Data fusion and multivariate analysis for food authenticity analysis

Yunhe Hong, Nicholas Birse, Brian Quinn, Yicong Li, Wenyang Jia, Philip McCarron, Di Wu, Gonçalo Rosas Silva, Lynn Vanhaecke, Saskia Ruth and Christopher T. Elliott ()
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Yunhe Hong: Queen’s University Belfast
Nicholas Birse: Queen’s University Belfast
Brian Quinn: Queen’s University Belfast
Yicong Li: Queen’s University Belfast
Wenyang Jia: Queen’s University Belfast
Philip McCarron: Queen’s University Belfast
Di Wu: Queen’s University Belfast
Gonçalo Rosas Silva: Queen’s University Belfast
Lynn Vanhaecke: Queen’s University Belfast
Saskia Ruth: Wageningen University and Research
Christopher T. Elliott: Queen’s University Belfast

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract A mid-level data fusion coupled with multivariate analysis approach is applied to dual-platform mass spectrometry data sets using Rapid Evaporative Ionization Mass Spectrometry and Inductively Coupled Plasma Mass Spectrometry to determine the correct classification of salmon origin and production methods. Salmon (n = 522) from five different regions and two production methods are used in the study. The method achieves a cross-validation classification accuracy of 100% and all test samples (n = 17) have their origins correctly determined, which is not possible with single-platform methods. Eighteen robust lipid markers and nine elemental markers are found, which provide robust evidence of the provenance of the salmon. Thus, we demonstrate that our mid-level data fusion - multivariate analysis strategy greatly improves the ability to correctly identify the geographical origin and production method of salmon, and this innovative approach can be applied to many other food authenticity applications.

Date: 2023
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DOI: 10.1038/s41467-023-38382-z

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