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Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor
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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386728/ https://www.ncbi.nlm.nih.gov/pubmed/22778629 http://dx.doi.org/10.3390/s120506023 |
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author | 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 |
author_facet | 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 |
author_sort | Zakaria, Ammar |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3386728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-33867282012-07-09 Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor 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 Sensors (Basel) Article 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. Molecular Diversity Preservation International (MDPI) 2012-05-10 /pmc/articles/PMC3386728/ /pubmed/22778629 http://dx.doi.org/10.3390/s120506023 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article 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 Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor |
title | Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor |
title_full | Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor |
title_fullStr | Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor |
title_full_unstemmed | Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor |
title_short | Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor |
title_sort | improved maturity and ripeness classifications of magnifera indica cv. harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386728/ https://www.ncbi.nlm.nih.gov/pubmed/22778629 http://dx.doi.org/10.3390/s120506023 |
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