DOI : https://doi.org/10.18517/ijaseit.14.2.19850

Electrical Properties for Non-destructive Determination of Free Fatty Acid and Moisture Content in Oil Palm Fruit

Verra Mellyana (1), I Wayan Budiastra (2), Irmansyah (3), Yohanes Aris Purwanto (4)
(1) Department of Mechanical and Biosystem Engineering, IPB University, Bogor, Indonesia
(2) Agency of Agricultural Extension and Human Resources Development (IAAEHRD), Ministry of Agriculture, Indonesia
(3) Department of Physics, IPB University, Bogor, Indonesia
(4) Department of Mechanical and Biosystem Engineering, IPB University, Bogor, Indonesia
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How to cite (IJASEIT) :
Mellyana, Verra, et al. “Electrical Properties for Non-Destructive Determination of Free Fatty Acid and Moisture Content in Oil Palm Fruit”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 2, Apr. 2024, pp. 641-9, doi:10.18517/ijaseit.14.2.19850.
Citation Format :
FFA content is one of the essential qualities of oil palm fruit. FFA content exceeding 5% is considered unsuitable for human consumption.  Commonly, FFA content is determined by chemical methods in the laboratory, but it is destructive, time-consuming, and costly. Several non-destructive methods have been investigated, but a maturity prediction approach has been primarily used with no direct relation to FFA content. Thus, there is a pressing need for a non-destructive framework to assess the FFA levels in the oil palm fruit directly. An attempt has been explored using a non-destructive palm fruit quality assessment that relied on electrical properties (impedance, admittance, resistance, and capacitance) in the frequency range from 50 Hz to 5 MHz was investigated to predict FFA and moisture content directly. Two statistical analyses were employed: stepwise multiple linear regressions (MLR) and artificial neural networks (ANN) to calibrate and validate electrical properties with FFA and moisture content. The best-performing models ANN showcased significant results: r= 0.96, R2=0.92, SEC at 0.86%, SEP at 0.97%, CV at 19.45%, consistency at 88.54, and RPD at 3.43 for FFA prediction, and r= 0.99, R2=0.98, SEC at 3.09 %, SEP at 3.46 %, CV at 5.44 %, consistency at 89.08 and RPD at 7.02 for predicting moisture content. In modeling oil palm quality determination, applying the ANN method significantly improved model performances, demonstrating its efficacy in predicting non-destructively both FFA levels based on admittance and moisture content based on impedance.

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