The effect of data fusion on improving the accuracy of olive oil quality measurement.

Autor:	Zarezadeh MR; Department of Agrotechnology, College of Aburaihan, University of Tehran, Tehran, Iran., Aboonajmi M; Department of Agrotechnology, College of Aburaihan, University of Tehran, Tehran, Iran., Ghasemi-Varnamkhasti M; Department of Mechanical Engineering of Biosystem, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran.
Jazyk:	angličtina
Zdroj:	Food chemistry: X [Food Chem X] 2023 Mar 06; Vol. 18, pp. 100622. Date of Electronic Publication: 2023 Mar 06 (Print Publication: 2023).
DOI:	10.1016/j.fochx.2023.100622
Abstrakt:	Olive oil is one of the healthiest and most nutritious edible oils, and it has a great potential to be adulterated. In this research, fraud samples of olive oil were detected with six different classification models by fusion of two methods of E-nose and ultrasound. The samples were prepared in six categories of adulteration. The E-nose system included eight various sensors. 2 MHz probes were used in through transmission ultrasound system. Principal Component Analysis method was used to reduce features and six classification models were used for classification. Feature with the greatest influence in the classification was "percentage of ultrasonic amplitude loss." It was found that the ultrasound system's data had worked more effectively than the E-nose system. Results showed that the ANN method was recognized as the most effective classifier with the highest accuracy (95.51%). The accuracy of classification in all the classification models significantly increased with data fusion. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2023 The Author(s).)
Databáze:	MEDLINE
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