Background:

the spoilage of cooked ham is a complex process that may be manifested by alterations of microbiological, physicochemical and sensory type, which has been determined by traditional methodologies including long periods of monitoring with a significant number of samples.

Objective:

in the present study a model for probabilistic prediction of spoilage of cooked ham was proposed by principal component analysis (PCA) and logistic regression with Bayesian approach.

Methods:

to perform the analysis, independent samples from 300 batches of cooked pork ham were evaluated, which were stored in cold at temperatures 12 ± 1°C. Three types of variables were experimentally determined: physicochemical (pH, syneresis, residual nitrite, lactic acid percentage), texture (adhesiveness, toughness, puncture and instrumental firmness) and microbiological (aerobic mesophilic bacteria and lactic acid bacteria) in two times: 3 and 40 days. To make this model were randomly selected 60% of the data and to validate it the remaining 40 %. The obtained data were processed with R package statistical Core Team 2012.

Results:

residual nitrite and syneresis were the most representative variables, since its distribution, correlation and loadings were the most significant, more discriminant of the phenomenon of spoilage in the two times of measurement. The developed model allowed correlating the parameters and predicting the spoilage probability of cooked ham, and classifying them according to their quality status. The first principal component (PC1) (biochemical variables) explained 73.3 % of the total variation, the residual nitrite and syneresis were the factors that related the spoilage.

Conclusions:

The logistic regression model with Bayesian approach allowed obtaining the probability of deterioration of cooked ham, stored at 12°C using physicochemical parameters. Applying the PCA permited to correlate and classify the factors of spoilage.

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