2020
DOI: 10.1111/1750-3841.15000
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Microbiological and physicochemical parameters for predicting quality of fat and low‐fat raw ground beef during refrigerated aerobic storage

Abstract: The aim of the current study was to identify quality indicators of fat (14.50 ± 0.75%) and low-fat (4.79 ± 0.63%) raw ground beef by monitoring changes in physicochemical and microbiological parameters during aerobic refrigerated storage, such as water-holding capacity, pH, thiols, carbonyl compounds, thiobarbituric acid reactive substances (TBARS), metmyoglobin, deoxymyoglobin, oxymyoglobin color indices, pseudomonads, Brochothrix thermosphacta, and total viable counts. Meat packaged in air-permeable polyethy… Show more

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Cited by 9 publications
(9 citation statements)
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“…Many empirical models are used for understanding microbiological profiles of meat and meat products, but a step forward in advanced mathematical and statistical modelling is still not widely applied. Only a few studies included using predictive tools for an explanation of microbiological experimental data, such as partial least squares regression analysis [ 47 ], growth kinetics modelling [ 48 , 49 ], nonlinear least square regression analysis with the Levenberg–Marquardt method of estimation [ 50 ], Lorentzian distribution function and Nonlinear regression [ 51 ], etc. On the other hand, when attempting to build a mathematical model in the area of predictive microbiology, the artificial neural network (ANN) model is perceived as one of the most relevant and universal anticipating tools [ 52 , 53 ].…”
Section: Discussionmentioning
confidence: 99%
“…Many empirical models are used for understanding microbiological profiles of meat and meat products, but a step forward in advanced mathematical and statistical modelling is still not widely applied. Only a few studies included using predictive tools for an explanation of microbiological experimental data, such as partial least squares regression analysis [ 47 ], growth kinetics modelling [ 48 , 49 ], nonlinear least square regression analysis with the Levenberg–Marquardt method of estimation [ 50 ], Lorentzian distribution function and Nonlinear regression [ 51 ], etc. On the other hand, when attempting to build a mathematical model in the area of predictive microbiology, the artificial neural network (ANN) model is perceived as one of the most relevant and universal anticipating tools [ 52 , 53 ].…”
Section: Discussionmentioning
confidence: 99%
“…In relation to the ISO standards for the enumeration of spoilage population in meat its shelf-life under vacuum packages (VP) spans from ten to fourteen days (Pothakos, Snauwaert, De Vos, Huys, & Devlieghere, 2014). However, the number of biochemical reactions rises considerably in parallel to the microbial growth and compounds related to undesirable sensory changes are produced even before the use-by date (Hultman, Johansson, & Björkroth, 2020;Pothakos, Devlieghere, Villani, Björkroth, & Ercolini, 2015;Valerio et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…One of the major causes of off-flavours and off-odours in ground beef is the lipid oxidation with the following formation of Volatile Organic Compounds (VOCs) like aldehydes and ketones (Valerio et al, 2020). Overall, VOCs accumulation over certain thresholds can lead to products rejection due to rancidity odours and meat colour change (Casaburi, Piombino, Nychas, Villani, & Ercolini, 2015).…”
Section: Introductionmentioning
confidence: 99%
“…For example, microbial spoilage in ground meat is translated in undesirable sensory changes like flavor, aroma, and appearance (Gonzalez et al, 2014). Simultaneously, chemical reactions like protein and lipid degradation produces compounds as amines and free fatty acids related to sensory characteristics degradation, thereby limiting the shelf life of meat (Valerio et al, 2020). Ground meat is especially sensitive to spoilage due to an increased surface area and, consequently, the exposure of cell fluids for microbial growth and chemical reactions.…”
Section: Introductionmentioning
confidence: 99%