Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra
Abstract The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests wil...
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doaj-ac6d2f9ce6dc405a8b3cd611c632b4152021-09-19T11:27:27ZengNature Publishing GroupScientific Reports2045-23222021-09-0111111110.1038/s41598-021-97857-5Classification of oil palm fresh fruit maturity based on carotene content from Raman spectraThinal Raj0Fazida Hanim Hashim1Aqilah Baseri Huddin2Aini Hussain3Mohd Faisal Ibrahim4Peer Mohamed Abdul5Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaDepartment of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan MalaysiaAbstract The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm−1 is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.https://doi.org/10.1038/s41598-021-97857-5 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Thinal Raj Fazida Hanim Hashim Aqilah Baseri Huddin Aini Hussain Mohd Faisal Ibrahim Peer Mohamed Abdul |
spellingShingle |
Thinal Raj Fazida Hanim Hashim Aqilah Baseri Huddin Aini Hussain Mohd Faisal Ibrahim Peer Mohamed Abdul Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra Scientific Reports |
author_facet |
Thinal Raj Fazida Hanim Hashim Aqilah Baseri Huddin Aini Hussain Mohd Faisal Ibrahim Peer Mohamed Abdul |
author_sort |
Thinal Raj |
title |
Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra |
title_short |
Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra |
title_full |
Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra |
title_fullStr |
Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra |
title_full_unstemmed |
Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra |
title_sort |
classification of oil palm fresh fruit maturity based on carotene content from raman spectra |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-09-01 |
description |
Abstract The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm−1 is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits. |
url |
https://doi.org/10.1038/s41598-021-97857-5 |
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