Data fusion and multivariate analysis for food authenticity analysis
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 prod...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-38382-z |
| _version_ | 1827928592561995776 |
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| author | Yunhe Hong Nicholas Birse Brian Quinn Yicong Li Wenyang Jia Philip McCarron Di Wu Gonçalo Rosas da Silva Lynn Vanhaecke Saskia van Ruth Christopher T. Elliott |
| author_facet | Yunhe Hong Nicholas Birse Brian Quinn Yicong Li Wenyang Jia Philip McCarron Di Wu Gonçalo Rosas da Silva Lynn Vanhaecke Saskia van Ruth Christopher T. Elliott |
| author_sort | Yunhe Hong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T06:10:55Z |
| format | Article |
| id | doaj.art-e7798e03eb484b10b5d8087acb830050 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T06:10:55Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-e7798e03eb484b10b5d8087acb8300502023-06-11T11:19:04ZengNature PortfolioNature Communications2041-17232023-06-0114111410.1038/s41467-023-38382-zData fusion and multivariate analysis for food authenticity analysisYunhe Hong0Nicholas Birse1Brian Quinn2Yicong Li3Wenyang Jia4Philip McCarron5Di Wu6Gonçalo Rosas da Silva7Lynn Vanhaecke8Saskia van Ruth9Christopher T. Elliott10National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastFood Quality and Design Group, Wageningen University and ResearchNational Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen’s University BelfastAbstract 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.https://doi.org/10.1038/s41467-023-38382-z |
| spellingShingle | Yunhe Hong Nicholas Birse Brian Quinn Yicong Li Wenyang Jia Philip McCarron Di Wu Gonçalo Rosas da Silva Lynn Vanhaecke Saskia van Ruth Christopher T. Elliott Data fusion and multivariate analysis for food authenticity analysis Nature Communications |
| title | Data fusion and multivariate analysis for food authenticity analysis |
| title_full | Data fusion and multivariate analysis for food authenticity analysis |
| title_fullStr | Data fusion and multivariate analysis for food authenticity analysis |
| title_full_unstemmed | Data fusion and multivariate analysis for food authenticity analysis |
| title_short | Data fusion and multivariate analysis for food authenticity analysis |
| title_sort | data fusion and multivariate analysis for food authenticity analysis |
| url | https://doi.org/10.1038/s41467-023-38382-z |
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