Guangdong Province Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China.
Institute for Global Food Security, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DJ, United Kingdom.
J Agric Food Chem. 2021 Aug 11;69(31):8861-8873. doi: 10.1021/acs.jafc.1c02630. Epub 2021 Jul 28.
In this work, an untargeted and pseudotargeted metabolomics combination approach was used for authentication of three shrimp species (, , and ). The monophasic extraction-based untargeted metabolomics approach enabled comprehensive-coverage and high-throughput analysis of shrimp tissue and revealed 26 potential markers. The pseudotargeted metabolomics approach confirmed 21 markers (including 9 key markers), which realized at least putative identification. The 21 confirmed markers, as well as 9 key markers, were used to develop PLS-DA models, correctly classifying 60/60 testing samples. Furthermore, DD-SIMCA and PLS-DA models were integrated based on the 9 key markers, with 59/60 and 20/20 samples of the species that were involved and uninvolved in model training correctly classified. The results demonstrated the potential of this untargeted and pseudotargeted metabolomics combination approach for shrimp species authentication.
本工作采用非靶向和伪靶向代谢组学组合方法对三种虾类(凡纳滨对虾、中国明对虾和日本囊对虾)进行鉴定。基于单相提取的非靶向代谢组学方法能够全面、高通量地分析虾组织,揭示了 26 个潜在的标志物。伪靶向代谢组学方法验证了 21 个标志物(包括 9 个关键标志物),实现了至少部分鉴定。21 个确证的标志物和 9 个关键标志物用于建立 PLS-DA 模型,可正确分类 60/60 个测试样本。此外,基于 9 个关键标志物,整合了 DD-SIMCA 和 PLS-DA 模型,对参与和未参与模型训练的物种的 59/60 和 20/20 个样本进行了正确分类。结果表明,这种非靶向和伪靶向代谢组学组合方法在虾类物种鉴定方面具有潜力。