Modeling and Optimization of Ultrasound-Assisted Extraction of Antioxidants from Yerba Mate (Ilex paraguariensis St. Hill) Using Artificial Neural Networks and ANFIS.

Alini Rafaela Neitzki Peters, Fabio Fiorin Cardoso Filho, Jéssyca Ketterine Carvalho, Edson Antonio da Silva, Emmanuel Zullo Godinho, Fernando de Lima Caneppele, Camila da Silva, Salah Din Mahmud Hasan

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Article in Applied biochemistry and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Alini Rafaela Neitzki PetersChemical Engineering Department. Rua Guaíra, State University of West Parana, 3141, Toledo, Brazil.

Fabio Fiorin Cardoso FilhoChemical Engineering Department. Rua Guaíra, State University of West Parana, 3141, Toledo, Brazil.

Jéssyca Ketterine CarvalhoChemical Engineering Department. Rua Guaíra, State University of West Parana, 3141, Toledo, Brazil. jessycakcarvalho@gmail.com.ORCID http://orcid.org/0000-0002-9935-4401

Edson Antonio da SilvaChemical Engineering Department. Rua Guaíra, State University of West Parana, 3141, Toledo, Brazil.

Emmanuel Zullo GodinhoFaculty of Zootechnics and Food Engineering. , University of São Paulo, Pirassununga, SP, 13635-900, Brazil.

Fernando de Lima CaneppeleFaculty of Zootechnics and Food Engineering. , University of São Paulo, Pirassununga, SP, 13635-900, Brazil.

Camila da SilvaChemical Engineering Department. CEP, State University of Maringá, Maringá, Paraná, 87020-900, Brazil.

Salah Din Mahmud HasanChemical Engineering Department. Rua Guaíra, State University of West Parana, 3141, Toledo, Brazil.

Funding

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6 · The paper itself

Abstract

The leaves of yerba mate, commonly found in the southern region of Brazil, are consumed as teas and contain a variety of antioxidant compounds with nutritional and pharmacological value. This study aimed to modeling and optimizing the extraction process of these compounds using ultrasound, where operational parameters such as temperature (T), ultrasound power (P), and the solvent-to-solid ratio of yerba mate (R) were evaluated, and four responses measured, including extraction yield, total phenolic content (TPC), and antioxidant capacity determined by the FRAP and DPPH methods. The Box-Behnken design (BBD) was initially used to optimize the variables and generate a response surface model. Subsequently, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were applied to enhance prediction and optimization of the results. The findings indicated that ultrasound-assisted extraction (UAE) achieved a TPC of approximately 500 mg GAE g⁻

Indexed as

AntioxidantsFuzzy LogicIlex paraguariensisNeural Networks, ComputerPlant ExtractsUltrasonic WavesPhenolsPlant LeavesAntioxidantsPhenolsPlant ExtractsANFISANNExtractionUltrasoundYerba mate

Identifiers

PMID41074930

DOI10.1016/j.ultsonch.2021.105467

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