| Autor: |
Babatunde Olawoye, Oladapo F. Fagbohun, Saka O. Gbadamosi, Charles T. Akanbi |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Artificial Intelligence in Agriculture, Vol 4, Iss , Pp 219-228 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2589-7217 |
| DOI: |
10.1016/j.aiia.2020.09.004 |
| Popis: |
The study investigated the improvement of slowly digestible starch fraction of cardaba banana via octenyl succinic anhydride (OSA) modification process. A nonlinear (Response surface methodology [RSM] and artificial neural network [ANN]) and linear (partial least square [PLS]) models were employed and their predictability was compared. The result revealed that all the modelling techniques were accurate in predicting the experimental process. The optimized RSM values for the production of slowly digestible starch (SDS) fraction were OSA concentration of 4%, reaction time of 47.49 min, and pH of 10 with a predicted SDS value of 44.64%. Among the modelling techniques, ANN was adjudged as the predictive model for improving the SDS yield. The regression coefficient coupled with the variable important in the projection (VIP) values of the PLS model indicated that the OSA concentration was the most important factors responsible for high SDS yield. Finally, a structural comparison of the optimized starch against native starch revealed the formation of high ordered crystalline structure of the starch due to the impregnation of the modifying agent to the hydroxyl group of the cardaba banana starch. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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