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IJMLC 2019 Vol.9(5): 609-614 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.5.847

Food and Formalin Detector Using Machine Learning Approach

Kanij Tabassum, Afsana A. Memi, Nasrin Sultana, Ahmed W. Reza, and Surajit D. Barman

Abstract—Unethical use of formalin, in the preservation of food items posturing threat to communal nutrition. Without chemical experts accurately Formalin detection is a time consuming and complicated task. Moreover, the presence of naturally occurring formalin in food items may interfere in detecting artificially added formalin. This paper presents a dynamic and reliable food and formalin detection technique based on machine learning approaches. Different machine learning algorithms i.e., Naïve Bayes, Logistic regression, Support Vector Machine, K-NN Classifier are applied to the experimental dataset to build a predictive model. Conductive properties were used to detect the type of foods. The designed system is able to detect 1-50 ppm of formalin using VOC HCHO gas sensor combining with arduino-uno. Several Tests are conducted and polynomial regression has been applied to presume the application of formalin.

Index Terms—Artificially added formalin, arduino uno, HCHO gas sensor, machine learning.

Kanij Tabassum, Nasrin Sultana, Ahmed W. Reza, and Surajit D. Barman are with East West University, Dhaka, Bangladesh (e-mail: tabassumkanij@gmail.com, dinabintemisbah@yahoo.com, wasif@ewubd.edu, surajitbarman012@ewubd.edu).
A. A. Memi was with East West University, Dhaka, Bangladesh. She is now with Opus Technology Limited, Dhaka, Bangladesh (e-mail: afsana.ewu@yahoo.com).

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Cite: Kanij Tabassum, Afsana A. Memi, Nasrin Sultana, Ahmed W. Reza, and Surajit D. Barman, "Food and Formalin Detector Using Machine Learning Approach," International Journal of Machine Learning and Computing vol. 9, no. 5, pp. 609-614, 2019.

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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