Using Convolutional Neural Networks for Detecting Acrylamide in Biscuit Manufacturing Process

Topçuoğlu, DilrubaMentes, Berat UtkanAşkın, NurŞengül, Ayşe DamlaCankut, Zeynep DenizAkdemir, TalipCeylan, Murat2022-10-082022-10-082022978-989-758-583-8https://doi.org/10.5220/0011310100003269https://hdl.handle.net/20.500.13091/293111th International Conference on Data Science, Technology and Applications (DATA) -- JUL 11-13, 2022 -- Lisbon, PORTUGALBased 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.eninfo:eu-repo/semantics/openAccessAcrylamideDeep LearningImage ProcessingCNN AlgorithmUsing Convolutional Neural Networks for Detecting Acrylamide in Biscuit Manufacturing ProcessConference Object10.5220/0011310100003269