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Aécio Luís de Sousa Dias¹, Emmanuelle Meudec¹, Arnaud Verbaere¹

¹SPO, Univ Montpellier, INRAE, Institut Agro, Montpellier, France; INRAE, CALIS Research Infrastructure, PROBE Research Infrastructure, PFP Polyphenol analysis facility, Montpellier, France.

|September 4, 2025

概括

捕获离子流动性谱法 (TIMS) 通过改善特征检测和使复杂异构体的分离,显著增强了可可豆中的多分析. 这种先进的技术有助于更准确地识别化合物.

科学领域:

代谢学
分析化学
食品科学

背景情况:

聚异构体在染色分离过程中经常共,使其在像可可这样的复杂矩阵中难以识别.
基于特征的分子网络 (FBMN) 是分析新陈代谢数据的强大工具,但可能受到共问题的限制.
离子移动光谱 (IMS) 提供了一个额外的分离维度,可以解决异构干扰.

研究的目的:

评估与FBMN相结合的捕获离子移动性光谱法 (TIMS) 对于可可多的代谢分析的附加值.
评估TIMS对黑色和棕色可可豆中发现的统计学意义上的特征的影响.
研究UHPLC-TIMS-HRMS-FBMN在分解和注释多异构体方面的能力.

主要方法:

使用非定位超高性能液态色谱 (UHPLC) 与被困离子移动性光谱-四极飞行时间质谱 (TIMS-Q-TOF) 进行可可豆分析.
为了比较数据采集策略,使用TIMS维度和不使用TIMS维度进行了分析.
对处理的数据进行了统计分析和FBMN,以识别和注释化合物.

主要成果:

黑色可可豆的区分特征数量几乎翻了一番,棕色可可豆的区分特征数量增加了50%.
TIMS成功地分离了异构聚,包括二元体,三元体和C-O-糖化物,它们在分子网络中分解成不同的集群.
在UHPLC-TIMS-HRMS-FBMN方法中,发现了新型的分离因子,如黑豆中的乙烯桥式醇和棕豆中的各种糖化醇和氨酸.

结论:

TIMS通过改善共化聚异构体的分离,显著提高了FBMN的结构解能力.
整合TIMS提供了更全面的可可豆性分析,揭示了以前未知的化合物.
这种TIMS增强的代谢学方法为可可和相关食品的详细性表征提供了有价值的策略.

关键词:

FBMN 公司基于特征的分子网络类化合物时间豆可可捕获的离子移动性谱学

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