Uncertainty of Microbial Shelf‐life Estimations for Refrigerated Foods Due to the Experimental Variability of the Model Parameters

Abstract

The uncertainty of shelf‐life estimations for refrigerated foods exposed to changing temperature was quantified by considering the contribution of the experimental variability of the model parameters. Assuming the real distribution of the parameters can be replaced by an unknown empirical distribution, this uncertainty was analyzed by a bootstrap methodology. Independent sets of heat transfer and microbial growth parameters were chosen randomly from experimental values. Shelf‐life values were estimated for each set, with the variability evaluated using an SD value. The procedure was repeated 10 times to obtain 10 shelf‐life SDs. The variation coefficient of these SDs is an absolute measurement of shelf‐life variability. At the recommended variation coefficient of 0.1, a sample size of 25 was found acceptable. The shelf‐life variability estimated was similar (±1 day) for surimi in cardboard and expanded polystyrene containers with different shelf life and temperature profiles. The method is applicable to other predictions of food stability. PRACTICAL APPLICATIONS The effect of the experimental variability of model parameters on shelf‐life estimations confirmed the need to assess the uncertainty of model predictions. Another beneficial aspect of the approach presented is that it encourages an integrated approach to food safety and shelf life. Decisions on raw materials and modifications to processing operations affect the initial microbial load of a product. Improvement of storage, distribution and retail facilities reduces temperature abuse. Product size, geometry and packaging choice are also aspects that should be considered. The impact on shelf life of all these factors can be examined by the integrated microbial and heat‐transfer models of the type here presented. The increasing availability of microbial growth parameters and electronic recorders to monitor temperature during processing, storage and distribution facilitate their implementation. The impact of the experimental variability of model parameters is important. For example, this study suggested the need for microbial determinations with lower experimental variability.

Effect of Model Parameter Variability on the Uncertainty of Refrigerated Microbial Shelf‐life Estimates

“…These conditions can be used to identify critical points in product manufacturing and distribution (Zwietering et al 1990;Zwietering et al, 1991). Predictive models describing microbial growth mathematically are also used to estimate the microbial shelf life of foods Li and Torres 1993a,b,c;Mcmeekin et al 1993a;Ross 1996;McDonald and Sun 1999;Ross 1999;Peleg 2006;Almonacid-Merino and Torres 2010). Models allowing estimations of lag time, generation time and exponential growth rate are classified as primary or secondary (McDonald and Sun 1999).…”

“…1993; Li and Torres 1993a,b,c; Mcmeekin et al . 1993a; Ross 1996; McDonald and Sun 1999; Ross 1999; Peleg 2006; Almonacid‐Merino and Torres 2010). Models allowing estimations of lag time, generation time and exponential growth rate are classified as primary or secondary (McDonald and Sun 1999).…”

“…The impact of temperature fluctuation and packaging material can also be assessed (Torres 1989; Almonacid‐Merino et al . 1993; Almonacid‐Merino and Torres 1993, 2010). …”

Effect of Input Data Variability on Estimations of the Equivalent Constant Temperature Time for Microbial Inactivation by HTST and Retort Thermal Processing

“…Mathematical approaches that include data variability in product and process design have been reported (for example, Lenz and Lund 1977; Lund 1978; Almonacid‐Merino and Torres 2010); however, they require high computational competency and thus their use is limited to large corporations where such expertise is available. This research shows examples of a simple and flexible calculation approach based on Monte Carlo simulations and descriptive statistics implemented as Excel™ spreadsheets.…”

Pengaruh jenis kemasan dan posisi penyimpanan terhadap kualitas surimi ikan swanggi selama penyimpanan suhu chilling

“…Namun, faktor yang paling utama adalah suhu penyimpanan surimi (Tolstorebrov et al, 2014;Ba et al, 2016). Proses pembuatan, suhu penyimpanan, dan distribusi surimi juga dapat memengaruhi jumlah awal mikroba dalam surimi (Almonacid & Torres, 2008).…”