液相色谱-高分辨质谱法结合同位素轮廓分析鉴别咖啡因和洛伐他汀来源

Source identification of caffeine and lovastatin by liquid chromatography-high resolution mass spectrometry combined with isotope profile analysis

中文关键词:  咖啡因  洛伐他汀  液相色谱-高分辨质谱  同位素轮廓分析  色谱同位素分馏  变点检测算法

英文关键词:caffeine  lovastatin  liquid chromatography-high-resolution mass spectrometry  isotope profile analysis  chromatographic isotope fractionation  change-point detection algorithm

中文摘要:

      目的 建立液相色谱-高分辨质谱法(liquid chromatography-high-resolution mass spectrometry, LC-HRMS)的化合物特异性同位素轮廓分析方法, 鉴别食品中天然提取、化学合成及生物工程发酵来源的咖啡因、洛伐他汀。方法 采用Waters XBridge BEH C18柱(250 mm×4.6 mm, 5 μm), 通过平缓洗脱梯度适度展宽目标化合物色谱洗脱时间; 采用分辨率140000半峰全宽(full width at half maximum, FWHM)的四极杆-静电场轨道阱质谱仪, 对色谱峰内不少于20个非等间隔时间点的目标分子完整同位素峰进行连续采集; 数据处理阶段应用理论同位素杂原子贡献扣除模型, 结合Grubbs离群值检验与修剪精确线性时间(pruned exact linear time, PELT)变点检测算法, 量化色谱峰内同位素比值的不均一性及阶跃变化, 辅以全洗脱去卷积技术构建食品基质二维同位素特征分布模型。结果 天然茶源、化学合成及生物工程发酵来源的咖啡因, 以及天然红曲发酵与药用级来源的洛伐他汀, 其基础同位素指纹存在显著差异。模拟掺杂体系中, 当外源合成物质添加量达目标物总量10%时, PELT算法可在P<0.01置信水平下准确识别同位素比值变点; 当比例大于等于20%时, 可锁定阶跃突变点。通过同位素质量平衡模型反推掺假比例, 相对偏差控制在±15%以内。方法在0.1~10.0 μg/mL范围内连续5 d测试的δ13C日间相对标准偏差为0.8%, 最大系统偏差+0.8‰, 表明具有高精密度和良好抗基质干扰能力。结论 本研究建立的多维同位素轮廓时序分析方法, 可在无需目标物完全色谱分离的条件下, 利用色谱同位素分馏效应实现同一分子天然与人工合成形态的定性区分, 为解决天然存在与人为添加来源易混淆物质的定性难题提供了可行技术手段, 为食品安全监管模式优化提供方法学参考。

英文摘要:

      Objective To establish a compound-specific isotope profiling method by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) for identify caffeine and lovastatin derived from natural extraction, chemical synthesis and bioengineered fermentation in food matrices. Methods Waters XBridge BEH C18 column (250 mm×4.6 mm, 5 μm) coupled with a mild elution gradient was used to moderately broaden the chromatographic elution window of target compounds. A quadrupole-orbitrap mass spectrometer operated at a resolution of 140000 full width at half maximum (FWHM) was used to continuously acquire the complete isotopic peaks of target molecules at no fewer than 20 non-equidistant time points within each chromatographic peak. During data processing, a theoretical correction model was applied to subtract heteroatom isotope contributions. Grubbs’ outlier test and the pruned exact linear time (PELT) change-point detection algorithm were then used to quantify intra-peak isotopic heterogeneity and stepwise shifts. Full-elution deconvolution was further used to construct a two-dimensional isotope landscape model of the food matrix. Results Caffeine from natural tea sources, chemical synthesis and bioengineered fermentation, as well as lovastatin from natural red-yeast fermentation and pharmaceutical-grade sources, showed distinct baseline isotopic fingerprints. In simulated adulteration systems, when exogenous synthetic substances accounted for 10% of the total target compound, the PELT algorithm accurately detected isotope-ratio change points at the P<0.01 confidence level; When the proportion was greater than or equal to 20%, stepwise transition points were reliably localized. The adulteration proportions back-calculated using the isotope mass-balance model showed relative deviations within ±15%. Over the concentration range of 0.1–10.0 μg/mL, the 5 consecutive days of testing gave an inter-day relative standard deviation of 0.8% for δ13C, with a maximum systematic bias of +0.8‰, demonstrating high precision and strong resistance to matrix interference. Conclusion The multidimensional isotope-profile time-series method established in this study enables qualitative differentiation between natural and artificially synthesize forms of the same molecule by exploiting chromatographic isotope fractionation without requiring complete chromatographic separation. This approach provides a feasible technical solution for resolving source-identification challenges involving substances with overlapping natural occurrence and artificial addition, and offers a methodological reference for improving food safety regulatory strategies.