Prediction of Pork Loin Quality Using Postmortem Temperature and pH Decline Curves

Introduction

In modern swine slaughter facilities in the United States, a focus on the attributes affecting pork quality is generally considered important for product acceptability, whether for domestic or export markets. For consumers, initial willingness to purchase is often determined by the physical appearance of the meat, including color, marbling, and firmness (Garmyn, 2020). Many factors are involved in development of pork quality, such as genetics, preslaughter animal handling, and postmortem muscle temperature and pH decline.

During the conversion of muscle to meat, skeletal muscles, in the effort of generating adenosine triphosphate and maintaining homeostasis, switch to anaerobic glycolysis, which, through complex metabolic processes, leads to a buildup of lactic acid (Hamoen et al., 2013; Zumbaugh et al., 2022). In pork carcasses, the pH decreases from about 7.4 in muscle to about 5.6 in meat due to lactic acid accumulation in the first 24 h postmortem (Huff-Lonergan, 2006). When pH nears the isoelectric point of the major muscle proteins of 5.2, the reactive groups lose their ability to bind water, therefore compromising the water-holding capacity of meat. Pork with lower ultimate pH (pHu) is associated with lighter color and greater drip loss (Huff-Lonergan et al., 2002; Bee et al., 2007; Zuber et al., 2021), which influences consumer purchasing decision. Moreover, pork with lower pHu tends to have higher cook loss, decreased tenderness, and more off-flavor (Huff-Lonergan et al., 2002; Lomiwes et al., 2014) that can affect a consumer’s willingness to repurchase. In addition to the extent of pH decline, the rate of decline also affects meat quality. A rapid pH decline accompanied with high carcass temperature at the early postmortem period can be detrimental to protein structures. Myoglobin, the pigmented protein in meat, is denatured in acidic, high temperature environments and loses its ability to bind iron, creating a paler, less desirable color in meat (Barbut et al., 2008).

Because pigs are generally considered to be susceptible to preslaughter handling stress, they can experience a rapid postmortem pH decline resulting in meat with pale, soft, and exudative appearance, often referred to as PSE pork (Sosnicki et al., 2025). Major genes, such as the halothane or rendement napole genes, increase the prevalence of PSE conditions (Salas and Mingala, 2017). Furthermore, preslaughter management practices such as transportation, animal handling, length of feed withdrawal, and lairage resting time have a critical impact on development of PSE pork (Guàrdia et al., 2004; Vermeulen et al., 2015; Trevisan and Brum, 2020; PIC, 2021). In a pork supply chain audit conducted in 2017 in the United States, around 1.3% of loins were identified as PSE (Boler, 2017). The distorted protein structure combined with increased drip loss in PSE product leads to great economic losses in the industry. Therefore, implementation of precise control of pH decline is important in enhancing pork quality.

One practical approach to reduce the rate of pH decline involves controlling carcass temperature decline postmortem. In modern abattoirs, blast chilling is often used to quickly remove heat from carcasses at an early postmortem stage, preventing the incidence of PSE meat formation (Huff-Lonergan, 2006). Studies suggest that blast chilling slows down the pH decline, as indicated by a higher pH value at early postmortem stage, but 24-h pH or pHu after aging does not necessarily differ from alternate chilling methods (Shackelford et al., 2012; Rybarczyk et al., 2015; Blakely et al., 2019).

The effect of rapid temperature decline on meat quality remains controversial. Some studies reported blast chilling resulted in darker loins, while others found no differences in color (Springer et al., 2003; Shackelford et al., 2012; Rybarczyk et al., 2015; Blakely et al., 2019). Blakely et al. (2019) reported an increased purge loss in tenderloins and increased cook loss in loins resulting from blast chilling. Sensory traits evaluated by Blakely et al. (2019) and Shackelford et al. (2012) reported higher shear force values on loins from blast chilling, indicating that fast temperature decline may have a negative impact on tenderness.

Prediction of pork quality from an early postmortem stage has been difficult because of the various attributes affecting meat quality. For this study, postmortem temperature and pH decline curves were generated from different commercial abattoirs. The objectives of this study were to: 1) generate continuous postmortem pH and temperature decline curves; 2) establish the correlations between the rate of temperature and pH decline, and pork loin quality; and 3) develop prediction equations of pork loin quality using early postmortem temperature and pH data.

Material and Methods

Results

Abattoir effect on temperature, pH decline, and loin quality traits

The effect of abattoir was determined for postmortem temperature and pH data (Tables 4 and 5, respectively), and decline curves were plotted (Figures 1 and 2, respectively). Initially, all abattoirs had similar loin temperatures at 40 min postmortem, but the rate of temperature decline was different among abattoirs due to different chilling methods. Abattoir D chilled slowest, followed by abattoir B, then by abattoirs A and C. Abattoir A chilled faster during the first 7 h postmortem, but abattoir C chilled faster after 13 h postmortem (P < .05). The rate of pH decline was also affected by abattoir. pHi at 0.67 h (40 min) was highest at abattoir C and abattoir D, lowest at abattoir B, with abattoir A being intermediate (P < .05). Abattoir D had the fastest rate of pH decline from 2 to 20 h postmortem (P < .05). From 3 to 20 h postmortem, pH values for abattoir B were higher than abattoir D but lower than abattoirs A and C (P < .05). No differences were observed in pH values between abattoir A and abattoir C from 1 to 16 h postmortem (P > .05); however, from 17 to 20 h postmortem, abattoir C had higher loin pH values compared with abattoir A (P < .05).

Table 4.

Effect of commercial abattoir on postmortem temperature decline in pork loins

Time Postmortem, h	Pork Loin Temperature, °C				Pooled SEM
	Abattoir A	Abattoir B	Abattoir C	Abattoir D
0.67	40.0ab	39.8b	40.0a	39.9ab	0.1
1	36.9b	36.6b	37.2b	38.3a	0.3
1.5	26.7c	29.9b	27.2c	34.1a	0.5
2	19.7 c	24.0b	19.7c	30.6a	0.6
3	10.3c	16.5b	10.6c	25.3a	0.5
4	6.1d	13.3b	7.3c	21.4a	0.4
5	4.5d	11.3b	5.9c	18.5a	0.4
6	3.8d	9.7b	5.1c	16.1a	0.4
7	3.4d	8.4b	4.5c	14.3a	0.3
8	3.2c	7.3b	4.0c	12.7a	0.3
9	3.0c	6.4b	3.6c	11.4a	0.3
10	3.0c	5.6b	3.2c	10.1a	0.3
11	2.9c	5.0b	2.8c	8.8a	0.3
12	2.9c	4.5b	2.5c	7.7a	0.3
13	2.9c	4.0b	2.2c	6.6a	0.3
14	2.9c	3.5b	1.8d	5.7a	0.2
15	2.9b	3.1b	1.6c	5.0a	0.2
16	2.9b	2.8b	1.4c	4.3a	0.2
17	3.0b	2.5c	1.2d	3.8a	0.2
18	2.9a	2.2b	1.2c	3.3a	0.2
19	2.9a	2.0b	1.2c	2.8a	0.1
20	2.9a	1.8c	1.1d	2.5b	0.1

• SEM, standard error of mean.

• Means within a row lacking a common superscript were significantly different (P < .05).

Table 5.

Effect of commercial abattoir on postmortem pH decline in pork loins

Time Postmortem, h	Pork Loin pH				Pooled SEM
	Abattoir A	Abattoir B	Abattoir C	Abattoir D
0.67	6.49b	6.41c	6.60a	6.60a	0.03
1	6.13b	6.15b	6.17b	6.26a	0.03
1.5	6.10a	6.06a	6.11a	6.08a	0.03
2	6.09a	6.02a	6.13a	5.97b	0.03
3	6.10a	5.99b	6.17a	5.85c	0.03
4	6.10a	5.95b	6.15a	5.79c	0.02
5	6.09a	5.91b	6.13a	5.76c	0.02
6	6.08a	5.89b	6.11a	5.74c	0.02
7	6.07a	5.87b	6.09a	5.73c	0.02
8	6.05a	5.85b	6.07a	5.72c	0.02
9	6.03a	5.84b	6.05a	5.72c	0.02
10	6.02a	5.83b	6.04a	5.71c	0.02
11	6.00a	5.82b	6.03a	5.71c	0.02
12	5.99a	5.81b	6.02a	5.71c	0.02
13	5.97a	5.80b	6.01a	5.71c	0.02
14	5.96a	5.80b	6.00a	5.71c	0.02
15	5.95a	5.79b	6.00a	5.71c	0.02
16	5.94a	5.78b	5.99a	5.72c	0.02
17	5.93b	5.78c	5.98a	5.71d	0.02
18	5.92b	5.77c	5.98a	5.71d	0.02
19	5.91b	5.77c	5.97a	5.72d	0.02
20	5.90b	5.77c	5.96a	5.72d	0.02

• SEM, standard error of mean.

• Means within a row lacking a common superscript were significantly different (P < .05).

Figure 1.

Rate of temperature decline in pork loins from 4 commercial abattoirs.

Figure 2.

Rate of pH decline in pork loins from 4 commercial abattoirs.

Average loin quality traits among the commercial abattoirs are presented in Table 6. Abattoirs A and C tended to have the highest quality compared to abattoirs B and D, although some traits varied. Abattoir C had the highest pH for both bone-in and boneless values, lowest L*, and lowest b* values (P < .05). Abattoir D had the highest L* values, lowest shoulder JCS, highest firmness, and highest drip loss (P < .05).

Table 6.

Effect of commercial abattoir on pork loin quality traits

Loin Quality	Abattoir				Pooled SEM
	Abattoir A	Abattoir B	Abattoir C	Abattoir D
n	47	76	51	36	—
pHi1	6.49b	6.41c	6.60a	6.60a	0.03
pH, bone-in2	5.90b	5.77c	5.96a	5.72d	0.02
pH, boneless3	5.61b	5.58b	5.72a	5.59b	0.02
pH differential	0.30a	0.18c	0.24b	0.12d	0.02
L*4	41.96c	43.41b	40.51d	44.99a	0.42
a*4	7.45a	7.01b	7.11ab	7.19ab	0.15
b*4	2.02a	2.02a	1.05b	2.17a	0.13
JCS avg5	3.57a	3.41ab	3.56ab	3.40b	0.06
JCS shld6	3.12a	3.05a	3.25a	2.81b	0.08
Firmness	2.60ab	2.47b	2.62ab	2.81a	0.10
Drip loss, %7	1.85b	2.02ab	2.12ab	2.59a	0.23

• SEM, standard error of mean.

• Means within a row with different superscripts were significantly different (P < .05).

• pHi: initial pH measured at 40 min postmortem.

• pH (bone-in): bone-in pork loin pH measured at 20 h postmortem.

• pH (boneless): boneless pork loin pH measured at approximately 22 h postmortem.

• L*, a*, b*: instrumental color measured using a Minolta CR-400 Chroma Meter on the ventral surface of boneless pork loins.

• JCS avg: average Japanese color score on the ventral surface of boneless pork loins.

• JCS shld: Japanese color score on the shoulder-end of boneless pork loins.

• Drip loss percentage is calculated as: exudate weight (TE-T)/initial sample weight (TS-T) * 100, where T = weight of tube, TS = weight of tube and sample, and TE = weight of tube and exudate.

Correlations among postmortem loin temperature, pH, and quality traits

As expected, the correlation between postmortem loin temperature and pH values was predominantly negative (Figure 3). This negative relationship between pH and temperature was stronger after about 120 min postmortem as shown by the darker blue color. The green-yellow color at the bottom right corner of the heat map suggests a weak correlation between early temperature and late pH value.

Figure 3.

Pearson correlations between postmortem pork loin temperature and pH. Columns indicate postmortem pH and rows indicate postmortem temperature. The red color indicates a more positive correlation, and purple color indicates a more negative correlation. TMP, temperature.

pHi was negatively correlated with postmortem temperature at 40 min (Figure 4). Bone-in pH was positively correlated with early postmortem temperature from 40 to 55 min but had a negative correlation from 70 to 1200 min, with greater magnitude of correlation from 180 to 480 min as indicated by a darker blue color. Similarly, boneless pH was positively correlated with postmortem temperature from 40 to 65 min but negatively correlated with postmortem temperature from 105 to 1200 min. Average and shoulder JCS were both moderately negatively correlated with postmortem temperature from 70 to 1020 min. Correlations among a*, marbling, firmness, and 24 h drip loss to postmortem temperature were all weak.

Figure 4.

Pearson correlations between postmortem pork loin temperature and pork loin quality traits. Columns indicate postmortem temperature, and rows indicate boneless pork loin quality traits. pHi is the initial pH measured at 40 min postmortem. pH bone-in is the bone-in pork loin pH measured at 20 h postmortem. pH boneless loin is the boneless pork loin pH measured at approximately 22 h postmortem. L*, a*, b* is the instrumental color measured using a Minolta CR-400 Chroma Meter on the ventral surface of boneless pork loins. JCS avg is the average Japanese color score on the ventral surface of boneless pork loins. JCS min is the Japanese color score on the shoulder-end of boneless pork loins. Drip loss percentage is calculated as: exudate weight (TE-T)/initial sample weight (TS-T) * 100, where T = weight of tube, TS = weight of tube and sample, and TE = weight of tube and exudate. JCS, Japanese color score; pHi, initial pH; TMP, temperature.

A decreasing magnitude of correlation between pHi and postmortem pH was observed, but the correlation was still moderately high at the end of the chilling process (Figure 5). The correlation between 20-h bone-in pH and postmortem pH values decreased from 40 to 70 min, but, as expected, the correlation between 20-h bone-in pH and postmortem pH was stronger as the postmortem pH time approached 1200 min. A similar positive relationship was observed between 22-h boneless pH and postmortem pH values, but the correlation was not as strong. Average and shoulder JCS were correlated with postmortem pH after approximately 180 min. The negative correlation among L*, b* and postmortem pH values weakened from 40 to 55 min and then strengthened after the 80 min postmortem period. Marbling and firmness scores and a* were poorly correlated with postmortem pH values. Drip loss percentage was negatively correlated with postmortem pH values, with the strongest relationship from 240 to 540 min postmortem.

Figure 5.

Pearson correlations between postmortem pork loin pH and pork loin quality traits. Columns indicate postmortem pH, and rows indicate boneless pork loin quality traits. pHi is the initial pH measured at 40 min postmortem. pH bone-in is the bone-in pork loin pH measured at 20 h postmortem. pH boneless loin is the boneless pork loin pH measured at approximately 22 h postmortem. L*, a*, b* is the instrumental color measured using a Minolta CR-400 Chroma Meter on the ventral surface of boneless pork loins. JCS avg is the average Japanese color score on the ventral surface of boneless pork loins. JCS min is the Japanese color score on the shoulder-end of boneless pork loins. Drip loss percentage is calculated as: exudate weight (TE-T)/initial sample weight (TS-T) * 100, where T = weight of tube, TS = weight of tube and sample, and TE = weight of tube and exudate. JCS, Japanese color score; pHi, initial pH.

There were also significant correlations between among loin quality traits (Table 7). Both pHi and pH (bone-in and boneless) were negatively correlated (P < .05) with L*, b*, and drip loss and correlated with average JCS and marbling score (P < .05). The instrumental color values were moderately correlated (a*) or negatively correlated (L* and b*) with average and shoulder JCS. Average and shoulder JCS were correlated with marbling score (P < .05) but negatively correlated with drip loss percentage (P < .05). Firmness was weakly correlated with boneless pH, a*, and marbling (P < .05).

Table 7.

Pearson correlations among postmortem pH, subjective color, objective color, marbling, firmness, and drip loss of boneless pork loins

Item	pHi1	pH (Bone-In)2	pH (Boneless)3	L*4	a*4	b*4	JCS Avg5	JCS Shld6	Marbling	Firmness
pH (bone-in)2	0.36
pH (boneless)3	0.32	0.59
L*4	−0.29	−0.57	−0.68
a*4	−0.10	−0.02	−0.09	−0.29
b*4	−0.28	−0.46	−0.66	0.67	0.32
JCS avg5	0.16	0.38	0.46	−0.61	0.31	−0.39
JCS shld6	0.07	0.33	0.33	−0.45	0.28	−0.30	0.70
Marbling	0.25	0.17	0.32	−0.13	0.10	−0.13	0.21	0.17
Firmness	0.12	0.02	0.16	−0.12	0.14	−0.04	0.03	−0.07	0.14
Drip loss7	−0.15	−0.28	−0.34	0.33	0.05	0.27	−0.35	−0.33	−0.05	0.00

• Bolded numbers indicate significant correlations (P < .05).

• pHi: initial pH measured at 40 min postmortem.

• pH (bone-in): bone-in pork loin pH measured at 20 h postmortem.

• pH (boneless): boneless pork loin pH measured at approximately 22 h postmortem.

• L*, a*, b*: instrumental color measured using Minolta a CR-400 Chroma Meter on the ventral surface of boneless pork loins.

• JCS avg: average Japanese color score on the ventral surface of boneless pork loins.

• JCS shld: Japanese color score on the shoulder-end of boneless pork loins.

• Drip loss percentage is calculated as: exudate weight (TE-T)/initial sample weight (TS-T) * 100, where T = weight of tube, TS = weight of tube and sample, and TE = weight of tube and exudate.

Discussion

The information collected on temperature and pH decline curves among abattoirs suggests that faster temperature decline is associated with reduced rate and extent of pH decline. Furthermore, the slowest chilling abattoir (abattoir D) had the palest loins with highest moisture loss as evidenced by the highest L* value, lowest average and shoulder JCS, and greatest 24-h drip loss. The results from this study suggest that slow temperature decline combined with fast pH decline result in compromised pork loin quality. Our results were not surprising as various research has been done on establishing impact on pork quality due to chilling in packing abattoirs (Kapper et al., 2012; Shackelford et al., 2012; Blakely et al., 2019; Overholt et al., 2019). However, previous studies have been unable to measure continuous postmortem pH values in carcasses but rather recorded pH at specific times during the chilling process (Maribo et al., 1998; Henckel et al., 2000). Therefore, previously reported pH decline curves were the predicted estimates of pH values among the predefined measurement time points, which were often long intervals (>2 h). In the current study, postmortem pH values were measured and recorded accurately every minute.

Blast chilling systems, which result in a lower ambient temperature early postmortem, can significantly reduce postmortem carcass temperature compared to conventional chilling systems (Tomović et al., 2008; Blakely et al., 2019). Even though all 4 curves fell within a normal range, there was a significant difference in the rate and extent of pH decline across the different commercial settings. Our data confirmed that rapidly reducing carcass temperature decreases the rate of pH decline, which was also supported by Rybarczyk et al. (2015) and Blakely et al. (2019). Moreover, this study revealed that the dynamic between pH and time postmortem did not always follow a conventional, smooth, gradual, or inversely proportional relationship that is often depicted in pH decline curves seen in the literature. In abattoirs with blast chilling systems, there was a rapid decrease in pH values initially, followed by a buffering-like effect that resulted in stabilization or slight elevation in pH, then eventually a slow and gradual reduction over time. Maribo et al. (1998) suggested that rate of pH decline was independent from loin temperature when it was over 37°C and decreased with decreasing temperature when it was below 37°C, possibly related to reduced metabolic enzyme activity. This could explain the rapid early postmortem pH decline rate across all abattoirs regardless of chilling methods but slower pH decline rate in blast chilling abattoirs later. This is confirmed by the weak correlation between temperature and pH values in the first 75 min postmortem followed by a moderate to strong, negative correlation after 120 min postmortem.

These data suggest that postmortem temperature and pH are correlated with loin quality. More specifically, loins with a higher temperature after 70 min had a greater propensity to have paler color. The weak to no correlation between temperature and firmness with 24-h drip loss suggest that temperature decline rate had a minimum impact on those quality attributes. Additionally, higher pH from 4 to 10 h postmortem could potentially improve water-holding capacity in loins. Loins with higher pH at a later postmortem period had a greater propensity to have darker color. Overall, rapidly reducing loin temperature early postmortem improved pork quality as evidenced by a darker average color, less shoulder discoloration, and reduced drip loss percentage in abattoirs with blast chilling systems. Similar results were observed by Rybarczyk et al. (2015) in that blast chilling systems improved color characteristics and water-holding capacity. Interestingly, even though both were characterized as blast chilling abattoirs, abattoir C had slightly better loin quality than abattoir A, attributed to a higher boneless pH and lower L*. This suggests that aside from the rate of chilling during the first 2 h postmortem, there are other factors affecting the development of final meat quality. One of the possible attributes is pHi differences. Previous studies suggest that pHi is highly related with loin qualities such as color, water-holding capacity, and Warner-Bratzler shear force (Eikelenboom et al., 1974; Møller and Vestergaard, 1987). Similarly, the current study reports pHi as an important trait for prediction of pH (20 h bone-in and 22 h boneless loin) and color. This suggests that the lower pHi observed in abattoir A may result in a slightly compromised meat quality compared to those with higher pHi even with proper chilling. Another plausible explanation for the improved meat quality is that the carcasses chilled more quickly after 14 h postmortem in abattoir C as compared to abattoir A. The heat maps revealed that several loin quality traits had a stronger correlation with pH and temperature toward the mid- to late postmortem period. Many studies suggested that blast chilling does not cause a complete stop in postmortem glycolysis but rather slows down the rate of pH decline, as indicated by a similar pHu value compared to conventional chilling (Shackelford et al., 2012; Rybarczyk et al., 2015; Blakely et al., 2019). Therefore, allowing the carcasses to chill at a lower temperature during the mid- to late postmortem period could further slow the postmortem glycolysis process, resulting in higher pH and improved loin quality in abattoir C.

In the initial stages of this research, the discrepancy between 20 h bone-in loin pH and 22 h boneless loin pH was concerning, as the pH was dropping 0.10 to 0.30 during the 2-h time frame. Thus, with 1 group of 17 loins, we measured pH immediately before and after boning (<10 min time difference) with the same pH meter and saw a similar difference (5.81 vs. 5.65; P < .0001) that ranged from 0.06 to 0.26 on individual loins (data not shown). This provided us with confidence that the methodology we were using was valid.

There has been a continuous effort to predict pork quality using different genetic markers. Several studies have identified biomarkers associated with postmortem glycolysis, pHu, color, water-holding capacity, intramuscular fat, and tenderness (Berri et al., 2019; Sosnicki et al., 2025). However, using genetic markers to predict final pork quality in commercial settings was not always accurate (Berri et al., 2019). This is because environmental factors such as pre- and postharvest management often play a bigger role in the development of meat quality than genetics. Research has investigated the reliability of prediction models developed using the near infrared spectroscopy method (Balage et al., 2015; Kapper et al., 2012; Savenije et al., 2006) or computer vision systems (Sun et al., 2018), and these studies reported a high predictability for quality traits. Some of this research was conducted across multiple abattoirs, and the researchers discovered that the correlation was higher within abattoir than across abattoirs (Kapper et al., 2012; Sun et al., 2018). Kapper et al. (2012) used the criteria of R² > 0.70 as a standard to evaluate the reliability of the predictions, and, given this standard, only our models for 20 h bone-in pH using pH at 75, 300, 360 min and temperature at 345 and 1200 min would be considered reliable. Our data were collected across multiple abattoirs for developing robust models with sufficient sample sizes that would work in all abattoirs. Although not the purpose for this paper, we were able to get more highly predictive models (R² > 0.80) within a single abattoir (data not shown), which agrees with the findings of Kapper et al. (2012) and Sun et al. (2018). This suggests data should be collected within individual abattoirs to generate predictive models that are abattoir specific and account for unique variations within each system’s environment.

To our knowledge, the current study was the first to use continuously measured postmortem temperature and pH decline data to objectively evaluate and predict pork quality data in different commercial settings. However, using a similar approach, Hamoen et al. (2013) was able to predict the pH of veal meat as a function of time and temperature postmortem. When building predictive models for loin quality traits in this study, postmortem temperatures across all time points were evaluated. Recognizing the practical limitation in monitoring complete postmortem pH decline in commercial environments, the analysis is strategically focused on pH values at an early postmortem stage, using either just pHi or pH values up to 360 min postmortem. In a study by Schäfer et al. (2002), pH and temperature measurements taken within the first 2 h postmortem were identified as predictors of drip loss differences across various commercial settings. Furthermore, the models were refined by selecting less than 5 parameters of pH or temperature values. Reducing the number of parameters mitigated model complexity and allowed easier implementation of the model in commercial abattoirs. However, the predictive capacity of the models was still relatively low and requires further refinement. High predictability was only found in the model evaluating 20 h bone-in pH with postmortem pH up to 360 min. There was also limited predictive association among postmortem temperature and pH, redness, and firmness of boneless loins. Because all carcasses were randomly selected in each commercial abattoir, there are some confounding factors not considered when evaluating loin quality. One of the possible factors is hot carcass weight (HCW). Increased HCW reduced the rate of temperature decline in hams but not in loins (Overholt et al., 2019). Harsh et al. (2017) revealed that heavier carcasses had darker, redder loins and hams, but HCW only explained less than 2.87% variability in loin and ham pHu and color. Price et al. (2019) found that even though increasing HCW increased loin depth and back fat, there was less than 1% variation in loin quality that could be explained by HCW. These studies suggest that HCW had a relatively poor relationship with loin quality traits and is unlikely to affect the accuracy of the predictive models. Genetics are another factor contributing to loin quality differences (Brewer et al., 2002; Lowell et al., 2019; Kowalski et al., 2020; Rodrigues dos Santos et al., 2023). Feed withdrawal variation may also impact loin quality in commercial abattoirs. A 12 to 18 h fasting period prior to slaughter is often recommended for pigs, including a 2 to 3 h lairage time in the abattoir (Faucitano, 2018; Driessen et al., 2020). Fasting of pigs contributed to a higher pHu, darker color, and greater water-holding capacity compared with pigs that had not been fasted (Leheska et al., 2002; Frobose et al., 2014). However, it can be difficult to implement proper feed withdrawal time, depending on the farm management, transportation time, and unforeseen challenges at the abattoir.

Conclusions

In conclusion, this research determined the differences in temperature and pH decline curves from different commercial facilities. Novel equipment was used to collect pH values every minute throughout the chilling process, including through the blast chilling systems. Temperature decline during the first 70 min postmortem was shown to have the greatest impact on color, while temperature decline between 120 and 360 min had the greatest impact on water-holding capacity. Generally, the abattoirs with faster temperature decline rates tended to have better meat quality. Moreover, the current results generated models to predict loin quality traits prior to fabrication using temperature and pH at selected times postmortem. For future applications, these models could be used by commercial abattoirs to identify critical time points postmortem to improve chilling systems and pork quality. Although abattoir-specific validation is proposed to increase accuracy of the models, using temperature and pHi to predict pork quality is a promising and cost-effective method with potential to benefit the industry.

Conflict of Interests

The authors declare no conflicts of interest.

Author Contributions

Xuenan Chen: data collection and writing—original draft; Bruno Valente: lead statistical analysis and writing—original draft; Neal Matthews: concept, methodology, data collection, statistical analysis, and writing—edits and revisions; L. Clay Eastwood: data collection and writing—edits and revisions; Andrzej Sosnicki: concept and writing—edits and revisions; and Brandon Fields: concept, methodology, data collection, and writing—edits and revisions.