Fauzia Izzati
Pusat Penelitian Bioteknologi, Lembaga Ilmu Pengetahuan Indonesia, Bogor

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OPTIMASI SELULASE PADA ENZYME ASSISTED EXTRACTION LEMAK DARI Caulerpa lentillifera SEGAR MENGGUNAKAN RESPONSE SURFACE METHODOLOGY Ishmah Hanifah; Fauzia Izzati; Siti Irma Rahmawati; Joko Hermanianto; Puspo Edi Giriwono; Yatri Hapsari; Bustanussalam; Fauzy Rahman; Eris Septiana; Partomuan Simanjuntak
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 13 No. 1 (2021): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v13i1.32654

Abstract

Caulerpa lentillifera is one of the most potential green seaweed to explored. It is abundantly available and cultivated in several region in Indonesia. Seaweed is well-known as a low lipid content but it is arranged by polyunsaturated fatty acid. Generally, organic solvent is used for lipid extraction. In an extraction method needs pre-treatment such as enzyme assisted extraction for degrading its cell wall and increasing solvent access to entry the cell. This research was designed to study the optimum condition of lipid enzyme assisted extraction process using cellulase from fresh green macroalga C. lentillifera. The optimization was carried out by Response Surface Methodology (RSM) using Central Composite Design (CCD) model with 15 runs. The aim of this study was to analyze the effect of some independent variables namely enzyme concentrations, hydrolysis temperatures, and hydrolysis times respectively to the dependent variables of lipid content and antioxidant activity. The optimum condition obtained from this experiment was 2% enzyme concentration, 30 °C hydrolysis temperature, and 1 h. The optimum condition could then be verified by making 2 or more replications of the chosen treatment approached the predicted result based on software Design Expert vers. 10 prediction. After methylation, extracted fatty acids were identified as palmitic acid and lauric acid using GC-MS. Extraction optimization enables to explore C. lentillifera’s lipid based on influence factors.