Optimization of ultrasound-assisted extraction of bioactive chemicals from Hemidesmus indicus (L.) R.Br. using response surface methodology and adaptive neuro-fuzzy inference system

Vellur, Senthilkumar; Pavadai, Parasuraman; Pandian, Sureshbabu Ram Kumar; Palanichamy, Chandrasekar; Kabilan, Shanmugampillai Jeyarajaguru; Sundar, Krishnan; Kannan, S.; Kunjiappan, Selvaraj

doi:10.1007/s10068-023-01351-9

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Cited by 4 publications

References 34 publications

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A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

Vijayalakshmi,

Kunjiappan,

Ravindranath

et al. 2024

Separation Science and Technology

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A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

Vijayalakshmi,

Kunjiappan,

Ravindranath

et al. 2024

Separation Science and Technology

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Mimosa pudica Linn. extract improves aphrodisiac performance in diabetes-induced male Wister rats

Palanichamy,

Nayak Ammunje,

Pavadai

et al. 2023

Journal of Biomolecular Structure and Dynamics

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Ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using optimized conditions of Central Composite Design of RSM, ANFIS modeling, and Machine Learning algorithm

Kunjiappan,

Ramasamy,

Kannan

et al. 2023

Preprint

Self Cite

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Grape seeds are a cost-effective source of antioxidant and potential therapeutic compounds in the form of polyphenols. Therapeutic important polyphenols were completely extracted from grape seeds using an ultrasound-aided extraction technique and examined the antioxidant properties. The present study aimed to determine the optimized condition and green process for maximum extraction of polyphenols from grape seeds through RSM (response surface methodology), ANFIS (adaptive neuro-fuzzy inference system), and machine learning (ML) algorithm models. Effect of five independent variables and their ranges, particle size (X1: 0.5-1 mm), methanol concentration (X2: 60-70% in distilled water), ultrasound exposure time (X3:18-28 min), temperature (X4: 35-45 °C), and ultrasound intensity (X5: 65-75 W cm-2) at five levels (-2, -1, 0, +1, and +2) concerning dependent variables, total phenolic contents (y1), total flavonoid contents (y2), %DPPH*sc (y3), %ABTS*sc (y4) and FRAP (y5) were selected. The optimized condition was observed at X1= 0.155 mm, X2= 65% methanol in water, X3= 23 min ultrasound exposure time, X4= 40 °C, and X5=70 W cm-2 ultrasound intensity. Under this situation, the optimal yields of TPC, TFC, and antioxidant scavenging potential were achieved to be 670.32 mg GAE/g, 451.45 mg RE/g, 81.23% DPPH*sc, 77.39% ABTS*sc and 71.55 μg mol (Fe(II))/g FRAP. This optimal condition yielded equal experimental and expected values. A well-fitted quadratic model was recommended. Furthermore, the validated extraction parameters were optimized and compared using the ANFIS and random forest regressor-ML algorithm. Additionally, GC-MS and LC-MS analyses were performed to find the existence of the bioactive compounds in the optimized extract.

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Optimization of ultrasound-assisted extraction of bioactive chemicals from Hemidesmus indicus (L.) R.Br. using response surface methodology and adaptive neuro-fuzzy inference system

Cited by 4 publications

References 34 publications

A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

Mimosa pudica Linn. extract improves aphrodisiac performance in diabetes-induced male Wister rats

Ultrasound-aided extraction of bioactive ingredients from Vitis vinifera seeds using optimized conditions of Central Composite Design of RSM, ANFIS modeling, and Machine Learning algorithm

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