Akademik Veri Yönetim Sistemi
SENSORS, cilt.26, sa.7, 2026 (SCI-Expanded, Scopus)
Highlights What are the main findings? An innovative electronic nose system was successfully developed and validated against the TS EN 13725 dynamic olfactometry standard using n-butanol as a reference, enabling the digitalization of odor analysis traditionally dependent on human perception. Among the tested machine learning models, Support Vector Regression (SVR) demonstrated superior performance with a Test R2 of 0.987 and a low Test MAPE of 11.09%, effectively quantifying odor concentrations. What are the implications of the main findings? The proposed methodology provides a standardized and objective instrumental alternative to highly variable and error-prone human perception-based odor measurements in environmental monitoring. The high generalization capability of the SVR model on independent datasets validates the system's potential for reliable and low-cost odor quantification in industrial and environmental applications, paving the way for real-time and dynamic field-deployable odor monitoring technologies.Highlights What are the main findings? An innovative electronic nose system was successfully developed and validated against the TS EN 13725 dynamic olfactometry standard using n-butanol as a reference, enabling the digitalization of odor analysis traditionally dependent on human perception. Among the tested machine learning models, Support Vector Regression (SVR) demonstrated superior performance with a Test R2 of 0.987 and a low Test MAPE of 11.09%, effectively quantifying odor concentrations. What are the implications of the main findings? The proposed methodology provides a standardized and objective instrumental alternative to highly variable and error-prone human perception-based odor measurements in environmental monitoring. The high generalization capability of the SVR model on independent datasets validates the system's potential for reliable and low-cost odor quantification in industrial and environmental applications, paving the way for real-time and dynamic field-deployable odor monitoring technologies.Abstract Electronic nose systems are advanced technological tools that enable the objective evaluation of odors through sensor arrays mimicking the human olfactory mechanism and sophisticated data processing algorithms. These systems facilitate rapid, reproducible, and standardized measurement of chemical components in applications such as food safety, environmental monitoring, medical diagnostics, and industrial quality control. In this study, measurements obtained from electronic nose sensors were compared with olfactometry panelist assessments using n-butanol as a reference substance in accordance with the TS EN 13725 standard. Furthermore, machine learning algorithms, including Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian Process Regression (GPR), were applied to model the sensor data and evaluate their predictive accuracy. The results demonstrated the reliability and applicability of the electronic nose system, achieving training mean absolute percentage error (MAPE) values of 6.53% for PLS, 10.89% for SVR, and 0.15% for GPR. This study presents an innovative approach that systematically assesses the performance of electronic nose technology using a standardized reference odor and highlights the effectiveness of the modeling approach.