2026-09-19T21:37:34Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1060152026-06-06T00:56:08Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Regina Barzilay and Tommi S. Jaakkola.
Gu, Youyang
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T15:18:39Z
2016-12-22T15:18:39Z
2016
2016
http://hdl.handle.net/1721.1/106015
965829142
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).
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.
by Youyang Gu.
M.Eng.
100 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Food adulteration detection using neural networks
Thesis
application/pdf
Publication
unknown
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<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.</Abstract>
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