Implementation of an app-based time-temperature-indicator system for the real-time shelf life prediction in a pork sausage supply chain

Time-temperature-indicators (TTIs) combined with predictive modeling are helpful tools for avoiding the increasing amount of food waste and the associated waste of resources along the food supply chain. Successful implementation of these systems in practice is still absent due to missing digital technologies for real-time shelf life prediction. This study aimed to validate a novel app system developed for the digital read-out of OnVu™ TTIs and the shelf life prediction of perishable products along the raw pork sausage supply chain. Therefore, a kinetic shelf life model of raw pork sausage was developed based on microbial parameters. A dynamic TTI model was developed based on app-measured TTI data and validated on a laboratory scale to prepare for the pilot study. In the pilot study, the shelf life prediction of TTIs based on app measurements was validated under practical conditions. Results showed that the spoilage kinetics of raw pork sausage could, in general, be reflected by the OnVu™ TTI kinetics based on the app's color measurements. The pilot study showed that predicted and measured TTI color values by the app were in good agreement with accuracy factors of 1.02–1.03; however, slight differences in shelf lives revealed that the prediction model must be further improved by integrating more data. Although variances in the hourly range could be seen between the predicted shelf life based on TTI app measurements and the real shelf life of raw pork sausage, the study serves as a proof of concept for the general useability of the app for shelf life prediction because it showed that TTI and product kinetics were highly comparable. Further technical adjustments to the app and the adaptation of the charging time may further improve the shelf life prediction by the app along the raw pork sausage supply chain.