Optimization of Supercritical Carbon Dioxide Extraction of Quercus infectoria Oil

Abel, Stashia ELeaness Rosland; Zahedi, Gholamreza; Rahman, Raja Noor Zaliha Raja Abdul; Nasir, Hasmida Mohd; Faua’ad, Syed Anuar Syed

doi:10.11113/jt.v74.4703

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…In order to develop a reliable model to represent the data, neural network have been applied to several supercritical fluid extraction processes in predicting yield of extraction [1][2][3][4][5][6] and solubility predictions [7][8][9]. Besides that, fuzzy systems also has been applied to determine the extractant quantities at desired temperatures and pressures [10].…”

Section: Previous Study Applying Soft Intelligent Computing Techniquementioning

confidence: 99%

Overview of soft intelligent computing technique for supercritical fluid extraction

Idris

Markom

2020

IJAAS

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<span>Optimization of Supercritical Fluid Extraction process with mathematical modeling is essential for industrial applications. The response surface methodology (RSM) has been proven to be a useful and effective statistical method for studying the relationships between measured responses and independent factors. Recently there are growing interest in applying smart system or artificial technique to model and simulate a chemical process and also to predict, compute, classify and optimize as well as for process control. This system works by generalizing the experimental result and the process behavior and finally predict and estimate the problem. This smart system is a major assistance in the development of process from laboratory to pilot or industrial. The main advantage of intelligent systems is that the predictions can be performed easily, fast, and accurate way, which physical models unable to do. This paper shares several works that have been utilizing intelligent systems for modeling and simulating the supercritical fluid extraction process.</span>

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Section: Previous Study Applying Soft Intelligent Computing Techniquementioning

confidence: 99%

Overview of soft intelligent computing technique for supercritical fluid extraction

Idris

Markom

2020

IJAAS

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“…There are various types of learning methods for neural networks, whereby one of the most applied methods is the backpropagation learning rule. Neural networks have been applied to several SFE processes in predicting the yield of extraction [46][47][48][49][50][51] , initial slope 49 , and solubility [52][53][54] . Table-2 lists some of the works that applied ANNs in the SFE of plants.…”

Section: Predictive Toolsmentioning

confidence: 99%

AN OVERVIEW OF THE Gynura procumbens LEAVES EXTRACTION AND POTENTIAL OF HYBRID PREDICTIVE TOOLS APPLICATION FOR PREDICTION AND SIMULATION IN SUPERCRITICAL FLUID EXTRACTION

Idris

Markom²

2019

RJC

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Supercritical Fluid (SCF) technology has been applied in many areas, such as the pharmaceutical and food sectors due to its outstanding features. It is an efficient technology that performs extraction and leaves none or less organic residues compared to conventional processes. Recently, the simulation and prediction of process output from supercritical fluid extraction (SFE) have been determined using intelligent system predictive tools, such as artificial neural networks. The prediction of the set of results from SFE for designing and scale up purposes is because apart from reducing the usage of extraction solvent, and the energy and time of the process, it can also generate a solution for problems that a complex mathematical model cannot solve. For example, the prediction of solubility is important because this particular fundamental value contributes to the optimizing process. A neural network is considered as one of the artificially intelligent systems, and furthermore a key technology in Industry 4.0. Moreover, the use of hybrid predictive tools is also a developing area in the prediction and simulation of supercritical fluid extraction (SFE), which would be discussed further in this paper. Currently, a limited number of studies related to the analysis of processing technology on extracting Gynura procumbens leaves can be found. Most of the research was focused on pre-clinical studies of Gynura procumbens extracts using conventional methods to prove the effectiveness of the herbal product application. Therefore, this overview will discuss and describe previous studies using Gynura procumbens and several recommendations for subsequent analysis.

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“…Supercritical fluid extraction (SFE) is one of the methods that have been applied to extract tannin and phenolic compounds from plants and herbs (Salleh et al., 2015). The advantage offers by SFE compare to other methods is that used non‐toxic, non‐flammable, easily evaporate chemical as their extraction solvent so that no solvent residue in the extract unless a small addition of polar modifier needed in case of extracting polar component; whereas others applied solvent mostly alcohol in their procedure (Putra et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimization of the supercritical carbon dioxide extraction ofQuercus infectoriagalls extracts and its bioactivities

Nasir

Putra

Chuo

et al. 2020

J. Food Process. Preserv.

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The aims of this study were to optimize the supercritical carbon dioxide extraction conditions on the extraction yield and hydrolyzable tannins content of Quercus infectoria (QI) galls and determine the bioactivities of the gall extracts related to wound healing. Experimentally, the QI gall was extracted using the supercritical carbon dioxide (SC‐CO2) extraction method at different operating conditions which were pressure (20 to 30 MPa), temperature (50 to 70°C), and mean particle size (0.50 to 1.50 mm). The optimum values of pressure, temperature and mean particle size to achieve optimum responses (galls extraction yield: 6.02%, tannic acid concentration: 6,149.71 mg/g sample; gallic acid concentration: 96.85 mg/g sample) were 28.11 MPa, 50.43°C, and 1.25 mm, respectively. The findings also demonstrated that the extracts possess antioxidant activity and cell proliferation and migration abilities. Novelty Impact Statement We declare that the work submitted for publication is original, previously unpublished in English or any other language(s), and not under consideration for publication elsewhere. I also certify that all the authors have approved the paper for release and are in agreement with its content. Furthermore, there are no conflict interest in this study. Thank you for your consideration of this manuscript.

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Optimization of Supercritical Carbon Dioxide Extraction of Quercus infectoria Oil

Cited by 3 publications

References 17 publications

Overview of soft intelligent computing technique for supercritical fluid extraction

Overview of soft intelligent computing technique for supercritical fluid extraction

AN OVERVIEW OF THE Gynura procumbens LEAVES EXTRACTION AND POTENTIAL OF HYBRID PREDICTIVE TOOLS APPLICATION FOR PREDICTION AND SIMULATION IN SUPERCRITICAL FLUID EXTRACTION

Optimization of the supercritical carbon dioxide extraction ofQuercus infectoriagalls extracts and its bioactivities

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