Czech J. Food Sci., 2014, 32(6):538-548 | DOI: 10.17221/113/2014-CJFS

Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM)Original Paper

Alireza SANAEIFAR1, Seyed Saeid MOHTASEBI1, Mahdi GHASEMI-VARNAMKHASTI2, Hojat AHMADI1, Jesus LOZANO3
1 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Department of Mechanical Engineering of Biosystems, Shahrekord University, Shahrekord, Iran
3 Research Group on Sensory Systems, University of Extremadura, Badajoz, Spain

Potential application of a metal oxide semiconductor based electronic nose (e-nose) as a non-destructive instrument for monitoring the change in volatile production of banana during the ripening process was studied. The proposed e-nose does not need any advanced or expensive laboratory equipment and proved to be reliable in recording meaningful differences between ripening stages. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Soft Independent Modelling of Class Analogy (SIMCA) and Support Vector Machines (SVM) techniques were used for this purpose. Results showed that the proposed e-nose can distinguish between different ripening stages. The e-nose was able to detect a clear difference in the aroma fingerprint of banana when using SVM analysis compared with PCA and LDA, SIMCA analysis. Using SVM analysis, it was possible to differentiate and to classify the different banana ripening stages, and this method was able to classify 98.66% of the total samples in each respective group. Sensor array capabilities in the classification of ripening stages using loading analysis and SVM and SIMCA were also investigated, which leads to develop the application of a specific e-nose system by applying the most effective sensors or ignoring the redundant sensors.

Keywords: ripening; electronic nose; non-destructive; support vector machine; sensors

Published: December 31, 2014  Show citation

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SANAEIFAR A, MOHTASEBI SS, GHASEMI-VARNAMKHASTI M, AHMADI H, LOZANO J. Development and application of a new low cost electronic nose for the ripeness monitoring of banana using computational techniques (PCA, LDA, SIMCA and SVM). Czech J. Food Sci. 2014;32(6):538-548. doi: 10.17221/113/2014-CJFS.
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