Kinetic modeling of proteolysis, tenderness, and microbial growth during beef accelerated aging by the freeze/thaw process

Kinetic modeling of proteolysis, tenderness, and microbial growth during beef accelerated aging by the freeze/thaw process | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Kinetic modeling of proteolysis, tenderness, and microbial growth during beef accelerated aging by the freeze/thaw process Johnathan de Lima Ramos, Marielle Maria de Oliveira Paula, Marcelo Stefanini Tanaka, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5284770/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Aging has been the main practice used by the industry to improve beef palatability, but requiring up to 21 d of cooler storage to reach the desired tenderness. As an alternative to accelerate the aging process, both the use of the prior freeze/thaw process and high storage temperatures have been suggested. The present study aimed to develop kinetic models to evaluate the effects of freezing/thawing process and different aging temperatures (1, 7, 14 and 20°C) on vacuum-packed Nellore beef steaks. Changes on fragmentation index (FI), shear force (SF), cooking loss (CL) and total bacteria count (TBC) of raw beef during aging followed a first-order kinetic model; The reaction rate constant ( k ) increases with increasing aging temperature, and the activation energy ( Ea ) was lower in frozen/thawed samples than nonfrozen ones for FI and SF. The increase in aging temperature had a lower effect on the FI and SF of frozen/thawed samples than on the nonfrozen ones. Forzen/thawed samples required a shorter aging time than nonfrozen samples to reach the same SF. CL was affected only by aging temperature. The specific growth rate ( µ ) of TBC increases with increasing aging temperature, but the Ea was not affected by the freezing/thawing process. The developed kinetic models provide a deeper understanding of the mechanism of the quality changes of frozen/thawed beef during aging. Beef quality storage temperature shelf life shear force myofibrillar index Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The palatability is one of the most important factors in the perception of beef quality by consumers, who are generally willing to pay more for tender meat. Therefore, the meat industry stores vacuum-packed raw meat under refrigeration (-1 to 2°C) for a period of 14 to 28 days to maximize its palatability before marketing. During this process, called wet aging, there is an increase in tenderness and juiciness, in addition to the development of the characteristic flavor, due to the degradation of the myofibrillar protein structure by endogenous proteases (Guimarães et al. 2024 ). Currently, the storage of primal or sub-primary cuts without protective packaging, in a higher temperature range (1 to 4°C), and under varying conditions of airflow and relative humidity, has been conducted by food retailers to differentiate their products. This process, termed dry aging, improves the meat's overall palatability, with a deeper and more intense flavor, and creates a premium price for beef products (Haddad et al. 2022 ). However, although these techniques are widely used, meat aging has high operating costs, requiring physical space and energy (Karwowska et al. 2021 ), and several research has been conducted to develop faster aging processes to obtain consistently tender meats. Among the alternatives that can be used to accelerate aging, freezing and thawing beef before aging is a promising technique that has been suggested both for wet (Wu et al. 2023 ; Guimarães et al. 2024 ; Aroeira et al. 2016 ; Grayson et al. 2014 ) and dry (Guimarães et al. 2024 ; Haddad et al. 2022 ) aging processes. During the freezing and thawing process, the activity of calpastatin, inhibitor of the main enzymes (calpains) responsible for meat proteolysis, is greatly inhibited (Whipple and Koohmaraie 1992 ), and this effect combined with the loss of myofibrillar structural integrity caused by ice crystals formed (Crouse and Koohmaraie 1990 ; Grujić et al. 1993 ; Sales et al. 2020 ) consequently increases the meat tenderness. However, the cell rupture and disruption of muscle fibers caused by cryogenic damage promote the release of exudate creating favorable conditions for enhanced microbial proliferation (Bernardo et al. 2020 ; Guimarães et al. 2024 ), and further investigation is needed. Another alternative to accelerate aging is the use of higher temperatures than that typically used since increases the activity of endogenous meat proteases, including the cathepsin system, allowing the tenderizing time to be shortened (Pomponio and Ertbjerg 2012 ; Rodrigues et al. 2022 ). However, the use of high temperatures also favors microbial growth (Nethra et al. 2023 ), which could alter the shelf life and safety for consumers. Due to these reports that aging after freezing and thawing can induce both tenderness and the propensity for microbial multiplication and that despite the aging systems being accelerated in higher temperatures there is also a microbial risk, it becomes necessary to know more precisely the effects of the interaction of both factors on the technological and microbiological quality of aged meat. One way of determining the quality of a food to predict its shelf life is by analyzing its kinetics. In this sense, there are several research that used mathematical models to quantify and predict quality changes and growth rate of microorganisms to ensure the hygienic and technological quality of meats, thus determining its shelf life (Koutsoumanis et al. 2006 ; Rabeler and Feyissa 2018 ; Olivera et al. 2013 ; Wang et al. 2020 ). However, to date, there is little information available on kinetic modelling that has been used in fresh beef aged in different systems. Moreover, no published work with a kinetic approach to examine the quality of frozen/thawed/aged meat under different temperatures has been conducted. Therefore, this study aimed to perform a kinetic study of microbial growth and technological quality indices such as fragmentation index, cooking loss, and shear force of frozen/thawed beef within a wide aging temperature range. Materials and Methods Raw material and aging process The striploins (left and right) of four Nelore cattle breed (male, with an average age of 30 months old) with 48 h postmortem were obtained from a commercial beef plant under Federal Inspection and conducted to the Laboratory of Meat Technology (LabCarnes) at Federal University of Lavras (UFLA). The left and right striploins of each animal were randomized into two pretreatment groups (n = 4 loins/pretreatment): the control group (never frozen, NF), with the samples being aged while still fresh; and the frozen/thawed (FT) group, with freezing in a commercial freezer (-18°C) for 24 h, followed by thawing in a commercial refrigerator (4°C) for 24 h and then aging. First, pieces of lean meat from a section of approximately 5 cm of each striploin was aseptically divided into 12 sub samples of 25 g and vacuum-packed (BS420; R. Baião, Ubá, MG, Brazil) in nylon-polyethylene bags. The sub samples were randomized into four aging temperatures (1, 7, 14, and 20°C) and stored in different climatic chambers (EletroLab, São Paulo, SP, Brazil) for up to 4 days. Microbiological analyses were performed at time zero (before storage) and after 2 and 4 days for aging temperatures of 1 and 7°C and after 1 and 2 days for aging temperatures of 14 and 20°C. For technological analyses, the remaining striploin was cut transversally into 2.5 cm thick steaks, individually weighed and vacuum-packed in nylon-polyethylene bags. For each striploin, one steak was used for the analyses at zero time, while 12 steaks were randomized into four aging temperatures (1, 7, 14, and 20°C) and stored in different climatic chambers for up to 6 days. On days 2, 4, and 6, one steak was taken out for analyses of fragmentation index, cooking loss and shear force. Technological analyses associated with tenderness A standard-sized rectangular (8.0 x 4.0 x 2.5 cm) lean meat sample (M. longissimus lumborum ; LL) of each 2.5-cm steak was removed, weighed and vacuum-packed for cooking loss (CL) and shear force (SF) determination. The remaining lean meat was grounded and frozen (at -18°C) for fragmentation index (FI) analysis. The standard-sized rectangular packaged samples were stored at 4°C for 1 h to stabilize their temperature and then cooked in a water bath at 80°C until the internal temperature reached 72°C (Rodrigues et al. 2020 ). After cooking, the samples were stored again at 4°C for 1 h, removed from the packaging, dried with paper towels and weighed. The CL was calculated as the difference between the weight before and after cooking divided by the initial weight, expressed as a percentage. Then, the lateral edges of the samples were discarded, and four 1-cm thick transverse sections were obtained. This resulted in standard-sized slices with a rectangular section 3.5 cm long × 2.5 cm high (with the muscle fibers at an angle of ~ 45°) and 1 cm thick. The sections were cut in half, parallel to the length (perpendicular to the muscle fibers), by a flat blade at a speed of 3.33 mm/s in a texturometer (TA. XTplus, Stable Micro Systems Ltd., Godalming, Surrey, UK). The maximum force (N) required to completely shear each section (Shear force; SF) was measured, and the average of the readings for each steak was used in the statistical analysis. The FI was determined in triplicate according to the methodology proposed by Aroeira et al. ( 2020 ), with minor modifications. About 10 grams of grounded frozen meat was homogenized (Turratec TE 102; TECNAL, Piracicaba, SP, Brazil) in 50 mL of refrigerated (4°C) extraction solution (KCl 2 mM and sucrose 0.25 M) at 15,000 rpm for 40 s. The homogenate obtained was vacuum-filtered (Vacuum pump NOF-650, New Pump, Brazil) through a 250 µm nylon mesh, previously dried and weighed (W M ). The set was placed on previously dried filter paper for 10 min at room temperature. Then, the set (filtrate + mesh) was weighed again (W S ), and the IF was expressed as 100 × (W S – W M ). Microbiological Analysis To determine the total bacterial count (TBC), the packages were aseptically opened, 225 mL of 0.1% peptone water was added and homogenized (490 strokes/min) for 5 min in a Stomacher (Metroterm, Brazil). Successive decimal dilutions were prepared with sterile 0.1% peptone water, plated on Plate Count Agar (PCA) medium, and incubated inverted in an oven at 37°C for 48 h (Silva et al. 2017 ). The TBC was expressed as the logarithm of colony-forming units per gram (log CFU/g). Kinetic Modelling Considering that the reduction in FI and SF throughout aging can be characterized by an exponential equation (Lanari et al. 1987 ; Aroeira et al. 2016 ), a first-order kinetic equation (Eq. 1 ) was used to fitted the technological attribute (included CL) as a function of aging time for each combination of pretreatment and aging temperature. $$\:{Q=Q}_{0}\times\:\text{e}\text{x}\text{p}(-kt)\:$$ 1 where Q 0 and Q are the technological attribute at the initial time and at time t (in days), respectively, and k is the reaction rate constant, which is dependent on pretreatment and temperature. The effects of pretreatment and aging on microbial multiplication were evaluated by the specific growth rate (µ), considering that the contaminating bacteria would be in the logarithmic phase of development, using a first-order equation (Eq. 2 ). $$\:{N=N}_{0}\times\:\text{e}\text{x}\text{p}\left(\mu\:t\right)\:$$ 2 where N 0 and N are the microbial population at the initial time and at time t (in days), respectively, and µ is the specific growth rate. Equations ( 1 ) and ( 2 ) were fitted using linear regression of ln ( Q/Q 0 ) or ln ( N/N 0 ) by time to estimate the reaction rate constant ( k ) and growth rate ( µ ). Subsequently, the equation of the Arrhenius model (Eqs. 3 and 4 ) was used to evaluate the effect of aging temperature on k and µ (Singh 1994 ). $$\:k={k}_{ref}\times\:\text{e}\text{x}\text{p}\left[-\frac{{E}_{a}}{RT}\times\:\left(\frac{1}{T}-\frac{1}{{T}_{ref}}\right)\right]$$ 3 $$\:\mu\:={\mu\:}_{ref}\times\:\text{e}\text{x}\text{p}\left[-\frac{{E}_{a}}{RT}\times\:\left(\frac{1}{T}-\frac{1}{{T}_{ref}}\right)\right]$$ 4 where T is the absolute temperature (K), T is the reference absolute temperature (273.15 K), k ref and µ ref are the reaction and growth rate constants (1/day), respectively, at T ref , E a is the activation energy (kJ/mol), and R is the universal gas constant (8.31 J/mol K). Statistical Analysis The experiment was arranged in a randomized block design (CBD) with four genuine replications, in which the block consisted of each animal, in a split-plot design, with a factorial of 2 (pretreatments) x 4 (aging temperatures) in the plot and 4 aging times in the subplot. The mean and standard deviation of the kinetic parameters, the analysis of variance (ANOVA) and Tukey's test for differences of means were performed using Statistica® 8.0 software (StatSoft Inc., Tulsa, USA) with a significance level of 5%. Results and discussion Technological Characteristics Associated with Tenderness There was no interaction ( P > 0.05) between pretreatment, aging temperature and aging time for FI and CL, but the SF was affected ( P < 0.05) by the pretreatment × aging time interaction (Table 1 ). Table 1 Effects (mean ± standard deviation) of pretreatment (P) 1 and temperature (T) and time (D) of aging on fragmentation index (FI), cooking loss (CL) and shear force (SF) in Nellore cattle muscles ( L. lumborum ). Effects Source of Variation FI CL (%) SF (N) Pretreatment (P) Nonfrozen 187 ± 71 x 21.01 ± 5.02 159.32 ± 44.03 Frozen/Thawed 176 ± 62 y 21.08 ± 6.15 138.29 ± 35.47 Aging temperature (T), 1 200 ± 66 a 19.33 ± 5.35 b 161.88 ± 38.72 °C 7 184 ± 66 b 20.58 ± 5.63 b 152.24 ± 33.20 14 176 ± 65 bc 21.14 ± 5.83 ab 145.96 ± 42.55 20 165 ± 66 c 23.18 ± 5.08 a 136.90 ± 47.94 Aging time (D), days 0 275 ± 39 a 20.01 ± 6.70 c 184.86 ± 31.29 2 193 ± 41 b 19.14 ± 5.42 bc 153.43 ± 37.02 4 146 ± 31 c 22.41 ± 5.09 ab 132.83 ± 36.19 6 117 ± 23 d 23.22 ± 3.51 a 122.50 ± 32.95 Pr > F 2 P 0.026 0.911 < 0.001 T < 0.001 0.024 0.001 D < 0.001 0.001 < 0.001 P×T 0.353 0.769 0.332 P×D 0.934 0.760 0.004 T×D 0.108 0.703 0.230 P×T×D 0.912 0.920 0.518 1 Nonfrozen = fresh (never frozen) aged samples; and Frozen/thawed = frozen (-18°C/24 h) e thawed (4°C/24 h) samples. 2 Significant probabilities ( P < 0.05) were marked in bold. x,y Means followed by different letters, in the column for treatment, differ ( P < 0.05) by F test. a−d Means followed by different letters, in the column within temperature or time of aging, differ ( P < 0.05) by Tukey test. The pretreatment significantly affected the FI values, being lower (P < 0.05) for the frozen/thawed (FT) samples than those without prior freezing before aging (NF). This indicates greater fragmentation of the myofibrillar structure after FT pretreatment, which may be due the combined effect on loss of myofibrillar structural integrity caused by the formation of ice crystals (Grujić et al. 1993 ) and an increase in postmortem proteolysis during aging, resulting from freezing induced denaturation of calpastatin (Whipple and Koohmaraie 1992 ). The calpastatin is the main inhibitor of the meat proteolytic calpain system, and its inactivation favors the activity of calpains I and II and, therefore, myofibrillar fragmentation during aging. A susceptibility to calpastatin inactivation (activity reduction by 50 to 55%) by freezing, while the calpains were unaffected in frozen post-rigor meats, was reported for beef (Koohmaraie et al. 1991 ; Koohmaraie 1990 ) and lamb (Ingolfsson and Dransfield 1991 ). Moreover, the cryodamage due to the formation of intracellular ice crystals disrupts physical structures and induces a large increase in free calcium, which also favors the meat proteolysis (Sales et al. 2020 ). These effects of freezing associated with a reduction in FI was observed both in meat frozen after ageing (Lagerstedt et al. 2008 ; Shanks et al. 2002 ) and in meat frozen/thawed before ageing (Grayson et al. 2014 ; Aroeira et al. 2016 ; Sales et al. 2020 ; Haddad et al. 2022 ; Guimarães et al. 2024 ). Stafford et al. ( 2024a ) observed greater desmin and troponin-T degradation in the frozen unaged samples than in their unfrozen counterparts. Therefore, the increased proteolysis in the frozen steaks is likely a consequence of an increase in endogenous protease activity triggered by the disruption of key cellular organelles. Furthermore, lower (P < 0.05) FI values with increasing aging temperature were observed (Table 1 ), i.e., greater proteolysis of the myofibrillar structure occurred in meats aged at higher temperatures. This could be attributed to an increase in the activity of meat endogenous proteases, which are more active at high aging temperatures (Rodrigues et al. 2022 ). Geesink et al. ( 2000 ) observed greater degradation of calpastatin and myofibrillar proteins due to greater activation of µ-calpain when sheep meat was stored at higher temperatures (up to 35°C). Moreover, it is known that cathepsins become more active during maturation at high temperatures (Hwan and Bandman 1989 ), thus increasing myofibrillar degradation. Similarly, a higher myofibrillar fragmentation in beef aged at high temperatures was observed when aging temperatures of 2, 15, and 30°C (Lee et al. 1996 ), 4 and 14°C (Kim et al. 2018 ), and 1, 7, and 15°C (Rodrigues et al. 2022 ) were evaluated. The increase in proteolysis caused by pretreatments and higher aging temperatures is also evident when observing the first-order predictive models of FI (Fig. 1 ), in which an increase in the reaction rate constant ( k ) was observed with increasing aging temperature, as well as a reduction in activation energy ( Ea ) with pre-freezing treatment before aging (Table 2 ). Olivera et al. ( 2013 ) also found an increase in the k values with increasing aging temperature in beef using a first-order kinetic model. However, in our experiment, the k values of the frozen samples were greater than that of the NF ones only at an aging temperature of 1°C, making the FT curve steeper than the NF ones. Furthermore, the increase in temperature had a greater effect on the NF samples than on the FT ones, as can be seen from the greater difference between the frozen curves (Fig. 1 B) when compared to the NF ones (Fig. 1 A). Table 2 Reaction rate constant ( k ) and activation energy ( Ea ) of the Arrhenius model for the effects of pretreatment 1 and aging temperature on the fragmentation index (FI), cooking loss (CL) and shear force (SF) of Nellore cattle muscles ( L. lumborum ). Characteristic Pretreatment Aging temperature (°C) k (/day) R 2 E a (kJ/mol) R 2 FI Nonfrozen 1 0.1058 0.94 23.83 0.93 7 0.1463 0.91 14 0.1717 0.97 20 0.1898 0.94 Frozen/Thawed 1 0.1376 0.94 13.30 0.99 7 0.1485 0.95 14 0.1650 0.95 20 0.1768 0.97 CL (%) Nonfrozen 1 0.0096 0.02 64.57 0.85 7 0.0389 0.31 14 0.0456 0.76 20 0.0716 0.73 Frozen/Thawed 1 0.0042 0.01 85.73 0.75 7 0.0258 0.14 14 0.0494 0.26 20 0.0547 0.55 SF (N) Nonfrozen 1 0.0348 0.84 47.46 0.84 7 0.0886 0.90 14 0.1171 0.81 20 0.1435 0.74 Frozen/Thawed 1 0.0442 0.74 24.99 0.96 7 0.0642 0.77 14 0.0750 0.68 20 0.0932 0.79 1 Nonfrozen = fresh (never frozen) aged samples; and Frozen/thawed = frozen (-18°C/24 h) e thawed (4°C/24 h) samples. As expected, longer aging times reduced ( P < 0.05) the FI values, and consequently, resulted in greater degradation of the myofibrillar structure (Table 1 ). This behavior can be explained by the proteolysis of the main myofibrillar proteins, which is the main reason for the improvement in meat tenderness during postmortem storage (Koohmaraie et al. 1987 ). The CL was not affected ( P > 0.05) by the pretreatment, but there was a significative increase with the aging temperature, with greater values for meat aged at 20°C than for that aged at 1 and 7°C (Table 1 ). However, the results obtained at the temperature conventionally used in the aging process (1°C) did not differ ( P > 0.05) from those obtained at 7 and 14°C. This is in agreement with Rodrigues et al. ( 2022 ) work which did not observe any effect on CL values ​​in beef aged at 1, 7, or 15°C. Conversely, Aroeira et al. ( 2016 ) reported a lower CL in frozen/thawed beef before aging (at 1°C) than in fresh (nonfrozen) aged beef. Stafford et al. ( 2024b ) demonstrates that freezing/thawing had a minor effect on CL of unaged beef. First-order predictive models for CL as a function of aging time for the pretreatments and aging temperatures were obtained but did not explain most of the observed variation, having low coefficients of determination ( R 2 < 0.55; Table 2 ). However, the k reaction rate increased ( P < 0.05) with increasing aging time, leading to higher CL values. As observed in this experiment, Rodrigues et al. ( 2022 ) also reported that the beef CL values increased from the 4th day of aging and then remained constant for 21 days, regardless of the temperature of aging. Despite observing higher CL values ​​in the frozen/thawed samples, Aroeira et al. ( 2016 ) also reported that the beef CL values increased from the 7th day of aging onwards for both treatments (nonfrozen and frozen). Furthermore, according to Hughes et al. ( 2014 ), the water lost after cooking beef aged for at least 3 to 6 days is greater than that in unaged beef. For SF, there was ( P < 0.05) an isolated effect of aging temperature and an interaction effect between pretreatment and aging time (Table 1 ). When comparing the effect of pretreatment within each aging time, a reduction in SF values during aging for all temperatures was observed but there was a greater tenderness (lower SF values) in the FT samples than in NF ones (Fig. 2 ) in the early days of aging. Freezing induced a reduction in the SF of the unaged samples, having the samples with prior freezing (FT) the same SF values as the nonfrozen samples (NF) with two days less aging (at day 2 vs day 4). This may be due to the combined effect on loss of myofibrillar structural integrity caused by the formation of ice crystals and an increase in postmortem proteolysis induced by freezing as previously discussed. Therefore, aging thawed samples accelerated the meat tenderization during the process as observed by Grayson et al. ( 2014 ), Aroeira et al. ( 2016 ) and Guimarães et al. ( 2024 ) for wet-aging and by Haddad et al. ( 2022 ) and Guimarães et al. ( 2024 ) for dry-aging. As observed for FI, the k values increase with increasing aging temperature, and the Ea values are reduced with the pre-freezing treatment before aging for SF (Table 2 ). Penny and Dransfield ( 1979 ) reported that for the reduction in toughness, the Ea was 63 kJ/mol between 5°C and 15°C and about 40 kJ/mol between 15°C and 35°C. Moreover, they observed that the rates of increase with temperature gave an energy of activation of 72 kJ/mol for troponin-T breakdown. Still according to these authors loss of troponin-T accounted for about 60% of the variation in toughness. A similar effect was observed by Lanari et al. ( 1987 ), evaluating the tenderness of beef aged at different temperatures (0, 4, 10, and 13°C); the authors reported higher k values at higher aging temperatures and an Ea value of 62 kJ/mol in cooked beef. The increase in meat tenderness (lower SF) with pretreatment and higher aging temperatures can be observed in the first-order predictive models represented in Fig. 3 . Also as observed for FI, the increase in temperature has a greater effect on the NF samples than on the FT ones. Moreover, despite the lower SF of the thawed samples at time zero (Fig. 2 ), the tenderness rate was higher in the NF than FT samples, especially in temperatures higher than 1°C. This suggests that the increased tenderness observed in the FT samples was primarily due to the loss of cellular integrity due to cryodamage. During aging, proteolysis in the NF samples was greater (Fig. 1 ), increasing the tenderness rate (Fig. 4 ) and causing the SF values ​​to reach the same values ​​observed in the FT samples only after four days (Fig. 3 ). However, one should consider the possibility that proteolysis could have also occurred during thawing, potentially contributing to the improved tenderness. Stafford et al. ( 2024a ) observed an increase in calpain-1 autolysis and cathepsin B activity, and a elevated levels of free calcium and mitochondrial dysfunction on frozen/thawed samples than nonfrozen ones. They attributed these effects as a consequence of ice crystals disrupting cellular organelles, leading to the release of factors that initiate protease activation. Microbial Growth The values of the specific growth rate ( µ ) and Ea of the Arrhenius model were calculated to verify the differences in the changes in the total bacterial count (TBC) with aging time for each combination of pretreatment and aging temperature (Table 3 ). The µ value increases with increasing aging temperature but the E a values of the pretreatments are similar. Nevertheless, the mean µ values of the FT samples (0.62 ± 0.33 /day) being slightly higher ( P < 0.05) than those of the NF ones (0.58 ± 0.30 /day). This higher rate of microbial development in FT samples can also be observed in the first-order predictive models of the TBC (Fig. 4 ). Table 3 Specific growth rate (µ) and activation energy (Ea) of the Arrhenius model for the effects of pretreatment 1 and aging temperature on the total bacterial count (TBC) in Nellore cattle muscles ( L. lumborum ). Pretreatment Aging temperature (°C) µ (/day) E a (kJ/mol) R 2 Nonfrozen 1 0.2495 50.42 0.97 7 0.3590 14 0.7163 20 0.9842 Frozen/Thawed 1 0.2531 52.42 0.99 7 0.3731 14 0.8156 20 1.0262 1 Nonfrozen = fresh (never frozen) aged samples; and Frozen/thawed = frozen (-18°C/24 h) e thawed (4°C/24 h) samples. Freezing is not intended to reduce bacterial contamination; it only interrupts bacterial proliferation potential. According to Lu et al. ( 2022 ), temperatures between − 5 and − 8°C tend to be limiting for bacterial growth, as the microbes become dormant at the commonly used storage temperature of -18°C. However, the formation of ice crystals by slow freezing can damage the cell membrane of microorganisms, resulting in the extravasation of potassium ions or RNA and decreasing their viability; in addition, the cells may die due to osmotic dehydration (Rahman and Velez-Ruiz 2007 ). Cells exposed to this heat stress may have reversible or irreversible lesions, and those that survive can recover and the growth could be even accelerated during the thawing process (Leygonie et al. 2012 ; Coombs et al. 2017 ). In this experiment, no reduction in the TBC count was observed with freezing, probably because the small pieces of meat were fast-frozen, and, therefore, large ice crystals were not formed (Grujić et al. 1993 ), probably leading to less cell cryodamage. Nevertheless, higher rate of microbial development during aging was observed in FT samples than NF ones, could be explained by the meat thawing process (4°C/24 h), which, in addition to providing a longer time for adaptation and multiplication of the microorganisms, generally provides a greater release of meat exudates due to cryodamage (Rahman et al. 2014 ). The meat exudate leads to an increase in moisture and nutrient availability during an increase in temperature by thawing, which is an excellent medium for microbial growth (Leygonie et al. 2012 ; Coombs et al. 2017 ). Haddad et al. ( 2022 ) and Guimarães et al. ( 2024 ) reported that freezing/thawing resulted in greater numbers of mesophiles and psychrotrophs in wet- and dry-aged beef. The effects of higher temperatures on microbial growth are well known. An increase in aging temperature favors microbiological development by increasing the rate of chemical and enzymatic reactions and altering the structure, fluidity and functionality of membranes and the folding of DNA, RNA and ribosomes (Adams et al. 2024 ). According to these authors, in general, a 10°C increase in temperature can double or triple the µ value due to this increase in the metabolic activity of the microorganism. This agrees with what was observed in this experiment (Table 3 ). Conclusion This study develops kinetic models that describe the tenderization and microbiological growth during aging beef as function of pretreatment, frozen/thawed and nonfrozen, and aging temperature. Overall, the developed kinetic models provide a deeper understanding of the mechanism of the quality changes of frozen/thawed beef during aging. The activation energy for myofibrillar fragmentation and meat tenderization during ageing was lower in the frozen/thawed samples, and these samples required a shorter ageing time to be tender than did the nonfrozen ones. Data suggest that the increased tenderization in the frozen/thawed samples is primarily due to cellular damage rather than the increase in proteolysis rate. In addition, freezing/thawing prior to aging did not affect cooking loss and had little effects on the beef microbiological safety. Furthermore, the increase in the ageing temperature also favored myofibrillar fragmentation and, therefore, the beef tenderness, and this effect was greater in the nonfrozen samples. However, the increase in temperature also favored microbial growth, regardless of the pretreatment used (frozen and nonfrozen samples). Therefore, it can be concluded that freezing prior aging is a potential technique for accelerating the aging process in beef using lower temperatures (1°C), but in higher temperatures additional conservation methods are needed to control microbial multiplication and ensure food security. Declarations Funding This study was financial supported by the Minas Gerais State Research Support Foundation (FAPEMIG; CVZ APQ 02904-17). Conflicts of interest/Competing interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Consent for publication All authors and the Institute (Federal University of Lavras - UFLA) where the work was performed agree with this submission. Author Contribution Ramos, JL: methodology, investigation, writing—original draft. Paula, MMO: validation, writing—review and editing. Tanaka, MS: validation, writing—review and editing. Torres Filho: validation, formal analysis, writing—review and editing; Ramos, ALS: supervision, methodology, writing—review and editing. Ramos, EM: conceptualization, methodology, project administration, supervision, validation, writing—review and editing. Acknowledgement The authors thank the National Council for Scientific and Technological Development (CNPq/Brazil) for granting a postdoctoral scholarship to the second author (152596/2022-4) and a Research Productivity Fellow (PQ) to the last two authors. References Adams, M. R., McClure, P. J., & Moss, M. O. (2024). Food Microbiology . Royal Society of Chemistry. Aroeira, C. N., Filho, T., Fontes, R. A., Gomide, P. R., Ramos, L. A. M., Ladeira, A. L. S., M. M., et al. (2016). Freezing, thawing and aging effects on beef tenderness from Bos indicus and Bos taurus cattle. Meat Science , 116 , 118–125. https://doi.org/10.1016/j.meatsci.2016.02.006 Aroeira, C. N., Filho, T., Fontes, R. A., Ramos, P. R., Castillo, A. L. S. C., Hopkins, C. J., D. L., et al. (2020). Comparison of different methods for determining the extent of myofibrillar fragmentation of chilled and frozen/thawed beef across postmortem aging periods. Meat Science , 160 , 107955. https://doi.org/10.1016/j.meatsci.2019.107955 Bernardo, A. P. S., Silva, A. C. M., Francisco, V. C., Ribeiro, F. A., Nassu, R. T., Calkins, C. R., et al. (2020). Effects of freezing and thawing on microbiological and physical-chemical properties of dry-aged beef. Meat Science , 161 , 108003. https://doi.org/10.1016/j.meatsci.2019.108003 Coombs, C. E. O., Holman, B. W. B., Friend, M. A., & Hopkins, D. L. (2017). Long-term red meat preservation using chilled and frozen storage combinations: A review. Meat Science , 125 , 84–94. https://doi.org/10.1016/j.meatsci.2016.11.025 Crouse, J. D., & Koohmaraie, M. (1990). Effect of freezing of beef on subsequent postmortem aging and shear force. 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Effect of post-mortem storage on Ca++-dependent proteases, their inhibitor and myofibril fragmentation. Meat Science , 19 (3), 187–196. https://doi.org/10.1016/0309-1740(87)90056-8 Koohmaraie, M., Whipple, G., Kretchmar, D. H., Crouse, J. D., & Mersmann, H. J. (1991). Postmortem proteolysis in longissimus muscle from beef, lamb and pork carcasses. Journal of Animal Science , 69 (2), 617–624. Koutsoumanis, K., Stamatiou, A., Skandamis, P., & Nychas, G. J. E. (2006). Development of a Microbial Model for the Combined Effect of Temperature and pH on Spoilage of Ground Meat, and Validation of the Model under Dynamic Temperature Conditions. Applied and Environmental Microbiology , 72 (1), 124–134. https://doi.org/10.1128/AEM.72.1.124-134.2006 Lagerstedt, Å., Enfält, L., Johansson, L., & Lundström, K. (2008). Effect of freezing on sensory quality, shear force and water loss in beef M. longissimus dorsi. Meat Science , 80 (2), 457–461. https://doi.org/10.1016/j.meatsci.2008.01.009 Lanari, M. C., Bevilacqua, A. E., & Zaritzky, N. E. (1987). Changes in tenderness during aging of vacuum-packaged beef. Journal of Food Processing and Preservation , 11 (2), 95–109. https://doi.org/10.1111/j.1745-4549.1987.tb00040.x Lee, M., Sebranek, J., & Parrish, F. C. (1996). Accelerated postmortem aging of beef utilizing electronbeam irradiation and modified atmosphere packaging. Journal of Food Science , 61 (1), 133–136. https://doi.org/10.1111/j.1365-2621.1996.tb14742.x Leygonie, C., Britz, T. J., & Hoffman, L. C. (2012). Impact of freezing and thawing on the quality of meat: Review. Meat Science , 91 (2), 93–98. https://doi.org/10.1016/j.meatsci.2012.01.013 Lu, N., Ma, J., & Sun, D. W. (2022). Enhancing physical and chemical quality attributes of frozen meat and meat products: Mechanisms, techniques and applications. Trends in Food Science & Technology , 124 , 63–85. https://doi.org/10.1016/j.tifs.2022.04.004 Nethra, P. V., Sunooj, K. V., Aaliya, B., Navaf, M., Akhila, P. P., Sudheesh, C., et al. (2023). Critical factors affecting the shelf life of packaged fresh red meat – A review. Measurement: Food , 10 , 100086. https://doi.org/10.1016/j.meafoo.2023.100086 Olivera, D. F., Bambicha, R., Laporte, G., Cárdenas, F. C., & Mestorino, N. (2013). Kinetics of colour and texture changes of beef during storage. Journal of Food Science and Technology , 50 (4), 821–825. https://doi.org/10.1007/s13197-012-0885-7 Penny, I. F., & Dransfield, E. (1979). Relationship between toughness and troponin T in conditioned beef. Meat Science , 3 (2), 135–141. https://doi.org/10.1016/0309-1740(79)90015-9 Pomponio, L., & Ertbjerg, P. (2012). The effect of temperature on the activity of µ- and m-calpain and calpastatin during post-mortem storage of porcine longissimus muscle. Meat Science , 91 (1), 50–55. https://doi.org/10.1016/j.meatsci.2011.12.005 Rabeler, F., & Feyissa, A. H. (2018). Kinetic Modeling of Texture and Color Changes During Thermal Treatment of Chicken Breast Meat. Food and Bioprocess Technology , 11 (8), 1495–1504. https://doi.org/10.1007/s11947-018-2123-4 Rahman, M. H., Hossain, M. M., Rahman, S. M., Hashem, M. A., & Oh, D. H. (2014). Effect of repeated freeze-thaw cycles on beef quality and safety. Korean J Food Sci Anim Resour , 34 (4), 482–495. https://doi.org/10.5851/kosfa.2014.34.4.482 Rahman, M. S., & Velez-Ruiz, J. F. (2007). Food preservation by freezing. Handbook of food preservation (pp. 653–684). CRC. Rodrigues, L. M., Guimarães, A. S., Ramos, J. L., Fontes, P. R., Ramos, A. L. S., & Ramos, E. M. R. (2022). Application of gamma radiation in the beef texture development during accelerated aging. Journal of Texture Studies , 53 (6), 923–934. https://doi.org/10.1111/jtxs.12714 Rodrigues, L. M., Sales, L. A., Fontes, P. R., Filho, T., Andrade, R. A., Ramos, M. P. D., A. L. S., et al. (2020). Combined effects of gamma irradiation and aging on tenderness and quality of beef from Nellore cattle. Food Chemistry , 313 , 126137. https://doi.org/10.1016/j.foodchem.2019.126137 Sales, L. A., Rodrigues, L. M., Silva, D. R. G., Fontes, P. R., Filho, T., Ramos, R. A., A. L. S., et al. (2020). Effects of freezing/irradiation/thawing processes and subsequent aging on tenderness, color and oxidative properties of beef. Meat Science , 163 , 108078. https://doi.org/10.1016/j.meatsci.2020.108078 Shanks, B. C., Wulf, D. M., & Maddock, R. J. (2002). Technical note: The effect of freezing on Warner-Bratzler shear force values of beef longissimus steaks across several postmortem aging periods. Journal of Animal Science , 80 (8), 2122–2125. https://doi.org/10.1093/ansci/80.8.2122 Silva, N., Junqueira, V. C. A., Silveira, N. F. A., Taniwaki, M. H., Gomes, R. A. R., & Okazaki, M. M. (2017). Manual of methods for microbiological analysis of food and water (in portuguese) (5 ed.). ed.). Blucher. Singh, R. P. (1994). Scientific principles of shelf life evaluation. In C. M. D. Man, & A. A. Jones (Eds.), Shelf Life Evaluation of Foods (pp. 3–26). Springer US. Stafford, C. D., Taylor, M. J., Dang, D. S., Alruzzi, M. A., Thornton, K. J., & Matarneh, S. K. (2024a). Freezing promotes postmortem proteolysis in beef by accelerating the activation of endogenous proteolytic systems. Meat and Muscle Biology , 8 (17760), 1–15. https://doi.org/10.22175/mmb.17760 Stafford, C. D., Taylor, M. J., Spurling, R. A., Crump, Z. C., Alberto, A. F., Alruzzi, M. A., et al. (2024b). The influence of different freezing and thawing conditions on the quality of beef rib primals. LWT , 209 , 116771. https://doi.org/10.1016/j.lwt.2024.116771 Wang, Z., He, Z., Zhang, D., Li, H., & Wang, Z. (2020). Using oxidation kinetic models to predict the quality indices of rabbit meat under different storage temperatures. Meat Science , 162 , 108042. https://doi.org/10.1016/j.meatsci.2019.108042 Whipple, G., & Koohmaraie, M. (1992). Freezing and calcium chloride marination effects on beef tenderness and calpastatin activity. Journal of Animal Science , 70 (10), 3081–3085. https://doi.org/10.2527/1992.70103081x Wu, G., Yang, C., Bruce, H. L., Roy, B. C., Li, X., & Zhang, C. (2023). Effects of alternating electric field assisted freezing-thawing-aging sequence on longissimus dorsi muscle microstructure and protein characteristics. Food Chemistry , 409 , 135266. https://doi.org/10.1016/j.foodchem.2022.135266 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Dec, 2024 Reviews received at journal 18 Nov, 2024 Reviews received at journal 03 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviewers agreed at journal 28 Oct, 2024 Reviewers invited by journal 28 Oct, 2024 Editor assigned by journal 18 Oct, 2024 Submission checks completed at journal 17 Oct, 2024 First submitted to journal 17 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5284770","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371463099,"identity":"41ce1b6d-dca4-407c-b514-20f33594f26b","order_by":0,"name":"Johnathan de Lima Ramos","email":"","orcid":"","institution":"Universidade Federal de Lavras (UFLA)","correspondingAuthor":false,"prefix":"","firstName":"Johnathan","middleName":"de Lima","lastName":"Ramos","suffix":""},{"id":371463100,"identity":"210e02ae-2e78-41ab-9bc4-b5a4111f0756","order_by":1,"name":"Marielle Maria de Oliveira Paula","email":"","orcid":"","institution":"Universidade Federal de Lavras (UFLA)","correspondingAuthor":false,"prefix":"","firstName":"Marielle","middleName":"Maria de Oliveira","lastName":"Paula","suffix":""},{"id":371463101,"identity":"4d51b4b0-90fa-4012-822e-ac95c56d46d2","order_by":2,"name":"Marcelo Stefanini Tanaka","email":"","orcid":"","institution":"Universidade Federal de Lavras (UFLA)","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"Stefanini","lastName":"Tanaka","suffix":""},{"id":371463102,"identity":"1e62ab6d-cc4b-4df7-a53e-3f40482a35ac","order_by":3,"name":"Robledo de Almeida Torres Filho","email":"","orcid":"","institution":"Universidade Federal de Viçosa (UFV)","correspondingAuthor":false,"prefix":"","firstName":"Robledo","middleName":"de Almeida Torres","lastName":"Filho","suffix":""},{"id":371463103,"identity":"eb6662d3-47a8-4707-a584-f1909dac1407","order_by":4,"name":"Alcinéia de Lemos Souza Ramos","email":"","orcid":"","institution":"Universidade Federal de Lavras (UFLA)","correspondingAuthor":false,"prefix":"","firstName":"Alcinéia","middleName":"de Lemos Souza","lastName":"Ramos","suffix":""},{"id":371463104,"identity":"7f7e69f6-489d-4758-9d01-46a60a73df6e","order_by":5,"name":"Eduardo Mendes Ramos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBADOQbmww0MjA0kaDFmYEskUUtiA9Fa+MXOPvx0o6IufcMxxsYPjDvuEdYiOTvdWDrnzOFcoJZmCcYzxYS1GNxOY5DObTuQu+F+Y4MEY1sCYS32t9OYf+e21aUbAG35QZQWA+k0NqAtzAlALW3E2SJxO43NGugXw5lALRaJZ4jQwj87jfl2TkWdPN8x5sM3Pu4gQgsqIFnDKBgFo2AUjALsAACiljmvr97HLAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidade Federal de Lavras (UFLA)","correspondingAuthor":true,"prefix":"","firstName":"Eduardo","middleName":"Mendes","lastName":"Ramos","suffix":""}],"badges":[],"createdAt":"2024-10-17 18:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5284770/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5284770/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67739085,"identity":"45f76986-e8ee-46da-b672-e068f151e492","added_by":"auto","created_at":"2024-10-29 08:34:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124401,"visible":true,"origin":"","legend":"\u003cp\u003ePredictions of changes in the fragmentation index (FI) compared to the initial values (FI\u003csub\u003e0\u003c/sub\u003e) of muscles (\u003cem\u003eL. lumborum\u003c/em\u003e) of Nellore cattle during aging. Effects of pretreatment and aging temperature (1, 7, 14 and 20 °C), according to the first-order model (Eq.1) and the estimated \u003cem\u003ek\u003c/em\u003e values (Table 2). Nonfrozen (NF) = fresh (never frozen) samples; and Frozen/thawed (FT) = frozen (-18°C/24 h), thawed (4°C/24 h) and aged samples. The points represent the mean values.\u003c/p\u003e","description":"","filename":"Fig1FreezingAgingKinects.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5284770/v1/64dae3199a781c60fc18b03e.jpg"},{"id":67738729,"identity":"a754f587-a29e-4863-97cc-9d167f99082f","added_by":"auto","created_at":"2024-10-29 08:26:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75494,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of pretreatment on the shear force (SF) of muscles (\u003cem\u003eL. lumborum\u003c/em\u003e) of Nellore cattle during aging. Nonfrozen (NF) = fresh (never frozen) samples; Frozen/thawed (FT) = frozen (-18 °C/24 h), thawed (4 °C/24 h) and aged samples. \u003csup\u003e\u003cem\u003ea-c \u003c/em\u003e\u003c/sup\u003eMeans followed by different letters differ (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) by Tukey's test. The bars represent the standard error of the mean (\u003cem\u003en\u003c/em\u003e = 16).\u003c/p\u003e","description":"","filename":"Fig2FreezingAgingKinects.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5284770/v1/000bb73e9bc755610fafb641.jpg"},{"id":67738733,"identity":"86a424bb-e0a6-46f1-a9af-caeb04600dd4","added_by":"auto","created_at":"2024-10-29 08:26:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117722,"visible":true,"origin":"","legend":"\u003cp\u003ePredictions of changes in shear force (SF) compared to the initial values (SF\u003csub\u003e0\u003c/sub\u003e) of muscles (\u003cem\u003eL. lumborum\u003c/em\u003e) of Nellore cattle during aging. Effects of pretreatment and aging temperature (1, 7, 14 and 20 °C), according to the first-order model (Eq.1) and the estimated \u003cem\u003ek\u003c/em\u003e values (Table 2). Nonfrozen (NF) = fresh (never frozen) samples; and Frozen/thawed (FT) = frozen (-18°C/24 h), thawed (4°C/24 h) and aged samples. The points represent the mean values observed.\u003c/p\u003e","description":"","filename":"Fig3FreezingAgingKinects.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5284770/v1/fa0c219cd131d81f8af90b72.jpg"},{"id":67738731,"identity":"e3299431-9358-4d2a-b3f1-b227850eba03","added_by":"auto","created_at":"2024-10-29 08:26:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":117993,"visible":true,"origin":"","legend":"\u003cp\u003ePredictions of changes in the total bacterial count (TBC) population (N) in relation to the initial count (N\u003csub\u003e0\u003c/sub\u003e) in muscles (\u003cem\u003eL. lumborum\u003c/em\u003e) of Nellore cattle during aging. Effects of pretreatment and aging temperature (1, 7, 14 and 20 °C), according to the first-order model (Eq.2) and the estimated \u003cem\u003eµ\u003c/em\u003e values (Table 3). Nonfrozen (NF) = fresh (never frozen) samples; and Frozen/thawed (FT) = frozen (-18°C/24 h), thawed (4°C/24 h) and aged samples. The points represent the mean values observed.\u003c/p\u003e","description":"","filename":"Fig4FreezingAgingKinects.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5284770/v1/b4d9e51b59ed349d8a23238b.jpg"},{"id":67740468,"identity":"802c132f-dbf8-4e1c-bfd4-5e9474e4d094","added_by":"auto","created_at":"2024-10-29 08:42:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1303055,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5284770/v1/f90d519f-3116-4971-88f4-7f1a27c74827.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Kinetic modeling of proteolysis, tenderness, and microbial growth during beef accelerated aging by the freeze/thaw process","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe palatability is one of the most important factors in the perception of beef quality by consumers, who are generally willing to pay more for tender meat. Therefore, the meat industry stores vacuum-packed raw meat under refrigeration (-1 to 2\u0026deg;C) for a period of 14 to 28 days to maximize its palatability before marketing. During this process, called wet aging, there is an increase in tenderness and juiciness, in addition to the development of the characteristic flavor, due to the degradation of the myofibrillar protein structure by endogenous proteases (Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Currently, the storage of primal or sub-primary cuts without protective packaging, in a higher temperature range (1 to 4\u0026deg;C), and under varying conditions of airflow and relative humidity, has been conducted by food retailers to differentiate their products. This process, termed dry aging, improves the meat's overall palatability, with a deeper and more intense flavor, and creates a premium price for beef products (Haddad et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, although these techniques are widely used, meat aging has high operating costs, requiring physical space and energy (Karwowska et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and several research has been conducted to develop faster aging processes to obtain consistently tender meats.\u003c/p\u003e \u003cp\u003eAmong the alternatives that can be used to accelerate aging, freezing and thawing beef before aging is a promising technique that has been suggested both for wet (Wu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Aroeira et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Grayson et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and dry (Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Haddad et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) aging processes. During the freezing and thawing process, the activity of calpastatin, inhibitor of the main enzymes (calpains) responsible for meat proteolysis, is greatly inhibited (Whipple and Koohmaraie \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), and this effect combined with the loss of myofibrillar structural integrity caused by ice crystals formed (Crouse and Koohmaraie \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Grujić et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Sales et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) consequently increases the meat tenderness. However, the cell rupture and disruption of muscle fibers caused by cryogenic damage promote the release of exudate creating favorable conditions for enhanced microbial proliferation (Bernardo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and further investigation is needed.\u003c/p\u003e \u003cp\u003eAnother alternative to accelerate aging is the use of higher temperatures than that typically used since increases the activity of endogenous meat proteases, including the cathepsin system, allowing the tenderizing time to be shortened (Pomponio and Ertbjerg \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rodrigues et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the use of high temperatures also favors microbial growth (Nethra et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which could alter the shelf life and safety for consumers.\u003c/p\u003e \u003cp\u003eDue to these reports that aging after freezing and thawing can induce both tenderness and the propensity for microbial multiplication and that despite the aging systems being accelerated in higher temperatures there is also a microbial risk, it becomes necessary to know more precisely the effects of the interaction of both factors on the technological and microbiological quality of aged meat. One way of determining the quality of a food to predict its shelf life is by analyzing its kinetics. In this sense, there are several research that used mathematical models to quantify and predict quality changes and growth rate of microorganisms to ensure the hygienic and technological quality of meats, thus determining its shelf life (Koutsoumanis et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rabeler and Feyissa \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Olivera et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, to date, there is little information available on kinetic modelling that has been used in fresh beef aged in different systems. Moreover, no published work with a kinetic approach to examine the quality of frozen/thawed/aged meat under different temperatures has been conducted. Therefore, this study aimed to perform a kinetic study of microbial growth and technological quality indices such as fragmentation index, cooking loss, and shear force of frozen/thawed beef within a wide aging temperature range.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRaw material and aging process\u003c/h2\u003e \u003cp\u003eThe striploins (left and right) of four Nelore cattle breed (male, with an average age of 30 months old) with 48 h postmortem were obtained from a commercial beef plant under Federal Inspection and conducted to the Laboratory of Meat Technology (LabCarnes) at Federal University of Lavras (UFLA). The left and right striploins of each animal were randomized into two pretreatment groups (n\u0026thinsp;=\u0026thinsp;4 loins/pretreatment): the control group (never frozen, NF), with the samples being aged while still fresh; and the frozen/thawed (FT) group, with freezing in a commercial freezer (-18\u0026deg;C) for 24 h, followed by thawing in a commercial refrigerator (4\u0026deg;C) for 24 h and then aging.\u003c/p\u003e \u003cp\u003eFirst, pieces of lean meat from a section of approximately 5 cm of each striploin was aseptically divided into 12 sub samples of 25 g and vacuum-packed (BS420; R. Bai\u0026atilde;o, Ub\u0026aacute;, MG, Brazil) in nylon-polyethylene bags. The sub samples were randomized into four aging temperatures (1, 7, 14, and 20\u0026deg;C) and stored in different climatic chambers (EletroLab, S\u0026atilde;o Paulo, SP, Brazil) for up to 4 days. Microbiological analyses were performed at time zero (before storage) and after 2 and 4 days for aging temperatures of 1 and 7\u0026deg;C and after 1 and 2 days for aging temperatures of 14 and 20\u0026deg;C.\u003c/p\u003e \u003cp\u003eFor technological analyses, the remaining striploin was cut transversally into 2.5 cm thick steaks, individually weighed and vacuum-packed in nylon-polyethylene bags. For each striploin, one steak was used for the analyses at zero time, while 12 steaks were randomized into four aging temperatures (1, 7, 14, and 20\u0026deg;C) and stored in different climatic chambers for up to 6 days. On days 2, 4, and 6, one steak was taken out for analyses of fragmentation index, cooking loss and shear force.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTechnological analyses associated with tenderness\u003c/h3\u003e\n\u003cp\u003eA standard-sized rectangular (8.0 x 4.0 x 2.5 cm) lean meat sample (M. \u003cem\u003elongissimus lumborum\u003c/em\u003e; LL) of each 2.5-cm steak was removed, weighed and vacuum-packed for cooking loss (CL) and shear force (SF) determination. The remaining lean meat was grounded and frozen (at -18\u0026deg;C) for fragmentation index (FI) analysis.\u003c/p\u003e \u003cp\u003eThe standard-sized rectangular packaged samples were stored at 4\u0026deg;C for 1 h to stabilize their temperature and then cooked in a water bath at 80\u0026deg;C until the internal temperature reached 72\u0026deg;C (Rodrigues et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). After cooking, the samples were stored again at 4\u0026deg;C for 1 h, removed from the packaging, dried with paper towels and weighed. The CL was calculated as the difference between the weight before and after cooking divided by the initial weight, expressed as a percentage. Then, the lateral edges of the samples were discarded, and four 1-cm thick transverse sections were obtained. This resulted in standard-sized slices with a rectangular section 3.5 cm long \u0026times; 2.5 cm high (with the muscle fibers at an angle of ~\u0026thinsp;45\u0026deg;) and 1 cm thick. The sections were cut in half, parallel to the length (perpendicular to the muscle fibers), by a flat blade at a speed of 3.33 mm/s in a texturometer (TA. XTplus, Stable Micro Systems Ltd., Godalming, Surrey, UK). The maximum force (N) required to completely shear each section (Shear force; SF) was measured, and the average of the readings for each steak was used in the statistical analysis.\u003c/p\u003e \u003cp\u003eThe FI was determined in triplicate according to the methodology proposed by Aroeira et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with minor modifications. About 10 grams of grounded frozen meat was homogenized (Turratec TE 102; TECNAL, Piracicaba, SP, Brazil) in 50 mL of refrigerated (4\u0026deg;C) extraction solution (KCl 2 mM and sucrose 0.25 M) at 15,000 rpm for 40 s. The homogenate obtained was vacuum-filtered (Vacuum pump NOF-650, New Pump, Brazil) through a 250 \u0026micro;m nylon mesh, previously dried and weighed (W\u003csub\u003eM\u003c/sub\u003e). The set was placed on previously dried filter paper for 10 min at room temperature. Then, the set (filtrate\u0026thinsp;+\u0026thinsp;mesh) was weighed again (W\u003csub\u003eS\u003c/sub\u003e), and the IF was expressed as 100 \u0026times; (W\u003csub\u003eS\u003c/sub\u003e \u0026ndash; W\u003csub\u003eM\u003c/sub\u003e).\u003c/p\u003e\n\u003ch3\u003eMicrobiological Analysis\u003c/h3\u003e\n\u003cp\u003eTo determine the total bacterial count (TBC), the packages were aseptically opened, 225 mL of 0.1% peptone water was added and homogenized (490 strokes/min) for 5 min in a Stomacher (Metroterm, Brazil). Successive decimal dilutions were prepared with sterile 0.1% peptone water, plated on Plate Count Agar (PCA) medium, and incubated inverted in an oven at 37\u0026deg;C for 48 h (Silva et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The TBC was expressed as the logarithm of colony-forming units per gram (log CFU/g).\u003c/p\u003e\n\u003ch3\u003eKinetic Modelling\u003c/h3\u003e\n\u003cp\u003eConsidering that the reduction in FI and SF throughout aging can be characterized by an exponential equation (Lanari et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Aroeira et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a first-order kinetic equation (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was used to fitted the technological attribute (included CL) as a function of aging time for each combination of pretreatment and aging temperature.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Q=Q}_{0}\\times\\:\\text{e}\\text{x}\\text{p}(-kt)\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Q\u003csub\u003e0\u003c/sub\u003e and Q are the technological attribute at the initial time and at time \u003cem\u003et\u003c/em\u003e (in days), respectively, and \u003cem\u003ek\u003c/em\u003e is the reaction rate constant, which is dependent on pretreatment and temperature.\u003c/p\u003e \u003cp\u003eThe effects of pretreatment and aging on microbial multiplication were evaluated by the specific growth rate (\u0026micro;), considering that the contaminating bacteria would be in the logarithmic phase of development, using a first-order equation (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{N=N}_{0}\\times\\:\\text{e}\\text{x}\\text{p}\\left(\\mu\\:t\\right)\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eN\u003c/em\u003e are the microbial population at the initial time and at time \u003cem\u003et\u003c/em\u003e (in days), respectively, and \u003cem\u003e\u0026micro;\u003c/em\u003e is the specific growth rate.\u003c/p\u003e \u003cp\u003eEquations\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were fitted using linear regression of ln (\u003cem\u003eQ/Q\u003c/em\u003e \u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) or ln (\u003cem\u003eN/N\u003c/em\u003e \u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) by time to estimate the reaction rate constant (\u003cem\u003ek\u003c/em\u003e) and growth rate (\u003cem\u003e\u0026micro;\u003c/em\u003e). Subsequently, the equation of the \u003cem\u003eArrhenius\u003c/em\u003e model (Eqs.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was used to evaluate the effect of aging temperature on \u003cem\u003ek\u003c/em\u003e and \u003cem\u003e\u0026micro;\u003c/em\u003e (Singh \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:k={k}_{ref}\\times\\:\\text{e}\\text{x}\\text{p}\\left[-\\frac{{E}_{a}}{RT}\\times\\:\\left(\\frac{1}{T}-\\frac{1}{{T}_{ref}}\\right)\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\mu\\:={\\mu\\:}_{ref}\\times\\:\\text{e}\\text{x}\\text{p}\\left[-\\frac{{E}_{a}}{RT}\\times\\:\\left(\\frac{1}{T}-\\frac{1}{{T}_{ref}}\\right)\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eT\u003c/em\u003e is the absolute temperature (K), \u003cem\u003eT\u003c/em\u003e is the reference absolute temperature (273.15 K), \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eref\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003eref\u003c/em\u003e\u003c/sub\u003e are the reaction and growth rate constants (1/day), respectively, at \u003cem\u003eT\u003c/em\u003e\u003csub\u003eref\u003c/sub\u003e, \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e is the activation energy (kJ/mol), and R is the universal gas constant (8.31 J/mol K).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe experiment was arranged in a randomized block design (CBD) with four genuine replications, in which the block consisted of each animal, in a split-plot design, with a factorial of 2 (pretreatments) x 4 (aging temperatures) in the plot and 4 aging times in the subplot. The mean and standard deviation of the kinetic parameters, the analysis of variance (ANOVA) and Tukey's test for differences of means were performed using Statistica\u0026reg; 8.0 software (StatSoft Inc., Tulsa, USA) with a significance level of 5%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTechnological Characteristics Associated with Tenderness\u003c/h2\u003e \u003cp\u003eThere was no interaction (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between pretreatment, aging temperature and aging time for FI and CL, but the SF was affected (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by the pretreatment \u0026times; aging time interaction (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation) of pretreatment (P) \u003csup\u003e1\u003c/sup\u003e and temperature (T) and time (D) of aging on fragmentation index (FI), cooking loss (CL) and shear force (SF) in Nellore cattle muscles (\u003cem\u003eL. lumborum\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCL (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSF (N)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretreatment (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonfrozen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187\u0026thinsp;\u0026plusmn;\u0026thinsp;71\u003csup\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159.32\u0026thinsp;\u0026plusmn;\u0026thinsp;44.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrozen/Thawed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176\u0026thinsp;\u0026plusmn;\u0026thinsp;62\u003csup\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.08\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.29\u0026thinsp;\u0026plusmn;\u0026thinsp;35.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAging temperature (T),\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u0026thinsp;\u0026plusmn;\u0026thinsp;66\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161.88\u0026thinsp;\u0026plusmn;\u0026thinsp;38.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184\u0026thinsp;\u0026plusmn;\u0026thinsp;66\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.63\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152.24\u0026thinsp;\u0026plusmn;\u0026thinsp;33.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176\u0026thinsp;\u0026plusmn;\u0026thinsp;65\u003csup\u003e\u003cem\u003ebc\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.83\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145.96\u0026thinsp;\u0026plusmn;\u0026thinsp;42.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165\u0026thinsp;\u0026plusmn;\u0026thinsp;66\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.18\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136.90\u0026thinsp;\u0026plusmn;\u0026thinsp;47.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAging time (D), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275\u0026thinsp;\u0026plusmn;\u0026thinsp;39\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.01\u0026thinsp;\u0026plusmn;\u0026thinsp;6.70\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184.86\u0026thinsp;\u0026plusmn;\u0026thinsp;31.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193\u0026thinsp;\u0026plusmn;\u0026thinsp;41\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003csup\u003e\u003cem\u003ebc\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153.43\u0026thinsp;\u0026plusmn;\u0026thinsp;37.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.09\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.83\u0026thinsp;\u0026plusmn;\u0026thinsp;36.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;23\u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.50\u0026thinsp;\u0026plusmn;\u0026thinsp;32.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePr\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eF\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.911\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026times;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.353\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.769\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.332\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026times;D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.934\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.760\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u0026times;D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.108\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.703\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.230\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026times;T\u0026times;D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.912\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.920\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.518\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e Nonfrozen\u0026thinsp;=\u0026thinsp;fresh (never frozen) aged samples; and Frozen/thawed\u0026thinsp;=\u0026thinsp;frozen (-18\u0026deg;C/24 h) e thawed (4\u0026deg;C/24 h) samples.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e Significant probabilities (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were marked in bold.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003ex,y\u003c/em\u003e\u003c/sup\u003e Means followed by different letters, in the column for treatment, differ (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by F test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003ea\u0026minus;d\u003c/em\u003e\u003c/sup\u003e Means followed by different letters, in the column within temperature or time of aging, differ (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) by Tukey test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe pretreatment significantly affected the FI values, being lower (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the frozen/thawed (FT) samples than those without prior freezing before aging (NF). This indicates greater fragmentation of the myofibrillar structure after FT pretreatment, which may be due the combined effect on loss of myofibrillar structural integrity caused by the formation of ice crystals (Grujić et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and an increase in postmortem proteolysis during aging, resulting from freezing induced denaturation of calpastatin (Whipple and Koohmaraie \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The calpastatin is the main inhibitor of the meat proteolytic calpain system, and its inactivation favors the activity of calpains I and II and, therefore, myofibrillar fragmentation during aging. A susceptibility to calpastatin inactivation (activity reduction by 50 to 55%) by freezing, while the calpains were unaffected in frozen post-rigor meats, was reported for beef (Koohmaraie et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Koohmaraie \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) and lamb (Ingolfsson and Dransfield \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Moreover, the cryodamage due to the formation of intracellular ice crystals disrupts physical structures and induces a large increase in free calcium, which also favors the meat proteolysis (Sales et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These effects of freezing associated with a reduction in FI was observed both in meat frozen after ageing (Lagerstedt et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shanks et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and in meat frozen/thawed before ageing (Grayson et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Aroeira et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sales et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Haddad et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Stafford et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) observed greater desmin and troponin-T degradation in the frozen unaged samples than in their unfrozen counterparts. Therefore, the increased proteolysis in the frozen steaks is likely a consequence of an increase in endogenous protease activity triggered by the disruption of key cellular organelles.\u003c/p\u003e \u003cp\u003eFurthermore, lower (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) FI values with increasing aging temperature were observed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), i.e., greater proteolysis of the myofibrillar structure occurred in meats aged at higher temperatures. This could be attributed to an increase in the activity of meat endogenous proteases, which are more active at high aging temperatures (Rodrigues et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Geesink et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) observed greater degradation of calpastatin and myofibrillar proteins due to greater activation of \u0026micro;-calpain when sheep meat was stored at higher temperatures (up to 35\u0026deg;C). Moreover, it is known that cathepsins become more active during maturation at high temperatures (Hwan and Bandman \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), thus increasing myofibrillar degradation. Similarly, a higher myofibrillar fragmentation in beef aged at high temperatures was observed when aging temperatures of 2, 15, and 30\u0026deg;C (Lee et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), 4 and 14\u0026deg;C (Kim et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and 1, 7, and 15\u0026deg;C (Rodrigues et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were evaluated.\u003c/p\u003e \u003cp\u003eThe increase in proteolysis caused by pretreatments and higher aging temperatures is also evident when observing the first-order predictive models of FI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), in which an increase in the reaction rate constant (\u003cem\u003ek\u003c/em\u003e) was observed with increasing aging temperature, as well as a reduction in activation energy (\u003cem\u003eEa\u003c/em\u003e) with pre-freezing treatment before aging (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Olivera et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) also found an increase in the \u003cem\u003ek\u003c/em\u003e values with increasing aging temperature in beef using a first-order kinetic model. However, in our experiment, the \u003cem\u003ek\u003c/em\u003e values of the frozen samples were greater than that of the NF ones only at an aging temperature of 1\u0026deg;C, making the FT curve steeper than the NF ones. Furthermore, the increase in temperature had a greater effect on the NF samples than on the FT ones, as can be seen from the greater difference between the frozen curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) when compared to the NF ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReaction rate constant (\u003cem\u003ek\u003c/em\u003e) and activation energy (\u003cem\u003eEa\u003c/em\u003e) of the Arrhenius model for the effects of pretreatment\u003csup\u003e1\u003c/sup\u003e and aging temperature on the fragmentation index (FI), cooking loss (CL) and shear force (SF) of Nellore cattle muscles (\u003cem\u003eL. lumborum\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAging temperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(kJ/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonfrozen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrozen/Thawed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonfrozen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrozen/Thawed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonfrozen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrozen/Thawed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e Nonfrozen\u0026thinsp;=\u0026thinsp;fresh (never frozen) aged samples; and Frozen/thawed\u0026thinsp;=\u0026thinsp;frozen (-18\u0026deg;C/24 h) e thawed (4\u0026deg;C/24 h) samples.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs expected, longer aging times reduced (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) the FI values, and consequently, resulted in greater degradation of the myofibrillar structure (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This behavior can be explained by the proteolysis of the main myofibrillar proteins, which is the main reason for the improvement in meat tenderness during postmortem storage (Koohmaraie et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CL was not affected (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) by the pretreatment, but there was a significative increase with the aging temperature, with greater values for meat aged at 20\u0026deg;C than for that aged at 1 and 7\u0026deg;C (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, the results obtained at the temperature conventionally used in the aging process (1\u0026deg;C) did not differ (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) from those obtained at 7 and 14\u0026deg;C. This is in agreement with Rodrigues et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) work which did not observe any effect on CL values ​​in beef aged at 1, 7, or 15\u0026deg;C. Conversely, Aroeira et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported a lower CL in frozen/thawed beef before aging (at 1\u0026deg;C) than in fresh (nonfrozen) aged beef. Stafford et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) demonstrates that freezing/thawing had a minor effect on CL of unaged beef.\u003c/p\u003e \u003cp\u003eFirst-order predictive models for CL as a function of aging time for the pretreatments and aging temperatures were obtained but did not explain most of the observed variation, having low coefficients of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.55; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the \u003cem\u003ek\u003c/em\u003e reaction rate increased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with increasing aging time, leading to higher CL values. As observed in this experiment, Rodrigues et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also reported that the beef CL values increased from the 4th day of aging and then remained constant for 21 days, regardless of the temperature of aging. Despite observing higher CL values ​​in the frozen/thawed samples, Aroeira et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also reported that the beef CL values increased from the 7th day of aging onwards for both treatments (nonfrozen and frozen). Furthermore, according to Hughes et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the water lost after cooking beef aged for at least 3 to 6 days is greater than that in unaged beef.\u003c/p\u003e \u003cp\u003eFor SF, there was (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) an isolated effect of aging temperature and an interaction effect between pretreatment and aging time (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When comparing the effect of pretreatment within each aging time, a reduction in SF values during aging for all temperatures was observed but there was a greater tenderness (lower SF values) in the FT samples than in NF ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in the early days of aging. Freezing induced a reduction in the SF of the unaged samples, having the samples with prior freezing (FT) the same SF values as the nonfrozen samples (NF) with two days less aging (at day 2 vs day 4). This may be due to the combined effect on loss of myofibrillar structural integrity caused by the formation of ice crystals and an increase in postmortem proteolysis induced by freezing as previously discussed. Therefore, aging thawed samples accelerated the meat tenderization during the process as observed by Grayson et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Aroeira et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Guimar\u0026atilde;es et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for wet-aging and by Haddad et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Guimar\u0026atilde;es et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for dry-aging.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs observed for FI, the \u003cem\u003ek\u003c/em\u003e values increase with increasing aging temperature, and the \u003cem\u003eEa\u003c/em\u003e values are reduced with the pre-freezing treatment before aging for SF (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Penny and Dransfield (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) reported that for the reduction in toughness, the \u003cem\u003eEa\u003c/em\u003e was 63 kJ/mol between 5\u0026deg;C and 15\u0026deg;C and about 40 kJ/mol between 15\u0026deg;C and 35\u0026deg;C. Moreover, they observed that the rates of increase with temperature gave an energy of activation of 72 kJ/mol for troponin-T breakdown. Still according to these authors loss of troponin-T accounted for about 60% of the variation in toughness. A similar effect was observed by Lanari et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), evaluating the tenderness of beef aged at different temperatures (0, 4, 10, and 13\u0026deg;C); the authors reported higher \u003cem\u003ek\u003c/em\u003e values at higher aging temperatures and an Ea value of 62 kJ/mol in cooked beef.\u003c/p\u003e \u003cp\u003eThe increase in meat tenderness (lower SF) with pretreatment and higher aging temperatures can be observed in the first-order predictive models represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Also as observed for FI, the increase in temperature has a greater effect on the NF samples than on the FT ones. Moreover, despite the lower SF of the thawed samples at time zero (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the tenderness rate was higher in the NF than FT samples, especially in temperatures higher than 1\u0026deg;C. This suggests that the increased tenderness observed in the FT samples was primarily due to the loss of cellular integrity due to cryodamage. During aging, proteolysis in the NF samples was greater (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), increasing the tenderness rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and causing the SF values ​​to reach the same values ​​observed in the FT samples only after four days (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, one should consider the possibility that proteolysis could have also occurred during thawing, potentially contributing to the improved tenderness. Stafford et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) observed an increase in calpain-1 autolysis and cathepsin B activity, and a elevated levels of free calcium and mitochondrial dysfunction on frozen/thawed samples than nonfrozen ones. They attributed these effects as a consequence of ice crystals disrupting cellular organelles, leading to the release of factors that initiate protease activation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial Growth\u003c/h2\u003e \u003cp\u003eThe values of the specific growth rate (\u003cem\u003e\u0026micro;\u003c/em\u003e) and Ea of the \u003cem\u003eArrhenius\u003c/em\u003e model were calculated to verify the differences in the changes in the total bacterial count (TBC) with aging time for each combination of pretreatment and aging temperature (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The \u003cem\u003e\u0026micro;\u003c/em\u003e value increases with increasing aging temperature but the \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e values of the pretreatments are similar. Nevertheless, the mean \u003cem\u003e\u0026micro;\u003c/em\u003e values of the FT samples (0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 /day) being slightly higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than those of the NF ones (0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 /day). This higher rate of microbial development in FT samples can also be observed in the first-order predictive models of the TBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecific growth rate (\u0026micro;) and activation energy (Ea) of the Arrhenius model for the effects of pretreatment\u003csup\u003e1\u003c/sup\u003e and aging temperature on the total bacterial count (TBC) in Nellore cattle muscles (\u003cem\u003eL. lumborum\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAging\u003c/p\u003e \u003cp\u003etemperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e\u0026micro;\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(kJ/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonfrozen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrozen/Thawed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e Nonfrozen\u0026thinsp;=\u0026thinsp;fresh (never frozen) aged samples; and Frozen/thawed\u0026thinsp;=\u0026thinsp;frozen (-18\u0026deg;C/24 h) e thawed (4\u0026deg;C/24 h) samples.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFreezing is not intended to reduce bacterial contamination; it only interrupts bacterial proliferation potential. According to Lu et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), temperatures between \u0026minus;\u0026thinsp;5 and \u0026minus;\u0026thinsp;8\u0026deg;C tend to be limiting for bacterial growth, as the microbes become dormant at the commonly used storage temperature of -18\u0026deg;C. However, the formation of ice crystals by slow freezing can damage the cell membrane of microorganisms, resulting in the extravasation of potassium ions or RNA and decreasing their viability; in addition, the cells may die due to osmotic dehydration (Rahman and Velez-Ruiz \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Cells exposed to this heat stress may have reversible or irreversible lesions, and those that survive can recover and the growth could be even accelerated during the thawing process (Leygonie et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Coombs et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this experiment, no reduction in the TBC count was observed with freezing, probably because the small pieces of meat were fast-frozen, and, therefore, large ice crystals were not formed (Grujić et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), probably leading to less cell cryodamage. Nevertheless, higher rate of microbial development during aging was observed in FT samples than NF ones, could be explained by the meat thawing process (4\u0026deg;C/24 h), which, in addition to providing a longer time for adaptation and multiplication of the microorganisms, generally provides a greater release of meat exudates due to cryodamage (Rahman et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The meat exudate leads to an increase in moisture and nutrient availability during an increase in temperature by thawing, which is an excellent medium for microbial growth (Leygonie et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Coombs et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Haddad et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Guimar\u0026atilde;es et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that freezing/thawing resulted in greater numbers of mesophiles and psychrotrophs in wet- and dry-aged beef.\u003c/p\u003e \u003cp\u003eThe effects of higher temperatures on microbial growth are well known. An increase in aging temperature favors microbiological development by increasing the rate of chemical and enzymatic reactions and altering the structure, fluidity and functionality of membranes and the folding of DNA, RNA and ribosomes (Adams et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to these authors, in general, a 10\u0026deg;C increase in temperature can double or triple the \u003cem\u003e\u0026micro;\u003c/em\u003e value due to this increase in the metabolic activity of the microorganism. This agrees with what was observed in this experiment (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study develops kinetic models that describe the tenderization and microbiological growth during aging beef as function of pretreatment, frozen/thawed and nonfrozen, and aging temperature. Overall, the developed kinetic models provide a deeper understanding of the mechanism of the quality changes of frozen/thawed beef during aging. The activation energy for myofibrillar fragmentation and meat tenderization during ageing was lower in the frozen/thawed samples, and these samples required a shorter ageing time to be tender than did the nonfrozen ones. Data suggest that the increased tenderization in the frozen/thawed samples is primarily due to cellular damage rather than the increase in proteolysis rate. In addition, freezing/thawing prior to aging did not affect cooking loss and had little effects on the beef microbiological safety.\u003c/p\u003e \u003cp\u003eFurthermore, the increase in the ageing temperature also favored myofibrillar fragmentation and, therefore, the beef tenderness, and this effect was greater in the nonfrozen samples. However, the increase in temperature also favored microbial growth, regardless of the pretreatment used (frozen and nonfrozen samples). Therefore, it can be concluded that freezing prior aging is a potential technique for accelerating the aging process in beef using lower temperatures (1\u0026deg;C), but in higher temperatures additional conservation methods are needed to control microbial multiplication and ensure food security.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was financial supported by the Minas Gerais State Research Support Foundation (FAPEMIG; CVZ APQ 02904-17).\u003c/p\u003e \u003ch2\u003eConflicts of interest/Competing interests\u003c/strong\u003e \u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eAll authors and the Institute (Federal University of Lavras - UFLA) where the work was performed agree with this submission.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRamos, JL: methodology, investigation, writing\u0026mdash;original draft. Paula, MMO: validation, writing\u0026mdash;review and editing. Tanaka, MS: validation, writing\u0026mdash;review and editing. Torres Filho: validation, formal analysis, writing\u0026mdash;review and editing; Ramos, ALS: supervision, methodology, writing\u0026mdash;review and editing. Ramos, EM: conceptualization, methodology, project administration, supervision, validation, writing\u0026mdash;review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe authors thank the National Council for Scientific and Technological Development (CNPq/Brazil) for granting a postdoctoral scholarship to the second author (152596/2022-4) and a Research Productivity Fellow (PQ) to the last two authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams, M. R., McClure, P. J., \u0026amp; Moss, M. O. (2024). \u003cem\u003eFood Microbiology\u003c/em\u003e. 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Effects of alternating electric field assisted freezing-thawing-aging sequence on longissimus dorsi muscle microstructure and protein characteristics. \u003cem\u003eFood Chemistry\u003c/em\u003e, \u003cem\u003e409\u003c/em\u003e, 135266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2022.135266\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2022.135266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"food-and-bioprocess-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food and Bioprocess Technology](https://www.springer.com/journal/11947)","snPcode":"11947","submissionUrl":"https://submission.nature.com/new-submission/11947/3","title":"Food and Bioprocess Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Beef quality, storage temperature, shelf life, shear force, myofibrillar index","lastPublishedDoi":"10.21203/rs.3.rs-5284770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5284770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAging has been the main practice used by the industry to improve beef palatability, but requiring up to 21 d of cooler storage to reach the desired tenderness. As an alternative to accelerate the aging process, both the use of the prior freeze/thaw process and high storage temperatures have been suggested. The present study aimed to develop kinetic models to evaluate the effects of freezing/thawing process and different aging temperatures (1, 7, 14 and 20\u0026deg;C) on vacuum-packed Nellore beef steaks. Changes on fragmentation index (FI), shear force (SF), cooking loss (CL) and total bacteria count (TBC) of raw beef during aging followed a first-order kinetic model; The reaction rate constant (\u003cem\u003ek\u003c/em\u003e) increases with increasing aging temperature, and the activation energy (\u003cem\u003eEa\u003c/em\u003e) was lower in frozen/thawed samples than nonfrozen ones for FI and SF. The increase in aging temperature had a lower effect on the FI and SF of frozen/thawed samples than on the nonfrozen ones. Forzen/thawed samples required a shorter aging time than nonfrozen samples to reach the same SF. CL was affected only by aging temperature. The specific growth rate (\u003cem\u003e\u0026micro;\u003c/em\u003e) of TBC increases with increasing aging temperature, but the \u003cem\u003eEa\u003c/em\u003e was not affected by the freezing/thawing process. The developed kinetic models provide a deeper understanding of the mechanism of the quality changes of frozen/thawed beef during aging.\u003c/p\u003e","manuscriptTitle":"Kinetic modeling of proteolysis, tenderness, and microbial growth during beef accelerated aging by the freeze/thaw process","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-29 08:26:26","doi":"10.21203/rs.3.rs-5284770/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-03T18:03:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-19T01:55:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-04T02:15:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182744042589329658103392771270873783410","date":"2024-10-30T08:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255932825117659129684027496538480562246","date":"2024-10-28T22:47:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-28T18:24:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-18T07:45:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-17T23:03:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food and Bioprocess Technology","date":"2024-10-17T18:23:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-and-bioprocess-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food and Bioprocess Technology](https://www.springer.com/journal/11947)","snPcode":"11947","submissionUrl":"https://submission.nature.com/new-submission/11947/3","title":"Food and Bioprocess Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cbde3174-b7ea-4056-a6fc-1f9b453b40d8","owner":[],"postedDate":"October 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-01-08T15:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-29 08:26:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5284770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5284770","identity":"rs-5284770","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}