Dataset Information

A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry.

ABSTRACT:

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.

SUBMITTER: Black C

PROVIDER: S-EPMC5668337 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry.

Black Connor C Chevallier Olivier P OP Haughey Simon A SA Balog Julia J Stead Sara S Pringle Steven D SD Riina Maria V MV Martucci Francesca F Acutis Pier L PL Morris Mike M Nikolopoulos Dimitrios S DS Takats Zoltan Z Elliott Christopher T CT

Metabolomics : Official journal of the Metabolomic Society 20171102 12

<h4>Introduction</h4>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.<h4>Objectives</h4>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. Addition ...[more]

PMID: 29151824

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Project description:BackgroundRe-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20-25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.MethodsElectrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.ResultsA classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).ConclusionsThe REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Dataset Information

A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry.

Introduction

Objectives

Methods

Results

Conclusions

Publications

A real time metabolomic profiling approach to detecting fish fraud using rapid evaporative ionisation mass spectrometry.

Similar Datasets

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