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Membranes
Volume 16
Issue 7
10.3390/membranes16070221
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Article

Coconut Water Microfiltration Optimization Using Response Surface Modeling, Neural Networks, and Genetic Algorithms: Performance and Nutritional Retention

by
José Diogo da Rocha Viana
1,
Arthur Claudio Rodrigues de Souza
2,
Paulo Riceli Vasconcelos Ribeiro
2,
Lorena Mara Alexandre Silva
2,
Kirley Marques Canuto
2,
Katia Rezzadori
3,
Giordana Demaman Arend
1,*,
Ana Paula Dionísio
2,* and
José Carlos Cunha Petrus
1
1
Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil
2
Embrapa Tropical Agroindustry, Fortaleza 60511-110, CE, Brazil
3
Department of Food Science and Technology, Federal University of Santa Catarina, Av. Ademar Gonzaga, 1346, Itacorubi, Florianópolis 88034-000, SC, Brazil
*
Authors to whom correspondence should be addressed.
Membranes 2026, 16(7), 221; https://doi.org/10.3390/membranes16070221 (registering DOI)
Submission received: 30 April 2026 / Revised: 4 June 2026 / Accepted: 18 June 2026 / Published: 26 June 2026
(This article belongs to the Special Issue Application of Membrane Technologies in Food Processing)
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Abstract

Although coconut water is recognized for its desirable sensory appeal and nutritional composition, its broader industrial use is constrained by the rapid deterioration that occurs after extraction. In this study, crossflow microfiltration of coconut water with a silicon carbide membrane was optimized by investigating pressure and temperature through a face-centered design (FCD) and artificial neural network modeling coupled with a genetic algorithm (ANN–GA). Permeate flux and fouling index were used as process responses, and the optimized condition was further examined in terms of hydraulic resistance, fouling behavior, and retention of minerals and primary metabolites. Pressure and temperature affected the process differently: permeate flux showed marked nonlinear behavior, whereas fouling index was governed mainly by pressure. At the sample level, ANN described permeate flux more accurately than FCD (R2 = 0.99 vs. 0.96), whereas FCD showed better grouped cross-validated predictivity across unseen pressure–temperature conditions (Q2 = 0.85 vs. 0.57). For the fouling index, FCD outperformed ANN in both sample-level fit and grouped validation (R2 = 0.95 vs. 0.60; Q2 = 0.70 vs. 0.61). Both approaches converged on the same favorable operating window, and experimental validation at 60 kPa and 35 °C yielded 1085.23 ± 23.12 L h−1 m−2 and 83.56 ± 1.56%. During concentration mode, flux decline was severe but predominantly reversible, with high clean-water permeance recovery after chemical cleaning. Resistance partition and fouling modeling indicated that the main hydraulic limitation was associated with concentration polarization and external cake-layer buildup rather than irreversible membrane damage. The clarified fraction also preserved high transmission of major minerals and relevant primary metabolites, indicating that the selected condition combined high productivity, manageable fouling, and satisfactory nutritional retention.
Keywords: Cocos nucifera L.; crossflow microfiltration; response surface methodology; membrane fouling; nuclear magnetic resonance (NMR); nutritional retention Cocos nucifera L.; crossflow microfiltration; response surface methodology; membrane fouling; nuclear magnetic resonance (NMR); nutritional retention
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MDPI and ACS Style

Viana, J.D.d.R.; Souza, A.C.R.d.; Ribeiro, P.R.V.; Silva, L.M.A.; Canuto, K.M.; Rezzadori, K.; Arend, G.D.; Dionísio, A.P.; Petrus, J.C.C. Coconut Water Microfiltration Optimization Using Response Surface Modeling, Neural Networks, and Genetic Algorithms: Performance and Nutritional Retention. Membranes 2026, 16, 221. https://doi.org/10.3390/membranes16070221

AMA Style

Viana JDdR, Souza ACRd, Ribeiro PRV, Silva LMA, Canuto KM, Rezzadori K, Arend GD, Dionísio AP, Petrus JCC. Coconut Water Microfiltration Optimization Using Response Surface Modeling, Neural Networks, and Genetic Algorithms: Performance and Nutritional Retention. Membranes. 2026; 16(7):221. https://doi.org/10.3390/membranes16070221

Chicago/Turabian Style

Viana, José Diogo da Rocha, Arthur Claudio Rodrigues de Souza, Paulo Riceli Vasconcelos Ribeiro, Lorena Mara Alexandre Silva, Kirley Marques Canuto, Katia Rezzadori, Giordana Demaman Arend, Ana Paula Dionísio, and José Carlos Cunha Petrus. 2026. "Coconut Water Microfiltration Optimization Using Response Surface Modeling, Neural Networks, and Genetic Algorithms: Performance and Nutritional Retention" Membranes 16, no. 7: 221. https://doi.org/10.3390/membranes16070221

APA Style

Viana, J. D. d. R., Souza, A. C. R. d., Ribeiro, P. R. V., Silva, L. M. A., Canuto, K. M., Rezzadori, K., Arend, G. D., Dionísio, A. P., & Petrus, J. C. C. (2026). Coconut Water Microfiltration Optimization Using Response Surface Modeling, Neural Networks, and Genetic Algorithms: Performance and Nutritional Retention. Membranes, 16(7), 221. https://doi.org/10.3390/membranes16070221

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