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A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry
INTRODUCTION: Fish fraud detection is mainly carried out using a genomic profiling approach requiring long and complex sample preparations and assay running times. Rapid evaporative ionisation mass spectrometry (REIMS) can circumvent these issues without sacrificing a loss in the quality of results....
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668337/ https://www.ncbi.nlm.nih.gov/pubmed/29151824 http://dx.doi.org/10.1007/s11306-017-1291-y |
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author | Black, Connor Chevallier, Olivier P. Haughey, Simon A. Balog, Julia Stead, Sara Pringle, Steven D. Riina, Maria V. Martucci, Francesca Acutis, Pier L. Morris, Mike Nikolopoulos, Dimitrios S. Takats, Zoltan Elliott, Christopher T. |
author_facet | Black, Connor Chevallier, Olivier P. Haughey, Simon A. Balog, Julia Stead, Sara Pringle, Steven D. Riina, Maria V. Martucci, Francesca Acutis, Pier L. Morris, Mike Nikolopoulos, Dimitrios S. Takats, Zoltan Elliott, Christopher T. |
author_sort | Black, Connor |
collection | PubMed |
description | INTRODUCTION: Fish fraud detection is mainly carried out using a genomic profiling approach requiring long and complex sample preparations and assay running times. Rapid evaporative ionisation mass spectrometry (REIMS) can circumvent these issues without sacrificing a loss in the quality of results. OBJECTIVES: To demonstrate that REIMS can be used as a fast profiling technique capable of achieving accurate species identification without the need for any sample preparation. Additionally, we wanted to demonstrate that other aspects of fish fraud other than speciation are detectable using REIMS. METHODS: 478 samples of five different white fish species were subjected to REIMS analysis using an electrosurgical knife. Each sample was cut 8–12 times with each one lasting 3–5 s and chemometric models were generated based on the mass range m/z 600–950 of each sample. RESULTS: The identification of 99 validation samples provided a 98.99% correct classification in which species identification was obtained near-instantaneously (≈ 2 s) unlike any other form of food fraud analysis. Significant time comparisons between REIMS and polymerase chain reaction (PCR) were observed when analysing 6 mislabelled samples demonstrating how REIMS can be used as a complimentary technique to detect fish fraud. Additionally, we have demonstrated that the catch method of fish products is capable of detection using REIMS, a concept never previously reported. CONCLUSIONS: REIMS has been proven to be an innovative technique to help aid the detection of fish fraud and has the potential to be utilised by fisheries to conduct their own quality control (QC) checks for fast accurate results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-017-1291-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5668337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-56683372017-11-16 A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry Black, Connor Chevallier, Olivier P. Haughey, Simon A. Balog, Julia Stead, Sara Pringle, Steven D. Riina, Maria V. Martucci, Francesca Acutis, Pier L. Morris, Mike Nikolopoulos, Dimitrios S. Takats, Zoltan Elliott, Christopher T. Metabolomics Original Article INTRODUCTION: Fish fraud detection is mainly carried out using a genomic profiling approach requiring long and complex sample preparations and assay running times. Rapid evaporative ionisation mass spectrometry (REIMS) can circumvent these issues without sacrificing a loss in the quality of results. OBJECTIVES: To demonstrate that REIMS can be used as a fast profiling technique capable of achieving accurate species identification without the need for any sample preparation. Additionally, we wanted to demonstrate that other aspects of fish fraud other than speciation are detectable using REIMS. METHODS: 478 samples of five different white fish species were subjected to REIMS analysis using an electrosurgical knife. Each sample was cut 8–12 times with each one lasting 3–5 s and chemometric models were generated based on the mass range m/z 600–950 of each sample. RESULTS: The identification of 99 validation samples provided a 98.99% correct classification in which species identification was obtained near-instantaneously (≈ 2 s) unlike any other form of food fraud analysis. Significant time comparisons between REIMS and polymerase chain reaction (PCR) were observed when analysing 6 mislabelled samples demonstrating how REIMS can be used as a complimentary technique to detect fish fraud. Additionally, we have demonstrated that the catch method of fish products is capable of detection using REIMS, a concept never previously reported. CONCLUSIONS: REIMS has been proven to be an innovative technique to help aid the detection of fish fraud and has the potential to be utilised by fisheries to conduct their own quality control (QC) checks for fast accurate results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-017-1291-y) contains supplementary material, which is available to authorized users. Springer US 2017-11-02 2017 /pmc/articles/PMC5668337/ /pubmed/29151824 http://dx.doi.org/10.1007/s11306-017-1291-y Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Black, Connor Chevallier, Olivier P. Haughey, Simon A. Balog, Julia Stead, Sara Pringle, Steven D. Riina, Maria V. Martucci, Francesca Acutis, Pier L. Morris, Mike Nikolopoulos, Dimitrios S. Takats, Zoltan Elliott, Christopher T. A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry |
title | A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry |
title_full | A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry |
title_fullStr | A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry |
title_full_unstemmed | A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry |
title_short | A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry |
title_sort | real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668337/ https://www.ncbi.nlm.nih.gov/pubmed/29151824 http://dx.doi.org/10.1007/s11306-017-1291-y |
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