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Drying Kinetics of Oven Dried Pellonula leonensis Fish from Congo River

Received: 22 May 2023     Accepted: 13 June 2023     Published: 27 June 2023
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Abstract

The populations of the riparian areas of the Congo River, have fishing as their main activity. The majority of fish caught and regularly consumed consists of a small fish called Pellonula leonensis or “Nsangui”. This species is of significant economic interest and is marketed in dried form. However, there does not appear to be any scientific information available on the drying of Pellonula leonensis fish in Congo. Thus, the objective of this work was to study the drying characteristics in a laboratory oven of Pellonula leonensis fish and to fit the drying data into five mathematical models to determine which one is better validated by experimental data. Pellonula leonensis fish were dried at two different air temperatures (50 and 70°C) in a natural convection oven. Fish moisture loss was systematically recorded, converted to moisture content, and fitted to five semi-theoretical mathematical drying models: the Lewis, Page, Henderson and Pabis, Avhad and Marchetti, and Diffusion Approach models. Chi-square (χ2), coefficient of determination (R2), root mean square error (RMSE), and mean bias error (MBE) are statistical parameters used to determine the quality of the model fit. It was found that the drying temperature of 70°C is the best temperature because it dries the Pellonula leonensis fish at 14 hours of drying time which is faster compared to the drying temperature of 50°C. This result shows that the increase in air temperature leads to a reduction in the drying time of the fish, so the moisture content decreases sharply with the increase in drying temperature. The drying rate decreased continuously with time. The drying process exhibited a period of decreasing drying speed and a period of constant speed. Among the models tested, the models of Avhad and Marchetti and that of Page showed the best fit to the experimental data with coefficient of determination values equal to 0.99911 and 0.99910, respectively when analyzing the 70°C temperature. The drying rate constants, coefficients and statistical parameters were determined by nonlinear regression analysis, and as a result, it could be observed that there was a good correlation between the experimental and predicted data of Avhad and Marchetti and Page models.

Published in American Journal of Chemical Engineering (Volume 11, Issue 2)
DOI 10.11648/j.ajche.20231102.12
Page(s) 39-45
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Drying Kinetics, Mathematical Modeling, Rate Constant, Pellonula Leonnensis, Statistical Measurements, Drying Temperature, Water Content

References
[1] Gourène G. et Teugels G. G., (1989). Clupeidae. In: Faune des poissons d’eaux douces et saumâtres de l’Afrique de l’Ouest (Lévêque C., Paugy D. & G. G. Teugels, eds), pp. 98-111. Tervuren: MRAC et Paris: Editions ORSTOM, coll. Faune tropicale.
[2] Kone N, Berte S, Kraidy A L B, Kouamelan E P et Kone T, (2011). Biologie de la reproduction du Clupeidae Pellonula leonensis Boulenger, 1916 dans le lac de barrage de Kossou (Côte d’Ivoire). Journal of Applied Biosciences 41: 2797 – 2807.
[3] Mujumdar A. S. and Menon A. S., (1995). “Drying of solids: Principles, classification, and selection of dryers,” in Handbook of Industrial Drying, A. S. Mujumdar, Ed., 2nd ed., Marcel Dekker, New York, vol. 1, pp. 1-39.
[4] Perea-Flores, M., Garibay-Febles, V., Chanona-Perez, J. J., Calderon-Dominguez, G., Mendez-Mendez, J. V., Palacios-González, E., Gutierrez-Lopez, G. F., (2012). Mathematical modelling of castor oil seeds (Ricinus communis) drying kinetics in fluidized bed at high temperatures Ind. Crops Prod. 38, 64–71.
[5] Simal S., Femenía A., Llull P., and Rosselló C., (2000). “Dehydration of aloe vera: simulation of drying curves and evaluation of functional properties,” J. of Food Eng., vol. 43, pp. 109-114.
[6] Aremu A. K. and Akintola, (2016). Drying Kinetics of Moringa (Moringa oleifera) Seeds. Journal of Life Sciences and Technologies Vol. 4, No. doi: 10.18178/jolst.4.1.7-10.
[7] Aremu A. K., Adedokun A. J., and Raji A. O., (2013). “Effect of slice thickness and temperature on the drying kinetics of mango (Mangifera indica),” IJRRAS, vol. 15, no. 1, pp. 41-50.
[8] Premi M., Sharma H. K., Sarkar B. C., and Singh C., (2010). “Kinetics of drumstick leaves during convective drying,” African Journal of Plant Science, vol. 4, no. 10, pp. 391-400.
[9] Gampoula R. H., Dzondo M. G., Tamba Sompila A. W. G., Pambou-Tobi N. P. G., Moussounga J. E., Diakabana P., Nguie R., (2021). Modeling of the Drying Kinetics of Two Extreme Parts of the Pulp of Gamboma Yam (Dioscorea cayenensis): Yellow Part (Head) and White Part (Tail). Open Journal of Applied Sciences, 11, 1218-1229.
[10] Ismail N F, Mat Nawi H N, Zainuddin N, (2019). Mathematical modelling of drying kinetics of oven dried Hibiscus Sabdariffa seed Journal of Physics: Conference Series 1349 (2019) 012144 doi: 10.1088/1742-6596/1349/1/01214.
[11] Keneni Y. G., Hvoslef-Eide A. K. (Trine), Marchetti J. M., (2019). Mathematical modelling of the drying kinetics of Jatropha curcas L. seeds. Industrial Crops & Products 132, 12–20.
[12] Saeed I E, (2010). Solar Drying of Roselle (Hibiscus Sabdariffa L.): modelling, drying experiments, and effects of the drying conditions Agric Eng Int: CIGR J. 12, 3 p. 115-123.
[13] Siqueira, V. C., Resende, O., Chaves, T. H., 2012. Drying kinetics of jatropha seeds. Rev. Ceres 59, 171–177.
[14] Inyang, U. E., Oboh, I. O. and Etuk, B. R. (2018). Kinetic Models for Drying Techniques—Food Materials. Advances in Chemical Engineering and Science, 8, 27-48. https://doi.org/10.4236/aces.2018.82003
[15] Ghodake, H., Goswami, T., Chakraverty, A., (2006). Mathematical modeling of withering characteristics of tea leaves. Drying Technol. 24, 159–164.
[16] Pandey, S. K., Diwan, S. and Soni, R., (2015). Review of Mathematical Modeling of Thin Layer Drying Process. International Journal of current Engineering and Scientific Research, 3, 96-107.
[17] Sridhar, D. and Madhu, G. M., (2015). Drying Kinetics and Mathematical Modeling of Casuarinas Equisetifolia Wood Chips at Various Temperatures. Periodica Polytechnica Chemical Engineering, 59, 288-295. https://doi.org/10.3311/PPch.7855
[18] Chen, J., Zhou, Y., Fang, S., Meng, Y., Kang, X., Xu, X. and Zuo, X., (2013). Mathematical Modeling of Hot Air Drying Kinetics of Momordica charantia Slices and Its Color Change. Advance Journal of Food Science and Technology, 5, 1214-1219.
[19] Doymaz, I., (2004). “Convective air-drying characteristics of thin layer carrots,” Journal of Food Engineering, vol. 61, pp. 359-364.
[20] Younis, M., Abdelkarim, D., El-Abdein, A. Z., (2018). Kinetics and mathematical modeling of infrared thin-layer drying of garlic slices. Saudi J. Biol. Sci. 25, 332–338.
[21] Zhang, Q.-A., Song, Y., Wang, X., Zhao, W.-Q., Fan, X.-H., (2016). Mathematical modeling of debittered apricot (Prunus armeniaca L.) kernels during thin-layer drying. CyTA-J. Food 14, 509–517.
[22] Gunhan, T., Demir, V., Hancioglu, E., Hepbasli, A., (2005). Mathematical modelling of drying of bay leaves. Energy Convers. Manage. 46, 1667–1679.
[23] Togrul I T and Pehlivan D 2003 Modeling of drying kinetics of single apricot J. Food Eng. 58, 1 p. 23-32.
[24] Kashaninejad M Mortazavi A Safekordi A and Tabil L G 2007 Thin Layer drying characteristics and modelling of pistachio nuts J. Food Eng. 78, p. 98-108.
[25] Roberts J S Kidd D R and Olga P Z 2008 Drying Kinetics of grape seeds J. Food Eng. 89, p. 460-465.
[26] Lahsasni S Kouhlia M Mahrouz M and Jaouhari 2004). Drying kinetics of prickly pear fruit (Opuntia ficus indica) J. Food Eng. 61, p. 173-179.
[27] Lílian Moreira Costa, Osvaldo Resende, Daniel Emanoel Cabral de Oliveira, José Mauro Guimarães Carvalho, Sarah Gabrielle Sousa Bueno, Wellytton Darci Quequeto (2020). Drying kinetics of Hyola 430 hybrid canola (Brassica napus L.) seeds AJCS 14 (10): 1623-1629. doi: 10.21475/ajcs.20.14.10.p2400.
[28] Yu, W., Liu, X., Su, H. S. and Zhang, Y. J., (2017) Drying Kinetics of Paper Mill Sludge. Energy and Power Engineering, 9, 141-148. https://doi.org/10.4236/epe.2017.94B017
[29] Fudholi, A., Ruslan, M. H., Haw, L. C., Mat, S., Othman, M. Y., Zaharim, A., Sopian, K., 2012. Mathematical modeling of brown seaweed drying curves. Proceedings of the WSEAS International Conference on Applied Mathematics in Electrical and Computer Engineering. pp. 207–211.
[30] Doymaz, I., (2010). Evaluation of mathematical models for prediction of thin-layer drying of banana slices. Int. J. Food Prop. 13, 486–497.
[31] Bogers, R. J., Craker, L. E. and Lange, D., (2006). Medicinal and aromatic plants - agricultural, commercial, ecological, legal, pharmacological and social aspects (Netherland: Springer) Chapter: Drying of medicinal plants p. 237-252.
[32] Adeyeye, S. A. O. (2019). "An overview of fish drying kinetics", Nutrition & Food Science, Vol. 49 No. 5, pp. 886-902. https://doi.org/10.1108/NFS-10-2018-0296.
[33] Wafa R H, T W Agustini, and A S Fahmi. (2021). Drying Kinetics and Study of Physical Characteristic Using Image Analysis of Dried Salted Striped Catfish (Pangasius hypophthalmus). The 6th International Conference on Tropical and Coastal Region Eco-Development. Earth and Environmental Science 750 (2021) 012045.
[34] Mambou L B, Loumouamou B W, Dzondo Gadet M. (2023). Bioavailability of Docosahexaenoic (DHA) and Eicosapentaenoic (EPA) Acids in the Oil Extracted from Pellonula leonensis from the Congo River and Nianga Lake. American Journal of Applied Chemistry. 11 (2): 66-74.
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  • APA Style

    Mambou Lea Beatrice, Loumouamou Bob Wilfrid, Dzondo Gadet Michel. (2023). Drying Kinetics of Oven Dried Pellonula leonensis Fish from Congo River. American Journal of Chemical Engineering, 11(2), 39-45. https://doi.org/10.11648/j.ajche.20231102.12

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    ACS Style

    Mambou Lea Beatrice; Loumouamou Bob Wilfrid; Dzondo Gadet Michel. Drying Kinetics of Oven Dried Pellonula leonensis Fish from Congo River. Am. J. Chem. Eng. 2023, 11(2), 39-45. doi: 10.11648/j.ajche.20231102.12

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    AMA Style

    Mambou Lea Beatrice, Loumouamou Bob Wilfrid, Dzondo Gadet Michel. Drying Kinetics of Oven Dried Pellonula leonensis Fish from Congo River. Am J Chem Eng. 2023;11(2):39-45. doi: 10.11648/j.ajche.20231102.12

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  • @article{10.11648/j.ajche.20231102.12,
      author = {Mambou Lea Beatrice and Loumouamou Bob Wilfrid and Dzondo Gadet Michel},
      title = {Drying Kinetics of Oven Dried Pellonula leonensis Fish from Congo River},
      journal = {American Journal of Chemical Engineering},
      volume = {11},
      number = {2},
      pages = {39-45},
      doi = {10.11648/j.ajche.20231102.12},
      url = {https://doi.org/10.11648/j.ajche.20231102.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajche.20231102.12},
      abstract = {The populations of the riparian areas of the Congo River, have fishing as their main activity. The majority of fish caught and regularly consumed consists of a small fish called Pellonula leonensis or “Nsangui”. This species is of significant economic interest and is marketed in dried form. However, there does not appear to be any scientific information available on the drying of Pellonula leonensis fish in Congo. Thus, the objective of this work was to study the drying characteristics in a laboratory oven of Pellonula leonensis fish and to fit the drying data into five mathematical models to determine which one is better validated by experimental data. Pellonula leonensis fish were dried at two different air temperatures (50 and 70°C) in a natural convection oven. Fish moisture loss was systematically recorded, converted to moisture content, and fitted to five semi-theoretical mathematical drying models: the Lewis, Page, Henderson and Pabis, Avhad and Marchetti, and Diffusion Approach models. Chi-square (χ2), coefficient of determination (R2), root mean square error (RMSE), and mean bias error (MBE) are statistical parameters used to determine the quality of the model fit. It was found that the drying temperature of 70°C is the best temperature because it dries the Pellonula leonensis fish at 14 hours of drying time which is faster compared to the drying temperature of 50°C. This result shows that the increase in air temperature leads to a reduction in the drying time of the fish, so the moisture content decreases sharply with the increase in drying temperature. The drying rate decreased continuously with time. The drying process exhibited a period of decreasing drying speed and a period of constant speed. Among the models tested, the models of Avhad and Marchetti and that of Page showed the best fit to the experimental data with coefficient of determination values equal to 0.99911 and 0.99910, respectively when analyzing the 70°C temperature. The drying rate constants, coefficients and statistical parameters were determined by nonlinear regression analysis, and as a result, it could be observed that there was a good correlation between the experimental and predicted data of Avhad and Marchetti and Page models.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Drying Kinetics of Oven Dried Pellonula leonensis Fish from Congo River
    AU  - Mambou Lea Beatrice
    AU  - Loumouamou Bob Wilfrid
    AU  - Dzondo Gadet Michel
    Y1  - 2023/06/27
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajche.20231102.12
    DO  - 10.11648/j.ajche.20231102.12
    T2  - American Journal of Chemical Engineering
    JF  - American Journal of Chemical Engineering
    JO  - American Journal of Chemical Engineering
    SP  - 39
    EP  - 45
    PB  - Science Publishing Group
    SN  - 2330-8613
    UR  - https://doi.org/10.11648/j.ajche.20231102.12
    AB  - The populations of the riparian areas of the Congo River, have fishing as their main activity. The majority of fish caught and regularly consumed consists of a small fish called Pellonula leonensis or “Nsangui”. This species is of significant economic interest and is marketed in dried form. However, there does not appear to be any scientific information available on the drying of Pellonula leonensis fish in Congo. Thus, the objective of this work was to study the drying characteristics in a laboratory oven of Pellonula leonensis fish and to fit the drying data into five mathematical models to determine which one is better validated by experimental data. Pellonula leonensis fish were dried at two different air temperatures (50 and 70°C) in a natural convection oven. Fish moisture loss was systematically recorded, converted to moisture content, and fitted to five semi-theoretical mathematical drying models: the Lewis, Page, Henderson and Pabis, Avhad and Marchetti, and Diffusion Approach models. Chi-square (χ2), coefficient of determination (R2), root mean square error (RMSE), and mean bias error (MBE) are statistical parameters used to determine the quality of the model fit. It was found that the drying temperature of 70°C is the best temperature because it dries the Pellonula leonensis fish at 14 hours of drying time which is faster compared to the drying temperature of 50°C. This result shows that the increase in air temperature leads to a reduction in the drying time of the fish, so the moisture content decreases sharply with the increase in drying temperature. The drying rate decreased continuously with time. The drying process exhibited a period of decreasing drying speed and a period of constant speed. Among the models tested, the models of Avhad and Marchetti and that of Page showed the best fit to the experimental data with coefficient of determination values equal to 0.99911 and 0.99910, respectively when analyzing the 70°C temperature. The drying rate constants, coefficients and statistical parameters were determined by nonlinear regression analysis, and as a result, it could be observed that there was a good correlation between the experimental and predicted data of Avhad and Marchetti and Page models.
    VL  - 11
    IS  - 2
    ER  - 

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Author Information
  • Multidisciplinary Food and Nutrition Research Team: Regional Center of Excellence in Food and Nutrition, Faculty of Science and Technology, Marien Ngouabi University, Brazzaville, Congo

  • Multidisciplinary Food and Nutrition Research Team: Regional Center of Excellence in Food and Nutrition, Faculty of Science and Technology, Marien Ngouabi University, Brazzaville, Congo

  • Molecular and Sensory Food Engineering Laboratory, National Polytechnic School (ENSP), Marien Ngouabi University, Brazzaville, Congo

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