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• Zakaria, A
• Shakaff, A
• Masnan, M
• Saad, F
• Adom, A
• Ahmad, M
• Jaafar, M
• Abdullah, A
• Kamarudin, L
• [+] on Google Scholar
• Zakaria, A
• Shakaff, A
• Masnan, M
• Saad, F
• Adom, A
• Ahmad, M
• Jaafar, M
• Abdullah, A
• Kamarudin, L
• [+] on PubMed
• Zakaria, A
• Shakaff, A
• Masnan, M
• Saad, F
• Adom, A
• Ahmad, M
• Jaafar, M
• Abdullah, A
• Kamarudin, L

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Sensors 2012, 12(5), 6023-6048; doi:10.3390/s120506023

Article

Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

Ammar Zakaria* , Ali Yeon Md Shakaff, Maz Jamilah Masnan, Fathinul Syahir Ahmad Saad, Abdul Hamid Adom, Mohd Noor Ahmad, Mahmad Nor Jaafar, Abu Hassan Abdullah and Latifah Munirah Kamarudin

Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), 01000, Kangar, Perlis, Malaysia

* Author to whom correspondence should be addressed.

Received: 22 April 2012; in revised form: 3 May 2012 / Accepted: 3 May 2012 / Published: 10 May 2012

(This article belongs to the Section Chemical Sensors)

Download PDF Full-Text [495 KB, uploaded 10 May 2012 11:33 CEST]

Abstract: In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

Keywords: electronic nose; acoustic sensor; volatiles; mango ripeness classification

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