Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106101
DC Field | Value | Language |
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dc.contributor | Department of Computing | en_US |
dc.contributor | Department of Industrial and Systems Engineering | en_US |
dc.creator | Chung, TRS | en_US |
dc.creator | Tam, IYS | en_US |
dc.creator | Lam, NYY | en_US |
dc.creator | Yang, YN | en_US |
dc.creator | Liu, BY | en_US |
dc.creator | He, B | en_US |
dc.creator | Li, WG | en_US |
dc.creator | Xu, J | en_US |
dc.creator | Yang, ZG | en_US |
dc.creator | Zhang, L | en_US |
dc.creator | Cao, JN | en_US |
dc.creator | Lau, LT | en_US |
dc.date.accessioned | 2024-05-03T00:45:11Z | - |
dc.date.available | 2024-05-03T00:45:11Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106101 | - |
dc.language.iso | en | en_US |
dc.publisher | Nature Publishing Group | en_US |
dc.rights | © The Author(s) 2022 | en_US |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | en_US |
dc.rights | The following publication Chung, T., Tam, I.Y.S., Lam, N.Y.Y. et al. Non-targeted detection of food adulteration using an ensemble machine-learning model. Sci Rep 12, 20956 (2022) is available at https://doi.org/10.1038/s41598-022-25452-3. | en_US |
dc.title | Non-targeted detection of food adulteration using an ensemble machine-learning model | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.doi | 10.1038/s41598-022-25452-3 | en_US |
dcterms.abstract | Recurrent incidents of economically motivated adulteration have long-lasting and devastating effects on public health, economy, and society. With the current food authentication methods being target-oriented, the lack of an effective methodology to detect unencountered adulterants can lead to the next melamine-like outbreak. In this study, an ensemble machine-learning model that can help detect unprecedented adulteration without looking for specific substances, that is, in a non-targeted approach, is proposed. Using raw milk as an example, the proposed model achieved an accuracy and F1 score of 0.9924 and 0. 0.9913, respectively, when the same type of adulterants was presented in the training data. Cross-validation with spiked contaminants not routinely tested in the food industry and blinded from the training data provided an F1 score of 0.8657. This is the first study that demonstrates the feasibility of non-targeted detection with no a priori knowledge of the presence of certain adulterants using data from standard industrial testing as input. By uncovering discriminative profiling patterns, the ensemble machine-learning model can monitor and flag suspicious samples; this technique can potentially be extended to other food commodities and thus become an important contributor to public food safety. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Scientific reports, 2022, v. 12, 20956 | en_US |
dcterms.isPartOf | Scientific reports | en_US |
dcterms.issued | 2022 | - |
dc.identifier.isi | WOS:000984275000039 | - |
dc.identifier.eissn | 2045-2322 | en_US |
dc.identifier.artn | 20956 | en_US |
dc.description.validate | 202405 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | HKSAR ITF project grant titled Big-data-enabled Collaborative Database for Non-targeted Contaminants Detection | en_US |
dc.description.fundingText | Logistics and Supply Chain MultiTech R&D Centre Limited (LSCM) | en_US |
dc.description.fundingText | Hong Kong Polytechnic University Strategic Fund | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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s41598-022-25452-3.pdf | 2.48 MB | Adobe PDF | View/Open |
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