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

Alireza Sanaeifar, Seyed Saeid Mohtasebi, Mahdi Ghasemi-Varnamkhasti, Hojat Ahmadi and Jesus Lozano
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Alireza Sanaeifar: Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
Seyed Saeid Mohtasebi: Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
Mahdi Ghasemi-Varnamkhasti: Department of Mechanical Engineering of Biosystems, Shahrekord University, Shahrekord, Iran
Hojat Ahmadi: Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
Jesus Lozano: Research Group on Sensory Systems, University of Extremadura, Badajoz, Spain

Czech Journal of Food Sciences, 2014, vol. 32, issue 6, 538-548

Abstract: 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 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjf:v:32:y:2014:i:6:id:113-2014-cjfs

DOI: 10.17221/113/2014-CJFS

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