Assessment of Shelf Life of Processed Cheese by Expert Cascade Artificial Neural Network Computing Models
This paper highlights the significance of cascade single layer models for predicting the shelf life of processed cheese stored at 7-8 o C. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient were used for testing the prediction ability of the developed models. The developed cascade model with a combination of 5-->8-->1 showed excellent agreement between the actual and the predicted values, suggesting that single layer cascade models are efficient in predicting the shelf life of processed cheese.
1. The author(s) retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. The author(s) are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
3. The author(s) warrants that the article is substantially different from any that the author(s) have already published elsewhere.
4. The author(s) have not sent this article or any article substantially the same as the one named above for publication elsewhere.