基于高分辨质谱法结合化学计量学筛选猪肉储藏过程中风险标志物

Screening of risk markers in pork storage process based on high-resolution mass spectrometry combined with chemometrics

中文关键词:  超高效液相色谱-四极杆-静电场轨道阱高分辨质谱法  化学计量学  猪肉  标志物

英文关键词:ultra performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry  stoichiometry  pork  Symbols

中文摘要:

      目的 基于超高效液相色谱-四级杆-静电场轨道阱高分辨质谱法(ultra performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry, UPLC-Q-Orbitrap HRMS)结合化学计量学筛选猪肉储藏过程中风险标志物。方法 样品经过前处理后，采用UPLC-Q-Orbitrap HRMS对猪肉进行代谢物检测，获取高精度的色谱和质谱数据，运用主成分分析（principal component analysis, PCA）进行降维以探索样本间的分布特征，通过正交偏最小二乘法判别分析（orthogonal partial least squares-discriminant analysis, OPLS-DA）筛选出变量重要性因子（variable importance factor, VIP）值大于1.0的差异代谢物。进一步通过T检验（P < 0.05）评估差异的统计显著性，并结合倍数变化分析（Fold Change, FC > 2或FC < 0.5）确认最终的关键代谢物。结果 PCA结果显示储藏温度显著影响猪肉不同部位（里脊、五花肉、后腿肉）的代谢物分布，不同温度下样品间在主成分空间中分离良好（总方差R2= 0.861, 预测能力Q2= 0.478）。OPLS-DA模型进一步优化了组间差异的分离（R2= 0.811, Q2= 0.653），储藏温度对代谢物丰度和类型的影响更加清晰。真实模型的R2和Q2明显高于置换模型，验证了分析的可靠性。通过筛选条件（VIP > 1, P < 0.05, FC > 2或< 0.5），进一步结合结构确证，确定了N-乙酰腐胺和6-甲基喹啉为风险标志物。结论 UPLC-Q-Orbitrap HRMS结合化学计量学能够有效筛选出猪肉储藏过程中的风险标志物。

英文摘要:

      Objective To screen risk markers in the storage process of pork using ultra-performance liquid chromatography-quadrupole-orbitrap high-resolution mass spectrometry (UPLC-Q-Orbitrap HRMS) combined with chemometrics. Methods After sample pretreatment, UPLC-Q-Orbitrap HRMS was employed to detect metabolites in pork, obtaining high-precision chromatographic and mass spectrometric data. Principal component analysis (PCA) was applied for dimensionality reduction to explore the distribution characteristics among samples. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was then used to screen differential metabolites with a variable importance factor (VIP) > 1.0. The statistical significance of the differences was further evaluated using t-tests (P < 0.05), and the final key metabolites were confirmed by combining fold change analysis (Fold Change, FC > 2 or FC < 0.5). Results PCA revealed that storage temperature significantly influenced metabolite distribution in different pork cuts (loin, belly, and ham), with samples under different temperatures showing clear separation in the principal component space (total variance R2 = 0.861, predictive ability Q2 = 0.478). The OPLS-DA model further optimized the separation of intergroup differences (R2 = 0.811, Q2 = 0.653), making the impact of storage temperature on metabolite abundance and type more evident. The R2 and Q2 values of the real model were significantly higher than those of the permutation model, confirming the reliability of the analysis. Through screening conditions (VIP > 1, P < 0.05, FC > 2 or < 0.5) and structural verification, N-acetylputrescine and 6-methylquinoline were identified as risk markers. Conclusion UPLC-Q-Orbitrap HRMS combined with chemometrics effectively screened risk markers in pork during storage.