ICYTAC (Instituto de Ciencia y Tecnología de Alimentos Córdoba), CONICET and Universidad Nacional de Córdoba, Bv. Dr. Juan Filloy s/n; Cdad. Universitaria, 5000 Córdoba, Argentina; Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Orgánica and ISIDSA-SECyT, Medina Allende esq. Haya de La Torre, Edif. Ciencias II, Cdad. Universitaria, 5000 Córdoba, Argentina.
ICYTAC (Instituto de Ciencia y Tecnología de Alimentos Córdoba), CONICET and Universidad Nacional de Córdoba, Bv. Dr. Juan Filloy s/n; Cdad. Universitaria, 5000 Córdoba, Argentina; Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Orgánica and ISIDSA-SECyT, Medina Allende esq. Haya de La Torre, Edif. Ciencias II, Cdad. Universitaria, 5000 Córdoba, Argentina.
Food Chem. 2022 Mar 1;371:131355. doi: 10.1016/j.foodchem.2021.131355. Epub 2021 Oct 7.
Chia, flax, and sesame seeds are well known for their nutritional quality and are commonly included in bakery products. So far, the development of methods to verify their presence and authenticity in foods is a requisite and a raised need. In this work we applied untargeted metabolomics to propose authenticity markers. Seeds were analyzed by HPLC-MS/MS and 9938 features in negative mode and 9044 in positive mode were obtained by Mzmine. After isotopes grouping, alignment, gap-filling, filtering adducts, and normalization, PCA was applied to explore the dataset and recognize pre-existent classification patterns. OPLS-DA analysis and S-Plots were used as supervised methods. Twenty-five molecules (12 in negative mode and 13 in positive mode) were selected as discriminant for the three seeds, polyphenols and lignans were identified among them. To the best of our knowledge, this is the first approach using non-target HPLC-MS/MS for the authentication of chia, flax and sesame seeds.
奇亚籽、亚麻籽和芝麻籽以其营养价值而闻名，通常被添加到烘焙食品中。到目前为止，开发验证其在食品中存在和真实性的方法是必需的，也是一个新的需求。在这项工作中，我们应用非靶向代谢组学来提出真实性标志物。通过 HPLC-MS/MS 分析种子，Mzmine 获得了负模式下的 9938 个特征和正模式下的 9044 个特征。经过同位素分组、对齐、间隙填充、过滤加合物和归一化后，应用 PCA 来探索数据集并识别预先存在的分类模式。OPLS-DA 分析和 S-图被用作有监督的方法。选择了 25 种分子（负模式下 12 种，正模式下 13 种）作为这三种种子的鉴别特征，其中鉴定出了多酚和木脂素。据我们所知，这是首次使用非靶向 HPLC-MS/MS 方法对奇亚籽、亚麻籽和芝麻籽进行鉴定。
