Next Article in Journal
Previous Article in Journal
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

* ,
,
,
,
,
,
,
 and
Received: 22 April 2012; in revised form: 3 May 2012 / Accepted: 3 May 2012 / Published: 10 May 2012
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [495 KB, uploaded 21 June 2014]
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 electronic nose; acoustic sensor; volatiles; mango ripeness classification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Zakaria, A.; Shakaff, A.Y.M.; Masnan, M.J.; Saad, F.S.A.; Adom, A.H.; Ahmad, M.N.; Jaafar, M.N.; Abdullah, A.H.; Kamarudin, L.M. Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor. Sensors 2012, 12, 6023-6048.

AMA Style

Zakaria A, Shakaff AYM, Masnan MJ, Saad FSA, Adom AH, Ahmad MN, Jaafar MN, Abdullah AH, Kamarudin LM. Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor. Sensors. 2012; 12(5):6023-6048.

Chicago/Turabian Style

Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah. 2012. "Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor." Sensors 12, no. 5: 6023-6048.



Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert