# Understanding wheat dough behavior through prediction of quality profiles with explainable Machine Learning

Mélanie Munch, Kamal Kansou, Flavien Alonzo, Luc Saulnier, Cedric Baudrit

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Mélanie Munch, Kamal Kansou, Flavien Alonzo, Luc Saulnier, Cedric Baudrit. Understanding wheat dough behavior through prediction of quality profiles with explainable Machine Learning. Journal of Cereal Science, 2025, 126, pp.104292. 10.1016/j.jcs.2025.104292. hal-05322419

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1 Understanding Wheat Dough Behavior Through Prediction of Quality Profiles with Explainable Machine Learning

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4 Melanie Munch<sup>a,c,*</sup>, Kamal Kansou<sup>b</sup>, Flavien Alonzo<sup>b</sup>, Luc Saulnier<sup>b</sup> and Cedric Baudrit<sup>a</sup>

5

6 <sup>a</sup>Université de Bordeaux, USC1368, Institut de Mécanique et d'Ingénierie (I2M), INRAE, 351

7 Cours de la Libération, Talence, 33405, France

8 <sup>b</sup>INRAE UR BIA, Imp. Yvette Cauchois, Nantes, 44300, France

9 <sup>c</sup>INRAE UMR STLO, 65 Rue de Saint-Brieuc, Rennes, 35000, France

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11 Corresponding author: Melanie Munch

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13 **Introduction**

14 A variety of measurements, based on the dosage of flour components (such as protein
15 content, damaged starch,...) or obtained from commercial tools (farinograph®, alveograph®)
16 are used to assess the breadmaking quality of wheat flour. To get closer to performance in a
17 bakery, baking tests are carried out by experienced bakers, in controlled conditions and at
18 small scale. They are generally the benchmarks that determine the baking quality of wheat
19 flour for professionals. Given the significant amount of resources they needed, a lot of
20 research works strived for predicting the results from more accessible measurements of
21 wheat characteristics (e.g. (Gabriel et al., 2017; Hermannseder et al., 2017; Ziegler et al.,
22 2025)).

23 The bread loaf volume, measured in all baking tests, is considered the primary criterion for
24 defining baking quality (Ziegler et al., 2025). Consequently, most studies have focused on
25 predicting loaf-specific volume, often supplemented with additional bread characteristics
26 such as weight, height, and crumb firmness (Gabriel et al., 2017; Rousu et al., 2003; Ziegler
27 et al., 2025). Other studies have aimed at predicting flour water absorption, which has
28 significant implications for baking quality (Dowell et al., 2008). Key breadmaking parameters
29 are also predicted to support decision-making, such as kneading time/power at peak, dough
30 development/stability during proofing (Hermannseder et al., 2017; Kansou et al., 2013;
31 Parenti et al., 2021) or kneading power over time (Hugon et al., 2023).

32 However, many types of bread across the world are not baked in pans, and for these breads,
33 focusing solely on loaf volume is a rather incomplete view of baking quality. This is
34 particularly true for French breads, the most well-known example being the "baguette". The
35 corresponding standardized baking test, AFNOR standard NF V03-716 (« AFNOR. 2023.
36 Flours of common wheat (Triticum aestivum L.) - French bread (pain courant français) baking
37 test. NF V03-716 », 2023), is routinely used in France to evaluate wheat flour quality for
38 breeders, millers, and bakers. Conducted by experienced bakers, this test includes subjective
39 scoring of several dough characteristics (such as elasticity, extensibility, consistency,
40 stickiness...) in addition to measuring the more conventional loaf volume. Decades of use of
41 this test produced a large amount of baking quality data, which remains underutilized in
42 research, mainly due to its subjective nature. Indeed, subjective scoring presents similarities
43 with sensory evaluation, including inter-individual variability and non-linearities, which make
44 prediction challenging (Ruan & Zeng, 2004).

45 To unlock this untapped potential usable for modelling baking quality, the data must be first
46 transformed. In a recent study, (Munch et al., 2024)) demonstrated that expert bakers
47 interpret the results of the baking test as quality profiles (QPs) and provided a data analysis
48 method to identify them. These QPs correspond to patterns within the data that experts rely
49 on for decision-making (Raufaste et al., 1998). Although the evaluation is subjective and the
50 criteria vary depending on the baking test, bakers consistently recognize only a few
51 consensual QPs, making these profiles valuable indicators of baking quality.

52 The use of data analysis tools to predict baking quality is now a well-established approach in
53 both research and the bakery industry. Simple linear regression and multivariate statistical
54 methods, such as Partial Least Square Regression, are standard techniques for developing
55 baking quality prediction models (Castro-Reigía et al., 2024). Machine Learning (ML)
56 techniques have also been applied in cereal science, notably Artificial Neural Networks
57 (Hermannseder et al., 2017) or ensemble methods (Price et al., 2025). One of the earliest
58 comparisons of multiple ML techniques applied to the bakery process can be found in
59 (Rousu et al., 2003).

60 The present work explores the prediction of QPs describing dough behaviors during the
61 baking test, based on wheat characteristics, using ML models. ML offers computational
62 techniques for linking the inputs and outputs of complex systems involving multidimensional
63 data and non-linear interactions, opening up new avenues for modelling food quality
64 (Mavani et al., 2022).

65 The application of ML to food science faces two main limitations: the lack of large datasets
66 and the relative opacity of the models, which can act as "black boxes" making their
67 outcomes difficult to interpret based on existing knowledge. While the data scarcity must be
68 addressed at the organizational level, the opacity issue is tackled in the field of explainable
69 AI (XAI) (Guidotti, 2021). Among the various methods, SHapley Additive exPlanation (SHAP)
70 (Lundberg & Lee, 2017) is model agnostic, meaning it can adapt to any ML model and
71 calculates SHAP values to explain the model's predictions.
The aim of this study is to propose a prediction model for wheat quality based on classical wheat quality criteria. Its novelty lies in the combinations of QP classes with an explainable ML approach. It uses an open dataset of 287 wheat samples (Munch et al., 2025), whose representativeness was validated against historical data from breadmaking assays collected over the past two decades. Several ML algorithms are evaluated based on their ability to predict QPs from technological measurements. SHAP values are employed to identify the key factors influencing the QP, suggesting improvements in the measurements used in baking. Finally, threshold values for two technological measurements, i.e., the index of elasticity and extensibility, are computed from the best model to demonstrate how ML can effectively support decision-making regarding crop and product quality.

## 2. Materials and Methods

### 2.1. Dataset Description

The dataset used in this study is an extract from an open-access dataset described in Munch et al. (2025). The full dataset comprises 290 wheat samples but three were excluded from the present study due to missing data. The dataset contains the results of 99 varieties grown across 38 locations in France and 1 in Belgium, during the years 2020 to 2022. These samples were selected and provided by Arvalis (the French technical institute for arable crops France), Limagrain (France), and Axiane Meunerie (France) to cover a wide range of end-use properties. All 287 samples were described using the measurements presented in the following sections.

#### 2.1.1 Technological measurements of wheat quality criteria

Wheat grain samples were characterized using the following routine measurements: **Protein Content** (near-infrared spectroscopy (NIRS), NF EN 15948); **Hardness** (NIRS; Particle Size Index calibration); **Damaged Starch** (SDMatic; Chopin, France); **Hagberg Falling Number** (Falling Number 1500, ISO 3093:2009); and **Gluten Index** (Glutomatic, NF EN ISO 21415-2).

White flour, with an average ash content of 0.55% (corresponding to Euro 550 flour type and American all-purpose flour, composed predominantly of the grain endosperm), was produced by milling wheat grains in 10 kg batches using a laboratory Buhler mill (Buhler MCKA, Buhler Group Company, Uzwil, Switzerland). Flours were characterized using Chopin alveograph measurements (Jødal & Larsen, 2021): P represents the maximum overpressure,
also called Tenacity; L represents the average abscissa to rupture, also called Extensibility; P/L is the Tenacity to Extensibility ratio; W represents the deformation energy, also known as Baking Strength; and le is the Elasticity Index (CHOPIN Alveogram, NF EN ISO 27971). Additionally, farinograph measurements were performed using the Mixolab system (NF V03-765) including Water Absorption, Development Time, Stability and Weakening Degree. Water Absorption refers to the percentage of water required for the dough to reach a torque of 1.1 Nm.

### 2.1.2 Wheat Quality profile

White flour produced from the 287 wheat samples was used to conduct standardized Baking Tests (NF-V03-716), which are widely employed to evaluate the quality of national wheat production in France. This test involves the assessment of 28 criteria characterizing dough and bread properties throughout the breadmaking process. Among these, only the bread volume is quantitatively measured, while the remaining criteria rely on sensory evaluations.

Each of these 28 criteria is assigned a score on either a seven-point or four-point scale, reflecting deviations from the expected standard of French bread. To facilitate the decision-making and data analysis, an overall baking score is then calculated as the weighted sum of these individual scores, ranging from 0 to 300, with scores higher than 250 indicating dough and bread quality meeting the standard for French bread. Lower baking scores, on the other hand, reflect important deviations from the expected standard and thus defective products.

The baking score does not convey information about the nature of the defect, because distinct defects can give the same baking score.

To address this limitation, in Munch et al., (2024) we presented a method to associate observations of specific wheat dough defects, called signatures, to an interpretation of the dough quality, called QP. While the main QPs of a food product are generally well known and shared by domain experts, the signatures of these QPs present the risk of depending on the evaluator's sensibility. Determining the signatures of the QPs is then an issue.

In the case of wheat dough behavior, the QPs and their signatures were obtained from a clustering analysis of database gathered by Arvalis over the past two decades years, representing 10,897 baking tests. Analysis revealed combinations of dough defects frequently observed together, highlighting three behaviours for domain experts:
* **No Defect:** Dough whose signature presents little to no deviation for all 28 criteria. It presents properties expected for French bread, typically associated with the highest baking scores (IQR 231-266).

* **Slackening:** Dough whose signature includes excessive slackening, stickiness, and lack of firmness and elasticity. This dough behavior is linked to a weak gluten network and corresponds to the lowest baking scores (IQR 127-177).

* **Resistant:** Dough whose signature includes excessive elasticity, insufficient extensibility and lengthening at shaping, and excessive bread section. This depicts a dough that withstands intensive mechanical action but tends to retract excessively. This profile is associated with an overly elastic gluten network and corresponds to low baking scores (IQR 177-245).

To capture a gradation, a total of seven QPs was proposed (Figure 1): Slackening (SI), Slackening-No Defect (SI-ND), No Defect-Slackening (ND-SI), No Defect (ND), No Defect-Resistant (ND-Re), Resistant-No Defect (Re-ND), Resistant (Re).

To assign baking results to one of the seven QPs, two quantitative scores are calculated in addition to the baking score: the Slackening Score and the Resistance Score, derived from weighted sums of QP-specific criteria (see Munch et al., 2024). QPs are then assigned using classification rules based on these three scores. This method, validated by experts (technical institutes, millers, bakers), correlates well with technological measurements. The article's goal is to test whether a predictive model can directly assign QPs, avoiding the standardized baking test, which is resource- and time-intensive. To do so QPs were automatically assigned for all 287 wheat samples based on this methodology.

### 2.1.3. Dataset representativity through multidimensional analysis

Assessing data variability is essential for building broadly applicable models, but it is difficult without a large reference dataset. In this study, such a dataset exists: over the past two decades, Arvalis has compiled 10,897 baking test results, providing a fair approximation of the variability in French wheat production.

The variability of the working dataset is compared to this baseline, using the Uniform Manifold Approximation and Projection (UMAP) method (McInnes et al., 2020). Unlike Principle Component Analysis (PCA) which focuses on maximizing the covariance between
variables, UMAP preserves local sample neighborhoods by constructing a high-dimensional graph, grounded in principles of Riemannian geometry and algebraic topology. Despite its complex theoretical foundation, the UMAP is an effective dimensionality reduction technique, capable of producing visually intuitive representations of data even in two-dimensional space.

For the UMAP analysis, all criteria used to compute the Baking Value in the standard NF-V03-716 test were included. Each one was normalized according to its distribution, and variables previously identified as key contributors to QPs (Munch et al., 2024) were assigned a customized weight of 2 (default weight being 1). To assess the representativeness of this study's dataset, we examined whether the projected points in the UMAP visualization exhibited a spatial distribution similar to that of the baseline dataset.

## 2.2. Machine Learning Settings

All experiments were implemented using the Python ML library Scikit-learn (Pedregosa et al., 2018) using the library's algorithm's default parameters.

### 2.2.1. Model Used

This study investigates the relationships between the 14 technological measurements (Section 2.1.1) and QPs (Section 2.2.2) by training predictive models. The QP is an ordinal variable with seven possible categories or "classes" (see Figure 1), framing this task as a classification problem. A variety of classification ML models exist, performing differently according to the bias-variance trade-off inherent to each method and the characteristics of the dataset (Wolpert & Macready, 1997).

Different algorithms are tested: **linear methods** (Ridge logistic regression and Stochastic Gradient Descent (SGD)), **kernel machines** (Support Vector Machine (SVM)), **local methods** (K-nearest Neighbors (KNN) and Decision Trees (DT)), and **ensemble methods** (Gradient Boosting (GB) and Random Forests (RF)). Linear methods generally offer robustness and interpretability but rely on strong assumptions about data structure and relationships. In contrast, ensemble methods are capable of capturing complex, non-linear interactions within the data, often achieving high predictive accuracy at the expense of model transparency, resulting in so-called "black-box" models.

### 2.2.2. Model Evaluation and Selection
Model performance is evaluated by the ability to predict the correct QP class from technological variables, using two metrics: the macro F-score (denoted as F<sub>m</sub>) and a custom weighted accuracy (denoted as Acc<sub>w</sub>).

The F-score is the harmonic mean calculated between the **precision** (fraction of correct predictions of a QP among the total predictions of this QP) and the **recall** (fraction of correct predictions of a QP among the actual number of this QP). This metric can be extended to multi-class problems by calculating the arithmetic mean of the F-score for each class, referred to as F<sub>m</sub>, to provide an indication of the average performance of the model.

In a multi-class classification, **accuracy** is typically defined as the proportion of correct predictions across all classes, offering a general measure of model correctness. However, since the QPs are ordinal, the severity of misclassification varies. It is preferable to predict a class that is fairly close rather than a class describing the opposite behaviour. This nuanced understanding of error is considered in the Acc<sub>w</sub> metric, which incorporates weighted penalties for different types of misclassifications based on their ordinal proximity. We define P<sub>i,j</sub> the number of samples j predicted as i. Given the set s<sub>QP</sub> of the seven ordered QPs and the weights w<sub>0</sub>=1, w<sub>1</sub>= 0.5 and w<sub>k</sub>= 0 if k > 1, our weighted accuracy Acc<sub>w</sub> is computed as

$$Acc_w = \frac{1}{|S_{QP}|} \sum_{j \in QP} \left( \frac{\sum_{i \in S_{QP}} w_k P_{i,j}}{\sum_{i \in S_{QP}} P_{i,j}} \right), \text{where } k = \text{range}(i,j) \quad (1)$$

In this study, Acc<sub>w</sub> illustrates the performance of a model in terms of closeness to the actual values, which is related to the user's expectations.

Both metrics range from 0 to 1, with higher values indicating better performance.

Each model is evaluated using a stratified 4-fold cross validation procedure repeated 50 times. While K-fold cross-validation is widely used, it is known to lack an unbiased estimator of variance (Bengio & Grandvalet, 2004), meaning that performance estimates can be influenced by the specific partitioning of the dataset. By repeating the cross-validation 50 times, the variability impact is reduced, enabling a reliable comparison of the different algorithms' predictive capabilities.

Specifically, for each repetition, the dataset is separated into four folds those proportions respect the original dataset QPs' distribution. Three folds are used for training, and the
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fourth isused for prediction and F<sub>m</sub> and Acc<sub>w</sub>'s computation, resulting in a total of 200
evaluations (4 folds x 50 repetitions) per model.

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2.2.3. Model Selection Process

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All fourteen technological variables (Section 2.1.1) were considered as candidate predictors.

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Variable selection used *Sequential Feature Selection* from Scikit-learn, a greedy algorithm that iteratively adds variables to maximize performance. The process stops when no variable improves the F<sub>m</sub> metric by more than 10<sup>-3</sup>, a threshold chosen given confidence intervals of about 10<sup>-2</sup>: we considered that below this limit, improvement was negligible.

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2.2.4. Model analysis using SHAP-Values

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Based on game theory principles, the Shapley Additive exPlanation (SHAP) method provides
insights into the contribution of each variable to model prediction (Lundberg & Lee, 2017).

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Thus, given a sample a SHAP value is computed for each input variable in order to determine
whether the sample's data aligns with the given QP **(positive SHAP value)** or not **(negative SHAP value)**). The procedure is applied for each of the seven QPs. The magnitude of the SHAP
value reflects the importance of the variable in the prediction decision.

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3. Results and Discussion

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3.1. Dataset representativity with regard to historical data

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The resulting UMAP projection reveals distinct clusters, reflecting groups of wheat samples
with similar baking performance profiles (Figure 2).

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The selected 287 samples are distributed across all major clusters, confirming
comprehensive coverage of the wheat diversity captured in the baseline. The presence of
several outliers (sample very different from the baseline) further suggests that the dataset
encompasses rare or extreme profiles, supporting its robustness and suitability for reliable
predictive modelling. This result shows that the samples in the dataset cover the overall
variability of the baking test results relative to the reference fairly well, which should
contribute to the models' ability to adapt to wheat not included in the training data.

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3.2 Dataset analysis

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Some variables show visible associations with specific QPs. For example, Slackening dough
generally exhibits low Elasticity Index (Ie<40) and low Gluten Index, whereas Resistant dough

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248 is characterized by high le values (le > 60) and Gluten Index values approaching 100%.
249 Protein Content (PC), often linked to dough behavior, also shows an increasing trend from
250 Slackening to Resistance profiles. However, the protein content distributions remain broad
251 across profiles, except for Slackening dough, which consistently shows low values.
252 Conversely, other variables, such as extensibility (L), do not exhibit clear relationships with
253 the QPs. To further investigate potential linear relationships among variables, a Principal
254 Component Analysis was performed. Results do not support the use of a linear framework
255 for the predictive task at hand. More details of these results are presented in supplementary
256 material.

257 **3.3 Quality Profiles Prediction**

258 **3.3.1. Model Selection**

259 Full model (all 14 technological variables) performances are presented in Table 1. The macro
260 F-score, F<sub>m</sub>, ranges from 0.39 to 0.49, indicating moderate performance in terms of precision
261 and recall. In contrast, the weighted accuracy, Acc<sub>w</sub>, ranges from 0.61 to 0.71, suggesting
262 that most classifiers achieve accuracy levels that are of practical interest to users. Worth
263 noting that since Acc<sub>w</sub> (Eq.1) differentiates between minor misclassifications (e.g., ND-SI
264 instead of No defect) and major errors (e.g., Slackening instead of Resistant), whereas F<sub>m</sub>
265 reflects a binary assessment, Acc<sub>w</sub> provides a more meaningful indication of model usability.

266 Among the tested algorithms, the SVM model achieves the best performance, with an F<sub>m</sub> of
267 0.49 and an Acc<sub>w</sub> of 0.71. These results are closely followed by Gradient Boosting and
268 Random Forest models, which is consistent with the strength of these algorithms in handling
269 non-linear datasets. In contrast, linear and local methods perform less well, underlining the
270 importance of models capable of capturing complex, non-linear interactions between
271 variables.

272 Compared to the full model performance, the model selection process marginally improved
273 the performances. The SVM still remains the top performer, with its best model including
274 seven of the fourteen possible variables.

275 Interestingly, the Elasticity Index (le) emerged as a key predictor, consistently selected
276 across the most performant models. According to Section 3.1, le exhibits the clearest
277 differentiation across the seven QPs, a result consistent with previous findings (Munc
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h et al., 2024). Notably, training an SVM model using le alone yields an F<sub>m</sub> of 0.42±0.01 and an Acc<sub>w</sub> of 0.67±0.01, making le by far the most informative single predictor in this dataset. This aligns with earlier work (Dowell et al., 2008; Rezette, Della Valle, et al., 2025), which established strong correlations between le and both the insoluble glutenin content and rheological properties such as the Strain Hardening Index (also in (Jødal & Larsen, 2021)) and modulus ratio (E'max/E'min).

The baking strength (W) appears in six out of seven best models, although it is naturally correlated with le (R = 0.83 in this dataset). Beyond le, no other variable stands out as an obvious complementary predictor.

### 3.3.2. SVM Model Performance Analysis

For SVM, the le-L pair was the best two-variable combination, reaching F<sub>m</sub> = 0.50 ± 0.01 and Acc<sub>w</sub> = 0.72 ± 0.01. le and L are weakly correlated (R = 0.26). L alone is a poor predictor but it accounts for complementary variability to le, improving the prediction accuracy.

Figure 3 shows one-fold results (75% training, 25% testing) for three SVM models: the two-variable le-L model, the best seven-variable model (Table 1), and the full fourteen-variable model. While results differ slightly from Table 1 due to the single fold, this comparison illustrates each model's performance on the same unseen dataset.

In this example, the seven-variable model (Figure 3b) matches or outperforms the full model (Figure 3c), which shows signs of overfitting for the full model. The two-variable model (Figure 3a) is slightly less accurate but still reasonable. Overall, SVM predicts extreme profiles (Slackening, Sl.-ND, Resistant, Re.-ND) reliably, while intermediate profiles (No defect, ND-Sl, ND-Re) remain harder to predict.

## 3.4. Explaining the best ML model predictions

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Beyond evaluating predictive performance, interpreting ML model outputs is essential to assess the relevance and validity of the relationships the model has learned from the
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dataset. In this section, we first investigate the two-variable SVM model (Section 3.3.1), then
the seven-variable SVM model (Section 3.3.2).

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**3.4.1. Model's Decisions Thresholds**

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As a first step, we computed a threshold map of technological variables guiding QP prediction. For clarity, this was done with the two-variable SVM (Ie and L) trained on the full dataset. The resulting map is shown in Figure 4.

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The thresholds for Ie and L delineate the ranges from which each of the seven QPs is predicted. Zones corresponding to the slackening profiles (Slackening, Sl-ND, left side of the plot) and the Resistant profile (right side) are well defined. In contrast, the boundaries between other profiles (e.g. between No Defect and ND-Re) appear to overlap. Scattered point color represents the true QP, background color the model's prediction. A match between both indicates a correct prediction by the model (e.g. within the slackening area); a mismatch corresponds to misclassifications (e.g. the Resistant profile). However, most of these errors remain moderate, as the incorrect predictions generally correspond to neighboring profiles.

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The figure also highlights the dominant influence of Ie on model predictions. This variable effectively distinguishes the two extreme QPs: low Ie values (<40) are mainly associated with slackening profiles, while high Ie values (>60) are characteristic of resistant profiles. According to (Kitissou, 1995), extreme values of Ie reflect potential defects in dough properties: high values describe doughs that are difficult to stretch and overly elastic, which aligns with resistant profiles, whereas low Ie values correspond to excessively extensible doughs typical of slackening profiles. (Duyvejonck et al., 2012) further demonstrated that low Ie values are often associated with reduced bread volume.

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Within the intermediate range of Ie values (40-60), the distribution of QPs is significantly influenced by L. In practice, high L values promote the prediction of Sl-ND and ND-Sl profiles while disfavoring the prediction of No Defect profiles.

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**3.4.2. SHAP-Values for Model Explanation**

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While Figure 4 presents the interactions between the two main predicting variables (Ie and L), this approach is rather limited when dealing with three or more factors. In this case, using an XAI approach such as SHAP value gives a valuable insight into how predictions are made.
Figure 5 shows the distribution of the SHAP values across the seven technological variables for each of the seven profiles. Each point represents a single contribution for a given feature: each sample has thus seven points (one per feature) for each QP. In the end, the SVM model predicts for the sample the QP with the highest sum of contributions. The concept of SHAP values helps to understand this decision by quantifying the contribution of each feature. As such, Figure 5 does not focus on each prediction but rather on the trends of the largest model: the higher (or lower) the contributions are, the more they are of use for the model predictions. This analysis supports the role of each feature in the QPs predictions.

For example, looking at the influence of le over all the QPs reveals that for all predictions, low le values (blue points) are associated with high positive SHAP values in Figure 5 (a-b-c), meaning that low le contributes positively to the prediction of slackening profiles. Conversely, in Figure 5 (f-g) high le values (red points) present positive SHAP values, meaning that they promote the prediction of Resistant and Re-ND profiles. The No defect and ND-Re profiles exhibit an intermediate pattern (Figure 5 (d-e)) for which average le values (purple points) contribute positively to its prediction, whereas both high (red) and low (blue) le values contribute negatively.

The SHAP analysis sheds light on why the SVM model predicts more accurately the extreme profiles (Slackening and Resistant), as shown by the confusion matrices (Figure 3). Specifically, Figures 5 (a-g) for, respectively, the Slackening profile and the Resistant profile, reveal clear positive contributions of extreme values for several variables of the model. For the Slackening profile, low values of le, Gluten Index, W, Protein content, and L all contribute positively, while it is the high values of the same variables that favor the prediction of the Resistant profile. It can be noted that the role of Stability is ambiguous, as low Stability values contribute positively to the Slackening profile (as expected) but also to the Resistant profiles.

For profiles closer to the No defect profile, such as ND-SI and ND-Re, the contributions of the technological variables are less clear-cut. Here average values can present positive contributions while they have near-zero SHAP contributions for extreme QPs. For example, average le values push towards ND-Re (Figure 5e), and average L values push towards ND-SI (Figure 5c). In other situations, positive contributions to ND-Re and ND-SI are obtained from average and from extreme (high or low) values; for example, average and high values of W
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push towards ND-Re. It appears also that high values of Gluten Index, i.e., close to 100%, contribute positively to ND-Re, Re-ND and Resistant profiles. The Gluten index then ceases to be discriminating for dough with these profiles.

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This analysis shows that extreme values for usual baking technological criteria allow for predicting well-pronounced defective profiles. On the other hand, predicting the no defect or the two slightly defective profiles appears more difficult, as these QPs are characterized by balanced values for several technological criteria. In other terms, there is no variable in the dataset that can strongly drive the prediction of No defect, ND-Re and ND-SI profiles, contrary to Slackening and the Resistant profiles which are characterized by doughs with gluten network deficiencies. To be noted, L plays a role in discriminating slightly defective profiles, as reflected by high SHAP values for classes such as SI-ND, ND-SI and ND-Re.

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### 3.5. Interpretation of the technological variables influence in ML models

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Across all tested models, the elasticity index (le) consistently emerged as the most effective predictor of QPs. The alveograph W was also frequently selected among the best predictors (Tab.1). The importance of these measurements is well established in the literature, as they capture the extensional properties of the dough, which are critical in breadmaking operations (Hugon et al., 2023; Yue et al., 2020). Notably, le effectively discriminates the two "extreme" QPs: Slackening and Resistant profiles, as shown in section 3.4. Our results showed that the Slackening profiles correspond to le values below 40, characterizing doughs that flow excessively during breadmaking, lacking firmness and elasticity, and sometimes exhibiting stickiness. Conversely, Resistant profiles are associated with le values above 60, indicating doughs that are difficult to stretch, exhibit insufficient lengthening during the shaping step and display excessive recovery after deformation.

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Previous research has linked le positively with glutenin and total protein content (Rezette, Della Valle, et al., 2025), both of which are critical components of the visco-elastic gluten protein networks and traditionally associated with breadmaking quality, via the impact on the flour water absorption, rheological properties of the dough at optimum mixing, and gas retention properties of the dough, which in turn affect loaf volume and crumb (Dowell et al., 2008; Rezette, Kansou, et al., 2025)). Thus, le can reflect well the dough viscosity and elasticity (also called strength) main imbalances: for le<40 the dough viscosity is predominant and excessive, while for le>60 the dough elasticity is excessive. When le falls
396

within the intermediate range of 40-60, the dough can still exhibit a variety of QPs, ranging
from SI-ND to Re-ND. This suggests that additional indicators are required to determine the
balance between dough viscosity and elasticity and then refine predictions.

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SHAP values identify two variables, the Gluten Index and W, whose high values seem to
favor the prediction of ND-Re to Resistant profiles (Figure 5 (e-f-g)). Gluten Index and W
both inform on the quantity and quality of the gluten protein network present in the dough
(Jødal & Larsen, 2021; Migliorini et al., 2016), however, they are largely redundant with le
(Pearson correlation coefficient of 0.58 and 0.83, respectively) and therefore do not improve
much the prediction of the profiles.

400

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On the other hand, our results show that the extensibility parameter (L) provides
complementary information. L is the time of inflation at rupture of the bubble; it is a
measurement of the biaxial extensibility in the dough, which must be sufficient to retain gas
in the cells during fermentation. It has been shown that L is affected by the gluten protein
network quality and by multiple other factors such as the water content of the dough and
flour water absorption (Cappelli et al., 2018; Jødal & Larsen, 2021). Given that the dough
hydration level for the alveograph assays is 50% on a flour basis and 59% on average for the
baking tests, L is probably affected by the sub-optimal flour component hydration, which
introduces uncontrolled variability. For this reason, interpreting L in the context of the
baking test is difficult, as observed in previous work for the dough at mixing (Hugon et al.,
2023).

406

407

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409

410

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The measurements of the bi-extensional properties of the dough at large deformations by
means of Lubricated Squeezing Flow (LSF) might complement technological criteria used in
the present work and improve the prediction of the QPs. LSF is a uniaxial compression of the
dough, the result of which should provide information on the dough's gas retention
properties. In a related work, (Rezette, Della Valle, et al., 2025) assessed the extensional
properties of dough from flours with comparable le values (50-60) and demonstrated that
the extensional viscosity is influenced by minor flour components, notably the intrinsic
viscosity of water-extractable arabinoxylan. These molecular features of minor flour
components can affect the viscosity of the liquid phase surrounding the gluten network,
thereby impacting overall dough consistency. Consequently, minor flour components may
hold the key to understanding what differentiates the QPs when le is in the range of 40-60.

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420

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422

423

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425

426
As AI approaches and data-driven models open up new prospect in the food industry (Mavani et al., 2022), these promising results pave the way for the definition of decision-making tools that could guide both the determination of wheat quality and the development of new quality criteria. Being able to rely on data-driven models for predictions is a key-issue in a context of food engineering. Because each decision may have an environmental, economic, or qualitative cost (Perrignon et al., 2024), stakeholders must be kept informed of the model's predictions and their motivation.

## 4. Conclusion

ML methodologies are the key to unravelling the complex non-linear and multi-factorial relationships in wheat baking quality assessment. In this study, we developed predictive models to classify wheat samples according to QPs which synthetize their performance at a baking test. A range of ML models were trained on an original, comprehensive dataset designed to encompass the broad diversity of wheat quality encountered in practical contexts. The SVM model produced the best results, both when trained with all parameters and when trained with a selection of the best performing parameters. The SHAP values highlighted the relative importance of different variables in influencing the model's decisions. Comparing these findings with related studies increased confidence in the model, as its key decision-making patterns aligned with previously reported results.

While the ML models perform well in predicting extreme dough behaviors, the limitations of the model also underscore a "grey zone" where the selected technological variables lack sufficient discriminative power, in particular for not extreme profiles. This suggests that further investigation into underexplored factors, particularly those related to minor flour components, is warranted to refine predictive capabilities.

This work makes several key contributions to the scientific community and industry practitioners. By introducing the concept of QPs to systematically capture and integrate expert sensory evaluations, it advances the application of ML frameworks for predicting wheat baking quality, moving beyond linear modelling limitations. In particular, the combination between expert knowledge and explainability techniques offers a critical appraisal of the discriminative capacity of conventional technological measurements. This paves the way for the development of innovative factors.
457 Collectively, these contributions decisively enhance our understanding of the determinants
458 of wheat baking quality and provide actionable insights for breeding, selection, and quality
459 control strategies in cereal-based food production systems.

460 Acknowledgments

461 This work was funded by the ANR Evagrain (ANR-20-CE21-0008).

462 The authors would like to thank the ARVALIS technical institute for providing the data set
463 and the experts from the AXIANE MEUNERIE and LIMAGRAIN milling industries for their
464 input on the quality profiles.
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<table><thead><tr><th colspan="2">Algorithm</th><th colspan="2">All Predictors</th><th colspan="2">Selected Predictors</th><th rowspan="2">Selected</th></tr><tr><th>Type</th><th>Name</th><th>F<sub>m</sub></th><th>Acc<sub>w</sub></th><th>F<sub>m</sub></th><th>Acc<sub>w</sub></th></tr></thead><tbody><tr><td rowspan="2">Linear Method</td><td>Ridge*</td><td>0.40</td><td>0.62</td><td>0.41</td><td>0.63</td><td>Proteins - Hardness - W - P - L - P/L - Ie - Development Time - Stability - Weakening Degree (10)</td></tr><tr><td>SGD*</td><td>0.40</td><td>0.62</td><td>0.40</td><td>0.62</td><td>Proteins - Hardness - W - P - L - P/L - Ie - Development Time - Stability (9)</td></tr><tr><td>Kernel Machine</td><td>SVM*</td><td><strong>0.49</strong></td><td><strong>0.71</strong></td><td><strong>0.52</strong></td><td><strong>0.74</strong></td><td>Proteins - Gluten Index - W - L - Ie - Development Time - Stability (7)</td></tr><tr><td rowspan="2">Local Method</td><td>Decision Tree</td><td>0.39</td><td>0.61</td><td>0.41</td><td>0.63</td><td>Gluten Index - W - P/L - Ie - Stability (5)</td></tr><tr><td>KNN*</td><td>0.42</td><td>0.64</td><td>0.47</td><td>0.67</td><td>P - Ie (2)</td></tr><tr><td rowspan="2">Ensemble Method</td><td>Gradient Boosting</td><td>0.44</td><td>0.66</td><td>0.46</td><td>0.67</td><td>W - P - Ie - Damaged Starch - Weakening Degree (5)</td></tr><tr><td>Random Forest</td><td>0.46</td><td>0.68</td><td>0.49</td><td>0.7</td><td>Proteins - Gluten Index - W - Ie - Development Time - Stability - Weakening Degree (7)</td></tr></tbody></table>

Table 1. Score comparison for models learned over 50 4-folds CV with all predictors, and best set of predictors selection. F<sub>m</sub>: macro F-score, Acc<sub>w</sub>: weighted accuracy. * indicates when a standard scaling was applied. All measures have a confidence interval of ±0.01. For predictor selection, attributes were added to the set until the F<sub>m</sub> score improvement was inferior to 10-3.

579 580 581 582 583 584
<figure><img src="image_4.png" /><figcaption>**Figure 1:** Definition of the seven quality profiles based on three distinct dough behaviours observed during bread-making. Sensory characteristics were evaluated following the AFNOR standard NF-V03-716 bread-making test.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 2:** UMAP projection of bread assessment data. Left: baseline dataset. Right: projected bread assessments from the study dataset. Points are coloured according to their assigned quality profile. Close points show similar baking performance.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Figure 3:** Confusion matrices for three SVM models trained and tested on identical, randomly selected subsets (training: 215 samples; testing: 72 samples). Rows indicate true quality profiles, columns indicate predicted profiles. Input variables for (b) were selected based on Table 2 best performances: Proteins - Gluten Index - W - L - le - DT - Stability.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Figure 4:** Decisions thresholds for SVM predictions based on combination of le (elasticity index) and L (extensibility). The model was trained using the full dataset with only le and L as input variables. White areas indicate extrapolations beyond the observed data space. Data points are represented by circles, colored according to their true quality profile.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 5:** SHAP summary plot for predicting the seven QPs. The color gradient (blue to red) represents *Predictor values* from minimum to maximum value (observed in the dataset). The SHAP values on the x-axis indicate the magnitude and direction of each predictor's contribution to the model's prediction: the higher their sum, the more the model tends to predict. Each point represents the impact of a predictor for a given profile. It shows for example that low le values have a positive contribution to the Slackening QP, while high le values have a positive contribution to the Resistant QP.</figcaption></figure>