Cooked cured ham is a ready-to-eat food that is popular among consumers. Stored temperature has a key effect on the quality and shelf life of ham. In this work, the quality changes and shelf life prediction of cooked cured ham stored at different temperatures were investigated. Sensory evaluation, physical and chemical indicators, and aerobic plate count were determined. Results showed that high storage temperature of cooked ham accelerates quality deterioration. Partial least squares (PLS) regression analysis based on the variable importance for projection (VIP) identified nine important variables for predicting the shelf life of cooked cured ham. Compared with either PLS or back-propagation artificial neural network (BP-ANN), the hybrid PLS–BP-ANN model better predicts the shelf life of cooked cured ham using the nine important variables. This study provides a theoretical basis and data support for the quality control of cooked cured ham and a new idea for research on the shelf life prediction of cooked cured ham.
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