# Insights into the mechanisms involved in the evolution of the structural and physicochemical properties of tomato during air drying – A study combining MRI, unilateral NMR and conventional techniques

Maja Musse, Amidou Traore, Alexandre Leca, Marc Lahaye, Clémentine Lorho, Gauvain Pierre, Nathan Rausch de Traubenberg, Caroline Garcia, Yves Diascorn, Stéphane Quellec, et al.

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Maja Musse, Amidou Traore, Alexandre Leca, Marc Lahaye, Clémentine Lorho, et al. Insights into the mechanisms involved in the evolution of the structural and physicochemical properties of tomato during air drying – A study combining MRI, unilateral NMR and conventional techniques. Food Research International, 2025, 212, 10.1016/j.foodres.2025.116385. hal-05051288

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# Insights into the mechanisms involved in the evolution of the structural and physicochemical properties of tomato during air drying – A study combining MRI, unilateral NMR and conventional techniques☆

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

Maja Musse<sup>a,*</sup>, Amidou Traore<sup>b</sup>, Alexandre Leca<sup>c</sup>, Marc Lahaye<sup>d</sup>, Clémentine Lorho<sup>a</sup>, Gauvain Pierre<sup>b</sup>, Nathan Rausch de Traubenberg<sup>c</sup>, Caroline Garcia<sup>c</sup>, Yves Diascorn<sup>a</sup>, Stéphane Quellec<sup>a</sup>, Carine Le Bourvellec<sup>c</sup>, Sylvie Clerjon<sup>b</sup>, Sylvain Challois<sup>a</sup>, Guylaine Collewet<sup>a</sup>, Nadia Bertin<sup>e</sup>

## ARTICLE INFO

**Keywords:**

Magnetic resonance imaging

Transverse relaxation

Fruit processing

Dehydration

Fleshy fruit

Solanum lycopersicum

## ABSTRACT

Most fruits and vegetables have high water content, making them highly perishable. Drying preserves them by reducing water content, slowing microbial activity and chemical spoilage. It also enhances product range and reduces transport and storage costs. This study explored how water and air distribution and chemical properties of fruits affect the drying process and quality of dried tomatoes, focusing on texture and nutrient composition. MRI and unilateral NMR, the unique spatially resolved techniques to characterize water dynamics and multiscale tissue structure non-destructively, were combined with conventional techniques assessing chemical composition, histological features and texture properties.

MRI and unilateral NMR succeeded in capturing tissue evolution and heterogeneous features of tomato slices during drying by accessing information on water status and distribution and apparent micro-porosity. Results indicated that fruit microstructure influenced drying kinetics: larger cells and less heterogeneous tissue promoted dehydration. Drying also changed biochemical compounds and phenolic profiles, affecting nutrient quality. Despite differences in drying rates between the two cultivars investigated, their texture and nutrient composition remained similar, suggesting potential for improvement of cultivars destined for drying and/or of the drying process itself. These findings could help optimize drying processes and develop tomato cultivars better suited to drying. Last, the experimental approaches adopted here, i.e., MRI for in situ drying investigation and unilateral NMR, can help gain deeper insights into fleshy fruit transformation, with possible industrial applications.

## 1. Introduction

Most fruit and vegetable edible organs contain over 80 % water and are therefore highly spoilable. The time constraints on both their availability and consumption make preservation essential for use outside the harvest season. Drying provides a transformation process that slows down microbial activity and chemical spoilage by removing water from the fruit. It not only improves preservation, it also diversifies

the range of consumable products (dried fruits, sauces, powders, etc.), cutting transport and storage costs by reducing weight and volume. Both the properties of the fresh fruit, i.e., multiscale structure and composition, and the drying method influence dehydration rates and the texture and nutritional value of the dried fruit (Altino et al., 2018; Pravitha et al., 2024). It can be expected that the particular organization of water at tissue and subcellular levels in individual fruits will play an important role in drying behaviors.

<footnote><sup>a</sup> INRAE, OPAALE, 35044 Rennes, France</footnote>

<footnote><sup>b</sup> INRAE, QuaPA, 63122 Saint-Genès Chamanelle, France</footnote>

<footnote><sup>c</sup> INRAE, Avignon Université, UMR SQPOV, 84914 Avignon, France</footnote>

<footnote><sup>d</sup> INRAE, BIA, 44316 Nantes, France</footnote>

<footnote><sup>e</sup> INRAE, PSH, 84914 Avignon, France</footnote>

<footnote>* This article is part of a Special issue entitled: 'Magnetic Resonance in Food Science' published in Food Research International.</footnote>

<footnote>* Corresponding author at: UR OPAALE, INRAE, 17 Avenue de Cucillé, CS 64427, 35044 Rennes, France.
E-mail address: maja.musse@inrae.fr (M. Musse).</footnote>
The structure of plant parenchyma tissues is complex, but a simplified model of it can be proposed by considering the three main aqueous compartments separated by semi-permeable membranes, i.e. the cell vacuole, the cytoplasm and the cell wall, and the air-filled intercellular spaces. In fresh tissue, the turgor pressure of water on the membranes and cell wall produces cell rigidity (Bruce, 2003) and contributes to the tissue texture (Oey et al., 2007). Drying induces histological changes: first, reversible cell shrinkage, where turgor can be restored; then, collapse of both cells and intercellular pores as water content decreases (Prothon et al., 2003). In a low-field (LF)-Nuclear Magnetic Resonance (NMR) study of apple parenchyma (Hills & Remigereau, 1997) it was suggested that the water loss induced by air drying occurred mainly from the vacuole, whereas water loss from the cytoplasm and cell wall was not significant. Water also plays a crucial role in the structure of the cell wall and its interactions with phyto-micronutrients, affecting the texture (Lahaye et al., 2013) of fruit tissues and the bioaccessibility and bioavailability of these nutrients (Borel, 2003; Ke et al., 2023; Le Bourvellec et al., 2019; Morelli & Rodriguez-Concepcion, 2023). Recent studies indicate the key role of cell wall water and its interactions with pectin on the poroviscoelastic properties of plant tissues (Lahaye et al., 2020; Lopez-Sanchez et al., 2020), suggesting that cell wall properties may significantly influence water retention and diffusion during tissue dehydration. In particular, advanced stages of ripening have been reported to lengthen hot air drying of fleshy fruits due to ripening-related breakdown of cell wall polysaccharides (Li et al., 2022; Ni et al., 2023; Niu et al., 2024). Although dried fruit has a higher concentration of nutrients than fresh fruit, drying methods may also cause the loss of bioactive fruit components (Jorge et al., 2018; Wojdylo et al., 2016). A better understanding of all these phenomena is needed to adapt the drying process to variations in fruit traits determined by genotype and/or growing conditions, and/or to select tomato varieties for drying, thereby allowing the production of high-quality dried products in an energy-efficient manner.

The main interest of LF-NMR and magnetic resonance imaging (MRI) for the study of plant tissues is their ability to provide information on water dynamics and structural features at different length scales. Studies have used MRI to visualize macroscopic morphological features in plant organs during transformation (Genovese et al., 2023; Leforestier et al., 2022), and to estimate the gas fraction locally (Winisdorffer et al., 2015). LF-NMR and MRI can also be used to access information at subcellular level by quantifying transverse relaxation ($T_2$), which is determined by the mobility, molecular environment and diffusivity of water molecules and membrane properties (Hills & Duce, 1990; Van As, 2007). This is possible because water molecules experience specific physical and chemical environments in the main cell compartments, resulting in different $T_2$ values. Although the assignment of relaxation times to specific compartments is not straightforward – the diffusive exchange of water between compartments depends on several factors (including membrane state and compartment size) and tends to partially average the signals, NMR relaxometry has been successfully applied in studies on fruit and vegetable processing (Genovese et al., 2023; Gonzalez et al., 2010; Hills & Lefloch, 1994; Hills & Remigereau, 1997; Leca et al., 2021), some of which discuss the drying process. These studies involved LF-NMR (Hills & Remigereau, 1997; Musse, Cambert, & Mariette, 2010), portable unilateral NMR (Proietti et al., 2018), designed to analyze water from the object volume near the sensor, and MRI, the last two approaches enabling spatial information on $T_2$ to be obtained in one (1D) or two directions (2D) respectively. However, a very limited number of studies have investigated how tissue changes induced by drying (water content, cell shrinkage, membrane permeability) affect multi-exponential $T_2$ relaxation (Hills & Remigereau, 1997; Leforestier et al., 2022), in order to make it possible to exploit the capacity of LF-NMR, portable unilateral NMR and/or MRI to provide relevant information on changes in tissue microstructure and composition. Further investigation is therefore called for, to enable comparison of transverse relaxation parameters with histological and biochemical

tissue traits. Combined with the application of protocols to access information on global shrinkage and the spatial distribution of microporosity, this would allow the full benefits of MRI and unilateral NMR to be accessed in the investigation of local changes in fruit properties during drying. Being non-destructive and non-invasive, these approaches have the further major advantage of providing continuous measurements of the samples as they evolve during the drying process.

This study set out to better understand how the evolution of structural and chemical properties during air drying determines the final quality of dried tomatoes, i.e., their composition and texture. To this end, MRI was used to provide $T_2$, proton density and micro-porosity maps, along with morphological parameters to monitor water distribution and quantity at scales ranging from the whole fruit to cellular compartmentalization. In addition, a portable unilateral NMR providing $T_2$ and proton density profiles in tomato slices was evaluated as a potential analytical device for industrial applications. In parallel to the MRI and unilateral NMR experiments, we investigated variations in the histological and biochemical characteristics and texture of the fruit pericarp. The first aim of these additional measurements was to improve understanding of the changes in the nutritional and textural qualities of the dried fruit. The second aim was to establish links between the observed variations in texture and histological and biochemical tissue traits and the recorded changes in transverse relaxation times and microporosity, thus allowing MRI/NMR parameters to be proposed as indicators for the assessment of the evolution of fruit structure and composition during fruit drying. The study investigated two industrial tomato cultivars that were expected to differ in their histological characteristics and micronutrient composition (de Castro et al., 2017), with a focus on the parenchyma tissue as the main consumable part of the fruit.

## 2. Materials and methods

### 2.1. Experimental design

Tomato (Solanum lycopersicum) fruits of two industrial cultivars (H1311 and Terradou) were greenhouse produced at INRAE Avignon in Spring 2023. Fruits of similar colour and size were harvested at the red ripe stage for each cultivar, packed in plastic bags to prevent water loss, and transported to the laboratories. These fruits formed a single batch from which the different batches were produced and sent to the different laboratories. Harvested fruits were stored at 4 °C until analysis was performed a few days after delivery. In order to approximate industrial conditions, tomato slices were prepared as described below and subjected to a drying process.

Two experiments (shown in the diagram in Fig. 1) were performed to meet the objectives described above. In the first (Experiment A), MRI images were acquired continuously during the drying of the tomato slices in a temperature-controlled chamber placed inside the imager. The aim was to measure the main changes in the tissues by means of $T_2$ mapping and morphological 3D images and to compare the two cultivars. An in-depth analysis (Experiment B) of fresh and dried tomato slices was then performed, combining MRI ($T_2$ and µ-porosity mapping and morphological analysis), unilateral NMR ($T_2$ and intensity profiles) and conventional destructive analysis (of chemical composition, histology and texture). To ensure that all analysis were carried out on comparable samples, the tomato slices to be used for the different measurement methods were dried to three predefined drying degrees (DD1, DD2, DD3, from least dried to most dried) under similar conditions.

### 2.2. Experiment A: continuous in situ MRI monitoring of tomato drying

The fruit analyzed in Experiment A (batch A), comprised a total of eight H1311 and eight Terradou tomatoes (Fig. 1). The tomato samples were prepared for drying as follows: central slices 24 mm thick were cut from the equatorial region perpendicular to the pedicle axis, and the
<figure><img src="image_4.png" /><figcaption>Fig. 1. Overall design of the drying experiments.</figcaption></figure>

core, locular tissue and seeds were then removed, leaving only the
pericarp and cuticle. Eight of the prepared fresh slices (four from each
cultivar) were analyzed simultaneously. They were first positioned on a
10 mm mesh grid, raised by 10 mm, to allow uniform drying inside the
MRI device. The grid and tomato slices were then together placed in the
temperature-controlled chamber, which provided a constant airflow
(~1 m/s) at 45 ± 1 °C, and inserted into the MRI radiofrequency (RF)
coil. Continuous MRI acquisitions were performed over approximately
15 h and the experiment was replicated (on another four H1311 and four
Terradou tomatoes).

## 2.3. *Experiment B: step drying*

Three batches of fruit (Fig. 1) were each assigned to a method as
follows: batch B1 - MRI; batch B2 - unilateral NMR; batch B3 - texture,
histology and chemical analysis.

MRI was performed on the central section of 15 entire fresh to-
matoes. Following this initial MRI analysis, 24 mm-thick slices with
centers corresponding to those of the virtual equatorial tomato slices in
the MRI images were cut and prepared as described in the previous
section (Experiment A) for drying (5 tomatoes per drying degree). For
the unilateral NMR and conventional destructive analysis (chemical
composition, histology and texture), different tomatoes were used for
measurements of fresh and dried slices. In the case of the unilateral
NMR, for H1311, 4 tomatoes were used for each drying degree while for
Terradou 3, 4 and 6 samples were used for fresh, drying degree 2 and
drying degree 3, respectively. Note that for the unilateral NMR mea-
surements, slices were 20 mm thick, due to the experimental constraints,
as described below.

Air drying of the tomato slices was performed at 45 ± 1 °C using
comparable ventilated ovens in the different laboratories (Froilabo
AP120, France for Batches B1 and B2, Memmert UF110, Memmert,
Germany, for Batch B3). Because oven humidity was not regulated, the
different laboratory environment and ambient conditions introduced
slight discrepancies in drying kinetics. The slices were placed on a 10
mm mesh grid, raised by 10 mm (as in Experiment A) and were left to
dry for the time required to reach three approximate target relative
water content (WC) values determined in preliminary experiments: 1)

90 % for drying degree 1 (DD1); 2) 85 %, for drying degree 2 (DD2); and
3) 50 % for drying degree 3 (DD3). Details of the drying process are
provided in Supplementary Material 1 and images of representative
FRESH and dried (DD1, DD2 and DD3) tomato slices are shown in
Supplementary Material 2. It should be noted that a significant range of
variation in water content was generated at DD3, demonstrating the
sample-dependent nature of drying behavior (Supplementary Material
1). Indeed, the mean WC values for H1311 batches B1, B2 and B3 at DD3
were 50 ± 8 %, 27 ± 9 % and 64 ± 6 %, respectively, while for Terradou
they were 41 ± 13 %, 37 ± 16 % and 58 ± 6 % respectively. After each
drying step, the tomato slices were placed in at 4 °C for 15 min for
thermal rebalancing. They were wrapped in plastic film and left for at
least 12 h at 4 °C prior to analysis in order to stabilize the tissues after
dehydration. MRI and unilateral NMR measurements were performed at
10 ± 1 °C, regulated by the temperature-controlled MRI device and
laboratory air-conditioning respectively.

## 2.4. MRI Acquisition

MRI measurements were performed on a 1.5 T imager (Magnetom
Avanto, Siemens, Erlangen, Germany) equipped with a circular polar-
ized head array receive RF coil. 15 entire fresh fruits (positioned on their
sides) were analyzed in groups of five. For the dried tomatoes slices, 7 or
8 of them (wrapped in plastic film) were imaged simultaneously. Before
Experiment B, entire fresh tomatoes were stored overnight at 10 °C in a
climatic chamber (Aralab Fitoclima D1200PLH, Portugal), while dried
tomato slices were placed in climatic chambers for 30 min at 10 °C for
thermal rebalancing.

2D MRI experiments were performed with the following geometric
parameters: coronal sections in the middle of tomato slices (Experiments
A and B) and transverse sections in the middle of entire tomatoes
(Experiment B) were imaged with a imaging slice thickness of 5 mm, a
192 × 192 pixel matrix, and a 192 × 192 mm² FOV. The imaging pro-
tools were:

1. Experiments A and B: multi spin echo (MSE) sequence to measure T₂ maps (Adriaensen et al., 2013) with 512 echoes, where the first echo (TE) was equal to an inter-echo spacing (ΔTE) of 6.5 ms. Pixel bandwidth (BW) was 290 Hz/pixel, number of averages (NA) was 2 and
repetition time (TR) was fixed at 20 s to prevent T₁-weighting.

2. Experiment B: multi gradient echo (MGE) sequence to measure T₂* maps, with 12 echoes (first echo time TE₁ = 2.27 ms and ΔTE = 1.58 ms), TR = 5 s, BW = 930 Hz/pixel, flip angle = 90°, and 2 NA.

In both Experiments A and B, 3D morphological images with a spatial resolution of 0.8 × 0.8 × 0.8 mm³ were acquired using a fast recovery turbo spin echo (FR-TSE) sequence with the following parameters: 256 × 256 × 80(96) pixel matrix for fresh tomatoes and 256 × 256 × 44 pixel matrix for dried tomato slices, TR = 140 ms, effective TE = 28 ms, ΔTE = 9.2 ms, BW = 199 Hz/pixel, Turbo Factor = 6, 42% interpolation in the imaging slice direction and 3 NA.

## 2.5. Image processing

Transverse relaxation times T₂ were estimated using MSE images acquired at different echo times tᵢ = TE(i). The signal in each voxel is the sum of the signals from all the protons included within it, whose T₂ values follow a continuous distribution of values. However, in MRI, the temporal sampling of the signal is not fine enough to allow estimation of such a distribution. For this reason, the signal is modelled by a discrete sum of a limited number of components, which nevertheless provides an informative description of the signal for our study. Thus, the signal sᵢ in each voxel was modelled as the sum of exponential decays of M (components)

$$s_i = \sum_{j=1}^{M} A_j e^{-t_i/T_{2j}} \quad (1)$$

where T₂j and Aⱼ are respectively the transverse relaxation time T₂ of component j and its associated amplitude (conventionally, components are numbered from the shortest to longest T₂). T₂j and Aⱼ were calculated for each voxel using the method described in (Collewet et al., 2022). The latter is a compromise between the maximization of the likelihood of the data and the spatial regularization necessary to reduce the effects of noise. In fact, spatial regularization reduces the variance but, in return, induces a bias. Thus, the regularization parameters are adjusted to achieve an acceptable bias-variance compromise. In this study, regularization was necessary when the number of components to be estimated were greater than or equal to two, otherwise the result had a noise level too high to be exploited. In the mono-exponential case the contribution of the regularization was not significant, which is why, in this case, the estimation was carried out without it to avoid any bias.

Calculations were performed for mono-exponential (M = 1) and tri-exponential (M = 3). The mono-exponential model allows a robust evaluation of the average T₂ in each voxel, while the tri-exponential model gives more detailed information on the different water pools producing the signal. The tri-exponential model was selected by applying the Bayesian criterion (Collewet et al., 2022). In this instance, the calculation of the component with the shortest T₂ (representing less than 10% and 25% of the signal for fresh and dried (DD2) tomatoes respectively) appears to have been compromised by the presence of acquisition artifacts; this component was therefore excluded from our analysis.

Apparent micro-porosity maps were computed following the method used in (Musse, De Guio, et al., 2010), which uses both T₂ and T₂* values. T₂* measurements differ from those for T₂, in that they use the gradient echo sequence and are not adjusted to compensate for the effects of the inhomogeneity of the magnetic field inside each voxel. Since this inhomogeneity is linked to the quantity of air, it becomes possible to estimate micro-porosity by using both T₂ and T₂*. These two sets of relaxation times were calculated with the mono-exponential model without spatial regularization.

The MRI maps (T₂ and micro-porosity) for each sample were calculated in two steps. First, the mean values of the parameters were computed for the region of interest (ROI), delimited manually using ImageJ software (Schneider et al., 2012) to select the whole pericarp

area. The mean value of T₂ and micro-porosity as a function of the distance from the cuticle was then calculated.

## 2.6. Volume

Semi-automatic segmentation was performed on 3D FR-TSE images using Avizo software. First, an automatic thresholding method based on the Otsu criterion (Otsu, 1979) was applied. Morphological operations were then performed to clean up the resulting image by filling holes and removing small spots. Last, the volume of each binary object was obtained following label analysis. For fresh tomatoes, additional preliminary processing steps were necessary because the fruit was scanned in its entirety. First, 30 central slices of each fresh tomato were selected, corresponding to the thickness of the fruit slices (physically) cut for drying tomatoes. Next, the pericarp was segmented manually with a brush tool for all the slices in the images. The region of interest thus obtained was extended to the intermediate slices using the interpolate tool. Last, the volume of each slice was computed using label analysis.

Shrinkage was expressed as a relative ratio between volume (Vᵢ) at a given drying degree (Experiment A) or time t (Experiment B) and the initial volume (V₀).

$$\psi_i = 100 \frac{V_i}{V_0} \quad (2)$$

## 2.7. Unilateral NMR

### 2.7.1. Acquisition

Experiments were performed at 10 °C in a climatic room using a unilateral NMR device (Mobile Universal Surface Explorer, MOUSE, PM25, Magritek, Aachen, Germany) equipped with a Kea® console, with a 1H resonance frequency of 13.29 MHz (Supplementary Material 3). This device has a permanent magnet geometry that generates a steep gradient of approximately 7.5 Tm⁻¹ in the B₀-(z)-direction, away from the magnet surface. Combined with a linear surface coil for RF transmission and signal reception, this linear gradient enables selective signal measurement within a flat sensitive volume of a thickness of 0.1 mm at a theoretical distance of 25 mm from the magnet surface. To optimize the signal to noise ratio (SNR), the configuration with a 10-mm thick spacer positioned between the magnet and the 4 cm × 4 cm surface RF coil was used, resulting in a distance of 15.8 mm between the coil and the sensitive volume. Accordingly, for the unilateral NMR study, the thickness selected for the fresh tomato slices was 20 mm. Since only trend comparisons are made with MRI results, no signal normalization for slice thickness differences was required. Measurements at various depths, i.e., profiling in the direction of the tomato slice thickness, were achievable using a high-precision lift that moved both the magnet and the RF coil downwards along the z-direction. Each tomato slice under investigation was wrapped in food plastic and placed on a 0.5 mm-thick glass sheet on top of the 4 cm × 4 cm measurement window.

For the profiles measurements, i.e., signal intensity (SI) as a function of depth, the signal was acquired for 80 sensitive volumes (0.1 mm × 40 mm × 40 mm) in 200 µm steps moving parallel to the tomato slice thickness (Supplementary Material 3). It was acquired, at each of the 80 depths, using a Carr-Purcell-Meiboom-Gill (CPMG) sequence (Meiboom & Gill, 1958) with the following parameters: 256 echoes, excitation pulse duration 12 µs, TE 100 µs, TR 3 s and 4 NA, resulting in a total acquisition time of 32 min for all 80 depths. To maximize the SNR, SI was estimated by averaging the 256 CPMG decay echoes (Blümich et al., 2020), producing a signal weighted by both proton density and T₂. For the T₂ measurements, CPMG experiments with 2048 echoes were recorded for at least 3 depths in the middle of the tomato slice for comparison with MRI results, with TE 100 µs, TR 20 s and NA 16 for a total acquisition time of 5 min.
### 2.7.2. Signal processing

Unlike MRI, the signal acquired with the unilateral NMR device was sufficiently well sampled and with a sufficiently high SNR to allow the estimation of a continuous T₂ distribution. To investigate the T₂ distribution in each sample, inverse Laplace transform (ILT) was used to fit the 2048 echo decays to a multi-exponential model. The signal decays were first pre-processed by removing the first two echoes (Bālibanu et al., 2000) and those following the first negative amplitude value, i.e., noise. ILT was then performed using an in-house Matlab® 2022a (MathWorks, Natick, MA, USA) implementation of the non-negative least square (NNLS) inversion algorithm (Whittall & Mackay, 1989), fed with 100 T₂ values logarithmically spaced from 0.3 ms to 1000 ms. The resulting solution was a sparse matrix to which a Tikhonov regularization parameter alpha (Whittall & Mackay, 1989) with selected values between 1.025 and 1.030 was applied to stabilize the fit. The final T₂ distribution consists of probability density functions representing amplitudes (weightings) applied to exponential decays with distinct T₂ time constants which characterize different water pools in the sample.

### 2.7.3. Water content

Water content, WC, expressed as a percentage of fresh weight for each tomato slice, was computed following each analysis in Experiments A and B using the following equation:

$$ WC = 100 \frac{(FW - DW)}{FW} \qquad (3) $$

where FW and DW represent, respectively, the fresh and dry weights of the tomato slices.

DW was determined by freeze drying (Experiment A) or oven drying at 70 °C for 72 h (Experiment B, Batches 1 and 3) or at 105 °C for 12 h (Experiment B, Batch 2).

### 2.7.4. Histology

Cell size and morphological descriptors were assessed through macro-vision histology. Two samples at each drying degree (FRESH/ DD1/DD2/DD3) were selected from Batch B3, cut into thin slices (approx. 1 mm thick) with a razor blade, then left to stain with Calco-fluor White (Fluorescent Brightener 28, Sigma-Aldrich, Germany) at 0.1 g.L⁻¹ for 5 min. A high-resolution C-mount digital camera (Baumer VCXU31C, Baumer SAS, France), equipped with a 0.5×-1.3× macro lens (VSTech 0513, VS technology Corporation, Japan) and a 20 mm extension tube, was used to visualize the sample slices under a UV lamp at 350 nm in order to highlight the cell wall.

Images were analyzed with FIJI software (Schindelin et al., 2012) and MorphoLibJ package (Legland et al., 2016) for particle segmentation. Post-control of the segmented images was systematically performed by the same human operator to correct and improve cell segmentation. For each cell, the Feret diameter, i.e. the longest distance between any two points along the cell perimeter, was used as the main cell size descriptor. This was automatically measured in FIJI through the ‘Analyze particles’ function. The relationship between the Feret diameter and the distance from the cuticle was studied by manually selecting cells and measuring their relative distance from the cuticle in FIJI.

### 2.7.5. Texture

Texture Profile Analysis was performed on three samples of each drying degree (FRESH/DD1/DD2/DD3) from the B3 batch with a Ta-Plus Texture Analyzer (Lloyd Ametek, France) equipped with a 50 N load sensor and a 5 cm × 5 cm compression plate, moving downwards at 1 mm.s⁻¹ until 20 % deformation was reached. The plate was then raised 1 mm at 2 mm.s⁻¹. A second cycle of downward/upward movement was performed under the same conditions after a 10 s pause. From the two compressions, values for sample hardness, springiness and gumminess were retained for analysis. Sample thickness was also measured as an indicator of sample shrinkage from the oven drying.

### 2.8. Composition

Three samples of each cultivar and drying degree were prepared for composition analysis. The absorbance of sugars and acids was measured with a microplate reader (SAFAS Xenius spectrofluorimeter, SAFAS, Monaco) using samples treated with enzymatic kits (R-Biopharm AG, Darmstadt, Germany). Polyphenols were analyzed on freeze-dried samples in accordance with (Le Bourvellec et al., 2011) and compared with external standards (Le Bourvellec et al., 2018). The very small sample size made it hard to repeat the polyphenol analysis, generating high uncertainty and high standard deviations. Carotenoids were analyzed using fresh samples prepared in accordance with Serino et al. (Sérino et al., 2009) and analyzed by HPLC-DAD, as in (Page et al., 2012).

### 2.9. Data analysis

Statistical analysis of MRI data was performed with Statgraphics Centurion XVII data mining software (StatPoint Technologies, Inc., Warrenton, VA, USA). The nonparametric Kruskal-Wallis test, suited for dataset with a non-normal distribution, followed by a post-hoc Bonferroni test for multiple pairwise comparisons were used to determine significant differences at 95 % confidence level between drying degrees. The nonparametric Wilcoxon test was applied to paired samples to compare the cultivars at each drying degree.

Drying, histology, composition and texture data were analyzed using R (Team RC, 2022) (RStudio software, version 2023.6.1.524), including tidyverse (Wickham et al., 2019) and multcomp (Hothorn et al., 2008) packages. Upon validating the normality and homoscedasticity of the composition dataset, ANOVA were performed at 95 % confidence level, followed by post-hoc Tukey. Honestly Significant Difference tests for pairwise comparison. To overcome lack of normality, the cell size dataset was studied with Kruskal-Wallis test at 95 % confidence level, followed by a post-hoc Bonferroni for pairwise comparison.

## 3. Results

### 3.1. Continuous in situ MRI monitoring of tomato drying

Fig. 2 A shows two representative pairs (first replicate) of mono-exponential T₂ maps, illustrating the drying process of the tomato slices inside the MRI imager, one for each cultivar. The surface area of the tomato slices can clearly be seen to decrease in all four examples, with a slower reduction in pericarp surface being visible in the H1311 cultivar compared with the Terradou cultivar. The 3D FR-TSE MRI images enabled this difference to be quantified using the volume shrinkage index (Eq. 2), indicating that the significant variations in the rate of volume decrease between the two cultivars occurred at the initial and final stages of the drying process (Fig. 2 B, Supplementary Material 4). The mono-exponential T₂, averaged across the entire pericarp (Fig. 2 C), for the Terradou cultivar fell most quickly in comparison with H1311 from the second to the fifth measurement points (~3 h to ~7 h after drying began). At the first measurement point, the appearance of the H1311 tomato pericarp was relatively heterogeneous (Fig. 2 A). Here, T₂ mapping made it possible to distinguish three ring-shaped zones, with shorter T₂ values (~ 400 ms) in the 1-mm outer and large inner zones compared to those in the very thin central zone (~ 450–500 ms). By contrast, in the pericarp of Terradou tomatoes, T₂ values showed a more uniform decrease from the cuticle to the inner pericarp edge. This heterogeneity within the pericarp is illustrated in Figs. 2 D and E, which represent mono-exponential T₂ as a function of distance from the tomato cuticle at the different drying steps. T₂ was observed to shorten in both cultivars as drying progressed. The results of the second replicate, corroborating those of the initial trial (Fig. 2), are shown in Supplementary Material 5.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Experiment A. (A) Mono-exponential T₂ maps of 2 H1311 and 2 Terradou tomatoes during continuous drying inside the MRI device (first replicate). (B) Volume shrinkage index (computed from morphological images measured by 3D FR-TSE sequence) during drying. (C) Mean T₂ values calculated in ROIs corresponding to the whole pericarp shown in (A). (D) and (E) Mono-exponential T₂ as a function of distance from the cuticle of tomato pericarp during drying (the maximal distance from the skin corresponds to the values observed in at least 3 of 4 tomatoes) for H1311 and Terradou respectively (colour sequence from fresh to driest: light blue, orange, gray, yellow, middle blue, green, brown, black, khaki. Values shown are the mean value ± standard deviation for 3 or 4 tomatoes. In (B) and (C), stars indicate differences (*P* < 0.05) between the two cultivar slopes (defined as a rate of change between previous and subsequent points. Statistical analysis of the differences (*P* < 0.05) between T₂ values at different distances for each drying time is presented in Supplementary Material 4. The results of the replication of</figcaption></figure>
the experiment are presented in Supplementary Material 5. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

## 3.2. Experiment B

Following Experiment A, which produced an initial overview of the principal changes in tomatoes during the drying process and highlighted discrepancies between cultivars, Experiment B was conducted on the step-dried tomato slices combining multiple methods (MRI, unilateral NMR and destructive measurements) to provide more comprehensive analysis. In addition to mono-exponential $T_2$ mapping and slice volume estimation, the MRI measurements in Experiment B generated multi-exponential $T_2$ and apparent micro-porosity maps, enabling spatial analysis of micro-structural changes to be carried out.

## 3.3. MRI

Fig. 3 shows a typical set of $T_2$ and apparent micro-porosity maps for the H1311 cultivar before (FRESH, first row) and after drying tomato slices to 3 drying degrees (DD1, DD2 and DD3). By imaging FRESH tomato slices before removing the core, locular tissue and seeds in Experiment B, it was possible to differentiate all tissues on $T_2$ maps. As observed in Experiment A (Fig. 2), the tomato pericarp had relatively heterogeneous mono-exponential $T_2$ values, especially for the H1311 cultivar (Fig. 4 A and B). As in the mono-exponential $T_2$ maps, ring-shaped zones could be detected in the $T_{22}$ and $T_{23}$ maps, while less details were observable in $A_0$ maps. The external part of the FRESH pericarp was characterized by very low apparent micro-porosity (~2 %), while the micro-porosity of the internal area was slightly greater (~3 %). Compared to the FRESH samples, the morphological features of the mono-exponential $T_2$ and µ-porosity maps at DD1 and DD2 were similar, with lower $T_2$ and higher µ-porosity values compared to fresh pericarp. An increase could be observed in apparent micro-porosity values with distance from the cuticle in the pericarp of fresh and DD1 tomatoes of both cultivars (Fig. 4 C and D). Although the apparent micro-porosity of DD2 tomatoes appeared to follow the same pattern of increase from the cuticle to the inner pericarp edge, this variation was not significant due to the high variability of the samples.

As described above, tri-exponential relaxation parameters were also computed to obtain information on the different water pools contributing to the MRI signal. From fresh tomatoes to DD2, the tri-exponential fitting of the relaxation curves was accurate, indicating three major water pools at voxel scale. At DD3, the tomato pericarp had undergone major changes and it was no longer possible to compute tri-exponential $T_2$ maps. This was also true for the micro-porosity maps, which were corrupted by magnetic susceptibility artifacts, as previously observed in dried fruits (Leforestier et al., 2022).

To investigate the differences in relaxation parameters and apparent micro-porosity between fresh and dried H1311 and Terradou tomatoes, mean values were calculated in ROIs corresponding to the entire pericarp (Table 1). Note that, for DD3, individual sample results are shown rather than an average value because of the wide range of WC values. In the pericarp of FRESH samples, there was no significant difference in the relaxation parameters (mono and tri-exponential) of the two tomato cultivars. The mean $T_{2}$-mono was around 430 ms while, respectively, $T_{22}$ and $T_{23}$ were around 300 ms and 490 ms, representing around 26 % and 67 % of total signal intensity. $T_{2}$-mono, $T_{22}$ and $T_{23}$ decreased with drying, but remained similar for both varieties. Note that the decrease in $T_{2}$-mono appears less marked for H1311 than for the Terradou cultivar (22 % and 31 % respectively from fresh to DD1). Notably, the apparent micro-porosity in the pericarp of FRESH tomatoes was significantly higher in H1311 than in Terradou. For both, the drying process resulted in a marked increase in micro-porosity, with values for the H1311 and Terradou cultivars nearly doubling and tripling respectively. The shrinkage index (Table 1), calculated from the 3D FR-TSE MRI images, showed no significant differences between cultivars at either DD1 or DD2.

## 3.4. Unilateral NMR

Fig. 5 (A and B) shows the evolution of the signal profiles (SI of a sensitive volume as a function of depth) at FRESH, DD2 and DD3. The highest signal in the FRESH tomato slice profiles of both cultivars

<figure><img src="image_6.png" /><figcaption>Fig. 3. Experiment B. Mono ($T_{2}$-mono) and tri-exponential ($T_{22}$, $A_{02}$, $T_{23}$, $A_{03}$) transverse relaxation and apparent micro-porosity maps of H1311 tomato slices prior to (FRESH) and after (DD1/DD2/DD3) drying.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Experiment B. Mono-exponential $T_2$ and apparent micro-porosity as a function of the distance from the cuticle of tomato pericarp prior to drying (FRESH, blue circle) and after drying at DD1 (orange triangle) and DD2 (gray square). Maximal distance from the skin corresponds to the values observed in at least 11 of 15 FRESH and 3 of 4 dried tomato slices. Values shown are the mean value ± standard deviation for 11–15 fresh and 4 or 5 DD1, DD2 and DD3 tomato slices. Letters indicate differences (P < 0.05) between $T_2$ values at different distances for each drying time. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

**Table 1**

Experiment B. Water content of the tomato slices and mono- and tri-exponential ($T_{22}$, $A_{02}$, $T_{23}$, $A_{03}$) transverse relaxation parameters and apparent micro-porosity in tomato pericarp calculated in ROIs corresponding to the whole pericarp, prior to and after drying (DD1/DD2/DD3). Volume shrinkage was computed from morphological images measured by a 3D FR-TSE sequence. Values are presented as mean value ± standard deviation for 15 fresh, 5 DD1, 5 DD2 and 5 DD3 samples. Letters indicate differences (P < 0.05) between drying degrees and a star sign (*) indicates differences between cultivars. DD3 data in the table follow the order of the samples in Supplementary Material 2.

<table><thead><tr><th></th><th>WC (%)</th><th>T<sub>2</sub>mono (ms)</th><th>T<sub>22</sub> (ms)</th><th>A<sub>02</sub> (%)</th><th>T<sub>23</sub> (ms)</th><th>A<sub>03</sub> (%)</th><th>μ-porosity (%)</th><th>shrinkage (%)</th></tr></thead><tbody><tr><td colspan="9"><strong>H1311</strong></td></tr><tr><td>FRESH</td><td>92.6 ± 0.8 <sup>a</sup></td><td>422 ± 33 <sup>a</sup></td><td>293 ± 24<sup>a</sup></td><td>26 ± 1<sup>a*</sup></td><td>486 ± 37<sup>a</sup></td><td>66 ± 1<sup>a</sup></td><td>2.9 ± 0.3<sup>a*</sup></td><td>-</td></tr><tr><td>DD1</td><td>89.8 ± 0.7 <sup>b</sup></td><td>331 ± 26 <sup>b</sup></td><td>216 ± 41<sup>b</sup></td><td>34 ± 11<sup>ab</sup></td><td>421 ± 42<sup>b</sup></td><td>48 ± 8<sup>b</sup></td><td>4.2 ± 0.6<sup>b</sup></td><td>74 ± 1<sup>a</sup></td></tr><tr><td>DD2</td><td>83.7 ± 1.8 <sup>c</sup></td><td>212 ± 33 <sup>c</sup></td><td>143 ± 38<sup>c</sup></td><td>40 ± 8<sup>b</sup></td><td>299 ± 57<sup>c</sup></td><td>39 ± 5<sup>c</sup></td><td>5.7 ± 0.9<sup>c*</sup></td><td>53 ± 5<sup>b</sup></td></tr><tr><td>DD3 (25 h)</td><td>35.6</td><td>14</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2</td></tr><tr><td>DD3 (18 h)</td><td>50.4</td><td>16</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>5</td></tr><tr><td>DD3 (22h)</td><td>52.2</td><td>21</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>8</td></tr><tr><td>DD3 (19 h)</td><td>56.4</td><td>20</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>6</td></tr><tr><td>DD3 (18 h)</td><td>53.4</td><td>18</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>4</td></tr><tr><td colspan="9"><strong>Terradou</strong></td></tr><tr><td>FRESH</td><td>92.2 ± 0.5 <sup>a</sup></td><td>435 ± 53<sup>a</sup></td><td>316 ± 36<sup>a</sup></td><td>27 ± 1<sup>a*</sup></td><td>491 ± 59<sup>a</sup></td><td>66 ± 2<sup>a</sup></td><td>1.5 ± 0.4<sup>a*</sup></td><td>-</td></tr><tr><td>DD1</td><td>89.9 ± 0.3 <sup>b</sup></td><td>300 ± 36<sup>b</sup></td><td>189 ± 41<sup>b</sup></td><td>28 ± 3<sup>a</sup></td><td>374 ± 49<sup>b</sup></td><td>49 ± 3<sup>b</sup></td><td>3.2 ± 0.7<sup>b</sup></td><td>81 ± 9<sup>a</sup></td></tr><tr><td>DD2</td><td>85.3 ± 1.0 <sup>c</sup></td><td>203 ± 48<sup>c</sup></td><td>117 ± 39<sup>c</sup></td><td>34 ± 5<sup>b</sup></td><td>282 ± 67<sup>c</sup></td><td>43 ± 8<sup>c</sup></td><td>4.4 ± 0.4<sup>c*</sup></td><td>53 ± 1<sup>b</sup></td></tr><tr><td>DD3 (18 h)</td><td>36.2</td><td>8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0</td></tr><tr><td>DD3 (18 h)</td><td>27.1</td><td>8</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0</td></tr><tr><td>DD3 (18 h)</td><td>49.2</td><td>21</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>8</td></tr><tr><td>DD3 (18 h)</td><td>57.6</td><td>28</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>12</td></tr><tr><td>DD3 (18 h)</td><td>47.8</td><td>13</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>4</td></tr></tbody></table>

occurred at the first tomato depth analyzed (near 1 mm) with a progressive decrease from 1 mm to 16 mm. This elevated signal may be attributed to the water being expelled at the cut interface as it disappeared during drying. The signal void between 0 and 0.4 mm corresponds to the glass plate. Signal did not reach zero at its final depth (16 mm); this was probably due to the initial thickness of the tomato slice (20 mm).

The integral of SI over the whole sample for FRESH H1311 (A) was 20% higher than that for Terradou (B) tomato slices. This difference can be explained by the effects of the sample weights, as the average weight
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Experiment B. NMR signal (mean value ± standard deviation) measured by unilateral NMR as a function of depth (profiles) for different drying degrees of (A) H1311 and (B) Terradou tomato slices: FRESH (blue, *n* = 4 for H1311 and *n* = 3 for Terradou) DD2 (gray, *n* = 4) and DD3 (purple, *n* = 4 for H1311 and *n* = 6 for Terradou). (C) Relationship between the integral (sum of 80 signal intensities) over the tomato slice depth of the 25 samples (12 H1311 and 13 Terradou) and water quantity at each drying time. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

of fresh Terradou tomato slices from the B-2 batch (unilateral NMR analysis) was lower than that of H1311 fresh slices (20 ± 3 g vs 23 ± 2 g, about 20 %). Further, differences in fruit shape (H1311 fruits were elongated and Terradou spherical) meant that some Terradou slices could conceivably have been very slightly larger than the RF coil. At DD2, the maximum signal amplitude for H1311 was 0.17 a.u.. Despite a wide dispersion of final depths (10 mm–16 mm), a decrease of almost 27 % from the plateau was observed for fresh samples and the extent of this plateau was also reduced to 10 mm–4 mm depths. The signal decreased at both extremities without reaching zero for the final depth (16 mm), suggesting that the sample was still more than 16 mm thick at DD2. The Terradou samples reached nearly the same level as the H1311 samples, but the decrease of the profiles at DD2 was smaller, i.e., 23 % of the fresh samples. At the final drying degree (DD3; 16 h for Terradou and 18 h for H1311) both cultivars displayed an almost flat profile with a limited plateau range of 5 mm–8 mm and maximums of less than 0.05 a.u.

To relate the depth profiles to the evolution of water quantity during drying, the integral of the signal profile for each drying degree was plotted against the water quantity (in grams) of the tomato slices (Fig. 5C). Given that the combined thickness of the sample (20 mm) and the glass plate (0.4 mm) was 20.4 mm, while the maximum measurement depth in this configuration was 16 mm, the integral signal values for each fresh sample were increased by 22 % to take account of the water contained in the unmeasured part. A similar compensation should have been applied to the integral values measured at DD2 for samples exceeding 16 mm in thickness. However, it should be noted that, in the latter instance, the soft texture and irregular edges of the tomatoes made it impossible to obtain accurate measurements of the thickness of the tomato slices after drying. Consequently, the data was more scattered for

this drying degree, especially for the Terradou samples. Despite these potential biases, the integrals of the profiles of the two cultivars displayed good linear correlation with their water quantity, with an R² of 0.98 and 1.02 for H1311 and Terradou respectively.

Fig. 6 A provides an example of T₂ distribution for FRESH tomato slices in the central depths of the tomato slice. T₂ distributions were very similar between the two cultivars, with three T₂ peaks. The relative amplitude of the peak with the longest T₂ (~100 ms) was 75–85 %, that of the intermediate peak (~10-15 ms) was 12–18 %, while the shortest T₂ peak (~1 ms) had an amplitude of 2–7 %. The locations and relative intensities of these two T₂ peaks were somewhat variable (Fig. 6B), attributable to variations in the biological variability and/or in the intrinsic signal-to-noise ratios of their decay relaxation signals (which resulted in a variable regularization parameter). While tri-exponential relaxation was measured by both unilateral NMR (Fig. 6) and MRI (Fig. 3, Table 1), the unilateral NMR T₂ values in Fig. 6 were considerably lower, mainly as a consequence of the effects of diffusion in the magnetic field gradients intrinsic to the unilateral device (Bălibanu et al., 2000). Figs. 6C and D show the evolution of T₂ distribution with drying of the central depth for H1311 and Terradou, respectively. For both cultivars, the distribution pattern at DD2 appears similar to that of the FRESH samples, except that a slight dispersion is observable in the intermediate T₂ component. By contrast, the distributions obtained after DD3 were notably different, either in the number of their components or in their T₂ values. Indeed, what was previously the main component (T₂ ~ 100 ms) almost disappeared, the shortest T₂ became dominant (> 60 %), and the intermediate peak, when present, slightly decreased. It is noteworthy that the pattern of the evolution of these T₂ distributions resembles the results of the fit of the average decays, despite the SNR
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Experiment B. (A) Representative T₂ distribution from the NNLS inversion of the CPMG echo decay measured for fresh tomato slices. (B) Distribution of the integral of T₂ peaks in (A) at central depths (4 to 5 per tomato sample) for FRESH H1311 (1 to 15) and Terradou (16 to 30) samples. (C) Distribution of T2 peak integrals at 5 central depths for H1311 (1–20) and Terradou (21–40) at DD2. (D) Distribution of T2 peak integrals at 5 central depths for H1311 (1–25) and Terradou (26–60) at DD3. T = Terradou and H=H1311.</figcaption></figure>

increase in the latter (Supplementary Material 6B).

### 3.5. Histology

Spatial analysis of FRESH tomato cell size evidenced a gradient that followed a parabolic evolution along the pericarp (Fig. 7 A). The structure of both cultivars was similar, but Terradou cells were larger, with a thinner pericarp. This observation is consistent with the MRI maps (Figs. 3, 4A and B).

Cell size decreased significantly between FRESH and DD3 ($p < 0.005$) samples and the cell size range decreased from a large multi-modal distribution at FRESH, DD1 and DD2, to a narrow monomodal distribution at DD3 (Fig. 7 B and C). These results can, however, only show a tendency, for lack of a large dataset. Terradou cells were larger than H1311 cells at all drying steps. Despite their size reduction, the cells maintained their integrity, evidencing incomplete cell disruption.

### 3.6. Texture

Hardness and gumminess decreased dramatically down to 90 % water content and then remained at floor values without significant change (Fig. 8). Drying caused the samples to shrink up to 60 % at DD3. Springiness values showed no significant change, remaining high and reflecting a strong capacity in samples of all thicknesses to regain between 82 and 99 % of initial thickness after the first compression.

### 3.7. Composition

Changes in composition of carotenoids, sugars, acids and polyphenols in tomato samples are expressed as a percentage of variation from the FRESH samples. Carotenoid content at DD3 differed significantly from that at DD1 and DD2, with the exception of lycopene. The changes in phytoene, phytofluene and luteine levels evolved differently in the two cultivars (Table 2). No isomerization was evidenced under our chosen analytic conditions.

In sugars and acids, no molecule showed a statistical difference between drying steps other than citric acid in Terradou (Table 2). Sugars and acids did not vary significantly between Terradou and H1311. A very strong increase (>100 %) in citric acid between DD2 and DD3 was observed only in H1311. Uncertainty was also high (>100 %), evidencing high variability in the citric acid content of DD3 samples.

One of the polyphenol molecules of interest, naringenin chalcone, showed no significant differences between drying steps for H1311 and Terradou, however very high variations with very high uncertainty were observed in H1311 at all drying degrees. In particular, DD2 displayed an increase of naringenin chalcone of 448 % with a standard error of 430 %, highlighting an improvement in the detection of this molecule that was nevertheless accompanied by very high variability. Significant differences between cultivars were observed in five polyphenol molecules at various drying degrees (Table 2).
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Experiment B. (A) Cell size of FRESH tomato cells as a function of relative distance from the cuticle. Size is expressed as the Feret diameter calculated in FIJI software. Dotted and dashed lines are degree 2 polynomial regressions of, respectively, Terradou and H1311 cell sizes. (B) Density plots of cell size distribution for H1311 ('H', left) and Terradou ('T', right) tomatoes at each drying step. Sizes are expressed as the Feret diameter calculated from image analysis performed with FIJI software. Sample images show the cellular structure of H1311 (left) and Terradou (right) at different steps (from top: FRESH, DD1, DD2, DD3).</figcaption></figure>

# 4. Discussion

## 4.1. Use of quantitative MRI for the study of fruit drying – highlights and further improvements

MRI and unilateral NMR enabled the quantification of water dynamics and structural features at different length scales during fruit

drying, as described above. The strengths of these methods, their limitations and possible ways to improve them in the context of fruit processing are discussed below.

The principal macroscopic information obtained from 3D MRI imaging was sample volume, which, when measured continuously during drying, provided an accurate and dynamic quantification of overall shrinkage (Fig. 2). This original measurement was made possible by the
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Experiment B. Sample thickness and texture profile analysis indicators (Gumminess, maximum Hardness, Springiness and Thickness) measured for H1311 and Terradou tomatoes at all drying steps. The water content shown is that measured at the end of the drying experiment, rather than FR/DD1/DD2/DD3 factor levels.</figcaption></figure>

**Table 2**

Changes in composition of carotenoids, sugars, acids and polyphenols in tomato samples. Variations are expressed by mean values and standard errors. All values are expressed as a percentage of variation from the FRESH samples (initial values were estimated per gram of dry matter). A star sign * indicates significant differences between tomato cultivars at the same drying state (no star signs means no significant difference), based on ANOVA at 95 % confidence level. Letters a, b and c represent statistically homogeneous groups within the same molecule, based on a Tukey test at 95 % confidence level. N.S. = no significant differences between drying states.

<table><thead><tr><th rowspan="3">Cultivar<br>State</th><th colspan="6">H1311</th><th colspan="6">TERRADOU</th></tr><tr><th colspan="2">DD1</th><th colspan="2">DD2</th><th colspan="2">DD3</th><th colspan="2">DD1</th><th colspan="2">DD2</th><th colspan="2">DD3</th></tr><tr><th>Relative<br>value<br>(%)</th><th>Standard<br>Error (%)</th><th>Relative<br>value<br>(%)</th><th>Standard<br>Error (%)</th><th>Relative<br>value<br>(%)</th><th>Standard<br>Error (%)</th><th>Relative<br>value<br>(%)</th><th>Standard<br>Error (%)</th><th>Relative<br>value<br>(%)</th><th>Standard<br>Error (%)</th><th>Relative<br>value<br>(%)</th><th>Standard<br>Error (%)</th></tr></thead><tbody><tr><td>Glucose</td><td>-15.69<br>N.S.</td><td>13.97</td><td>-12.22<br>N.S.</td><td>16.62</td><td>-28.32<br>N.S.*</td><td>4.69</td><td>2.51 N.S.</td><td>6.39</td><td>-51.24<br>N.S.</td><td>22.70</td><td>0.63 N.S.*</td><td>6.78</td></tr><tr><td>Fructose</td><td>-35.49<br>N.S.</td><td>21.71</td><td>-11.38<br>N.S.</td><td>2.45</td><td>-26.29<br>N.S.</td><td>4.79</td><td>-2.53 N.<br>S.</td><td>7.96</td><td>-20.48<br>N.S.</td><td>50.37</td><td>-13.73<br>N.S.</td><td>2.08</td></tr><tr><td>Citric acid</td><td>23.19 N.<br>S.</td><td>49.04</td><td>15.80 N.<br>S.</td><td>16.16</td><td>142.77 N.<br>S.</td><td>107.48</td><td>47.79 a</td><td>15.97</td><td>-33.11<br>b-</td><td>7.33</td><td>-33.52 b</td><td>2.29</td></tr><tr><td>Malic acid</td><td>15.69 N.<br>S.</td><td>9.75</td><td>37.37 N.<br>S.</td><td>33.23</td><td>4.87 N.S.</td><td>11.69</td><td>42.32 N.<br>S.</td><td>0.64</td><td>-7.73 N.<br>S.</td><td>8.43</td><td>8.14 N.S.</td><td>11.25</td></tr><tr><td>Rutin</td><td>-70.61<br>N.S.</td><td>0.50</td><td>5.52 N.S.</td><td>17.28</td><td>112.75 N.<br>S.</td><td>83.16</td><td>-4.82 ab</td><td>25.90</td><td>-47.99 a</td><td>9.52</td><td>137.16 b</td><td>60.19</td></tr><tr><td>Quercetin</td><td>-65.84<br>N.S.*</td><td>9.20</td><td>-23.44<br>N.S.</td><td>18.22</td><td>68.84 N.<br>S.</td><td>49.08</td><td>15.83 N.<br>S.*</td><td>14.92</td><td>-24.64<br>N.S.</td><td>4.24</td><td>152.95 N.<br>S.</td><td>76.25</td></tr><tr><td>Caffeoylglucose<br>acid</td><td>-30.94<br>N.S.</td><td>7.57</td><td>-7.84 N.<br>S.*</td><td>5.49</td><td>-18.49<br>N.S.</td><td>17.02</td><td>-19.17<br>N.S.</td><td>9.73</td><td>-35.82<br>N.S.*</td><td>2.55</td><td>-5.62 N.<br>S.</td><td>15.77</td></tr><tr><td>5-caffeoylquinic<br>acid</td><td>-13.29<br>N.S.</td><td>1.73</td><td>51.16 N.<br>S.</td><td>42.90</td><td>41.67 N.<br>S.</td><td>19.48</td><td>-27.34<br>N.S.</td><td>10.00</td><td>-45.44<br>N.S.</td><td>4.68</td><td>-8.42 N.<br>S.</td><td>24.69</td></tr><tr><td>Feruloyl acid</td><td>-35.31<br>N.S.</td><td>8.09</td><td>3.50 N.S.*</td><td>10.66</td><td>-15.46<br>N.S.</td><td>16.87</td><td>-14.48<br>N.S.</td><td>7.91</td><td>-28.12<br>N.S.*</td><td>3.96</td><td>3.89 N.S.</td><td>13.33</td></tr><tr><td>Caffeic acid</td><td>-32.96<br>N.S.</td><td>13.32</td><td>-12.16<br>N.S.*</td><td>12.76</td><td>-8.15 N.<br>S.</td><td>12.70</td><td>-42.16<br>N.S.</td><td>6.31</td><td>-59.34<br>N.S.*</td><td>7.03</td><td>-9.52 N.<br>S.</td><td>33.89</td></tr><tr><td>Naringenin<br>chalcone</td><td>121.61 N.<br>S.</td><td>122.05</td><td>448.66 N.<br>S.</td><td>430.73</td><td>27.78 N.<br>S.*</td><td>32.04</td><td>-70.30<br>N.S.</td><td>9.84</td><td>-82.43<br>N.S.</td><td>3.35</td><td>-91.34<br>N.S.*</td><td>1.48</td></tr><tr><td>Phytoene</td><td>-29.45<br>ab*</td><td>3.69</td><td>-44.94 a</td><td>3.39</td><td>-2.11 b*</td><td>7.94</td><td>13.98 a*</td><td>7.97</td><td>-40.80 b</td><td>2.30</td><td>29.13 a*</td><td>1.68</td></tr><tr><td>Phytofluene</td><td>-31.64<br>a*</td><td>2.18</td><td>-49.23 a</td><td>5.67</td><td>29.91 b*</td><td>11.42</td><td>6.46 a*</td><td>5.18</td><td>-46.56 b</td><td>2.68</td><td>61.12 c*</td><td>7.16</td></tr><tr><td>Luteine</td><td>-31.07<br>a*</td><td>2.88</td><td>-50.40 a</td><td>5.35</td><td>30.69 b</td><td>11.85</td><td>6.18 a*</td><td>5.20</td><td>-49.52 b</td><td>0.51</td><td>66.65 c</td><td>10.49</td></tr><tr><td>β-Carotene</td><td>-30.86 a</td><td>8.80</td><td>-43.96 a</td><td>6.89</td><td>7.33 b*</td><td>9.37</td><td>2.27 a</td><td>9.20</td><td>-47.20 b</td><td>0.36</td><td>38.97 c*</td><td>3.30</td></tr><tr><td>Lycopene</td><td>-38.41<br>N.S.</td><td>11.20</td><td>-45.92<br>N.S.</td><td>12.29</td><td>-52.31<br>N.S.</td><td>4.63</td><td>-41.68<br>N.S.</td><td>8.89</td><td>-65.61<br>N.S.</td><td>1.73</td><td>-58.44<br>N.S.</td><td>8.18</td></tr></tbody></table>
non-destructive and non-invasive nature of MRI and by use of a temperature-controlled chamber to allow in situ drying. It has the potential to be used for the characterization of anisotropic shape deformation in the context of the development of the drying process.

Unilateral NMR allowed the quantification of changes in total water quantity (Fig. 5), while the recorded changes in profile shape can be of use in assessing how water loss occurs along the thickness of the sample. In the present study, limitations emerged concerning the specific shape and size of tomato slices that, in some cases, did not fit within the 4 × 4 cm FOV of the RF coil of the unilateral NMR device or the maximum configured depth of 16 mm. In Terradou tomatoes at DD2 (Fig. 5B), the signal value for maximum slice thickness did not reach zero, indicating that the slices were still over 16-mm thick. Investigation of thinner and slightly smaller tomato slices would allow systematic capture of the total signal and more accurate calculation of water loss. We can also point out the relative dispersion of signal amplitude in the depth profiles for the intermediate drying degree, i.e., DD2. This dispersion can simply be attributed to changes in T₂ associated with the drying process (see, e.g., Fig. 6C), as the signal amplitude at each depth is made up of the average of 256 CPMG echoes and is thus T₂ weighted. Indeed, the morphological changes to the outer edges of the tomatoes at DD2 (Supplementary Material 2) suggest uneven drying and variable depth signal contributions as the result of vertical tissue shrinkage. A lateral T₂ gradient from the cuticle to the inner pericarp (Figs. 4 A and B) was also recorded. Nonetheless, good linear correlation was observed between the integral of the signal profiles and tomato water quantity at different drying degrees (Fig. 5C). This demonstrates the suitability of unilateral NMR for monitoring water loss in whole fruit slices during the drying process, bringing the experimental setup one step closer to a realistic situation on the production lines, compared to previous experimentation on small pear samples (~2.5 cm in size) (Proietti et al., 2018).

MRI allowed changes in apparent micro-porosity distribution at sample scale to be quantified and provided a unique record of the formation of porosity gradients within the whole sample as a result of the drying process (Figs. 4 and 4C and D). Its analysis of real-size samples and capture of heterogeneous features made it possible to obtain additional information compared with studies using microscopy (Lewicki & Pawlak, 2003; Nieto et al., 2004) or micro-computed tomography (CT) (Prawiranto et al., 2019) based on small cell quantities (~ a few cells) or samples (~1 cm³) respectively. MRI micro-porosity measurements were not, however, appropriate to the study of highly dehydrated tomatoes (DD3), due to the presence of magnetic susceptibility artifacts, as previously reported in (Leforestier et al., 2022). Several experimental strategies could be considered to overcome this issue. One would be the use of an MRI system with a lower magnetic field, which would reduce the effects of magnetic susceptibility (Musse & Van As, 2018). Another experimental strategy is the use of a mono-exponential GE sequence with a small incremental increase in TE, which allows more adequate sampling of the rapid T₂* decay but significantly increases the measurement time. Finally, the use of ultra-short echo time (UTE) sequences with a minimum TE of less than 01 ms, could be used to measure short T₂* (Araujo et al., 2017) and also improve the signal detection limit. In the present study, spatial information was made available through the use of a virtual equatorial 5-mm thick sample (imaging) slice. Although 3D protocols are not suitable for this sort of parametric approach because of the long acquisition times, it would be of interest to acquire an additional micro-porosity map that was orthogonally oriented, enabling the capture of heterogeneities in the direction of the sample thickness (as for the water content profiles obtained by unilateral NMR).

MRI relaxometry, combining the methods described in (Adriaensen et al., 2013; El Hajj et al., 2020), allowed multi-exponential T₂ relaxation to be calculated at voxel level (Fig. 3), while unilateral NMR allowed similar calculations to be performed on a sensitive volume 0.1 mm thick (Fig. 6). These approaches made it possible to access information on different water pools at voxel/thin-volume levels. As in fresh fruit, the optimal relaxation signal model for the dried samples was

found to be tri-exponential at DD1 and DD2, indicating that cell structures remained undamaged, in accordance with the histological measurements. The decrease in T₂ observed by both approaches and for all signal components cannot be attributed to any single factor because of the concomitant decrease in water content in cell compartments, cell shrinkage and possible changes in membrane properties (Nieto et al., 2004) during drying. Indeed, changes in water content have been found to lead to variations in bulk relaxation times via chemical exchange mechanisms (Leforestier et al., 2021), while changes in cell size and membrane permeability may modify diffusional exchanges of water molecules between cell compartments (Musse et al., 2021; Van der Weerd et al., 2002). However, the analysis in (Leforestier et al., 2022) indicates that cell size probably has the dominant effect on T₂ values during the dehydration of tomato pericarp, assuming constant pH. Our results showed a trend of a decrease in the relative intensity of the third component (A₀₃) and an increase in A₀₂ with the drying degree (Table 1, Fig. 6), agreeing with the studies reported in the literature (Hills & Nott, 1999; Leforestier et al., 2022). Leforestier et al. (Leforestier et al., 2022) hypothesized that the second and third T₂ components of a tri-exponential model correspond to the vacuoles of two cell populations with distinct volume distributions, where dehydration would induce a decrease in the number of large cells relative to that of small cells. According to this hypothesis, variations in relaxation parameters would correspond to the greater impact of the dehydration process on larger cells. Our results, which show a wide distribution of multimodal cell sizes in FRESH, DD1 and DD2 tissues (Fig. 7) followed by a tendency towards narrow monomodal distribution in DD3 tissues, support this hypothesis. For further improvement, studies are needed to elucidate and eliminate artifacts that corrupt maps of the shortest T₂ component (Snaar & Van As, 1992) in order to include cell wall water information in the analyses. At DD3, neither MRI nor unilateral NMR could provide the data for multi-exponential relaxation maps, because either T₂ values became too short or cell compartments lost their integrity (membrane damage). Furthermore, the loss of more than 60–70 % of initial water quantity led to SNR that was insufficient for multi-exponential analysis. Some of the information provided through the evolution of mono-exponential T₂ maps, such as approximations of cell shrinkage and data on tissue heterogeneities, could nevertheless be used to study tomatoes at the advanced stages of drying.

A comparative analysis of MRI T₂ maps (Figs. 3 and 4) and the cell size distributions revealed by histology (Fig. 7 A) showed similar variation patterns within the pericarp as a function of distance from the tomato cuticle. This supports the hypothesis that cell size has the greatest effect on T₂ under the measurement conditions applied in this study (1.5 T, TE = 6.5 ms), although effects of micro-porosity, composition and membrane permeability cannot be excluded (Leforestier et al., 2022). In the present experiment, analysis of such factors was not possible using the unilateral NMR, as the histological and NMR measurements were made at right angles to each other. Depth profiling of a tomato slice positioned cuticle-down and pericarp-up on the spectrometer would necessitate more processing and tissue pressure, leading to cell damage.

This study demonstrates the potential of NMR profilers for deployment in online quality control of the drying process, although some improvements in method would appear necessary. Indeed, to allow the quantitative analysis of water status and distribution for a whole tomato fruit during drying, sample size must fit in the 4 × 4 cm MOUSE coil and have a slice thickness less than the maximum depth accessible would have to extend across the full current 25 mm width (of which 16 mm was used in our configuration) of the PM25 MOUSE. To accommodate sample sizes over these dimensions, technical improvements which are in the scoop of manufacturer are needed. The technical difficulties of increasing depth resolution while maintaining reasonable magnet size and weight are discussed in (Blümich & Anders, 2021). The portability of the current Profile NMR-MOUSE along with its small size (and low magnetic field) allows the device to be moved around at a production
site for in situ measurements. An improvised carrying device to allow
handling and repositioning of the sensor would help in the design of a
derivative (bypass) line for discontinuous process sampling. Indeed, the
current temporal resolution of NMR measurements is not compatible
with continuous process follow-up. The temporal resolution of depth
profiling can, however, be improved by using an acquisition protocol
with reduced number of CPMG echoes, to decrease acquisition time (low
TR), while adjusting the number of averages for SNR. Such a reduction
will also limit the impact of the changes in T₂ with drying on the aver-
aged signal amplitude. Another quick way to obtain an accurate mea-
surement of the initial signal amplitude involves the use of phase cycling
methods to cancel ringing in the production of first CPMG echoes
(Utsuzawa et al., 2022). Such an implementation will allow speed ac-
quisitions of only initial CPMG echoes if not the first one.

In addition to improvements in the MRI and NMR protocols, the control of drying conditions should be optimized in subsequent studies to reduce the differences in drying kinetics observed for similar ovens (Supplementary Materiel 1). Indeed, flow and humidity of the air entering the oven, depending on its volume, should be controlled in future studies by using a humidification system. However, within the same experiment, despite the fact that ovens were ventilated, we observed significant differences between tomatoes for DD3, demonstrating that the drying process depends on the characteristics of each sample.

## 4.2. Tomato slices dry differently, depending on the physicochemical characteristics of the fresh tissues

The characteristics of H1311 and Terradou tomatoes at harvest differed on several grounds, with the most marked differences being in structure. At the macroscopic level, H1311 tomatoes tended to be slightly elongated and generally had lower weights than Terradou fruit (for example, in the B1 batch, m<sub>H1311</sub> = 70 ± 6 g, m<sub>Terradou</sub> = 84 ± 6 g), but the pericarp of H1311 fruits was slightly thicker in appearance, resulting in slices (as prepared for drying) of similar weight (for example, again in B1, m<sub>H1311</sub> = 25 ± 2, m<sub>Terradou</sub> = 23 ± 2 g). Compared to that of H1311, the pericarp of Terradou tomatoes consisted of larger, thinner packed cells with lower micro-porosity (Figs. 4 and 7, Table 1). Both cultivars were characterized by similar broad multimodal cell size distributions (Figs. 7 B and C), as further evidenced by the strong heterogeneity of their T<sub>2</sub> maps (Figs. 2–4), which showed a more pronounced heterogeneity in H1311 than Terradou. Water status and compartmentalization as estimated by MRI and unilateral NMR relaxometry experiments were found to be similar between the two cultivars. No differences were found in water content (~92 %, Table 1, Supplementary Material 2) and acid composition between the two cultivars, while they displayed differences in the content of sugars and some carotenoids. The uncertainty of the values mainly results from the limited sample quantity. However, all analyses were based on robust protocols, thus ensuring the representativeness of the values presented.

A typical curve for global shrinkage due to cell dehydration (Proietti et al., 2018; Prothon et al., 2003) was observed from the 3D MRI images acquired during continuous drying (Experiment A, Fig. 2 B and Supplementary Material 5), highlighting the two specific stages at the beginning and end of the drying process. The decrease in the shrinkage rate as drying progressed is explained by the decreasing ability of the fruit tissue to transport water as the cells dehydrate (Prawiranto et al., 2018). Cell reduction from FRESH to DD1, DD2 and DD3 was quantified in Experiment B (Fig. 7). In both Experiments A and B, shrinkage was also reflected by a marked decrease in T<sub>2</sub> from the beginning of the drying process (Figs. 2 and 3), in line with the literature (Leforestier et al., 2022; Proietti et al., 2018). Overall slice shrinkage was probably mitigated to some extent by the replacement of moisture by air. Indeed, as expected from the literature (Aprajeeta et al., 2015; Lewicki & Pawlak, 2003; Prawiranto et al., 2019; Prothon et al., 2003), micro-porosity increased significantly with the degree of drying (Fig. 3, Table 1),

especially in the tissue layer furthest from the cuticle (Fig. 3). Drying
had similar significant effects on texture in both cultivars, despite
marked differences in structure. Every drying stage (DD1, DD2 and DD3)
induced a significant loss of firmness compared to fresh tomatoes, in
accordance with (Askari et al., 2009). This loss is due to decreasing
turgor in the course of drying, and is consistent with the water loss
observed by both MRI and unilateral NMR, and the cellular degradation
observed histologically. Nevertheless, springiness remained stable at all
drying stages, reflecting the preservation of cellular integrity and the
remains of a structured cell wall complex. Although of close initial cell
wall polysaccharide compositions, pectin and hemicellulose are altered
but distinctly during the drying of the two cultivars (Lahaye et al.,
2025). These different cell wall disassembly processes accompanying
cell shrinkage was proposed to be responsible for discrete water diffu-
sion processes in the contrasted cultivars. To our knowledge, no study
has addressed the correlation between the microstructural changes in
dried tomatoes and their springiness and TPA-related indicators. Two
different phenomena might explain the change in composition during
drying. First, the decrease in concentration from FRESH to DD2 suggests
a degradation of the matrix induced by the drying process – thermal
damage to the cells caused by heat accumulation could in turn lead to
the degradation of the vacuolar membrane (Leca et al., 2021), resulting
in oxidation. This is consistent with the other observations in Experi-
ment B, since the first stage of drying significantly alters the samples.
Second, the increase between DD2 and DD3 could result from the
improved extractability of the molecules, again due to heat accumula-
tion, as described in the existing literature (Leca et al., 2021). This could
also be related to the cell membrane damage suggested by the unilateral
NMR and MRI relaxometry data at DD3 (Table 1, Fig. 6). Surprisingly,
we detected no lycopene isomers. To our knowledge, no existing study
has investigated the presence or absence of lycopene in dried tomatoes,
although it is known that thermal processing induces lycopene isomer-
ization in tomato purees (Yu et al., 2019). Our observations suggest that
tomato oven drying does not lead to a detectable lycopene isomer con-
tent. The high uncertainty, as evidenced by the standard errors in
Table 2, can be explained by the small sample quantity for analysis,
especially for naringenin chalcone, which was found in small but vari-
able concentrations.

Cultivar-specific characteristics had a marked effect on drying rates. Compared to H1311, Terradou was found to have a significantly higher drying rate (Fig. 2, SM 1). This difference occurred during the first (Bruce, 2003; Oey et al., 2007) hours of drying and at the end of the process (Fig. 2). The higher drying rate of Terradou could be partly due to its larger cells, as cell size has a positive effect on tissue permeability (Prawiranto et al., 2018). According to (Prawiranto et al., 2018), for the same cell wall thickness, larger cells have a lower cell wall volume ratio and a higher protoplast volume ratio than smaller cells. Since the cell wall has a higher resistance to moisture transport than the protoplast, water permeability increases with cell size. The differences in dehydration kinetics between the two cultivars could also be partially explained by barriers to water diffusion in this tissue, hypothetically reflected by different degrees of pericarp heterogeneity. Future studies should be conducted to further investigate this phenomenon. Apparent micro-porosity did not result in faster drying, as might be expected given the higher surface exposure, probably because average pore size and distribution also play an important role in the drying process (Ramos et al., 2003).

Because of the water-holding capacity of cell wall polysaccharides, they are chiefly responsible for water retention and diffusion induced by drying (Prothon et al., 2003; Wang et al., 2024). A possible explanation for the differences in the two cultivars’ drying behavior may lie in the differences in their cell wall structure and composition, as suggested by Sinkora et al. (Sinkora et al., 2024). This hypothesis is supported by further analysis of the cell walls in the tomato samples from Experiment B, which revealed that drying has a major impact on the composition, structure, organization and interaction with water of polysaccharides
within the cell walls (Lahaye et al., 2025). Pectin was markedly affected by ripening-related metabolization (with a decrease in pectin methyl-esterification and in the content of neutral pectic side-chains). Mannan appeared better preserved, as did the organization of cellulose in the faster drying Terradou, suggesting that mannan contributes to the maintenance of cellulose-hemicellulose network porosity and water diffusion.

## 5. Conclusion

The present study reports an in-depth in situ investigation of the interrelated and simultaneous effects of the drying process of fruits from two tomato cultivars, from chemical composition and multi-scale structure to texture characteristics, with particular attention to changes in water quantity and distribution accessed by MRI and unilateral NMR. Our study also constitutes an important step forward in the reliable interpretation of changes in transverse relaxation parameters during plant tissue dehydration, allowing NMR parameters to be used to characterize fruit during drying. MRI and unilateral NMR were shown to be relevant to the characterization of the evolution and heterogeneous features of tomato slices during drying, allowing information on water status and distribution and apparent micro-porosity to be accessed. They indicated that tomato microstructure affects drying kinetics, with larger cells and lower tissue heterogeneity facilitating dehydration. A spatially dependent increase in the proportion of intercellular air spaces was shown to occur with drying, but higher micro-porosity did not accelerate drying. The drying process caused biochemical compounds and phenolic profiles to evolve, changing the nutrient quality and health value of the product. Indeed, the changes in nutrient composition would indicate greater bioaccessibility of health-beneficial nutrients. Despite the differences in drying kinetics observed between the two cultivars studied, the final texture and nutritional composition of the dried fruits remained consistent between the cultivars. This suggests that there is significant scope for the development or selection of varieties better suited to drying and/or for optimization of the drying process. In particular, the promotion of large pericarp cells through genetic and agronomic factors could reduce drying time without significant changes in nutritional and sensory quality. This allows the drying time to be adapted to the microstructure of the fruit to optimize energy costs with drying process. The multiscale data collected during this study could act as a calibration validation dataset for a hybrid model (mass transfer and mathematical relationships) describing the changes in tomato slices microstructure and quality indicators during oven-drying. It would be beneficial to use the in situ MRI experimental approaches implemented in this study to gain a deeper insight into fleshy fruit drying and to continue the development of unilateral NMR for industrial applications.

### CRediT authorship contribution statement

**Maja Musse:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Amidou Traore:** Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Alexandre Leca:** Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization. **Marc Lahaye:** Writing – review & editing, Conceptualization. **Clémentine Lorho:** Investigation. **Gauvain Pierre:** Investigation. **Nathan Rausch de Traubenberg:** Investigation. **Caroline Garcia:** Investigation. **Yves Diascorn:** Methodology, Investigation. **Stéphane Quellec:** Methodology, Investigation. **Carine Le Bourvelle:** Investigation, Formal analysis, Conceptualization. **Sylvie Clerjon:** Visualization, Conceptualization. **Sylvain Challois:** Investigation, Data curation. **Guylaine Collewet:** Writing – original draft, Validation, Methodology, Investigation, Data curation, Conceptualization. **Nadia Bertin:** Writing – original draft, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

Part of this work has been performed using the PRISM core facility (Biogenouest, Univ Rennes, Univ Angers, INRAE, CNRS, France). We thank Didier Anato (INRAE, A2M, 40509 Avignon, France) and Valérie Serra (INRAE, PSH) for their experimental support in tomato fruit production and Mireille Cambert (INRAE, OPAALE) for her help in the experiments performed at OPAALE.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at. Supplementary data to this article can be found online at [https://doi.org/10.1016/j.foodres.2025.116385].

## Appendix B. Supplementary data

Supplementary data to this article can be found online at [https://doi.org/10.1016/j.foodres.2025.116385].

### Data availability

Data will be made available on request.

### References

Adriaensen, H., Musse, M., Quellec, S., Vignaud, A., Cambert, M., & Mariette, F. (2013). MSE-MRI sequence optimisation for measurement of bi- and tri-exponential T2 relaxation in a phantom and fruit. *Magnetic Resonance Imaging, 31*(10), 1677-1689.

Altino, H. O. N., Locatelli, K. M. D., & da Cunha, R. N. (2018). Influence of drying conditions on the final quality of cherry and grape tomatoes. *Brazilian Archives of Biology and Technology, 61*.

Aprajaeta, J., Gopirajah, R., & Anandharamakrishnan, C. (2015). Shrinkage and porosity effects on heat and mass transfer during potato drying. *Journal of Food Engineering, 144*, 119-128.

Araujo, E. C. A., Azzabou, N., Vignaud, A., Guillot, G., & Carlier, P. G. (2017). Quantitative ultrashort TE imaging of the short-T<sub>sub</sub>>2<sub>sub</sub>> components in skeletal muscle using an extended echo-subtraction method. *Magnetic Resonance in Medicine, 78*(3), 997-1008.

Askari, G. R., Emam-Djomeh, Z., & Tahmasbi, M. (2009). Effect of various drying methods on texture and color of tomato halves. *Journal of Texture Studies, 40*(4), 371-389.

Bălibanu, F., Hailu, K., Eymael, R., Demco, D. E., & Blümich, B. (2000). Nuclear magnetic resonance in inhomogeneous magnetic fields. *Journal of Magnetic Resonance, 145*(2), 246-258.

Blümich, B., & Anders, J. (2021). When the MOUSE leaves the house. *Magnetic Resonance, 2*(1), 149-160.

Blümich, B., Baias, M., Rehorn, C., Gabrielli, V., Jaschtschuk, D., Harrison, C., et al. (2020). Comparison of historical violins by non-destructive MRI depth profiling. *Microchemical Journal, 158*, Article 105219.

Borel, P. (2003). Factors affecting intestinal absorption of highly lipophilic food microconstituents (fat-soluble vitamins, carotenoids and phytosterols). *Clinical Chemistry and Laboratory Medicine, 41*(8), 979-994.

Bruce, D. M. (2003). Mathematical modelling of the cellular mechanics of plants. *Philosophical Transactions of the Royal Society, B: Biological Sciences, 358*(1437), 1437-1444.

de Castro, A., Vilas Boas, A., Page, D., Giovinazzo, R., Bertin, N., & Fanciullino, A.-L. (2017). Combined effects of irrigation regime, genotype, and harvest stage determine tomato fruit quality and aptitude for processing into puree. *Frontiers. Plant Science, 8*.

Collewet, G., Musse, M., El Hajj, C., & Moussaoui, S. (2022). Multi-exponential MRI T2 maps: A tool to classify and characterize fruit tissues. *Magnetic Resonance Imaging, 87*, 119-132.

El Hajj, C., Moussaoui, S., Collewet, G., & Musse, M. (2020). Multi-exponential transverse relaxation times estimation from magnetic resonance images under Rician noise and spatial regularization. *IEEE Transactions on Image Processing, 29*, 6721-6733.

Genovese, J., Strucic, M., Sersa, I., Novickij, V., Rocculi, P., Miklavvic, D., et al. (2023). PEF treatment effect on plant tissues of heterogeneous structure no longer an enigma: MRI insights beyond the naked eye. *Food Chemistry, 405*.
Gonzalez, M. E., Barrett, D. M., McCarthy, M. J., Vergeldt, F. J., Gerkema, E.,
Matser, A. M., et al. (2010). 1H-NMR study of the impact of high pressure and
thermal processing on cell membrane integrity of onions. *Journal of Food Science*, **75**
(7). E417-E25.

Hills, B. P., & Duce, S. L. (1990). The influence of chemical and diffusive exchange on water proton transverse relaxation in plant tissues. *Magnetic Resonance Imaging*, **8**(3), 321-331.

Hills, B. P., & Nott, K. P. (1999). NMR studies of water compartmentation in carrot parenchyma tissue during drying and freezing. *Applied Magnetic Resonance*, **17**(4), 521-535.

Hills, B. P., & Remigereau, B. (1997). NMR studies of changes in subcellular water compartmentation in parenchyma apple tissue during drying and freezing. *International Journal of Food Science and Technology*, **32**(1), 51-61.

Hills, B. P., & Lefloch, G. (1994). NMR-studies of non-freezing water in cellular plant-tissue. *Food Chemistry*, **51**.

Hothorn, T., Bretz, F., & Westfall, P. (2008). Simultaneous inference in general parametric models. *Biometrical Journal*, **50**(3), 346-363.

Jorge, A., Leal, E. S., Sequinel, R., Sequinel, T., Kubaski, E. T., & Tebcherani, S. M. (2018). Changes in the composition of tomato powder (<i>Lycopersicon esculentum</i>/*<i>I</i> Mill) resulting from different drying methods. *Journal of Food Processing & Preservation*, **42**(5).

Ke, Y., Deng, L., Dai, T., Xiao, M., Chen, M., Liang, R., et al. (2023). Effects of cell wall polysaccharides on the bioaccessibility of carotenoids, polyphenols, and minerals: An overview. *Critical Reviews in Food Science and Nutrition*, **63**(32), 11385-11398.

Lahaye, M., Devaux, M. F., Poole, M., Seymour, G. B., & Causse, M. (2013). Pericarp tissue microstructure and cell wall polysaccharide chemistry are differently affected in lines of tomato with contrasted firmness. *Postharvest Biology and Technology*, **76**, 83-90.

Lahaye, M., Falourd, X., Laillet, B., & Le Gall, S. (2020). Cellulose, pectin and water in cell walls determine apple flesh viscoelastic mechanical properties. *Carbohydrate Polymers*, **232**, Article 115768.

Lahaye, M., Falourd, X., Le Bot, L., Bertin, N., & Musse, M. (2025). Impact of drying on the composition and organization tomato fruit cell walls: a biochemical and structural study. *submitted to FRI*.

Le Bourvellec, C., Bagano Vilas Boas, P., Lepercq, P., Comtet-Marre, S., Auffret, P., Ruiz, P., et al. (2019). Procyanidin—Cell Wall interactions within apple matrices decrease the Metabolization of Procyanidins by the human gut microbiota and the anti-inflammatory effect of the resulting microbial metabolome in vitro. *Nutrients*, **11** (3), 664.

Le Bourvellec, C., Bouzerzour, K., Ginies, C., Regis, S., Plé, Y., & Renard, C. M. G. C. (2011). Phenolic and polysaccharidic composition of applesauce is close to that of apple flesh. *Journal of Food Composition and Analysis*, **24**(4), 537-547.

Le Bourvellec, C., Gouble, B., Bureau, S., Reling, P., Bott, R., Ribas-Agusti, A., et al. (2018). Impact of canning and storage on apricot carotenoids and polyphenols. *Food Chemistry*, **240**, 615-625.

Leca, A., Clerjon, S., Bonny, J. M., Renard, C., & Traore, A. (2021). Multiscale NMR analysis of the degradation of apple structure due to thermal treatment. *Journal of Food Engineering*, **294**.

Leforestier, R., Fleury, A., Mariette, F., Collewet, G., Challois, S., & Musse, M. (2022). Quantitative MRI analysis of structural changes in tomato tissues resulting from dehydration. *Magnetic Resonance in Chemistry*, **60**(7), 637-650.

Leforestier, R., Mariette, F., & Musse, M. (2021). Impact of chemical exchange on transverse relaxation at low and moderate magnetic field strengths for sugar solutions representative of fruit tissues analyzed by simulation and MRI experiments. *Journal of Magnetic Resonance*, **322**, Article 106872.

Legland, D., Arganda-Carreras, I., & Andrey, P. (2016). MorphoLibJ: Integrated library and plugins for mathematical morphology with ImageJ. *Bioinformatics*, **32**(22), 3532-3534.

Lewicki, P. P., & Pawlak, G. (2003). Effect of drying on microstructure of plant tissue. *Drying Technology*, **21**(4), 657-683.

Li, D., Deng, L., Dai, T., Chen, M., Liang, R., Liu, W., et al. (2022). Ripening induced degradation of pectin and cellulose affects the far infrared drying kinetics of mangoes. *Carbohydrate Polymers*, **291**, Article 119582.

Lopez-Sanchez, P., Martinez-Sanz, M., Bonilla, M. R., Sonni, F., Gilbert, E. P., & Gidley, M. J. (2020). Nanostructure and poroviscoelasticity in cell wall materials from onion, carrot and apple: Roles of pectin. *Food Hydrocolloids*, **98**.

Meiboom, S., & Gill, D. (1958). Modified spin-Echo method for measuring nuclear relaxation times. *The Review of Scientific Instruments*, **29**, 688-691.

Morelli, L., & Rodriguez-Concepcion, M. (2023). Open avenues for carotenoid biofortification of plant tissues. *Plant Communications*, **4**(1), Article 100466.

Musse, M., Bidault, K., Quellec, S., Brunel, B., Collewet, G., Cambert, M., et al. (2021). Spatial and temporal evolution of quantitative magnetic resonance imaging parameters of peach and apple fruit - Relationship with biophysical and metabolic traits. *The Plant Journal*, **105**(1), 62-78.

Musse, M., Cambert, M., & Mariette, F. (2010). NMR study of water distribution inside tomato cells: Effects of water stress. *Applied Magnetic Resonance*, **38**.

Musse, M., De Guio, F., Quellec, S., Cambert, M., Challois, S., & Davenel, A. (2010). Quantification of microporosity in fruit by MRI at various magnetic fields: Comparison with X-ray microtomography. *Magnetic Resonance Imaging*, **28**(10), 1525-1534.

Musse, M., & Van As, H. (2018). NMR imaging of air spaces and metabolites in fruit and vegetables. In G. A. Webb (Ed.), *Modern magnetic resonance* (pp. 1-15). Cham: Springer International Publishing.

Ni, J. B., Zielinska, M., Wang, J., Fang, X. M., Prakash Sutar, P., Li, S. B., et al. (2023). Post-harvest ripening affects drying behavior, antioxidant capacity and flavor release of peach via alteration of cell wall polysaccharides content and nanostructures, water distribution and status. *Food research international* (Ottawa, Ont.), **170**, Article 113037.

Nieto, A. B., Salvatori, D. M., Castro, M. A., & Alzamora, S. M. (2004). Structural changes in apple tissue during glucose and sucrose osmotic dehydration: Shrinkage, porosity, density and microscopic features. *Journal of Food Engineering*, **61**(2), 269-278.

Niu, X. X., Deng, L. Z., Wang, H., Wang, Q. H., Xu, M. Q., Li, S. B., et al. (2024). Transformation of cell wall pectin profile during postharvest ripening process alters drying behavior and regulates the sugar content of dried plums. *Food Chemistry*, **458**, Article 140093.

Oey, M. L., Vanstreels, E., De Baerdemaeker, J., Tijskens, E., Ramon, H., Hertog, M., et al. (2007). Effect of turgor on micromechanical and structural properties of apple tissue: A quantitative analysis. *Postharvest Biology and Technology*, **44**(3), 240-247.

Otsu, N. (1979). A threshold selection method from gray-level histograms. *IEEE Transactions on Systems, Man, and Cybernetics, SMC-9*(1), 62-66.

Page, D., Van Stratum, E., Degrou, A., & Renard, C. M. (2012). Kinetics of temperature increase during tomato processing modulate the bioaccessibility of lycopene. *Food Chemistry*, **135**(4), 2462-2469.

Pravitha, M., Agrahar, M. D., & Kumar, V. A. (2024). Recent developments in tomato drying techniques: A comprehensive review. *J Food Process Engr.*, **47**(2).

Prawiranto, K., Defraeye, T., Derome, D., Buhlmann, A., Hartmann, S., Verboven, P., et al. (2019). Impact of drying methods on the changes of fruit microstructure unveiled by X-ray micro-computed tomography. *RSC Advances*, **9**(19), 10606-10624.

Prawiranto, K., Defraeye, T., Derome, D., Verboven, P., Nicolai, B., & Carmeliet, J. (2018). New insights into the apple fruit dehydration process at the cellular scale by 3D continuum modeling. *Journal of Food Engineering*, **239**, 52-63.

Proietti, N., Adiletta, G., Russo, P., Buonocore, R., Mannina, L., Crescitelli, A., et al. (2018). Evolution of physicochemical properties of pear during drying by conventional techniques, portable-NMR, and modelling. *Journal of Food Engineering*, **230**, 82-98.

Prothon, F., Ahrné, L., & Sjöholm, I. (2003). Mechanisms and prevention of plant tissue collapse during dehydration: A critical review. *Critical Reviews in Food Science and Nutrition*, **43**(4), 447-479.

Ramos, I. N., Brandao, T. R. S., & Silva, C. L. M. (2003). Structural changes during air drying of fruits and vegetables. *Food Science and Technology International*, **9**(3), 201-206.

Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., et al. (2012). Fiji: An open-source platform for biological-image analysis. *Nature Methods*, **9**(7), 676-682.

Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH image to ImageJ: 25 years of image analysis. *Nature Methods*, **9**(7), 671-675.

Sérino, S., Gomez, L., Costagliola, G., & Gautier, H. (2009). HPLC assay of tomato carotenoids: Validation of a rapid microextraction technique. *Journal of Agricultural and Food Chemistry*, **57**(19), 8753-8760.

Sinkora, M., Fanciullino, A.-L., Bertin, N., Giovinazzo, R., Zuber, F., Leca, A., et al. (2024). Variety, growing conditions and processing method act on different structural and biochemical traits to modify viscosity in tomato puree. *Food Research International*, **189**, Article 114495.

Snaar, J. E. M., & Van As, H. (1992). Probing water compartments and membrane permeability in plant cells by H NMR relaxation measurements. *Biophysical Journal*, **63**, 1654-1658.

Team RC. (2022). R: A language and environment for statistical computing. *R foundation for statistical Computing*.

Utsuzawa, S., Donaldson, M., & Song, Y.-Q. (2022). Ringing cancellation in Carr-Purcell-Meiboom-Gill-type sequences. *Magnetic Resonance Letters*, **2**(4), 233-242.

Van As, H. (2007). Intact plant MRI for the study of cell water relations, membrane permeability, cell-to-cell and long distance water transport. *Journal of Experimental Botany*, **58**(4), 743-756.

Van der Weerd, L., Claessens, M., Efde, C., & Van As, H. (2002). Nuclear magnetic resonance imaging of membrane permeability changes in plants during osmotic stress. *Plant, Cell & Environment*, **25**(11), 1539-1549.

Wang, B., Li, Y., Lv, Y., Jiao, X., Wang, Z., He, Y., et al. (2024). Dehydration-rehydration mechanism of vegetables at the cell-wall and cell-membrane levels and future research challenges. *Critical Reviews in Food Science and Nutrition*, **64**(30), 1-17.

Whittall, K. P., & Mackay, A. L. (1989). Quantitative interpretation of nmr relaxation data. *Journal of Magnetic Resonance*, **84**(1), 134-152.

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., et al. (2019). Welcome to the Tidyverse. *Journal of Open Source Software*, **4**, 1686.

Winisdorffer, G., Musse, M., Quellec, S., Devaux, M.-F., Lahaye, M., & Mariette, F. (2015). MRI investigation of subcellular water compartmentalization and gas distribution in apples. *Magnetic Resonance Imaging*, **33**(5), 671-680.

Wojdylo, A., Figiel, A., Legua, P., Lech, K., Carbonell-Barrachina, A. A., & Hernández, F. (2016). Chemical composition, antioxidant capacity, and sensory quality of dried jujube fruits as affected by cultivar and drying method. *Food Chemistry*, **207**, 170-179.

Yu, J., Gleize, B., Zhang, L., Caris-Veyrat, C., & Renard, C. M. G. C. (2019). Heating tomato puree in the presence of lipids and onion: The impact of onion on lycopene isomerization. *Food Chemistry*, **296**, 9-16.