Year-end Sale % OFF 
Abstract
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. 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. 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. 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.
Highlights
The emerging use of rapidly collected, complex data in unprecedented quantities is ushering the world into the era of big data [1]
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 einvoices of manufacturers who may produce other merchandise and (2) to identify suspicious edible oil manufacture based on irrational transactions from the e-invoices sieved
Compulsory usage of e-invoice for food producing can increase the accuracy of this alarm system
Summary
The emerging use of rapidly collected, complex data in unprecedented quantities is ushering the world into the era of big data [1]. A variety of food fraud incidents have been reported in many countries Such incidents have had a profound impact on public health and consumer confidence in the safety of food [4]. In response to these incidents, one of the main focuses of food fraud prevention has been on novel prediction models of food fraud using a big data approach, which considered different factors from within and outside the food supply chain. Other approaches like ISAR-Tool (Import Screening for the Anticipation of Food Risks) [8] facilitates a descriptive analysis of the food commodity listed in the national trade statistic, and enables automated detection of unexpected changes in volumes and prices for potential food fraud. We present an alarm system for edible oil manufacture that can prevent a food safety crisis rather than trace problematic sources post-crisis
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
