Box-Behnken Response Surface Design of Polysaccharide Extraction from Rhododendron arboreum and the Evaluation of Its Antioxidant Potential.
Ajaz AhmadMuneeb U RehmanAdil Farooq WaliHamed A El-SerehyFahad A Al-MisnedSaleh N MaodaaHossam M AljawdahTahir Maqbool MirParvaiz AhmadPublished in: Molecules (Basel, Switzerland) (2020)
In the present investigation, the ultrasound-assisted extraction (UAE) conditions and optimization of Rhododendron arboreum polysaccharide (RAP) yield were studied by a Box-Behnken response surface design and the evaluation of its antioxidant potential. Three parameters that affect the productivity of UAE, such as extraction temperature (50-90 °C), extraction time (10-30 min), and solid-liquid ratio (1-2 g/mL), were examined to optimize the yield of the polysaccharide percentage. The chromatographic analysis revealed that the composition of monosaccharides was found to be glucose, galactose, mannose, arabinose, and fucose. The data were fitted to polynomial response models, applying multiple regression analysis with a high coefficient of determination value (R2 = 0.999). The data exhibited that the extraction parameters have significant effects on the extraction yield of polysaccharide percentage. Derringer's desirability prediction tool was attained under the optimal extraction conditions (extraction temperature 66.75 °C, extraction time 19.72 min, and liquid-solid ratio 1.66 mL/g) with a desirability value of 1 yielded the highest polysaccharide percentage (11.56%), which was confirmed through validation experiments. An average of 11.09 ± 1.65% of polysaccharide yield was obtained in optimized extraction conditions with a 95.43% validity. The in vitro antioxidant effect of polysaccharides of R. arboreum was studied. The results showed that the RAP extract exhibited a strong potential against free radical damage.
Keyphrases
- oxidative stress
- magnetic resonance
- type diabetes
- transcription factor
- adipose tissue
- climate change
- blood pressure
- water soluble
- electronic health record
- metabolic syndrome
- computed tomography
- blood glucose
- ionic liquid
- deep learning
- skeletal muscle
- machine learning
- insulin resistance
- weight loss
- diffusion weighted imaging
- contrast enhanced