Instituto de Ciência e Tecnologia de Alimentos, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, Brazil.
Instituto de Ciência e Tecnologia de Alimentos, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, Brazil.
Food Res Int. 2023 Jan;163:112315. doi: 10.1016/j.foodres.2022.112315. Epub 2022 Dec 7.
LC-HR-MS/MS is the predominant analytical technique in phenolic compound (PC) research. However, the manual interpretation of mass spectra is a heavy nontrivial time-consuming task and depends on mass spectrometry and phenolic compounds fragmentation deep knowledge. We think this manual approach should be partially translated into a practical software that allows users to perform such complicated analyses. In silico fragmentation software have been tested for small molecule identification, MS-FINDER and SIRIUS stood out at identification contests and challenges. We evaluated both software to identify PC from two data categories: 1 MS/MS spectra from 18 phenolic compound standards (PCS) and 2 phenolic compounds from 8 food samples (FPC) (coffee, green tea, cranberry juice, grape juice, orange juice, apple juice, soy extract and parsley extract). MS-FINDER and SIRIUS were able to correctly identifymore than 90% of the PCS by LC-HR-MS/MS. The main FPC were also correctly identified by MS-FINDER (70%) and SIRIUS (38%). We highlight that these software were unable to differentiate PC isomers. This task is only possible by using additional information, such as chromatographic behavior and manual analysis of the relative intensity of fragments in the MS/MS spectra. Therefore, the combination of initial screening by using MS-FINDER and SIRIUS with manual analyses of additional information is a powerful and efficient approach for identifying phenolic compounds.
LC-HR-MS/MS 是酚类化合物（PC）研究中的主要分析技术。然而，对质谱进行手动解释是一项繁重且耗时的非平凡任务，并且依赖于质谱和酚类化合物的碎片深入知识。我们认为这种手动方法应该部分转化为实用的软件，使用户能够执行此类复杂的分析。已经对虚拟碎片软件进行了小分子鉴定测试，MS-FINDER 和 SIRIUS 在鉴定竞赛和挑战中脱颖而出。我们评估了这两种软件，以从两个数据类别中鉴定 PC：1）来自 18 种酚类化合物标准品（PCS）的 MS/MS 光谱和 2）来自 8 种食品样品（FPC）的酚类化合物（咖啡、绿茶、蔓越莓汁、葡萄汁、橙汁、苹果汁、大豆提取物和欧芹提取物）。LC-HR-MS/MS 能够正确鉴定超过 90%的 PCS。MS-FINDER 和 SIRIUS 也能够正确鉴定主要的 FPC（70%和 38%）。我们强调，这些软件无法区分 PC 异构体。只有使用其他信息，例如色谱行为和对 MS/MS 光谱中碎片相对强度的手动分析，才能完成此任务。因此，使用 MS-FINDER 和 SIRIUS 进行初步筛选，并结合对其他信息的手动分析，是一种强大且高效的鉴定酚类化合物的方法。
