Prediction of the moisture ratio of Atama (Heinsia Crinita) leaves using artificial neural network (ANN)

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    In this work, an artificial neural network (ANN) model was used to predict the moisture ratio of atama (Heinsia crinita) dried under different drying temperatures of 40 0C, 50 0C, 60 0C, and 70 0C using a laboratory dry oven. The experimental data collected (140 data points in all) which was partitioned into three sets: training (70%), validation (15%), and testing (15%) were modeled using artificial neural network (ANN), an Artificial Intelligence approach. The ANN model architecture of ANN (3 – 4 - 1) used in this work was selected by trial-and-error approach. The input layer had three (3) inputs (drying rate, temperature, time), the hidden layer had four (4) neurons, and the output layer had one (1) output (moisture ratio). Levenberg-Marquardt (LM) algorithm was used for training the network, and TANSIG and Purelin transfer/activation functions were used for the hidden layer and output layer, respectively. The model had a learning rate of 0.7, and the number of epochs was set at 1000. The results obtained showed that the ANN methodology could precisely predict experimental data with high correlation coefficient (R-Squared) value of 0.9995 – 0.9977 and low mean square error (RMSE) of 0.00052568, as the artificial neural network model more accurately predict the drying parameter (moisture ratio). The sensitivity analysis performed shows that temperature has the greatest impact on the moisture ratio of atama. From the finding, the ANN technology which is embedded in the neural toolbox of MATLAB mathematical software is indeed a tool of choice when it comes to the prediction of parameters of non-linear and complex processes like drying. The unique modelling technique and the model it evolved represent a huge step in the trajectory of achieving full automation of moisture ratio estimation which will increase the utilization of atama as well as other vegetables to curb the unending events of food spoilage currently plaguing the global food and agriculture industry.

     

     

     


  • Keywords


    Prediction; Moisture Ratio; Artificial Neural Network; Atama.

  • References


      [1] J. K. Mensah, R.I. Okoli, J.O. Ohaju-Obodo and K. Eifediyi, “Phytochemical, nutritional and medical properties of some leafy vegetables consumed by Edo people of Nigeria:” African Journal of Biotechnology, 7 (14), 2304 -2309, 2008

      [2] D. Okafor and S. M. Okoro “The useful plants of West Tropical Africa. Families” MR, Royal botanic Garden 4: 805, 2004

      [3] H. C. Gaga and H. E. Gaga “Nutritive value and of blanching on trypsin and chymotrypsin inhibitor activities of selected leafy vegetables” Plant Foods Human Nutrition, 54(3): 271 - 283. 1999 https://doi.org/10.1023/A:1008157508445.

      [4] E. T. Ufot, F. E Comfort and E. N Anne “Physical Properties, Nutritional Composition and Sensory Evaluation of Cookies prepared from Rice, unripe Banana and Sprouted Soybean Flour Blends” International Journal of Food Science and Biotechnology, 3(2):70 - 76, 2018. https://doi.org/10.11648/j.ijfsb.20180302.15.

      [5] M. Aghbashlo, M. H. Kianmehr and H. Samimi-Akhijahani, “Influence of Drying Conditions on the Effective Moisture Diffusivity, Energy of Activation and Energy Consumption during the Thin-layer Drying of Berberis fruit (Berberidaceae)” Energy Conversion Management, 49(10):2865 – 2871, 2008. https://doi.org/10.1016/j.enconman.2008.03.009.

      [6] S. K.Pandev, S. Diwan and R. Soni, “Review of Mathematical Modeling of Drying of Potato Slices in a Forced Convective Dryer Based on Important Parameters” Food Science and Nutrition, 4:110 - 118, 2015. https://doi.org/10.1002/fsn3.258.

      [7] P. C. Panchariya, D. Popovic and A. L. Sharma, “Thin-Layer Modeling of Black Tea Drying Process” Journal of Food Engineering Davis, 52:349 - 357, 2002. https://doi.org/10.1016/S0260-8774(01)00126-1.

      [8] P. Dilip, “Solids Drying: Basics and Applications” Chemical Engineering -New York- Mcgraw Hill Incorporated then Chemical Week Publishing Llc- 121(4), 2014

      [9] S. A. Adeleye “Comparative effects of drying on the quality of some leafy vegetables at a temperature of 600C” Scholarly Journal of Science Research and Essay, 7(4):58-64, 2018.

      [10] H. Fazeli, R. Soleimani, M. A. Ahmadi, R. Badrnezhad and A. H. Mohammadi, “Experimental Study and Modelling of Ultra-filtration of Refinery Effluents using a Hybrid Intelligent Approac” Journal of Energy Fuels, 27(6):3523 – 3537, 2013. https://doi.org/10.1021/ef400179b.

      [11] A .K. Babu, G. Kumaresan V. Antony Aroul Raja and R. Velraj, “Review of leaf drying: Mechanism and influencing parameters, drying methods, nutrient preservation, and mathematical models” Renewable and Sustainable Energy Reviews 90:536 - 556, 2018 https://doi.org/10.1016/j.rser.2018.04.002.

      [12] AOAC (Association of Official Analytical Chemists) (1990). Official Methods of Analysis, 15th edition, Washington, D.C., 210p.

      [13] S.E. Agarry, A.O. Ajani and M.O. Arem, “Thin Layer Drying Kinetics of Pineapple: Effect of Blanching Temperature – Time Combination” Nigerian Journal of Basic and Applied Science 21(1): 1 - 10, 2013. https://doi.org/10.4314/njbas.v21i1.1.

      [14] C. E. Onu, P. K. Igbokwe and J. T. Nwabanne” Effective Moisture Diffusivity, Activation Energy and Specific Energy Consumption in the Thin-Layer Drying of Potato “International Journal of Novel Research in Engineering and Science 3(2): 10 - 22, 2017,

      [15] C. E., Onu, P. K. Igbokwe and J. T. Nwabanne “Effective moisture diffusivity, activation energy and specific energy consumption in the thin-layer drying of potato” International Journal of Novel Research in Engineering and Science, 3(2): 10 – 22, 2018

      [16] A. Sarimeseli, M. A.Coskun, and M. Yuceer, “Modeling Microwave Oven Drying Kinetics of Thyme Leaves using ANN Methodology and Dried Product Quality” Journal of Food Processing and Preservation, 38(1): 558-564, 2012. https://doi.org/10.1111/jfpp.12003.


 

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Article ID: 31809
 
DOI: 10.14419/ijet.v10i2.31809




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