Definition of predictor variables for MAP poultry filets stored under different temperature conditions.

Storage tests under different temperatures (2, 4, 10, and 15°C) were conducted to identify the best predictor variable that is most effective to explain the loss of the shelf life and quality of modified atmosphere packed (MAP) poultry, and constitutes the basis for the prediction of the remaining shelf life. The samples were packed in 70% O2 and 30% CO2, which is the common used gas atmosphere for poultry filets in Germany. Typical spoilage microorganisms (Pseudomonas spp., Brochothrix thermosphacta, Enterobacteriaceae, and Lactobacillus spp.) and total viable count (TVC) were enumerated frequently. Additionally, samples were analyzed for sensory changes, pH, and gas concentration. The data extraction and selections by stepwise regression and principle component analysis (PCA) was carried out to identify a variable which has the main influence on shelf life and freshness loss. The results accentuate that the spoilage is caused by a wide range of microorganisms. No specific microorganism could be identified as the dominant originator for the deteriorative changes. Solely TVC showed significant correlations between the development of the sensory decay and the development of the TVC for each single storage temperature.

Active and Intelligent Packaging for Enhancing Modified Atmospheres and Monitoring Quality and Shelf Life of Packed Gilthead Seabream Fillets at Isothermal and Variable Temperature Conditions.

The study investigated the effect of active modified atmosphere packaging (20% CO2-60% N2-20% O2) with CO2 emitters (MAP-PAD) and conventional MAP (MAP) on the quality and shelf-life of gilthead seabream fillets during chill storage, while the most appropriate enzymatic Time Temperature Integrators (TTI) were selected for monitoring their shelf-life at isothermal and variable temperature storage conditions (Teff = 4.8 °C). The concentration of CO2 and O2 in the headspace of the package, volatile compounds and of the microbial population were monitored during storage. The kinetic parameters for bacterial growth were estimated at 0-10 °C using the Baranyi growth model. The MAP-PAD samples presented significantly lower microbial growth rates and longer lag phases compared to the MAP samples, leading to significant shelf-life extension: 2 days of extension at 2.5 °C and 5 °C, while 50% extension at variable conditions (Teff = 4.8 °C). CO2 emitters in the package improved the chemical freshness (K-values) and volatile compounds (characterizing freshness). The responses of different enzymatic TTI were modeled as the function of enzyme concentration, temperature and storage time. The activation energy (Ea) ranged from 97 to 148 kJ mol-1, allowing the selection of appropriate TTIs for the shelf-life monitoring of each fish product: LP-150U for the MAP and M-25U for the MAP-PAD samples. The validation experiment at Teff = 4.8 °C confirmed the applicability of Arrhenius-type models, as well as the use of TTIs as effective chill chain management tools during distribution and storage.

Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions.

Systematic kinetic modeling is required to predict frozen systems behavior in cold dynamic conditions. A one-step procedure, where all data are used simultaneously in a non-linear algorithm, is implemented to estimate the kinetic parameters of both primary and secondary models. Compared to the traditional two-step methodology, more precise estimates are obtained, and the calculated parameter uncertainty can be introduced in realistic shelf life predictions, as a tool for cold chain optimization. Additionally, significant variability of the real distribution/storage conditions is recorded, and must be also incorporated in a kinetic prediction scheme. The applicability of the approach is theoretically demonstrated in an analysis of data on frozen green peas Vitamin C content, for the calculation of joint confidence intervals of kinetic parameters. A stochastic algorithm is implemented, through a double Monte Carlo scheme incorporating the temperature variability during distribution, drawn from cold chain databases. Assuming a distribution scenario of 130 days in the cold chain, 93 ± 110 days remaining shelf life was predicted compared to 180 days assumed based on the use by date. Overall, through the theoretical case study investigated, the uncertainty of models' parameters and cold chain dynamics were incorporated into shelf life assessment, leading to more realistic predictions.