ArticleScientific reports2021
Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Mechanistic Insight into the Antioxidant and Antimicrobial Activities of Palm Oil-Derived Biomaterials: Implications for Dental and Therapeutic Applications.International journal of molecular sciences · 2025Review
- Raman on the palm: handheld Raman spectroscopy for enhanced traceability of palm oil.NPJ science of food · 2025Article
- Transmission Raman Spectroscopy for Inner Layers Chemical Analysis of Fresh Produce.Sensors (Basel, Switzerland) · 2025Article
- Raman Spectroscopy in the Characterization of Food Carotenoids: Challenges and Prospects.Foods (Basel, Switzerland) · 2025Review
- Machine learning classification of mango maturity based on carotene content from Raman spectra.PeerJ · 2025Article
- Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8.Sensors (Basel, Switzerland) · 2023Article
- Enabling chlorophyll photo-response for in-line real-time noninvasive direct probing of the quality of palm-oil during mill process.Scientific reports · 2023Article
- Maturity Grading and Identification ofFoods (Basel, Switzerland) · 2022Article
- Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra.Sensors (Basel, Switzerland) · 2022Article
- Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra.Plants (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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