The design of equipments for shelf-life study

Armour Food Research Laboratory. Oak Brook. IL 60521
Journal of Food Science (Impact Factor: 1.7). 08/2006; 40(2):399 - 403. DOI: 10.1111/j.1365-2621.1975.tb02211.x
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    • "Moreover, the shelf life of frozen dough stored under temperature fluctuations must be confirmed by a sensory test. Due to the expense and the timeconsuming nature of sensory testing procedures for food products, the use of staggered sampling designs for shelf life studies in foods has been proposed (Gacula, 1975). The technique consists of evaluating an increasing number of samples as storage time progresses. "
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    ABSTRACT: The shelf life of frozen bread dough stored under both constant and fluctuating temperature conditions was investigated. Storage regimes were designed to mimic either good or poor practice likely to be experienced in the cold chain (-18 ± 0.1°C, -18 ± 1°C, -18 ± 3°C or -18 ± 5°C). Gas production, dough water mobility and bread crumb characteristics were measured as quality parameters. The acceptability of bread made from frozen dough was monitored using a modified Weibull hazard sensory method. The shelf life results were considered from a kinetic standpoint with a focus on the effect of temperature on the acceleration of deterioration. Temperature fluctuations during storage accelerated deterioration in frozen bread dough. Large temperature fluctuations (-18 ± 5°C) and storage at higher temperatures (a combination of -18°C, -13°C and -8°C) during frozen storage resulted in significantly more rapid loss of dough and bread quality than storage at constant temperatures. A broken (nuclear magnetic resonance) peak of frozen dough stored under large temperature changes indicated greater separation of water bound to the starch-gluten matrix. The shelf life of frozen dough stored under large temperature fluctuations and higher temperatures was about 12 weeks, whereas the shelf life of the dough stored under constant or less fluctuating temperatures was greater than 16 weeks.
    Kasetsart Journal - Natural Science 01/2009; 43:187-197.
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    • "Also, fitting empirical data to mathematical distributions has lead many researchers to use the Weibull distribution to model the deterioration rate. Among the items whose rate of deterioration was assumed to follow the Weibull distribution are refrigerated meats (Andujar and Herrera [1]), roasted and ground coffee (Cardelli and Labuza [3]), pasteurized milk (Duyvesteyn [7]), luncheon meats (Gacula [8]), breakfast cereal (Pickering [14]), cottage cheese (Schmidt and Bouma [20]), cassava flour (Shirose et al. [22]), corn seed (Tang et al. [23]), frozen foods (Tomasicchio et al. [24]), and ice cream (Wittinger and Smith [25]). Besides food stuff, there are many products, such as camera films, drugs, pharmaceuticals, chemicals, electronic components and radioactive substances that deteriorate while in stock. "
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    ABSTRACT: This paper is concerned with the optimal control of a production inventory system with deteriorating items. It is assumed that the deterioration rate follows the two-parameter Weibull distribution. The continuous-review and periodic-review policies are investi- gated. In each case, optimality conditions are derived. Also, numerical illustrative examples are presented.
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    • "This distribution has also been used to model experimental data in food experimentation. In particular, Gacula and Kubala (1975) used this distribution to model experimental data in shelf life studies. In a comparison between the lognormal and Weibull distributions, they argued that " the Weibull distribution is characterized by an increasing or a decreasing failure rate, while in the lognormal distribution, the failure rate is zero at time zero, increases with time to a maximum and then decreases back to zero with increasing time. "
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    ABSTRACT: Sensory evaluations to determine the shelf life of food products are routinely conducted in food experimentation as a part of each product development program, whether it includes a new product, product improvement or a change in type or specification of an ingredient. In such experiments, trained panelists are asked to judge food attributes by reference to a scale of numbers. The “failure time” associated with a product unit under test is usually defined as the time required to reach a cut-off point previously defined by the food company. Important issues associated with the planning and execution of this kind of testing are total sampling size, frequency of sample withdrawals, panel design, and statistical analysis of the panel data, to list a few. Different approaches have been proposed for the analysis of this kind of data. In particular, Freitas et al. proposed an alternative model based on a dichotomization of the score data and a Weibull as the underlying distribution for the time to failure. Also, through a simulation study, the bias and mean square error of the estimates obtained for percentiles and fraction defectives were evaluated. These quantities were used to estimate the shelf life. The simulation study used only the same sample plan implemented in the real situation. This paper focuses on the planning issues associated with these experiments. Sample plans are contrasted and compared in a simulation study, through the use of the approach proposed by Freitas et al.. The simulation results showed that, in general, one can get results much more precise and with smaller bias with a shorter follow-up time, allocating more panelists to each evaluation time.
    International Journal of Quality &amp Reliability Management 04/2004; 21(4):439-466. DOI:10.1108/02656710410530127
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