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探索高分辨率LC-MS的潜力,结合离子移动性分离和替代最小深度,以加强杏仁原产地认证

Henri Lösel¹, Maike Arndt¹, Soeren Wenck¹

¹Hamburg School of Food Science - Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146, Hamburg, Germany.

|January 15, 2024

概括

杏仁 (Prunus dulcis) 的地理来源会影响市场价值. 使用UPLC-ESI-IM-qToF-MS的脂质分析准确确定了杏仁的原产地,其中三糖类是关键标记物.

科学领域:

农业化学农业化学
分析化学分析化学
食品科学食品科学食品科学

背景情况:

杏仁 (Prunus dulcis) 是一个全球消费的商品.
地理来源显著影响杏仁的市场价值.
准确的原产地确定对于质量控制和真实性至关重要.

研究的目的:

开发和验证一种方法来确定杏仁的地理来源.
为了确定杏仁原产地分类的关键脂质生物标志物.
评估使用质谱仪进行常规原产地确定的可行性.

主要方法:

分析了来自六个国家的250个杏仁样本,使用超高性能液体染色学-电喷射电离-离子移动性-四极飞行时间质谱法 (UPLC-ESI-IM-qToF-MS).
在方法开发中包括各种品种,收获期,以及甜杏和苦杏.
应用主要组件分析 (PCA) 和随机森林 (RF) 机器学习算法进行分类.
使用替代最小深度 (SMD) 识别相关代谢物.

主要成果:

主要组件分析 (PCA) 显示了三个不同的地理分组:澳大利亚/美国,西班牙/意大利和伊朗/摩洛哥.
一个随机森林 (RF) 模型在确定杏仁的地理来源方面取得了88.8%的准确性.
含有氧化,奇链或短链脂肪酸的三糖化物 (TAG),以及某些脂,被确定为最有效的分类标记.
错误分类主要发生在澳大利亚和美国杏仁之间,因为品种和气候相似.

结论:

脂质分析,特别是专注于特定的三甘油和脂,为确定杏仁的地理来源提供了可靠的方法.
开发的UPLC-ESI-IM-qToF-MS方法显示了在杏仁真实性和原产地验证中常规应用的希望.
使用诸如三重四极质谱仪 (QqQ-MS) 等仪器进行有针对性的分析,可以促进这种方法转移到常规实验室环境中.

关键词:

杏仁是一种杏仁.食品认证的真实性食品认证的真实性离子流动性的离子流动性机器学习是机器学习.质谱测量质量谱测量在SMD中,它是SMD.

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