kruskal-Wallis检验筛选差异代谢物,利用随机森林回归分析和逐步多元回归分析获得基于代谢组学的冰鲜鸡新鲜度标志物并拟合预测模型。
进一步地,所述预测模型为:
Y=3.964+1.97E-7X1-4.22E-7X2-3.37E-7X3+8.80E-8X4+1.26E-8X5-5.57E-7X6,
如Y≤1,则为新鲜冰鲜鸡;如2<Y<5,则为次新鲜冰鲜鸡;如Y≥5,则为不新鲜冰鲜鸡,
所述Y为预测保存天数,X1、X2、X3、X4、X5、X6分别代表吲哚-3-甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺、丝氨酸-苯丙氨酸相对峰面积或相对含量。
进一步地,所述冰鲜肉为冰鲜鸡胸肌。
所述的基于代谢组学的冰鲜鸡新鲜度标志物在冰鲜鸡新鲜度检测中的用途。
有益效果:本发明可快速、准确的检测冰鲜鸡的新鲜度。和以往鉴定技术相比,本发明利用Agilent 1290UHPLC串联Q Exactive Orbitrap高分辨率质谱,具有更高的分辨率,可以更准确的检测到更多的物质,能够更全面的从整体上阐明冰鲜鸡保存过程中的代谢产物,所得结果更加可靠。本发明鉴定到6个重要标志物,并基于此拟合了预测模型,AUC值达0.89,相比于其它单指标检测方法,具有更高的准确性和可信度。
附图说明
图1为所有代谢物主成分分析(PCA)图,其中,A和B分别代表正离子和负离子模式下检测到的代谢物PCA结果,M1、M3、M5、M7代表4℃保存1、3、5、7天的冰鲜鸡样本,QC代表质控样本;
图2为265个差异代谢物聚类热图;
图3为随机森林回归模型5折交叉验证结果,横坐标表示代谢物数量,纵坐标表示交叉验证错误率,当选择38代谢物作为潜在标志物时,模型具有最低错误率;
图4为重要性排名前38代谢物多分类ROC曲线,横坐标表示假阳性率,纵坐标表示真阳性率,ROC曲线下面积(AUC)为0.90,表明利用38个代谢物能出色的预测冰鲜鸡的新鲜度;
图5为模型6中6个标志物合并ROC曲线,横坐标表示假阳性率,纵坐标表示真阳性率,ROC曲线下面积(AUC)为0.89,表明拟合的模型6能出色的预测冰鲜鸡的新鲜度。
具体实施方式
下述实施例中所用的材料、试剂等,如无特殊说明,均可从商业途径得到。
本实施例标志物的筛选和预测方法,包括以下步骤:
1.实验动物
实验动物为江苏京海禽业集团有限公司饲养的70日龄海扬黄鸡母鸡,共32只,饲养条件一致。
2.材料与方法
2.1仪器与试剂
实验仪器包括:超高效液相色谱仪(Agilent 1290)、Q Exactive Orbitrap高分辨率质谱仪(Thermo Fisher)、高速低温离心机(Eppendorf)、震动涡旋仪、4℃冰箱、纯水仪(TKA)。
实验试剂包括:乙腈(CMW,LCMS级)、醋酸铵(CMW,LCMS级)、氨水(CMW, LCMS级)、甲醇(CMW,LCMS级)、L-2-氯苯丙氨酸(上海恒柏生物,纯度≥98%)、纯水。
2.2样本采集
32只海扬黄鸡,电击晕,放血处死,取左侧胸肌置于无菌塑料袋中,于4℃下转移至实验室。在实验室中,按保存时间将样本分为M1、M3、M5、M7四组,分别对应保存1、3、5、7天,每组8个,每个样本取50g,转移至无菌塑料袋中,4℃保存备用。本步骤需在1h内完成。
2.3代谢物提取
步骤3.1中样本,分别在第1、3、5、7天,采集M1、M3、M5、M7组各样本50mg,转移至EP管中,每管添加1000μL提取溶剂(乙腈、甲醇、水比例为2∶2∶1),涡旋30 s,45Hz匀质4min,冰水浴中超声5min,匀质和超声循环重复3次,-20℃孵育1h, 12000rpm 4℃离心15min,取上清液,转移至LC-MS瓶中,-80℃保存备用。通过将所有样本的上清液取等量混合后制备质控样本。
2.4UHPLC-MS/MS分析
使用Agilent 1290超高效液相色谱串联Thermo Q Exactive Orbitrap高分辨率质谱检测,色谱柱为UPLC HSS T3。代谢物检测条件为:正模式:流动相A:0.1%甲酸水溶液,流动相B:乙腈;负模式:流动相A:5mM醋酸铵水溶液(用氨水调节pH 值至9.0),流动相B:乙腈。洗脱程序为:0min,1%B;1min,1%B;8min,99%B;10min,99%B; 10.1min,1%B;12min,1%B。洗脱速度为0.5mL/min,进样量为2μL。
质谱参数条件为:ESI离子源喷雾电压:3800V(正离子模式)或-3100V(负离子模式);毛细管温度:320℃;鞘气流速:45Arb;辅助气流速:15Arb;扫描范围: 70-1000m/z;一级分辨率:70000;二级分辨率:17500;分步碰撞能量的强度取值:3;分步碰撞能量:20eV,40eV,60eV;扫描速率:7Hz。
3.冰鲜鸡新鲜度标志物筛选
3.1代谢物定性定量
使用ProteoWizard将MS原始数据文件转换为mzML格式,并通过R包XCMS(版本3.2)进行处理,包括保留时间对齐,峰检测和峰匹配,随后根据内标将每个样品标准化,利用数据集中最小值的一半替换缺失值。预处理结果生成了一个数据矩阵,该数据矩阵由保留时间(RT),质荷比(m/z)值和峰强度组成。加工后的数据利用OSI-SMMS (1.0版,大连化学数据解决方案信息技术有限公司)基于二级质谱数据对质谱峰进行注释,所用数据库为广州基迪奥生物科技有限公司搭建的MS/MS数据库。结果在正离子和负离子模式下共检测到12522个质谱峰,基于二级质谱数据共成功鉴定并注释到 546个代谢物。
3.2PCA分析
使用R Project(http://www.r-project.org/),采用无监督降维方法主成分分析(PCA) 来描述不同组之间代谢轮廓的差异。PCA是一种统计过程,可将成千上万个相关代谢物变量转换为一组线性不相关变量(称为主成分)的值。图1显示了不同实验分组样本代谢物PCA结果。结果分别基于正离子模式和负离子模式数据获得。PCA分数图显示了QC样品的低分散性,这表明整个分析过程中仪器漂移最小。保存不同时间长度的组之间存在一定程度的分离。1天组可以与3天、5天和7天组区分开,3天组也可以与5 天和7天组区分开。但是,无法将5天小组与7天小组区分开。上述结果表明保存时间长度的差异会改变代谢谱图。从代谢组学的角度来看,我们研究中的冷冻鸡肉可分为三类:“新鲜”(第1天),“次新鲜”(2-4天)和“不新鲜”(5-7天)。以上结果表明,代谢组学方法可用于检测评价冰鲜鸡的新鲜度。
3.6差异代谢物筛选
利用SPSS 22.0软件中非参数Kruskal-Wallis检验,基于二级质谱定性定量的546个代谢物筛选差异代谢物,P<0.05为差异代谢物。结果共筛选到265个差异代谢物,图 2展示了差异代谢物在保存1、3、5、7天冰鲜鸡样本中的相对含量。
3.7随机森林回归分析
利用随机森林回归分析基于265个差异代谢物筛选潜在的冰鲜鸡新鲜度标志物。通过R中的randomForest软件包(https://CRAN.R-project.org/package=randomForest)进行了随机森林回归分析。使用均方误差增加百分比(%IncMSE)和节点纯度增量(IncNodePurity)评估代谢产物的重要性。进行了5次交叉验证,确定可以用作冰鲜鸡新鲜度潜在生物标记的代谢物的最佳数量为38个(图3)。然后,我们根据38个潜在的生物标记绘制了ROC曲线(图4),AUC值为0.90,显示筛选到的38个潜在标志物对冰鲜鸡新鲜度具有出色的预测能力。
3.8关键生物标志物筛选与预测模型拟合
利用SPSS 22.0中的逐步多元线性回归分析进一步筛选关键标志物,并拟合冰鲜鸡新鲜度预测模型。结果共筛选到了6个重要的标志物,分别为吲哚-3-甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺、丝氨酸-苯丙氨酸。6个标志物共拟合了6个预测模型,表 1展示了各个模型的参数,利用调整后的决定系数(R2)评价各个模型的拟合优度。模型6具有最高的拟合优度(R2=0.969),包含全部6个标志物,显示6个标志物的联合具有最高预测能力(表1)。绘制6个标志物的联合ROC曲线,评价对于冰鲜鸡新鲜度的预测能力,ROC曲线AUC值为0.89(图5),再次证明6个标志物联合对于冰鲜鸡新鲜度具有出色的预测能力。模型6方程如下:
Y=3.964+1.97E-7X1-4.22E-7X2-3.37E-7X3+8.80E-8X4+1.26E-8X5-5.57E-7X6
其中Y为预测值,X1、X2、X3、X4、X5、X6分别代表吲哚-3-甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺、丝氨酸-苯丙氨酸相对峰面积(相对含量)。
Y为冰鲜鸡的预测保存天数,将吲哚-3-甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺、丝氨酸-苯丙氨酸相对含量代入公式,计算得到预测保存天数,根据PCA结果,如 Y≤1,则为新鲜冰鲜鸡;如2<Y<5,则为次新鲜冰鲜鸡;如Y≥5,则为不新鲜冰鲜鸡。
表1关键标志物筛选和预测模型拟合结果
Claims (3)
1.一种冰鲜鸡新鲜度预测模型拟合方法,其特征在于:包括如下步骤:
(1)取冰鲜鸡样本添加乙腈、甲醇、水,涡旋、匀质,冰水浴中超声,静置孵育,离心,取上清液保存备用;
(2)对上清液进行超高效液相色谱串联质谱分析;
(3)通过冰鲜鸡代谢组学轮廓分析、数据的前处理、基于二级质谱数据定性定量代谢物,将代谢物的精确分子量与数据库进行比最终获得冰鲜鸡代谢物图谱;
(4)然后通过Kruskal-Wallis检验筛选差异代谢物,利用随机森林回归分析和逐步多元回归分析获得基于代谢组学的冰鲜鸡新鲜度标志物并拟合预测模型;
所述冰鲜鸡新鲜度标志物为吲哚-3-甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺和丝氨酸-苯丙氨酸;
所述预测模型为:
Y=3.964+1.97E-7X1-4.22E-7X2-3.37E-7X3+8.80E-8X4+1.26E-8X5-5.57E-7X6,
如Y≤1,则为新鲜冰鲜鸡;如2<Y<5,则为次新鲜冰鲜鸡;如Y≥5,则为不新鲜冰鲜鸡,
所述Y为预测保存天数,X1、X2、X3、X4、X5、X6分别代表吲哚-3-甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺、丝氨酸-苯丙氨酸相对峰面积或相对含量。
2.根据权利要求1所述的冰鲜鸡新鲜度预测模型拟合方法,其特征在于:所述冰鲜鸡为冰鲜鸡胸肌。
3.冰鲜鸡新鲜度标志物在冰鲜鸡新鲜度检测中的用途,其特征在于:所述冰鲜鸡新鲜度标志物为吲哚-3-甲醛、尿苷酸、苯巯基尿酸、葡萄糖酸、酪胺和丝氨酸-苯丙氨酸。
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