Alim24310 Vol. 23 No. 3 (2024)

Vol. 23, No. 3 (2024), Alim24310 https://doi.org/10.24275/rmiq/Alim24310

Impact of ultrasound-assisted process on enzymatic extraction of polyphenols from purple rice bran in Vietnam: Experimental kinetics and innovative artificial approach

Authors

L.T.K. Loan, B.T. Vinh, N.V. Tai

Abstract

Ultrasound waves is one of the emerging innovative techniques, that widely applied to assist the extraction process. Besides, enzymatic extraction process also considered as green technology. In addition, in Asia country, especially in Vietnam, the rice bran is abundant, which contained many health benefits compounds. This study is aimed to investigate the effect of power of ultrasound (20-100%) on the kinetic of recovery yield (RY, %) and total polyphenol content (TPC, mgGAE/g) in the extract. The statistical and artificial analysis were applied to study the behavior of extraction process. Among 5 statistical models, the first-order model showed the best prediction the extraction characteristics of ultrasound-assisted enzymatic extraction process under different levels of sonication waves (R2 > 99%). Artificial statistical analysis with 2 inputs (sonication power and extraction time) and two outputs (RY and TPC), also reflected more quick and powerful for prediction with the large and complex dataset with the structure of 2-5-2. The fitting between actual and predicted data set of statistical and artificial model presented that the artificial remained more advantage for describing the extraction process than the first-order model. Further studies could consider this approach for using in other extraction process in other kinds of materials.

Keywords

rice bran, modelling, artifical neural network, polyphenols, innovation.

References
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Optimizing artificial neural networks for the accurate prediction of global solar radiation: A performance comparison with conventional methods. Energies, 16(17), 6165. https://doi.org/10.3390/en16176165 Avinash, G., & Sharma, N. (2023). Unveiling the Proximate Composition of Pigmented and Non-Pigmented Rice Bran. International Journal of Plant & Soil Science, 35(21), 620-626. Bahmani, L., Aboonajmi, M., Arabhosseini, A., & Mirsaeedghazi, H. (2018). ANN modeling of extraction kinetics of essential oil from tarragon using ultrasound pre-treatment. Engineering in agriculture, environment and food, 11(1), 25-29. https://doi.org/10.1016/j.eaef.2017.10.003 Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31. https://doi.org/10.1016/S0167-7012(00)00201-3 Bhagya Raj, G., & Dash, K. K. (2022). Comprehensive study on applications of artificial neural network in food process modeling. 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Physicochemical and antioxidative characteristics of rice bran protein extracted using subcritical water as a pretreatment and stability in a functional drink model during storage. Biocatalysis and Agricultural Biotechnology, 44, 102466. https://doi.org/10.1016/j.bcab.2022.102466 Das, A. B., Goud, V. V., & Das, C. (2017). Extraction of phenolic compounds and anthocyanin from black and purple rice bran (Oryza sativa L.) using ultrasound: A comparative analysis and phytochemical profiling. Industrial Crops and Products, 95, 332-341. https://doi.org/10.1016/j.indcrop.2016.10.041 Dong, Z., Gu, F., Xu, F., & Wang, Q. (2014). Comparison of four kinds of extraction techniques and kinetics of microwave-assisted extraction of vanillin from Vanilla planifolia Andrews. Food Chemistry, 149, 54-61. https://doi.org/10.1016/j.foodchem.2013.10.052 Figueroa-Garcia, E., Farias-Cervantes, V., Segura-Castruita, M., Andrade-Gonzalez, I., Montero-Cortés, M., & Chávez-Rodríguez, A. (2021). 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Kinetics of high pressure homogenization assisted protein extraction from Chlorella pyrenoidosa. Innovative Food Science & Emerging Technologies, 88, 103438. https://doi.org/10.1016/j.ifset.2023.103438 Kayahan, S., & Saloglu, D. (2020). Optimization and kinetic modelling of microwave-assisted extraction of phenolic contents and antioxidants from Turkish artichoke. CyTA - Journal of Food, 18(1), 635-643. https://doi.org/10.1080/19476337.2020.1800103 Khadhraoui, B., Ummat, V., Tiwari, B., Fabiano-Tixier, A., & Chemat, F. (2021). Review of ultrasound combinations with hybrid and innovative techniques for extraction and processing of food and natural products. Ultrasonics Sonochemistry, 76, 105625. https://doi.org/10.1016/j.ultsonch.2021.105625 Kheddar, H., Hemis, M., Himeur, Y., Megías, D., & Amira, A. (2024). Deep learning for steganalysis of diverse data types: A review of methods, taxonomy, challenges and future directions. 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References

Abdolrasol, M. G. M., Hussain, S. M. S., Ustun, T. S., Sarker, M. R., Hannan, M. A., Mohamed, R., Ali, J. A., Mekhilef, S., & Milad, A. (2021). Artificial neural networks based optimization techniques: A review. Electronics, 10(21), 2689. https://doi.org/10.3390/electronics10212689
Agu, C. M., Menkiti, M. C., Ohale, P. E., & Ugonabo, V. I. (2021). Extraction modeling, kinetics, and thermodynamics of solvent extraction of Irvingia gabonensis kernel oil, for possible industrial application. Engineering Reports, 3(4), e12306. https://doi.org/10.1002/eng2.12306
Ahmad, A., & Singh, A. (2024). Predictive Modeling and Optimization of Engine Characteristics with Biogas–Biodiesel-Powered Dual-Fuel Mode: A Neural Network-Coupled Box–Behnken Design. Arabian Journal for Science and Engineering, 49(2), 2661-2680. https://doi.org/10.1007/s13369-023-08375-7
Ali, M. A., Elsayed, A., Elkabani, I., Akrami, M., Youssef, M. E., & Hassan, G. E. (2023). Optimizing artificial neural networks for the accurate prediction of global solar radiation: A performance comparison with conventional methods. Energies, 16(17), 6165. https://doi.org/10.3390/en16176165
Avinash, G., & Sharma, N. (2023). Unveiling the Proximate Composition of Pigmented and Non-Pigmented Rice Bran. International Journal of Plant & Soil Science, 35(21), 620-626.
Bahmani, L., Aboonajmi, M., Arabhosseini, A., & Mirsaeedghazi, H. (2018). ANN modeling of extraction kinetics of essential oil from tarragon using ultrasound pre-treatment. Engineering in agriculture, environment and food, 11(1), 25-29. https://doi.org/10.1016/j.eaef.2017.10.003
Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31. https://doi.org/10.1016/S0167-7012(00)00201-3
Bhagya Raj, G., & Dash, K. K. (2022). Comprehensive study on applications of artificial neural network in food process modeling. Critical Reviews in Food Science and Nutrition, 62(10), 2756-2783. https://doi.org/10.1080/10408398.2020.1858398
Bitwell, C., Indra, S. S., Luke, C., & Kakoma, M. K. (2023). A review of modern and conventional extraction techniques and their applications for extracting phytochemicals from plants. Scientific African, 19, e01585. https://doi.org/10.1016/j.sciaf.2023.e01585
Bunmusik, W., Suttiarporn, P., Phankaew, T., Thitisut, P., & Seangwattana, T. (2023). The effects of solvent–based ultrasonic–assisted extraction of bioactive compounds and antioxidant activities from pigmented rice bran. Materials Today: Proceedings, 77, 1073-1078. https://doi.org/10.1016/j.matpr.2022.11.391
Çakmak, G., & Yıldız, C. (2011). The prediction of seedy grape drying rate using a neural network method. Computers and Electronics in Agriculture, 75(1), 132-138.
Chaisuwan, B., & Supawong, S. (2022). Physicochemical and antioxidative characteristics of rice bran protein extracted using subcritical water as a pretreatment and stability in a functional drink model during storage. Biocatalysis and Agricultural Biotechnology, 44, 102466. https://doi.org/10.1016/j.bcab.2022.102466
Das, A. B., Goud, V. V., & Das, C. (2017). Extraction of phenolic compounds and anthocyanin from black and purple rice bran (Oryza sativa L.) using ultrasound: A comparative analysis and phytochemical profiling. Industrial Crops and Products, 95, 332-341. https://doi.org/10.1016/j.indcrop.2016.10.041
Dong, Z., Gu, F., Xu, F., & Wang, Q. (2014). Comparison of four kinds of extraction techniques and kinetics of microwave-assisted extraction of vanillin from Vanilla planifolia Andrews. Food Chemistry, 149, 54-61. https://doi.org/10.1016/j.foodchem.2013.10.052
Figueroa-Garcia, E., Farias-Cervantes, V., Segura-Castruita, M., Andrade-Gonzalez, I., Montero-Cortés, M., & Chávez-Rodríguez, A. (2021). Using artificial neural networks in prediction of the drying process of foods that are rich in sugars. Revista Mexicana de Ingeniería Química, 20(1), 161-171.
Franco-Vásquez, D., Carreón-Hidalgo, J., Gómez-Linton, D., Román-Guerrero, A., Franco-Vásquez, A., Arreguín-Espinosa, R., Alavez, S., & Pérez-Flores, L. (2023). Conventional and non-conventional extraction of functional compounds from jiotilla (Escontria chiotilla) fruits and evaluation of their antioxidant activity. Revista Mexicana de Ingeniería Química, 22(1), Alim2963-Alim2963.
Galván, E., & Mooney, P. (2021). Neuroevolution in deep neural networks: Current trends and future challenges. IEEE Transactions on Artificial Intelligence, 2(6), 476-493. https://doi.org/10.1109/TAI.2021.3067574
Harouna-Oumarou, H. A., Fauduet, H., Porte, C., & Ho, Y.-S. (2007). Comparison of kinetic models for the aqueous solid-liquid extraction of Tilia sapwood in a continuous stirred tank reactor. Chemical Engineering Communications, 194(4), 537-552. https://doi.org/10.1080/00986440600992511
Islam, M. R., Sablani, S., & Mujumdar, A. (2003). An artificial neural network model for prediction of drying rates. Drying Technology, 21(9), 1867-1884.
Ji, S., Yoo, T., Jin, S., Ju, H., Eom, S., Kim, J.-S., & Hyun, T. (2020). Changes in the phenolic compounds profile, antioxidant and anti-melanogenic activity from organs of Petasites japonicas under different extraction methods. Revista Mexicana de Ingeniería Química, 19(3), 1453-1464. https://doi.org/10.24275/rmiq/Bio1186
Kadiri, O., Gbadamosi, S. O., & Akanbi, C. T. (2019). Extraction kinetics, modelling and optimization of phenolic antioxidants from sweet potato peel vis-a-vis RSM, ANN-GA and application in functional noodles. Journal of Food Measurement and Characterization, 13(4), 3267-3284. https://doi.org/10.1007/s11694-019-00249-7
Katsimichas, A., Karveli, I., Dimopoulos, G., Giannakourou, M., & Taoukis, P. (2023). Kinetics of high pressure homogenization assisted protein extraction from Chlorella pyrenoidosa. Innovative Food Science & Emerging Technologies, 88, 103438. https://doi.org/10.1016/j.ifset.2023.103438
Kayahan, S., & Saloglu, D. (2020). Optimization and kinetic modelling of microwave-assisted extraction of phenolic contents and antioxidants from Turkish artichoke. CyTA - Journal of Food, 18(1), 635-643. https://doi.org/10.1080/19476337.2020.1800103
Khadhraoui, B., Ummat, V., Tiwari, B., Fabiano-Tixier, A., & Chemat, F. (2021). Review of ultrasound combinations with hybrid and innovative techniques for extraction and processing of food and natural products. Ultrasonics Sonochemistry, 76, 105625. https://doi.org/10.1016/j.ultsonch.2021.105625
Kheddar, H., Hemis, M., Himeur, Y., Megías, D., & Amira, A. (2024). Deep learning for steganalysis of diverse data types: A review of methods, taxonomy, challenges and future directions. Neurocomputing, 127528. https://doi.org/10.1016/j.neucom.2024.127528
Lara-Cerecedo, L., Pitalúa-Díaz, N., & Hinojosa-Palafox, J. (2023). Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA. Revista Mexicana de Ingeniería Química, 22(3), Ener2956.
Leonarski, E., Kuasnei, M., dos Santos, E. H., Benvenutti, L., Moraes, P. A. D., Cesca, K., de Oliveira, D., & Zielinski, A. A. F. (2024a). Ultrasound and microwave-assisted extractions as green and efficient approaches to recover anthocyanin from black rice bran. Biomass Conversion and Biorefinery, 1-14. https://doi.org/10.1007/s13399-024-05479-4
Leonarski, E., Kuasnei, M., Santos, E. H., Moraes, P. A., Cesca, K., Oliveira, D. d., & Zielinski, A. A. (2024b). The Potential of Crude and Partially Purified Black Rice Bran Extracts Obtained by Ultrasound-Assisted Extraction: Anti-Glycemic, Cytotoxicity, Cytoprotective, and Antitumoral Effects. Foods, 13(4), 597.
Li, J., Wang, W., Xu, W., Yu, S., Lv, R., Zhou, J., & Liu, D. (2024). Biomimetic hybrid porous microspheres with plant membrane-wall structure for evaluating multiscale mechanisms of ultrasound-assisted mass transfer. Chemical Engineering Journal, 149936. https://doi.org/10.1016/j.cej.2024.149936
Li, Z., & Ahammed, G. J. (2023). Plant stress response and adaptation via anthocyanins: A review. Plant Stress, 100230.
Loan, L. T. K., Tai, N. V., & Thuy, N. M. (2023a). Microwave-assisted extraction of “Cẩm” purple rice bran polyphenol: a kinetic study. Acta Scientiarum Polonorum Technologia Alimentaria, 22(3), 341-349. https://doi.org/10.17306/J.AFS.1140
Loan, L. T. K., Thuy, N. M., & Van Tai, N. (2023b). Ultrasound-Assisted Extraction of Antioxidant Compounds from “Cẩm” Purple Rice Bran for Modulation of Starch Digestion. International Journal of Food Science, 2023, 1086185. 10.1155/2023/1086185
Loan, L. T. K., & Vinh, B. T. (2024). Extraction of antioxidants from purple rice bran in Vietnam by green extraction approaches and its antidiabetic properties. Food Research, In press.
Mapholi, Z., & Goosen, N. J. (2023). Optimization of fucoidan recovery by ultrasound-assisted enzymatic extraction from South African kelp, Ecklonia maxima. Ultrasonics Sonochemistry, 101, 106710. https://doi.org/10.1016/j.ultsonch.2023.106710
McLoone, S., Brown, M. D., Irwin, G., & Lightbody, A. (1998). A hybrid linear/nonlinear training algorithm for feedforward neural networks. IEEE transactions on Neural Networks, 9(4), 669-684. https://doi.org/10.1109/72.701180
Ngo, T. V., Kunyanee, K., & Luangsakul, N. (2023). Insights into Recent Updates on Factors and Technologies That Modulate the Glycemic Index of Rice and Its Products. Foods, 12(19), 3659. https://doi.org/10.3390/foods12193659
Saini, L., & Soni, M. (2002). Artificial neural network based peak load forecasting using Levenberg–Marquardt and quasi-Newton methods. IEE Proceedings-Generation, Transmission and Distribution, 149(5), 578-584. https://doi.org/10.1049/ip-gtd:20020462
Sánchez-Mesa, N., Sepúlveda-Valencia, J., Ciro-Velásquez, H., & Meireles, M. (2020). Bioactive compounds from mango peel (Mangifera indica L. var. Tommy Atkins) obtained by supercritical fluids and pressurized liquids extraction. Revista Mexicana de Ingeniería Química, 19(2), 755-766. https://doi.org/10.24275/rmiq/Alim657
Sethi, S., Datta, A., Gupta, B. L., & Gupta, S. (2013). Optimization of cellulase production from bacteria isolated from soil. ISRN Biotechnology, 2013, 985685. 10.5402/2013/985685
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