Bayesian survival analysis modeling applied to sensory shelf life of foods

Data from sensory shelf-life studies can be analyzed using survival statistical methods. The objective of this research was to introduce Bayesian methodology to sensory shelf-life studies and discuss its advantages in relation to classical (frequentist) methods. A specific algorithm which incorporates the interval censored data from shelf-life studies is presented. Calculations were applied to whole-fat and fat-free yogurt, each tasted by 80 consumers who answered ‘‘yes’’ or ‘‘no’’ to whether they would consume samples with different storage times. The Bayesian approach was used to obtain the failure functions and the estimated shelf lives of the yogurts. Two approaches were considered: first non-informative priors were used, and then results from the fat-free yogurt were used to construct a more informative prior for the whole-fat product. For both approaches a Weibull distribution was assumed. For the non-informative priors the results were analogous to those obtained by the classical methods. When an informative prior was used to estimate the whole-fat shelf-life parameters, there was a tendency to closer credible limits in the percentile distributions. Advantages of the Bayesian approach are discussed: no need for asymptotical assumptions and the possibility of improving the precision of the results by introducing external information.