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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...

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Autores principales: Chang, Wan-Tzu, Yeh, Yen-Po, Wu, Hong-Yi, Lin, Yu-Fen, Dinh, Thai Son, Lian, Ie-bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
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.
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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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