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All Journal AGRITEPA: Jurnal Ilmu dan Teknologi Pertanian
Aththobarani, Muhammad Daffa'
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Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Optimization Approach of Microwave Assisted Extraction Anthocyanins Pigments Butterfly Pea Flowoers (Clitoria Ternatea L.) Using OFAT (One-Factor-At-a-Time) Method Aththobarani, Muhammad Daffa'; Priyanto, Anugerah Dany; Putra, Andre Yusuf Trisna; Wicaksono, Luqman Agung; Erliyanti, Nove Kartika; Panjaitan, Renova; Pujiastuti, Caecilia; Triani, Nova
AGRITEPA: Jurnal Ilmu dan Teknologi Pertanian Vol 12 No 1 (2025)
Publisher : UNIVED Press, Dehasen University Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/agritepa.v12i1.8268

Abstract

Optimization of anthocyanin pigment extraction from butterfly pea flowers (Clitoria ternatea L.) is an important focus to increase the efficiency of obtaining valuable active compounds. This study marks an initial approach in the application of Microwave-Assisted Extraction (MAE) using 0.75% aquadest-tartaric acid solvent, with the hope of producing more stable anthocyanins. The main objectives of this study were to identify the effect of the solvent-material ratio, extraction duration, and MAE power level on anthocyanin levels (mg/L) through the One-Factor-At-a-Time (OFAT) method. The results showed that the optimal treatment was obtained at condition P17, with a solvent-material ratio of 1:15, an extraction time of 15 minutes, and an MAE power of 450 watts, which produced an anthocyanin level of 33.89 ± 0.47 mg/L. These findings confirm that increasing the solvent-material ratio to the optimal point contributes to increasing anthocyanin recovery. However, excessive MAE time and power can cause pigment degradation. The optimal ratio variables were found to be in the range of 1:15 to 1:25, extraction time 9–15 minutes, and MAE power 270–450 watts. This study is expected to be the basis for further optimization using the Response Surface Methodology (RSM) method which allows for deeper analysis of the interactions between variables.
Co-Authors Anugerah Dany Priyanto, Anugerah Dany Erliyanti, Nove Kartika Panjaitan, Renova Pujiastuti, Caecilia Putra, Andre Yusuf Trisna triani, nova