Association Between Beef Ribeye Area Measurements and Steak Portion Size

Introduction

Average beef carcass weights have continued to increase over the past several years (US Department of Agriculture [USDA] Economic Research Service, 2025). While this has led to a potential increase in efficiency during beef processing, it has also led beef processors to apply some discounts on beef prices (when purchased on a grid-based marketing system) when carcasses are over 408 kg and additional discounts for carcasses over 476 kg (USDA Economics, Statistics and Market Information System, 2024a). Along with the increase in the hot carcass weight (HCW), the average ribeye area (REA) has also increased over the years. Specifically, the 2022 National Beef Quality Audit indicated that the average HCW has increased by 56.9 kg from 1991 (average HCW of 345.0 kg) to 2022 (average HCW of 401.9 kg), while the REA increased by 7.6 cm² (1.2 in²) from 1991 with the average REA of 91.0 cm² (14.1 in²) in 2022 (Lorenzen et al., 1993; Mayer et al., 2024). In general, REA is used to calculate the yield grade to predict cutability, but it can also affect the thickness of whole-muscle steaks if they are portioned to a specific weight.

Previous consumer surveys have reported that consumers prefer to purchase thicker steaks when comparing 0.5-in, 1.0-in, and 1.5-in thick ribeye and sirloin steaks (Maples et al., 2018). Moreover, steak thickness can influence the consumer’s eating experience. Miller et al. (2019) evaluated the interaction between steak thickness and grill temperature on consumers’ perceived liking and flavor of beef top loin steaks and reported that thickness impacted beef flavor with thick steaks (3.8 cm) cooked at low temperatures (177°C) or thin steaks (1.3 cm) cooked at high temperatures (232°C), having more positive sensory traits and consumer liking scores. Similarly, Kerth (2016) reported that the steak thickness influenced the generation of volatile compounds during cooking, with thin steaks (1.27 cm) producing volatiles originating from lipid degradation, whereas thick steaks (3.8 cm) produced more Maillard products. Differences in the volatile compounds generated could affect perceived flavor, thereby altering the overall liking of the steak. Additionally, Dunn et al. (2000) reported that steak thickness affected consumer perception and eating quality, with thicker steaks having a more intense flavor compared to thinner steaks.

Due to this association among REA, steak thickness, and eating quality, previous studies have attempted to utilize the REA to predict steak thickness (Bass et al., 2009; Steele et al., 2020). Steele et al. (2020) reported that the strip loins from the smaller REA categories were more likely to meet the optimal weight and thickness; however, sorting carcasses by REA was less associated with the tenderloin weight and thickness than it was for ribeye or strip loins, suggesting that sorting subprimals based on REA might improve boxed product consistency. Bass et al. (2009) also observed similar results, with only 7 of the 14 muscles examined showing direct effects of the REA on their retail portion weights. These authors reported that the portion characteristics for longissimus thoracis (ribeye) had moderate to low correlations with carcass REA, and portion characteristics for the longissimus lumborum (strip loin) had weak to very weak correlations with carcass REA.

The REA is also used as a metric for inclusion into several branded beef programs in the United States, which could offer a higher price for carcasses that meet their specifications. For example, the Certified Angus Beef program specifies that carcasses should have a REA between 64.52 cm² (10 in²) and 109.68 cm² (17 in²) based on measurements taken from the grading camera to be included in the brand’s program. Therefore, understanding the relationship between REA and steak thickness/weight is critical to ensure the consistency of beef products. The objective of this study was to evaluate the effect of differing REA on the steak dimension of longissimus lumborum muscle cut to various weights (340.19 g [12 oz] and 453.59 g [16 oz]) or thickness (2.54 cm [1 in]) and to estimate the change in portion size based on REA.

Materials and Methods

Results

One hundred carcasses were selected based on USDA-approved camera REA, with REA ranging from 63.2 to 125.8 cm² (9.8–19.5 in²). The distribution of carcasses in each REA bracket is presented in Figure 1. Even though the intent was to obtain 10 carcasses in each category, there were slightly lower numbers of carcasses in the less-than-70.97-cm² (11-in²; n = 7) and greater-than-122.58-cm² (19-in²; n = 6) categories. Descriptive summary statistics of the carcass data collected are presented in Table 2. The average REA from a USDA-approved camera measurement, gridded measurement, and traced measurement were 94.8 cm² (14.7 in²), 96.8 cm² (15.0 in²), and 93.5 cm² (14.5 in²), respectively (Table 2). The average HCW was 399.0 ± 66.0 kg with a minimum of 262.0 kg and a maximum of 534.0 kg. Fat thickness also varied, ranging from 0.8 to 3.3 cm. Furthermore, the average strip loin weight was 5.3 ± 1.0 kg with an average length of 41.4 ± 2.6 cm and an average height of 10.0 ± 0.1 cm (Table 2).

Figure 1.

Number of beef strip loins (n = 100) within each category of the incremental ribeye area category increasing by 1 in² from less than 71.0 cm² (<11 in²) through greater than 122.6 cm² (>19.0 in²). REA, ribeye area.

The mean thickness or weight of the portioned cuts is shown in Table 3. The mean thickness of the 340.19-g (12-oz) portion was 2.62 ± 0.39 cm (1.03 in), whereas the thickness of the 453.59-g (16-oz) portion was 3.53 ± 0.52 cm (1.39 in). The 2.54-cm (1-in) thick portion had a mean weight of 340 ± 50 g (12 ± 1.76 oz). When evaluating the relationship between traced REA and average thickness of the portioned cuts, both the thickness of 12-oz (340.19-g) and 16-oz (453.29-g) portioned cuts were negatively correlated with the traced REA measurement (R² = −0.71, P < .001; Figure 2), which means that as the REA increased, steak thickness decreased. Conversely, the average weight of each strip loin, when portioned to 1-in (2.54-cm) thick cuts, was positively correlated with traced REA (R² = 0.75, P < .001; Figure 2), which means that the average weight of the 1-in thick steaks increased with the increase in REA.

Figure 2.

Correlation between traced ribeye measurements (traced ribeye area) and 3 separate portion cut sizes: 12 oz (340.19g), 16 oz (453.59 g), and 1 in (2.54 cm; n = 100). (A) Traced REA vs the average thickness of a 12-oz portion (P < .001), (B) traced REA vs the average thickness of a 16-oz portion (P < .001), and (C) traced REA vs the average weight of a 1-in thick portion (P < .001). REA, ribeye area.

The correlation matrix for all factors measured in this study is presented in Table 4. Traced REA was correlated (P < .05) to all the parameters evaluated, except the marbling score and fat thickness. Similarly, HCW was correlated with all measurements (P < .05), except the marbling score. The strip loin length and all measured widths (anterior, middle, and posterior) were also highly correlated with steak thickness and weight (P < .05).

The results of the linear regression models indicated that traced REA was significant (P < .001) for estimating all 3 portion sizes (Table 5). A 2.54-cm (1-in) increase in REA was estimated to decrease the thickness of a 340.19-g (12-oz) portion and a 453.59-g (16-oz) portion by 0.138 cm (95% CI: −0.155, −0.120 cm) and 0.185 cm (95% CI: −0.209, −0.162 cm), respectively. Additionally, a 1-in increase in traced REA could increase the weight of a 1-in thick portion by 18 g (95% CI: 15, 20 g).

Discussion

Steak thickness and steak weight can influence both food-service operations and retail merchandising. In food service, steaks are commonly purchased and marketed by weight. However, when steaks vary in thickness, achieving consistent degrees of doneness can be challenging because chefs often rely on cooking time to manage multiple steaks simultaneously. In response, chefs who fabricate steaks in-house may cut portions thicker, and therefore heavier than the advertised menu, to maintain a more premium visual appearance. Although this approach may enhance perceived quality and customer satisfaction, it can also reduce yield and negatively affect profitability. In retail settings, subprimals with large REA could create merchandising challenges. When steaks are cut to a thickness that meets consumer expectations, the resulting package price may exceed what many consumers are willing to pay. Conversely, cutting steaks thinner to maintain a lower package price can reduce visual appeal, leading consumers to select alternative packages with more desirable thickness.

Previous studies have demonstrated that the thickness of beef ribeye, top loin, and top sirloin steaks could be related to consumers’ willingness to pay (WTP; Leick et al., 2012). Maples et al. (2018) also examined the relationship between portion size and consumers’ WTP for beef steaks using ribeye and sirloin steaks with 3 different REA (10 in², 14 in², 18 in²) and 3 different thicknesses (0.5 in, 1.0 in, 1.5 in). These authors reported that 90% of consumers perceived the thinnest steaks (0.5 in) as less desirable compared to 1-in thick steaks. Additionally, consumers would pay $7.07 less per package for steaks with a 10-in² ribeye and $3.51 less per package for steaks with an 18-in² ribeye when compared to a 14-in² REA (Maples et al., 2018). When assessing how steak thickness influenced consumers’ WTP, the authors found that WTP was $18.67 lower for 0.5-in thick steaks compared with 1-in thick steaks, while WTP increased by $4.66 for 1.5-in thick steaks relative to 1-in thick steaks (Maples et al., 2018).

In the current study, the 340.19-g (12-oz) portion strip loin steaks had a mean thickness of 2.62 cm, whereas the 453.59-g (16-oz) portion had a mean thickness of 3.53 cm (Table 3). Steak thickness can impact cookery and consumer perception. Thicker steaks had an overall higher consumer liking compared to thinner steaks when cooked the same way (Miller et al., 2019), which could be due to differences in volatile compounds generated during cooking (Kerth, 2016). Specifically, Miller et al. (2019) reported that thicker (3.8-cm) steaks cooked at grill surface temperature of 177°C had more intense umami basic taste and beef identity, whereas thinner (1.3-cm) steaks had lower levels of beef identity and brown/roasted flavor aromatics. These results indicated that consumers place high importance on steak thickness, which would be affected by the REA. Foster et al. (2021) also reported that portioning steaks by a constant weight resulted in significant variations in thickness, regardless of HCW or REA, while steaks cut to a uniform thickness showed significant differences in weight. Additionally, these authors reported that the steak thickness affected shear force values and sensory attributes. When steaks were portioned by weight, those in the small REA category received the highest sensory overall liking and tenderness scores, outperforming steaks from the medium and large categories. No differences were observed among REA categories when steaks were portioned by thickness (Foster et al., 2021). The difference in overall liking of steaks when portioned by weight (different thicknesses) indicates that sorting based on REA and portioning to a specific weight may lead to changes in overall liking of the steak.

The results of the current study also indicated that each of the 3 portion dimensions (12-oz, 16-oz, and 1-in thick) was correlated (P < .001; R² = −0.71, −0.71, 0.75, respectively) to the traced REA measurements (Figure 2). Dunn et al. (2000) also categorized REA into 1-in² categories, ranging from less than 11 in² to greater than 16 in², to examine the impact of REA on steak thickness, cooking parameters, and tenderness of the longissimus muscle in strip loin and T-bone steaks. As expected, steak thickness within groups portioned to a predetermined weight decreased with increasing REA, consistent with the results of the current study. These authors also indicated that REA affected tenderness and the cooking times of both strip steaks and T-bone steaks, with steaks portioned from carcasses with 77.4-cm² (11.99-in²) to 96.6-cm² (14.97-in²) REA having optimal tenderness and cooking times (Dunn et al., 2000), which reiterates the importance of having consistent steak thickness for the food-service industry. On contrary, Bass et al. (2009) reported that among the strip loin characteristics measured (whole-muscle maximum height, whole-muscle maximum width, whole-muscle maximum length, face length, and face width), only whole-muscle maximum height had a significant (still weak) correlation with REA. Previous studies attributed the variation in the correlation between muscle size and REA to differences in the breed of the cattle, sex, maturity, and management practices of the live animals (Dunn et al., 2000; Bass et al., 2009).

The REA is a key component of the current USDA yield grade equation used to estimate retail yield in beef carcasses. However, previous studies have discussed whether the original yield grade equations developed in 1965 should be modified to better reflect changes in beef carcass composition over time (Lawrence et al., 2008). Although yield grade equations were not evaluated in the current study, our results indicate that a larger REA results in thinner boneless strip loin steaks when portioned to specific weights. A clearer understanding of the relationship between REA and subprimal muscling could provide additional context for future refining yield grade equations. Furthermore, advances in carcass sorting technologies and improved subprimal characterization could enable secondary sorting beyond the grading chain, allowing strip loins to be more effectively aligned with specific processing and end-use requirements.

In US meat processing facilities, sorting carcasses on the grading chain is essential because this is where premiums and discounts are assigned under grid-based pricing systems (USDA Economics, Statistics and Market Information System, 2024b). For example, carcasses that conform to boxed beef program standards can be rewarded with a premium because they can be marketed through higher value programs. The current study revealed that REA was an accurate predictor of steak thickness when portioned to 12 or 16 oz or to 1-in thickness. Numerous additional factors, including differences in production systems, cattle sex, age class, and breed, can affect predictors such as REA, HCW, and fat thickness across commercial beef populations. For example, Steele et al. (2020) examined whether sorting by REA could be improved by sorting by the weight of the subprimal instead to increase the consistency of the product. These authors suggested that while weight may minimize some variation, it would not be fully sufficient to minimize most of the inter-carcass variation. This may be because these authors grouped subprimals into only 2 weight categories (heavy and light), whereas grouping into additional weight categories may reduce variability (Steele et al., 2020).

Conclusions

The results of the current study indicated a linear relationship between REA and strip loin weight and thickness. Thickness of the strip loin steaks decreased by an estimated 0.138 cm (0.054 in.) for 340.19-g (12-oz) steaks and 0.185 cm (0.073 in.) for 453.59-g (16-oz) steaks per 6.45-cm² (1-in²) increase in REA. On the other hand, the weight of 2.54-cm (1-in) steak portions increased by approximately 18 g for every 6.45-cm² (1-in²) increase in REA. The industry could benefit from additional research to determine an optimal REA range based on consumer acceptance and degree of doneness for steaks with varying thickness, which could optimize consumer acceptance and steak portion sizes for retail and food service.

Conflict of Interest

The authors declare that there are no conflicts of interest relevant to this article.

Author Contribution

A. F. Schiefelbein: data collection, data analysis, investigation, writing—original draft, reviewing, and editing; C. L. Smith: data collection, and writing—reviewing and editing; I. Geornaras: data collection and writing—reviewing and editing; J. A. Scanga: conceptualization, funding acquisition, and data collection; K. E. Belk: writing—reviewing and editing; R. J. Delmore: data collection and writing—reviewing and editing; D. L. Clark: conceptualization, funding acquisition, data collection, and writing—reviewing and editing; and M. N. Nair: conceptualization, funding acquisition, data collection, data analysis, investigation, principal investigator, and writing—reviewing and editing.