Determination of oil palm fresh fruit bunch ripeness—Based on flavonoids and anthocyanin content

• Published in: Industrial crops and products Vol. 36; no. 1; pp. 466 - 475
• Main Authors: Hazir, Mohd Hafiz Mohd, Shariff, Abdul Rashid Mohamed, Amiruddin, Mohd Din
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2012
• Subjects: Anthocyanins; Classification analysis; Flavonoids; Grading system; Oil palm FFB; Anthocyanins; Flavonoids; Classification analysis; Oil palm FFB; Grading system
• ISSN: 0926-6690; 1872-633X
• DOI: 10.1016/j.indcrop.2011.10.020

► Flavonoids and anthocyanins are potential as a predictor to classify the degree of oil palm FFB ripeness. ► Flavonoid and anthocyanin content decreased from immature to over mature oil palm FFBs. ► The highest overall classification accuracy was 87.7%. Non-destructive and real-time oil palm fresh fruit bunch (FFB) grading systems are of major exploratory concern for researchers in the oil palm industry. The objective is to reduce time, labour, costs, and most importantly, to increase the oil extraction rate, in order to achieve a good quality of palm oil at a more acceptable price. This research investigates the potential of flavonoids and anthocyanins as a predictor to classify the degree of oil palm FFB ripeness. This paper also discusses the relationship between these predictors and the ripeness categories period. One hundred and eighty oil palm FFB samples were collected from a private plantation in Malaysia, according to three maturity categories i.e., ripe, under-ripe, and over-ripe. Each sample was randomly scanned 10 times, both front and back using a hand-held Multiplex®3 multi-parameter fluorescence sensor. The results show that flavonoid and anthocyanin content decreased from immature to over mature oil palm FFBs. Overall, the relationship using Pearson's correlation between flavonoids and anthocyanins was r2=0.84 and the most outstanding relationship accuracy was at the over-ripe stage, at 90%. Statistical analysis using analysis of variance (ANOVA) and pair-wise testing proved that both predictors gave significance difference between under-ripe, ripe, and over-ripe maturity categories. This shows that both predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using both predictors together and separately through several methods. The highest overall classification accuracy was 87.7% using a Stochastic Gradient Boosting Trees model and with both predictors. The other classification methods used either independent or both predictors together and gave various results ranging from 50 to 85% accuracy. This research proves that flavonoids and anthocyanins can be used as predictors of oil palm maturity classification.