Year: 2025
Type: article
Abstract: Olive oil, an inseparable part of Mediterranean culture, is valued in a vast geography due to its healthiness and deliciousness. Higher quality olive oil is exposed to adulteration by mixing cheaper vegetable oils into it due to its high cost and decreased production volume due to the drought experienced in recent years. Mixing with lower-quality olive oil or mixing vegetable or seed oils such as sunflower, corn, cottonseed, peanut, soybean, rapeseed, or poppy seed oils is considered the most common type of adulteration in olive oil. In this study, sunflower oil was mixed into extra virgin olive oil at different concentrations, and the gases emitted by the mixtures were measured using high-sensitivity BME688 sensors produced by Bosh Sensortec. Various machine learning models were then trained using the data obtained from the sensors. It was revealed that both the classification and regression models detected extra virgin olive oil adulteration with near-perfect accuracy.
Source: Food Analytical Methods
Institution: Bolu Abant İzzet Baysal University
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $2,490
Funder: Abant Izzet Baysal Üniversitesi
OpenAlex ID: W4410002575