Response surface optimization of yield of agarwood (Aquilaria malaccensis) leaf extract using Soxhlet extraction
Extracts 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 c...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Blue Eyes Intelligence Engineering & Sciences Publication
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/79987/1/Paper_Zainurin%20et%20al%202020%20%28IJRTE%29.pdf http://irep.iium.edu.my/79987/ https://www.ijrte.org/download/volume-8-issue-6/ |
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| Summary: | Extracts 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 |
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