| Autor: |
Sadimantara, Muhammad Syukri, Argo, Bambang Dwi, Sucipto, Sucipto, Al Riza, Dimas Firmanda, Hendrawan, Yusuf |
| Předmět: |
|
| Zdroj: |
Journal of Global Innovations in Agricultural Sciences; 2024, Vol. 12 Issue 2, p315-325, 11p |
| Abstrakt: |
This study introduces an innovative approach to predicting aflatoxin contamination levels in cocoa beans by leveraging an optimized Artificial Neural Network (ANN) model coupled with UV-fluorescence imaging. Aspergillus flavus-inoculated cocoa beans underwent a 7-day incubation period, and UV lamp-based image acquisition facilitated data collection. Leveraging 289 color-texture features, the developed ANN model exhibited highly promising predictive capabilities. Validation results indicate a remarkably low Mean Square Error (MSE) of 0.0087 and a high R-value of 0.9910, affirming the efficacy of the proposed model. The seamless integration of UV-fluorescence imaging and ANN presents a viable and accurate alternative for detecting aflatoxin in cocoa beans, thereby enhancing food safety practices within the cocoa industry. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
