Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain; Research Institute in Food Nutrition and Food Safety, University of Barcelona, Av. Prat de la Riba 171, Edifici Recerca (Gaudí), E08921 Santa Coloma de Gramenet, Spain.
Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain; Research Institute in Food Nutrition and Food Safety, University of Barcelona, Av. Prat de la Riba 171, Edifici Recerca (Gaudí), E08921 Santa Coloma de Gramenet, Spain; Serra Húnter Fellow, Generalitat de Catalunya, Rambla de Catalunya 19-21, E08007 Barcelona, Spain.
Food Chem. 2022 Oct 1;390:133141. doi: 10.1016/j.foodchem.2022.133141. Epub 2022 May 6.
Ion mobility spectrometry (IMS) has proved its huge potential in many research areas, especially when hyphenated with chromatographic techniques or mass spectrometry (MS). However, focusing on food analysis, and particularly in classification and authentication issues, very few applications have been reported. In this study, differential mobility spectrometry coupled to mass spectrometry (DMS-MS) is presented for the first time as an alternative and high-throughput technique for food classification and authentication purposes using a fingerprinting strategy. As a study case, 70 Spanish paprika samples (from La Vera, Murcia, and Mallorca) were analyzed by DMS-MS to address their classification -using partial least squares regression-discriminant analysis (PLS-DA)- and authentication -through soft independent modeling of class analogy (SIMCA). As a result, after external validation, complete sample classification according to their geographical origin and excellent La Vera and Mallorca sample authentication were reached.
离子迁移谱（IMS）已在许多研究领域证明了其巨大的潜力，特别是与色谱技术或质谱（MS）联用。然而，在食品分析方面，特别是在分类和认证问题方面，报道的应用很少。在本研究中，首次提出了将差分迁移谱与质谱（DMS-MS）联用作为一种替代技术，用于使用指纹图谱策略进行食品分类和认证的高通量技术。作为研究案例，通过 DMS-MS 分析了 70 个西班牙甜椒样品（来自拉韦拉、穆尔西亚和马略卡岛），以利用偏最小二乘回归判别分析（PLS-DA）对其进行分类，并通过类间相似性的软独立建模（SIMCA）进行认证。结果，经过外部验证，根据其地理来源对样品进行了完全分类，并对拉韦拉和马略卡岛的样品进行了出色的认证。
