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dc.contributor.authorAmmar, Zakaria
dc.contributor.authorAli Yeon, Md Shakaff, Prof. Dr.
dc.contributor.authorMaz Jamilah, Masnan
dc.contributor.authorFathinul Syahir, Ahmad Saad
dc.contributor.authorAbdul Hamid, Adom, Prof. Dr.
dc.contributor.authorMohd Noor, Ahmad, Prof. Dr.
dc.contributor.authorMahmad Nor, Jaafar, Assoc. Prof. Dr.
dc.contributor.authorAbu Hassan, Abdullah
dc.contributor.authorLatifah Munirah, Kamarudin
dc.date.accessioned2013-05-14T04:38:34Z
dc.date.available2013-05-14T04:38:34Z
dc.date.issued2012-05-10
dc.identifier.citationSensors, vol.12 (5), 2012, pages 6023-6048en_US
dc.identifier.issn1424-8220
dc.identifier.urihttp://www.mdpi.com/1424-8220/12/5/6023
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/25442
dc.descriptionLink to publisher’s homepage at http://www.mdpi.comen_US
dc.description.abstractIn 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.en_US
dc.description.uri10.3390/s120506023
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.subjectElectronic noseen_US
dc.subjectAcoustic sensoren_US
dc.subjectVolatilesen_US
dc.subjectMango ripeness classificationen_US
dc.titleImproved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensoren_US
dc.typeArticleen_US
dc.contributor.urlammarzakaria@unimap.edu.myen_US


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