<figure><img src="image_1.png" /></figure>

# Utilising starchy tropical agri-resources through gluten-free bread-making: the case of cassava, green banana and sweet potato flours

Simon Carlier<sup>a,b</sup>, Layal Dahdouh<sup>a,b,*</sup> ●, Julien Ricci<sup>a,b</sup>, Lidwine Grosmaire<sup>a</sup>

## ABSTRACT

This work evaluated, for the first time, different gluten-free bread formulations using mixtures of cassava, sweet potato and green banana flours. A simplex centroid mixture experimental design with constraints was used to evaluate the role of each flour in the physical properties of dough and bread. Hence, the relationships between the pasting properties of the flours, rheological properties of the doughs and physical properties of the baked breads were investigated. The results showed that increasing the proportion of cassava flour (from 30 to 70 %) enhanced porosity (from 6.36 to 17.89 %) and led to a softer crumb (from 12.48 to 8.07 N); whereas a higher proportion of green banana flour (70 %) resulted in a harder crumb (12.48 N) despite an increase in the bread specific volume (1.33 cm³ g⁻¹). The sweet potato flour input enhanced the staling properties of bread. However, when the input of this flour exceeded the threshold of ~20 %, the specific volume of the bread (<1.16 cm³ g⁻¹) and its porosity (<11.20 %) and textural properties deteriorated. An optimal formulation containing equal proportions of cassava and green banana flours (43.3 %) and a lower proportion of sweet potato (13.3 %) was identified.

## 1. Introduction

Global food security is an increasingly pressing issue particularly in a context of sustainable food systems (Campi et al., 2021). This issue is even more challenging in island regions, where the reliance on imported food products often exacerbates sustainability concerns (Marzin et al., 2021). Addressing island food systems sustainability involves efforts and territorial strategies to efficiently utilise and make the most of local resources (Brial, 2023), namely those rich in starch (Marzin et al., 2021).

Starchy resources are of particular importance due to their versatility in food production, especially in the creation of staple foods such as pasta, noodles and bread (Shewry & Hey, 2015). Bread is a staple food and has played a central role in human diet for thousands of years (Shewry & Hey, 2015).

Traditionally, bread is made from wheat flour, water, yeast and salt. Hence it provides an essential source of complex carbohydrates, fibres, vitamins and minerals. Wheat plays a fundamental role in bread production due to its starch, which is the main energy source, while its gluten (a viscoelastic protein complex) confers textural properties and gas-holding capacity (Foschia et al., 2016; Martínez & Gómez, 2017). However, gluten is implicated in various health disorders such as celiac disease and gluten sensitivity (Horstmann et al., 2019; Mohammadi et al., 2014). Currently, the only effective treatment for these disorders is a strict gluten-free (GF) diet. In the past recent years, the demand for

GF food products has significantly increased due to the growing number of diagnosed patients, as well as people adopting this diet for other various reasons (Foschia et al., 2016).

Many tropical and Southern regions, including island territories, face a twin issue of dietary disorders such celiac disease and food sovereignty due to the drastic dependency on imported wheat. In these territories, commonly imported maize and rice flours are used in GF bread-making (Monteiro et al., 2021). However, these flours are not produced locally and their nutritional profile is not suited to the dietary needs encountered in island territories. In this context, roots tubers (cassava and sweet potato) and green bananas derived from local starchy agro-resources are useful options for producing gluten-free breads (GFB) (Guadalupe-Moyano et al., 2022; Monteiro et al., 2021; Nzossié & Temple, 2023; Sigüenza-Andrés et al., 2021). In addition, green banana flour provides a way of utilising pre-harvest losses, such as those caused by cyclones. Reunion Island is a production territory of all of these resources, which provides an opportunity to explore the synergistic potential of their flours to be used jointly in GFB making, thereby offering new prospects to cater to the growing GF market while dealing with food sovereignty (Nzossié & Temple, 2023).

GF breadmaking presents many technological challenges, mainly due to the absence of gluten (Naqash et al., 2017). The main problems concern the techno-functional properties and sensory quality of GFB, often characterized by a crumbly texture and dense crumbs (Amini

<footnote><sup>a</sup> QualiSud, Université de Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France</footnote>

<footnote><sup>b</sup> CIRAD, UMR Qualisud, F-97410, Saint-Pierre, France</footnote>

<footnote>* Corresponding author. QualiSud, Université de Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France. E-mail addresses: layal_dahdouh@hotmail.com, layal.dahdouh@cirad.fr (L. Dahdouh).</footnote>
Khoozani et al., 2020; Foschia et al., 2016). Enhancers such as hydrocolloids (Mohammadi et al., 2014), emulsifiers and proteins are used to mimic the viscoelastic properties of gluten (Monthe et al., 2019; Sciarini et al., 2012). GF breadmaking has already been widely studied (Monteiro et al., 2021; Naqash et al., 2017), but to date, the use of banana flour in GF breadmaking remains under-studied. Furthermore, its combination with sweet potato and cassava flours is an original and little-explored approach.

This research aims to contribute to food sovereignty and resilience in island regions, while dealing with local dietary needs, namely in a context of growing celiac disorders and changing dietary trends. Hence the objective of this study is to investigate the feasibility of GF breadmaking using green banana, sweet potato and cassava flours from Reunion. To do so, the specific role of each flour in the physical and physico-chemical properties of dough and bread, was evaluated. The interactions and synergies between these three flours were explored through a mixing plan with specific constraints (Horstmann et al., 2019; Monteiro et al., 2021) to approach an optimal gluten-free bread formulation.

## 2. Materials and method

### 2.1. Raw materials

GF baker's yeast (Culinat, Colombes, France), salt, sugar, sweet cassava (Ca) and orange sweet potato (SP) ("Carrote" variety) were purchased from local markets on Reunion Island. Xanthan gum was supplied by Cooper (Melun, France). Green banana (GB) bunches (Red Dacca variety) were harvested at CIRAD's "Bassin Plat" experimental stations. To produce the native flours, Ca roots, SP tubers and GB bunches were washed, peeled, cut, then soaked in a citric acid solution (10 g/L) for 10 min. They were then drained, dried for 48 h at 50 °C (Amarasinghe et al., 2021) in an oven (UF450, Memmert, Schwabach, Germany) before being ground and sieved simultaneously (mesh size 500 µm) with a rotor mill (model ZM200, RETSCH, Haan, Germany).

### 2.2. Bread-making process

For 100 g flour, the following ingredients were used: water (120 g),

brown sugar (1.47 g), freeze-dried baker's yeast (1.62 g), xanthan gum (2.06 g) and fine salt (1.76 g) (Martínez & Gómez, 2017; Monthe et al., 2019). Yeast and sugar were dissolved in warm water (35 °C) and re-generated in an oven (WON-155, WiseVen, Daejeon, South Korea) at 35 °C for 15 min. Flours (350 g total for 1 bread) were premixed for 1 min at speed 1 in a blender (model 5KSM125, KitchenAid, St Joseph, MI, USA) with Coated Flat Beater (K45B). An aliquot of 10 g of this flour blend was sampled. Next, salt, xanthan gum and water were incorporated, and kneaded first at speed 1 for 30 s, and then at speed 2 for 45 s. The dough was placed in a metal rectangular mould (Length 25 cm x Width 9 cm x Height 7.5 cm, Gobel, France) and fermented for 30 min at 35 °C and 85 % RH in an oven. After proofing, the bread was baked for 35 min at 180 °C in wet mode (Martínez & Gómez, 2017). The baked bread was left to cool for 2 h at room temperature (24 °C ± 1 °C) before weighing and the physical analyses.

### 2.3. Experimental design

A simplex centroid mixing plan with constraints was used to study and optimize bread formulation by mixing different proportions of Ca, GB and SP flours (Fig. 1). Upper and lower limits were chosen so as to increase the proportions of Ca and GB flours (30–70 %) while reducing the SP flour (0–30 %), in order to achieve acceptable bread properties (Monthe et al., 2019). Seven formulations, with different proportions of flours (flour blends), were tested and duplicated (F1 to F6). Only the central point (F7) was tested in triplicate.

### 2.4. Flours, doughs and breads characterization

#### 2.4.1. Flours chemical and physicochemical properties

#### 2.4.1.1. Starch content.
Starch content was determined using the enzymatic method adapted from Holm, J. 1986; Holm et al., 1986). Total carbohydrates were quantified by hydrolysis with the thermos-stable α-amylase enzyme A3403 (Sigma-Aldrich, St. Louis, MO, USA), followed by amyloglucosidase (Sigma-Aldrich, St. Louis, MO, USA). Total glucose released was measured by enzymatic colorimetry at 510 nm (Infinite 200 Pro, M Nano configuration, Tecan Austria GmbH, Männedorf, Switzerland) after reaction with the enzyme glucose oxidase

<figure><img src="image_2.png" /><figcaption>Fig. 1. Experimental mixture design and the 7 formulations with the content of the three flours used (Ca: Cassava; GB: Green Banana; SP: Sweet Potato).</figcaption></figure>
(GOD, Sigma-Aldrich, St. Louis, MO, USA) and peroxidase (POD, Sigma-Aldrich, St. Louis, MO, USA). Free glucose content was determined separately after extraction from flour with sulfuric acid (5 mM), and quantified using the GOD-POD assay (Holm et al., 1986). Starch content was then calculated as the difference between total carbohydrates and free glucose. All measurements were performed in triplicate.

### 2.4.1.2. Water holding capacity (WHC).
One-gram wet basis of flour (W1) was dispersed in 10 ml of distilled water and placed in a pre-weighed 15 ml centrifuge tube. The flour and water were mixed with a vortex mixer for 1 min, left at room temperature for 1 h and then centrifuged at 23 °C for 25 min at 3000 g (Sigma 3-18 KS, Sigma, Osterode am Harz, Germany). The supernatant was discarded, and the tube containing the hydrated sample was weighed (W2). WHC was calculated using the following equation (Eq. (1)), and expressed in grams of water retained per gram of flour (Amarasinghe et al., 2021). WHC was determined in triplicate.

$$WHC = \frac{W2 - W1}{W1} \qquad \text{Eq. 1}$$

### 2.4.1.3. Pasting properties.
The flour pasting properties were studied using a Physica MCR 301 rheometer (Anton Paar, Graz, Austria) combined with a ST24-2D-12 probe and SN32904 cells. The Rapid Visco Analyser method (RVA) was used to determine the viscous properties of each flour. RVA properties were analysed for 15 g flour/water mixture at 8 % dry basis flour, and the viscosity was recorded using the following temperature profile: held at 50 °C for 1 min, heated from 50 °C to 90 °C at 6 °C/min, held at 90 °C for 5 min and then cooled down to 50 °C at 6 °C/min with continuous stirring, first at 960 rpm for 10 s, and finally at 160 rpm for the rest of the experiment. Six parameters were recorded: pasting temperature (PT), peak viscosity (PV), breakdown (BD) and final viscosity (FV) (Alvarado et al., 2013; Dufour et al., 2009; Monthe et al., 2019). The analyses were performed in duplicate.

## 2.4.2. Dough physical properties

### 2.4.2.1. Dough rheological behaviour.
The rheological properties of kneaded dough (before proofing) were analysed using a rheometer (Physica MCR 301, Anton Paar, Graz, Austria) equipped with a plan-plan parallel serrated geometry (PP 50 - P2, 50 mm diameter, 2 mm gap). Measurements were made at 25 °C, temperature being maintained using a Peltier and refrigerant system (Viscotherm VT2, Anton Paar, Graz, Austria). The rheometer was supplied with a chamber previously sprayed with water to prevent dehydration of the sample during the measurements. The dough was placed between the plates and allowed to stand for 120 s, before the measurement for relaxing the sample after placing it on the rheometer. The storage (G') and loss (G'') moduli were measured at a constant frequency of 1 Hz, while the strain amplitude varied between 0.01 and 500 %. The storage modulus G'_{LVE} in the viscoelastic linear region (LVE) was determined and used as a rheological indicator reflecting the solid-like behaviour of the dough, representing its stiffness and the energy recovered when the stress is released. The linear viscoelastic region (LVE) of each sample was determined with an LVE limit defined by the strain causing a 5 % reduction in modulus values. The shear stress value (τf, Pa), corresponding to the flow point where the storage and the loss moduli are equal (G' = G''), was also recorded. Analyses were performed at least twice to ensure the consistency of the results, and the data were recorded with Rheoplus software (Amini Khoozani et al., 2020; Dahdouh et al., 2021; Monthe et al., 2019).

### 2.4.2.2. Dough texture.
Dough texture was measured with a textrometer (model TA-XT plus, Stable Micro Systems, Godalming, UK) (Sciarini et al., 2012) equipped with a 50 kg load cell and a stainless-steel spherical probe (P/1S diameter 1 inch = 25.4 mm). 100 g

of kneaded dough was sampled and placed in a cylindrical plastic cup (79 mm height × 58 mm diameter). The probe reached the surface of the dough with a speed test of 2 mm s⁻¹, and then penetrated into the dough with a test speed of 1 mm s⁻¹ to reach a target distance of 25 mm. Finally, the probes moved back in the opposite direction at a speed of 2 mm s⁻¹ to reach the start position. The test was performed in triplicate, and the recorded maximum force (Fmax) was used as an indicator of the dough texture.

### 2.4.2.3. Dough growth.
Dough growth (Dg) was calculated on the basis of the difference between dough volume before and after fermentation (Eq. (2)). To do so, 90 g of kneaded dough was fermented in a cylindrical plastic cup (height 79 mm × radius 29 mm) placed in an oven (WON-155, WiseVen, Daejeon, South Korea) for 30 min at 35 °C. During fermentation a dome formed in the dough, hence height measurements were taken before (Hi) and after fermentation at different positions (H₁: Height at the base of the growth dome and H₂: Maximum growth height, after fermentation).

$$Dg = \left( \left( \frac{V_f}{V_i} \right) - 1 \right) \times \left( \frac{90}{w} \right) \times 100 \qquad \text{Eq. 2}$$

$$\text{where } V_i = \pi \times r^2 \times H_i$$

$$\text{and } V_f = (\pi \times r^2 \times H_1) + \left( \frac{\pi \times (H_2 - H_1)}{6} \right) \times (3r^2 + (H_2 - H_1)^2)$$

$$\text{Where: } Dg = \text{dough growth}, r = 29 \text{ mm}, w = \text{dough weight}.$$

## 2.4.3. Bread physicochemical and textural properties

### 2.4.3.1. Conventional measurements.
Water loss was calculated from weight ratio of dough and baked bread, expressed as a percentage (Tao et al., 2024; Vouris et al., 2018). Specific Volume (SV) (cm³.g⁻¹) of the bread was determined by the seed displacement method using AACC method 10–05.01 (2008). Colour was determined on the crumb and crust using a Minolta CR-400 colorimeter (Konica Minolta Sensing Inc., Osaka, Japan), equipped with a CR-A33d measuring head (8 mm aperture), with a D65 illuminant and d/0° measurement geometry, after calibration with a standard white plate. according to the CIELAB colour system (L*, a*, b*). Browning index (BI) (Guadalupe-Moyano et al., 2022) was calculated as follows (Eq. (3)). All measurements were performed in triplicate.

$$BI = \frac{[100(x - 0.31)]}{0.172} \qquad \text{Eq. 3}$$

$$\text{where } x = (a^* + 1.75L^*) / (5.645L^* + a^* - 3.012b^*)$$

### 2.4.3.2. Crumb porosity.
For each bread, 3 slices (20 mm thickness) were scanned using a scanner (e-STUDIO2518A, TOSHIBA, Tokyo, Japan), and the images were analysed using Image J software (Schneider et al., 2012). An area measuring 4 × 4 cm at the centre of each slice was selected to image analysis. Images were processed in 8-bit grayscale at a resolution of 600 dpi with auto threshold mode and particle analysis >1 mm². The following parameters were calculated: pore density (number of pores.cm⁻²), mean pore size (ratio of pore surface area and number of pores), porosity (ratio of pore surface area and total surface area) (Vouris et al., 2018). Crumb uniformity was calculated by the ratio between small pores (0.15 < Surface area < 2.00 mm²) and large pores (2.00 < Surface area < 10.00 mm²), as described by Sciarini et al. (2012).

### 2.4.3.3. Textural analysis.
Crumb's textural profile analysis (TPA) was carried out using a texturometer (model TA-XT plus, Stable Micro Systems, Godalming, UK) equipped with a 25 mm cylindrical probe (P/25 probe, Stable Micro Systems, Godalming, UK) and a 50-kg load cell. TPA
was performed at three points on three bread slices with a thickness of 2 cm, 2 h after baking; and on one slice one after 24 h of storage at room temperature. Texture profile analysis (TPA) was carried out for each formulation. The data were collected using the “Texture Expert for Windows” software. The test was realized with the following parameters: Pre-test, test and post-test speeds were set at 1 mm s⁻¹ at interval of 5 s between the first and second compression; compression: 25 % with a trigger force of 0.049 N (Grassi de Alcántara et al., 2020; Martínez & Gómez, 2017; Monthe et al., 2019).

Crust hardness was analysed with the same TA-XT texturometer fitted with a craft knife probe (A/CKB probe, Stable Micro Systems, Godalming, UK). The test was carried out at two areas of the crust, on three crusts without crumbs (4 cm × 2 cm section). The test was realized with the following parameters: Pre-test, test and post-test speeds were set at 1, 1, 1 mm s⁻¹ respectively, with a trigger force of 0.098 N (Kang & Chen, 2015). A threshold of 294.3 N was set for maximum force to preserve the equipment. The maximum force recorded during each test was used as an indicator of crust hardness.

## 2.5. Statistical analyses

The data were analysed using ‘R software’ (version 4.3.2, (RStudio Team, 2024). Normality and variance homogeneity were assessed using Shapiro-Wilk and Levene’s tests, respectively. ANOVA or Kruskal-Wallis tests (p < 0.05) were applied, followed by Tukey post-hoc tests using the Benjamini-Hochberg procedure. Experimental design, models and ternary plots were generated with the ‘mixexp’ package (Lawson & Willden, 2016).

# 3. Results and discussion

## 3.1. Flour characterisation

### 3.1.1. Flour chemical and physicochemical properties

The flour physicochemical characterisation results (Table 1) indicate that GB flour exhibits the highest WHC, demonstrating that its superior water retention capacity can be linked for example to fibre content; while SP flour has the lowest WHC. WHC is a critical parameter in breadmaking, as it significantly impacts the functional properties of the dough and the final bread quality (Horstmann et al., 2019; Monteiro et al., 2021). Unlike wheat flours, which naturally exhibit high WHC due to their gluten content, (Calabi-Floody et al., 2019), GF flours typically have a lower WHC. Hence, it is essential to increase the dough’s hydration level by the incorporation of hydrocolloids that can also improve the texture of GF doughs (Mohammadi et al., 2014; Monteiro et al., 2021; Sahin et al., 2020). As for starch content, Ca and GB flours have the highest values (Table 1); while SP flour’s value is in accordance with literature (Amarasinghe et al., 2021; Monthe et al., 2019). This low starch content can affect negatively the functionality of SP flour in breadmaking and baking processes, as well as other processes where starch plays an important role in the organoleptic properties of the end products (Sudheesh et al., 2021).

<caption>**Table 1**</caption>

Water holding capacity and starch content of cassava (Ca), sweet potato (SP) and green banana (GB) flours.

<table><thead><tr><th>Flour</th><th>WHC (g/g)</th><th>Starch (%)</th></tr></thead><tbody><tr><td>Ca</td><td>1.28 ± 0.04 ab</td><td>82.73 ± 3.68 a</td></tr><tr><td>GB</td><td>1.39 ± 0.04 a</td><td>80.60 ± 1.38 a</td></tr><tr><td>SP</td><td>1.23 ± 0.07 b</td><td>61.34 ± 2.26 b</td></tr></tbody></table>

WHC: Water holding capacity. Mean ± standard deviation. a-b: Different letters, within each column, indicate significant differences at p < 0.05 (Tukey test; n = 3).

### 3.1.2. Pasting properties

RVA analysis provides essential information on the technological characteristics of flours, and their suitability for bread-making purposes (Juhász & Salgó, 2008; Zaidul et al., 2007). Understanding RVA parameters aids selection of flours to meet specific textural and functional properties. The RVA parameters of the 3 studied flours and the generated blends are summarized in Table 2. While no significant difference is observed in Pasting Temperature (PT) when comparing the flour blends, it can be noticed that SP flour has the highest PT of the individual flours, which is consistent with the literature (Amini Khoozani et al., 2020; Zaidul et al., 2007). This observation could be explained by starch granule size, as smaller granules resist swelling (Juhász & Salgó, 2008). Indeed, some authors have reported smaller granule sizes for SP flour (7–12 µm) (Jangchud et al., 2003) than Ca flour (15.7 µm) (Alvarado et al., 2013; Zaidul et al., 2007) and GB flour (24.3 µm) (Reyes-Atrizco et al., 2019). Amylose content also influences pasting temperature (Juhász & Salgó, 2008), but does not explain the observed trend, as SP’s lower amylose content (16.6–18.6 %) (Bodjrenou et al., 2023; Monthe et al., 2019) than those of Ca (18.8–21.2 %) (Alvarado et al., 2013) and GB (27.7–29.9 %) (Reyes-Atrizco et al., 2019; Yee et al., 2021) would typically lead to a lower PT. This unexpected discrepancy could be explained by the predominance of the role of starch granule size in PT, or varietal effects (Alvarado et al., 2013).

SP shows the lowest Peak Viscosity (PV), Breakdown (BD), and Final Viscosity (FV) values, probably due to its lower starch content (Table 1). For flour blends, these values decrease as the proportion of SP increases. Conversely, an input of GB flour consistently increases the values of these pasting parameters proportionally to its ratio in the blends. This effect is not solely related to starch content, as the same phenomenon is not observed for Ca, which contains a similar amount of starch to GB. These results could be explained by differences in amylose, phosphorus and fibre contents, as well as starch granule size (Juhász & Salgó, 2008; Zaidul et al., 2007). These findings suggest that GB flour positively contributes to breadmaking with its good swelling capacity, but has poorer textural stability during baking and storage due to its significant high viscosity breakdown and high retrogradation tendency. Combining GB with SP and Ca would be beneficial for limiting retrogradation and viscosity loss at high temperatures, but SP must be incorporated in small amounts to ensure good dough expansion (Martínez & Gómez, 2017).

## 3.2. Dough physical properties

Fig. 2 illustrates an example of the viscoelastic behaviour of bread dough (Fig. 2A) for the central point (F7). The same viscoelastic behaviour was observed for all dough formulations (F1 to F7). It should be noticed that all formulations had a linear viscoelastic region is observed up to 0.1 % strain, where the storage (G') and loss (G'') moduli remain steady, with G' exceeding G'', indicating the predominance of a solid-like behaviour. Beyond a strain of 0.1 %, both moduli decrease slightly and more sharply after a strain of 1 %, marking the breakdown of the dough structure. At 100 % strain, the storage and loss moduli have similar values, indicating the transition from a solid-like (G' > G'') to a viscous-like behaviour (G'' > G'). The recorded stress value at this crossover corresponds to the flow point (rf). Hereafter, the flow point (rf) and the storage G'LVE modulus in the viscoelastic linear region (LVE) were used as rheological indicators to compare dough rheological properties (Dahdouh et al., 2021). Fig. 2B depicts, as for example, the force (N) measured over time (s) during textural analysis carried out on the F7 dough. The same textural behaviour was observed for all dough formulations (F1 to F7). Indeed, for all doughs, the force increases as the probe penetrates the dough (positive area), reaching a maximum force Fmax before withdrawing (negative area, due to the adhesiveness of the dough on the probe). Since all parameters follow a similar trend, only Fmax is used hereafter as an indicator of the dough hardness. Significant differences are observed between the formulations in terms of
<table><caption>**Table 2**
Pasting properties of flours and flours blends obtained by RNA (Rapid visco analyser).</caption><thead><tr><th>Flours/blends</th><th>PT (°C)</th><th>PV (mPa.s)</th><th>BD (mPa.s)</th><th>FV (mPa.s)</th></tr></thead><tbody><tr><td>Ca</td><td>63.90 ± 0.58 c</td><td>1509.84 ± 111.87 b</td><td>948.95 ± 153.23 a</td><td>812.24 ± 32.38 e</td></tr><tr><td>GB</td><td>65.33 ± 0.52 bc</td><td>2898.25 ± 334.10 a</td><td>1110.00 ± 236.17 a</td><td>2548.25 ± 71.06 a</td></tr><tr><td>SP</td><td>70.50 ± 1.41 a</td><td>290.79 ± 348.70 c</td><td>293.20 ± 359.43 bcd</td><td>22.66 ± 18.52 h</td></tr><tr><td>F1</td><td>65.14 ± 0.06 bc</td><td>1515.41 ± 59.37 b</td><td>616.05 ± 27.37 abc</td><td>1244.91 ± 28.96 d</td></tr><tr><td>F2</td><td>65.64 ± 0.26 bc</td><td>2026.11 ± 38.51 b</td><td>729.00 ± 33.94 ab</td><td>1929.11 ± 35.68 b</td></tr><tr><td>F3</td><td>67.42 ± 1.19 b</td><td>87.61 ± 7.01 c</td><td>59.10 ± 2.67 d</td><td>46.66 ± 6.81 h</td></tr><tr><td>F4</td><td>64.91 ± 0.79 bc</td><td>1649.62 ± 14.78 b</td><td>605.50 ± 14.85 abc</td><td>1505.12 ± 4.31 c</td></tr><tr><td>F5</td><td>66.68 ± 0.00 bc</td><td>191.22 ± 6.71 c</td><td>143.05 ± 8.41 cd</td><td>83.42 ± 2.20 gh</td></tr><tr><td>F6</td><td>67.06 ± 0.57 b</td><td>243.80 ± 12.61 c</td><td>191.22 ± 2.74 fg</td><td>130.05 ± 10.25 cd</td></tr><tr><td>F7</td><td>66.52 ± 0.65 b</td><td>321.04 ± 6.18 c</td><td>201.85 ± 3.93 f</td><td>199.33 ± 3.46 cd</td></tr></tbody></table>

PT: pasting temperature; PV: peak viscosity; BD: breakdown; FV: final viscosity. Mean ± standard deviation. a-h: Different letters, within each column, indicate significant differences at p < 0.05 (Tukey test). Composite flour formulations (cassava (Ca), green banana (GB), sweet potato (SP)): F1 (70;30;0); F2 (30;70;0); F3 (30;30;40); F4 (50;50;0); F5 (50;30;20); F6 (30;50;20); F7 (43.3;43.3;13.3).

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** A) Evolution of storage modulus (G', Pa) and the loss modulus (G", Pa) as a function of strain (%), B) Evolution of the force (N) as a function of time (s) for dough, C) Storage modulus of dough (G'LVE, Pa), D) Flow point of dough (τf, Pa), E) Maximum penetration force of dough (Fmax, N) and F) Dough growth (Dg, %) for the 7 formulations (F1 to F7). a-c: Letters indicate significant differences at p < 0.05 (Tukey test). Flour formulations (cassava (Ca), green banana (GB), sweet potato (SP)): F1 (70;30;0); F2 (30;70;0); F3 (30;30;40); F4 (50;50;0); F5 (50;30;20); F6 (30;50;20); F7 (43.3;43.3;13.3).</figcaption></figure>

rheological and textural properties (Fig. 2C-F). F2 has the highest G' and Fmax (Fig. 2C-E), indicating the highest solid-like behaviour and hardness. This is in accordance with GB flour's chemical and physicochemical properties, since it is the major flour of this formulation (Amini Khoozani et al., 2020). Moreover, the flow point (τf) of this formulation's dough is the highest one (Fig. 2D), showing that a higher stress is required for the dough to flow, and thus change from solid-like to the

viscous-like behaviour. We can observe that the presence of SP flour reduces solid-like behaviour and hardness, as seen in doughs F3, F5, F6 and F7 (Fig. 2C-E). For dough growth (Dg) (Fig. 2F), F7 shows the best value, while F2 and F3 have the lowest values. The low solid behaviour of F7 enhances the dough's expansion capacity, and could result in a good gas retention during baking (Martínez & Gómez, 2017). Even though dough F3 also has low values for storage modulus G'LVE and
$F_{max}$, it has low growth unlike F7. This could be explained by the techno-functional properties of SP flour. As for F2, the higher GB proportions lead to higher solid-like behaviour and hardness, resulting in a low dough growth.

### 3.3. Bread analysis

#### 3.3.1. Physicochemical properties

Specific Volume (SV) is a good indicator of bread quality, which has an influence on consumer choice (Monteiro et al., 2021). As shown in Table 3, F2 and F7 exhibit higher SV, suggesting that GB flour enhances bread specific volume; and, in contrast, SP flour beyond a critical threshold decreases SV (Monthe et al., 2019; Sigüenza-Andrés et al., 2021). The interaction of the three flours seems to have a positive synergistic effect, improving SV, as in the case of F7.

Bread colour strongly influences consumer acceptance (Monteiro et al., 2021). The crumb of SP-rich breads has lower lightness (L\*) and higher yellowness (b\*) and redness (a\*), resulting in a higher Browning Index (BI) (Table 3). Crust colour follows similar trends (results not shown). Many factors can explain these differences, since the colour developed during baking is the result of chemical reactions such as pyrolysis, caramelisation and the Maillard reaction (Monteiro et al., 2021). Formulations containing less sugar and more starch produce lighter colours like Ca flour, while formulations containing less starch and more sugar produce darker colours like SP flour (Guadalupe-Moyano et al., 2022). The high carotenoid and polyphenol content in SP flour, especially in orange varieties, contributes to the redder colour of SP-rich breads (Nogueira et al., 2018; Rodriguez-Amaya et al., 2011).

#### 3.3.2. Structure and texture

Crumb structure is a crucial sensory attribute and it has an important role in the overall bread quality attributes, such as bread volume and crumb softness (Amini Khoozani et al., 2020; Mohammadi et al., 2014). Formulations F1 and F7 exhibit higher crumb porosity, pore density and mean pore size, which are favourable for good crumb structure (Table 4 and Fig. 3E). These formulations contain low SP, a higher proportion of Ca and less than 50 % GB flour. In contrast, F3, with a high SP flour proportion (40 %), shows the lowest values for these parameters. The ternary diagrams in Fig. 3A-B confirm that crumb porosity and pore density are higher with high Ca content, and when GB content is lower than 50 % and SP lower than 20 %. Formulation F7 has the highest pore density; this could be explained by the positive effect of using equal contents of GB and CA and a lower SP content. Indeed, we can observe that an input of SP flour adversely affects these quality parameters above a threshold of ~20 % (Fig. 3E). These findings are in line with dough properties reported previously, namely dough growth and viscoelastic properties. Similar trends are reported in the literature for SP flour behaviour in breadmaking (Monthe et al., 2019; Tao et al., 2024), and on the role of Ca flour in crumb porosity (Monteiro et al., 2021; Monthe et al., 2019; Sigüenza-Andrés et al., 2021). The uniformity of crumb structure is highest in F2, indicating a greater number of small pores,

which is a negative quality attribute (Sciarini et al., 2012). This may be linked to the more solid-like behaviour of dough rich in GB flour. Formulations F5 and F7 led to breads with a more open structure, lower uniformity and a better balance between small and large pores, which are key attributes in baked products.

Table 4 summarizes the main textural parameters of the crumbs. Crumb hardness is a crucial textural characteristic in bread, and it significantly influences consumer perception of bread hardness (Amini Khoozani et al., 2020; Monteiro et al., 2021). F7 shows the lowest value for this parameter, while F2, F3 and F4 have higher values, with F2 being the hardest. Hardness increases with higher GB or SP proportions, but decreases with higher Ca content. Ternary diagram analysis confirms that optimised proportions of Ca, GB and SP reduce hardness and subsequently enhance bread textural quality (Fig. 3C). Adhesiveness is an undesirable crumb property, and it reflects its stickiness. Formulations F2 and F4 do not exhibit stickiness, as they are free from SP, and contain more than 50 % GB (positive values). For other formulations, the measured adhesiveness values, even if they are negative, remain low - indicating a crumb with minimal stickiness. Springiness and cohesiveness are also critical for texture. Springiness contributes to a pleasant texture, and cohesiveness prevents crumbling. These parameters are lower in formulations with low GB (<40 %) and high SP content, such as F3 and F5. The ternary diagram of springiness (Fig. 3D) clearly indicates the opposite effects of GB and SP on this parameter. Formulations F3 and F5 are therefore the least desirable because they are stickier, less elastic and crumblier. Moreover, these two formulations have the lowest crust hardness values (Table 4), resulting in a soft texture. Thus, increasing the SP content beyond 20 % with a GB content of 30 % leads to breads with a less desirable crust. On the contrary, increasing Ca and GB flour content increases crust hardness. These findings show that bread structure and texture are significantly influenced by the proportions of Ca, GB and SP flours. The results of this study showed that formulations with balanced GB (30–50 %) and low SP (<20 %) improve structural and textural characteristics, whereas a higher SP content had negative impact on these quality attributes.

#### 3.3.3. Stored bread quality

After 24 h of storage, crumb hardness increases significantly for all the formulations (Fig. 4A), showing crumb hardening due to starch retrogradation and the syneresis effect, which reduces the consumer appeal of the bread (Curti et al., 2014). Breads without SP flour (F1, F2 and F4) show a more significant increase in hardness during ageing (Fig. 4B). This result could be explained by the higher starch and amylose content in Ca and GB flours, leading to a more pronounced retrogradation and syneresis process, thus resulting in harder, crumblier and less appealing breads (Amini Khoozani et al., 2020; Juhász & Salgó, 2008; Tovar et al., 2002). This behaviour is consistent with the FV values (Table 2). Indeed, this parameter, reflecting the retrogradation tendency, had the highest values in F1, F2 and F4. In order to maintain the product's sensory qualities during ageing and avoid bread staling, it appears that formulations containing a low proportion of SP flour are

<table><caption>**Table 3**

Physicochemical properties of breads for the 7 formulations.</caption><thead><tr><th rowspan="2">Formulation</th><th>Bread</th><th colspan="4">Crumb</th></tr><tr><th>SV (cm³/g)</th><th colspan="2">L* (l)</th><th colspan="2">BI (l)</th></tr></thead><tbody><tr><td>F1</td><td>1.19 ± 0.04 bc</td><td>54.65 ± 1.09 a</td><td>3.39 ± 0.27 d</td><td>15.03 ± 1.56 d</td><td>35.74 ± 3.12 d</td></tr><tr><td>F2</td><td>1.33 ± 0.04 a</td><td>52.65 ± 2.18 abc</td><td>5.79 ± 0.28 bcd</td><td>14.4 ± 2.22 d</td><td>39.05 ± 3.89 d</td></tr><tr><td>F3</td><td>1.10 ± 0.04 c</td><td>47.03 ± 1.27 d</td><td>10.63 ± 3.47 a</td><td>25.76 ± 1.40 a</td><td>92.35 ± 13.88 a</td></tr><tr><td>F4</td><td>1.24 ± 0.08 ab</td><td>52.69 ± 0.98 ab</td><td>4.63 ± 0.21 cd</td><td>15.44 ± 1.09 cd</td><td>40.09 ± 2.09 cd</td></tr><tr><td>F5</td><td>1.16 ± 0.05 bc</td><td>48.83 ± 1.51 cd</td><td>7.8 ± 2.29 ab</td><td>21.33 ± 3.83 b</td><td>67.56 ± 15.8 b</td></tr><tr><td>F6</td><td>1.15 ± 0.06 bc</td><td>48.90 ± 3.79 d</td><td>8.48 ± 1.70 ab</td><td>20.69 ± 2.68 b</td><td>67.15 ± 15.42 b</td></tr><tr><td>F7</td><td>1.29 ± 0.06 a</td><td>49.96 ± 2.17 bcd</td><td>6.87 ± 1.15 bc</td><td>18.89 ± 2.21 bc</td><td>56.56 ± 9.86 bc</td></tr></tbody></table>

Mean ± standard deviation. a-d: Different letters, within each column, indicate significant differences at p < 0.05 (Tukey test). SV: Specific Volume; BI: Browning Index. Flours formulations (cassava (Ca), green banana (GB), sweet potato (SP)): F1 (70;30;0); F2 (30;70;0); F3 (30;30;40); F4 (50;50;0); F5 (50;30;20); F6 (30;50;20); F7 (43.3;43.3;13.3).
<table><caption>**Table 4**
Structure and texture properties of breads for each formulation.</caption><thead><tr><th rowspan="3">Formulation</th><th colspan="2">Crumb</th><th colspan="2">Texture</th><th colspan="2">Crust</th></tr><tr><th colspan="2">Structure</th><th colspan="2">Hardness (N)</th><th colspan="2">Crust hardness (N)</th></tr><tr><th>Porosity (%)</th><th>Pore density (pore/cm²)</th><th>Mean pore size (mm²)</th><th>Uniformity (/)</th><th>Adhesiveness (N's)</th><th>Springiness (/)</th></tr></thead><tbody><tr><td>F1</td><td>17.89 ± 3.63 a</td><td>4.38 ± 0.82 a</td><td>4.14 ± 0.89 a</td><td>1.18 ± 0.38 ab</td><td>8.07 ± 1.01 cd</td><td>76.2 ± 8.7 a</td></tr><tr><td>F2</td><td>11.77 ± 3.27 b</td><td>3.85 ± 0.45 ab</td><td>3.03 ± 0.60 b</td><td>1.81 ± 0.58 a</td><td>12.48 ± 1.47 a</td><td>74.3 ± 13.9 a</td></tr><tr><td>F3</td><td>6.36 ± 0.76 c</td><td>2.09 ± 0.29 d</td><td>3.05 ± 0.27 b</td><td>1.23 ± 0.41 ab</td><td>11.80 ± 1.76 ab</td><td>45.8 ± 14.5 c</td></tr><tr><td>F4</td><td>11.52 ± 3.23 b</td><td>3.99 ± 0.69 ab</td><td>2.89 ± 0.55 b</td><td>1.52 ± 0.52 ab</td><td>10.81 ± 1.23 b</td><td>70.2 ± 16.4 ab</td></tr><tr><td>F5</td><td>10.66 ± 1.70 bc</td><td>2.80 ± 0.44 cd</td><td>3.82 ± 0.36 ab</td><td>0.77 ± 0.19 b</td><td>8.04 ± 1.75 cd</td><td>50.9 ± 10.8 c</td></tr><tr><td>F6</td><td>11.12 ± 1.63 b</td><td>3.36 ± 0.62 bc</td><td>3.35 ± 0.42 ab</td><td>1.48 ± 0.63 ab</td><td>8.84 ± 1.62 c</td><td>56.9 ± 13.7 bc</td></tr><tr><td>F7</td><td>17.8 ± 2.58 a</td><td>4.39 ± 0.46 a</td><td>4.08 ± 0.65 a</td><td>1.05 ± 0.25 b</td><td>7.40 ± 1.84 d</td><td>56.8 ± 12.9 bc</td></tr></tbody></table>

Mean ± standard deviation. a-d: Different letters, within each column, indicate significant differences at *p* < 0.05 (Tukey test). DM: Dry Matter; WL: Water Loss; SV: Specific Volume; BI: Browning Index. Composite flour formulations (cassava (Ca), green banana (GB), sweet potato (SP)): F1 (70;30;0); F2 (30;70;0); F3 (30;30;40); F4 (50;50;0); F5 (50;30;20); F6 (30;50;20); F7 (43.3;43.3;13.3).

less likely to undergo textural changes after 24 h. However, an excessive input of SP (>20 %) may adversely affect the organoleptic qualities of the bread before ageing, as discussed previously.

### 3.4. Principal component analysis

The principal component analysis (PCA) (Fig. 5) emphasises the relationships between the different variables, to jointly describe the characteristics of flours blends, doughs and breads. The two main dimensions (PC1 and PC2) explain 76.9 % of the total variance (54.1 % for PC1 and 22.8 % for PC2). This PCA provides a relevant perspective for differentiating between formulations according to flour content (CA, GB, SP), and understanding qualitative correlations between flour, dough and bread characteristics. PC1 is positively correlated with GB content, and negatively correlated with SP content. Ca flour content does not have any significant correlation with either PC1 or PC2. The projection of the 7 formulations on PC1 confirms that formulations rich in GB such as F2 have high BD, PV and FV; with positive scores on PC1, contrasting with SP-rich F3, which has higher PT scores. Meanwhile, Ca content has no significant correlations with these parameters. PCA also highlights that high-viscosity flours, such as blends rich in GB flour, tend to produce dough with a higher solid-like behaviour - leading to higher cohesiveness, springiness, and staling (ΔH). Conversely, a high SP flour content results in a more viscous dough - leading to high crumb adhesiveness, which is characterized by a higher BI and lower staling. For example, the doughs from F5 and F3, in the lower left quadrant, exhibit less solid behaviour, in contrast to F2. Moreover, as observed in Fig. 3E, formulations F5 and F3 rich in SP content, with negative scores on Dim1, exhibit high Browning index (BI) and adhesiveness; in contrast with F2, which shows higher cohesiveness, springiness, crust hardness, staling and bread specific volume. Hardness is negatively correlated to PC2, whereas structural parameters like porosity, pore size and pore density are positively correlated to this component. F7, with a positive score on PC2, has higher porosity and larger pores, while F3 and F2, with negative scores, have denser structure, as observed in Fig. 3E. Moreover, the dough from formulation F7 is characterized by its high dough growth - which is positively correlated with PC2, and leads to a more homogeneous, aerated crumb with a softer texture. This formulation has a balanced content of Ca and GB flours, and a smaller content of SP. The discussed parameters above are related to the synergistic role of the GB, Ca and SP flours. The interaction between Ca and GB flours is highlighted in formulations F1, F2, and F4, which do not contain sweet potato flour. These formulations are located on the right side of the PCA, which is positively correlated with axis 1, itself constructed from the textural and structural parameters of the bread. The interaction between Ca and GB flours results in a crumb with a more porous and dense structure and high cohesiveness and springiness. In addition, the interaction between these two flours results in crumbs and crusts with a harder texture. These properties of baked breads are consistent with the properties observed in mixtures of these two flours, namely a peak viscosity (PV) and breakdown (BD), which result in breads with higher porosity and harder crumb and crust. Furthermore, the interaction between these Ca and GB flours leads to a higher final viscosity, which affects the staling of the bread, whose hardness increases significantly within 24 h. The addition of SP flour to the formulation drastically changes the textural and structural properties of the bread. The greater the input, the more the adhesiveness and mean pore size increase, while the other parameters described above decrease. It is important to point out that the textural and structural parameters change proportionally to the input of SP flour, particularly above the 20 % threshold, suggesting very little interaction or even a lack of synergy between this flour and the other two. In fact, above a proportion of 20 % SP flour, the behaviour of the mixture is driven by the pasting properties of the SP flour, which become predominant.
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Response surface models of A) Porosity (%), B) Pore density (pore/cm²), C) Hardness (N), and D) Springiness of the crumb and E) Scanning of bread slices. For porosity the fitted model is a special cubic equation: $y = 39.855Ca +24.556 GB + 247.688SP - 82.747Ca*GB - 922.949Ca*SP - 834.863 GB*SP + 2954.102Ca*GB*SP (R²: 0.963). For pore density the fitted model is a special cubic equation: $y = 5.422Ca +4.120 GB + 37.948SP - 3.125Ca*GB - 140.322Ca*SP - 119.697 GB*SP + 431.543Ca*GB*SP (R²: 0.977). For hardness the fitted model is a special cubic equation: $y = 1.964Ca +12.983 GB + 4.524SP + 13.343Ca*GB + 118.237Ca*SP + 83.349 GB*SP - 552.401Ca*GB*SP (R²: 0.974). For springiness the fitted model is a quadratic equation: $y = 0.841Ca +0.909 GB + 0.801SP - 0.054Ca*GB - 0.747Ca*SP + 0.375 GB*SP (R²: 0.999).</figcaption></figure>

## 4. Conclusion

This study demonstrated the feasibility of producing a gluten-free bread from tropical green banana, sweet potato and cassava flours, thereby contributing to encouraging and developing food sovereignty and meeting local nutritional needs in island territories. The rheological properties of GF doughs, as well as the structural and textural characteristics of GF breads, are influenced by the proportions of Ca, GB and SP flours. These three flours exhibit useful techno-functional properties, but

also have drawbacks and lead to contrasting breads with different quality indicators. Ca flour appears to improve dough growth, which has a positive influence on crumb porosity and hardness. GB flour increases the solid-like behaviour of the dough and contributes to crumb and crust firmness, but reduces the dough growth and the bread porosity. Conversely, SP decreases the solid-like behaviour of the dough, produces darker breads and improves bread ageing, but if used beyond a certain threshold (~20 %) an input of this flour has adverse effects on the bread structure and texture. The optimal formulation consists of a blend of
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** A) Evolution of hardness (N) of the bread crumb during storage and B) Delta Hardness (ΔH, N)) for different formulations. The ΔH represents the difference between the hardness measured on day 1 and the average hardness on day 0 for each formulation. a–c: Letters indicate significant differences at *p* < 0.05 (Tukey test). Composite flour formulations (cassava (Ca), green banana (GB), sweet potato (SP)): F1 (70;30;0); F2 (30;70;0); F3 (30;30;40); F4 (50;50;0); F5 (50;30;20); F6 (30;50;20); F7 (43.3;43.3;13.3).</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Analysis in principal components of flours, doughs and breads variables. b.: bread variables; d.: dough variables; f.: flour variables; Adhesiveness (N*s); BD: breakdown (cP); BI: Browning Index (/); Crust: Crust hardness (N); Cohesiveness (/); Dg: Dough growth (%); ΔH: Delta Hardness (N); Fmax: Force max (N); FV: Final viscosity (cP); G' LVE: Storage modulus of dough (Pa); Hardness (N); Porosity (%); Pore density (pore/cm2); PT: Pasting temperature (°C); PV: Peak viscosity (cP); SV: Specific Volume (cm3/g); Springiness (/). (Composite flour formulations (cassava (Ca), green banana (GB), sweet potato (SP)): F1 (70;30;0); F2 (30;70;0); F3 (30;30;40); F4 (50;50;0); F5 (50;30;20); F6 (30;50;20); F7 (43.3;43.3;13.3)).</figcaption></figure>

these three flours, comprising 43.3 % Ca, 43.3 % GB and 13.3 % SP. These contents appear to represent the best compromise in terms of bread texture, structure and ageing. Thus, using mixed flours from local

resources appears to be a promising approach for developing gluten-free bakery products. These results offer new prospects for enhancing GF-bread formulations blending GB, SP and CA flours. This approach
makes it possible to take into account sensory preferences and local production constraints, in a context where food sovereignty and sustainability are causes for concern.

## CRediT authorship contribution statement

**Simon Carlier:** Writing – review & editing, Writing – original draft, Validation, Methodology, Conceptualization. **Layal Dahdouh:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Methodology, Funding acquisition, Conceptualization. **Julien Ricci:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **Lidwine Grosmaire:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Conceptualization.

## Funding and acknowledgments

The authors received a fellowship grant from the European Union and the Reunion Regional Council. This work was supported by the SADur research programme (2023–2027), funded by the European Regional Development Fund (“L’Europe s’engage à La Réunion avec le FEDER” with reference convention 2024-1248-005756) and the Reunion Regional Council.

## 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.

## Data availability

Data will be made available on request.

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