Using Convolutional Neural Networks for Detecting Acrylamide in Biscuit Manufacturing Process

Abstract

Based on a research in 2002 (Ozkaynak & Ova, 2006), acrylamide substance is formed when excessive heat treatment (e.g. frying, grilling, baking) is applied to starch-containing products. This substance contains carcinogenic and neurotoxicological risks for human health. The acrylamide levels are controlled by random laboratory sampling. This control processes which are executed by humans, cause a prolonged and error prone process. In this study, we offer a Convolutional Neural Network (CNN) model, which provides acceptable precision and recall rates for detecting acrylamide in biscuit manufacturing process.