Summary of Study ST002169
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001380. The data can be accessed directly via it's Project DOI: 10.21228/M8BH86 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002169 |
| Study Title | Identifying putative key metabolites from fingerprinting metabolomics for the authentication of rice origin: A case study of Sengcu rice |
| Study Type | MS Untargeted analysis |
| Study Summary | 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 1.1310-4, 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. |
| Institute | Institute of Chemistry, Vietnam Academy of Science and Technology |
| Laboratory | Laboratory of Environmental and Bioorganic Chemistry |
| Last Name | Dao |
| First Name | Yen Hai |
| Address | 18 Hoang Quoc Viet Street, Hanoi, 100000, Vietnam |
| hoasinhmoitruong.vast@gmail.com | |
| Phone | +84 985859795 |
| Submit Date | 2022-05-08 |
| Num Groups | 2 |
| Total Subjects | 71 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | mzML |
| Analysis Type Detail | LC-MS |
| Release Date | 2022-06-08 |
| Release Version | 1 |
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Collection:
| Collection ID: | CO002248 |
| Collection Summary: | A total of 71 Sengcu rice samples (hereafter referred to as ‘Sengcu samples’) of the 2020 spring crop were employed for the assessment of metabolic variation between authentic (n = 37) and commercial groups (n = 34). The authentic group comprised 37 samples, each of which was originally cultivated from a separate rice paddy in Muong Vi village, Bat Xat district, Lao Cai province (22.607916 N, 103.704403 E, approximately 2314 m above sea level). Meanwhile, the commercial Sengcu samples were collectively obtained from different local market vendors in Lao Cai and Dien Bien provinces, with 24 and 10 samples per region, respectively. Since the cultivation locations of those commercial samples were not available, each commercial sample was collected from a local market, and the distance between every two markets ranged from 10 to 15 km to minimize pseudoreplication. These commercial samples were intentionally gathered immediately after the spring harvesting season of 2020 to ensure compatibility with authentic samples. |
| Sample Type: | Rice grain |
