دورية أكاديمية
أعرض في EDS

Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra.

التفاصيل البيبلوغرافية
العنوان: Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra.
المؤلفون: Azmi MHIM; Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia., Hashim FH; Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.; Research Centre for Sustainable Process Technology (CESPRO), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia., Huddin AB; Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia., Sajab MS; Research Centre for Sustainable Process Technology (CESPRO), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.; Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.
المصدر: Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Sep 19; Vol. 22 (18). Date of Electronic Publication: 2022 Sep 19.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI, c2000-
مواضيع طبية MeSH: Arecaceae*/chemistry, Amino Acids/analysis ; Correlation of Data ; Fruit/chemistry ; Organic Chemicals ; Palm Oil/analysis ; beta Carotene/analysis
مستخلص: The degree of maturity of oil palm fresh fruit bunches (FFB) at the time of harvest heavily affects oil production, which is expressed in the oil extraction rate (OER). Oil palm harvests must be harvested at their optimum maturity to maximize oil yield if a rapid, non-intrusive, and accurate method is available to determine their level of maturity. This study demonstrates the potential of implementing Raman spectroscopy for determining the maturity of oil palm fruitlets. A ripeness classification algorithm has been developed utilizing machine learning by classifying the components of organic compounds such as β-carotene, amino acid, etc. as parameters to distinguish the ripeness of fruits. In this study, 47 oil palm fruitlets spectra from three different ripeness levels-under ripe, ripe, and over ripe-were examined. To classify the oil palm fruitlets into three maturity categories, the extracted features were put to the test using 31 machine learning models. It was discovered that the Medium, Weighted KNN, and Trilayered Neural Network classifier has a maximum overall accuracy of 90.9% by using four significant features extracted from the peaks as the predictors. To conclude, the Raman spectroscopy method may offer a precise and efficient means to evaluate the maturity level of oil palm fruitlets.
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معلومات مُعتمدة: FRGS/1/2020/TK0/UKM/02/7 Ministry of Higher Education
فهرسة مساهمة: Keywords: Fresh Fruit Bunches (FFB); Raman spectroscopy; machine learning; oil palm fruitlets; ripening
المشرفين على المادة: 0 (Amino Acids)
0 (Organic Chemicals)
01YAE03M7J (beta Carotene)
5QUO05548Z (Palm Oil)
تواريخ الأحداث: Date Created: 20220923 Date Completed: 20220926 Latest Revision: 20220928
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC9506033
DOI: 10.3390/s22187091
PMID: 36146439
قاعدة البيانات: MEDLINE
الوصف
تدمد:1424-8220
DOI:10.3390/s22187091