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Identifying putative key metabolites from fingerprinting metabolomics for the authentication of rice origin: A case study of Sengcu rice

ABSTRACT: The expanding scale and nature of rice fraud in the global food system has caused major economic and human health concerns. Herein, an untargeted metabolomics approach based on the UHPLC-Q-Orbitrap-HRMS system was utilized for the discrimination between authentic and commercial Sengcu rice, a local specialty cultivated by terraced farming in northern Vietnam. A total of 8398 positive and 5250 negative mode compounds were introduced to multivariate statistical analyses for the construction of classification models. The first two principal components explaining 52% of the total variance in both datasets exhibited distinguished clusters of authentic against commercial Sengcu rice. Partial least squares-discriminant analysis models were optimized to obtain the optimal number of retained components, the optimal number of variables retained in each component and the best prediction distance type for model evaluation. One component containing five positive (DMG, RSA, RCA, PAL and BOSe) and six negative mode variables (PXP, RXP, TDHP, ISS, MXP and RGB) was sufficient to discriminate between authentic and commercial Sengcu rice. The classification error rate was less than 1.1310-4, as determined from repeated k-fold cross validation. These putative signature metabolites clearly separated authentic and commercial Sengcu rice in the hierarchical clustering models. In addition, the isolated metabolites also reflected the cultivation practices of terraced farming of authentic Sengcu rice. Overall, we have proposed an effective method for the identification of key metabolites from fingerprinting metabolomics, and it could serve as a fundamental approach for other in-depth food authentication studies.

ORGANISM(S): Oryza Sativa Rice

TISSUE(S): Seeds

SUBMITTER: Yen Hai Dao

PROVIDER: ST002169 | MetabolomicsWorkbench | Sun May 08 00:00:00 BST 2022

REPOSITORIES: MetabolomicsWorkbench

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Project description:Use of Information Dependent Acquisition mass spectra and Sequential Window Acquisition of all Theoretical fragment-ion mass spectra for fruit juices metabolomics and authentication. LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry. Objectives To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study. Methods IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets. Results IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%. Conclusion SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.

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Identifying putative key metabolites from fingerprinting metabolomics for the authentication of rice origin: A case study of Sengcu rice

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