PREDICTION OF LETHALITY BY NONLINEAR ARTIFICIAL NEURAL NETWORK MODELING METIN GULDAS 1,4 , FERHAT KURTULMUS 2 and OZAN GURBUZ 3 1 Department of Food Processing, Uludag University Karacabey Vocational School, Bursa 16700, Turkey 2 Department of Biosystems Engineering, Uludag University Agricultural Faculty, Bursa 16059, Turkey 3 Department of Food Engineering, Uludag University Agricultural Faculty, Bursa 16059, Turkey 4 Corresponding author. TEL: 190 224 2942662/61653; FAX: 190 224 6765562; EMAIL: mguldas@uludag.edu.tr Received for Publication March 15, 2016 Accepted for Publication June 28, 2016 doi:10.1111/jfpe.12457 ABSTRACT In 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 5 0.9982, mean square error (MSE) 5 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 5 0.9914, MSE 5 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 APPLICATIONS In 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. INTRODUCTION F value (Lethality) is a term or measurement for determining thermal process efficiency in canned foods in order to pro- duce healthy and safe foods. It is also known as a sterilization value. Sterilization is also an unavoidable heating process in canned foods with low acidity (pH > 4.5), mainly aimed to eradicate spore forming bacteria (especially Clostridium bot- ulinum) and vegetative forming bacteria. The conditions of heat processing required to terminate microorganisms in canned foods can be determined through thermal death time (TDT) studies. In order to compare the sterilization efficiencies of heat processes, a unit of lethality or lethality rate needs to be determined. D and Z values should be defined at first to determine lethality. These values are used to designate the heat resistance of the target micro- organisms. The D value is described as the time, at a desig- nated temperature, required to reduce a determined number of microorganisms by 90% or to result in a 1-log reduction. This is also called the decimal reduction time because expo- sure for this length of time decreases the number of counts by 90%, therefore relocating the decimal point in the num- ber of microorganisms remaining by one place to the left. To determine the D values at certain temperatures, a Z value can be determined from the slope of the line that results from plotting the log of D values versus temperature. The Z value, indicative of the change in the death rate based on temperature, is the number of degrees between a 10-fold change (1 log cycle) in a microorganism’s resistance. As a Journal of Food Process Engineering 00 (2016) 00–00 V C 2016 Wiley Periodicals, Inc. 1 Journal of Food Process Engineering ISSN 1745-4530