Open AccessResponse Surface Optimization of Yield of Agarwood (Aquilaria Malaccensis) Leaf Extract using Soxhlet Extraction
Author(s) -
Nur Aimi Aliah Zainurin,
Nurhusna Samsudin,
Yumi Zuhanis Has-Yun Hashim,
Ma An Fahmi Rashid Al-Khatib,
Nor Fadhillah Mohamed Azmin,
Mohd Hafidz Mahamad Maifiah
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8013.038620
Subject(s) - agarwood , yield (engineering) , mathematics , central composite design , response surface methodology , extraction (chemistry) , horticulture , botany , statistics , chemistry , chromatography , biology , medicine , materials science , alternative medicine , pathology , metallurgy
from plant materials are continuously being tested using various techniques in the quest to find new therapeutic agents including from agarwood species. To date, the current existing method and parameters of extracting A. malaccensis leaves are still indefinite. Hence, this study was carried out to redefine the standard method of Soxhlet extraction by optimizing the parameters in order to maximize the yield of A. malaccensis leaves. The yields of Aquilaria malaccensis leaves extract (ALEX) were statistically optimized using Response Surface Methodology (Central Composite Design) with two factors: (A) extraction time (12, 15 and 18 hours) and (B) solid to solvent ratio (1:50, 1:60 and 1:70). The optimization of ALEX yield revealed that Run 5 had the highest yield of 184.482 ± 5.849 mg/g (18.45% wt/wt) with A: 18 hours and B: 1:70 while the lowest yield was at Run 12, 160.173 ± 15.342 mg/g (16.02% wt/wt) with A: 12 hours and B: 1:70. Subsequently, the analysis of variance (ANOVA) revealed that optimization study was well explained by a quadratic polynomial model (R2=0.7964 and Adj. R2=0.6510) implying the acceptable accuracy and general availability of the polynomial model. The data presented that only the effect of A was highly significant (P-value = 0.0123) towards the yield of ALEX although the interaction between variables A and B were significant as indicated by a small P-value=0.0220 (<0.05). Subsequently, the model validation showed that the experimental value accorded considerably well with the predicted value and ultimately the yield of ALEX was successfully optimized.