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An automated alarm system for food safety by using electronic invoices
BACKGROUND: Invoices had been used in food product traceability, however, none have addressed the automated alarm system for food safety by utilizing electronic invoice big data. In this paper, we present an alarm system for edible oil manufacture that can prevent a food safety crisis rather than tr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980643/ https://www.ncbi.nlm.nih.gov/pubmed/31978198 http://dx.doi.org/10.1371/journal.pone.0228035 |
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author | Chang, Wan-Tzu Yeh, Yen-Po Wu, Hong-Yi Lin, Yu-Fen Dinh, Thai Son Lian, Ie-bin |
author_facet | Chang, Wan-Tzu Yeh, Yen-Po Wu, Hong-Yi Lin, Yu-Fen Dinh, Thai Son Lian, Ie-bin |
author_sort | Chang, Wan-Tzu |
collection | PubMed |
description | BACKGROUND: Invoices had been used in food product traceability, however, none have addressed the automated alarm system for food safety by utilizing electronic invoice big data. In this paper, we present an alarm system for edible oil manufacture that can prevent a food safety crisis rather than trace problematic sources post-crisis. MATERIALS AND METHODS: Using nearly 100 million labeled e-invoices from the 2013‒2014 of 595 edible oil manufacturers provided by Ministry of Finance, we applied text-mining, statistical and machine learning techniques to “train” the system for two functions: (1) to sieve edible oil-related e-invoices of manufacturers who may also produce other merchandise and (2) to identify suspicious edible oil manufacture based on irrational transactions from the e-invoices sieved. RESULTS: The system was able to (1) accurately sieve the correct invoices with sensitivity >95% and specificity >98% via text classification and (2) identify problematic manufacturers with 100% accuracy via Random Forest machine learning method, as well as with sensitivity >70% and specificity >99% through simple decision-tree method. CONCLUSION: E-invoice has bright future on the application of food safety. It can not only be used for product traceability, but also prevention of adverse events by flag suspicious manufacturers. Compulsory usage of e-invoice for food producing can increase the accuracy of this alarm system. |
format | Online Article Text |
id | pubmed-6980643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-69806432020-02-07 An automated alarm system for food safety by using electronic invoices Chang, Wan-Tzu Yeh, Yen-Po Wu, Hong-Yi Lin, Yu-Fen Dinh, Thai Son Lian, Ie-bin PLoS One Research Article BACKGROUND: Invoices had been used in food product traceability, however, none have addressed the automated alarm system for food safety by utilizing electronic invoice big data. In this paper, we present an alarm system for edible oil manufacture that can prevent a food safety crisis rather than trace problematic sources post-crisis. MATERIALS AND METHODS: Using nearly 100 million labeled e-invoices from the 2013‒2014 of 595 edible oil manufacturers provided by Ministry of Finance, we applied text-mining, statistical and machine learning techniques to “train” the system for two functions: (1) to sieve edible oil-related e-invoices of manufacturers who may also produce other merchandise and (2) to identify suspicious edible oil manufacture based on irrational transactions from the e-invoices sieved. RESULTS: The system was able to (1) accurately sieve the correct invoices with sensitivity >95% and specificity >98% via text classification and (2) identify problematic manufacturers with 100% accuracy via Random Forest machine learning method, as well as with sensitivity >70% and specificity >99% through simple decision-tree method. CONCLUSION: E-invoice has bright future on the application of food safety. It can not only be used for product traceability, but also prevention of adverse events by flag suspicious manufacturers. Compulsory usage of e-invoice for food producing can increase the accuracy of this alarm system. Public Library of Science 2020-01-24 /pmc/articles/PMC6980643/ /pubmed/31978198 http://dx.doi.org/10.1371/journal.pone.0228035 Text en © 2020 Chang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Chang, Wan-Tzu Yeh, Yen-Po Wu, Hong-Yi Lin, Yu-Fen Dinh, Thai Son Lian, Ie-bin An automated alarm system for food safety by using electronic invoices |
title | An automated alarm system for food safety by using electronic invoices |
title_full | An automated alarm system for food safety by using electronic invoices |
title_fullStr | An automated alarm system for food safety by using electronic invoices |
title_full_unstemmed | An automated alarm system for food safety by using electronic invoices |
title_short | An automated alarm system for food safety by using electronic invoices |
title_sort | automated alarm system for food safety by using electronic invoices |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980643/ https://www.ncbi.nlm.nih.gov/pubmed/31978198 http://dx.doi.org/10.1371/journal.pone.0228035 |
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