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Shelf life

Predictive modelling to determine shelf-life – what can it tell us?

By Linda Everis - December 2019


How do we define shelf-life? It’s typically the time after production during which a food or drink product remains acceptable for consumption. A straightforward definition, right? But the food and drink industry faces many hurdles when setting shelf-life protocol. It’s a complex task. From manufacture to consumer storage, products can encounter a range of fluctuating temperatures as they are passed from one stage of the cold chain to the next. Determining what these temperatures are and the time spent at them is a challenge. But it must be done to deliver a relevant shelf-life protocol.


Retailers have often developed their own company-specific shelf-life protocols for manufacturers to follow. When developing them, they ask questions such as, “How long will the product be out of chill when purchased?” and “What temperatures will the product be stored at during purchase and in the customer’s fridge?” Often, protocols can differ between retailers, meaning a manufacturer may end up supplying similar products to different customers while following different test protocols.


This leaves us to question whether these differences in shelf-life protocol significantly impact a product’s assigned shelf-life. Will spoilage organisms behave in the same way? How much faster may they grow and spoil the product? How much of a potential safety risk is there? Predictive modelling helps us to answer these questions.


What is predictive modelling?


Predictive modelling is a tool used to assess the survival and/or growth of a microorganism under a range of temperatures, pH and water activities (aw). The models are generated using computer-based software packages and can be used to quickly assess the effect of different storage regimes on the microbiological levels in products. It’s also extremely useful at the product development stage, allowing a quick evaluation of the impact of recipe changes - for example, the effect of salt reduction on Listeria. Overall, it can be used to help determine the likely shelf-life of products from both a quality and a safety viewpoint.


Modelling can be used alongside testing real-life samples. Challenge testing involves deliberately inoculating a product with relevant microorganisms and assessing their growth over time. This approach considers all factors that may influence growth. Predictive modelling, however, will usually only consider three factors: pH, aw and temperature. The unaccounted factors include:



How does predictive modelling work?


Laboratory generated microbial growth curves are used to produce the models. Microbiological growth media (broths) with different intrinsic parameters (e.g. varying pH and salt levels) are inoculated with the relevant organism or cocktail of organisms and incubated at a range of temperatures. Mathematical equations are then applied to the data and used to construct models that allow us to predict how the organisms are likely to grow in conditions similar to that of a particular product. As a result, we can then use this data to predict what kind of shelf-life this product will have.


What predictive modelling can tell us about shelf-life protocols


We used this technique to investigate how seven different protocols, that can be used to set a shelf-life, showed variations in the growth of different microorganisms. Cooked ham and raw meat were chosen for the study. The goal was to understand how slight differences in shelf-life protocols impacted shelf-life.


To do this, two approaches were taken. One to assess the product’s safety (by measuring the growth of two pathogenic species: Listeria and C. botulinum) and the other which assessed the product’s quality (by measuring the level of the spoilage organism Enterobacteriaceae). For each organism, the ‘end of shelf-life’ was taken to be the time at which there was an increase of 0.5 log cfu/g for C. botulinum, an increase from 10 to 100 cfu/g for Listeria or the time taken for Enterobacteriaceae to reach 106 cfu/g. At this point, the product was then taken to be unsafe or spoilt.


What did we find?


The results highlighted considerable differences in the growth potential of microorganisms depending on the storage protocol used. This would affect the setting of shelf-life for those products. This is significant, especially where safety is concerned. Neurotoxin-producing C. botulinum showed this variation most clearly in the duration of the shelf-life that was predicted to be with the consumer, and therefore at the highest temperature of the cold chain. The protocols that predicted the product to be with the consumer for most of its shelf-life showed significant growth of this organism. In fact, the shelf-life was shortened by 14 days in the most extreme case compared to the protocol that hadn’t factored in as much time with the consumer.


Figure 1: Potentially achievable shelf-life in standard cooked ham – the number of days taken for each organism to reach its specified cut-off value

[pH : 6.29 aw: 0.980]. Assumed Initial level: 10 cfu/g.


*106 cfu/g level not reached by Enterobacteriaceae for protocol ‘19d 4°C, 2d 8°C’.


Figure 2: Potentially achievable shelf-life in raw meat – the number of days taken for each organism to reach its specified cut-off value

[pH : 5.66 aw: 0.986]. Assumed Initial level: 10 cfu/g.


*106 cfu/g level not reached by Enterobacteriaceae for protocol ‘16d 4°C, 2d 8°C’


As these results have shown, following one protocol can lead to unsafe food two weeks earlier than another (when both are applied to the same product), so it does make you question which protocol is more appropriate and which is most realistic. The food and drink industry should be aware of these differences and think about how realistic their testing protocols are when all factors are taken into consideration.


We’re about to publish a new guideline on setting the shelf-life of chilled foods for the industry. It discusses in detail what manufacturers and retailers must do to produce a shelf-life protocol which ensures the safety and quality of their products. Contact Linda Everis for more information.


At Campden BRI, we can perform both challenge testing and predictive modelling as a practical service to establish the shelf-life of food and drink products. There are specific benefits to each of these tests, but you can find out which is most suitable for your needs by getting in touch.


How else can we help?


To help you prevent the threat caused by the major pathogens, in early 2020 we’re putting on two seminars that will cover the latest detection methods and initiatives to control E. coli STEC and Salmonella, and Campylobacter. Why not book while there’s still time?


Linda Everis, Microbiologist
+44(0)1386 842063
linda.everis@campdenbri.co.uk

About Linda Everis

Linda Everis joined Campden BRI in 1995 as a Senior Technician in the Microbiological Analytical Services group having graduated from the University of Wales Aberystwyth with a BSc in Biology. Read more...