Monte Carlo simulation of the shelf life of pasteurized milk as affected by temperature and initial concentration of spoilage organisms (original) (raw)
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Journal of Food Process Engineering, 2010
ABSTRACTThe 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.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 APPLICATIONSThe 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.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.
Determination of the End of Shelf-life for Milk using Weibull Hazard Method
LWT - Food Science and Technology, 2001
The shelf life of pasteurized milk is traditionally estimated by the counts of both total and psychrotrophic microbial load. However, the values reported to date for both microbial populations at the end of the sensory shelf life of milk vary, and are not consistent. The present study examined the relation between the total and psychrotophic microbial growth in milk and its sensory shelf life as measured using the Weibull hazard method. Milk was stored at 5 constant temperatures (2, 5, 7, 12, and 15°C) and both total and psychrotrophic microbial counts were used to obtain the lag time and the growth rate values. The lag time of the total and psychrotrophic growth responded to temperature following the Arrhenius equation. The loss of sensory quality of the milk followed a log shelf life vs. temperature dependency. It was found that there was no correlation between the microbial count at the end of shelf life and the sensory quality of the milk. It is therefore suggested that microbial counts should not be used to determine the sensory shelf life of milk. The Weibull method gave end of shelf life values fairly similar to that of prior work using the ADSA scoring method.
2018
Agri-food industriesmust guarantee the safety of the produced foods through the application of the existing regulations, by correctly implementing quality control systems. In relation to the quality of drinking milk, it is extremely important to monitor the industrial treatments to which it is subjected to avoid the multiplication of spoilage and pathogenic microorganisms. Raw milk must undergo strict quality controls at the primary production level based on the knowledge of themain factors that influence their quality andmicrobiological safety: hygienic practices, health status of cows, frequency andmoment of collection, storage temperature and time of transportation. To improve food safety and estimate food shelf life, predictive microbiology is a widely used tool for the estimation of microbial behavior as a function of intrinsic and extrinsic by using mathematical models. Throughout this chapter, a description of the current food quality management systems (FQMS) carried out by ...
Influence of time and storage temperature on raw milk deteriorating microbiota
Brazilian Journal of Veterinary Research and Animal Science, 2020
The quality of raw milk depends on initial microbial contamination and conditions of storage until industry processing. Considering the influence of time and storage temperature on raw milk microbiota, the objective of this work was to quantify and monitor the multiplication of these groups under different conditions. For this purpose, 41 samples of raw milk were collected immediately after milking, stored in the following storage conditions: 25 °C/2 h; 35 °C/2 h; 7 °C/24 h; 7 °C/48 h and 7 °C/60 h and analyses of aerobic mesophilic, psychrotrophic and proteolytic psychrotrophic microorganisms. The milk samples analyzed in the study had an initial mean count of mesophilic aerobes of 5.38 Log CFU/mL at Time Zero. The milk stored at 25 °C/2 h and 35 °C/2 h kept the mesophilic aerobic counts within the limits established by the legislation (5.48 Log CFU/mL), with an increase in counts of psychrotrophic and proteolytic microorganisms. When stored at 7 °C/24 h and 7 °C/48 h, the count of...
Beverages
The consumer rejection threshold (RjT) method was applied to determine the total microbial numbers (TMNs) where consumers find that the quality of whole fresh chilled pasteurised milk (WFCPM) and skim milk (Trim) stored at 4.5 ± 0.5 °C is no longer acceptable. Food spoilage progression was supported by measurements of VOCs and the terms consumers used to describe the ageing fresh chilled pasteurised milk (FCPM). RjTs for TMN of 7.43 and 7.34 log10 CFU.mL−1 for WFCPM and Trim, respectively were derived using Hill’s equation from a series of paired preference tests comparing fresh and aged milks (3–26 days) assessed by consumers (WFCPM, n = 55; Trim, n = 52). A poor relationship between storage time and TMN was found, owing mainly to batch-to-batch and within-batch variation in the milk’s post-pasteurization contamination (PPC) levels. At the RjT, there was a significant change in the signal intensities for a number of spoilage-related VOCs that occurred in the FCPM headspace (p ≤ 0.0...
Foods
Bacillus cereus is relatively resistant to pasteurization. We assessed the risk of B. cereus growth during warming and subsequent storage of pasteurized banked milk (PBM) in the warmed state using a predictive mathematical model. Holder pasteurization followed by storage below −18 °C was used. Temperature maps, water activity values, and B. cereus growth in artificially inoculated PBM were obtained during a simulation of manipulation of PBM after its release from a Human Milk Bank. As a real risk level, we chose a B. cereus concentration of 100 CFU/mL; the risk was assessed for three cases: 1. For an immediate post-pasteurization B. cereus concentration below 1 CFU/mL (level of detection); 2. For a B. cereus concentration of 10 CFU/mL, which is allowed in some countries; 3. For a B. cereus concentration of 50 CFU/mL, which is approved for milk formulas. In the first and second cases, no risk was detected after 1 h of storage in the warmed state, while after 2 h of storage, B. cereus...
Characterization of Pasteurized Fluid Milk Shelf-life Attributes
Journal of Food Science, 2004
Pasteurized fluid milk samples were systematically collected from 3 commercial dairy plants. Samples were evaluated for microbial, chemical, and sensory attributes throughout shelf life. In general, product shelf lives were limited by multiplication of heat-resistant psychrotrophic organisms that caused undesirable flavors in milk. The predominant microorganisms identified were Gram-positive rods including Paenibacillus, Bacillus, and Microbacterium. Principal component analysis of sensory data collected using quantitative descriptive analysis showed that attributes related to milk flavor defects explained the largest amount of variance. These findings highlight the need to develop specific strategies for excluding bacterial contaminants from milk to further extend product shelf lives.
Acta Horticulturae, 2005
Microbiological modelling techniques (predictive microbiology, the Bayesian Markov Chain Monte Carlo method and a probability risk assessment approach) were combined to assess the shelf-life of an in-pack heat-treated, low-acid sauce intended to be marketed under chilled conditions. From a safety perspective, the product and process design for the chilled sauce was focused on the spore forming microorganism Bacillus cereus. Different scenarios of time/temperature profiles in the food supply chain from manufacture up to the consumer were analysed in terms of growth of B. cereus (growth rate and lag phase) and of the consequence of this on the shelf-life. The end of the shelf-life was considered to be the time at which B. cereus reaches a concentration of 10 5 cfu g-1. For example, we have found equivalence in term of model output between scenarios in which the temperature in both retail and at the consumer's home was below 6°C for 60 days, below 8°C for 28 days, and below 10°C for 17 days. These results can be used to support decisions relating to new product design, such as maximum shelf-life, target markets and labelling.
Foods
Shiga toxin-producing Escherichia coli O157:H7 is a food-borne pathogen and the major cause of hemorrhagic colitis. Pseudomonas is the genus most frequent psychrotrophic spoilage microorganisms present in milk. Two-species bacterial systems with E. coli O157:H7, non-pathogenic E. coli, and P. fluorescens in skimmed milk at 7, 13, 19, or 25 °C were studied. Bacterial interactions were modelled after applying a Bayesian approach. No direct correlation between P. fluorescens’s growth rate and its effect on the maximum population densities of E. coli species was found. The results show the complexity of the interactions between two species in a food model. The use of natural microbiota members to control foodborne pathogens could be useful to improve food safety during the processing and storage of refrigerated foods.