一种改进的数据过滤策略，用于使用漂移管离子淌度-质谱法对复杂基质中的聚合物进行靶向特征分析：在分析葡萄籽提取物中原花青素中的应用。
A modified data filtering strategy for targeted characterization of polymers in complex matrixes using drift tube ion mobility-mass spectrometry: Application to analysis of procyanidins in the grape seed extracts.
机构信息
State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing 210009, China.
State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing 210009, China.
出版信息
Food Chem. 2020 Aug 15;321:126693. doi: 10.1016/j.foodchem.2020.126693. Epub 2020 Mar 29.
BACKGROUNDS
Polymers, widely existing in food or dietary materials, have been attracting researchers, facing challenges, and needing effective strategies on targeted characterization in complex matrixes.
METHODS
A modified data filtering strategy (including locating with drift time and m/z ranges, multiple mass defect filtering, validating MS information, and evaluating MS/MS spectra) was developed and applied for procyanidins in the grape seed extracts (GSE) using drift tube ion mobility-mass spectrometry. The procyanidin ions' trendlines were predicted by multi-model regression. Their collision cross-sections (CCSs) were calculated using single-field methods.
RESULTS AND DISCUSSION
Totally, 769 CCSs belonging to 686 procyanidins with polymer degrees at 1-15 were characterized. The exponent regression was the most reasonable model (r ≥ 0.9379) to reveal the trendlines. The change tendency of CCSs with their polymer degrees, charge states, and linkage types were investigated.
CONCLUSION
This study provided an innovative strategy for targeted characterization of polymers in complex matrixes.
背景
聚合物广泛存在于食品或膳食材料中，已引起研究人员的关注，他们面临着在复杂基质中进行目标特征分析的挑战，需要有效的策略。
方法
采用一种改进的数据过滤策略（包括漂移时间和 m/z 范围定位、多重质量缺陷过滤、MS 信息验证和 MS/MS 谱评估），并应用于利用漂移管离子淌度-质谱对葡萄籽提取物（GSE）中原花青素进行分析。采用多模型回归预测原花青素离子的趋势线。使用单场方法计算它们的碰撞截面（CCS）。
结果与讨论
共鉴定出 686 种聚合度为 1-15 的原花青素，其 CCSs 达 769 个。指数回归是揭示趋势线的最合理模型（r≥0.9379）。研究了 CCS 与其聚合度、电荷状态和键合类型的变化趋势。
结论
本研究为复杂基质中聚合物的目标特征分析提供了一种创新策略。
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