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Food and Formalin Detector Using Machine Learning Approach

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dc.contributor.author Memi, Afsana Azad
dc.contributor.author Sultana, Nasrin
dc.contributor.author Tabassum, Kanij
dc.date.accessioned 2019-04-01T10:29:26Z
dc.date.available 2019-04-01T10:29:26Z
dc.date.issued 2018-09-17
dc.identifier.uri http://dspace.ewubd.edu/handle/2525/3029
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. en_US
dc.description.abstract Unethical use of formalin, in the preservation of food items posing threat to public health. 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. Purpose of the study was to develop a simple cost-effective and reliable detection technique that can detect contaminated food. Therefore, quantifying artificially added formalin and naturally formed extent it is important to dynamically detect food for the comparison. With this view in mind, we have applied different machine learning algorithms like Naïve Bayes, Logistic regression, Support Vector Machine, K-NN Classifier on fruit’s feature dataset to build a predictive model. We found that the K-NN algorithm works best in terms of accuracy. Finally using food conductance to electricity Rules have been developed and uploaded to the microcontroller unit. Combining with Arduino and the VOC HCHO gas sensor our own android application is able to detect 1-50 ppm of formalin. Several Tests are conducted and polynomial regression has been applied to predict the concentration of formalin in a given sample. en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;CSE00183
dc.subject Food and Formalin Detector Using Machine Learning Approach en_US
dc.title Food and Formalin Detector Using Machine Learning Approach en_US
dc.type Thesis en_US


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