Pierre Jacob
email: pierre.jacob@ensea.fr

David Picard

Aymeric Histace

Edouard Klein

DIABLO: Dictionary-based attention block for deep metric learning

Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks. Recent contributions mostly address the training part (loss functions, sampling strategies, etc.), while a few works focus on improving the discriminative power of the image representation. In this paper, we propose DIABLO, a dictionary-based attention method for image embedding. DIABLO produces richer representations by aggregating only visually-related features together while being easier to train than other attention-based methods in deep metric learning. This is experimentally confirmed on four deep metric learning datasets (Cub-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval) for which DIABLO shows state-of-the-art performances.

Introduction

Deep Metric Learning (DML) is an important yet challenging topic in the Computer Vision community, that has a broadspectrum in terms of applications such as: person or vehicle identification [START_REF] Zhou | Efficient online local metric adaptation via negative samples for person re-identification[END_REF], visual product search [START_REF] Liu | Deepfashion: Powering robust clothes recognition and retrieval with rich annotations[END_REF][START_REF] Song | Deep metric learning via lifted structured feature embedding[END_REF] or multi-modal retrieval [START_REF] Wehrmann | Bidirectional retrieval made simple[END_REF][START_REF] Carvalho | Cross-modal retrieval in the cooking context: Learning semantic text-image embeddings[END_REF]. By learning the image representations and an embedding space together, DML methods produce compact representations where visually-related images (e.g., images of the same car model) are close to each other and dissimilar images (e.g., images of two cars from the same brand but from different models) are distant.

Recent contributions mainly address the training part of deep metric learning, proposing loss functions (e.g. Angular loss [START_REF] Wang | Deep metric learning with angular loss[END_REF]), sampling strategies (e.g., DAMLRMM [START_REF] Xu | Deep asymmetric metric learning via rich relationship mining[END_REF]) and ensemble methods (e.g., BIER [START_REF] Opitz | Bier -boosting independent embeddings robustly[END_REF]). All of these methods are built upon a backbone network such as GoogleNet [START_REF] Szegedy | Going deeper with convolutions[END_REF] to extract the local features from which the image representations and the corresponding metric are computed. Nowadays, global average pooling is the most used pooling strategy to compute the image representations. This is due to interesting properties such as full back-propagation of the gradient or localization ability without direct supervision [START_REF] Zhou | Learning deep features for discriminative localization[END_REF]. In almost every method, these representations are computed using the mean of the deep features and used as they are.

However, the average is also known to be a non-robust representation because it is very sensitive to outliers and to sampling problems (Jacob et al., 2019b). A famous solution is to strengthen this representation in order to compute the average on only visually-related features, using a set of attention maps such as ABE [START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF]. ABE is based on what we next call dimension-wise selection with pre-attention -this method shows very good results with few additional parameters, through both pooling of visually-related features and feature denoising. Also, ABE is trained with a divergence loss which ensures that the attention maps are complementary by enforcing two attention maps to be dissimilar, even for visuallyrelated images. Due to this optimization criterion and the tradeoff parameter, both the training procedure and the parameterization become more complex. NetVLAD [START_REF] Arandjelovic | Netvlad: Cnn architecture for weakly supervised place recognition[END_REF] is based on what we next call feature-wise selection with post-attention -this method aggregates visually-similar features using a structural constraint based on a dictionary strategy. However, the feature-wise selection does not contribute to feature denoising.

In this paper, we introduce the method DIABLO, a DIctionary-based Attention BLOck, that produces robust im-age representations by taking advantage of both ABE and NetVLAD benefits. We evaluate attention strategies named preattention and post-attention in the DML setup, together with two selection strategies named feature-wise and dimension-wise selection. We show in practice that DIABLO consistently improves the state-of-the-art on four DML datasets (Cub-200-2011, Cars-196, Stanford Online Products and In-Shop Clothes Retrieval).

The remaining of the paper is organized as follows: in the next section, we present the related work on deep metric learning and support our proposition. In section 3, we give an overview of the proposed architecture, then we detail the attention strategies and the feature-wise and dimension-wise selection methods. In section 4, we show ablation studies on the selection and attention strategies, the dictionary size and the comparison to ABE. Finally, in section 5 we compare our approach to the state-of-the-art methods on four image retrieval datasets (Cub-200-2011, Cars-196, Stanford Online Products and In-Shop Clothes Retrieval).

Related work

In DML, we learn the image representations and an embedding together in such a way that the Euclidean distance corresponds with the semantic content of the images. The standard strategy is to extract deep features using a backbone network such as GoogleNet [START_REF] Szegedy | Going deeper with convolutions[END_REF] and to learn the target representation with a linear projection on the average of these deep features. The whole network is fine-tuned to solve the metric learning task according to three criteria: a similarity-based loss function, a sampling strategy, and an ensemble method.

Standard loss functions rely on pairs or triplets of similar/dissimilar samples. Most recent ones extend these formulations by considering larger tuples [START_REF] Ustinova | Learning deep embeddings with histogram loss[END_REF] or by improving the design [START_REF] Wang | Deep metric learning with angular loss[END_REF]. The batch construction can either be done by random sampling, mining strategies [START_REF] Xu | Deep asymmetric metric learning via rich relationship mining[END_REF]), proxy-based approximations (Movshovitz-Attias et al., 2017) or generation [START_REF] Lin | Deep variational metric learning[END_REF]. Finally, ensemble methods have recently become a popular way of improving the performances of DML architectures [START_REF] Opitz | Bier -boosting independent embeddings robustly[END_REF][START_REF] Opitz | Deep metric learning with BIER: boosting independent embeddings robustly[END_REF].

The latest DML approaches consider using a codebook strategy [START_REF] Arandjelovic | Netvlad: Cnn architecture for weakly supervised place recognition[END_REF] or even attention maps [START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF]. [START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF] propose ABE, an attentionbased ensemble method to enforce diversity within the embedding space. With this aim, they train a set of attention blocks to select several feature map entries per block. These blocks are then trained with a divergence loss function, which ensures that all attention maps are complementary. Their proposed design relies on what we call a dimension-wise selection with preattention. they select a set of dimensions from the feature maps using a given attention map. Then, these selected dimensions are further processed with Inception blocks. We call this selection schema a pre-attention strategy -and we argue that one of its properties results in denoising the deep features. Thus, this improves the image representations by post-processing features that contain only relevant information. However, there are two major drawbacks in using a divergence loss. On the one hand, we need to cross-validate the additional trade-off hyper-parameter. On the other hand, the training time is increased because the divergence loss plays the opposite role of the similarity-based loss function. [START_REF] Arandjelovic | Netvlad: Cnn architecture for weakly supervised place recognition[END_REF] propose NetVLAD which takes advantage of a dictionary strategy that avoids the divergence loss. In their case, the optimization constraint is replaced by a structural one, that simplifies the training procedure by directly optimizing the task-dependent loss function. However, NetVLAD's design relies on a strategy that we call feature-wise selection with post-attention. Feature-wise selection ensures that only visually-related features are pooled together. This selection strategy does not have the denoising property of the dimension-wise selection. Also, in the case of NetVLAD, the pre-processing is a centering and an intra-projection, that can be improved using non-linear transformation learned by a CNN.

In DIABLO, we show the benefits of dimension-wise selection versus feature-wise selection with pre-attention or postattention strategies. With this aim, we propose to replace the divergence loss in ABE by a structural constraint, which is based on a dictionary strategy. This dictionary is trained without direct supervision as it is the case with NetVLAD. We show that it leads to better results than ABE on four DML datasets.

Proposed method

In this section, we start by giving an overview of DIABLO. Then, we describe the attention maps computation and the two selection strategies named feature-wise and dimension-wise selection.

Method overview

We give an overview of the method illustrated in Figure 1. We start by extracting a deep feature map F ∈ R h×w×c using the local feature extractor F where h and w are the height and width of the feature map and c is the deep feature dimension. We further process the extracted feature map using a non-linear function φ implemented by a convolutional neural network (CNN). In order to compute the N attention maps A where N is the dictionary size, we pass the feature map into the selection block S, that is either the feature-wise selection (subsection 3.3) or the dimension-wise selection (subsection 3.4).

In the post-attention setup (Figure 1a), the feature map F is further processed with a non-linear function ψ (implemented by a CNN) and transformed into a feature map G. It is then combined with the attention maps A using the block M to produce the N new feature maps. In the pre-attention setup (Figure 1b), the feature map F is directly combined with the attention maps A using the block M. In this case, we further process these N feature maps using a non-linear function ψ. The different blocks M used to combine the attention maps and the original feature map are illustrated in Figure 2.

These N feature maps are pooled using a global average pooling with adding an embedding layer for each branch. So, the output representation is obtained by concatenating these N branches.

Attention strategies

This section focuses on computing a set of attention maps A using one of the selection blocks S and its corresponding dictionary D as well as the feature map F ∈ R h×w×c . For this purpose, we process the feature map F with a non-linear function φ : R c -→ R m implemented by a CNN. Then, the set of attention maps are computed by a selection block S such that A = S(φ(F ); D). A is used with one of the following attention strategies.

Post-attention

In the post-attention strategy illustrated in Figure 1a, we process the feature map F into an intermediate feature map G:

G = ψ (F ) (1)
Then, we combine this feature map G with the attention maps A using the merging block M:

H = M(G; A) (2)
with H representing the set of feature maps obtained by the post-attention strategy. From these, we perform a spatial pooling of the local features and we add an embedding layer to generate the corresponding image representation.

The main idea of post-attention consists of aggregating only the related features, unlike global pooling that aggregates unrelated features. For example, features that describe the background are aggregated using a different attention block than those which represent the desired object. One can note that this approach is strongly related to NetVLAD for which the function ψ is a centering. However, it differs from NetVLAD in two ways: First, because it learns a non-linear clustering of the local features using the function φ; second because it learns a nonlinear pre-processing of the deep features using the function ψ.

Pre-attention

In the pre-attention strategy illustrated in Figure 1b, we perform the operation in reverse order. In order to do so, we combine the feature map F with the set of attention maps A using the merging block M to produce the set of N (one per attention block) feature maps G:

G = M(F ; A) (3)
Then, we process these N feature maps using a non-linear function ψ to generate the feature maps H:

H = ψ (G) (4)
From these N feature maps, we pool the local features and we add an embedding layer to generate the corresponding image representation.

The pre-attention strategy is similar to a refinement approach. Indeed, the attention selects dimensions or features, and a refinement function ψ improves these extracted features before that they are aggregated. Hence, the attention maps role is to select only the relevant information for a given attention block. The function ψ is trained to refine this information so that it improves generalization.

Feature-wise selection

In this section, we give details about the computation of the selection block S and the merging block M for the featurewise selection illustrated in Figure 2a. We start from a feature map F ∈ R h×w×c from which we compute a set of attention maps A = {A (n) ∈ R h×w×c } n using a dictionary D such that A = S(F ; D). Before computing the feature-wise selection, the feature map F is processed by a non-linear function φ : R c -→ R m implemented by a CNN. We denote f i, j ∈ R c a feature from F at spatial location (i, j) and φ( f i, j ) ∈ R m its transformation in φ(F ) that is in the same spatial location.

Selection block

In the feature-wise selection, the objective is to assign each feature f i, j from the feature map F to one of the dictionary entries D = {d (n) ∈ R m } n . To do so, we consider the cosine similarity between the transformed feature φ( f i, j ) and the dictionary entry:

s(φ( f i, j ), d (n) ) = φ( f i, j ) ; d (n) φ( f i, j ) 2 d (n) 2 (5)
Using the cosine similarity has a main advantage when compared to the Euclidean distance: during the training, it is more stable by means of the 2 -normalization.

From this similarity measure, we compute A (n) i, j,k , the weight for the n-th feature-wise attention map and k-th dimension of the feature in spatial location (i, j):

A (n) i, j,k =        1 if d (n) = arg max d (l) ∈ D s(φ( f i, j ), d (l) ) 0 else. (6)
However, this one-hot encoder is not differentiable due to the arg max operator. We relax the constraint using the soft-max operator to train in an end-to-end way the deep network:

A (n) i, j,k = e α s(φ( f i, j ),d (n) ) l e α s(φ( f i, j ),d (l) ) (7)
such that, α is a hyper-parameter to control the hardness of the assignment. For this formulation, we rely on a given feature f i, j that is assigned to a dictionary entry d (n) according to the similarity between φ( f i, j ) and d (n) . We show in section 5 that the feature-wise selection increases the performance of the attention-based models when compared to the baseline model (without attention map). Then, we detail the block M to merge the feature-wise selection based attention map with the raw features.

Merging block

The combination of the attention maps A and the feature map F is illustrated in Figure 2a. For the k-th dimension of the n-th feature map in spatial location (i, j), the corresponding entry in H, H (n) i, j,k is computed using the following equation:

H (n) i, j,k = A (n) i, j,k F i, j,k (8)
It must be considered that A (n) i, j,k has the same value independently from the value of k. Thus, Equation 8 is easily implemented using the Kronecker product (⊗) as illustrated in Figure 2a:

H i, j = A i, j ⊗ F i, j (9)
where A i, j is composed by the N-th assignment weights and f i, j ∈ R c is the feature for all spatial locations (i, j).

Dimension-wise selection

In this section, we extend the feature-wise selection from the subsection 3.3 to the dimension-wise selection. Similarly, we give details about the attention maps computation through the selection block S before that we explain the merging strategy with the block M.

Selection block

The selection block is composed of a set of directions per dictionary entry D = {d (n) k ∈ R m } n,k of size N × c, on the contrary to the feature-wise selection that has a dictionary D = {d (n) ∈ R m } n of size N. Thus, for a given feature f i, j ∈ F in spatial location (i, j), the cosine similarity is computed between the transformed feature φ( f i, j ) and the k-th direction of the n-th dictionary entry d (n) k ∈ D:

s(φ( f i, j ), d (n) k ) = φ( f i, j ) ; d (n) k φ( f i, j ) 2 d (n) k 2 (10)
Then, one entry A (n) i, j,k from the attention map A is computed using the following equation:

A (n) i, j,k = e α s(φ( f i, j ),d (n) k ) l e α s(φ( f i, j ),d (l) k ) (11)
The attention map has an attention weight for each dimension of the input feature and for each of the N attention blocks. section 5 highlights that the dimension-wise selection produces stronger image representations than the feature-wise selection, showing much higher performances.

Merging block

The merging block M is used in the case of dimension-wise selection as illustrated in Figure 2b. The entry H (n) i, j,k in H is computed for the k-th dimension of the n-th feature map in the spatial location (i, j) using the following equation:

H (n) i, j,k = A (n) i, j,k F i, j,k (12)
Note that A (n) i, j,k depends on the value of k. The computation can easily be re-written using the Kronecker product and the Hadamard product ( ) as illustrated in Figure 2b. Using the Kronecker product, we duplicate N times the local feature f i, j Then, H is computed using the element-wise product between A and the duplicated feature map:

H = (F ⊗ 1 N ) A (13)

Implementation details

For a fair comparison with other methods, we use a pretrained GoogleNet [START_REF] Szegedy | Going deeper with convolutions[END_REF] on ImageNet. The embedding size is fixed to 512 for all models. As it is done in common practice, we set the triplet margin α = 0.1, the contrastive and the binomial margin β = 0.5 and the negative sample weight C = 25 in the binomial deviance. We follow the same training procedure as state-of-the-art methods [START_REF] Opitz | Bier -boosting independent embeddings robustly[END_REF][START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF]: training hyper-parameters (learning rate, batch size, etc.) are empirically chosen based on the training loss after a few epochs. Model hyper-parameters (number of layers, etc.) are set to comparable values as the ones of models reported in ABE [START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF]. We use the following data augmentation on the images: multi-resolution where the size is uniformly sampled in [0.8, 1.8] times the crop size as well as random 256 × 256 crop and horizontal flip during the training. During testing, we re-scale the images to 256 × 256. We use Adam [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF] with the default parameters and a learning rate of 10 -5 . For all models, the function ψ is shared across the attention maps to reduce the large number of parameters induced. In practice, it still leads to strong experimental results and avoids over-fitting on small datasets, for instance: Cub-200-2011 and Cars-196.

Ablation studies

In this section, we compare our approach to the baseline in terms of model complexity and computation. Then, we present the evaluation protocol of our ablation studies. Ablation studies are performed to evaluate the benefits of pre and post-attention, the assignment approaches (feature and dimension-wise), the dictionary strategy and the dictionary size.

Model complexity and computation cost

In this section, we give the architecture choices to compute the functions φ and ψ for the post-attention and for the preattention architectures. Then, we analyze the induced computations and the complexity introduced by the dictionary approach.

In post-attention, the GoogleNet backbone extracts the feature map including and up to the max-pooling between the fourth and the fifth scales. To compute φ, we add the two inception blocks named '5a' and '5b' upon these features. The function ψ is also composed by two independent inception blocks '5a' and '5b'. The attention maps and the weighted features are computed using one of the two proposed strategies. Then, each branch is pooled using a global average pooling as well as an embedding layer of size 512/N and a 2 -norm are added. The output representation is the concatenation of these N branches which leads to a 512d representation.

In pre-attention, we use the GoogleNet features including and up to the max-pooling between the third and the fourth scales. The non-linear function φ is composed of the five inception blocks from the fourth scale of GoogleNet pre-trained on ImageNet. The refinement function ψ is composed by the fourth and fifth scales of GoogleNet, they are shared for each map but they are independent from φ. The attention maps and the weighted feature maps are computed using either dimension-wise or feature-wise selection. As it the case with pre-attention, each branch is pooled using a global average pooling, an embedding layer of size 512/N and a 2 -norm are added before the concatenation of all branches in order to produce the full 512d representation.

These choices directly follow ABE [START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF] and we refer the reader to the related paper for more ablations on the architecture, including the multi-head approach, the attention module and the sharing of parameters across the attention modules.

All additional parameters are included in the computation of the function φ. By using the five Inception blocks from the fourth scale of GoogleNet, this leads to 3.5M additional parameters. Note that these parameters are shared across the dictionary entries and this drastically reduces the number of parameters. Also, note that the function ψ is already included in the number of parameters of GoogleNet.

In terms of computation, the most important additional computations come from the function ψ which is computed on each attention map. The computation of the fourth and fifth Inception scales are estimated to 0.7 Gflop (see [START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF], Table 1). In comparison, the whole GoogleNet requires 1.6 Gflop to produce the image embedding. Note that in the case of the pre-attention, all parameters of ψ are shared across the attention maps, which leads to fewer additional parameters. Overall, these choices lead to higher experimental results for both ABE and DIABLO compared to the baseline.

Model selection protocol

In this section, we detail the evaluation protocol for all ablation studies on the Cub-200-2011 dataset that are performed to select the best model. We perform 10 random train-val splits on the training set of Cub-200-2011 for deep metric learning: We randomly choose 50 classes for the training set and we keep the rest of the classes for the validation set. Then, we train each model on the training set and we select the model that gives the best performances on the validation set. We then compute Recall@K on the testing set of Cub-200-2011 for each train-val split. All reported results in Table 4 and Table 3 are the average and the standard-deviation of Recall@K on the testing set for the ten runs.

Feature selection and attention strategy

First, we evaluate the benefit of the pre and post-attention strategies with respect to the assignment strategy. To that end, we fix the dictionary size to 8 and we use the training procedure from subsection 3.5. Results are reported in Table 3 for the dataset Cub-200-2011 with binomial loss. We remark that all strategies with the exception of the feature-wise selection with pre-attention improve over the baseline and this confirms the benefit of attention maps. This experiment also shows that feature-wise attention and dimension-wise attention impact differently the model.

In Feature-wise attention, the post-attention leads to stronger representations with +1.7% on Recall@1 over the pre-attention. We argue that selecting features make better sense with postattention than pre-attention: only related features are aggregated together with post-attention whereas in pre-attention, the refinement part mostly processes sparse feature maps. In the case of dimension-wise attention, both approach provide stronger results with +3.8% and +4.3% over the best featurewise strategy, even though it is still better to use the preattention approach. We argue that pre-attention is better with dimension-wise selection because the refinement part processes denoised features. Indeed, certain dimensions may be useless for a given dictionary entry and the dimension-wise approach can select only the relevant dimensions. Then, the refinement part is trained with sparse vectors which contain only the relevant information. Moreover, feature-wise selection with postattention leads to aggregate sparse vector together, which might bring to sub-optimal results because some dimensions are rarely used. [START_REF] Xu | Deep asymmetric metric learning via rich relationship mining[END_REF] 55.1 66.5 76.8 85.3 --73.5 82.6 89.1 93.5 --HDC [START_REF] Yuan | Hard-aware deeply cascaded embedding[END_REF] 53.6 65.7 77.0 85.6 91.5 95.5 73.7 83.2 89.5 93.8 96.7 98.4 BIER [START_REF] Opitz | Bier -boosting independent embeddings robustly[END_REF] 55.3 67.2 76.9 85.1 91.7 95.5 78.0 85.8 91.1 95.1 97.3 98.7 DVML [START_REF] Lin | Deep variational metric learning[END_REF] 52.7 65.1 75.5 84.3 --82.0 88.4 93.3 96.3 --HTG [START_REF] Zhao | An adversarial approach to hard triplet generation[END_REF] 59.5 71.8 81.3 88.2 --76.5 84.7 90.4 94.0 --HTL [START_REF] Ge | Deep metric learning with hierarchical triplet loss[END_REF] 57.1 68.8 78.7 86.5 92.5 95.5 81.4 88.0 92.7 95.7 97.4 99.0 A-BIER [START_REF] Opitz | Deep metric learning with BIER: boosting independent embeddings robustly[END_REF] 57.5 68.7 78.3 86.2 91.9 95.5 82.0 89.0 93.2 96.1 97.8 98.7 JCF (Jacob et al., 2019a) 60

Comparison to ABE

In a second time, we want to evaluate the impact of the structural constraints imposed by the dictionary (Equation 7and Equation 11) by comparing our method to ABE [START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF]. In ABE, the authors show that a M-head approach already provides strong results on the Cars-196 dataset with Table 4: Impact of the dictionary size for the feature-wise post-attention mapping and for the dimension-wise pre-attention mapping. Reported results are Recall@1 (R@1) on the Cub-200-2011 [START_REF] Kim | Attention-based ensemble for deep metric learning[END_REF]). Among the drawback of the divergence loss, despite of the additional hyper-parameter, figure that optimizing the loss generates gradients which are in opposition with the metric learning loss ones. Indeed, it is designed to reduce the similarity between different branches even when the images are similar. We solve this issue in DIABLO where this optimization constraint is replaced by a structural constraint (softmax in Equation 7and Equation 11). The orthogonality is ensured by the design of DIABLO, which allows to simply remove the divergence loss at the price of a reduced expressiveness: feature map entries can be chosen independently in ABE whereas in DIABLO they are constrained to only one dictionary entry. In Table 1 andTable 2, DIABLO performs well compared to ABE with similar results on the Cars-196 dataset (-0.2% compared to ABE) and higher ones on other datasets such as Cub-200-2011 (+2.2%), Stanford Online Products (+1.5%) and Inshop Clothes Retrieval (+3.4%) for this set of parameters.

Dictionary size

In this section, we evaluate the impact of the dictionary size on DIABLO. We evaluate both the feature-wise and the dimension-wise with dictionary sizes in {2, 4, 8, 16}. Recall@1 on the Cub-200-2011 dataset are reported in Table 4.

In post-attention with feature-wise selection, extreme combinations (e.g., with 16 branches with 32 dimensions) lead to results under the baseline. Thus, to increase the performances of such attention strategy, there is a compromise between the representation size of each branch and the number of branches (+1.1% over the baseline). In pre-attention with dimensionwise selection, all parameter combinations for this approach lead to better results than the baseline (+4.9% to +5.4%). The number of branches increases the performance with the log of the dictionary size on the contrary to the previous strategy.

Comparison to the state-of-the-art

In this compare DIABLO to the state-of-theart on 4 DML datasets, named Cub-200-2011[START_REF] Wah | The Caltech-UCSD Birds-200-2011 Dataset[END_REF]), Cars-196 (Krause et al., 2013), Stanford Online Products (Oh [START_REF] Song | Deep metric learning via lifted structured feature embedding[END_REF] and In-Shop Clothes Retrieval [START_REF] Liu | Deepfashion: Powering robust clothes recognition and retrieval with rich annotations[END_REF]. For Cub-200-2011, Cars-196 and Stanford Online Products, we follow the standard splits from (Oh [START_REF] Song | Deep metric learning via lifted structured feature embedding[END_REF] and for In-Shop Clothes Retrieval we follow the one from [START_REF] Liu | Deepfashion: Powering robust clothes recognition and retrieval with rich annotations[END_REF]. Especially, the Cub-200-2011 training set is composed of the first 100 classes (i.e., the training and the validation sets from the ablation studies) for a total of 5864 images and its testing set is composed of the last 100 classes for a total of 5924 images. The Cars-196 training set is composed of the first 98 classes for a total of 8054 images and its testing set is composed of the last 98 classes for a total of 8131 images. The Stanford Online Products training set is composed of 11318 classes for a total of 59551 images and its testing set is composed of 11316 classes for a total of 60502 images. Finally, the In-Shop Clothes Retrieval training set is composed of 3997 classes for total of 25882 images and its testing set is composed 3985 classes and it is split into query set of 17218 images and a collection set of 12612 images. We report the Recall@K which evaluates, for a given query, if there is at least one image with the same label in the top-K retrieved images. We use K ∈ {1, 2, 4, 8, 16, 32} for Cub-200-2011 andCars-196, K ∈ {1, 10, 100, 1000} for Stanford Online Products andK ∈ {1, 10, 20, 30, 40, 50} for In-Shop Clothes Retrieval. We report the results for Cub-200-2011 and Cars-196 in Table 1 and in Table 2 for Stanford Online Products and In-Shop Clothes Retrieval. DIABLO consistently improves the already strong baseline on the four datasets and for three different loss functions. E.g., using the binomial loss, the baseline is improved from 59.6% to 63.9% (+4.3%) on Cub-200-2011 and from 78.8% to 85.4% (+6.6%) on the Cars-196 dataset. The same observation is made for both the other loss functions on these datasets.

Moreover, DIABLO leads to better results when compared to the similar approach ABE. Their best reported approach, ABE-8, is outperformed by DIABLO with N = 8 and c = 64 (total dimension 512) by 2.2% in R@1 on Cub-200-2011, by 1.5% on Stanford Online Products and by 4% on In-Shop Clothes Retrieval. We also report results with N = 16 and c = 32 (total dimension 512) which are further improved on these datasets: 1.1% in Recall@1 on the Cub-200-2011 and by 0.4% on the Cars-196 dataset, leading to the state-of-the-art on the four deep metric learning datasets.

Conclusion

In this paper, we have presented a dictionary-based attention method named DIABLO which consistently improves DML models. An ablation study is undertaken to evaluate the benefits of the feature-wise and the dimension-wise selections for two attention strategies named pre-attention and post-attention. We show that DIABLO consistently outperforms the baseline for three different loss functions on four datasets (Cub-200-2011, Cars-196, Stanford Online Products, and Inshop Clothes Retrieval). Moreover, it outperforms the current state-of-the-art methods on the four datasets.

  Fig.1: Illustration of post and pre-attention strategies. In pre-attention, we first compute the attention maps and then we process each of them. In post-attention, the feature map is further processed before computing the attention maps. In pre-attention, we first compute the attention maps and then we process each of them. F is a feature extractor and φ and ψ are two non-linear transformation of the deep features. The blocks S correspond to either Equation7or Equation11. The blocks M are illustrated in Figure2.

Table 1 :

 1 Comparison with the state-of-the-art onCub-200-2011 and Cars-196 datasets using GoogleNet as feature extractor. Results are in percents. State-of-the-art results are in bold and results which improve the baseline are underlined.

				Cub-200-2011					Cars-196
	R@	1	2	4	8	16	32	1	2	4	8	16	32
	RML (Roy et al., 2019)	52.3 64.5 75.3 84.0	-	-	73.2 82.2 88.6 92.2	-	-
	Angular loss (Wang et al., 2017) 54.7 66.3 76.0 83.9	-	-	71.4 81.4 87.5 92.1	-	-
	DAML (Duan et al., 2018)	52.7 65.4 75.5 84.3	-	-	75.1 83.8 89.7 93.5	-	-
	HDML (Zheng et al., 2019)	53.7 65.7 76.7 85.7	-	-	79.1 87.1 92.1 95.5	-	-
	DAMLRMM

Table 2 :

 2 Comparison with the state-of-the-art on Stanford Online Products and In-Shop Clothes Retrieval using GoogleNet as feature extractor. Results in percents. State-of-the-art results are in bold and results which improve the baseline are underlined.

		.1 72.1 81.7 88.3	-	-	82.6 89.2 93.5 96.0	-	-
	HORDE (Jacob et al., 2019b)	59.4 71.0 81.0 88.0 93.1 96.5 83.2 89.6 93.6 96.3 98.0 98.8
	ABE (Kim et al., 2018)	60.6 71.5 79.8 87.4	-	-	85.2 90.5 94.0 96.1	-	-
	Contrastive (Ours)	58.7 69.7 79.4 87.0 92.6 96.1 78.5 85.9 90.9 94.4 96.7 98.1
	Contrastive + DIABLO	62.3 73.6 82.6 89.2 94.0 96.9 84.8 90.5 94.3 96.6 98.1 98.9
	Triplet (Ours)	55.9 67.0 77.7 86.1 92.0 95.5 73.1 81.7 87.9 92.9 95.9 97.6
	Triplet + DIABLO	59.6 70.6 80.3 87.7 92.7 96.2 75.0 83.4 89.4 93.5 96.4 98.1
	Binomial (Ours)	59.6 70.8 81.0 88.1 93.1 96.2 78.8 86.2 91.5 94.8 97.0 98.4
	Binomial + DIABLO N = 8	62.8 73.9 82.4 89.3 94.0 96.7 85.0 90.8 94.0 96.4 98.0 98.9
	Binomial + DIABLO N = 16	63.9 74.3 82.4 88.8 94.0 96.8 85.4 91.3 95.0 97.2 98.5 99.1
		Stanford Online Products		In-Shop Clothes Retrieval
	R@	1	10	100	1000	1	10	20	30	40	50
	Angular loss Wang et al. (2017)	70.9	85.0	93.5	98.0	-	-	-	-	-	-
	DAML (Duan et al., 2018)	68.4	83.5	92.3	-	-	-	-	-	-	-
	HDML (Zheng et al., 2019)	68.7	83.2	92.4	-	-	-	-	-	-	-
	RML (Roy et al., 2019)	69.2	83.1	92.7	-	-	-	-	-	-	-
	DAMLRMM (Xu et al., 2019)	69.7	85.2	93.2	-	-	-	-	-	-	-
	HDC (Yuan et al., 2017)	69.5	84.4	92.8	97.7	62.1	84.9	89.0	91.2	92.3	93.1
	BIER (Opitz et al., 2017)	72.7	86.5	94.0	98.0	76.9	92.8	95.2	96.2	96.7	97.1
	DVML (Lin et al., 2018)	70.2	85.2	93.8	-	-	-	-	-	-	-
	HTG (Zhao et al., 2018)	-	-	-	-	80.3	93.9	95.8	96.6	97.1	-
	HORDE (Jacob et al., 2019b)	72.6	85.9	93.7	97.9	84.4	95.4	96.8	97.4	97.8	98.1
	HTL (Ge, 2018)	74.8	88.3	94.8	98.4	80.9	94.3	95.8	97.2	97.4	97.8
	A-BIER (Opitz et al., 2018)	74.2	86.9	94.0	97.8	83.1	95.1	96.9	97.5	97.8	98.0
	ABE (Kim et al., 2018)	76.3	88.4	94.8	98.2	87.3	96.7	97.9	98.2	98.5	98.7
	JCF (Jacob et al., 2019a)	77.4	89.9	95.8	98.6	-	-	-	-	-	-
	Contrastive (Ours)	75.0	87.9	94.5	98.1	89.0	97.2	98.0	98.4	98.6	98.7
	Contrastive + DIABLO N = 8	77.8	89.5	95.3	98.4	91.3	98.1	98.7	99.0	99.1	99.1
	Triplet (Ours)	70.6	85.7	94.0	98.2	85.6	96.5	97.7	98.2	98.4	98.6
	Triplet + DIABLO N = 8	73.5	87.8	95.0	98.5	87.4	97.2	98.1	98.6	98.8	98.9

Table 3 :

 3 Impact of the pre-attention or post-attention on the performances for the three proposed attention strategies. Reported Recall@1 (R@1) is on the Cub-200-2011 dataset in percent.

			Feature	Dimension
		Baseline	Pre-att	Post-att	Pre-att	Post-att
	R@1	52.9 ± 0.2	52.3 ± 0.4	54.0 ± 0.4	58.3 ± 0.2	57.8 ± 0.1

  dataset in percent.

	performance from 69.7% without the divergence loss to 85.2%
	(+15.5%) in Recall@1 (see Table 2 in
			Feat. + Post-att.
	N	2	4	8	16
	R@1	53.4 ± 0.3 53.3 ± 0.3	54.0 ± 0.4	48.0 ± 0.4
			Dim. + Pre-att.
	N	2	4	8	16
	R@1	57.1 ± 0.3 57.4 ± 0.3	58.3 ± 0.2	58.9 ± 0.3
	76.1% (+8.9%) Recall@1 for M = 8. However, this archi-
	tecture tends to overfit due to the large number of parameters.
	Then, they propose an enhanced version named ABE that takes
	advantage of attention maps. The divergence loss increases the

Acknowledgments

Authors would like to acknowledge the COMUE Paris Seine University, the Cergy-Pontoise University and M2M Factory for their financial and technical support.


Florian Bahut

Remy Romanet

Nathalie Sieczkowski

Philippe Schmitt-Kopplin

Maria Nikolantonaki
email: maria.nikolantonaki@u-bourgogne.fr

Régis D Gougeon

Rémy Romanet

Philippe Schmitt- Kopplin

Antioxidant activity from inactivated yeast: Expanding knowledge beyond the glutathione-related oxidative stability of wine

Keywords: Oxidative stability, glutathione, nucleophile, wine, Chardonnay Chemical compounds studied 9958), Hydrogen sulfide (PubChem CID: 402), Acetaldehyde ), Sulfite (PubChem CID: 1099), Hexanal

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Early oxidation by nonenzymatic reactions could affect wine quality and thus its economic value. The natural occurrence of transition metals in wine is thought to initiate metal-catalyzed reduction of oxygen, leading to generate hydroperoxyl radicals, which are highly reactive radical oxygen species (ROS), and polyphenol-derived quinones [START_REF] Danilewicz | Review of Reaction Mechanisms of Oxygen and Proposed Intermediate Reduction Products in Wine: Central Role of Iron and Copper[END_REF]. The hydroperoxyl radical reacts quickly and non-selectively with ethanol in wine to yield 85% of 1-hydroxyethyl radical [START_REF] Elias | Key factors affecting radical formation in wine studied by spin trapping and EPR spectroscopy[END_REF]. The latter is then involved in further chemical reactions with main wine compounds, such as phenols or thiols, resulting in color browning and varietal aroma loss, which are key attributes of wine organoleptic quality, in particular for white wines [START_REF] Kreitman | Investigation of ethyl radical quenching by phenolics and thiols in model wine[END_REF][START_REF] Li | Mechanisms of oxidative browning of wine[END_REF][START_REF] Nikolantonaki | A method to quantify quinone reaction rates with wine relevant nucleophiles: A key to the understanding of oxidative loss of varietal thiols[END_REF]. The genuine antioxidant composition of the wine (phenolic compounds, sulfhydryl compounds, organic acids) regulates the oxidation rate and thus the shelf-life of the wine [START_REF] Kontogeorgos | Total free sulphydryls of several white and red[END_REF]. In order to preserve wine longer, sulfur dioxide (SO2) is one of the most versatile and efficient wine antioxidants used to prevent early oxidation. However, intolerances caused by SO2 derivatives have led to the reduction of its concentration in wines. In a competitive global winemaking market strategy, it is crucial to reduce or even eliminate the use of SO2 as a preservative and to search for new healthier and safer strategies.

Yeast derivatives (YDs) applied biotechnology was proposed a decade ago, as a new strategy to control wine oxidation during bottle storage through oxygen consumption and release of antioxidants [START_REF] Comuzzo | Antioxidant properties of different products and additives in white wine[END_REF][START_REF] Pozo-Bayon | Scientific evidences beyond the application of inactive dry yeast preparations in winemaking[END_REF]. Indeed, YDs refer to a class or fraction of yeasts produced on an industrial scale as additives [START_REF] Pozo-Bayon | Scientific evidences beyond the application of inactive dry yeast preparations in winemaking[END_REF]. Depending on the industrial process, YDs can be found under the form of inactivated yeast, yeast autolysate, yeast protein extract, yeast cell wall and yeast mannoprotein [START_REF] Comuzzo | Effect of different lysis treatments on the characteristics of yeast derivatives for winemaking[END_REF][START_REF] Pozo-Bayon | Scientific evidences beyond the application of inactive dry yeast preparations in winemaking[END_REF].

Amongst the numbers of compounds released by YDs in wine, glutathione (GSH) receives most of the scientific attention [START_REF] Bahut | Metabolic diversity conveyed by the process leading to glutathione accumulation in inactivated dry yeast: A synthetic media study[END_REF][START_REF] Kritzinger | Role of glutathione in winemaking: A review[END_REF]. This tripeptide containing a cysteine residue is well known to be present naturally in grapes, wine and yeasts. The reductive property supported by the free sulfhydryl enables it to have various beneficial effects during wine aging. GSH has the ability to form colorless products which delay the browning of model white wine under accelerated oxidative conditions [START_REF] Sonni | Impact of glutathione on the formation of methylmethine-and carboxymethine-bridged (+)-catechin dimers in a model wine system[END_REF]. In addition, GSH exhibited a protective effect on aromas during aging, notably volatiles esters and terpenes [START_REF] Papadopoulou | Inhibition of the decrease of volatile esters and terpenes during storage of a white wine and a model wine medium by glutathione and Nacetylcysteine[END_REF] and volatile thiols, and also reduced atypical flavors [START_REF] Dubourdieu | The role of glutathione on the aromatic evolution of dry white wine. Vinidea[END_REF]. In parallel, inactivated yeast rich in GSH showed stabilization of wine varietal aromas, such as volatile thiols and terpenes [START_REF] Gabrielli | Additions of glutathione or specific glutathione-rich dry inactivated yeast preparation (DYP) to sauvignon blanc must: Effect on wine chemical and sensory composition[END_REF]. Interestingly this study showed that pure GSH at the same concentration as the one released by inactivated yeast had a lower impact, notably on volatile thiols preservation. The combination of GSH with wine antioxidants (phenolic compounds and sulfites at different doses) has shown a positive impact on volatile compounds in long-term wine storage when compared with the use of sulfites alone [START_REF] Roussis | Protection of aroma volatiles in a red wine with low sulphur dioxide by a mixture of glutathione, caffeic acid and gallic acid[END_REF]. These results allowed us to hypothesize that the complex chemical composition brought into the must during fermentation by YDs could enhance the formation and/or the stabilization potential of wine aroma at the end of the fermentation.

According to a recent study on Chardonnay aged wines, Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) based metabolomics along with multivariate statistical analyses provided evidence that the GSH efficiency against oxidation during bottle aging is dependent on the wine's global antioxidant metabolome, including in particular N-and Scontaining compounds like amino acids, aromatic compounds and peptides. These compounds possess a strong nucleophilic character and their reactivity with wine electrophiles, such as oxidized polyphenols, suggests the formation of stable adducts possessing lower oxidative potential [START_REF] Nikolantonaki | Impact of Glutathione on Wines Oxidative Stability: A Combined Sensory and Metabolomic Study[END_REF]. YDs rich in GSH are thus gaining interest since they are a natural way for winemakers to increase the concentration of reduced GSH during winemaking and pre-bottling without direct addition of GSH, which is not yet allowed by European food additives regulations.

However, many of these studies about the impact of YDs on wine stability agree to highlight the combined effect of GSH with other compounds released by YDs. Indeed, the metabolic changes related to GSH accumulation in yeast can subsequently impact the diversity of metabolites released by YDs [START_REF] Bahut | Metabolic diversity conveyed by the process leading to glutathione accumulation in inactivated dry yeast: A synthetic media study[END_REF]. The objective of the present study was to characterize the antiradical effect of different YD products and to give insights into their chemical composition. This work constitutes a primary approach in understanding the action mechanisms of YDs and in establishing better criteria for their use in winemaking. Essentially, no study has shown a clear relationship between the diversity of compounds released by YDs and the potential oxidative stability of wine or other beverages. Our study is dedicated to exploring the stabilization potential properties of different yeast derivatives with a particular emphasis on the non-GSH soluble molecular fraction released in wine-like acidic medium.

Materials and methods

Chemicals

The water used in this study was ultrapure water (18.2 MΩ; Millipore, Germany). Ethanol was purchased from Honeywell (United States); formic acid (MS grade), 2,2-diphenyl-1-picrylhydrazyl (DPPH), citric acid and phosphate dibasic from Sigma-Aldrich (St. Louis, MO). Methanol (MS grade and HPLC grade) and acetonitrile (MS grade) were purchased from Biosolve Chimie (Dieuze, France). FeSO4,7H2O (99,5%) was purchased from Carlo Erba (Milan, Italy)

Sample sets

Seven yeast derivatives produced on a laboratory scale were used for this study, labeled YD1 to YD4 and YD6 to YD8 (Supplementary Information 1). Each yeast derivative was mixed at 1 g/L in hydro alcoholic solution (12% (v/v) of ethanol with 0.01% (v/v) of formic acid to reach a pH of 3.2) previously deoxygenated by bubbling nitrogen through for 10 min. After an hour of stirring at room temperature and in the dark with a rotary stirrer, samples were centrifuged (15 min at 9,000 g at 10 °C) and the supernatants were separated and kept at 4 °C until analysis [START_REF] Bahut | Metabolic diversity conveyed by the process leading to glutathione accumulation in inactivated dry yeast: A synthetic media study[END_REF].

All samples were prepared freshly and analyzed within 24 h to prevent deterioration.

DPPH radical scavenging activity

The DPPH assay was performed following the protocol previously described [START_REF] Romanet | The Antioxidant Potential of White Wines Relies on the Chemistry of Sulfur-Containing Compounds: An Optimized DPPH Assay[END_REF]. A solution of 2,2-diphenyl-1-picrylhydrazyl (DPPH) was prepared by mixing 27 mg of DPPH with 1L of 60:40 (v/v) 0.3 M buffer citrate-phosphate:methanol to reach a pH of 3.6. In the absence of oxygen, 3.9 mL of the DPPH solution were mixed with 0.1 mL of sample at different mass ratio YD/DPPH (Rm). After 4-h incubation in darkness, sample absorbance was measured in a UV-Vis spectrometer at 525 nm. Absorbance was normalized with the blank (buffer with 0.1 mL of model wine). Results were expressed as the Rm needed to reduce the initial absorbance by 20%, designated Rm20% and translated into the equivalent YD mass to provide the Rm20% (calculation details in Supplementary Information 2).

Nucleophilic compounds derivatization

The derivatization was performed using an adaptation of the protocol described by Nikolantonaki and collaborators (Nikolantonaki & Waterhouse, 2012;[START_REF] Romanet | Molecular characterization of white wines antioxidant metabolome by ultra high performance liquid chromatography high-resolution mass spectrometry[END_REF].

Firstly, the freshly prepared quinone, 4-methyl-1,2-benzoquinone (4MeQ), is added to 1 mL of sample in excess concentration (final concentration 4MeQ is 1 mM). After 30 min of reaction at room temperature, 1.5 mM SO2 is added to reduce the remaining 4MeQ in the sample. The addition of quinone in excess allows all nucleophilic compounds present in the soluble fraction of the yeast derivatives to be derivatized. A second set of analyses was performed by adding limiting amounts of 4MeQ, in order to derivatize compounds with the highest affinity for quinone. Six different limiting concentrations (from 30 µM to 625 µM) were added to samples for 30 min before quenching the reaction by addition of 1.5 mM of SO2. Samples were then analyzed by high resolution UHPLC-Q-ToF-MS in positive and negative modes with the protocol described below.

High resolution UHPLC-Q-ToF MS(/MS) analysis

The separation was performed with an ultra-high-pressure liquid chromatography system (Dionex Ultimate 3000; ThermoFisher) coupled to a MaXis plus MQ ESI-Q-TOF mass spectrometer (Bruker, Bremen, Germany). The non-polar and low polar metabolites were separated through reverse-phase liquid chromatography (RP-LC) by injecting 5 µL in an Acquity BEH C18 1.7 µm column, 100 × 2.1 mm (Waters, Guyancourt, France). Elution was performed at 40 °C using (A) acidified water (0.1% (v/v) formic acid) and (B) acetonitrile (0.1% (v/v) formic acid) with the following gradient: isocratic step from 0 to 1.10 min with 5% (v/v) B, then the percentage of B was increased to 95% (v/v) until 6.40 min, held there for 3 min and finally returned to the initial condition in 0.1 min for 5 min of re-equilibration. The flow rate was set to 400 µL min -1 .

Electrospray and mass spectrometer acquisition parameters for positive and negative polarity are summarized in Supplementary Information 3. A divert valve was used to inject four times diluted ESI-L Low Concentration Tuning Mix (Agilent, Les Ulis, France) at the beginning of each run, allowing a recalibration of each spectrum. The mass spectrometer was calibrated with undiluted Tuning Mix before batch analysis in enhanced quadratic mode, with less than 0.5 ppm errors after calibration. Acquisitions were done in the m/z 100 to 1500 mass range in positive ionization mode.

Quality control was used to guarantee the stability of the UHPLC-Q-ToF MS system before each run. Calibrated ions were restricted to those with S/N better than 30 and an absolute intensity of at least 1000. Before features extraction (couple of m/z values and retention time), the spectral background noise was removed. The extracted features were aligned with an in-house R script with a maximum m/z tolerance of 3 ppm and retention time tolerance of 0.5 min and variables absent from more than 80% of the samples were removed from the analysis.

Features fragmentation was performed using the Scheduled Precursors List MS/MS function.

The fragmentation was performed at two different collision energies: 20 and 35 eV. Parent ions and fragments were submitted to different databases through the massTRIX interface (http://masstrix.org) [START_REF] Suhre | MassTRIX: mass translator into pathways[END_REF] and YMDB 2.0 (http://www.ymdb.ca) and both compositional (obtained isotopic profile) and structural information were used to annotate compounds with high confidence level.

Chemical oxidation monitoring

The monitoring of chemical oxidation reactions was performed by UHPLC-Q-ToF MS analysis in negative mode using analytical conditions as described in Section 2.5, with the temperature of the auto-sampler set to 30 °C. Chemical oxidation was initiated by mixing 1 mL of YD soluble fraction at 1 g/L with 50 µL of 4MeC (20 mM) and 5 µL of FeSO4,7H2O (18 mM). Oxidation reactions were monitored after 5 minutes, and then every 50 minutes up to 40 hours after initiation.

Samples were analyzed in triplicate.

Data Analysis

All experiments were performed at least in triplicate and if not specified, results were expressed as average ± standard deviation for the triplicate. The basic data mining and data visualization were performed with R software v. 3.5.1. Nonparametric Kruskal-Wallis rank sum tests and Spearman tests were used for median comparison and correlation estimation, respectively. Curve fitting was performed with OriginPro 2017 (b.9.4.0.220).

After alignment, features that were positively correlated (Spearman correlation, ρ > 0.1) with the addition of the quinone were extracted. They were considered to be associated with compounds which had reacted with quinones. The nucleophile-quinone derivative is the result of the

combination of n*4MeC (1 ≤ n) with m*nucleophiles (1 ≤ m ≤ n+2) leading to m*Nu + n*4MeC
addition products [START_REF] Ma | Tracing oxidation reaction pathways in wine using 13 C isotopolog patterns and a putative compound database[END_REF]. Thus, a specific m*Nu + n*4MeC derivative carrying the m/z information of the corresponding free nucleophile and the combination of all the derivatives will be used to further express the nucleophilic fingerprints of the YDs. The SmartFormula tool from the DataAnalysis software (v.4.3,Bruker,Germany) allows attribution of a raw formula based on the detected m/z and the isotopic profile (Supplementary information 5). Since sulfites were used to quench the derivatization reaction, nucleophilic addition of x*HSO3 (0 ≤ x) on 4MeC moiety can be observed to form the nucleophilic addition product m*Nu + n*4MeC + x*HSO3. The putative raw formula (and thus the corresponding neutral mass) of the free nucleophiles was calculated by subtracting the raw formula of n*4MeC + x*HSO3 -2*(n + x + m -1)*H from the total raw formula of the quinone derivative. The resulting nucleophile neutral formula was submitted to a database search (Metlin and YMDB) for putative annotation.

Results and discussions

Radical scavenging activity

The measurement of radical scavenging activity was firstly applied to estimate antioxidant properties of pure compounds or mixtures. The radical scavenging activity of the different YD soluble fractions was measured using the DPPH assay (Figure 2), recently adapted for wine-like media [START_REF] Romanet | The Antioxidant Potential of White Wines Relies on the Chemistry of Sulfur-Containing Compounds: An Optimized DPPH Assay[END_REF]. DPPH is a stable radical in solution with a difference in absorbance between the radical and the protonated form. The decrease of the absorbance can be related to the protonation of the DPPH. It is thus possible to follow the reduction potential of a compound (or a mixture of compounds) with the amount/volume of sample needed to reduce the initial absorbance to a specific range (Supplementary Information 2).

Figure 1 shows the mass needed from different YDs to get a 20% decrease (mass equivalent) of the initial absorbance of the DPPH solution. The results of the DPPH assay allowed the following classification of YDs soluble fractions, going from the highest scavenging activity to the lowest: YD8 > YD7 > YD3 > YD1 > YD4 > YD6 > YD2. All tested YDs could reduce the DPPH radical, with YDs rich in GSH (YD8, YD7 and YD3) having the highest antiradical capacity compared to those without GSH accumulation. This result clearly demonstrated the wide range of potential antioxidant properties among the different YDs, with YD8 exhibiting nearly 10 times higher efficiency than YD2.

It is important to mention that only soluble fractions were used in this assay (insoluble fractions were removed by centrifugation). Thus, these results represent only a part of the antioxidant potential of the product (except for YD4, which was totally soluble). Indeed, it is known that insoluble fractions also have antioxidant activity, notably due to sulfur-containing compounds present in cell walls and mannoproteins [START_REF] Jaehrig | Antioxidative activity of (1→3), (1→6)-β-d-glucan from Saccharomyces cerevisiae grown on different media[END_REF]. In that assay, insoluble fractions were removed to prevent the adsorption of DPPH on cell walls and consequently reduce the concentration (and absorbance) of the radical, which would lead to an overestimation of the antiradical activity of YDs.

YD6 which was obtained from the same yeast strain as YD7 (strain B) but without the process leading to GSH accumulation in the intracellular medium, exhibited a 3.8 times lower antiradical activity than that of YD7. The antiradical capacity of YD soluble fractions was related not only to the GSH enrichment industrial process, but also to the yeast strain used. Thus, YD1 (strain A) and YD2 (strain B), which were produced without GSH enrichment exhibited significantly different antiradical scavenging activities, with YD1 being more efficient than YD2. It is also interesting to note the biological variability apparent between YD6 and YD2 obtained from the same strain (B) and the same procedure but in different batches. YD6 was significantly more efficient than YD2 (0.72 mg against 0.92 mg respectively) indicating that the technology used to produce the inactive yeast (inactivation procedure, drying system for example) may influence the final activity of the product.

To estimate the impact of the GSH concentration on the DPPH results, it was possible to quantify the concentration of GSH in each YD soluble fraction during the assay (red square in Figure 1). However, there was no clear relationship between the concentration of GSH during the assay and the antiradical activity of the YDs (Spearman correlation ρ = 0.46, p-value > 0.3) despite the known antiradical activity of GSH. YD2, which showed the lowest antiradical activity, was the YD with the highest concentration of GSH during the assay. In contrast, considering equivalent amounts of GSH released by YD8, YD3 and YD1 samples, we observed significant differences in their global antiradical capacity. Therefore, these results led to the conclusion that the activity of the whole soluble fraction (and not only GSH) must impact the antiradical capacity of the YDs estimated by DPPH assay and hence should better explain the classification among YDs than GSH alone. This result is in agreement with a previous study comparing GSH, yeast autolysates and wine lees, where the yeast autolysate (200 mg/L GSH equivalent) showed a greater impact on DPPH discoloration than pure GSH at 500 mg/L [START_REF] Comuzzo | Antioxidant properties of different products and additives in white wine[END_REF]. The potential of the non-GSH fraction on wine aroma stability has also highlighted [START_REF] Andújar-Ortiz | Role of Glutathione Enriched Inactive Yeast Preparations[END_REF][START_REF] Rodríguez-Bencomo | Fate of the glutathione released from inactive dry yeast preparations during the alcoholic fermentation of white musts[END_REF]. Compounds with reducing property such as cysteine-containing compounds could be more abundant than GSH and thus contribute more than GSH to the pool of reductive compounds [START_REF] Jaehrig | In Vitro Potential Antioxidant Activity of (1→3), (1→6)-β-dglucan and Protein Fractions from Saccharomyces cerevisiae Cell Walls[END_REF][START_REF] Roussis | Inhibition of the decline of volatile esters and terpenols during oxidative storage of Muscat-white and Xinomavro-red wine by caffeic acid and N-acetyl-cysteine[END_REF]. GSH is the most abundant nonproteinaceous thiol in yeast, but the accumulation of low concentrations of other sulfhydrylcontaining compounds could greatly impact the global reactivity of the matrix against free radicals, or oxidative species [START_REF] Rodriguez-Bencomo | Impact of glutathione-enriched inactive dry yeast preparations on the stability of terpenes during model wine aging[END_REF][START_REF] Roussis | Protective Effect of Thiols on Wine Aroma Volatiles[END_REF]. Therefore, the increasing level of these compounds with the enrichment process could explain the differential activities of YD7 and YD6 (which differ only in the production process) and also the highest antiradical activity of YD3, YD7 and YD8 [START_REF] Bahut | Metabolic diversity conveyed by the process leading to glutathione accumulation in inactivated dry yeast: A synthetic media study[END_REF].

Estimation of molecular nucleophilic fingerprints of YD soluble fractions

To go beyond the DPPH method, which does not provide any molecular information related to the antioxidant activity of YDs, we applied a derivatization procedure proposed by [START_REF] Romanet | Molecular characterization of white wines antioxidant metabolome by ultra high performance liquid chromatography high-resolution mass spectrometry[END_REF] with an electrophilic probe specifically designed to mimic oxidants in wine, coupled with mass spectrometry detection. Derivatization procedures for the detection and quantification of specific compounds are commonly used to increase the limit of detection (LOD) of targeted compounds. For wine oxidative stability studies, the 4-methylquinone (4MeQ), obtained by oxidation of 4-methylcatechol (4MeC), has been used as a model compound for oxidized polyphenols due to its electrophilic carbon site which could be subject to nucleophilic addition in wine [START_REF] Danilewicz | Review of Reaction Mechanisms of Oxygen and Proposed Intermediate Reduction Products in Wine: Central Role of Iron and Copper[END_REF][START_REF] Danilewicz | Reactions involving iron in mediating catechol oxidation in model wine[END_REF][START_REF] Danilewicz | Further Studies on the Mechanism of Interaction of Polyphenols, Oxygen, and Sulfite in Wine[END_REF]. In addition to sulfites, GSH and ascorbic acid, other nucleophilic compounds, such as thiols, amines and polyphenols, can also competitively react with quinones to form nucleophilic addition products [START_REF] Nikolantonaki | Measuring protection of aromatic wine thiols from oxidation by competitive reactions vs wine preservatives with orthoquinones[END_REF][START_REF] Waterhouse | Oxidation of Wine Phenolics: A Critical Evaluation and Hypotheses[END_REF].

The innovative use of untargeted analysis on derivatized and non-derivatized samples enables the detection of nucleophilic compounds specific for wine relevant antioxidants [START_REF] Inoue | Foodomics platform for the assay of thiols in wines with fluorescence derivatization and ultra performance liquid chromatography mass spectrometry using multivariate statistical analysis[END_REF][START_REF] Romanet | Molecular characterization of white wines antioxidant metabolome by ultra high performance liquid chromatography high-resolution mass spectrometry[END_REF]. Molecular features (m/z pairs and retention time from UHPLC-Q-ToF-MS analyses in both negative and positive ionization modes) were extracted after addition of increasing amounts of 4MeQ in each YD soluble fractions. The reaction of nucleophiles with quinones resulted in the disappearance of the free nucleophiles and the appearance of new products formed after nucleophilic addition. Spearman correlation tests allowed classification of the compounds either as free nucleophiles (ρ < 0) or as their addition reaction products (ρ > 0). All Spearman correlation scores are given in Supplementary Information 4. The combination of positive and negative ionization modes enabled the detection of 85 compounds significantly impacted by the presence of the 4MeQ and common to at least three tested YDs.

The great majority of the 85 detected features were detected in negative mode (63) and only 10 features were solely detected in positive mode (the other 12 features were also detected in negative mode), which is in agreement with previous results on the efficiency of the negative ionization mode to detect quinone derivatives [START_REF] Ma | Tracing oxidation reaction pathways in wine using 13 C isotopolog patterns and a putative compound database[END_REF]. Features corresponding to nucleophilic addition reaction products (ρ > 0) represented the majority of the detected compounds in both positive (73% (16/22)) and negative ionization modes (83% (52/63)). Since the addition reaction products correspond to 4MeC addition on nucleophiles, the diversity of these products is related to the diversity of free nucleophiles, which means that the products of nucleophilic addition are representative of the diversity of nucleophiles present in the solution. The large abundance/diversity of quinone derivatives compared to the few free nucleophiles actually detected illustrates the potential of the proposed derivatization method to improve detection of poorly ionizable free nucleophilic compounds. With respect to these observations, quinone derivatives detected in negative mode were selected as representatives of the nucleophilic fraction (nucleophilic fingerprint, Figure 2) of the samples and used for further investigation.

These 52 nucleophiles enabled the discrination of the YDs according to their initial nucleophilic fingerprint and thus their potential antioxidant activity. As previously reported, YD4 appeared chemically significantly different from the others with few nucleophiles (26 detected) at low abundance. Besides YD4, three clusters were clearly defined: YD7-YD8, YD1-YD3 and YD6-YD2. The absolute number of nucleophiles was equivalent (average of 42 ± 2 compounds) between these YDs, but they showed important molecular diversity. Besides the 26 common nucleophiles shared by YD7-YD8, YD1-YD3 and YD6-YD2, 16 compounds were specific to some of these YDs. The chemical proximity between the YD nucleophilic fingerprints is relevant information to attribute similar antioxidant activities to similar samples. In order to estimate this parameter, the principal components analysis of the nucleophilic fingerprint features was performed to reduce the number of dimensions of the data and get an overview of the sample's distance (Figure 3). In that case, the nucleophilic fingerprint allowed the hierarchization of YDs consistent with that obtained from the DPPH assay.

The DPPH assay revealed the failure of the GSH concentration to explain the scavenging activity of YDs soluble fractions. However, the derivatization procedure highlighted the potential of nucleophiles that were unconsidered until now, to better characterize the antiradical activity of YD soluble fractions. This study showed the major importance of the non-targeted approach to consider the global nucleophilic fingerprint for a better assessment of the antioxidant potential of YD soluble fractions. In the present case, the correlation circle plots revealed the features which specifically discriminate the different YDs and pointed out the most relevant (out of the inner circle representing a correlation of 0.8).

In addition to the nucleophilic fingerprint, the high resolution of the UHPLC-Q-ToF-MS has been used to putatively annotate the nucleophiles. Based on the derivatization method, the nucleophilic addition reaction could occur between one or several nucleophiles with one or two electrophilic sites of the 4MeQ [START_REF] Ma | Tracing oxidation reaction pathways in wine using 13 C isotopolog patterns and a putative compound database[END_REF]Nikolantonaki et al., 2012[START_REF] Nikolantonaki | Measuring protection of aromatic wine thiols from oxidation by competitive reactions vs wine preservatives with orthoquinones[END_REF][START_REF] Nikolantonaki | A method to quantify quinone reaction rates with wine relevant nucleophiles: A key to the understanding of oxidative loss of varietal thiols[END_REF][START_REF] Romanet | Molecular characterization of white wines antioxidant metabolome by ultra high performance liquid chromatography high-resolution mass spectrometry[END_REF]. Table 1 summarizes the putative annotations of the quinone derivatives detected in negative ionization mode, which were positively correlated to the addition of 4MeQ. Almost 60% (26/44) of the nucleophiles detected were not found in online databases, whatever the combination of 4MeC or HSO3-4MeC used. Of the 41 attributed elemental formulas, 21 could be putatively assigned to sulfur containing compounds.

Within the annotated adducts, eight were related to GSH or GSH precursors such as cysteine ). This agrees with the nature of the YDs, since three of these products had been produced in order to accumulate GSH (YD3, YD7 and YD8). The high concentration of GSH and its precursors could explain the abundance of adducts containing these specific nucleophiles. This observation was also corroborated by the correlation circle plot in Figure 3 where the separation between YD3, YD7 and YD8 is strongly correlated (ρ >0.8) to the abundance of [371.0922_2.55] and [733.1809_2.13], which correspond to Glu-Cys and GSH derivatives, respectively, and to a lower extent to [428.1136_2.45] and [857.234_2.49], which also correspond to GSH derivatives.

In Figure 3, the opposite direction is driven by the abundance of sulfite derivatives [203.0021_2.87] and [203.0020_1.31] in the samples YD2 and YD6. These two compounds correspond to sulfite addition on 4MeC, likely in different C electrophilic sites. The nucleophilic addition of SO2 is known to be minor in comparison with the reduction of the quinone (11% yield against 89%, respectively) [START_REF] Nikolantonaki | A method to quantify quinone reaction rates with wine relevant nucleophiles: A key to the understanding of oxidative loss of varietal thiols[END_REF]. Thus, the abundance of sulfonated 4MeC could provide relative information about the underivatized fraction of the 4MeQ.

Besides the GSH and GSH precursor derivatives, few other compounds had been annotated from online databases. Notably two peptides Tyr-Pro-Ala (C24H28N3O10S, m/z = 550.1502) and Pro-Cys-Gln-Glu (C25H34N5O13S2, m/z = 676.1594), which were strongly correlated with the abundance of glutamyl-cysteine and GSH derivatives (Figure 3). The correlation between two compounds could indicate the co-accumulation of these peptides during the GSH accumulation process, or the degradation product coming from specific macromolecules involved in the GSH accumulation process. The nucleophilic property of these peptides highlights the wider effect of the GSH enrichment process on a diversity of other metabolites. The quality of the growing environment, notably nutritious factors, is known to impact the genome expression and thus the metabolome of yeasts [START_REF] Kresnowati | When transcriptome meets metabolome: Fast cellular responses of yeast to sudden relief of glucose limitation[END_REF]. During the industrial process, this leads to the accumulation of specific metabolites, such as reduced GSH. It was shown that the transient presence of specific nutrients in the yeast culture media can produce yeast with distinct growth and compositional characteristics [START_REF] Alfafara | Cysteine addition strategy for maximum glutathione production in fed-batch culture of Saccharomyces cerevisiae[END_REF]. Moreover, it was recently shown that the accumulation of GSH in inactivated Saccharomyces cerevisiae yeasts is associated with an increased production of multiple cysteine-containing peptides and other sulfur containing compounds. Finally, Table 1 shows that 13 nucleophiles were formed with compounds which do not contain sulfur. This further indicates the clear potential of chemical families other than thiols to contribute to the antiradical activity of YDs. For example, aldehydes such as acetaldehyde (C9H9O3, m/z = 165.0557) and hexanal (C13H17O3, m/z = 221.1183) had been annotated as potential 4MeQ binders. These results are in accordance with those found previously in wine, where the reaction between aldehydes and polyphenols is a major step in pigments formation [START_REF] Li | Mechanisms of oxidative browning of wine[END_REF][START_REF] Oliveira | Oxidation mechanisms occurring in wines[END_REF][START_REF] Waterhouse | Oxidation of Wine Phenolics: A Critical Evaluation and Hypotheses[END_REF].

The role of soluble fraction of YDs on chemically initiated oxidation under wine-like conditions

If the derivatization procedure provided the total relevant nucleophiles present in the solution, which were able to react with an excess concentration of quinone, it must be considered that the oxidation of catechol into quinone is a gradual process catalyzed by metal transition. During this slow oxidation, distinct nucleophiles do not have the same affinity towards the quinone and thus are submitted to competitive additional reactions (Nikolantonaki et al., 2012[START_REF] Nikolantonaki | Measuring protection of aromatic wine thiols from oxidation by competitive reactions vs wine preservatives with orthoquinones[END_REF][START_REF] Nikolantonaki | A method to quantify quinone reaction rates with wine relevant nucleophiles: A key to the understanding of oxidative loss of varietal thiols[END_REF]. Under chemical oxidation conditions in wine-like medium, the oxidation reaction rate is related to the rate of reactive oxygen species formation via the oxidation of polyphenols catalyzed by the presence of metals [START_REF] Danilewicz | Mechanism of Interaction of Polyphenols, Oxygen, and Sulfur Dioxide in Model Wine[END_REF][START_REF] Elias | Controlling the fenton reaction in wine[END_REF].

In order to investigate the reactivity of YDs under chemical oxidation conditions, YD2, YD3 and YD8 soluble fractions, selected for their low, medium and high nucleophilicity (respectively), according to the different clusters shown Figure 2 and their antiradical properties (Figure 1), were submitted to chemical oxidation in the presence of a model polyphenol (4MeC) and of Fe 2+ .

Reactions were conducted at 30 °C and monitored over 40 hours, with data collection every 50 min, and then processed to identify nucleophiles strongly correlated with oxidation (Spearman correlation, ρ ≥ |0.85|). For each m/z_retention time couple, the peak area was centered on its initial value (time = 5 min) and divided by the standard deviation for behavior comparison on a common scale. Figure 4 represents the evolution of five features during chemical oxidation, considered as representative for kinetic profiles of all detected features.

The production of the oxidant (4MeQ) begins immediately after mixing Fe 2+ and 4MeC. The rapid increase of GSH-4MeC in the three YDs shows that the 4MeQ was quickly quenched by the GSH which decreased gradually in parallel. After 20 h of oxidation, the free GSH in YD2 is completely depleted while GSH-4MeC reaches a plateau. In contrast, GSH consumption in YD3 and YD8 was not complete even after 40 h (Supplementary Information 6-8). The observed differences between GSH consumption kinetic rates could be guided by the concentration effect and synergic/antagonistic effects, due to the presence of other compounds competing during its nucleophilic addition reaction with the quinone. Indeed, similar to GSH, the unknown nucleophile C8H16N2O9S2 (m/z = 347.0232) specific to YD3 and YD8, was totally depleted in YD3 but not in YD8 after 30 h. These results highlight the importance of the chemical diversity of the nucleophilic fingerprints of the YDs for the comprehension of their antioxidant capacity.

Besides the production of GSH-4MeC, the double addition product GSH-4MeC-GSH (equivalent to C27H37N6O14S2, m/z = 733.1805) was not observed in all samples. It never appeared in YD2 while it appeared with delay in YD3 (after 14 hours) and YD8 (after 10 hours). The production of GSH-4MeC-GSH can be related to (i) a further oxidation of GSH-4MeC derivatives and (ii) a nucleophilic addition of GSH to this specific electrophile. Thus, the higher the concentration of the GSH-4MeC adduct (and the corresponding oxidized form) and that of the remaining free GSH, the quicker the onset of the double adduct production. This agrees with the quicker appearance and evolution of this double adduct for YD8 than for YD3, where the concentration of free GSH was initially higher. The feature [299.0706_1.96] (annotated as [(Gly-Cys+4MeC-2H)-H] -) exhibited a particular behavior in this example. In YD2, it quickly increased during the first 13 h, reached a maximum and then decreased until total disappearance 15 h later.

This reaction could be explained by the rapid nucleophilic addition of Gly-Cys on 4MeQ until a maximum corresponding to the total derivatization of the Gly-Cys. Then the decrease would be interpreted as a second reaction occurring on the simple adduct, such as a second nucleophile addition on the Gly-Cys-4MeC. It is also interesting to note the highly different kinetic of appearance of the Gly-Cys-4MeC. In YD2, the reaction was fast and led to the total derivatization of Gly-Cys, whereas in YD3 and YD8 the reaction occurred with delay and was slow. The presence of competitive reactions for nucleophilic addition could explain these differences as already reported in the literature [START_REF] Nikolantonaki | Measuring protection of aromatic wine thiols from oxidation by competitive reactions vs wine preservatives with orthoquinones[END_REF]. Nucleophilic competition can therefore actively modulate the production rate of specific quinone adducts and thus modulate the final chemistry of the wine.

In order to semi-quantify the nucleophilic potential of YDs soluble fractions, the reaction rates of GSH consumption and GSH-4MeC production were calculated directly in their native complex chemical environments. To that purpose, first order kinetic (Equation 1) was used to express GSH and GSH-4MeC reaction rates during oxidation:

𝐴 (𝑡) = 𝐴 0 + 𝐵 * 𝑒 -𝑅 * 𝑡 Equation 1
With the following constants: This indicates that the nucleophilic addition of GSH to 4MeQ must be the main reaction leading to the consumption of GSH. In contrast, the production of GSH-4MeC seemed more complex. Since GSH-4MeC-GSH was found in YD3 and YD8, it showed that an equilibrium exists between the GSH-4MeC increase (nucleophilic addition of GSH on 4MeQ) and GSH-4MeC decrease by further nucleophilic addition (for example, nucleophilic addition of GSH on GSH-4MeQ to form GSH-4MeC-GSH present in YD3 and YD8). In the latter reaction, GSH could be replaced by any other nucleophile present in the solution. The apparent first order of the reaction actually hides a complex balance between the appearance of GSH-4MeC and the disappearance of this compound in further reactions. For example, the progressive disappearance of GSH-4MeC is clearly visible in YD2

A(
(Figure 4) after 20 h. On the basis of these few acknowledged reaction pathways for GSH, we only considered the GSH kinetics for further analysis. The kinetic rate of GSH given by the R parameter in Equation 1 indirectly estimates the dynamic of reactions occurring in the solution between this compound and its chemical environment. B (a.u.) R (h -1 ) t1/2 (h) adj. R² YD2 2.6×10 6 ± 3.9×10 4 a -2.1×10 6 ± 1.1×10 5 a 0.36 ± 0.01 a 1.9 0.88 YD3 3.4×10 6 ± 3.2×10 5 ab -2.3×10 6 ± 3.4×10 4 a 0.07 ± 0.02 ab 9.9 0.92 YD8 4.9×10 6 ± 1.4×10 5 b -3.7×10 6 ± 1.7×10 5 a 0.03 ± 0 b 23.1 0.94 Parameters A0 and B give information about the initial and final area of the compound, while R is representative of the global rate of the reaction. In order to compare the rates of reaction between YDs, the half-life constants (t1/2, time needed to achieve 50% of the reaction) were calculated as follows (Equation 2): 2YD2 was the only sample were GSH reached total depletion, which gave access to the experimental t1/2. The t1/2 was estimated to be 4.6 hours (Equation 2) with the fit, whereas the experimental value was 3.2  0.3 hours. The estimation is thus close to the experimental t1/2 which consolidates the validity of first order kinetics to estimate the rate of the reaction. Consumption of GSH is significantly quicker for YD2 with a t1/2 fifteen and five times lower than YD8 and YD3, respectively.

𝑡 1/2 = ln (2) 𝑅 Equation
These results suggest that compounds present in YD samples can interfere with the oxidative chain reaction, normally leading to the reaction of GSH with 4MeQ to produce GSH-4MeC, through possibly more kinetically favorable reactions. Our study enabled us to appreciate the influence of these reactions by tracing the kinetics of GSH consumption. GSH can be used as an indirect marker to estimate the activity of the pool of nucleophiles to preserve GSH itself. Indeed, the slower kinetic of consumption of GSH (YD8) would be the result of a matrix able to strengthen the action of GSH and thus to preserve this oxidation-sensitive compound. Samples with higher diversity and abundance of nucleophiles (YD3 and YD8) present longer t1/2 for consumption of GSH and production of the addition product. In this context, YDs can be ordered from the more nucleophilic to the less nucleophilic: YD8 > YD3 > YD2 based on their t1/2. The low abundance of additional nucleophiles in YD2 led to a rapid decrease of GSH until total depletion. YD3 and YD8 released much more GSH in solution compared to YD2 and did not reach the plateau after 40 h of oxidation. But interestingly, they exhibited high differences in t1/2. Associated with the higher amount of GSH, YD8 exhibited a slower (but not significant at p-value ≤ 0.05) consumption rate than YD3. This shows that independently of the concentration of GSH, the co-accumulated nucleophiles effectively preserve the pool of GSH. This result is in accordance with our previous results showing the positive impact of the GSH accumulation process on the quality and quantity of potential nucleophilic compounds [START_REF] Bahut | Metabolic diversity conveyed by the process leading to glutathione accumulation in inactivated dry yeast: A synthetic media study[END_REF].

In addition, this nucleophilic order (YD8 > YD3 > YD2) order matches perfectly with the antiradical activity of these samples found with the DPPH assay. The DPPH assay demonstrates that GSH alone does not allow characterization of the antiradical activity of YDs [START_REF] Comuzzo | Antioxidant properties of different products and additives in white wine[END_REF]. But here, the observation of the activity of the other nucleophilic compounds allows better understanding of the potential pool of compounds behind the antiradical activity. These results put emphasis on the significance of a complex pool of nucleophilic compounds, rarely considered so far, which contributes to the overall antioxidant activity of samples such as YDs.

Conclusions

The metabolomics approach provided evidence of specific fingerprints for YD soluble fractions. The DPPH assay of these soluble fractions was performed in order to assess the YD antiradical activity. The higher radical scavenging activity of yeast derivatives naturally rich in GSH pointed out the positive influence of this specific production process on the antioxidant activities of the YDs. Nevertheless, GSH concentrations appeared poorly correlated with the DPPH scores implying the potential contribution of a larger pool of compounds from YDs to the oxidative stability. The use of a model electrophile (4-methyl-1,2-benzoquinone) as a derivatization agent revealed a pool of nucleophiles which may react with quinones in model wine; 52 nucleophiles discriminated the YDs into four groups based on the number and the abundance of these reactive compounds. This innovative separation of samples only based on derivatized compounds matched very well with the DPPH scores, allowing samples to be ordered based on their stabilizing activity: YD8 > YD7 > YD3 > YD1 > YD4 > YD6 > YD2. The UHPLC-Q-ToF-MS further enabled annotation of most of the nucleophiles, which mostly belong to sulfur and nitrogen-sulfur compounds. Our results confirmed the need to consider the whole chemical diversity of the nucleophilic fraction present in the sample, beyond the sole GSH concentration. However, monitoring the consumption rate of specific nucleophiles (for example GSH) can be used as an indirect marker to estimate the activity of the entire pool of nucleophiles. Indeed, YDs with the highest number of kinetically favorable nucleophilic reactions (longest GSH half-life in this example) also appeared to possess the best antiradical activity. The important issue for the practical application of GSH-enriched YDs in wine, would thus be that the slower the kinetic rate of the GSH consumption (longer t1/2), the higher the YD antioxidant potential, because wine could thus benefit from a long-lasting reservoir of GSH antioxidant. This work opens new perspectives for the analysis and development of yeast preparations dedicated to improving wine oxidative stability. YD mass equivalent (mg)

-1 0 1 2 Normalized Area 6 9 8 .9 1 0 _ 8 .9 3 0 9 .1 7 4 1 _ 6 .7 2 8 3 .0 3 9 5 _ 1 .3 1 3 0 9 .1 7 0 9 _ 5 .0 5 2 9 3 .1 7 8 9 _ 7 .9 7 4 3 9 .0 8 0 7 _ 0 .6 5 3 2 5 .0 3 5 2 _ 0 .6 9 2 4 5 .0 8 2 _ 3 .6 4 3 5 3 .2 4 8 5 _ 7 .0 7 5 2 6 .0 8 0 9 _ 2 .4 5 1 6 5 .0 5 5 7 _ 3 .1 6 4 8 1 .0 2 4 8 _ 2 .5 3 9 8 .2 3 3 6 _ 7 .2 4 3 2 .0 7 5 7 _ 0 .6 9 4 2 3 .0 8 2 1 _ 0 .6 5 2 2 1 .1 1 8 3 _ 4 .5 4 3 8 6 .0 3 3 9 _ 0 .6 5 1 6 5 .0 4 0 8 _ 6 .1 8 6 7 6 .1 5 9 4 _ 2 .2 6 4 5 1 .0 4 8 8 _ 1 .3 5 1 0 .0 6 8 9 _ 1 .1 5 5 5 0 .1 5 0 2 _ 3 .4 4 3 5 3 .2 0 0 2 _ 7 .4 4 8 0 0 .2 1 2 1 _ 2 .5 5 5 7 2 .1 3 1 8 _ 3 .4 4 2 1 5 .0 6 7 5 _ 2 .5 5 3 5 5 .1 5 8 1 _ 7 .3 9 2 4 7 .0 9 7 7 _ 3 .2 1 8 5 7 .2 3 4 _ 2 .4 9 4 3 1 .1 1 5 1 _ 2 .4 8 7 3 3 .1 8 0 9 _ 2 .1 3 1 2 6 .9 0 5 1 _ 0 .6 7 2 4 2 .0 4 9 2 _ 1 .8 8 2 0 3 .0 0 2 1 _ 2 .8 7 1 5 5 .0 1 7 1 _ 1 .8 9 5 5 7 .3 9 9 6 _ 8 .2 1 4 0 9 .3 1 1 1 _ 7 .8 4 2 5 0 .1 4 5 _ 6 .2 6 2 6 5 .1 4 8 _ 5 .9 2 5 0 2 .9 6 3 _ 5 .3 9 2 4 5 .0 8 1 9 _ 4 .3 1 1 2 3 .0 4 5 2 _ 3 .3 3 0 0 .0 0 9 2 _ 3 .2 1 1 2 2 .0 3 7 5 _ 3 .2 1 3 7 1 .0 9 2 2 _ 2 .5 5 4 2 6 .0 9 7 9 _ 2 .4 9 2 7 2 .0 8 9 _ 2 .4 8 4 2 8 .1 1 3 6 _ 2 .4 5 2 9 9 .0 7 0 7 _ 2 2 0 4 .9 9 8 2 _ 1 .3 2 0 3 .0 0 2 _ 1 .3 1 5 0 8 .0 7 0 4 _ 1 .1 5

(

  C3H7NO2S, m/z = 120.0125) and glutamyl-cysteine (C8H14N2O5S, m/z = 249.0551): [(Cysteine-4MeC-2H)-H] -(C10H12NO4S, m/z = 242.0492), [(Cys-Gly+4MeC-2H)-H] -(C12H15N2O5S, m/z = 299.0707), [(Glu-Cys+4MeC-2H)-H] -(C15H19N2O7S, m/z = 371.0922), [(GSH+4MeC-2H)-H] - (C17H22N3O8S, m/z = 428.1136), [(Glu-Cys+4MeC+H2SO3-4H)-H] -(C15H19N2O10S2, m/z = 451.0488), [(GSH+4MeC+H2SO3-4H)-H] -(C17H22N3O11S2, m/z = 508.0704), [2(GSH+4MeC-2H)-H] -(C27H37N6O14S2, m/z = 733.1809) and 2[(GSH+4MeC-2H)-H] -(C34H45N6O16S2, m/z = 857.234_2.49

Figure 1 :Figure 2 :

 12 Figure 1: Mass of yeast derivatives needed to reduce the initial absorbance of DPPH by 20% (left

Figure 3 :

 3 Figure 3: PCA analysis (scores plot and correlation circle plot) of YD soluble fraction nucleophilic

Figure 4 :

 4 Figure 4: Representative kinetic profiles of nucleophiles consumption and nucleophile derivatives production under chemical oxidation conditions (4MeC, Fe 2+ , 30°C) during 40 h. Each point

Table 1 :

 1 Relevant nucleophiles detected in negative ionization mode and their putative annotation based on mass precision and isotopic profiles. The isotopic profile and the MS² profile are available in

	Supplementary Information 5 for compounds detected with relative intensities higher than 1000. Mono
	and di-deprotonated free forms of 4-methylcatechol C7H7O2 (m/z = 123.0452) and C7H6O2 (m/z = 122.0375)
	respectively as well as the auto-polymerization products C14H13O4 (m/z = 245.0819) and C14H13O4 (m/z =
	247.0977) were excluded from the data mining.
	m/zexperimental RT	Ion putative formula	Δppm	Adduct	[M]	[M] neutral Formula	YMDB ID	Annotation*
	155.0171	1.9	C7H7O2S	-0.80	[(M+4MeC-2H)-H]-	33.9877	H2S	YMDB00653	hydrogen sulfide
	165.0557	3.2	C9H9O3	-0.11	[(M+4MeC-2H)-H]-	44.0262	C2H4O	YMDB00022	acetaldehyde
	203.002	1.3	C7H7O5S	0.16	[(M+4MeC-2H)-H]-	81.9725	H2SO3	YMDB00114	sulfite*
	203.0021	2.9	C7H7O5S	0.65	[(M+4MeC-2H)-H]-	81.9725	H2SO3	YMDB00114	sulfite
	215.0675	2.6	C8H11N2O5	0.72	[(M+4MeC-2H)-H]-	94.0378	CH6N2O3	-	unknown
	221.1183	4.5	C13H17O3	-0.08	[(M+4MeC-2H)-H]-	100.0888	C6H12O	YMDB16016	hexanal*
	242.0492	1.9	C10H12NO4S	-0.21	[(M+4MeC-2H)-H]-	121.0197	C3H7NO2S	YMDB00046	cysteine
	250.145	6.3	C14H20NO3	0.53	[(M+4MeC-2H)-H]-	129.1154	C7H15NO	YMDB16052 isopentylacetamide*
	265.148	5.9	C12H25O4S	0.36	[(M+4MeC-2H)-H]-	142.1028	C5H18O2S	-	unknown
	272.089	2.5	C10H14N3O6	0.70	[(M+4MeC-2H)-H]-	151.0593	C3H9N3O4	-	unknown
	283.0395	1.3	C11H11N2O5S	0.30	[(M+4MeC-2H)-H]-	162.0099	C4H6N2O3S	-	unknown
	293.1789	8.0	C14H29O4S	-1.04	[(M+4MeC-2H)-H]-	172.1497	C7H24O2S	-	unknown
	299.0707	2.0	C12H15N2O5S	-0.05	[(M+4MeC-2H)-H]-	178.0412	C5H10N2O3S	YMDB00690	Cys-Gly
	300.0092	3.2	C13H6N3O4S	2.50	[(M+4MeC-2H)-H]-	178.9789	C6HN3O2S	-	unknown*
	309.1709	5.1	C17H25O5	0.49	[(M+4MeC-2H)-H]-	188.1412	C10H20O3	YMDB16207	hydroxydecanoic acid
	309.1741	6.7	C14H29O5S	-0.06	[(M+4MeC-2H)-H]-	216.1508	C7H24N2O3S	-	unknown
	325.0352	0.7	C9H13N2O9S	1.46	[(M+4MeC+H2SO3-4H)-H]-	124.0484	C2H8N2O4	-	unknown
	353.2002	7.4	C16H33O6S	-0.38	[(M+4MeC-2H)-H]-	232.1708	C9H28O4S	-	unknown
	353.2485	7.1	C16H37N2O4S	1.55	[(M+4MeC-2H)-H]-	232.2184	C9H32N2O2S	-	unknown

× 10 6 ; m/ztheoretical corresponds to the exact m/z of the ion putative formula. *Features for which MS/MS profile is provided in supplementary information 5

Table 2

 2 presents the fitting parameters for the curves of GSH consumption and GSH-4MeC production for YD2, YD3 and YD8, using Equation1. Extensive results such as fitted curves for all replicates and residuals plots are available in Supplementary Information 9-11. The fit with a first order equation matched well with the GSH raw data (adjusted R² > 0.98 for all replicates).

Table 2 :

 2 Parameters of the first order fit (Equation1) and associated half-life time (t1/2) and adjusted R².Different letters represent significant differences after Wilcoxon test, n = 3 and p-value < 0.05.

		GSH
	A0 (a.u.)	B (a.u.)	R (h -1 )		t1/2 (h) adj. R²
	YD2 -9.8×10 3 ± 4.4×10 3 a	5.9×10 5 ± 2.7×10 4 a	0.15 ± 0.01 a	4.6	0.98
	YD3 -3.1×10 3 ± 2.3×10 5 a	1.4×10 6 ± 2.5×10 5 ab	0.03 ± 0	ab 23.1	0.98
	YD8 -1.5×10 6 ± 7.8×10 5 a	4.0×10 6 ± 8.2×10 5 b	0.01 ± 0	b	69.3	0.99
		GSH-4MeC
	A0 (a.u.)

ACKNOWLEDGEMENTS

The authors acknowledge the Regional Council of Bourgogne -Franche-Comté, the "Fonds Européen de DEveloppement Régional (FEDER)" and Lallemand SA (31, Blagnac) for financial support. The authors would like to thank Lallemand SA (31) and Oenobrand (34) for the yeast derivatives supply. They also would like to thank Dr. Eveline Bartowsky for a careful reading of the manuscript by a native English speaker.

5862); Isopentylacetamide (PubChem CID: 263768); Cysteinyl-glycine (PubChem CID: 439498); Hydroxydecanoic acid (PubChem CID: 21488); Glutamyl-cysteine (PubChem CID: 10171468); Glutathione (PubChem CID: 124886)

Abbreviations

4MeC, 4-methylcatechol; 4MeQ, 4-methyl-1,2-benzoquinone; DPPH, 2,2-diphenyl-1picrylhydrazyl; ESI, electrospray ionization; FTICR-MS, Fourier-transform ion cyclotron resonance mass spectrometry; LOD, limit of detection; m/z, mass/charge; PCA, principal component analysis; Rm20%, mass ratio to reduce 20% initial absorbance; RT, retention time; SO2, sulfur dioxide (sulfite); t1/2, half-life constant; UHPLC-Q-ToF-MS, ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometer; YD, yeast derivative

Conflicts of interest

The authors wish to confirm that there are no conflicts of interest associated with this publication.