2023-08-112023-08-112016-06-28Güldaş, M. vd. (2017). ''Prediction of lethality by nonlinear artificial neural network modeling''. Journal of Food Process Engineering, 40(3).0145-88761745-4530https://doi.org/10.1111/jfpe.12457https://onlinelibrary.wiley.com/doi/10.1111/jfpe.12457http://hdl.handle.net/11452/33479In this research, the aim was to predict F value (lethality or sterilization value) of canned peas by using a nonlinear auto-regressive artificial neural network model with exogenous input (NARX-ANN). During the model testing, training, validation and reliability steps were followed, respectively. It was found that the model tested was a useful tool to predict the F value for the canned foods with high reliability. Cross-validation rules were performed for training and testing of the model. F value of the 5 kg canned peas could be predicted with a high degree of accuracy (R-2=0.9982, mean square error (MSE)=0.1088) using training the data yielded from 0.5 kg canned peas despite huge mass differences between cross-validated data sets. When the same data sets were trained and tested inversely, a high degree of prediction accuracy (R-2=0.9914, MSE=0.6262) was also observed. The model is also significant in terms of reducing the operational costs due to the fact that higher temperatures and longer process times lead to increased energy costs. Practical ApplicationsIn this research, it was found that nonlinear auto-regressive artificial neural network model with exogenous input is a reliable model for the prediction of lethality rate (F value) in canned food factories. It also provides the advantage of estimating process time more accurately in the retort and thus, reducing operational costs.eninfo:eu-repo/semantics/closedAccessEngineeringFood science & technologyHeat-transferGenetic algorithmsRetortFoodSterilizationOptimizationCanningCost reductionCostsForecastingMean square errorArtificial neural network modelingCross validationHigh degree of accuracyHigh reliabilityMass differenceNonlinear artificial neural networksPrediction accuracyTraining and testingNeural networksArticle0004001535000352-s2.0-85018923737403Engineering, chemicalFood science & technologySterilization; Temperature; Preserved Food