College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
China Certification & Inspection Group Hunan Co., Ltd., Changsha 410021, China.
J Agric Food Chem. 2021 Sep 8;69(35):10379-10393. doi: 10.1021/acs.jafc.1c04122. Epub 2021 Aug 26.
α-Dicarbonyls (α-DCs) are key reactive Maillard intermediates with structural diversity and are widely found in foods and in vivo, but little is known regarding the complete molecular profiles of these potentially harmful electrophiles. Herein, we reported a novel isotope-coding derivatization (ICD) strategy for the broad-spectrum, quantitative profiling of (non)target α-DC species in natural foodstuffs. It utilized differential isotope labeling (DIOL) with a reagent pair -phenylenediamine (OPD)/OPD- (deuterated) to form stable quinoxalines for class-specific fragmentation-dependent acquisition using liquid chromatography-hybrid quadrupole linear ion trap mass spectrometry (LC-QqLIT). A combination of facile one-pot quantitative labeling and convenient cleanup protocol afforded satisfactory sensitivity, linearity, accuracy (81-116%), and process recovery (86-109% with RSDs < 10%) by matrix-matched ICD-internal standard calibration, without significant matrix interference (-9 to 5%), isotopic effect (<0.5%), and cocktail effect. A more generic DIOL-based LC-QqLIT algorithm integrated double precursor ion and neutral loss scan to trigger enhanced product ions with the unique isobaric doublet tags (4 Da shift), enabling simultaneous screening and relative quantitation of nontarget α-DC analogues in a single analysis. This study has widened the vision on complex α-DC profiles in traditional botanicals, which revealed a wide occurrence of α-DCs in such processed sugar-rich products, yet their abundance varied greatly among different samples.
α-二羰基化合物（α-DCs）是具有结构多样性的关键反应性美拉德中间产物，广泛存在于食品和体内，但对于这些潜在有害亲电体的完整分子谱知之甚少。在此，我们报道了一种新的同位素编码衍生化（ICD）策略，用于广泛、定量分析天然食品中非靶向 α-DC 物种。它利用试剂对 - 苯二胺（OPD）/OPD-（氘代）的差异同位素标记（DIOL）形成稳定的喹喔啉，用于使用液相色谱 - 混合四极杆线性离子阱质谱（LC-QqLIT）进行基于特征碎片的依赖性采集。简便的一锅定量标记和方便的净化方案相结合，通过基质匹配 ICD-内标校准，提供了令人满意的灵敏度、线性度、准确性（81-116%，RSDs < 10%）和过程回收率（86-109%，RSDs < 10%），没有显着的基质干扰（-9 至 5%）、同位素效应（<0.5%）和鸡尾酒效应。更通用的基于 DIOL 的 LC-QqLIT 算法集成了双前体离子和中性丢失扫描，以触发具有独特等质量双峰标签（4 Da 位移）的增强产物离子，从而能够在单次分析中同时筛选和相对定量非靶向 α-DC 类似物。这项研究拓宽了对传统植物中复杂 α-DC 谱的认识，揭示了 α-DCs 在这些加工糖丰富产品中的广泛存在，但它们在不同样品中的丰度差异很大。
