FoodAlytics: A formalin detection system incorporating a supervised learning approach

The rampant use of dangerous levels of formalin in food items has incited anxiety and concern amongst the Bangladeshi citizens. Sporadic monitoring, amalgamated with the paucity of well-grounded and inexpensive testing devices has created a need for the citizens to address the issue themselves. Impelled by promising results from a survey, FoodAlytics, a portable, economical and non-contact formalin detection system has been developed using locally sourced COTS components. Integrated with our own android application and bundled with the Grove VOC HCHO gas sensor, a recent innovation, our system has been able to detect up to 50 ppm of formalin. Polynomial regression, linear regression and the Levenberg-Marquardt algorithm, three supervised machine learning algorithms have been incorporated in our system to accurately predict the correct concentration of formalin at all temperatures. By applying logistic regression and support vector machines, our system is also able to correctly classify between artificially added and naturally formed formalin.