AUTHOR(S)
Stefan Ivanov^1,2*, Todor Todorov^1,2, Toshko Nenov^1,2

ABSTRACT
This work presents the use of a multisensor system of the electronic nose type for the classification of different types of vegetable oils - sunflower oil, olive oil and avocado oil. The system is built using gas sensors MICS-6814 and MICS-4514, which provide registration of characteristic volatile compounds. The collected data are analyzed by an artificial neural network with one hidden layer, trained using the Scaled Conjugate Gradient (SCG) algo-rithm. The results show high classification accuracy for both the training sample and the test data, which confirms the effectiveness of the proposed system for reliable classification of the studied vegetable oils. The study demon-strates the potential of electronic noses as an affordable and effective technology for quality control of food prod-ucts and for detecting possible counterfeits.

DOI

www.doi.org/10.70456/AXET6145

DOWNLOAD
https://unitech.tugab.bg/images/2025/dokladi/5-Automation%20and%20Robotics/p34_s5_u28_id30.pdf

How to cite this article:
Stefan Ivanov^1,2*, Todor Todorov^1,2, Toshko Nenov^1,2, CLASSIFICATION OF DIFFERENT TYPES OF VEGETABLE OILS USING GAS SENSORS, UNITECH – SELECTED PAPERS - 2025