# Application of machine learning for optimizing biomarker combinations and guiding decisions on meat authentication

Lucille Rey-Cadilhac, Sophie Prache

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Lucille Rey-Cadilhac, Sophie Prache. Application of machine learning for optimizing biomarker combinations and guiding decisions on meat authentication. Meat Science, 2025, 227, pp.109852. 10.1016/j.meatsci.2025.109852. hal-05077824v2

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# Application of machine learning for optimizing biomarker combinations and guiding decisions on meat authentication

Lucille Rey-Cadilhac<sup>a, b</sup>, Sophie Prache<sup>a, *</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

Colorimetry

Lamb

Pasture-feeding

Decision tree

Random forest

Skatole

## ABSTRACT

This paper tested the relevance of two machine learning approaches (decision trees, DTs; and random forest models, RFs) applied to meat authentication. DT allow to select and rank potential biomarkers according to their respective discriminatory power, optimize their combinations, and guide decisions on classification of samples according to their production systems, all of which has so far been under-researched. RFs were also developed as they are particularly robust. We applied both methods on 19 compounds/variables measured on different tissues (perirenal fat (PF), dorsal fat (DF) and *longissimus thoracis et lumborum* (LTL) muscle) in an experiment using *Romane* male lambs pasture-finished on lucerne for four durations pre-slaughter (n = 34–36 lambs per group). Several DTs/RFs were constructed including measurements that are relatively easy to carry out in the abattoir/ point of sale, or measurements requiring laboratory analyses. The DTs/RFs distinguished carcasses of lambs pasture-finished from stall-fed lambs with an accuracy of up to 95.1–95.7 %, and showed that PF skatole and PF carotenoid pigment content (out of 19 variables) played a prominent role in classification. The DT/RF designed for use at the point of sale, which was based on DF spectrocolorimetric characteristics and LTL muscle colour coordinates, achieved 84.3–85.4 % accuracy. This is the first research to use DTs for meat authentication, and threshold values for classification decisions will probably need to be validated further on larger databases. These findings nevertheless raise prospects for broad application of decision trees for authentication.

## 1. Introduction

Biomarkers are molecules which can be used to classify, identify, diagnose or determine the origin of animal foods under specific production systems (Horcada, García-Infante, Liu, & Álvarez, 2024). Numerous candidate biomarkers such as fatty acid composition, skatole content, carotenoid content, measured on various different animal tissues, have been proposed to authenticate meat from specific production systems, chiefly pasture-based production systems (Horcada et al., 2024; Monahan, Schmidt, & Moloney, 2018; Prache, Martin, & Coppa, 2020; Whyte et al., 2024). However, the scientific literature shows that none of these markers alone can guarantee completely reliable and unfalsifiable authentication. Consequently, studies consistently recommend combining different markers (Horcada et al., 2024; Monahan et al., 2018; Prache et al., 2020; Zhao et al., 2023), possibly measured in different animal tissues (Harrison et al., 2011; Osorio, Moloney, Schmidt, & Monahan, 2011; Prache et al., 2009; Prache, Priolo, & Grolier, 2003; Zhao et al., 2023). Given that some of these biomarkers

show variable patterns of change according to duration of consumption of a given diet or animal tissue sampled (Prache et al., 2003; Prache, Graulet, & Rey-Cadilhac, 2024), there is a strong rationale for combining multiple biomarkers in order to enhance the reliability of authentication, especially given that different sample tissues are available and accessible in different authentication settings (at the abattoir or point of sale). Importantly, establishing “cut-off values” for these biomarkers is essential for practical application. Cut-off values provide clear, actionable thresholds for decision-making in meat and carcass authentication, facilitating their use by industry stakeholders in real-world settings. Despite their practical importance, questions regarding which biomarkers to combine, which to prioritize, and how to define cut-off values for meat and carcass authentication decisions remain largely unexplored.

Machine learning emerges as a promising approach to address these challenges. García-Infante et al. (2024) successfully used machine learning algorithms to authenticate 3 lamb diets using fatty acids and volatile compounds, while Birkinshaw, Sutter, Nussbaum, Kreuzer, and

<footnote><sup>a</sup> Université Clermont Auvergne, INRAE, VetAgro Sup, UMR12133 Herbivores, 63122 St-Genès-Champanelle, France</footnote>

<footnote><sup>b</sup> PEGASE, INRAE, Institut Agro, 35590 Saint-Gilles, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: Sophie.prache@inrae.fr (S. Prache).</footnote>
Reidy (2024) used it to authenticate the share of grass pasture in dairy cow's diet from milk MIR spectra. However, these approaches do not define cut-off values for authentication decisions, limiting their practical utility in decision-making.

Here we applied a machine learning approach, i.e. decision trees, to support decisions on meat and carcass authentication. Decisions trees are particularly able to i) rank the different biomarkers on importance for inclusion in the authentication process, ii) identify combinations of biomarkers that give the best authentication process, and iii) define cut-off values for the biomarkers. These features make decision trees particularly well-suited for developing practical tools for meat authentication. While recent studies have applied the decision tree approach to identify combinations of farming practices that result in the best intrinsic quality of meat (Monteils, Sibra, & Laurent, 2021) or milk (Rey-Cadilhac, Ferlay, Gelé, Léger, & Laurent, 2023), to our knowledge, this method has not yet been applied with the specific aim of identifying biomarkers combinations and providing practical cut-off values for meat authentication. To address this gap, we used a dataset from a previous experiment that investigated the effect of lucerne pasture-finishing duration on lamb meat composition (Prache, Rey-Cadilhac, & Prunier, 2023), and the authentication of those finishing durations from spectral data (Prache et al., 2024; Rey-Cadilhac, Andueza, & Prache, 2024; Rey-Cadilhac, Andueza, & Prache, 2025). The present paper adopts a more comprehensive approach by utilizing decision trees to analyse the entire dataset, identifying biomarker combinations and their cut-off values to authenticate the duration of lucerne pasture-finishing. The dataset included measurements of perirenal fat (PF) skatole and indole content, the colour coordinates of PF, dorsal fat (DF) and longissimus thoracis et lumborum (LTL) muscle, and an indicator of PF and DF carotenoid content calculated from the fat reflectance spectrum (Prache et al., 2003; Prache & Theriez, 1999), in lambs pasture-finished on lucerne for 0, 21, 42, or 63 days before slaughter. We then used decision tree models to classify lambs of the different treatment groups from those measurements. Recognizing that decision trees can be prone to overfitting and instability (Breiman, 1996), we also developed random forest models to strengthen the robustness of our results. This study highlights how decision trees, supported by random forests, can serve as practical tools for meat authentication by providing interpretable results, reliable biomarker combinations, and actionable cut-off values.

## 2. Material and methods

The datasets used in this paper come from datasets previously published in Prache et al. (2023 and 2024). The experimental design and description of the datasets are briefly presented here.

### 2.1. Experimental design

The experiment took place at the INRAE's 'Herbipôle' experimental farm (doi: 10.15454/1.5572318050509348E12) over 3 consecutive years, under a protocol approved by the French Ministry of Higher Education, Research and Innovation ethics committee (committee approval #14289-2017042411419479V4). Each year, four groups of 18–19 male Romane lambs were monitored: one group of lambs that were not pasture-finished but fed on grain in the barn ('0d' group), and three groups of lambs that were first reared in the barn on grain then finished on lucerne pasture for the final 21, 42, or 63 days before slaughter ('21d' group, '42d' group, and '63d' group, respectively). All lambs were born in April, and their slaughter dates were planned so that they were all aged around 5 months old at slaughter. These lambs came from a total of 99 ewes and 15 rams over the two experimental years whose data were used in this study.

Indoors, the lambs were all kept in a single group and fed a commercial concentrate with ad libitum access to hay, water and salt blocks. Level of concentrate fed to lambs kept in-barn was adjusted to obtain a similar pattern of growth between concentrate-fed and pasture-fed

lambs. Outdoors, the lambs were all kept in the same lucerne plot to limit potential confounding factors linked to sward characteristics. More details on animal and pasture management can be found in Prache et al. (2023).

The lambs were slaughtered at the INRAE's experimental abattoir as described in Prache et al. (2023). The fresh carcasses were kept at 4 °C until 24 h post-mortem before collection and analysis of PF, DF and LTL muscle as described in Prache et al. (2023 and 2024).

### 2.2. Description of the dataset

The datasets used in this paper come from the first two years of the experiment described above, and were previously published in Prache et al. (2023) (years 1 and 2 of the experiment) for PF skatole and indole content, and in Prache et al. (2024) for PF and DF lightness (L*), redness (a*), yellowness (b*), chroma, hue angle, and absolute value of the mean integral (AVMI, i.e. the mean area under the curve of the translated reflectance spectrum between 450 and 510 nm), which is an indicator of fat carotenoid content as described in Prache et al. (2003) (see Supplementary Material 1 for the AVMI calculation method), and LTL muscle L*, a*, b*, chroma and hue angle. Details of the measurement equipment and methods used can be found in the above papers and in Supplementary Material 1. In total, the database included 35, 36, 36 and 34 lambs in groups 0d, 21d, 42d and 63d, respectively.

### 2.3. Statistical analysis

#### 2.3.1. ANOVA

Analytical and spectrocolorimetric data were subjected to ANOVA followed by a Tukey test for pairwise comparisons, with treatment group as a fixed factor and year as a random factor, where applicable. All statistical analyses were performed using R software version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria).

#### 2.3.2. Decision trees

Initially, the dataset was randomly divided into a training set (comprising 70 % of individuals) and a testing set (comprising 30 % of individuals), ensuring that the treatment groups were evenly distributed between the two sets. Each decision tree was developed on the training set to predict the treatment groups from biomarkers compounds/variables measured on animal tissues, using the 'Classification And Regression Tree' (CART) decision tree algorithm (Breiman, Friedman, Stone, & Olshen, 1984) bundled with the cart package in R (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria) (Fig. 1). The decision tree algorithm divides all the data concentrated at the root node at the top of the tree into two child nodes, based on an explanatory variable (splitter) and a cut-off value for the variable (splitting value) that together best separate the treatment groups measured by Gini index (an index of purity; De'ath & Fabricius, 2000). For a node with n individuals and K possible treatment groups, the Gini index is calculated as:

$$ \text{Gini index} = 1 - \sum_{i=1}^{K} \left( \frac{n_i}{n} \right) $$

The process is iteratively repeated for each child node until additional splits do not further increase the purity. The final nodes, called leaves, determine the predicted class (Fig. 1). To avoid over-fitting, this exhaustive tree is then pruned using the complexity parameter, which minimizes the cross-validated error. Finally, the decision tree is validated on the testing set and its performance is assessed using its accuracy, which represents the percentage of correctly classified lambs, and the percentage of misclassified lambs in each treatment group of the testing set. The structure of the R decision tree script can be found in Supplementary material 2A.

The decision tree ranks biomarkers according to their importance,
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Example of a decision tree. X denotes the splitting variable and c denotes the variable's splitting value. At the top, the first node (parent node) is called the root. It splits into child nodes, also called branches, and the final child nodes are called leaves. The treatment groups are better separated in the child nodes than in the parent node. The vertical depth of each split is proportional to the variation explained by that split. Variables at the top of the tree are considered more influent on the predicted variable that variables lower in the tree. In each final box, the information in the first line refers to the predicted class, and the number in the bottom line refers to the number of lambs in that leaf. The observed distribution of lambs by class in each leaf is shown below.</figcaption></figure>

with those at the root of the tree being the most important and those at lower levels down to the leaves being less important. To test the information gain or loss (in terms of accuracy) of decision trees applied at the abattoir or at the point-of-sale (i.e. retail meat cuts), we set up different scenarios for using the available measurements. We thus constructed several types of decision trees to take into account the different animal tissues available or not at the time of classification (i.e., on the carcass at the abattoir or on a piece of meat at point-of-sale) and the ease of measurement of biomarker compounds (i.e., measurements possible on a slaughter line or measurements that require tissue sampling and laboratory analysis). A first decision tree (DT1) considered all the 19 variables measured in the experiment (see Table 1). A second decision tree (DT2) considered only the variables measured on PF, on the rationale that i) sampling PF does not devalue the carcass, ii) PF can always be readily sampled, even if the animal is lean, iii) PF has a higher carotenoid concentration than DF (Dian, Chauveau-Duriot, Prado, & Prache, 2007; Kirton, Crane, Paterson, & Clare, 1975; Priolo, Micol, Agabriel, Prache, & Dransfield, 2002) and its pattern of change with duration of pasture-finishing is more interesting for authentication purposes than in DF (Prache et al., 2024), and iv) PF generally has a higher skatole concentration than DF (Devincenzi, Prunier, Meteau, Nabinger, & Prache, 2014; Devincenzi, Prunier, Meteau, & Prache, 2019; Eiras, Prunier, Meteau, do Prado, & Prache, 2022), although the reverse has sometimes been observed (Rivaroli, Prunier, Meteau, do Prado, & Prache, 2019). A third decision tree (DT3) considered only the spectrocolorimetric variables measured on PF (colour coordinates and fat reflectance spectrum), as these variables can be measured at speeds fast enough to keep pace with the line speeds of carcasses in a commercial abattoir. Again, PF was chosen for the same reasons as above. A fourth decision tree (DT4) considered only the spectrocolorimetric variables measured on DF and LTL muscle (colour coordinates and fat reflectance spectrum), as they were the only variables measured in this experiment that are measurable at the point of sale. The four decision trees were

developed to discriminate the four treatment groups (Table 2, Fig. 2) or to discriminate stall-fed lambs from lambs pasture-finished on lucerne (Table 3, Figs. 3, 4 and 5).

### 2.3.3. *Random forest*

As the decision tree can be subject to overfitting and instability (Breiman, 1996), we also developed random forest models to consolidate the results, using R's *randomForest* package. Random forest improves the stability and accuracy of decision tree predictions (Breiman, 1996), but at the expense of interpretability (Breiman, 2001), the method being often considered as a 'black-box' method. In a random forest model, the dataset is randomly sampled with replacement to create bootstrap samples, which are used to train multiple deep decision trees independently (here the number of trees was set to 500). The final prediction is made by aggregating the outputs of these trees through majority voting, a process known as bagging (or bootstrap aggregation). This technique reduces variance by leveraging diverse subsets of data while maintaining the bias of the ensemble, resulting in a more robust model. The random forest model performance was evaluated using Out-of-Bag (OOB) error and accuracy. During the bootstrap sampling process, a part of the original dataset is not included in each bootstrap sample (OOB sample). After training a decision tree on its respective bootstrap sample, the model is tested on its corresponding OOB samples to evaluate its performance. The OOB error is calculated as the average prediction error for all OOB samples across all trees in the random forest. The variable importance measures the contribution of each predictor variable to overall model performance. As explained for decision trees, when a variable is used to divide a node in a decision tree, it reduces the impurity (or Gini index) at that location. The total reduction in impurity brought about by a variable is calculated over all the trees in the forest. This sum is normalized to give a relative importance score for each variable called mean decrease Gini. A similar calculation is made for the accuracy and gives a relative importance score for each variable called
<caption>**Table 1**</caption>

Characteristics of the dataset used: mean (range) values for the different variables measured on perirenal and dorsal fat and *longissimus thoracis et lumborum* muscle in lambs pasture-finished on lucerne for varying durations before slaughter.

<table><thead><tr><th rowspan="2">Characteristic<sup>1</sup></th><th colspan="4">Treatment groups<sup>2</sup></th><th rowspan="2">SEM</th><th rowspan="2">P-value<sup>3</sup></th></tr><tr><th>0d</th><th>21d</th><th>42d</th><th>63d</th></tr></thead><tbody><tr><td>N</td><td>35</td><td>36</td><td>36</td><td>34</td><td>-</td><td>-</td></tr><tr><td colspan="7"><strong>Perirenal fat</strong></td></tr><tr><td>Skatole (µg/g liquid fat)</td><td>0.07b (0.01-0.72)</td><td>0.19a (0.05-0.38)</td><td>0.15a (0.01-0.32)</td><td>0.22a (0.06-0.54)</td><td>0.010</td><td>&lt;0.001</td></tr><tr><td>Indole (µg/g liquid fat)</td><td>0.04c (0.01-0.16)</td><td>0.11ab (0.01-0.32)</td><td>0.10b (0.01-0.30)</td><td>0.15a (0.01-0.43)</td><td>0.007</td><td>&lt;0.001</td></tr><tr><td>Lightness (L*)</td><td>73.7c (67.4-77.4)</td><td>75.5b (69.9-80.2)</td><td>76.2ab (72.2-80.7)</td><td>77.0a (71.7-80.5)</td><td>0.21</td><td>&lt;0.001</td></tr><tr><td>Redness (a*)</td><td>3.0a (0.4-7.4)</td><td>3.0a (0.5-6.2)</td><td>2.7ab (0.0-5.0)</td><td>2.1b (0.5-5.0)</td><td>0.10</td><td>0.008</td></tr><tr><td>Yellowness (b*)</td><td>10.6b (7.9-15.9)</td><td>12.3a (7.5-19.7)</td><td>13.0a (8.9-18.0)</td><td>13.3a (8.7-17.3)</td><td>0.20</td><td>&lt;0.001</td></tr><tr><td>Chroma</td><td>11.1b (7.9-17.2)</td><td>12.7a (7.5-19.9)</td><td>13.3a (9.4-18.7)</td><td>13.5a (8.9-18.0)</td><td>0.20</td><td>&lt;0.001</td></tr><tr><td>Hue angle</td><td>74.5c (59.3-87.5)</td><td>76.4bc (66.6-86.0)</td><td>78.1ab (70.3-90.0)</td><td>81.0a (73.8-87.6)</td><td>0.45</td><td>&lt;0.001</td></tr><tr><td>AVMI<sup>4</sup> (units)</td><td>130c (78-206)</td><td>224b (117-500)</td><td>268 ab (148-459)</td><td>291a (88-445)</td><td>8.1</td><td>&lt;0.001</td></tr><tr><td colspan="7"><strong>Dorsal fat</strong></td></tr><tr><td>Lightness (L*)</td><td>64.4c (57.8-69.7)</td><td>68.7a (59.6-74.5)</td><td>67.5ab (59.1-74.5)</td><td>66.7b (61.1-72.6)</td><td>0.29</td><td>&lt;0.001</td></tr><tr><td>Redness (a*)</td><td>3.4a (-0.1-6.9)</td><td>1.6b (-3.5-6.3)</td><td>2.4ab (-3.1-8.6)</td><td>1.8b (-3.8-5.4)</td><td>0.20</td><td>&lt;0.001</td></tr><tr><td>Yellowness (b*)</td><td>15.0a (9.8-18.3)</td><td>13.1b (6.1-18.8)</td><td>13.9ab (1.4-21.5)</td><td>14.3ab (5.0-23.0)</td><td>0.36</td><td>0.044</td></tr><tr><td>Chroma</td><td>15.5a (9.8-18.7)</td><td>13.4b (7.0-18.8)</td><td>14.3ab (3.4-22.2)</td><td>14.6ab (5.4-23.7)</td><td>0.26</td><td>0.030</td></tr><tr><td>Hue angle</td><td>77.5b (64.5-90.3)</td><td>84.5a (68.6-119.5)</td><td>82.9ab (60.7-155.6)</td><td>84.9a (71.7-126.8)</td><td>1.02</td><td>0.007</td></tr><tr><td>AVMI<sup>4</sup> (units)</td><td>119b (67-207)</td><td>169a (63-332)</td><td>174a (21-329)</td><td>181a (57-301)</td><td>5.0</td><td>&lt;0.001</td></tr><tr><td colspan="7"><strong>Longissimus thoracis et lumborum muscle</strong></td></tr><tr><td>Lightness (L*)</td><td>32.9c (24.9-38.6)</td><td>33.9bc (27.0-39.7)</td><td>34.9ab (30.9-38.8)</td><td>33.6bc (29.7-39.1)</td><td>0.22</td><td>0.010</td></tr><tr><td>Redness (a*)</td><td>7.3b (4.9-9.9)</td><td>8.0ab (5.8-10.1)</td><td>8.3a (4.9-11.2)</td><td>8.5a (5.0-10.6)</td><td>0.11</td><td>&lt;0.001</td></tr><tr><td>Yellowness (b*)</td><td>11.1b (8.5-13.3)</td><td>11.5ab (9.6-13.2)</td><td>11.8a (10.7-13.6)</td><td>11.5ab (9.0-13.4)</td><td>0.08</td><td>0.011</td></tr><tr><td>Chroma</td><td>13.3b (10.5-16.0)</td><td>14.0ab (11.9-16.2)</td><td>14.4a (11.8-17.6)</td><td>14.3a (10.3-17.0)</td><td>0.11</td><td>&lt;0.001</td></tr><tr><td>Hue angle</td><td>56.8a (44.0-65.6)</td><td>55.2ab (49.6-61.6)</td><td>54.9ab (48.2-65.6)</td><td>53.6b (47.8-62.1)</td><td>0.35</td><td>0.016</td></tr></tbody></table>

<caption>**Table 2**</caption>

Accuracy of discrimination of the four treatment groups (0d vs 21d vs 42d vs 63d<fnref n="1" />).

<table><thead><tr><th rowspan="2">Decision tree (DT); Random forest (RF)</th><th colspan="2">Variables included</th><th rowspan="2">Accuracy (%)</th><th colspan="4">Class<fnref n="1" /> error rate (%)</th></tr><tr><th>Tissue<fnref n="3" /></th><th>Analyses<fnref n="2" /></th><th>0d</th><th>21d</th><th>42d</th><th>63d</th></tr></thead><tbody><tr><td>DT1</td><td>PF, DF,</td><td rowspan="2">L<sup>*</sup>, a<sup>*</sup>, b<sup>*</sup>, chroma, hue angle (all tissues); AVMI (PF, DF); skatole and indole content (PF)</td><td>43.6</td><td>0.0</td><td>80.0</td><td>70.0</td><td>77.8</td></tr><tr><td>RF1</td><td>LTL</td><td>57.1</td><td>2.9</td><td>44.4</td><td>75.0</td><td>48.5</td></tr><tr><td>DT2</td><td>PF</td><td rowspan="2">L<sup>*</sup>, a<sup>*</sup>, b<sup>*</sup>, AVMI, chroma, hue angle, skatole and indole content</td><td>50.0</td><td>0.0</td><td>50.0</td><td>100.0</td><td>50.0</td></tr><tr><td>RF2</td><td></td><td>54.3</td><td>5.7</td><td>52.8</td><td>75.0</td><td>48.5</td></tr><tr><td>DT3</td><td>PF</td><td rowspan="2">L<sup>*</sup>, a<sup>*</sup>, b<sup>*</sup>, AVMI, chroma, hue angle</td><td>40.0</td><td>30.0</td><td>80.0</td><td>80.0</td><td>50.0</td></tr><tr><td>RF3</td><td></td><td>39.7</td><td>17.1</td><td>77.8</td><td>86.1</td><td>58.8</td></tr><tr><td>DT4</td><td>DF, LTL</td><td rowspan="2">L<sup>*</sup>, a<sup>*</sup>, b<sup>*</sup>, chroma, hue angle (both tissues); AVMI (DF)</td><td>33.3</td><td>40.0</td><td>80.0</td><td>50.0</td><td>100.0</td></tr><tr><td>RF4</td><td></td><td>49.3</td><td>20.0</td><td>44.4</td><td>72.2</td><td>66.7</td></tr></tbody></table>

mean decrease accuracy. The higher the value of mean decrease accuracy or mean decrease Gini score, the higher the importance of the variable in the model. Random forest models 1 to 4 (RF1, RF2, RF3 and RF4) were developed using the same variables than for DT 1 to 4, respectively. The structure of the R random forest script can be found in Supplementary material 2B.

## 3. Results and discussion

### 3.1. Authentication of lucerne pasture-finishing duration (discrimination of the 4 treatment groups)

None of the 4 decision trees and random forest models developed reliably discriminated between the 4 treatment groups. Accuracy was in

the range 33%–57% (Table 2) depending on the decision tree/random forest model (i.e., depending on the tissues and variables initially included in the decision tree), which is not different from a random classification. Note, however, that although the first 2 decision trees/random forest models poorly classified pasture-finished lambs (>40% lambs misclassified), they correctly classified stall-fed lambs (the proportion of misclassified stall-fed lambs was 0.0% for DT1 and DT2, and 2.9% and 5.7% for RF1 and RF2, respectively). We therefore decided to test the reliability of discrimination of stall-fed lambs (0d from lucerne pasture-finished lambs (21d + 42d + 63d), as done by Rey-Cadilhac et al. (2024 and 2025) using spectral methods in the same experiment. Of all the variables tested for the development of DT1 (all the variables of all tissues), the ones selected by the algorithm were mostly measured on PF (Fig. 2). Out of all the variables used in this experiment, PF skatole

<footnote n="1">¹ Mean (minimum-maximum).</footnote>

<footnote n="2">² 0d: concentrate-fed lambs; 21d, 42d, 63d: lambs grazed on lucerne for 21, 42 and 63 days before slaughter, respectively.</footnote>

<footnote n="3">³ Within a row, means with different letters are significantly different at *P* ≤ 0.05.</footnote>

<footnote n="4">⁴ AVMI: absolute value of the mean integral of the translated reflectance spectrum between 450 and 510 nm, an indicator of fat carotenoid content.</footnote>

<footnote n="1">¹ 0d: concentrate-fed lambs; 21d, 42d, 63d: lambs grazed on lucerne for 21, 42 and 63 days before slaughter, respectively.</footnote>

<footnote n="2">² AVMI: absolute value of the mean integral of the translated reflectance spectrum between 450 and 510 nm, an indicator of fat carotenoid content; L*: lightness; a*: redness; b*: yellowness.</footnote>

<footnote n="3">³ PF: perirenal fat; DF: dorsal fat; LTL: *longissimus thoracis et lumborum* muscle.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Decision tree 1 obtained using all the analytical and spectrocolorimetric variables measured on perirenal fat (PF), dorsal fat, and *longissimus thoracis et lumborum* (LTL) muscle to discriminate the four treatment groups (0d, 21d, 42d, 63d).</figcaption></figure>

0d: concentrate-fed lambs; 21d, 42d, 63d: lambs grazed on lucerne for 21, 42 and 63 days before slaughter, respectively.

AVMI: absolute value of the mean integral of the translated reflectance spectrum of the fat between 450 and 510 nm, considered an indicator of fat carotenoid content.

<caption>**Table 3**</caption>

Accuracy of discrimination between stall-fed lambs and pasture-finished lambs (0d vs 21d + 42d+63d).<fnref n="1" />

<table><thead><tr><th rowspan="2">Decision tree (DT); Random forest (RF)</th><th colspan="2">Variables included</th><th rowspan="2">Accuracy (%)</th><th colspan="2">Class<fnref n="1" /> error rate (%)</th></tr><tr><th>Tissue<fnref n="3" /></th><th>Analyses<fnref n="2" /></th><th>0d</th><th>21d + 42d + 63d</th></tr></thead><tbody><tr><td>DT1</td><td>PF, DF,</td><td rowspan="2">L*, a*, b*, chroma, hue angle (all tissues); AVMI (PF, DF); skatole and indole content (PF)</td><td>95.1</td><td>10.0</td><td>3.2</td></tr><tr><td>RF1</td><td>LTL</td><td>95.7</td><td>2.9</td><td>4.8</td></tr><tr><td>DT2</td><td>PF</td><td rowspan="2">L*, a*, b*, AVMI, chroma, hue angle, skatole and indole content</td><td>95.1</td><td>10.0</td><td>3.2</td></tr><tr><td>RF2</td><td></td><td>92.9</td><td>11.4</td><td>5.7</td></tr><tr><td>DT3</td><td>PF</td><td rowspan="4">L*, a*, b*, AVMI, chroma, hue angle</td><td>90.2</td><td>10.0</td><td>9.6</td></tr><tr><td>RF3</td><td></td><td>87.2</td><td>22.9</td><td>9.4</td></tr><tr><td>DT4</td><td>DF, LTL</td><td>85.4</td><td>30.0</td><td>9.7</td></tr><tr><td>RF4</td><td></td><td>84.3</td><td>40.0</td><td>7.6</td></tr></tbody></table>

content ranked as the most important (at the root of the tree) and AVMI of perirenal fat (AVMI<sub>PF</sub>) as the second most important to consider in the classification process (second level variable in the tree). These two variables made it possible to discriminate stall-fed lambs (0d lambs) from lucerne pasture-finished lambs (Fig. 2). The threshold values for PF skatole content and AVMI<sub>PF</sub> were 0.05 µg/g liquid fat and 126 units, respectively. Lambs with a PF skatole content below 0.05 µg/g liquid fat were classified as stall-fed and lambs with a PF skatole content ≥0.05 µg/g liquid fat and an AVMI<sub>PF</sub> <126 units were also classified as stall-fed, whereas lambs with a PF skatole content ≥0.05 µg/g liquid fat and AVMI<sub>PF</sub> ≥126 units were classified as pasture-finished on lucerne. The next variables included in the DT1 were again AVMI<sub>PF</sub> and PF skatole content as well as LTL muscle chroma, but they provided little useful information for further discriminating lucerne pasture-finished lambs according to duration of pasture-finishing (Fig. 2). The most important variables in RF1 were also first AVMI<sub>PF</sub>, then PF skatole content, confirming the result of DT1 (Supplementary material 3 A). Results are not shown for DT3 and DT4, but note that DT4 (constructed from spectrocolorimetric variables measured on DF and LTL muscle, Table 2) gave more classification errors for all treatment groups, even if the class error was lower for stall-fed lambs (20.0 % misclassified 0d lambs) than for lucerne pasture-finished lambs (44%–72 % misclassified 21d, 42d or 63d-group lambs; Table 2). Overall, the random forest models gave similar or better accuracy, confirming that the decision trees had not been over-fitted (Table 2).

<footnote n="1">¹ 0d: concentrate-fed lambs; 21d, 42d, 63d: lambs grazed on lucerne for 21, 42 and 63 days before slaughter, respectively.</footnote>

<footnote n="2">² AVMI: absolute value of the mean integral of the translated reflectance spectrum between 450 and 510 nm, an indicator of fat carotenoid content; L*: Lightness; a*: redness; b*: yellowness.</footnote>

<footnote n="3">³ PF: perirenal fat; DF: dorsal fat; LTL: *longissimus thoracis et lumborum* muscle.</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Decision tree 1 obtained using all the analytical and spectrocolorimetric variables measured on perirenal fat (PF) to discriminate stall-fed lambs from pasture-finished lambs.</figcaption></figure>

AVMI: absolute value of the mean integral of the translated reflectance spectrum of the fat between 450 and 510 nm, considered an indicator of fat carotenoid content.

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Variable importance plot obtained from random forest 1 (RF1) using all the analytical and spectrocolorimetric variables measured on perirenal fat (PF) to discriminate stall-fed lambs from pasture-finished lambs.</figcaption></figure>

AVMI: absolute value of the mean integral of the translated reflectance spectrum between 450 and 510 nm, an indicator of fat carotenoid content; L = L*: lightness; a = a*: redness; b = b*: yellowness.

PF: perirenal fat; DF: dorsal fat; LTL: longissimus thoracis et lumborum muscle.

## 3.2. Authentication of pasture-finishing on lucerne (discrimination of stall-fed lambs from pasture-finished lambs)

### 3.2.1. Decision trees/random forest models 1 and 2

DT1 and DT2 gave similar results when used for discriminating stall-

fed lambs from pasture-finished lambs (Table 3, Fig. 3). Accuracy reached 95.1 %, and 10.0 % of stall-fed lambs (0d lambs) and 3.2 % of pasture-finished lambs were misclassified. This was a good level of accuracy, comparable to that obtained by discriminating the same treatment groups using PF reflectance spectrum in the visible range (Rey-Cadilhac et al., 2024). Out of all the 19 variables used, PF skatole content and AVMI<sub>PF</sub> were the only ones selected in the classification process. PF skatole content was the most important discriminatory variable (Fig. 3). The threshold values for PF skatole content and AVMI<sub>PF</sub> were 0.045 µg/g liquid fat and 116 units, respectively. Lambs with a PF skatole content below 0.045 µg/g liquid fat were classified as stall-fed, and lambs with a PF skatole content ≥0.045 µg/g liquid fat and AVMI<sub>PF</sub> <116 units were also classified as stall-fed, whereas lambs with a PF skatole content ≥0.045 µg/g liquid fat and AVMI<sub>PF</sub> ≥116 units were classified as pasture-finished on lucerne. Random forest models 1 and 2 had similar accuracy (95.7 % and 92.9 %, respectively) and placed PF skatole content and AVMI<sub>PF</sub> as the most important variables, far ahead the other variables (Fig. 4 and Supplementary material 3B), confirming the results obtained with the decision trees.

These results confirmed the importance of PF skatole content and AVMI<sub>PF</sub> as variables for authenticating pasture-fed lamb, as already described in the literature (Prache, Huang, & Andueza, 2018 for AVMI<sub>PF</sub>; Prache et al., 2023 for PF skatole content). However, these studies also showed that these variables had some limitations that could probably be partly overcome by combining them and hence strengthen the accuracy of the discrimination. Note that out of all the 19 variables measured or computed in the three tissues, the two selected variables were both on the same tissue (PF), which makes implementation of the
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Decision tree 3 obtained using the spectrocolorimetric variables measured on perirenal fat (PF) to discriminate stall-fed lambs from pasture-finished lambs. AVMI: absolute value of the mean integral of the translated reflectance spectrum of the fat between 450 and 510 nm, considered an indicator of fat carotenoid content; L*: lightness.</figcaption></figure>

method easier to operationalize. If we consider only lambs finished on lucerne pasture for 63 days to have comparable conditions to Prache et al. (2018), the threshold cut-off value for discriminating stall-fed lambs from pasture-fed lambs for AVMI<sub>PF</sub> was 212 units (not shown), which is close to the value obtained by Prache et al. (2018) (224 units) on a large database of 1054 lambs.

One of the 10 exclusively stall-fed lambs of the testing set was misclassified as a pasture-finished lamb as it had a skatole content that was above the cut-off threshold (0.10 µg/g liquid fat). As this lamb had no particular growth events, the misclassification was probably linked to inter-individual variability in PF skatole content, as already reported (some stall-fed lambs stocked higher-than-usual amounts of skatole; Devincenzi, Prunier, Meteau, & Prache, 2019; Eiras et al., 2022) and confirmed by Prache et al. (2023) on the present dataset. Out of the 31 lucerne pasture-finished lambs of the testing set, one 42d lamb was misclassified as a stall-fed lamb as it had a low PF skatole content of 0.04 µg/g liquid fat. It had no particular growth events, and so here again, the misclassification was probably linked to inter-individual variability in PF skatole content, as already reported (Prache et al., 2018; Prache et al., 2023).

### 3.2.2. Decision tree/random forest model 3

This third decision tree for discriminating stall-fed lambs from pasture-finished lambs (Fig. 5) was based on variables that can be measured on-line at the abattoir and are therefore more readily available than those used to construct DT1 and DT2. DT3 had a cross-validation accuracy of 90.2 % (Table 3), and misclassified 10.0 % of stall-fed lambs and 9.6 % of lucerne pasture-finished lambs. This level of reliability was fairly good, but lost 4.9 % accuracy compared to DT1 and DT2, and the increase in class error rate mainly concerned lambs

pasture-finished on lucerne (proportion of lucerne pasture-fed lambs that were misclassified reached 9.6 % for DT3 vs 3.2 % for DT1 and DT2; Table 3). Out of the 6 variables used, i.e., PF L*, a*, b*, AVMI, chroma and hue angle, AVMI<sub>PF</sub> and PF lightness were the ones selected in the classification process. AVMI<sub>PF</sub> was the most important discriminatory variable (Fig. 5). The threshold values for AVMI<sub>PF</sub> were 187 units for the first node and 115 units for the third node, and 75 for PF lightness. Lambs with an AVMI<sub>PF</sub> ≥187 units were classified as lucerne pasture-finished whereas lambs with an AVMI<sub>PF</sub> <115 units were classified as stall-fed. Lambs with AVMI<sub>PF</sub> <187 units and PF lightness <75 were classified as stall-fed, and lambs with PF lightness ≥75 and AVMI<sub>PF</sub> ≥115 units were classified as lucerne pasture-finished (Fig. 5). These results confirm the value of the AVMI<sub>PF</sub> variable for authenticating pasture-feeding in lamb, as mentioned above. The inclusion of the second variable (PF lightness) in DT3 was not expected, but it contributed less to discrimination. This outcome is consistent with Devincenzi et al. (2019) who observed higher PF lightness in pasture-fed than stall-fed lambs (the difference was not significant, but only 12 lambs per treatment group were used). Rivaroli et al. (2019) also observed significantly higher DF lightness in pasture-fed than stall-fed lambs (they did not measure PF lightness). This difference in fat lightness may be explained by better protection of adipose tissue against oxidation by the antioxidants contained in green pasture herbage (Gruffat, Durand, Rivaroli, do Prado, & Prache, 2020).

Out of the 10 exclusively stall-fed lambs of the testing set, 1 was misclassified as a lucerne pasture-finished lamb as its AVMI<sub>PF</sub> reached 204 units. Out of the 31 pasture-finished lambs of the testing set, 3 were misclassified as stall-fed (two 21d lambs, one 63d lamb). The two misclassified 21d lambs lost weight during the pasture-finishing period or gained very little (320 g). These lambs probably did not have sufficient
time to replenish their adipose tissue reserves on pasture, and so there was relatively little marking of adipose tissue by pasture, which may at least partly explain the low-to-moderate AVMI<sub>PF</sub> values (135–168 units). The 63-d misclassified lamb had no particular growth events, and so the misclassification here was probably linked to inter-individual variability in AVMI<sub>PF</sub> (159 units), as mentioned above. Random forest model 3 had similar accuracy (87.2 %) and placed AVMI<sub>PF</sub> as the most important variable, far ahead the other variables (Supplementary material 3C) as well as PF lightness in second position, confirming the results obtained with DT3.

### 3.2.3. Decision tree/random forest model 4

The fourth decision tree (Fig. 6) designed for distinguishing stall-fed from lucerne pasture-finished lambs was based on variables measured in this experiment that are readily measurable at the point-of-sale (spectrocolorimetric variables measured on DF and LTL muscle). DT4 had a cross-validation accuracy of 85.4 % (Table 3), and misclassified 30.0 % stall-fed lambs and 9.7 % lucerne pasture-finished lambs. Thus, it lost 4.8 % accuracy compared to DT3 that was built using the same variables but measured on PF (which is no longer available for sampling once the meat reaches the point of sale). The proportion of stall-fed lambs that were misclassified was higher with DT4 than with DT3, while the proportion of lucerne pasture-finished lambs that were misclassified remained unchanged (Table 3). Out of the 11 variables used, AVMI of DF (AVMI<sub>DF</sub>) and DF redness ($a^*$) were the ones selected in the classification process (Fig. 6). The threshold values were 139 units for AVMI<sub>DF</sub> and 0.05 for DF redness. Lambs with an AVMI<sub>DF</sub> ≥139 units were classified as lucerne pasture-finished, and lambs with an AVMI<sub>DF</sub> <139 units and DF redness <0.05 were also classified as lucerne pasture-finished, whereas lambs with an AVMI<sub>DF</sub> <139 units and DF redness ≥0.05 were classified as stall-fed (Fig. 6). Differences in AVMI<sub>DF</sub> between pasture-finished lambs and stall-fed lambs are widely reported in the scientific literature. AVMI<sub>DF</sub> is higher in pasture-finished lambs (Dian

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Decision tree 4 obtained using the spectrocolorimetric variables measured on dorsal fat (DF) and *longissimus thoracis et lumborum* muscle to discriminate stall-fed lambs from pasture-finished lambs.

AVMI: absolute value of the mean integral of the translated reflectance spectrum of the fat between 450 and 510 nm, considered an indicator of fat carotenoid content; $a^*$: redness.</figcaption></figure>

et al., 2007; Prache et al., 2003; Prache et al., 2024; Prache & Theriez, 1999) due to the ingestion of carotenoid pigments contained in green pasture herbage. The results concerning DF redness were less expected, as previous studies observed no significant differences in subcutaneous fat redness between pasture-fed lambs and stall-fed lambs (Devincenzi et al., 2019; Priolo et al., 2002; Rivaroli et al., 2019). However, these studies observed a consistent trend of higher DF redness in stall-fed lambs than in lucerne pasture-finished lambs, which may be attributed to a higher accumulation of haem pigments and end-products of polyunsaturated fatty acid peroxidation (Prache, Aurousseau, Theriez, & Renerre, 1990). Random forest model 4 had similar accuracy (84.3 %) and similarly placed AVMI<sub>DF</sub> as the most important variable, but the further sequence of variables involved DF and LTL muscle lightness and LTL muscle redness, Fig. 7) that were not present in the DT4, thus suggesting that the capacity of these variables and of DF redness to deliver information for meat authentication needs to be evaluated further.

## 3.3. *Genericity and practical application of the results*

In the particular dataset used in this study, the biomarker compounds/variables measured reliably discriminated lucerne pasture-finished lambs from stall-fed lambs but were not able to reliably authenticate duration of pasture-finishing on lucerne. This outcome confirms previous results obtained using spectral methods (Rey-Cadilhac et al., 2024; Rey-Cadilhac et al., 2025).

However, the conditions of this experiment were particularly favorable to the expression of differences between pasture-finished and grain-fed lambs, and the results obtained thus warrant further validation on a larger and more diverse database. Indeed, the grazing conditions used here included good-quality lucerne (Prache et al., 2024) that was offered *ad libitum* on pasture, thus favouring high levels of skatole and carotenoid pigments (AVMI) in fatty tissues. Furthermore, we used the Romane breed, which has been shown to have a high capacity to absorb and store carotenoid pigments (Macari, Graulet, Andueza, & Prache, 2017), even though the study by Prache et al. (2018) found no difference in discrimination thresholds of AVMI<sub>PF</sub> for pasture-fed vs stall-fed lambs between 3 breeds of sheep. Moreover, the range of biomarker compounds and variables potentially included to construct decision trees for product authentication could be extended beyond the variables measured in this study, such as by adding fatty acid composition (Cama-Moncunill, Moloney, Röhrle, Luciano, & Monahan, 2021; Moloney,

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Variable importance plot obtained from random forest 4 (RF4) using the spectrocolorimetric variables measured on dorsal fat (DF) and *longissimus thoracis et lumborum* (LTL) muscle to discriminate stall-fed lambs and pasture-finished lambs.</figcaption></figure>

AVMI: absolute value of the mean integral of the translated reflectance spectrum between 450 and 510 nm, an indicator of fat carotenoid content; $L = L^*$: lightness; $a = a^*$: redness; $b = b^*$: yellowness.

PF: perirenal fat; DF: dorsal fat; LTL: *longissimus thoracis et lumborum* muscle.
Keane, Monahan, & O'Callaghan, 2022; Varrà et al., 2023), stable isotope ratios (Devincenzi, Delfosse, et al., 2014; Moloney, O'Riordan, Schmidt, & Monahan, 2018), other volatile compounds (García-Infante et al., 2024; Horcada et al., 2024), muscle mineral composition (Whyte et al., 2024), and/or elemental profiles (Varrà et al., 2023).

The decision tree method presented here offers several advantages. It makes it possible to rank the importance of compounds/variables to be taken into account for authentication, i.e. PF skatole content and AVMI<sub>PF</sub> for authentication at the abattoir or AVMI<sub>DF</sub> and DF *a*\* for authentication at the point of sale, which has never been done before, and to account for loss of accuracy when a given compound/variable is no longer present in the analysis. The results show that DT4, which relies solely on data that are easily measured by spectrocolorimetry and are available at the point-of-sale, lost only around 5% accuracy compared to DT3, which is based on PF spectrocolorimetric data (available only at the abattoir). Additionally, DT4 showed an accuracy around 10% lower than DT1 and DT2, which incorporate all biomarkers on all tissues or both spectrocolorimetric and analytical biomarkers measured on PF (which are not always readily available and are only available at the abattoir). Despite this, DT4 maintained a good performance with 85.4% accuracy.

The method also offers further advantages in terms of operability, by indicating threshold values for biomarker compounds/variables to guide the classification decision. Few studies report threshold values for discriminating stall-fed lambs from pasture-finished lambs, making it difficult to discuss the thresholds values obtained in this study. Nevertheless, as discussed in the section on DT1 and RF1, a robust threshold for AVMI<sub>PF</sub> was determined by linear discriminant analysis in a study of over 1000 lambs (Prache et al., 2018), which was close to the one found for DT1. Furthermore, the threshold values determined in the DT<sub>s</sub> consider the combination of biomarkers. However, the limited number of individuals in this study does not allow us to establish definitive threshold values for the other variables measured, and these will need to be established and validated on larger databases to be set conclusively.

The DT method, compared to the RF method, allows easy interpretation of results and provision of a biological explanation of the underlying mechanisms. It would also enable potential users to explain how controls to authenticate the pastural origin of meat are carried out, on the basis of which biomarkers and threshold values, thus facilitating the societal acceptance of these controls.

## 4. Conclusion

This paper set out with a methodological objective of testing the relevance of two machine learning approaches (decision trees and random forest) for meat authentication by applying it to a set of potential biomarker compounds/variables measured during a previous experiment. The results confirmed perirenal fat skatole and carotenoid pigment content as the most valuable compounds/variables that can successfully authenticate lucerne pasture-finished lambs from stall-fed lambs, with 95.1 % of lambs correctly classified. However, AVMI and lightness of perirenal fat were also able to discriminate lucerne pasture-finished lambs from stall-fed lambs, with 90.2 % lambs correctly classified, while also offering the advantage of being rapid enough for use on real-world slaughterhouse lines. Spectrocolorimetric measurements on DF (AVMI<sub>DF</sub> and DF redness) provided less powerful but nevertheless non-negligible discriminatory power for use at points of sale. The accuracy and variables of importance obtained using random forest algorithm confirmed those results. The decision trees developed here also allowed to define cut-off values to guide authentication. One cut-off value was confirmed by a study on a large database, but others still need to be further validated. However, none of the decision trees developed was able to reliably authenticate duration of finishing on lucerne pasture. Future work could usefully investigate the inclusion of other compounds in the construction of the decision trees, such as fatty acid composition, volatile compounds, stable isotope ratios, muscle mineral composition, and elemental profiles in animal tissues.

## CRediT authorship contribution statement

**Lucille Rey-Cadilhac:** Writing – original draft, Software, Methodology, Investigation, Formal analysis. **Sophie Prache:** Writing – review & editing, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.

## Funding

This work was supported by the French Ministry of Agriculture, Agrifood and Forestry's Special Assignment Account for “Agricultural and Rural Development” (CASDAR), under project 19AIP5912 – REVABIO.

## Declaration of competing interest

None to declare.

## Acknowledgements

The authors thank L. Lavelle, C. Coustet and the staff of the INRAE's Herbipôle Experimental Unit, and S. Faure and C. Labonne from UMR Herbivores for their technical assistance at the abattoir. We also thank V. Bellet from the Institut de l'Elevage and C. Experton and S. Rouger from the Institut Technique de l'Agriculture Biologique for implementing and facilitating the REVABIO project.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.meatsci.2025.109852.

## Data availability

Data will be made available on request.

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