Lipid-extracted microalgae (LEM, Tetraselmis KCTC 12236BP), a solid waste by-product obtained from algal biodiesel production, is typically considered a rich source of antioxidant compounds, including phenolic compounds. The purpose of this study was to apply a statistically-based methodology to enhance the extraction of total phenolic compounds (TPCs) and antioxidant activity (AA) from LEM and to verify the production of epigallocatechin gallate (EGCG), a bioactive material, under optimum conditions. The optimal extractions of TPC and AA were explored by varying the key variables, including the extraction temperature, ethanol concentration, extraction time, and ultrasonic power, through statistical optimization. The optimal extraction conditions were identified through 27 runs following the central composite design. The regression analyses of TPC and AA showed good fit of the experimental data to the second-order polynomial models, with coefficient of determination (R2) values of 0.8769 and 0.8432, respectively. In the variation experiment, the maximum TPC and AA values of 9.8 mg GAE/g and 91.8% were obtained respectively with an extraction temperature of 74.4 °C, ethanol concentration of 55.4%, extraction time of 59.6 min, and ultrasonic power of 700 W. HPLC coupled with diode array detection was used to identify and quantify the phenolic compounds in the extracts, and EGCG (0.12 mg/g DM) was identified as a major peak in the analysis, demonstrating that high value-added material with a bioactive property can be produced from LEM. The results indicated that statistical optimization is applicable for optimizing the extraction of TPC and AA from LEM and provided a scientific basis for applying ultrasound-assisted extraction on an industrial scale by optimizing the conditions. LEM has a high TPC value, particularly with regard to EGCG, and excellent AA, considering it is highly used as a functional material for food, cosmetics, and medicine.
Response surface methodology was employed to optimize the ultrasound-assisted extraction (UAE) conditions for simultaneous optimization of dependent variables, including DPPH radical scavenging activity (RSA), tyrosinase activity inhibition (TAI), and collagenase activity inhibition (CAI) of peanut shell extracts. The effects of the main variables including extraction time (5.0~55.0 min, X1), extraction temperature (26.0~94.0 °C, X2), and ethanol concentration (0.0%~99.5%, X3) were optimized. Based on experimental values from each condition, quadratic regression models were derived for the prediction of optimum conditions. The coefficient of determination (R2) of the independent variable was in the range of 0.89~0.96, which demonstrates that the regression model is suitable for the prediction. In predicting optimal UAE conditions based on the superimposing method, extraction time of 31.2 min, extraction temperature of 36.6 °C, and ethanol concentration of 93.2% were identified. Under these conditions, RSA of 74.9%, TAI of 50.6%, and CAI of 86.8% were predicted, showing good agreement with the experimental values. A reverse transcription polymerase chain reaction showed that peanut shell extract decreased mRNA levels of tyrosinase-related protein-1 and matrix metalloproteinase-3 genes in B16-F0 cell. Therefore, we identified the skin-whitening and anti-wrinkle effects of peanut shell extracts at protein as well as gene expression levels, and the results show that peanut shell is an effective cosmetic material for skin-whitening and anti-wrinkle effects. Based on this study, peanut shell, which was considered a byproduct, can be used for the development of healthy foods, medicines, and cosmetics.
The aim of this study was to remove 5-hydroxymethyl furfural (5-HMF) and furfural, known as fermentation inhibitors, in acid pretreated hydrolysates (APH) obtained from Scenedesmus obliquus using activated carbon. Microwave-assisted pretreatment was used to produce APH containing glucose, xylose, and fermentation inhibitors (5-HMF, furfural). The response surface methodology was applied to optimize key detoxification variables such as temperature (16.5–58.5 °C), time (0.5–5.5 h), and solid–liquid (S-L) ratio of activated carbon (0.6–7.4 w/v%). Three variables showed significant effects on the removal of fermentation inhibitors. The optimum detoxification conditions with the maximum removal of fermentation inhibitors and the minimum loss of sugars (glucose and xylose) were as follows: temperature of 36.6 °C, extraction time of 3.86 h, and S-L ratio of 3.3 w/v%. Under these conditions, removal of 5-HMF, furfural, and sugars were 71.6, 83.1, and 2.44%, respectively, which agreed closely with the predicted values. When the APH and detoxified APH were used for ethanol fermentation by S. cerevisiae, the ethanol produced was 38.5% and 84.5% of the theoretical yields, respectively, which confirmed that detoxification using activated carbon was effective in removing fermentation inhibitors and increasing fermentation yield without significant removal of fermentable sugars.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.