Introduction
Muscle pH and temperature decline during the first 24 h following slaughter have a substantial effect on pork quality. A rapid pH decline during the early postmortem period, often associated with high muscle temperatures, may cause the onset of pale, soft, and exudative (PSE) conditions. Additionally, a low ultimate pH can also contribute to PSE, which is characterized by watery and pale muscle due to protein denaturation and reduced water-holding capacity (WHC) (Brewer, 2014; Tarczyński et al., 2018; Zuber et al., 2021). These factors collectively highlight the importance of managing pH and temperature during the early postmortem period to optimize pork quality, as they influence various quality traits such as water retention, color, and texture. Hence, understanding these dynamics is crucial for the pork industry to produce high-quality pork that meets consumer preferences.
pH values measured at 45 min and 24 h postmortem are among the most widely used criteria for evaluating pork quality in commercial slaughter facilities as well as in the research setting. Recently, the pork industry has increasingly incorporated these traits into genetic selection programs aimed at improving pork quality, ensuring that pork meets the desired standards for both visual appeal and moisture retention. Researchers commonly measure pH at 45 min and 24 h postmortem to predict pork quality. While Garrido et al. (1995) identified pH at 24 h postmortem as the best predictor of pork quality traits, the rate and extent of pH decline between 45 min and 24 h postmortem are critical determinants of pork quality that require further investigation (Lonergan, 2012; Trezona and Moore, 2021). Boler et al. (2010) reported a weak correlation among pH at 45 min postmortem and several pork qualities. Lonergan (2012) also stated that the most affected pork quality traits related to pH changes are WHC, color, texture, and shelf life. However, only 5% or 10% of the variation in these aforementioned pork quality traits is explained by measuring pH at 45 min and 24 h postmortem, respectively (Boler et al., 2010). Conversely, while pH measured at 45 min postmortem is less predictive than pH measured at 24 h postmortem, it offers valuable insights into the initial rate of pH decline, which can significantly impact pork quality, particularly related to rapid levels of pH decline like PSE conditions. It is important to note that pH values at 45 min and 24 h postmortem are influenced differently by temperature, slaughter practices, and the amount of muscle glycogen available for postmortem metabolism (Boler et al., 2010). Therefore, pH measured at 45 min and 24 h postmortem may not fully capture the dynamic changes in muscle biochemistry influenced by temperature variations during the early postmortem period.
Matthews et al. (2022) proposed that integrating temperature-specific pH readings may improve the accuracy of pork quality predictions. This approach considers the dynamic changes in muscle biochemistry and environmental conditions that affect pork quality (Zhao et al., 2023). While the evaluation and application of various sorting criteria are well documented (Boler et al., 2010; Honikel, 2014; Kim et al., 2016a; Popp et al., 2018; Tarczyński et al., 2018; Zuber et al., 2021), there remains a gap in the literature regarding the potential of temperature-specific pH measurements. We hypothesized in this study that stable measurements of pork longissimus thoracis (LT) muscle pH at specific temperatures during the early postmortem period would predict pork quality attributes with greater accuracy than pH readings at fixed times.
Materials and Methods
All experimental procedures were approved by the Agriculture and Agri-Food Canada Lacombe Research and Development Centre’s (AAFC-LRDC, AB, Canada) Animal Care Committee (#201807). Pigs were cared for as outlined under the guidelines established by the Canadian Council on Animal Care (2009).
Animal slaughtering condition and data collection
A total of 558 commercially sourced pigs, including 296 barrows and 262 gilts (from Large White × Landrace sows bred to Duroc boars; Genesus Genetic Technology, MB, Canada), were slaughtered at the AAFC-LRDC federally inspected abattoir over 28 different slaughter days. Prior to slaughter, pigs were without feed for 12 h but had continuous access to water. Stunning was performed using an electric stunner (2.1 A for 5 s), immediately followed by exsanguination.
Immediately after stunning, the time was recorded to aid in the assessment of pH at 45 min and 24 h postmortem. Following stunning, exsanguination, scalding, singeing, pasteurization, evisceration, and splitting, hot side weights were recorded along with muscle and fat depths of the loin using a Viewtrak PG-309 (Viewtrak Technologies Inc., Markham, ON, Canada). The targeted location for muscle and fat depth measures were between the 3rd and 4th last ribs and 7 cm off the midline, on the left sides of each carcass. Thereafter, initial pH and temperature were recorded between the 10th and 11th ribs on the left LT muscle, using a calibrated Hanna HI99163 pH meter equipped with a Hanna Smart electrode FC232 (Hanna Instruments, Laval, QC, Canada) and a Mark III MC4000 (Sumaq Wholesalers, Toronto, ON, Canada), respectively. The pH meter was standardized using pH 7 and 4 buffer solutions before each use, whenever the battery was replaced or approximately every hour during extended pH readings. Additionally, the electrode was acclimatized and calibrated to the ambient temperature of the slaughter floor. Carcass sides were then railed into a 2°C drip cooler room with wind speeds of 0.5 m/s.
Upon entry into the drip cooler room, data logging pH and temperature probes (REED SD-230 [REED Instruments, Newmarket, ON, Canada]) were inserted into the left side of the carcass at the same position of the LT muscle where the initial pH and temperature were recorded. The loggers were attached to the spinal column of the carcasses, set to record pH to the nearest 100th place and temperature to the nearest 10th of the °C at 5 min intervals, and left in the cooler until 24 h postmortem. After 24 h postmortem, the pH and temperature loggers were removed prior to the weighing of the carcass sides. The memory cards in each pH and temperature logger were inserted into a computer, and the data were downloaded. Additionally, 24 h postmortem pH and temperature measurements were collected at the same location postremoval of the data logging probes, utilizing the same instrument used for the 45 min postmortem assessments.
The left and right sides of each carcass were weighed to determine total cooler loss, and hot and cold dressing percentages were calculated. Left-side carcasses were then fabricated into primal cuts, picnic, butt, loin, belly, and ham, and weighed. Following fabrication, two 2.5-cm thick bone-in chops were collected, which originated from between the 3rd and 4th last ribs and between the 2nd and 3rd last ribs, respectively, to expose the LT muscle interface. Chop 1 of each sample, specifically evaluating only the LT muscle and excluding the spinalis muscle, was exposed to oxygen for 20 min. The samples were then evaluated by experienced carcass evaluators for Japanese color scoring and instrumental lightness (L*) and redness (a*) using a Minolta CM-700d (Folio Instruments, Kitchener, ON, Canada) with D65 illuminant at a 10° observer angle and an 8 mm aperture.
After 48 h postmortem, chop 1 was assessed for cook loss and slice shear force (SSF). First, precook temperature and raw weight of each sample were measured and then cooked for 19 min with a conveyor oven (Blodgett, Artic Mechanical, Red Deer, AB, Canada) set to 260°C. Afterward, 1 min of rest time was allowed, followed by sample blotting with paper towel to remove all excess moisture, then cooked weight and temperature (2 location readings) were recorded. Cook loss was determined using the following formula below:
Immediately after taking the cooked weight and temperature, the spinalis muscle, external fat, and bone were removed, and the method described by Shackelford et al. (2004) was used to determine the SSF of the samples. Immediately after recording the cooked weight and temperature, a 1-cm thick, 5-cm long slice was removed from each chop parallel to the muscle fibers. The slice was acquired by first cutting across the width of the LT at a point approximately 2 cm from the lateral end of the muscle. Using a sample sizer, a cut was made across the LT parallel to the 1st cut at a distance 5 cm from the 1st cut. Two parallel cuts were simultaneously made through the length of the 5-cm long chop portion at a 45° angle to the long axis of the LT and parallel with the muscle fibers using a knife that consisted of 2 parallel blades spaced at 1 cm apart. Each sample was sheared once with a flat, blunt-end blade using a texture analyzer (model TA-HD plus; Stable Micro Systems Ltd., Surrey, UK). A 100-kg load cell was used with a crosshead speed set at 8.33 mm/s−1.
Chop 2 of each sample was deboned and trimmed of fat, then placed on a preweighed Styrofoam tray with an absorbent pad. The sample was then weighed together with the tray and pad, wrapped, and subsequently stored in a walk-in cooler at 2°C. After 48 h of storage in the cooler, the sample was removed from the tray, blotted to remove excess moisture on the surface, and then weighed. The purge loss was calculated using the following equations:
After purge loss was determined, the procedure described by Lam et al. (2023) was employed to assess chop 2 for moisture and fat content. The sample from each carcass was finely comminuted using a Robot Coupe Blixir BX3 (Robot Coupe USA Inc., Ridgeland, MS, USA). A 50-g subsample of the resulting chop grind was collected into a prelabelled Whirl-Pak bag and frozen at −35°C. The subsample was then thawed for 24 h prior to analyzing the moisture and fat content using the CEM rapid analyzer systems (Smart Turbo Moisture Analyzer Model 907, 990 and Smart Trac Fat Analyzer Model 907, 955 [CEM Corporation, Matthews, NC, USA]).
Statistical analysis
pH values at different temperature points for all the left-side carcasses were merged with pork quality data. Temperature ranges used to extract pH values varied from 39°C (maximum carcass temperature upon entering the cooler) to 31°C (average temperature at the pH plateau). Summary statistics including mean, standard deviation, minimum, and maximum observations were determined using the MEANS procedure in SAS (version 9.4, SAS Inst. Inc., Cary, NC). Normality was tested to ensure the data were normally distributed using PROC UNIVARIATE in SAS. Additionally, the normality of residuals was tested after fitting a linear model to the data to ensure the normality assumption of the model was satisfied.
For correlation analysis, PROC CORR in SAS was used to calculate Pearson correlation coefficients among independent variables, with significance at P ≤ .05. For the regression analysis, however, forward stepwise selection via PROC REG was employed. Traits such as hot carcass weight, lean depth, dressing percentage, and fat depth were included in the regression models if they met the selection entry criterion (F statistic significant at P < .15).
Results and Discussion
The descriptive statistics for pH at specific times and temperatures, as well as pork quality traits, are presented in Table 1. Graphical representations of the relationships among pH, temperature, and time collected by the loggers are shown in Figures 1 and 2. In the present study, average pH values at 45 min and 24 h postmortem are consistent with the means found by Popp et al. (2018) and Arkfeld et al. (2016), respectively. With regards to pH values at specific temperatures (39–31°C), the mean pH values ranged from 5.47 to 5.85 with higher temperature points of measurement generally associated with higher pH values. However, pH values at specific times (i.e., pH at 45 min, pH 24 h at postmortem) and temperatures were relatively less variable than the other quality traits, except moisture (Table 1). The relatively low variability in the pH values recorded at these specific times and temperatures when compared to other quality traits in our study might be interpreted as a stable indicator of pork quality. Nevertheless, Jerez-Timaure et al. (2022) argued that pH alone cannot sufficiently predict quality attributes since different traits have various relevance and susceptibilities to fluctuations. They found that while early postmortem pH correlates with glycolytic potential and muscle glycogen content, various variables can influence these factors, such as genetic differences or environmental stressors that can distort stability. With regard to L*, there was consistency across samples with a calculated coefficient of variation estimate of 4.02. Conversely, a*, purge loss, SSF, fat content, cooler loss, and cook loss exhibited wide variations (Table 1). These high variabilities suggest that these traits are susceptible to various external influences. Consequently, they may serve as more reliable quality measures due to their responsiveness to practical handling and processing conditions (Gvozdanović et al., 2023).
Descriptive statistics for pH at 31°C to 39°C, 45 min and 24 h postmortem, carcass and quality traits.
| Trait | N | Mean | SD | CV, % | Minimum | Maximum |
|---|---|---|---|---|---|---|
| 45 min pH | 557 | 6.29 | 0.15 | 2.38 | 5.94 | 6.71 |
| HCW | 558 | 93.31 | 4.75 | 5.09 | 80.58 | 105.20 |
| 24 h pH | 558 | 5.62 | 0.17 | 3.02 | 5.18 | 6.08 |
| 39°C pH | 410 | 5.85 | 0.22 | 3.76 | 5.23 | 6.41 |
| 38°C pH | 446 | 5.77 | 0.23 | 3.99 | 5.16 | 6.30 |
| 37°C pH | 448 | 5.70 | 0.22 | 3.86 | 5.09 | 6.31 |
| 36°C pH | 443 | 5.63 | 0.22 | 3.91 | 5.08 | 6.21 |
| 35°C pH | 446 | 5.59 | 0.22 | 3.94 | 5.06 | 6.19 |
| 34°C pH | 452 | 5.54 | 0.22 | 3.97 | 5.04 | 6.10 |
| 33°C pH | 435 | 5.52 | 0.22 | 3.99 | 5.06 | 6.03 |
| 32°C pH | 448 | 5.48 | 0.20 | 3.65 | 5.05 | 5.97 |
| 31°C pH | 448 | 5.47 | 0.19 | 3.47 | 5.03 | 6.01 |
| L* | 558 | 56.2 | 2.26 | 4.02 | 50.3 | 63.0 |
| a* | 556 | 3.50 | 0.97 | 27.71 | 0.96 | 6.09 |
| Purge loss, % | 554 | 2.71 | 0.97 | 35.79 | 0.96 | 5.49 |
| JCS | 558 | 4.08 | 0.54 | 13.24 | 2.00 | 6.00 |
| Moisture, % | 477 | 73.7 | 0.84 | 1.14 | 71.2 | 75.6 |
| Fat, % | 477 | 3.79 | 1.06 | 27.97 | 1.58 | 6.89 |
| Cooler loss, % | 558 | 2.37 | 0.30 | 12.66 | 1.60 | 3.30 |
| Cook loss, % | 558 | 27.6 | 2.33 | 8.44 | 21.4 | 33.9 |
| SSF, kg | 556 | 20.2 | 4.56 | 22.57 | 11.9 | 33.4 |
| Fat depth, mm | 558 | 21.52 | 4.16 | 19.33 | 12.00 | 33.0 |
| Lean depth, mm | 558 | 64.76 | 4.84 | 7.47 | 52.40 | 77.40 |
CV, coefficient of variation; HCW, hot carcass weight; JCS, Japanese color score; SSF, slice shear force.
Graphical representation of pH and temperature decline in the cooler over 5 h. The solid line indicates the recorded temperature, while the broken line represents pH. The error bars represent the standard deviation of the mean values of all samples.
Postmortem pH values of pork measured at various temperatures (e.g., 39–31°C, etc.) over the postmortem period. Error bars indicate the standard deviation of the measurements.
Relationships among pH at specific temperature, time, and other pork quality traits
In most cases, measuring pH at specific temperatures postmortem in a commercial setting is impractical. However, understanding the associations among pH at specific temperatures along with traditional pH measurements taken at 45 min and 24 h, as well as other pork quality traits, will be useful in understanding the impact of pre- and postmortem factors on pork quality as well as in pursuing novel traits for improving pork quality through genetic selection. The correlations that are reported in Tables 2 and 3 provide more insight into some of these relationships. Our findings corroborate the assertion that pH at 45 min and 24 h may not fully capture the dynamic changes in muscle biochemistry influenced by temperature variations during the chilling process. pH measurements collected at different temperatures and times postmortem exhibited positive correlations with each other (Table 2). Specifically, the 45 min postmortem pH showed positive correlations with pH values at 35°C (r = 0.19; P = .01) and 34°C (r = 0.20; P = .01), while also displaying weak negative correlations (Table 3) with L*, a*, and purge loss (P < .05). Kim et al. (2016b) reported similar findings, noting weak negative correlations among pH at 45 min and L*, a*, and drip loss. However, the negative correlation regarding L* in this study contradicts Popp et al. (2018), who found a positive but nonsignificant correlation. Additionally, Popp et al. (2018) identified a moderate correlation between pH at 45 min and a* postmortem, which is inconsistent with our findings. No significant correlation was observed between pH at 45 min and 24 h postmortem (P = .08) or other individual carcass traits (P > .05; subjective color score, moisture, fat, cooler loss, cook loss, or SSF). This finding contrasts with previous research, which reported a weak correlation between pH at 45 min and 24 h postmortem (Boler et al., 2010; Kim et al., 2016b; Jankowiak et al., 2021). The discrepancy may be attributed to differences in experimental conditions, such as variations in animal handling, chilling rates, or muscle glycogen levels, all of which can impact pH changes over time. On the other hand, previous studies found significant correlations among pH measured at 45 min postmortem and various pork quality traits, including moisture and fat content, cook loss, and SSF (Boler et al., 2010; Kim et al., 2016b; Jankowiak et al., 2021). This suggests minimal variability in pH values at the 45 min postmortem point, as Boler et al. (2010) noted. Furthermore, the lack of significant correlations could imply that pH at 45 min is relatively stable across samples within this context, thereby reducing its reliability as a predictor of quality traits.
Correlation coefficients (r) among pH at 31°C to 39°C, 45 min, and 24 h.
| Trait | 45 min pH | 24 h pH | 39°C pH | 38°C pH | 37°C pH | 36°C pH | 35°C pH | 34°C pH | 33°C pH | 32°C pH | 31°C pH |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 45 min pH | 1 | NS | 0.132 | 0.152 | 0.179 | 0.170 | 0.193 | 0.198 | 0.217 | 0.211 | 0.200 |
| 24 h pH | 1 | 0.264 | 0.272 | 0.244 | 0.266 | 0.233 | 0.244 | 0.219 | 0.221 | 0.193 | |
| 39°C pH | 1 | 0.975 | 0.938 | 0.897 | 0.864 | 0.829 | 0.792 | 0.765 | 0.732 | ||
| 38°C pH | 1 | 0.981 | 0.957 | 0.924 | 0.897 | 0.854 | 0.832 | 0.792 | |||
| 37°C pH | 1 | 0.988 | 0.970 | 0.941 | 0.908 | 0.870 | 0.841 | ||||
| 36°C pH | 1 | 0.990 | 0.975 | 0.947 | 0.925 | 0.892 | |||||
| 35°C pH | 1 | 0.992 | 0.976 | 0.955 | 0.927 | ||||||
| 34°C pH | 1 | 0.992 | 0.980 | 0.957 | |||||||
| 33°C pH | 1 | 0.994 | 0.982 | ||||||||
| 32°C pH | 1 | 0.993 | |||||||||
| 31°C pH | 1 |
NS, nonsignificant (P > .05).
Correlation coefficients (r) among pH at 31°C to 39°C, 45 min, 24 h, carcass and quality traits.
| Trait | L* | a* | Purge Loss | JCS | Moisture % | Fat % | Cooler Loss | Cook Loss | SSF |
|---|---|---|---|---|---|---|---|---|---|
| 45 min pH | −0.232 | −0.095 | −0.196 | NS | NS | NS | NS | NS | NS |
| 24 h pH | −0.103 | 0.184 | −0.155 | 0.235 | −0.153 | 0.224 | −0.094 | NS | 0.122 |
| 39°C pH | −0.257 | NS | −0.439 | 0.134 | NS | NS | −0.177 | 0.141 | 0.280 |
| 38°C pH | −0.295 | NS | −0.507 | 0.138 | NS | NS | −0.109 | 0.154 | 0.281 |
| 37°C pH | −0.250 | NS | −0.488 | 0.173 | NS | NS | −0.161 | 0.147 | 0.306 |
| 36°C pH | −0.273 | NS | −0.532 | 0.141 | −0.118 | 0.113 | −0.112 | 0.174 | 0.324 |
| 35°C pH | −0.254 | NS | −0.506 | 0.141 | NS | NS | −0.156 | 0.131 | 0.333 |
| 34°C pH | −0.288 | NS | −0.519 | 0.178 | −0.114 | NS | −0.151 | 0.124 | 0.373 |
| 33°C pH | −0.262 | NS | −0.473 | 0.139 | NS | NS | −0.135 | 0.108 | 0.357 |
| 32°C pH | −0.273 | NS | −0.481 | 0.169 | NS | NS | −0.141 | 0.120 | 0.357 |
| 31°C pH | −0.234 | NS | −0.444 | 0.154 | NS | NS | −0.136 | 0.111 | 0.331 |
JCS, Japanese color score; SSF: slice shear force.
NS, nonsignificant (P > .05).
This study also examined the relationship among pH levels measured at 24 h postmortem and various quality traits. Our findings were consistent with previous research in terms of significance (Bidner et al., 1999; Huff-Lonergan et al., 2002; Kim et al., 2016b). Significant correlations were identified between postmortem pH at 24 h and several carcass and quality traits, except for cook loss (P = .07; Table 3). Bidner et al. (1999) reported significant correlations between the pH of the longissimus muscle at 24 h postmortem and purge loss, as well as L* and a* color values. However, they found strong correlations between postmortem pH at 24 h and L* and purge loss, which contrasts with our findings of weak correlations (r = −0.10 and −0.16, respectively; P ≤ .05). Additionally, other studies have revealed significant correlations between 24 h pH measurements and cook loss when loin chops were aged for 2 d (Kim et al., 2016b) and 10 d (Huff-Lonergan et al., 2002), which does not align with our results. This discrepancy may be attributed to differences in laboratory procedures.
The observed strong correlation between pH measurements at specific temperatures reinforces the consistency of pH measurements across different temperatures (P ≤ .05; data not shown). This consistency could be explained by the critical role temperature plays in the rate of pH decline, where higher postmortem temperatures accelerate glycolysis and lead to a more rapid decrease in pH, resulting in strongly correlated pH measurements at various temperatures (Matthews et al., 2022). Additionally, the proximity in measurement time could explain the strong correlations observed. Supporting this, researchers have observed that comparing measurements taken at the start of a production run with those taken at the end could account for the correlation (Bendall and Swatland, 1988; Boler et al., 2010). In the current study, pH values at 39°C, 38°C, and 37°C were strongly positively correlated with each other (e.g., 39°C pH and 38°C pH: r = 0.98; P = .01). However, these pH values were negatively correlated with L*, purge loss, and cooler loss (P < .05). This observed inverse relationship between pH measurement at specific temperatures and L*, purge loss, and cooler loss can be attributed mainly to the influence of pH on protein denaturation and the water retention capabilities in pork (Maribo et al., 1998; Kim et al., 2016b). Additionally, no significant correlation amongst these pH values (39–31°C) and a* was observed (P > .05). This suggests that pork color may be influenced by a multifaceted interplay of factors beyond pH, including myoglobin concentration, oxidation state, and muscle fiber type, which may obscure the effects of pH on protein denaturation and water retention (Mancini and Hunt, 2005). The correlation values between pH values (39–31°C) and multiple pork quality parameters further supports our hypothesis that pH measurements at these specific temperatures effectively capture the biochemical and physical transformations impacting pork quality during the cooling phase. Overall, pH measurements at specific temperatures showed higher correlations (P ≤ .05) with most pork quality traits, specifically L*, purge loss, subjective color score, cooler loss, cook loss, and SSF, compared to pH measurements at 45 min and 24 h postmortem. To our knowledge, no prior studies have specifically examined the use of pH measurements at specific carcass temperatures to predict pork traits. However, various studies (Martin et al., 1975; Garrido et al., 1994; Bowker et al., 1999; Huff-Lonergan et al., 2002; Lindahl et al., 2006; Moeller et al., 2010; Duan et al., 2013; Zuber et al., 2021; Matthews et al., 2022) have explored the impact of pH and temperature on pork quality traits at different time intervals. Notably, Trezona and Moore (2021) conducted a comparative assessment of pH/temperature data loggers and conventional manual pH/temperature meters for monitoring pH decline in pork carcasses in commercial processing. However, their analysis did not extend to investigating pH at specific temperatures as opposed to times or assessing its implications for pork quality traits.
According to Huff-Lonergan et al. (2002), different factors affect complex traits that determine meat quality, making it challenging to predict and develop strategies for improving pork quality. Therefore, understanding the associations between these quality parameters is essential. Quality traits, particularly L*, a*, purge loss, subjective color score, moisture, fat, cooler loss, cook loss, and SSF were weakly to strongly correlated with each other (0.09 ≤ | r | ≤ 0.85; P < .05). L* was found to be moderately correlated with purge loss, subjective color scores, and SSF (r = 0.30, −0.40, and −0.47, respectively; P < .01), whereas weak correlations were found with a*, moisture, and fat (P < .05). However, L* was not correlated with cooler (P = .50) and cook losses (P = .61). In contrast, Huff-Lonergan et al. (2002) found a moderate correlation between Hunter L* value measured at 48 h postmortem and cook loss. The difference in aging time between both studies may account for this discrepancy. Likewise, for a*, no correlation was observed with cooler loss (P = .33) and SSF (P = .85). A similar observation was made for subjective color score with moisture (P = .43), cooler loss (P = .17), and cook loss (P = .65). As expected, a significant negative correlation (r = −0.85; P = .01) was found between moisture content and fat content. Despite this observation, both moisture and fat contents were not significantly correlated with SSF (P > .05; Table 3). Generally, there are limited studies that have examined the relationship among these carcass quality traits at 24 and 48 h postmortem, thus warranting further studies.
Prediction of L*, a*, purge loss, and slice shear force of pork chops
To further determine which variables are most important for the selected traits, such as L*, a*, purge loss, and SSF, a stepwise regression analysis was performed. The results indicate that the initial model, which included the 35°C pH, accounted for 11.5% of the variance in L* (P < .001; Table 4). This suggests a small but potentially meaningful influence, while most variability is driven by other factors. Subsequently, variables added to the model, such as fat and moisture, further improved the model’s explanatory power, culminating in a final model that explained 33.4% of the variance in L*. In agreement, the interactive effect of temperature × time at a particular temperature × hydrogen ion concentration was reported to have an effect on L* (Freise et al., 2005). For instance, a higher temperature could speed up reactions that depend on pH levels, and the duration can influence the time these interactions take. Together, these factors can impact the L* value. pH at 45 min explained only 1.5% of the variation in L* when included in the model (Table 4). Similar results were reported by Boler et al. (2010) when the longissimus muscle was aged for 21 d postmortem. The inability of pH at 45 min to predict L* may result from depolarization events in prerigor muscle, which can introduce variations in early postmortem pH measurements (Boler et al., 2010). Interestingly, the pH measured 24 h postmortem did not seem to contribute to predicting the L* value. This finding, combined with the significant impact of pH measured at 45 min, suggests that pH taken at a specific time interval may not be an effective predictor of L*.
Stepwise regression analysis to predict L* in pork loin chops.
| Step | Variable Entered | No. Variables Included | Partial R2 | Model R2 | C(p) | F Value | Pr > F |
|---|---|---|---|---|---|---|---|
| 1 | 35°C pH | 1 | 0.115 | 0.115 | 86.00 | 33.00 | <0.001 |
| 2 | Fat % | 2 | 0.068 | 0.183 | 61.87 | 21.21 | <0.001 |
| 3 | Moisture % | 3 | 0.125 | 0.308 | 15.97 | 45.74 | <0.001 |
| 4 | 45 min pH | 4 | 0.015 | 0.323 | 12.22 | 5.59 | 0.019 |
| 5 | Fat depth | 5 | 0.010 | 0.334 | 10.08 | 4.08 | 0.044 |
In Table 5, the stepwise regression analysis sought to elucidate the factors influencing a* through a systematic inclusion and exclusion of independent variables. Initially, fat contributed 12.9% of the variance in a* (P < .001). While subsequent variables provided only marginal increases in explanatory power, most lacked statistical significance (P > .05), except lean depth, which approached significance (P = .05). Ultimately, lean depth proved to be the most significant addition among the later variables, leading to a model with a coefficient of determination (R2) of 18% upon its inclusion. The results indicate that fat content is the primary determinant with other factors, including lean depth, contributing to a lesser extent. Although none of the pH parameters is the main factor for a*, they can interact with other variables to influence the overall a* value.
Stepwise regression analysis to predict a* in pork loin chops.
| Step | Variable Entered | Variable Removed | No. Variables Included | Partial R2 | Model R2 | C(p) | F Value | Pr > F |
|---|---|---|---|---|---|---|---|---|
| 1 | Fat % | 1 | 0.129 | 0.129 | 8.06 | 37.67 | <0.001 | |
| 2 | 45 min pH | 2 | 0.011 | 0.140 | 6.74 | 3.27 | 0.072 | |
| 3 | Moisture % | 3 | 0.010 | 0.149 | 5.86 | 2.86 | 0.092 | |
| 4 | Fat % | 2 | 0.006 | 0.143 | 5.65 | 1.78 | 0.183 | |
| 5 | Cook loss | 3 | 0.008 | 0.151 | 5.26 | 2.38 | 0.124 | |
| 6 | 39°C pH | 4 | 0.009 | 0.161 | 4.53 | 2.73 | 0.100 | |
| 7 | HCW | 5 | 0.007 | 0.168 | 4.41 | 2.14 | 0.145 | |
| 8 | Lean depth | 6 | 0.013 | 0.180 | 2.63 | 3.84 | 0.051 |
HCW, hot carcass weight.
In relation to purge loss, the results indicated that fat content was the most significant predictor (P < .001; Table 6). Other variables, such as cook loss and cooler loss, showed varying degrees of influence, but none reached the same level of statistical significance (P > .05). When examining purge loss, it is important to consider how temperature, time, and pH interact with one another, as these factors can significantly influence the outcome. According to Freise et al. (2005), a 3-way interaction among temperature, time, and pH creates a complex interplay that affects purge loss. Additionally, a positive correlation was observed between purge loss and temperature at 2, 4, 6, and 24 h postmortem when measured at the sirloin 7 d after storage (Gardner et al., 2005). However, in our current study, fat content emerged as the most significant predictor, underscoring its crucial role in managing water retention and purge loss in pork.
Stepwise regression analysis to predict purge loss in pork loin chops.
| Step | Variable Entered | Variable Removed | No. Variables Included | Partial R2 | Model R2 | C(p) | F Value | Pr > F |
|---|---|---|---|---|---|---|---|---|
| 1 | 36°C pH | 1 | 0.275 | 0.275 | 55.94 | 95.82 | <0.001 | |
| 2 | HCW | 2 | 0.016 | 0.290 | 51.34 | 5.54 | 0.019 | |
| 3 | Moisture % | 3 | 0.015 | 0.305 | 47.03 | 5.39 | 0.021 | |
| 4 | Fat % | 4 | 0.096 | 0.401 | 8.44 | 40.03 | <0.001 | |
| 5 | Cook loss | 5 | 0.006 | 0.407 | 8.00 | 2.43 | 0.121 | |
| 6 | Cooler loss | 6 | 0.005 | 0.412 | 7.83 | 2.16 | 0.143 | |
| 7 | 31°C pH | 7 | 0.005 | 0.418 | 7.53 | 2.31 | 0.130 | |
| 8 | 32°C pH | 8 | 0.008 | 0.425 | 6.25 | 3.31 | 0.070 | |
| 9 | 39°C pH | 9 | 0.006 | 0.431 | 5.85 | 2.44 | 0.119 | |
| 10 | 38°C pH | 10 | 0.006 | 0.436 | 5.49 | 2.41 | 0.122 | |
| 11 | 36°C pH | 9 | 0.002 | 0.434 | 4.40 | 0.93 | 0.336 |
HCW, hot carcass weight.
Table 7 shows the stepwise regression analysis conducted to identify the most influential predictors of SSF in pork loin chops. The final model included 5 independent variables, which collectively accounted for 18.1% of the variation in SSF. Among these predictors, 32°C pH (P = .001) was the most significant contributor, explaining 12.7% of variation. This result aligns with previous research that highlights the role of early postmortem pH in determining ultimate meat tenderness (Warner et al., 1997). A higher early postmortem pH is associated with improved WHC and reduced protein denaturation, both of which contribute to lower shear force values and improved tenderness (Huff-Lonergan and Lonergan, 2005). Cooler loss (P = .024) was the 2nd variable in the model, contributing an additional 1.8% of variation to the model. Cooler loss reflects moisture loss during chilling, which could influence muscle structure and subsequently affect tenderness. Elevated cooler loss has been linked to increased drip loss and tougher meat due to the potential loss of proteolytic enzymes required for postmortem tenderization (Wheeler et al., 1997).
Stepwise regression analysis to predict slice shear force in pork loin chops.
| Step | Variable Entered | No. Variables Included | Partial R2 | Model R2 | C(p) | F Value | Pr > F |
|---|---|---|---|---|---|---|---|
| 1 | 32°C pH | 1 | 0.127 | 0.127 | 26.21 | 37.15 | <0.001 |
| 2 | Cooler loss | 2 | 0.018 | 0.145 | 22.63 | 5.18 | 0.024 |
| 3 | Fat depth | 3 | 0.017 | 0.161 | 19.27 | 5.05 | 0.025 |
| 4 | Cook loss | 4 | 0.011 | 0.173 | 17.70 | 3.39 | 0.067 |
| 5 | Lean depth | 5 | 0.008 | 0.181 | 17.02 | 2.57 | 0.110 |
Fat depth (P = .025) was the 3rd significant predictor, explaining an additional 1.7% of the variation in SSF. Greater subcutaneous fat thickness has been associated with reduced heat transfer during cooking, thereby preventing excessive moisture loss and protein denaturation (Smith et al., 1985). This insulating effect could contribute to lower shear force values and improved juiciness, enhancing overall palatability. Cook loss was the 4th variable included in the model, with a less pronounced contribution (R2 = 0.011; P = .067). Cook loss represents the moisture expelled during cooking, which directly affects meat texture and tenderness. Greater cook loss has been correlated with tougher meat due to reduced residual moisture, limiting the lubricating effect of water between muscle fibers (Kim et al., 2016a).
Finally, lean depth was the 5th variable but did not significantly contribute to the model (R2 = 0.008, P = .110). While lean depth is often associated with carcass yield and composition, its direct impact on tenderness is less established. The lack of significance in this study suggests that lean depth may not be a primary determinant of SSF in pork loin chops. Overall, while the stepwise regression model identified significant predictors of SSF, the relatively low model R2 (0.181) indicates that additional factors, such as proteolysis, connective tissue composition, and cooking methodology, may play a more substantial role in determining pork tenderness. Future studies incorporating biochemical markers of postmortem proteolysis and connective tissue properties may improve the predictive accuracy of tenderness models.
Overall, the study found that pH measurements at specific temperatures (39–31°C) during the early postmortem period were more consistent and showed higher correlations with pork quality traits compared to pH measurements at fixed times (45 min and 24 h postmortem). Significant predictors for various quality traits were identified through stepwise regression analysis with pH at specific temperatures, explaining a notable portion of the variability in traits such as L*, purge loss, and SSF. These findings suggest that incorporating temperature-specific pH measurements could improve the accuracy of pork quality predictions, although practical implementation in commercial settings may be challenging.
Conclusions
The findings of this study highlight the complex interaction between pH levels at various postmortem times and temperatures and pork quality traits. While pH measurements at 45 min postmortem serve as an important indicator, their ability to reliably predict overall pork quality appears limited, particularly due to varying external factors. The significant variability observed in other quality traits emphasizes the need for a comprehensive approach to assessing pork quality, incorporating a range of measurements beyond pH measurements at 45 min and 24 h postmortem to better capture the dynamic changes in muscle biochemistry and practical handling conditions. Measuring pH at specific temperatures (39–31°C) showed consistency and potential for predicting pork quality traits. These data partially confirm our hypothesis that stable pH measurements in pork LT muscle at specific temperatures during the early postmortem period can predict pork quality. Pig breeders should consider integrating temperature-specific pH measurements into their selection objectives to improve pork quality through genetic advancements. However, this procedure may not be feasible in a plant setting at line speed. Hence, efforts should be made to develop and validate protocols that allow for the efficient use of temperature-specific pH measurements in pork processing plants.
Future research should focus on longitudinal studies to assess the long-term impact of temperature-specific pH measurements on pork quality across different breeds and production systems. Investigating the role of biochemical markers of postmortem proteolysis and connective tissue properties could provide deeper insights into the mechanisms underlying pork tenderness and quality. Additionally, further studies are needed to explore how environmental conditions and handling practices influence the relationship between pH at specific temperatures and pork quality traits. Overall, while this study provides valuable insights, additional research is necessary to fully understand and optimize the use of temperature-specific pH measurements in predicting pork quality.
Conflict of Interest
The authors declare no conflicts of interest.
Acknowledgments
This study was funded by Swine Innovation Porc (Project #1787 “Classifying Canadian pork based on quality attributes”). Authors are also grateful for the technical support from the meat quality team at the Agriculture and Agri-Food Canada Lacombe Research and Development Centre (Alberta, Canada).
Author Contribution
Justice B. Dorleku: Data curation, Methodology, Investigation, Writing – original draft, Writing – review & editing. Tawanda Tayengwa: Software, Validation, Formal analysis, Data curation, Writing – review & editing. Benjamin M. Bohrer: Writing – review & editing. Manuel Juárez: Conceptualization, Methodology, Data curation, Supervision, Funding acquisition, Project administration, Writing – review & editing.
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