# Random Forest Approach Applied to Italian-French Beef Production Systems: Sex Differences and Key Meat Standards Australia Traits Affecting Beef Eating Quality

Matteo Santinello, Mauro Penasa, Nicola Rampado, Jean-François Hocquette, Dave Pethick, Massimo de Marchi

► To cite this version:

Matteo Santinello, Mauro Penasa, Nicola Rampado, Jean-François Hocquette, Dave Pethick, et al. Random Forest Approach Applied to Italian-French Beef Production Systems: Sex Differences and Key Meat Standards Australia Traits Affecting Beef Eating Quality. Meat and Muscle Biology, 2025, 9 (18329), pp.1-16. 10.22175/mmb.18329. hal-04999175

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# Random Forest Approach Applied to Italian-French Beef Production Systems: Sex Differences and Key Meat Standards Australia Traits Affecting Beef Eating Quality

Matteo Santinello<fnref n="1" />*, Mauro Penasa<fnref n="1" />, Nicola Rampado<fnref n="2" />, Jean-François Hocquette<fnref n="3" />, Dave Pethick<fnref n="4" />, and Massimo De Marchi<fnref n="1" />

**Abstract:** The European beef carcass grading scheme prioritizes meat yield over consumer eating experience, in contrast to the Meat Standards Australia (MSA) grading scheme, which is more focused on eating quality. The Italian-French beef production system, mostly characterized by young bulls and heifers imported from France and then fattened within Italian specialized fattening units, has been underexplored using the MSA grading system. This study examines the impact of animal sex on performance, MSA traits, and predicted MSA quality scores (MQ4 and MSA index) using carcasses from an Italian commercial abattoir. It also assesses how animal performance and MSA traits influence predicted beef eating quality. A Random Forest classifier demonstrated high performance with an accuracy of .98, a specificity of .99, and a sensitivity of .97 after 10-fold cross-validations, confirming key traits such as hot carcass weight, European carcass grading scheme fatness score, and general muscular characteristics as being significantly different due to sex. Entire males had greater muscle development, whereas females had notably higher MSA marbling scores, MQ4 scores, and MSA index (*P* < .05). Furthermore, categorizing the MSA index into 2 classes and applying a similar Random Forest classifier approach revealed that MSA marbling was the primary factor influencing variability of the MSA index. These findings suggest that beef cuts from females may better meet consumer expectations when using the MSA grading scheme in Europe, making them a suitable category for premium beef branding. Grading carcasses from entire male animals for sensory quality is also important, as it demonstrates their potential to produce good-quality beef with a higher lean meat yield; however, their sensory quality tends to be lower than that of heifers and this is in part reflected by their lower marbling score resulting from reduced intramuscular fat deposition.

**Key words:** beef cattle, marbling, Meat Standards Australia, meat quality, Charolais

*Meat and Muscle Biology* 9(1): 18329, 1–16 (2025)

doi:10.22175/mmb.18329

Submitted 30 August 2024

Accepted 16 January 2025

## Introduction

The European carcass grading scheme (EUROP) prioritizes animals with excellent carcass conformation and minimal superficial fat cover, referring mainly to aspects related to carcass yield (Hocquette et al.,

2018). Several studies have identified that the EUROP carcass grading scheme incorporates factors that have little or no relationship with the beef eating quality as perceived by consumers (Bonny et al., 2016b; Monteils et al., 2017; Liu et al., 2020). Therefore, implementing a carcass grading scheme

<figure><img src="image_2.png" /></figure>

<footnote n="1">¹Department of Veterinary Medicine and Animal Production, University of Naples 'Federico II', Naples, Italy, Via Federico Delpino, 1 80137, 35020</footnote>

<footnote n="2">²Associazione Zootecnica Veneta (AZOVE), Cittadella, 35013 Italy</footnote>

<footnote n="3">³INRAE, Clermont Auvergne University, VetAgro Sup, UMR Herbivores, Saint-Genès-Champanelle, 63122 France</footnote>

<footnote n="4">⁴School of Agricultural Sciences, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, Murdoch, Western Australia, 6150 Australia</footnote>

<footnote>*Corresponding author. Email: matteo.santinello@unipd.it (Matteo Santinello)</footnote>
capable of capturing sensory characteristics by consid-
ering a broader range of factors could be beneficial for
the European beef industry (Bonny et al., 2018).

The Meat Standards Australia (MSA) grading
scheme has prioritized meat-eating quality by consider-
ing a range of factors, including animal-related charac-
teristics (i.e., age, gender, breed, and percentage of
*Bos indicus* of the animal), *pre-slaughter* conditions
(i.e., milk-fed vealers and sale yard conditions), *post-
slaughter* procedures (i.e., carcass hanging method
and aging time), carcass characteristics (i.e., carcass
weight, ossification score, marbling score, ultimate
pH and temperature), and variations among commercial
cuts with respect to the most common cooking methods
(e.g., grilled and stir-fried; Bonny et al. 2018). The
MSA grading scheme was developed using a model
able to predict beef eating quality by combining most
of the aforementioned *in vivo* and *post-mortem* charac-
teristics measured for each animal with scores of tender-
ness, juiciness, flavor, and overall liking determined
through extensive taste panel tests with untrained con-
sumers (Polkinghorne et al., 2008; Watson et al., 2008).
During panel tests, several commercial cuts have been
evaluated under different hanging methods, aging
times, and 5 cooking methods to predict the average
sensory experience for consumers (i.e., MQ4 score
which is a combination of tenderness, juiciness,
flavor, and overall liking; Polkinghorne et al., 2008).
Depending on the combination of factors such as cut,
carcass hanging method, aging time, and cooking
method, the MQ4 score predicts the average consumer
experience as either unsatisfactory, 3-star, 4-star, or 5-
star. The value of each MQ4 score ranges from 0 to 100.
According to the benchmarks reported by Polkinghorne
et al. (2008), in Australia, the boundaries of 46, 64, and
77 correspond to the 2/3-, 3/4-, and 4/5-star thresholds
of the indicated grades, respectively.

Italy imports most of its beef cattle from France
for intensive fattening (European Commission, 2022),
with Charolais being the most common breed
(Santinello et al., 2020). Italian feeders import both
entire male and female cattle (Santinello et al., 2022).
Heifers are typically slaughtered at lower body weight
compared to males due to their reduced average daily
gain. For instance, in the study by Santinello et al.
(2024c), Charolais entire males and females achieved
an average hot carcass weight of 440 kg and 318 kg,
respectively. Recent studies have explored the use of
the MSA carcass grading scheme in Europe (Liu et al.,
2020; Liu et al., 2022; Liu et al., 2023), including the
integrated production system between Italy and France

(Santinello et al., 2024a). Other research has identified
relationships between European carcass evaluation and
the MSA grading scheme applied to young beef cattle
(Santinello et al., 2024c). However, implementing the
MSA system in Europe is challenging, because it was
originally developed in Australia, where production
systems and beef breeds often differ from those in
Europe (Hocquette et al., 2011). Cull cows hold signifi-
cant economic value for the French beef industry
(Garcia and Agabriel, 2008), whereas Italy predomi-
nantly slaughters young entire bulls within 24 months
of age (Gallo et al., 2014). To effectively reflect both
the commercial interests and the unique characteristics
of the European beef market, the MSA system should
consider these regional peculiarities in its framework.
Moreover, consumer preferences in Europe may differ
from those in Australia, adding complexity to adapting
the MSA system. Fořtová et al. (2022) observed that
European consumers generally prefer leaner beef even
if they rate higher-fat options positively within certain
limits. However, it remains unclear whether the MSA
model, when applied to Italian and French beef cattle,
assigns similar importance to the same traits as it does
for Australian cattle.

Therefore, the aim of this study was to investigate
the effects of sex on animal performance, MSA traits,
and MQ4 scores for Charolais young bulls and heifers
imported from France and fattened intensively in Italy.
Moreover, this study sought to understand how the var-
iables used in the MSA grading scheme impact the pre-
dicted beef eating quality in young cattle.

**Materials and Methods**

Animal selection and data collection

Data were collected in a commercial slaughter-
house of the Associazione Zootecnica Veneta (AZoVe,
Cittadella, Italy) by certified graders for the EUROP
grid on one hand and the MSA carcass grading scheme
(under the auspices of the International Meat Research
3G Foundation) on the other hand. The study involved
108 Charolais cattle, 39 entire males and 69 females.
The average slaughter age and body weight were
518 ± 60 d and 566 ± 15 kg for females, and 510 ±
53 d and 723 ± 41 kg for males. Animals were fattened
for 6 months, fed a diet rich in concentrates (Table 1),
and reared in 27 Italian specialized fattening units, cov-
ering the period between May 2021 and July 2022.
Although carcass grading using the MSA protocols
<table><caption>**Table 1.** Average characteristics of the diet provided to Charolais beef cattle during the finishing period</caption><thead><tr><th>Item</th><th>Females</th><th>Males</th></tr></thead><tbody><tr><td>Dry matter, kg</td><td>9.00</td><td>9.90</td></tr><tr><td>Metabolizable energy, UFV<sup>1</sup></td><td>8.60</td><td>10.0</td></tr><tr><td>Digestible protein in the small intestine, g</td><td>821</td><td>967</td></tr><tr><td>Digestible protein in the small intestine for nitrogen, g</td><td>796</td><td>890</td></tr><tr><td>Concentrates, %</td><td>54.2</td><td>63.7</td></tr><tr><td>Forages, %</td><td>45.8</td><td>36.3</td></tr></tbody></table>

is conducted at the 10th ribbing site, European commercial practices typically allow grading at the 5th ribbing site for logistical and commercial reasons (Santinello et al., 2024a). Grading at the 10th ribbing site depreciates the carcass value, limiting data access at this location and contributing to the uneven distribution of male and female carcasses in our dataset. Indeed, in the slaughterhouse where the present study took place, the hindquarters of heifers are more frequently cut at the 10th ribbing site to sell the loins to local butchers, whereas the hindquarters of entire males are usually sold whole to the large-scale retail sector, where they are subsequently processed. The 14-months data collection period in 27 Italian specialized fattening units considered in the present study allowed the capture a broad range of seasonal and environmental variability, improving the representativeness and usefulness of our findings.

The following MSA traits were assessed using the Australian beef chiller assessment system standards and Australian Meat standards (AUS-MEAT, 2018) in the chiller room from the surface of the *Longissimus thoracis et lumborum* at the 10th *thoracic vertebrae* of Achilles hung carcasses, cut 24 h after slaughtering (6–7°C): 1) rib fat depth (mm), which measures subcutaneous fat; 2) total rib fat depth (mm), which includes both subcutaneous and intermuscular fat, measured manually with a ruler along the rib eye muscle at the 10th *thoracic vertebrae*; 3) eye muscle area (EMA cm²), which measures the surface of *Longissimus thoracis et lumborum* at the 10th *thoracic vertebrae*, determined using the AUS-MEAT EMA standard grid by counting the number of square centimeters of *Longissimus thoracis et lumborum* at the quartering site (AUS-MEAT, 2005); 4) intramuscular fat, evaluated by comparing the amount, size, and distribution of marbling with MSA reference standards, on a scale from 100 to 1190 with 10-point increments (MLA, 2006); and 5) ultimate pH (measured using a pH meter,

RTD Thermometer, Delta OHM, HD2105.1, Italy). Additionally, for each carcass, the ossification score has been assessed as the degree of calcification in the *sacral, lumbar*, and *thoracic vertebrae* (Romans et al., 1994), and hump height has been measured as the distance (cm) from the highest point of the hump to the dorsal edge of the *Ligamentum nuchae*.

## Prediction of MQ4 scores and MSA index

The EUROP-accredited technician recorded animal details (sex, birth, arrival date at the Italian fattening unit, and slaughter date) and performances (hot carcass weight, EUROP conformation, and fatness scores; EU 2013/1308, 2013), from which the length of the fattening cycle (calculated as the difference between the slaughter date and the date of arrival to the fattening unit, in days), slaughter month, and age at slaughter (days) were derived. The EUROP conformation and fatness scores were converted to a continuous 15-point scale following the method proposed by Hickey et al. (2007). Traits collected at the abattoir were used to predict the MQ4 scores for 4 commercial cuts: CUB045 (*Longissimus thoracis et lumborum*), STA045 (*Longissimus thoracis et lumborum*, anterior striploin piece), RMP131 (*Gluteus medius*), and TFL052 (*Obliquus internus abdominis*). The predictions were performed for the different commercial cuts of the same animals assuming a carcass aging time of 10 d and using the most common cooking methods for each cut as described in Santinello et al. (2024a; 2024c): grilling for CUB045 and STA045 (CUB045 GRL and STA045 GRL, respectively), stir-frying for TFL052 (TFL052 SA), and grilling and roasting methods for RMP131 (RMP131 GRL and RMP131 RST, respectively). The carcass aging time was set to 10 d, as it is the most used period at the slaughterhouse where the trial was conducted, and to maintain consistency with our previous study (Santinello et al., 2024a), since the *Longissimus dorsi* reaches 80% of its aging in 10 d (Pogorzelski et al., 2022).

The MSA index was calculated for a standard aging time of 5 d by summing the predicted MQ4 scores at 5 d of aging of all MSA cuts using the SP2009 version of the MSA model (McGilchrist et al., 2019). The SP2009 model includes animal sex, carcass weight (kg), hanging method, hump height (mm), ossification score, MSA marbling score, rib fat depth (mm), and ultimate pH. The MSA index was developed to provide producers with feedback on the potential meat-eating quality of their beef carcasses (McGilchrist et al., 2019). Detailed information on the MSA index and the SP2009 model is

<footnote n="1">¹Unité fourrage viande.</footnote>
available in Polkinghorne et al. (2008), and McGilchrist et al. (2019).

## Statistical analysis

Slaughter age, length of the fattening cycle, EUROP conformation and fatness scores, hot carcass weight, MSA marbling score, ossification score, eye muscle area, hump height, rib fat depth, total rib fat depth, ultimate pH, MQ4 scores, and MSA index were used as dependent variables into the following linear mixed model in SAS software v.9.4 (SAS Institute Inc., Cary, NC):

$$y_{ijkl} = \mu + sex_i + slaughter\ month_j + fattening\ unit_k + slaughter\ date_l + e_{ijkl},$$

where $y_{ijkl}$ is the dependent variable; $\mu$ is the overall intercept of the model; $sex_i$ is the fixed effect of the ith sex of the animal ($i$=male or female); slaughter month_j is the fixed effect of the jth slaughter month ($j$=January to December); fattening unit_k is the random effect of the kth receiving fattening unit ~N(0, $\sigma^2_{fattening_unit}$), where $\sigma^2_{fattening_unit}$ is the fattening unit variance; slaughter date_l is the random effect of the date of slaughter ~N(0, $\sigma^2_{slaughter_date}$), where $\sigma^2_{slaughter_date}$ is the slaughter date variance; and $e_{ijkl}$ is the random residual ~N(0, $\sigma^2_e$), where $\sigma^2_e$ is the residual variance. Results are presented as least-squares means and standard error and the comparison of least-squares means for the fixed effects was performed using the Bonferroni post hoc correction ($P < .05$).

A correlation and a principal component analysis (PCA) were performed on raw data with R software (R version 2023.03.0; RStudio PBC, Boston, MA) to investigate the relationships between the investigated traits and to discriminate animals based on collected variables. Principal component analysis simplifies data interpretation by transforming correlated variables into uncorrelated principal components, effectively reducing dimensionality while retaining key sources of variability (Jolliffe and Cadima, 2016). This orthogonal transformation reduces redundancy and minimizes noise associated with overlapping variance, making it particularly useful in datasets with correlated variables, such as in the present study (Destefanis et al., 2000). Additionally, PCA can help cluster both variables and individuals, enhancing the ability to differentiate groups within complex datasets using features of several variables (Vichi and Saporta, 2009). After running the PCA, the first 2 principal components were plotted to discriminate animals according to carcass

and MSA traits. Correlation analysis was conducted on the entire dataset (raw data), including both males and females. Additionally, partial-correlation analysis was performed to identify any differences or changes in correlations between the 2 groups.

### Random Forest classifier applied to sex effect.
To investigate the most important variables that could explain most of the variability between males and females, a Random Forest (RF) classifier was employed. This analysis utilized the “randomForest” and “caret” packages of R software (R version 2023.03.0; RStudio PBC, Boston, MA). The RF classifier, a machine learning technique used for both classification and regression tasks, operates by generating an ensemble of decision trees, in which each tree is built on a random subset of the data and features, thus improving model robustness and reducing overfitting (Breiman, 2001; Breiman, 2017). The importance of each variable is assessed by evaluating its contribution to reducing node impurity across the ensemble, which is measured through the Gini index, with the mean decrease in Gini (MDG) serving as a metric for variable importance (Chen and Ishwaran, 2012). Higher MDG values indicate a greater impact on classification accuracy, helping to identify the most informative variables in the dataset (Machado et al., 2015).

The variables included in the RF classifier were categorized into 5 distinct features: (1) characteristics of the fattening cycle, including the length of the fattening cycle (days); (2) animal maturity, including ossification score and slaughter age (days); (3) muscle development, including eye muscle area (cm²), EUROP conformation score, hump height (cm), and hot carcass weight (kg); (4) fat development, including rib fat depth (mm), total rib fat depth (mm), MSA marbling, and EUROP fatness score as proxies for physiological maturity; and (5) meat quality characteristics, including ultimate pH, MQ4 scores, and MSA index. The groups were created based on the PCA plot, which, as mentioned previously, enabled visualization of how the variables cluster together in a specific direction (feature). Because the EUROP fatness score explained the variability in a different direction compared to MQ4 scores, MSA index, MSA marbling, rib fat, and total rib fat, another class was created to account for general fat deposition. Moreover, considering that ossification score and animal slaughter age are commonly used as proxies for maturity (Bonny et al., 2016a), they were clustered together.

The dataset was randomly divided into 2 subsets: 70% of the data were used for training the model
(training subset) and 30% for testing its performance (testing subset), which is useful when external validation is not available (Machado et al., 2015). The proportion of males and females was kept constant in both subsets. The training phase involved constructing 500 decision trees, with the number of variables considered for splitting nodes optimized using the “caret” package to enhance model precision. To validate the model and mitigate the risk of overfitting, a repeated 10-fold cross-validation approach was employed. This process entailed splitting the training data into 10 subsets, using 9 for training and 1 for validation in each fold. This procedure was repeated 2,000 times to ensure the stability of the results.

To evaluate model performance, for each iterated cross-validation, the confusion matrix was obtained, and accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and the area under the curve (AUC) were calculated. Then, all the performances were averaged across the 2,000 iterations. Similarly, the significance of each variable in differentiating between genders was determined by calculating and averaging MDG values. By analyzing these MDG values, the RF model identified the most influential factors in distinguishing between males and females, highlighting the key variables in each category.

**Random Forest classifier applied to predicted beef eating quality.** To identify the most significant variables explaining the variability in predicted beef eating quality, the MSA index (representing the average eating quality at the carcass level by integrating MQ4 scores of all cuts) was categorized into 2 homogeneous classes: low and high quality, based on predicted values. Then, with the same approach described before, a RF classifier was applied to these 2 classes by using the residuals of each response variable from the mixed model described earlier (slaughter age, length of the fattening cycle, EUROP conformation and fatness scores, hot carcass weight, MSA marbling score, ossification score, eye muscle area, hump height, rib fat depth, total rib fat depth, ultimate pH). This approach was used to adjust the variables for the fixed effects of sex and slaughter months, as well as the random effects of slaughter date and fattening unit.

To evaluate model performance, confusion matrices and various metrics were used (accuracy, sensitivity, specificity, ROC curves, and AUC). These metrics were averaged across 2,000 iterations of cross-validation. Additionally, the significance of each variable in differentiating between genders was assessed through the MDG values, as described previously.

# Results and Discussion

## Effects of sex on performance and MSA traits

Table 2 reports the least-squares means of animal and carcass characteristics (including observed MSA traits and predicted MQ4 scores) according to the sex of the animal. Males outperformed females for EUROP conformation score and hot carcass weight

<table><caption>**Table 2.** Least-squares means and standard error of the mean (SEM) of animals' characteristics, MSA traits, MSA predicted meat quality scores (MQ4),¹ and MSA index for the effect of sex</caption><thead><tr><th rowspan="2">Trait</th><th colspan="2">Sex</th><th rowspan="2">SEM</th></tr><tr><th>Female</th><th>Male</th></tr></thead><tbody><tr><td>Fattening cycle</td><td></td><td></td><td></td></tr><tr><td>Length of the fattening cycle (days)</td><td>194</td><td>196</td><td>3.77</td></tr><tr><td>Animal maturity</td><td></td><td></td><td></td></tr><tr><td>Slaughter age (days)</td><td>507</td><td>510</td><td>9.73</td></tr><tr><td>Ossification score</td><td>165<sup>b</sup></td><td>174<sup>a</sup></td><td>2.94</td></tr><tr><td>Muscle development</td><td></td><td></td><td></td></tr><tr><td>Eye muscle area (cm<sup>2</sup>)</td><td>97<sup>b</sup></td><td>124<sup>a</sup></td><td>1.75</td></tr><tr><td>EUROP conformation score</td><td>11.2<sup>b</sup></td><td>14.0<sup>a</sup></td><td>0.18</td></tr><tr><td>Hump height (cm)</td><td>6.5<sup>b</sup></td><td>9.8<sup>a</sup></td><td>0.46</td></tr><tr><td>Hot carcass weight (kg)</td><td>326<sup>b</sup></td><td>439<sup>a</sup></td><td>5.40</td></tr><tr><td>Fat development</td><td></td><td></td><td></td></tr><tr><td>Rib fat depth (mm)</td><td>7.45<sup>a</sup></td><td>4.58<sup>b</sup></td><td>0.63</td></tr><tr><td>Total rib fat depth (mm)</td><td>12.2<sup>a</sup></td><td>7.89<sup>b</sup></td><td>1.13</td></tr><tr><td>MSA marbling score</td><td>395<sup>a</sup></td><td>307<sup>b</sup></td><td>12.8</td></tr><tr><td>EUROP fatness score</td><td>7.99<sup>a</sup></td><td>5.10<sup>b</sup></td><td>0.05</td></tr><tr><td>Ultimate pH</td><td>5.55</td><td>5.54</td><td>0.02</td></tr><tr><td>Meat quality potential (predicted MQ4 values)</td><td></td><td></td><td></td></tr><tr><td>CUB045 GRL</td><td>66.3<sup>a</sup></td><td>64.3<sup>b</sup></td><td>0.43</td></tr><tr><td>RMP131 GRL</td><td>55.2<sup>a</sup></td><td>52.5<sup>b</sup></td><td>0.27</td></tr><tr><td>RMP131 RST</td><td>63.6<sup>a</sup></td><td>61.1<sup>b</sup></td><td>0.27</td></tr><tr><td>STA045 GRL</td><td>60.4<sup>a</sup></td><td>58.4<sup>b</sup></td><td>0.55</td></tr><tr><td>TFL052 SF</td><td>70.6<sup>a</sup></td><td>68.7<sup>b</sup></td><td>0.52</td></tr><tr><td>MSA index<sup>2</sup></td><td>60.9<sup>a</sup></td><td>58.9<sup>b</sup></td><td>0.34</td></tr></tbody></table>

¹Predicted MQ4 for 5 “cut × cooking method” combinations. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 10 d and cooked according to the most common cooking method for each cut: CUB045 GRL = MQ4 score of CUB045 (Longissimus thoracis et lumborum) assuming the grilling cooking method; RMP131 GRL = MQ4 score of RMP131 (Gluteus medius) assuming the grilling cooking method; RMP131 RST = MQ4 score of RMP131 (Gluteus medius) assuming the roasting cooking method; STA045 GRL = MQ4 score of STA045 (Longissimus thoracis et lumborum, anterior striploin piece) assuming the grilling cooking method; TFL052 SF = MQ4 score of TFL052 (Obliquus internus abdominis) assuming stir-frying cooking method.

²MSA index = carcass predicted MSA score calculated as the weighted sum of the predicted MQ4 scores of all MSA cuts. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 5 d and cooked according to the most common cooking method for each cut.

a,bMeans with different letters within a row differ (P < .05).
(P < .05; Santinello et al., 2024b), whereas no
differences were reported for slaughter age and length
of the fattening cycle (P > .05). Males had higher ossi-
fication scores, eye muscle area, and hump height, and
females had higher EUROP fatness scores, marbling
scores, rib fat depth, and total rib fat depth (P < .05),
in agreement with Venkata Reddy et al. (2015).
Males and females had similar ultimate pH (P < .05).
The MQ4 quality scores and MSA index were higher
for females than males (P < .05). The higher MSA mar-
bling score, rib fat depth, and total rib fat depth of
females compared with males are reflected also in
the higher MQ4 scores for females as previously
observed in different studies. For instance, the previous
study of Santinello et al. (2024a) conducted in Italy,
reported higher MSA marbling scores for females than
males (+40 marbling points). These sex differences

may be linked to the distinct hormonal profile between
males and females (Schumacher et al., 2022), which
favors higher muscle growth in males and higher intra-
muscular fat deposition in females. The hormonal sta-
tus of the animals significantly impacts hot carcass
weight, hump height, marbling score, and ossification
score (Packer et al., 2021). Among other factors, those
differences are due to testosterone production, which
promotes muscle growth without a concomitant
increased fat deposition in males (Lee et al., 1990).

PCA and correlation analysis

The results of the PCA analysis are depicted in
Figure 1 and confirm the results of the analysis of vari-
ance. The first 2 principal components explained
approximately 60% of the variability between males

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Principal component analysis using animal characteristics, MSA traits, predicted meat quality scores (MQ4),<fnref n="1" /> and MSA index.<fnref n="2" /></figcaption></figure>

<footnote n="1">¹Predicted MQ4 for 5 “cut × cooking method” combinations. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 10 d and cooked according to the most common cooking method for each cut: CUB045 GRL = MQ4 score of CUB045 (*Longissimus thoracis et lumborum*) assuming the grilling cooking method; RMP131 GRL = MQ4 score of RMP131 (*Gluteus medius*) assuming the grilling cooking method; RMP131 RST = MQ4 score of RMP131 (*Gluteus medius*) assuming the roasting cooking method; STA045 GRL = MQ4 score of STA045 (*Longissimus thoracis et lumborum*, anterior striploin piece) assuming the grilling cooking method; TFL052 SF = MQ4 score of TFL052 (*Obliquus internus abdominis*) assuming stir-frying cooking method.</footnote>

<footnote n="2">²MSA index = carcass predicted MSA score calculated as the weighted sum of the predicted MQ4 scores of all MSA cuts. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 5 d and cooked according to the most common cooking method for each cut.</footnote>
and females. The first 2 principal components are related to the contrast between muscle development, which is much higher in males, and fat deposition which is a characteristic of females. Indeed, females had higher EUROP fatness scores, total rib fat depth, rib fat depth, MSA marbling scores, MSA index, and MQ4 scores. Conversely, males had higher hump height, ossification scores, eye muscle area, and EUROP conformation scores. The results suggested that beef from males may have lower eating quality due to the higher hump height, ossification score, eye muscle area, and EUROP conformation score than females.

Coefficients of correlation (r) between the variables estimated on the whole dataset are presented in Table 3. Slaughter age and ossification score were moderately correlated (r = .42) and were slightly positively associated with MSA marbling (r = .19 and .16 respectively), suggesting that older and mature animals tend to potentially deposit more marbling in their meat (Soulat et al., 2023). However, the ossification score was negatively correlated with MQ4 for RMP131 GRL (r = -.50) and RMP131 RST (r = -.49), and with MSA index (r = -.32), which implies that mature animals tend also to have lower meat-eating quality likely due to maturity-related collagen crosslinking formation. The EUROP conformation score was negatively correlated with fat and meat quality measurements such as EUROP fatness score (r = -.86), MSA marbling score (r = -.32), rib fat depth (r = -.26), total rib fat depth (r = -.21), and all MQ4 scores and MSA index (r = -.19 to -.52), and positively correlated with hot carcass weight (r = .82), eye muscle area (r = .72), and hump height (r = .51). On the other hand, EUROP fatness score was negatively correlated with growth-related traits such as hot carcass weight (r = -.89), eye muscle area (r = -.70), and hump height (r = -.57), and positively correlated with MSA marbling score (r = .44), total and rib fat depth (r = .19 and .28), and MQ4 scores and MSA index (r = .29 to .63). However, it is important to note that 99% of the females were classified in EUROP fatness class 3 and 97% of the males in class 2. This classification may have influenced the results.

Tables 4 and 5 report the partial correlation between EUROP fatness score and hot carcass weight, which is -.10 for females and -.51 for males. The differences in the magnitude of correlations suggest how males and females have different patterns of fat deposition. The European fatness score may be a moderate proxy for marbling level and consumer satisfaction, even if it assesses the presence of superficial fat on the carcass and not marbling in the present study. Our results partially agree with those of Bonny et al.

(2016c) who reported that 3 muscles out of 17 had a positive association between EUROP fat score and tenderness. Indurain et al. (2009) assessed a correlation of .49 between the marbling score measured through image analysis and the EUROP fatness score in Pirenaica yearling bulls. In our study, MSA marbling score was positively and moderately to weakly correlated with rib fat depth and total rib fat depth (r = .33 and .27, respectively, Table 3), and with all MQ4 scores and MSA index (r = .51 to .80, Table 3). The MSA marbling was negatively correlated with eye muscle area (r = -.30), due to the dilution effects of marbling flecks into a greater surface. Hot carcass weight was negatively associated with MSA marbling (r = -.33), total and rib fat depth (r = -.14 and -.15), and all MQ4 scores and MSA index (r = -.22 to -.53), and positively associated with eye muscle area (r = .77) and hump height (r = .59). In turn, eye muscle area and hump height were negatively associated with total and rib fat depth (r = -.11 to -.30), all MQ4 scores and MSA index (r = -.18 to -.60), and positively correlated to each other (r = .38). The ossification score was positively correlated to hot carcass weight (r = .39) and eye muscle area (r = .30). Rib fat depth and total rib fat depth were positively correlated with all MQ4 scores and MSA index (r = .30 to .49). All MQ4 scores and MSA index were strongly positively correlated between them (r = .71 to .98).

## Random Forest classifier applied to sex effect

The accuracy, specificity, and sensibility of the RF classifier over all iterations of 10-fold cross-validation averaged .98, .99, and .97, respectively. The average AUC of the ROC curves over all repetitions of cross-validation was .99 (Figure 2). According to MDG, the most influential variables associated with sex variability were hot carcass weight, EUROP fatness score, eye muscle area, hump height, and EUROP conformation (Figure 3). Stability of MDG values across iterations is reported in Figure 4. The RF classifier showed good performance metrics and identified, as expected, hot carcass weight, EUROP fatness score, and general muscular characteristics (eye muscle area, EUROP conformation score) as the main contributors to differences between males and females. These results confirmed the output of the PCA analysis, with males being more developed in terms of muscularity than females, and females being more prone to deposit intramuscular fat and thus to have higher beef quality (MQ4 and MSA index). The low MDG for MSA marbling and the high MDG of EUROP fatness score
<table><caption>**Table 3.** Correlation analysis between the investigated traits</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Fattening Cycle</th><th colspan="2">Animal Maturity</th><th colspan="3">Muscle Development</th><th colspan="3">Fat Development</th><th colspan="7">Meat Quality</th></tr><tr><th>LFC</th><th>OSS</th><th>SA</th><th>EMA</th><th>CONF</th><th>HUMP</th><th>HCW</th><th>RIB</th><th>TRIB</th><th>MSA</th><th>FATNESS</th><th>pH</th><th>CUB045 GRL</th><th>RMP131 GRL</th><th>RMP131 RST</th><th>STA045 GRL</th><th>TFL052 SF</th><th>MSA index</th></tr></thead><tbody><tr><td>LFC</td><td>1.00</td><td></td><td>.05</td><td>.15</td><td>.06</td><td>.12</td><td>.08</td><td>.07</td><td>-.13</td><td>-.10</td><td>-.06</td><td>-.17</td><td>.05</td><td>-.10</td><td>-.10</td><td>-.19</td><td>-.18</td><td>-.07</td><td>-.14</td></tr><tr><td>OSS</td><td></td><td>1.00</td><td>.42</td><td>.30</td><td>.24</td><td>.36</td><td>.39</td><td>.15</td><td>.02</td><td>.16</td><td>-.25</td><td>-.03</td><td>-.12</td><td>-.11</td><td>-.11</td><td>-.49</td><td>-.50</td><td>-.14</td><td>-.32</td></tr><tr><td>SA</td><td></td><td></td><td>1.00</td><td>.03</td><td>.13</td><td>.02</td><td>.12</td><td>.03</td><td>.19</td><td>.07</td><td>.06</td><td>.00</td><td>.00</td><td>-.01</td><td>-.14</td><td>-.16</td><td>-.02</td><td>-.02</td><td>-.09</td></tr><tr><td>EMA</td><td></td><td></td><td></td><td>1.00</td><td>.72</td><td>.38</td><td>.77</td><td>-.29</td><td>-.30</td><td>-.30</td><td>-.70</td><td>-.05</td><td>-.23</td><td>-.18</td><td>-.47</td><td>-.45</td><td>-.19</td><td>-.32</td><td>-.32</td></tr><tr><td>CONF</td><td></td><td></td><td></td><td></td><td>1.00</td><td>.51</td><td>.82</td><td>-.26</td><td>-.21</td><td>-.32</td><td>-.86</td><td>.00</td><td>-.26</td><td>-.23</td><td>-.52</td><td>-.49</td><td>-.19</td><td>-.35</td><td>-.35</td></tr><tr><td>HUMP</td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.59</td><td>-.11</td><td>-.11</td><td>-.10</td><td>-.57</td><td>-.06</td><td>-.47</td><td>-.55</td><td>-.59</td><td>-.60</td><td>-.53</td><td>-.60</td><td>-.60</td></tr><tr><td>HCW</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>-.15</td><td>-.14</td><td>-.33</td><td>-.89</td><td>-.10</td><td>-.28</td><td>-.25</td><td>-.53</td><td>-.51</td><td>-.22</td><td>-.36</td><td>-.36</td></tr><tr><td>RIB</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.79</td><td>.33</td><td>.28</td><td>-.03</td><td>.44</td><td>.39</td><td>.48</td><td>.48</td><td>.33</td><td>.49</td><td>.44</td></tr><tr><td>TRIB</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.27</td><td>.19</td><td>-.13</td><td>.38</td><td>.34</td><td>.42</td><td>.44</td><td>.30</td><td>.44</td><td>.44</td></tr><tr><td>MSA</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.44</td><td>.14</td><td>.80</td><td>.73</td><td>.51</td><td>.51</td><td>.68</td><td>.63</td><td>.63</td></tr><tr><td>FATNESS</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.00</td><td>.37</td><td>.32</td><td>.63</td><td>.62</td><td>.29</td><td>.45</td><td>.45</td></tr><tr><td>pH</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.10</td><td>.13</td><td>.02</td><td>.00</td><td>.14</td><td>.07</td><td>.07</td></tr><tr><td>CUB045 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.98</td><td>.79</td><td>.80</td><td>.92</td><td>.95</td><td>.95</td></tr><tr><td>RMP131 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.76</td><td>.77</td><td>.94</td><td>.94</td><td>.94</td></tr><tr><td>RMP131 RST</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.97</td><td>.71</td><td>.92</td><td>.92</td></tr><tr><td>STA045 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.72</td><td>.93</td><td>.93</td></tr><tr><td>TFL052 SF</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.89</td><td>.89</td></tr><tr><td>MSA index</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>1.00</td></tr></tbody></table>

LFC = length of the fattening cycle (days); OSS = ossification score; SA = slaughter age (days); EMA = eye muscle area (cm²); CONF = EUROP conformation score; HUMP = hump height (cm); HCW = hot carcass weight (kg); RIB = rib fat thickness (mm); TRIB = total rib fat thickness (mm); MSA = MSA marbling; FATNESS = EUROP fatness score; pH = ultimate pH; Predicted MQ4 for 5 “cut × cooking method” combinations. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 10 d and cooked according to the most common cooking method for each cut: CUB045 GRL = predicted MQ4 score of CUB045 (*Longissimus thoracis et lumborum*) assuming the grilling cooking method; RMP131 RST = predicted MQ4 score of RMP131 (*Gluteus medius*) assuming the roasting cooking method; STA045 GRL = predicted MQ4 score of STA045 (*Longissimus thoracis et lumborum*, anterior striploin piece) assuming the grilling cooking method; TFL052 SF = predicted MQ4 score of TFL052 (*Obliquus internus abdominis*) assuming stir-frying cooking method; MSA index = carcass predicted MSA score calculated as the weighted sum of the predicted MQ4 scores of all MSA cuts. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 5 d and cooked according to the most common cooking method for each cut.
<table><caption>**Table 4.** Partial-correlation analysis performed on female animals</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Fattening Cycle</th><th colspan="2">Animal Maturity</th><th colspan="3">Muscle Development</th><th colspan="3">Fat Development</th><th colspan="3">Meat Quality</th></tr><tr><th>LFC</th><th>OSS SA</th><th>EMA</th><th>CONF</th><th>HUMP</th><th>HCW</th><th>RIB</th><th>TRIB</th><th>MSA</th><th>FATNESS</th><th>pH</th><th>CUB045 GRL</th><th>RMP131 GRL</th><th>RMP131 RST</th><th>STA045 GRL</th><th>TFL052 SF</th><th>MSA index</th></tr></thead><tbody><tr><td>LFC</td><td>1.00</td><td>.05</td><td>.17</td><td>.01</td><td>.11</td><td>-.04</td><td>-.02</td><td>.12</td><td>.17</td><td>.05</td><td>.31</td><td>.03</td><td>.04</td><td>.08</td><td>-.03</td><td>-.05</td><td>.10</td><td>.06</td></tr><tr><td>OSS</td><td></td><td>1.00</td><td>.44</td><td>.13</td><td>.01</td><td>.09</td><td>.38</td><td>.15</td><td>.10</td><td>.24</td><td>-.09</td><td>-.16</td><td>-.09</td><td>-.04</td><td>-.63</td><td>-.61</td><td>-.08</td><td>-.33</td></tr><tr><td>SA</td><td></td><td></td><td>1.00</td><td>.06</td><td>.10</td><td>-.02</td><td>.14</td><td>.01</td><td>-.01</td><td>.20</td><td>.05</td><td>.12</td><td>.02</td><td>.05</td><td>-.24</td><td>-.26</td><td>.05</td><td>-.08</td></tr><tr><td>EMA</td><td></td><td></td><td></td><td>1.00</td><td>.36</td><td>.01</td><td>.40</td><td>-.09</td><td>-.23</td><td>-.03</td><td>.00</td><td>-.08</td><td>-.04</td><td>-.07</td><td>-.11</td><td>-.12</td><td>-.05</td><td>-.11</td></tr><tr><td>CONF</td><td></td><td></td><td></td><td></td><td>1.00</td><td>.17</td><td>.34</td><td>-.10</td><td>-.07</td><td>.03</td><td>-.03</td><td>.13</td><td>.01</td><td>.01</td><td>.01</td><td>-.11</td><td>-.12</td><td>-.11</td></tr><tr><td>HUMP</td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.06</td><td>-.01</td><td>-.04</td><td>.14</td><td>-.01</td><td>.20</td><td>-.11</td><td>-.14</td><td>-.15</td><td>-.18</td><td>-.17</td><td>-.19</td></tr><tr><td>HCW</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.15</td><td>.08</td><td>.17</td><td>-.10</td><td>-.13</td><td>.16</td><td>.17</td><td>.00</td><td>-.03</td><td>.17</td><td>.09</td></tr><tr><td>RIB</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.78</td><td>.12</td><td>-.04</td><td>-.02</td><td>.35</td><td>.33</td><td>.35</td><td>.40</td><td>.32</td><td>.42</td></tr><tr><td>TRIB</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.09</td><td>-.04</td><td>-.06</td><td>.23</td><td>.24</td><td>.26</td><td>.31</td><td>.25</td><td>.32</td></tr><tr><td>MSA</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>-.02</td><td>.17</td><td>.80</td><td>.77</td><td>.34</td><td>.33</td><td>.70</td><td>.59</td></tr><tr><td>FATNESS</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>-.02</td><td>-.03</td><td>-.03</td><td>-.02</td><td>-.02</td><td>-.02</td><td>-.02</td></tr><tr><td>pH</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.14</td><td>.15</td><td>.13</td><td>.12</td><td>.23</td><td>.14</td></tr><tr><td>CUB045 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.98</td><td>.75</td><td>.75</td><td>.92</td><td>.94</td></tr><tr><td>RMP131 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.71</td><td>.71</td><td>.93</td><td>.93</td></tr><tr><td>RMP131 RST</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.95</td><td>.69</td><td>.91</td></tr><tr><td>STA045 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.70</td><td>.91</td></tr><tr><td>TFL052 SF</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.89</td></tr><tr><td>MSA index</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td></tr></tbody></table>

LFC = length of the fattening cycle (days); OSS = ossification score; SA = slaughter age (days); EMA = eye muscle area (cm²); CONF = EUROP conformation score; HUMP = hump height (cm); HCW = hot carcass weight (kg); RIB = rib fat thickness (mm); TRIB = total rib fat thickness (mm); MSA = MSA marbling; FATNESS = EUROP fatness score; pH = ultimate pH; Predicted MQ4 for 5 "cut × cooking method" combinations. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 10 d and cooked according to the most common cooking method for each cut: CUB045 GRL = predicted MQ4 score of CUB045 (*Longissimus thoracis et lumborum*) assuming the roasting cooking method; RMP131 GRL = predicted MQ4 score of RMP131 (*Gluteus medius*) assuming the grilling cooking method; RMP131 RST = predicted MQ4 score of RMP131 (*Gluteus medius*) assuming the roasting cooking method; STA045 GRL = predicted MQ4 score of STA045 (*Longissimus thoracis et lumborum*, anterior striploin piece) assuming the grilling cooking method; TFL052 SF = predicted MQ4 score of TFL052 (*Obliquus internus abdominis*) assuming stir-frying cooking method; MSA index = carcass predicted MSA score calculated as the weighed sum of the predicted MQ4 scores of all MSA cuts. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 5 d and cooked according to the most common cooking method for each cut.
<table><caption>Table 5. Partial-correlation analysis performed on male animals</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Fattening Cycle</th><th colspan="2">Animal Maturity</th><th colspan="4">Muscle Development</th><th colspan="4">Fat Development</th><th colspan="4">Meat Quality</th></tr><tr><th>LFC</th><th>OSS</th><th>SA</th><th>EMA</th><th>CONF</th><th>HUMP</th><th>HCW</th><th>RIB</th><th>TRIB</th><th>MSA</th><th>FATNESS</th><th>pH</th><th>CUB045 GRL</th><th>RMP131 GRL</th><th>RMP131 RST</th><th>STA045 GRL</th><th>TFL052 SF</th><th>MSA index</th></tr></thead><tbody><tr><td>LFC</td><td>1.00</td><td>.14</td><td>.18</td><td>.04</td><td>.27</td><td>.13</td><td>.02</td><td>-.16</td><td>-.18</td><td>-.07</td><td>-.26</td><td>-.13</td><td>-.23</td><td>-.31</td><td>-.35</td><td>-.19</td><td>-.31</td><td></td></tr><tr><td>OSS</td><td></td><td>1.00</td><td>.33</td><td>.24</td><td>.14</td><td>.36</td><td>.45</td><td>.33</td><td>.11</td><td>.13</td><td>-.21</td><td>.01</td><td>-.15</td><td>-.15</td><td>-.26</td><td>-.39</td><td>-.24</td><td>-.23</td></tr><tr><td>SA</td><td></td><td></td><td>1.00</td><td>.00</td><td>.29</td><td>.34</td><td>.24</td><td>.36</td><td>.23</td><td>-.08</td><td>-.26</td><td>.17</td><td>-.08</td><td>-.11</td><td>-.04</td><td>-.09</td><td>-.11</td><td>-.07</td></tr><tr><td>EMA</td><td></td><td></td><td></td><td>1.00</td><td>.10</td><td>.29</td><td>.00</td><td>-.04</td><td>.08</td><td>.19</td><td>-.06</td><td>-.11</td><td>.09</td><td>.05</td><td>-.06</td><td>-.04</td><td>.00</td><td>-.01</td></tr><tr><td>CONF</td><td></td><td></td><td></td><td></td><td>1.00</td><td>.16</td><td>.41</td><td>.07</td><td>.01</td><td>.07</td><td>-.84</td><td>-.14</td><td>.08</td><td>.06</td><td>.08</td><td>.00</td><td>.07</td><td>.05</td></tr><tr><td>HUMP</td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.47</td><td>.19</td><td>.19</td><td>.10</td><td>-.18</td><td>-.22</td><td>-.20</td><td>-.33</td><td>-.13</td><td>-.21</td><td>-.39</td><td>-.26</td></tr><tr><td>HCW</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.29</td><td>.09</td><td>.14</td><td>-.51</td><td>-.10</td><td>.07</td><td>.04</td><td>.25</td><td>.09</td><td>-.01</td><td>.12</td></tr><tr><td>RIB</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.83</td><td>.36</td><td>-.15</td><td>.05</td><td>.40</td><td>.35</td><td>.48</td><td>.43</td><td>.18</td><td>.44</td></tr><tr><td>TRIB</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.37</td><td>-.16</td><td>.02</td><td>.39</td><td>.32</td><td>.47</td><td>.47</td><td>.15</td><td>.42</td></tr><tr><td>MSA</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>-.13</td><td>-.06</td><td>.82</td><td>.75</td><td>.63</td><td>.58</td><td>.68</td><td>.69</td></tr><tr><td>FATNESS</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.18</td><td>-.11</td><td>-.08</td><td>-.16</td><td>-.08</td><td>-.08</td><td>-.09</td></tr><tr><td>pH</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.04</td><td>.05</td><td>-.01</td><td>-.02</td><td>.13</td><td>.05</td></tr><tr><td>CUB045 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.97</td><td>.90</td><td>.86</td><td>.89</td><td>.96</td></tr><tr><td>RMP131 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.88</td><td>.85</td><td>.92</td><td>.96</td></tr><tr><td>RMP131 RST</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.94</td><td>.80</td><td>.96</td></tr><tr><td>STA045 GRL</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.77</td><td>.94</td></tr><tr><td>TFL052 SF</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td><td>.89</td></tr><tr><td>MSA index</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.00</td></tr></tbody></table>

LFC = length of the fattening cycle (days); OSS = ossification score; SA = slaughter age (days); EMA = eye muscle area (cm²); CONF = EUROP conformation score; HUMP = hump height (cm); HCW = hot carcass weight (kg); RIB = rib fat thickness (mm); TRIB = total rib fat thickness (mm); MSA = MSA marbling; FATNESS = EUROP fatness score; pH = ultimate pH; Predicted MQ4 for 5 "cut × cooking method" combinations. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 10 d and cooked according to the most common cooking method for each cut: CUB045 GRL = predicted MQ4 score of CUB045 (*Longissimus thoracis et lumborum*) assuming the grilling cooking method; RMP131 GRL = predicted MQ4 score of RMP131 (*Gluteus medius*) assuming the roasting cooking method; STA045 GRL = predicted MQ4 score of STA045 (*Longissimus thoracis et lumborum*, anterior striploin piece) assuming the grilling cooking method; TFL052 SF = predicted MQ4 score of TFL052 (*Obliquus internus abdominis*) assuming stir-frying cooking method; MSA index = carcass predicted MSA score calculated as the weighted sum of the predicted MQ4 scores of all MSA cuts. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 5 d and cooked according to the most common cooking method for each cut.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Mean ROC curve calculated on each 10-fold cross-validation, iterated 2,000 times of RF classifier applied to sex effect.</figcaption></figure>

indicate that fat deposition in females is more concentrated in superficial fat rather than in intramuscular fat. Despite MSA marbling being significantly higher in females (Table 2), the potential for intramuscular fat deposition is not fully expressed due to the young age at slaughter. Marbling tends to develop more with increased slaughter age (Park et al., 2018), and this potential difference is expected to be even more pronounced in animals that are allowed to reach an older age, in which maximum intramuscular fat deposition occurs. This is aligned with the lower values of MDG obtained for all MQ4 scores and MSA index compared with the MDG of muscle development characteristics. Indeed, slaughter age and length of the fattening cycle are similar (Table 2), and this confirms that there are differences in marbling deposition between males and females. Moreover, the European grading scheme considers traits that are poorly or not related to the sensory properties of beef, and this can reduce consumer satisfaction (Monteils et al., 2017). Thus, the use of criteria to better reflect beef eating qualities

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Mean decrease in Gini illustrating variable importance as estimated by the RF model for sex classification, utilizing variables from Charolais cattle. The MDG values were calculated for each 10-fold cross-validation and averaged across 2,000 iterations.</figcaption></figure>

Animal maturity: OSS = ossification score; SA = slaughter age (days). Fat development: RIB = rib fat thickness (mm); TRIB = total rib fat thickness (mm); MSA = MSA marbling; FATNESS = EUROP fatness score. Fattening cycle: LFC = length of the fattening cycle (days). Meat quality: pH = ultimate pH; predicted MQ4 for 5 "cut × cooking method" combinations. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 10 d and cooked according to the most common cooking method for each cut: CUB045 GRL = MQ4 score of *Cub045 (Longissimus thoracis et lumborum)* assuming the grilling cooking method; RMP131 GRL = MQ4 score of *RMP131 (Gluteus medius)* assuming the grilling cooking method; RMP131 REST = MQ4 score of *RMP131 (Gluteus medius)* assuming the roasting cooking method; STA045 GRL = MQ4 score of *STA045 (Longissimus thoracis et lumborum)*, anterior striploin piece) assuming the grilling cooking method; TFL052 SF = MQ4 score of *TFL052 (Obliquus internus abdominis)* assuming stir-frying cooking method; MSA index = carcass predicted MSA score calculated as the weighted sum of the predicted MQ4 scores of all MSA cuts. The model assumes that all the carcasses were Achilles hung and all cuts were assumed to be aged for 5 d and cooked according to the most common cooking method for each cut. Muscle development: EMA = eye muscle area (cm²); CONF = EUROP conformation score; HUMP = hump height (cm); HCW = hot carcass weight (kg).
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Mean decrease in Gini averaged for each trait along iterations estimated through RF approach applied to sex effect.</figcaption></figure>

and the optimization according to sex could improve both product consistency and consumer enjoyment.

## Random Forest classifier applied to MSA index classes

Figure 5 shows the ROC curve of the RF classifier over all iterations of the 10-fold cross-validation

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Mean ROC curve calculated on each 10-fold cross-validation, iterated 2,000 times of RF classifier applied to MSA index classes.</figcaption></figure>

applied to the MSA index classes. The average accuracy, specificity, sensibility, and AUC were .76, .78, .73, and .85, respectively. The results presented in Figure 6 indicated that MSA marbling, EUROP fatness score, ossification score, rib fat thickness, total rib fat thickness, and slaughter age had the highest MDG

<figure><img src="image_8.png" /><figcaption>**Figure 6.** Mean decrease in Gini illustrating variable importance as estimated by the RF model for MSA index classification, utilizing variables from Charolais cattle. The MDG values were calculated for each 10-fold cross-validation and averaged across 2,000 iterations.</figcaption></figure>

Animal maturity: OSS = ossification score; SA = slaughter age (days). Fat development: RIB = rib fat thickness (mm); TRIB = total rib fat thickness (mm); MSA = MSA marbling; FATNESS = EUROP fatness score. Fattening cycle: LFC = length of the fattening cycle (days). Meat quality: pH = ultimate pH. Muscle development: EMA = eye muscle area (cm²); CONF = EUROP conformation score; HUMP = hump height (cm); HCW = hot carcass weight (kg).
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Mean decrease in Gini averaged for each trait along iterations estimated through RF approach applied to MSA index classes.</figcaption></figure>

values and thus explained most of the variability between the 2 MSA index classes. Stability of MDG values across iterations is reported in Figure 7. Meat Standards Australia marbling has the highest MDG, and if compared with the EUROP fatness score, it is more accurate to measure predicted beef eating quality. Indeed, a lot of quality traits of meat are significantly influenced by intramuscular fat content (Hocquette et al., 2010). Previous studies have highlighted the weak and inconsistent correlation between EUROP fatness scores and meat quality attributes like marbling. The moderate correlations reported by Indurain et al. (2009) and Conroy et al. (2009) imply that although EUROP scores are informative, they may not fully capture meat quality nuances, indicating the potential value of integrating measures like the MSA marbling score, which is more closely tied to beef eating quality (Hocquette et al., 2011). Thus, probably a modern grading scheme should maximize both sensory and yield outcomes.

Ossification score had a higher MDG value compared to slaughter age. Bonny et al. (2016a) found that ossification is a better indicator of beef eating quality than slaughter age in young European cattle. The current study showed that eye muscle area, hot carcass weight, hump height, and EUROP conformation score, having lower MDG values, reflect muscular growth instead of beef eating quality.

## Conclusions

This study highlighted significant sex-based differences in carcass traits and meat quality using the MSA classification system. Females showed higher MSA marbling, MQ4, and MSA index, suggesting superior predicted meat-eating quality compared with males. Therefore, females appear well-suited for inclusion in a parallel premium grading system, although additional research is necessary to better valorize males. Given the complexities of carcass composition and the limitations of the current EUROP grading scheme, the inclusion of MSA marbling in a parallel grading system could enhance market segmentation and provide consumers with more detailed information on beef eating quality. The EUROP system predominantly focuses on yield and conformation, neglecting key sensory traits that influence consumer satisfaction. Integrating MSA marbling scores alongside the EUROP classification would address these gaps by capturing nuances in beef eating quality, thus increasing the value of carcasses with higher marbling scores. Recent technological advancements offer promising avenues for implementing such a parallel grading system. Tools like pocket near-infrared devices for predicting MSA marbling scores (Kombolo-Ngah et al., 2023) and advanced imaging systems (Mendes et al., 2025; Stewart et al., 2024) provide opportunities for rapid, cost-effective screening. If integrated into a standardized protocol, these technologies could facilitate the widespread adoption of a premium grading system without excessive labor or costs. However, the adoption of a dual grading system should not only prioritize meat-eating quality but also consider broader industry challenges, including beef healthiness and environmental sustainability. Improvements must balance quality with practicality, ensuring the grading system remains simple and adaptable for brand development and marketing strategies (Farmer and Farrel, 2018). A holistic approach addressing both consumer preferences and production sustainability would strengthen the competitiveness of the European beef sector, aligning it with evolving market demands.

## Acknowledgments

The authors would like to thank AZoVe (Cittadella, Italy) for providing the data used in this study.
**Declaration of Competing Interest**

The authors declare that they have no competing interests or conflicts of interest.

**Author Contribution**

Matteo Santinello assisted with conceptualization, data curation, formal analysis, validation, visualization, original draft writing, reviewing, and editing. Mauro Penasa assisted with conceptualization, supervision, writing, reviewing, and editing. Nicola Rampado assisted with conceptualization and data curation. Jean-François Hocquette assisted with conceptualization, supervision, writing, reviewing, and editing. Dave Pethick assisted with conceptualization, supervision, writing, reviewing, and editing. Massimo De Marchi assisted with conceptualization, supervision, writing, reviewing, and editing, project administration, and funding acquisition.

**Ethics Statement**

This study did not require approval from the ethical committee for the care and use of experimental animals because information on animals and carcasses was obtained from a commercial slaughterhouse during routine processes.

**Data Availability Statement**

None of the data were deposited in an official repository. The data that support the findings presented in this study are available from the corresponding author or the last author upon reasonable request.

**Funding**

This study received support from Veneto Region (Italy) through the project SustaIn4Food funded within the call “POR FESR azione 1.1.4.”

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