Taguchi L9 (34) orthogonal array study based on methylene blue removal by single-walled carbon nanotubes-amine: Adsorption optimization using the experimental design method, kinetics, equilibrium and thermodynamics

Maazinejad, Behanm; Mohammadnia, Osveh; Ali, Gomaa A. M.; Makhlouf, Abdel Salam Hamdy; Nadagouda, Mallikarjuna N.; Sillanpää, Mika; Asiri, Abdullah M.; Agarwal, Shilpi; Gupta, Vinod Kumar; Sadegh, Hamidreza

doi:10.1016/j.molliq.2019.112001

Cited by 99 publications

(37 citation statements)

References 74 publications

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“…ESI-MS data for four ginsenosides agreed with previously reported results. 26) The four clearly changing ginsenosides were identified as Rb1, Rd, Rg3 and Rh2, with retention time of 44.18, 48.43, 54.47, and 63.44 min, respectively. Possibly, hydrolysis was hindered by the saponin moiety of protopanoxadiol because protopanoxadiol was not observed in the total ion chromatogram.…”

Section: Identification Of the Major Ginsenoside Products By Lc-ms/msmentioning

confidence: 95%

“…In fact some secondary glycosides of ginsenoside Rb1 were observed indeed, including Rd, Rg3 and Rh2. 26) Both ginsenosides Rb1 and Rd are PPD-type ginseng saponins. Transformation of ginsenoside Rb1 to ginsenoside Rd involved removal of glucosides at the C-20 position.…”

Section: Identification Of the Major Ginsenoside Products By Lc-ms/msmentioning

confidence: 99%

“…Some reports described enzymatic transformation of ginsenosides, including biotransformation of ginsenoside Rb2 to compound Y, 21) ginsenoside Rb1 to ginsenoside Rg3 and ginsenoside Rb1 to compound-K. [22][23][24] In our previous report, it was found that pectinase could hydrolyzes PPD-type ginsenoside and convert ginsenoside Rb1 into ginsenoside Rd in a way. 25,26) In addition, some optimum seeking methods including orthogonal design were often used to increase the yield of target products and to find the optimum operating conditions. 27,28) So far as we know, few reports involved the one-pot process coupling enzyme-assisted extraction and enzymatic transformation of ginsenosides from drug material.…”

Section: Introductionmentioning

confidence: 99%

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One-Pot Process for the Production of Ginsenoside Rd by Coupling Enzyme-Assisted Extraction with Selective Enzymolysis

Fang

Wei

Ya-qi

et al. 2020

Biological & Pharmaceutical Bulletin

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One-pot process for the production of ginsenoside Rd by coupling enzyme-assisted extraction with selective enzymolysis was explored in this paper. Several detection methods including HPLC-MS were used to identify and quantify the products in the enzymolysis solution of pectinase. Results showed that ginsenoside Rd was the main component in enzymolysis solution, pectinase specifically hydrolyzes protopanaxadiol (PPD)-type ginsenoside and was a selective enzyme to convert ginsenoside Rb1 to Rd in a way. In addition the influencing factors on the yield of ginsenoside Rb1 and Rd were optimized using L 9 (3 4) orthogonal design data. The enzymolysis conditions for the higher yield of Rd were 52.5 °C, pH 6 and 1 h with a yield of 0.8314 from 50 mg drug material. The controllable transformation hypothesis of the PPD-type ginsenoside was also explored from the perspective of the molecular steric hindrance. Pectinase could be used as an efficient enzyme for one-pot producing ginsenoside Rd.

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Section: Identification Of the Major Ginsenoside Products By Lc-ms/msmentioning

confidence: 95%

Section: Identification Of the Major Ginsenoside Products By Lc-ms/msmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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One-Pot Process for the Production of Ginsenoside Rd by Coupling Enzyme-Assisted Extraction with Selective Enzymolysis

Fang

Wei

Ya-qi

et al. 2020

Biological & Pharmaceutical Bulletin

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“…However, the intensity should still be controlled in a certain range so that the flocs cannot be broken into small pieces again. An L9 (3 4 ) orthogonal array design [47][48][49][50] based on jar tests was applied to determine the optimal hydraulic conditions for the chitosan flocculants. High-grade samples were used.…”

Section: Optimization Of Hydraulic Conditionsmentioning

confidence: 99%

Investigation and Optimization of Chitosan Performance in Flocculating Kaolin Suspensions Using a Real-Time Suspending Solid Concentration Measuring Method

Gan

Liang

et al. 2021

Water

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The performance of chitosan in flocculating kaolin suspensions was investigated. A real-time suspended solid (SS) concentration measuring method was applied. The influence of pH was examined from 2.0 to 13.0. The optimal pH value for low-grade (100 mg/L), middle-grade (500 mg/L), and high-grade (1000 mg/L) kaolin suspension samples was 8.0. The optimal dosage of chitosan for the three grades of kaolin suspensions was 5.0 mg/L, regardless of the initial SS concentration. An orthogonal array test was performed to determine the optimal hydraulic conditions. Range analysis and variance analysis showed that all four considered factors significantly influenced the flocculation performance of chitosan in the following order: paddle rotation speed in the mixing period > paddle rotation speed in the flocculating period > mixing period time > flocculating period time. Intermittent agitation tests were also performed, showing that alternating agitation and settling enhanced the flocculation performance of chitosan.

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“…This likely occurs because not all interactions are reflected when predicting values using the orthogonal array experiment design. For this reason, while orthogonal array experimental design techniques are used in many fields [50][51][52], their experimental predictions are not well utilized. In a study by Chen [53], an optimal process was designed using an orthogonal array table, but a loss function value was used instead of an experimental value.…”

Section: Experimental Evaluation Of the Model Accuracymentioning

confidence: 99%

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

Wie

Lee

et al. 2020

Materials

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The purpose of this study is to experimentally design the drying, calcination, and sintering processes of artificial lightweight aggregates through the orthogonal array, to expand the data using the results, and to model the manufacturing process of lightweight aggregates through machine-learning techniques. The experimental design of the process consisted of L18(3661), which means that 36 × 61 data can be obtained in 18 experiments using an orthogonal array design. After the experiment, the data were expanded to 486 instances and trained by several machine-learning techniques such as linear regression, random forest, and support vector regression (SVR). We evaluated the predictive performance of machine-learning models by comparing predicted and actual values. As a result, the SVR showed the best performance for predicting measured values. This model also worked well for predictions of untested cases.

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Taguchi L9 (34) orthogonal array study based on methylene blue removal by single-walled carbon nanotubes-amine: Adsorption optimization using the experimental design method, kinetics, equilibrium and thermodynamics

Cited by 99 publications

References 74 publications

One-Pot Process for the Production of Ginsenoside Rd by Coupling Enzyme-Assisted Extraction with Selective Enzymolysis

One-Pot Process for the Production of Ginsenoside Rd by Coupling Enzyme-Assisted Extraction with Selective Enzymolysis

Investigation and Optimization of Chitosan Performance in Flocculating Kaolin Suspensions Using a Real-Time Suspending Solid Concentration Measuring Method

The Experimental Process Design of Artificial Lightweight Aggregates Using an Orthogonal Array Table and Analysis by Machine Learning

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