Study on the geographical origin and characteristic differential components of Qianbei Ma lamb based on rapid evaporative ionization mass spectrometry

A rapid evaporative ionization mass spectrometry (REIMS) method combined with intelligent knife (iKnife) method was developed to explore the geographical origin and characteristic differential components of Qianbei Ma lamb. The REIMS conditions were initially refined, with the cauterization duration...

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Bibliographic Details
Main Authors: Ji Zhang, Rui Hou, Yuhang Luo, Xiaohong Zhang, Hong Luo, Lei Ling, Liping Xiang
Format: Article
Language:English
Published: Czech Academy of Agricultural Sciences 2025-02-01
Series:Czech Journal of Food Sciences
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Online Access:https://cjfs.agriculturejournals.cz/artkey/cjf-202501-0007_study-on-the-geographical-origin-and-characteristic-differential-components-of-qianbei-ma-lamb-based-on-rapid-e.php
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Summary:A rapid evaporative ionization mass spectrometry (REIMS) method combined with intelligent knife (iKnife) method was developed to explore the geographical origin and characteristic differential components of Qianbei Ma lamb. The REIMS conditions were initially refined, with the cauterization duration of 3 seconds, and the auxiliary solvent flow rate set to 100 μL.min-1 to prevent duplication. A database model was created from raw data through the proposed principal component analysis-linear discriminate analysis (PCA-LDA) in Live ID software, successfully applied to identify samples from 5 provinces in China and the real-time reliable identification rate with confidence higher than 99%. The obtained data by REIMS were used to establish the multivariate statistical models which using orthogonal projection to latent structures discriminant analysis (OPLS-DA), provided strong the discrimination power between composition and content changes of 16 specific ions such as m/z 726.3952 and m/z 744.4050, etc., including fatty alcohols, fatty acids and phosphatidylserines in mutton of different origin and the model displayed validation [R2(Y) = 0.968, Q2 = 0.924].
ISSN:1212-1800
1805-9317