Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach
There is an increasing demand to develop cost-effective and accurate approaches to analyzing biological tissue samples. This is especially relevant in the fishing industry where closely related fish samples can be mislabeled, and the high market value of certain fish leads to the use of alternative...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
Wiley - VCH Verlag GmbH & Co. KGaA
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/15635 |
| _version_ | 1848748947106955264 |
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| author | Bellgard, M. Taplin, Ross Chapman, B. Livk, A. Wellington, C. Hunter, A. Lipscombe, R. |
| author_facet | Bellgard, M. Taplin, Ross Chapman, B. Livk, A. Wellington, C. Hunter, A. Lipscombe, R. |
| author_sort | Bellgard, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | There is an increasing demand to develop cost-effective and accurate approaches to analyzing biological tissue samples. This is especially relevant in the fishing industry where closely related fish samples can be mislabeled, and the high market value of certain fish leads to the use of alternative species as substitutes, for example, Barramundi and Nile Perch (belonging to the same genus, Lates). There is a need to combine selective proteomic datasets with sophisticated computational analysis to devise a robust classification approach. This paper describes an integrated MS-based proteomics and bioinformatics approach to classifying a range of fish samples. A classifier is developed using training data that successfully discriminates between Barramundi and Nile Perch samples using a selected protein subset of the proteome. Additionally, the classifier is shown to successfully discriminate between test samples not used to develop the classifier, including samples that have been cooked, and to classify other fish species as neither Barramundi nor Nile Perch. This approach has applications to truth in labeling for fishmongers and restaurants, monitoring fish catches, and for scientific research into distances between species. |
| first_indexed | 2025-11-14T07:13:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-15635 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:13:08Z |
| publishDate | 2013 |
| publisher | Wiley - VCH Verlag GmbH & Co. KGaA |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-156352017-09-13T13:41:03Z Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach Bellgard, M. Taplin, Ross Chapman, B. Livk, A. Wellington, C. Hunter, A. Lipscombe, R. Fish classification Naïve Bayes classifier Biomarkers Bioinformatics There is an increasing demand to develop cost-effective and accurate approaches to analyzing biological tissue samples. This is especially relevant in the fishing industry where closely related fish samples can be mislabeled, and the high market value of certain fish leads to the use of alternative species as substitutes, for example, Barramundi and Nile Perch (belonging to the same genus, Lates). There is a need to combine selective proteomic datasets with sophisticated computational analysis to devise a robust classification approach. This paper describes an integrated MS-based proteomics and bioinformatics approach to classifying a range of fish samples. A classifier is developed using training data that successfully discriminates between Barramundi and Nile Perch samples using a selected protein subset of the proteome. Additionally, the classifier is shown to successfully discriminate between test samples not used to develop the classifier, including samples that have been cooked, and to classify other fish species as neither Barramundi nor Nile Perch. This approach has applications to truth in labeling for fishmongers and restaurants, monitoring fish catches, and for scientific research into distances between species. 2013 Journal Article http://hdl.handle.net/20.500.11937/15635 10.1002/pmic.201200426 Wiley - VCH Verlag GmbH & Co. KGaA restricted |
| spellingShingle | Fish classification Naïve Bayes classifier Biomarkers Bioinformatics Bellgard, M. Taplin, Ross Chapman, B. Livk, A. Wellington, C. Hunter, A. Lipscombe, R. Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach |
| title | Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach |
| title_full | Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach |
| title_fullStr | Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach |
| title_full_unstemmed | Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach |
| title_short | Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach |
| title_sort | classification of fish samples via an integrated proteomics and bioinformatics approach |
| topic | Fish classification Naïve Bayes classifier Biomarkers Bioinformatics |
| url | http://hdl.handle.net/20.500.11937/15635 |