Three leaved yam starch physical / engineering properties evaluation using Response Surface Methodology and Artificial Neural Network network
This study evaluated some physical properties of three-leaved yam starch (TLYS). The angle of repose (AOR) (wood and glass), bulk density, true density, bulk volume, and surface area were analyzed with varying temperatures (60 °C, 67.5 °C, and 75 °C) at a constant air velocity (1.75 m/s), as tempera...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Journal of Agriculture and Food Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154323002533 |
| _version_ | 1797385121875099648 |
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| author | Kenechi Nwosu-Obieogu Emmanuel Oke Ude Chiamaka Dirioha Cyprian Maureen Allen Simeon Bright Gabriel Ohabuike Christian Goodnews Ekeoma Nwankwo |
| author_facet | Kenechi Nwosu-Obieogu Emmanuel Oke Ude Chiamaka Dirioha Cyprian Maureen Allen Simeon Bright Gabriel Ohabuike Christian Goodnews Ekeoma Nwankwo |
| author_sort | Kenechi Nwosu-Obieogu |
| collection | DOAJ |
| description | This study evaluated some physical properties of three-leaved yam starch (TLYS). The angle of repose (AOR) (wood and glass), bulk density, true density, bulk volume, and surface area were analyzed with varying temperatures (60 °C, 67.5 °C, and 75 °C) at a constant air velocity (1.75 m/s), as temperature increased, the AOR glass decreased significantly. In contrast, the AOR metal increased, the highest bulk density (0.61 kg/m3) was observed at 75 °C, bulk volume and bulk density decreased significantly with an increase in temperature, and surface area increased with an increase in temperature. The effect of the operating parameters (time, temperature, and air velocity) on the responses (bulk density, true density, bulk volume, and surface area) was investigated, modeled, and optimized via Response Surface Methodology (RSM). The Analysis of Variance (ANOVA) showed a second-order polynomial model with bulk density (R2- 0.999, Adj R2-0.9997, Pred R2-0.9979), true density (R2- 0.999, Adj R2-0.9997, Pred R2-0.9977), bulk volume (R2- 0.9970, Adj R2-0.9932, Pred R2-0.9527) and surface area (R2- 0.9953, Adj R2-0.9892, Pred R2-0.9247) indicating a close relationship between the experimental and predicted responses. The 3D graphs showed a significant impact of the process factors on the response. The optimal bulk density (0.81 kg/m3), true density (0.55 kg/m3), bulk volume (25 m3), and surface area (684 m2) were obtained at a temperature (57.5 °C), time (3 h), and air velocity (2.25 m/s). Artificial Neural Network (ANN) technique with 3 backpropagation algorithm (B·P.) algorithm was employed to analyze TLYS engineering properties; each algorithm was evaluated with 3 neurons in the input layer, 10 neurons in the hidden layer, and an output layer with four neurons. Coefficient of determination (R2) and mean square error (M.S.E.) have been implemented and correlated to test the adequacy of the model. Results showed that the Bayesian regularization had the best prediction for all the algorithms with an MSE (2.5465E-9) and R2 (9.999E-1) for the responses. Scanning Electron Micrograph (SEM) and proximate analysis indicate that the TLYS contains starch. This information from this study can be effectively utilized in the design parameters of the TLYS post-harvest process/machinery. Hence the post-harvest process evaluates the nutritional quality, protects food safety, and reduces losses between harvest and consumption. |
| first_indexed | 2024-03-08T21:49:35Z |
| format | Article |
| id | doaj.art-f360dae1aec146478d4b9a3ed945ca0e |
| institution | Directory Open Access Journal |
| issn | 2666-1543 |
| language | English |
| last_indexed | 2024-03-08T21:49:35Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj.art-f360dae1aec146478d4b9a3ed945ca0e2023-12-20T07:37:14ZengElsevierJournal of Agriculture and Food Research2666-15432023-12-0114100746Three leaved yam starch physical / engineering properties evaluation using Response Surface Methodology and Artificial Neural Network networkKenechi Nwosu-Obieogu0Emmanuel Oke1Ude Chiamaka2Dirioha Cyprian3Maureen Allen4Simeon Bright5Gabriel Ohabuike6Christian Goodnews7Ekeoma Nwankwo8Department of Chemical Engineering, College of Engineering and Engineering Technology, Michael, Okpara University of Agriculture, Umudike, Abia State, Nigeria; Corresponding author.Department of Chemical Engineering, College of Engineering and Engineering Technology, Michael, Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Chemical Engineering, College of Engineering and Engineering Technology, Michael, Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Agricultural and Bioresources Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Mechanical Engineering, College of Engineering and Engineering Technology, Michael, Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Mechanical Engineering, College of Engineering and Engineering Technology, Michael, Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Chemical Engineering, College of Engineering and Engineering Technology, Michael, Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Chemical Engineering, College of Engineering and Engineering Technology, Michael, Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Chemical Engineering, College of Engineering and Engineering Technology, Michael, Okpara University of Agriculture, Umudike, Abia State, NigeriaThis study evaluated some physical properties of three-leaved yam starch (TLYS). The angle of repose (AOR) (wood and glass), bulk density, true density, bulk volume, and surface area were analyzed with varying temperatures (60 °C, 67.5 °C, and 75 °C) at a constant air velocity (1.75 m/s), as temperature increased, the AOR glass decreased significantly. In contrast, the AOR metal increased, the highest bulk density (0.61 kg/m3) was observed at 75 °C, bulk volume and bulk density decreased significantly with an increase in temperature, and surface area increased with an increase in temperature. The effect of the operating parameters (time, temperature, and air velocity) on the responses (bulk density, true density, bulk volume, and surface area) was investigated, modeled, and optimized via Response Surface Methodology (RSM). The Analysis of Variance (ANOVA) showed a second-order polynomial model with bulk density (R2- 0.999, Adj R2-0.9997, Pred R2-0.9979), true density (R2- 0.999, Adj R2-0.9997, Pred R2-0.9977), bulk volume (R2- 0.9970, Adj R2-0.9932, Pred R2-0.9527) and surface area (R2- 0.9953, Adj R2-0.9892, Pred R2-0.9247) indicating a close relationship between the experimental and predicted responses. The 3D graphs showed a significant impact of the process factors on the response. The optimal bulk density (0.81 kg/m3), true density (0.55 kg/m3), bulk volume (25 m3), and surface area (684 m2) were obtained at a temperature (57.5 °C), time (3 h), and air velocity (2.25 m/s). Artificial Neural Network (ANN) technique with 3 backpropagation algorithm (B·P.) algorithm was employed to analyze TLYS engineering properties; each algorithm was evaluated with 3 neurons in the input layer, 10 neurons in the hidden layer, and an output layer with four neurons. Coefficient of determination (R2) and mean square error (M.S.E.) have been implemented and correlated to test the adequacy of the model. Results showed that the Bayesian regularization had the best prediction for all the algorithms with an MSE (2.5465E-9) and R2 (9.999E-1) for the responses. Scanning Electron Micrograph (SEM) and proximate analysis indicate that the TLYS contains starch. This information from this study can be effectively utilized in the design parameters of the TLYS post-harvest process/machinery. Hence the post-harvest process evaluates the nutritional quality, protects food safety, and reduces losses between harvest and consumption.http://www.sciencedirect.com/science/article/pii/S2666154323002533ANNEngineering propertiesRSMTLYS starch |
| spellingShingle | Kenechi Nwosu-Obieogu Emmanuel Oke Ude Chiamaka Dirioha Cyprian Maureen Allen Simeon Bright Gabriel Ohabuike Christian Goodnews Ekeoma Nwankwo Three leaved yam starch physical / engineering properties evaluation using Response Surface Methodology and Artificial Neural Network network Journal of Agriculture and Food Research ANN Engineering properties RSM TLYS starch |
| title | Three leaved yam starch physical / engineering properties evaluation using Response Surface Methodology and Artificial Neural Network network |
| title_full | Three leaved yam starch physical / engineering properties evaluation using Response Surface Methodology and Artificial Neural Network network |
| title_fullStr | Three leaved yam starch physical / engineering properties evaluation using Response Surface Methodology and Artificial Neural Network network |
| title_full_unstemmed | Three leaved yam starch physical / engineering properties evaluation using Response Surface Methodology and Artificial Neural Network network |
| title_short | Three leaved yam starch physical / engineering properties evaluation using Response Surface Methodology and Artificial Neural Network network |
| title_sort | three leaved yam starch physical engineering properties evaluation using response surface methodology and artificial neural network network |
| topic | ANN Engineering properties RSM TLYS starch |
| url | http://www.sciencedirect.com/science/article/pii/S2666154323002533 |
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