| Literature DB >> 30854335 |
M Naceur Haouet1, Mauro Tommasino1, Maria Lucia Mercuri1, Ferdinando Benedetti1, Sara Di Bella1, Marisa Framboas1, Stefania Pelli1, M Serena Altissimi1.
Abstract
The most direct way to estimate the shelf life of a product is to conduct simulation tests which are time consuming and expensive. Conversely, accelerated shelf life tests can be successfully used for stable products having long expected shelf life. The aim of the study was directed to verify the possibility to apply an accelerated shelf life test to perishable food products having a short-expected shelf life, such as a new ready-to-eat processed food preparation, composed mainly by cereals, tuna and chicken, packed in thermo-sealed trays and pasteurised. Different samples of the product were stored in thermal abuse conditions, collected periodically and subjected to determinations of TVB-N, pH and sensorial characteristics. Q10 and activation energy were calculated allowing to obtain a predictive evaluation of the product shelf life at the 4°C recommended temperature. The product shelf life was assessed at 26 days vs the 30 days expected by the manufacturer, showing the possibility to apply successfully ASLT for products having short shelf life, saving both time and money.Entities:
Keywords: Accelerated shelf life test; RTE food; Spoilage
Year: 2019 PMID: 30854335 PMCID: PMC6379691 DOI: 10.4081/ijfs.2018.6919
Source DB: PubMed Journal: Ital J Food Saf ISSN: 2239-7132
Figure 1.Sampling plan of the shelf life study.
TVN (mg/100 g) and pH: means and standard errors (SE).
| Sampling, day | Storage temperature, °C | TVB-N | pH | Sensorial evaluation, scale | ||
|---|---|---|---|---|---|---|
| Mean | SE | Mean | SE | |||
| 0 | 8.0[ | 0.4 | 6.08[ | 0.003 | 6 | |
| 1 | 25 | 15.0[ | 0.9 | 6.18[ | 0.003 | 1 |
| 4 | 18 | 18.2[ | 0.6 | 5.28[ | 0.003 | 3-4 |
| 7 | 18 | 20.1d | 0.7 | 5.03d | 0.003 | 0 |
| 7 | 12 | 13.8[ | 0.6 | 5.99e | 0.004 | 3-4 |
| 11 | 12 | 17.4[ | 0.5 | 5.42f | 0.004 | 1 |
abcSignificant differences at P≤0.001
Figure 2.Linear regression curve for the tolerance time/temperature.
Figure 3.Linear regression curve for Arrhenius plot.