Food adulteration detection using neural networks
Name
965829142-MIT.pdf
Description
Full printable version
Size
1.38 MB
Format
Adobe PDF
Checksum (MD5)
f7f0633d640b2ab7fc1e9cc1c5bbe030
Author(s)
Gu, Youyang
Advisor(s)
Regina Barzilay and Tommi S. Jaakkola.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
In food safety and regulation, there is a need for an automated system to be able to make predictions on which adulterants (unauthorized substances in food) are likely to appear in which food products. For example, we would like to know that it is plausible for Sudan I, an illegal red dye, to adulter "strawberry ice cream", but not "bread". In this work, we show a novel application of deep neural networks in solving this task. We leverage data sources of commercial food products, hierarchical properties of substances, and documented cases of adulterations to characterize ingredients and adulterants. Taking inspiration from natural language processing, we show the use of recurrent neural networks to generate vector representations of ingredients from Wikipedia text and make predictions. Finally, we use these representations to develop a sequential method that has the capability to improve prediction accuracy as new observations are introduced. The results outline a promising direction in the use of machine learning techniques to aid in the detection of adulterants in food.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 99-100).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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