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Prediction of in vitro release of nanoencapsulated phenolic compounds using Artificial Neural Networks
Predicción de la liberación in vitro de compuestos fenólicos nanoencapsulados empleando Redes Neuronales Artificiales
DOI:
https://doi.org/10.15446/dyna.v87n212.72883Palabras clave:
phenolic compounds, ultrasound, nanoencapsulation, Artificial Neural Networks (ANN) (en)compuestos fenólicos, ultrasonido, nanoencapsulación, Redes Neuronales Artificiales (ANN) (es)
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In Vitro Release modeling (IVR) of nanoencapsulated phenolic compounds (PC) is complex, due to the number of factors involved in the process. Artificial Neural Networks (ANN) are useful tools for its prediction because they consider the effect of all factors on the response. The release at 5h is crucial in kinetics because, in most cases, it is an equilibrium point leading to a constant phase. The objective of this investigation was to predict the IVR of nanoencapsulated PC at 5h using ANN. A database with information from the scientific literature was used. This model permits mathematical correlation of the IVR at 5h with eleven factors. The optimal network configuration consisted of one hidden layer with one neuron. A mathematical model was obtained with a Mean Square Error (MSE) of 0.0516 and a correlation coefficient (r) of 0.8413.
La modelación de la liberación in vitro (LIV) de compuestos fenólicos (CF) nanoencapsulados es compleja debido a la cantidad de factores que intervienen en el proceso. Las Redes Neuronales Artificiales (RNA) constituyen una herramienta útil para predecirla gracias a que consideran el efecto de todos los factores sobre la respuesta. La LIV a 5h es determinante en las cinéticas debido a que en la mayoría de las investigaciones se alcanza un punto de equilibrio y se pasa a una fase constante. El objetivo de esta investigación fue predecir la LIV a 5h de CF nanoencapsulados empleando RNA. El modelo desarrollado permite correlacionar matemáticamente la LIV a 5h de CF nanoencapsulados con once factores. La configuración óptima de la red consistió de una capa oculta con una neurona. Se obtuvo un modelo matemático con un Error Cuadrático Medio (ECM) de 0.0516 y un coeficiente de correlación (r) de 0.8413.
Referencias
Kim, M. J., Moon, Y., Tou, J. C., Mou, B. and Waterland, N. L. Nutritional value, bioactive compounds and health benefits of lettuce (Lactuca sativa L.). Journal of Food Composition and Analysis, 49, pp. 19–34, 2016. https://doi.org/10.1016/j.jfca.2016.03.004
Bogue, J., Collins, O. and Troy, A. J. Chapter 2 – Market analysis and concept development of functional foods. In Developing New Functional Food and Nutraceutical Products, pp. 29–45, 2017. https://doi.org/10.1016/B978-0-12-802780-6.00002-X
Leong, T., Martin, G. and Ashokkumar, M. Ultrasonic encapsulation. A review. Ultrasonics sonochemestry 35, pp. 605-614, 2017. http://dx.doi.org/10.1016/j.ultsonch.2016.03.017
Jamshidi, M., Ghaedi, M., Dashtian, K., Ghaedi, a M., Hajati, S., Goudarzi, A. and Alipanahpour, E. Highly efficient simultaneous ultrasonic assisted adsorption of brilliant green and eosin B onto ZnS nanoparticles loaded activated carbon: Artificial neural network modeling and central composite design optimization. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy, 153, pp. 257–267, 2015. https://doi.org/10.1016/j.saa.2015.08.024
Ochoa-Martínes, C.I. Red neuronal artificial en respuesta a predicciones de parámetros de transferencia de masa (pérdida de humedad y ganancia de sólidos) durante la deshidratación osmótica de frutas. Acta Agronómica, 65(4), pp. 318–325, 2016.
Cubeddu, A., Rauh, C. and Delgado, A. Hybrid artificial neural network for prediction and control of process variables in food extrusion. Innovative Food Science & Emerging Technologies, 21, pp. 142–150, 2014. https://doi.org/10.1016/j.ifset.2013.10.010
Aktaş, M., Şevik, S., Özdemir, M. B. and Gönen, E. Performance analysis and modeling of a closed-loop heat pump dryer for bay leaves using artificial neural network. Applied Thermal Engineering, 87, pp. 714–723, 2015. https://doi.org/10.1016/j.applthermaleng.2015.05.049
Sudha, L., Dillibabu, R., Srivatsa Srinivas, S. and Annamalai, A. Optimization of process parameters in feed manufacturing using artificial neural network. Computers and Electronics in Agriculture, 120, pp. 1–6, 2016. https://doi.org/10.1016/j.compag.2015.11.004
Górska-Horczyczak, E., Horczyczak, M., Guzek, D., Wojtasik-Kalinowska, I. and Wierzbicka, A. Chromatographic fingerprints supported by artificial neural network for differentiation of fresh and frozen pork. Food Control, 73, pp. 1–8, 2017. https://doi.org/10.1016/j.foodcont.2016.08.010
Zeng, Z., Guo, X., Zhu, K., Peng, W., Zhou, H. Artifitial neural network. Genetic algorithm to optimize wheat germ fermentation condition: application to the production of two anti-tumor benzoquinones. Food chemestry 227, pp. 264-270, 2017. http://dx.doi.org/10.1016/j.foodchem.2017.01.077
Sulaiman, I. S., Basri, M., Fard Masoumi, H. R., Ashari, S. E., Basri, H. and Ismail, M. Predicting the optimum compositions of a transdermal nanoemulsion system containing an extract of Clinacanthus nutans leaves (L.) for skin antiaging by artificial neural network model. Journal of Chemometrics, e2894, pp. 1-13, 2017. https://doi.org/10.1002/cem.2894
Tao, Y., Wang, P., Wang, J., Wu, Y., Han, Y. and Zhou, J. Combining various wall materials for encapsulation of blueberry anthocyanin extracts: Optimization by artificial neural network and genetic algorithm and a comprehensive analysis of anthocyanin powder properties. Powder Technology, 311, pp. 77–87, 2017. https://doi.org/10.1016/j.powtec.2017.01.078
Shahsavari, S., Rezaie Shirmard, L., Amini, M. and Abedin Dokoosh, F. Application of Artificial Neural Networks in the Design and Optimization of a Nanoparticulate Fingolimod Delivery System Based on Biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate). Journal of Pharmaceutical Sciences, 106(1), pp. 176–182, 2017. https://doi.org/10.1016/j.xphs.2016.07.026
Elkomy, M. H., Elmenshawe, S. F., Eid, H. M. and Ali, A. M. A. Topical ketoprofen nanogel: artificial neural network optimization, clustered bootstrap validation, and in vivo activity evaluation based on longitudinal dose response modeling. Drug Delivery, 7544, pp. 1–13, 2016. https://doi.org/10.1080/10717544.2016.1176086
Pereira, M. C., Oliveira, D. A., Hill, L. E., Carlos, R., Borges, C. D., Vizzotto, M. and Gomes, C. L.. Effect of nanoencapsulation using PLGA on antioxidant and antimicrobial activities of guabiroba fruit phenolic extract. Food Chemistry, 240, pp. 396–404, 2018. https://doi.org/10.1016/j.foodchem.2017.07.144
Oliveira, D. A., Angonese, M., Ferreira, S. R. S. and Gomes, L. Food and Bioproducts Processing Nanoencapsulation of passion fruit by-products extracts for enhanced antimicrobial activity. Food and Bioproducts Processing, 104, pp. 137–146, 2017. https://doi.org/10.1016/j.fbp.2017.05.009
Wang, T., Ma, X., Lei, Y. and Luo, Y. Colloids and Surfaces B : Biointerfaces Solid lipid nanoparticles coated with cross-linked polymeric double layer for oral delivery of curcumin. Colloids and Surfaces B: Biointerfaces, 148, pp. 1–11, 2016. https://doi.org/10.1016/j.colsurfb.2016.08.047
Arunkumar, R., Prashanth, K. V. H., Manabe, Y., Hirata, T., Sugawara, T., Dharmesh, S. M. and Baskaran, V. Biodegradable Poly (Lactic-co-Glycolic Acid)-Polyethylene Glycol Nanocapsules: An Efficient Carrier for Improved Solubility, Bioavailability and Anticancer Property of Lutein. Journal of Pharmaceutical Sciences, 104(6), pp. 2085–2093, 2015. https://doi.org/10.1002/jps.24436
Liu, M., Yang, J., Ao, P. and Zhou, C. Preparation and characterization of chitosan hollow nanospheres for anticancer drug curcumin delivery. Materials Letters, 150, pp. 115–117, 2015. https://doi.org/10.1016/j.matlet.2015.03.013
Natrajan, D., Srinivasan, S., Sundar, K., & Ravindran, A. Formulation of essential oil-loaded chitosan-alginate nanocapsules. Journal of Food and Drug Analysis, 23(3), pp. 560–568, 2015. https://doi.org/10.1016/j.jfda.2015.01.001
Rigo, L. A., Da Silva, C. R., De Oliveira, S. M., Cabreira, T. N., De Bona Da Silva, C., Ferreira, J. and Beck, R. C. R.. Nanoencapsulation of rice bran oil increases its protective effects against UVB radiation-induced skin injury in mice. European Journal of Pharmaceutics and Biopharmaceutics, 93, pp. 11–17, 2015. https://doi.org/10.1016/j.ejpb.2015.03.020
Coradini, K., Lima, F. O., Oliveira, C. M., Chaves, P. S., Athayde, M. L., Carvalho, L. M. and Beck, R. C. R. Co-encapsulation of resveratrol and curcumin in lipid-core nanocapsules improves their in vitro antioxidant effects. European Journal of Pharmaceutics and Biopharmaceutics, 88(1), pp. 178–185, 2014. https://doi.org/10.1016/j.ejpb.2014.04.009
Hill, L. E. and Gomes, C. L. Characterization of temperature and pH-responsive nanoparticles for the release of antimicrobials. Materials Research Express, 1, pp. 1-18, 2015. https://doi.org/10.1088/2053-1591/1/3/035405
Silva, L. M., Hill, L. E., Figueiredo, E. and Gomes, C. L. Delivery of phytochemicals of tropical fruit by-products using poly (dl-lactide-co-glycolide) (PLGA) nanoparticles: Synthesis, characterization, and antimicrobial activity. Food Chemistry, 165, pp. 362–370, 2014. https://doi.org/10.1016/j.foodchem.2014.05.118
Hill, L. E., Taylor, T. M. and Gomes, C. Antimicrobial Efficacy of Poly (DL-lactide-co-glycolide) (PLGA) Nanoparticles with Entrapped Cinnamon Bark Extract against Listeria monocytogenes and Salmonella typhimurium. Journal of Food Science, 78(4), pp. 1-49, 2013. https://doi.org/10.1111/1750-3841.12069
Gomes, C., Moreira, R. G. and Castell-Perez, E. Poly (DL-lactide-co-glycolide) (PLGA) Nanoparticles with Entrapped trans-Cinnamaldehyde and Eugenol for Antimicrobial Delivery Applications. Journal of Food Science, 76(2), 16–24, 2011. https://doi.org/10.1111/j.1750-3841.2010.01985.x
Kumari, A., Kumar, S., Pakade, Y. B., Singh, B. and Chandra, S. Colloids and Surfaces B : Biointerfaces Development of biodegradable nanoparticles for delivery of quercetin. Colloids and Surfaces B: Biointerfaces, 80(2), pp. 184–192, 2010. https://doi.org/10.1016/j.colsurfb.2010.06.002
Mukerjee, A. and Vishwanatha, J. Formulation, characterization and evaluation of curcumin-loaded PLGA nanoespheres for cancer therapy. Anticancer research 29, pp. 3867-3876, 2009. Available at: https://www.researchgate.net/publication/38027789
Zigoneanu, I. G., Astete, C. E. and Sabliov, C. M. Nanoparticles with entrapped α -tocopherol: synthesis, characterization and controlled release. Nanotechnology, 19, pp. 1-8, 2008. https://doi.org/10.1088/0957-4484/19/10/105606
Matich, D. J. Redes Neuronales: Conceptos Básicos y Aplicaciones. Historia, 55, 2001. Retrieved from ftp://decsai.ugr.es/pub/usuarios/castro/Material-Redes-Neuronales/Libros/matich-redesneuronales.pdf
V.7, N. S. The neural network simulation environment. Getting started manual V. 7, 2015.
Hashad, R. A., Ishak, R. A. H., Fahmy, S., Mansour, S. and Geneidi, A. S. Chitosan-tripolyphosphate nanoparticles: Optimization of formulation parameters for improving process yield at a novel pH using artificial neural networks. International Journal of Biological Macromolecules, 86, pp. 50–58, 2016. https://doi.org/10.1016/j.ijbiomac.2016.01.042
Ochoa-Martínez, C. I. and Ayala-Aponte, A. A. Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks. LWT - Food Science and Technology, 40(4), pp. 638–645, 2007. https://doi.org/10.1016/j.lwt.2006.03.013
Bourbon, A. I., Cerqueira, M. A. and Vicente, A. A. Encapsulation and controlled release of bioactive compounds in lactoferrin-glycomacropeptide nanohydrogels: Curcumin and caffeine as model compounds. Journal of Food Engineering, 180, 110–119, 2016. https://doi.org/10.1016/j.jfoodeng.2016.02.016
Wang, J., Liao, X., Zheng, P., Xue, S. and Peng, R. Classification of Chinese Herbal Medicine by Laser Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network Classification of Chinese Herbal Medicine by Laser Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network. Analytical letters, 2719, pp. 1-24, 2017. https://doi.org/10.1080/00032719.2017.1340949
Haykin, S. Neural networks a comprehensive foundation. 2nd Ed. New Yersey. Pearson education, 1999.
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