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Optimization and analysis of ultrasound-assisted solvent extraction of bioactive compounds from Boerhavia diffusa Linn. using RSM, ANFIS and machine learning algorithm

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Abstract

Boerhavia diffusa L., a flowering plant is traditionally used as a therapy for protecting against several diseases. The present study aims to identify the optimal extraction conditions for ultrasound-assisted solvent extraction (USAE) for retrieving highest amount of biologically important compounds from B. diffusa L. through RSM (Response Surface Methodology) and validated by ANFIS (adaptive neuro-fuzzy inference system), and MLA (machine learning algorithm) models. Numerous extraction parameters have played a major role in the extraction process for obtaining maximum yield of bioactive compounds. The four majorly contributing independent parameters, ethanol concentration (X1: 60–70%), temperature (X2: 35–45 °C), particle size (X3: 300–500 µm), and ultrasonic-exposure time (X4: 15–20 min), and at five levels (− 2, − 1, 0, + 1, + 2) concerning dependent parameters, TPC (y1), TFC (y2), %DPPH*sc (y3), %ABTS*sc (y4) and %H2O2*sc (y5)) were chosen. The optimal extraction condition from RSM was observed at X1 = 67.5–70%, X2 = 35 °C, X3 = 300 µm and X4 = 20 min; under this situation, y1 = 312.59–316.271 mg gallic acid equivalents (GAE)/g, y2 = 138.748–142.052 mg rutin equivalents (RU)/g and their antioxidant potentials (y3 = 59.98–61.621%, y4 = 76.762–78.642%, and y5 = 64.623–62.362%) have been noted. ANFIS and MLA were used to authenticated the optimized extraction parameters of RSM. Many experimental values were well-matched with the predicted values of ANFIS and MLA. A well-fitted quadratic model was obtained. Further, GC–MS analysis performed to identify the compounds present in the optimized extract yielded 17 compounds. In silico molecular docking study was done to predict the nephroprotective effect of the compounds present in the optimized extract.

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All the data generated in this work is included in the manuscript.

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Acknowledgements

The authors are grateful to the Management of Kalasalingam Academy of Research and Education for the research facilities. The authors availed the laboratory facility established from the DBT-NER project (BT/ PR45283/NER/95/1919/2022).

Funding

This research was funded by Science and Engineering Research Board of India (Grant number: EMR/2016/003035 to K.S.) and Department of Biotechnology (Grant number: BT/PR36633/TRM/120/277/2020 to K.S., and S.K.).

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Authors and Affiliations

  1. Department of Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India

    Shanmugampillai Jeyarajaguru Kabilan, Oviya Sivakumar, Selvaraj Kunjiappan & Krishnan Sundar

  2. Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India

    Gorla Buchayyagari Sumanth

  3. Department of Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India

    Suthendran Kannan

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  1. Shanmugampillai Jeyarajaguru Kabilan
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Correspondence to Krishnan Sundar.

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Kabilan, S.J., Sivakumar, O., Sumanth, G.B. et al. Optimization and analysis of ultrasound-assisted solvent extraction of bioactive compounds from Boerhavia diffusa Linn. using RSM, ANFIS and machine learning algorithm. Food Measure 18, 4204–4220 (2024). https://doi.org/10.1007/s11694-024-02487-w

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