STATISTICAL EVALUATION OF ARRHENIUS MODEL AND ITS APPLICABILITY IN PREDICTION OF FOOD QUALITY LOSSES

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Abstract
To minimize quality losses occurring during processing and storage and to predict shelf-life, quantitative kinetic models, expressing the functional relationship between composition and environmental factors on food quality are required. The applicability of these models is based on the accuracy of the model and its parameters. In this paper, the calculation of the Arrhenius parameters and the accuracy of the derived model were compared, using three statistical methods, namely; linear least squares, nonlinear least squares and weighted nonlinear least squares. Results indicated that the traditional two-step linear method, was the least accurate and the derived energy of activation and the pre-exponential factor had the largest confidence interval. The latter was shown to have a profound effect on the precision of the calculated rate constant and the predicted shelf life. Based on previous reports that indexes of deterioration are log-normal distributed, the unweighted nonlinear least squares method was applied in a single-step on all the data points, following a logarithmic transformation. The overall better accuracy and superior performance of the nonlinear least squares method, suggests that this method should be utilized for routine kinetic data analysis.