# Thermal transitions of fonio (Digitaria exilis) starch: modeling and phase diagram from DSC analysis

Paloma Babadoudou, Romain Domingo, Nadia Fanou-Fogny, Comlan Evariste

Simon Mitchikpè, Christian Mestres, Philippe Bohuon

► To cite this version:

Paloma Babadoudou, Romain Domingo, Nadia Fanou-Fogny, Comlan Evariste Simon Mitchikpè, Christian Mestres, et al.. Thermal transitions of fonio (Digitaria exilis) starch: modeling and phase diagram from DSC analysis. LWT - Food Science and Technology, 2025, 237, pp.118749. 10.1016/j.lwt.2025.118749 . hal-05386928

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https://institut-agro-montpellier.hal.science/hal-05386928v1

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# Thermal transitions of fonio (Digitaria exilis) starch: modeling and phase diagram from DSC analysis

Paloma Babadoudou<sup>a,c</sup>, Romain Domingo<sup>a,b</sup>, Nadia Fanou Fogny<sup>c</sup>,
Comlan Evariste Simon Mitchikpè<sup>c</sup>, Christian Mestres<sup>a,b</sup>, Philippe Bohuon<sup>a,∗</sup> ◆

## ARTICLE INFO

### Keywords:

Fonio starch

DSC deconvolution

Starch phase diagram

Amylose

Lipid complex

Flory-Huggins model

## ABSTRACT

The thermal transitions of fonio (Digitaria exilis) starch were investigated over a wide range of temperatures (20–160 °C) and water contents (0.3–3.0 kg kg⁻¹ dry basis) using differential scanning calorimetry (DSC) combined with Gaussian deconvolution and thermodynamic modeling based on the Flory–Huggins equation. Three distinct endothermic transitions were identified: gelatinization (T<sub>G</sub> = 71–90 °C), crystallite melting (T<sub>M1</sub> = 74–126 C), and amylose–lipid complex dissociation (T<sub>Cx</sub> = 95–149 C). Under excess-water conditions, the total transition enthalpy was 15.0 J g⁻¹, consisting of contributions from gelatinization (7.1 J g⁻¹), melting (6.6 J g⁻¹), and complex dissociation (1.3 J g⁻¹). The gelatinization temperature stabilized at around 73 °C under excess-water conditions and increased to approximately 141 °C under dry-state extrapolation. The model accurately reproduced the peak shapes and enthalpy changes (R² > 0.90) and predicted a residual unconverted fraction ~ 10 % (w/w) at 100 °C, indicating incomplete complex dissociation during steaming. Compared with rice, wheat, and millet, fonio starch exhibited unusually shig thermal stability and a persistent secondary (M1) transition, which is consistent with its high-amylose, A-type crystalline structure. These findings emphasize the distinctive multiphase behaviour of fonio starch and demonstrate the predictive capacity of the proposed model in optimizing hydrothermal processing and texture development in fonio-based foods.

### Abbreviations

Cx Amylose–lipid complex dissociation endotherm

G Gelatinization endotherm

Δh Change in specific enthalpy (J g⁻¹)

Δhᵢ Change in specific enthalpy associated with peak i (J g⁻¹)

Δh<sub>total</sub> Cumulative change in specific enthalpy for T → +∞ (J g⁻¹)

Δh<sub>i,0</sub> Change in molar enthalpy of peak i per repeating unit (J mol⁻¹)

Δh<sub>i</sub>⁰ Change in specific enthalpy of peak i under zero water content (J g⁻¹)

Δh<sub>i∞</sub> Change in specific enthalpy of peak i under excess-water (J g⁻¹)

Δh<sub>∞</sub> Change in specific enthalpy under excess-water (J g⁻¹)

m Mass of dry matter in DSC pans (g)

M1 Crystallite melting endotherm

r the linear heating ramp (K s⁻¹)

R Gas constant (J mol⁻¹ K⁻¹)

RMSE Root-mean-square error

T Temperature (°C)

Tᵢ Peak temperature at maximum of transition i (°C or K)

T<sub>i,0</sub> Peak temperature of transition i for the pure polymer (K)

(continued on next column)

(continued)

ΔTᵢ Width parameter of peak i (°C)

ΔTᵢ⁰ Width parameter of peak i at zero water content (°C)

ΔTᵢ∞ Width parameter of peak i in excess-water (°C)

X Water content of starch flour (kg kg⁻¹ dry basis)

### Greek symbols

βᵢ Shape parameter of the Weibull function (see Eq. (10)) (dimensionless)

γᵢ Correlation parameter ΔTᵢ (see Eq. (9)) (dimensionless)

ζᵢ Scale parameter of the Weibull function (see Eq. (10)) (dimensionless)

ν<sub>g</sub> Molar volume of repeating unit (m³ mol⁻¹)

ν<sub>w</sub> Molar volume of water (m³ mol⁻¹)

τ Degree of starch conversion (dimensionless)

φ Specific heat flow (W g⁻¹)

φ̄ Specific enthalpy per degree Celsius (J g⁻¹ K⁻¹)

φ<sub>DSC</sub> Differential scanning calorimetry heat flow (mW)

φ Volume fraction of water in starch-water mixture (m³ m⁻³)

χᵢ Flory–Huggins interaction parameter for peak i (dimensionless)

<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-34398, Montpellier, France</footnote>

<footnote><sup>c</sup> Department of Nutrition and Food Sciences and Technologies, Faculty of Agronomic Sciences, University of Abomey-Calavi, 03 BP 2819, Jéricho, Cotonou, Benin</footnote>

<footnote>* Corresponding author. Institut Agro, UMR Qualisud, 1101 Av. Agropolis, 34090, Montpellier, France.
E-mail address: philippe.bohuon@institut-agro.fr (P. Bohuon).</footnote>
# 1. Introduction

Fonio (*Digitaria exilis*), one of the oldest cultivated cereals in West Africa, is well adapted to arid, nutrient-poor soils and requires minimal agricultural inputs, making it an attractive crop for low-input farming systems (Cruz and Béavogui, 2016). Fonio is nutritionally similar to other cereals, with high carbohydrates (≈90 %), moderate protein (≈6 %), low lipid (≈1 %) (Ballogou, Soumanou, et al., 2013) and starch content exceeding 80 % db (Zhu, 2020). In addition, fonio is rich in sulfur amino acids, gluten-free (Small, 2015) and low glycemic index, making it valuable crop for public health, nutrition, food security, and rural livelihoods in sub-Saharan Africa (Ballogou, Sagbo, et al., 2013; Cruz and Béavogui, 2016).

Recent interest in fonio has spurred its use in various food products, including porridge, dough, and couscous that includes often steaming (Ballogou, Sagbo, et al., 2013). Hydrothermal processing of these products modifies the grain's structure, thereby reducing antinutritional factors and enhancing nutrient bioavailability (Chung et al., 2012). During these processes, particularly steaming, heat and moisture induce starch transitions that shape the foods' texture, digestibility, and cooking quality. These transitions are strongly influenced by the crystalline polymorphism of starch, which can be in one of three forms: A-, B- or C-type. A-type starch, which is prevalent in cereals, has a tight, compact structure with minimal interstitial water. By contrast, B-type starch, found in tubers and high-amylose starches, has a more open hexagonal structure stabilized by a central water channel, giving it greater thermal stability (Imberty et al., 1991; He & Wei, 2017). C-type starch, typical of pulses, displays mixed A/B features, often producing multiple endotherms (Guo et al., 2020; Xu et al., 2020). When these crystalline structures are heated in the presence of water, they undergo irreversible changes through a process known as gelatinization. This involves water diffusion, granular swelling, loss of birefringence, dissociation of double helices, and amylose leaching (Lu et al., 2023; Rakhshi et al., 2024). The specific nature and stability of the crystalline polymorphs directly govern the temperature, enthalpy and complexity of the observed thermal transitions, providing a crucial framework for interpreting DSC profiles. Under excess water conditions ($X > 1.0 - 1.5 \text{ kg/kg db}$), DSC shows that cereal starches often exhibit three endothermic transitions (G, M1, Cx), influenced by testing conditions and composition. The G endotherm, typically occurring between 60 and 80 °C, corresponds to the initial swelling and partial crystalline disruption of starch granules (Wang et al., 2014). The M1 endotherm, which follows at slightly higher temperatures (85–110 °C), is attributed to the melting of residual, less hydrated crystalline regions. Cx endotherm, generally detected between 95 and 125 °C reflects the dissociation of amylose-lipid complexe (Cervantes-Ramírez et al., 2020; S. Wang and Copeland, 2013). Under excess water conditions ($X > 1.5 \text{ kg/kg db}$) potato and cassava starches, display a single G endotherm (Donovan, 1979). In contrast, rice starch typically exhibits two distinct peaks G followed by Cx (Briffaz et al., 2013). Pulse starches generally show G peak, occasionally followed by a faint shoulder that diminishes with increasing water content (Lefèvre et al., 2021). Maize starch, presents both G and M1, reflecting more complex crystalline arrangements (Xing et al., 2017a, Xing et al., 2017a). At intermediate water levels ($X$ between 0.5 and 1.5 kg/kg db), additional transitions emerge. In potato starch, G peak develops a trailing shoulder (M1), which becomes the dominant endotherm at lower water contents (Donovan, 1979). Rice starch shows all three peaks G, M1, and Cx clearly resolved (Briffaz et al., 2013), while pulse starches display distinct G and M1 under these conditions (Lefèvre et al., 2021). At low moisture levels ($X \le 0.4 \text{ kg/kg db}$), the G endotherm is inhibited. In potato and pulse starches, only the M1 peak remains visible (Donovan, 1979; Lefèvre et al., 2021), whereas in rice starch, M1 and Cx become partially superimposed, with Cx appearing as a high-temperature shoulder (Briffaz et al., 2013). Thus, DSC profiles show that hydration strongly modulates both the number and nature of thermal transitions, reflecting the supramolecular organization specific

to each starch.

For the world's most widely consumed cereals, the thermal transitions of their starches are well documented in the scientific literature. This is notably the case for wheat (Wang et al., 2018; Zhang & Zhang, 2019), maize (Cervantes-Ramírez et al., 2020; Xing et al., 2017b), Xing et al., 2017bnd rice (Briffaz et al., 2013), and more recently for pulse starches such as lentil, chickpea, and bean (Lefèvre et al., 2021). In contrast, to the best of our knowledge, very few studies have investigated fonio starch. Aside from the work of Deriu et al. (2022), which reported and characterized two peaks (G and Cx) under excess-water conditions, its thermal behavior remains largely unexplored. The distinctive compositional and structural traits of fonio starch may strongly affect its thermodynamic behaviour. It has a relatively high amylose content of around 30 % and a fine granular morphology of $\le 10$ µm, both of which are known to enhance crystallite stability and limit water penetration. Furthermore, fonio lipids are highly unsaturated (≈75 %), primarily consisting of linoleic acid (≈45 %) and oleic acid (≈30 %) (Ballogou, Sagbo, et al., 2013), Ballogou, Sagbo, et al., 2013oth of which are capable of forming heat-stable amylose-lipid complexes. Together, these features suggest that fonio starch may exhibit unique gelatinization, melting and complexation profiles compared with other cereals. This study examined the thermal transitions of fonio starch-water systems under controlled moisture conditions using DSC to model a starch conversion diagram. A deconvolution approach, following the method of Lefèvre et al. (2021), was applied to separate overlapping events of gelatinization, melting and dissociation of amylose-lipid complexes dissociation. Together, these analyses aim to predict the complete phase diagram of fonio starch transitions and discuss about the potential interest for use and processing of fonio.

# 2. Materials and methods

## 2.1. Materials

Fonio cultivar (Iporawan), harvested in August and September 2022, were collected from producers across ten villages in Boukombé, Bénin (10°00'-10°40' N, 0°75'-1°30' E). Samples were collected in May 2024 and mechanically dehulled using GMBF huller (Engelberg type, Mali). Due to frictional heat generated during dehulling, the grain surface temperature reached approximately 40–45 °C. The grains were spread in thin layers and cooled at ambient temperature (30 ± 2 °C) in a dry environment. After 14 h ambient cooling, grains underwent five washing cycles with tap water, followed by 45 min drainage in jute bags. This washing step aimed to remove fine pericarp residues, dust, and sand particles. This short treatment, necessary due to the small grain size ($\langle 1$ mm), did not affect starch integrity (Odunmbaku et al., 2018). The grains were then dried in a CSec-T continuous-flow dryer (CSec-T model, France) by spreading them evenly on fine-mesh trays at 45 °C for 5 h to achieve uniform moisture content of 10 ± 0.5 % as determined using a moisture meter (Model LST-7D, Suzhou Kehui Electronic Technology Research Institute, China). Dehulled dried grains were vacuum-packed and stored at 4 °C until analysis. Grains were then dry-milled using a ball grinder (Dangoumill 300-type, Prolabo, Paris, France), and the resulting fine flours were stored at 4 °C in airtight plastic bags until further analyses.

## 2.2. Physicochimical analyses

The dry matter content of the samples was determined by oven-drying at 130 ± 2 °C for 2 h, followed by differential weighting, according to the ISO 712 (2010) method. Total starch content was determined following the enzymatic hydrolysis method of Holm et al. (1986). Briefly, finely ground samples ($\langle 500$ µm) were suspended in demineralized water and subjected to hydrolysis by thermostable α-amylase (A3403-1MU) at 98 °C for 15 min, followed by amyloglucosidase (10, 115, 70 U/mg, Sigma) at 60 °C for 30 min to ensure complete conversion
of starch into glucose. After cooling, the hydrolysates were diluted, and 75 µL aliquots were transferred for glucose quantification. Then, 1.5 mL of glucose oxidase-peroxidase (GOD-POD) reagent was added, and the mixture was incubated at 37 °C for 10 min. Absorbance was measured at 510 nm against glucose standards processed in parallel. Starch content was calculated from the amount of glucose released after enzymatic hydrolysis, using a stoichiometric conversion factor of 0.9 (glucose × 0.9). Given that fonio flour contains negligible amounts of free glucose (<0.1 % dry matter), the measured glucose was considered to originate exclusively from starch hydrolysis.

Amylose content of the grains was determined using differential scanning calorimetry (DSC), as outlined by Mestres et al. (1996). Ten milligrams of lysophosphatidylcholine (LPL) were dissolved in 0.5 mL of ultrapure water at 60 °C to ensure complete solubilization. Approximately 3 mg of ground fonio flour were weighed into DSC aluminum pans, to which 45 µL of the LPL solution were added. The pans were hermetically sealed and equilibrated for 1 h at room temperature before analysis. DSC measurements were performed by heating the pans from 25 to 160 °C at a rate of 50 °C/min, followed by cooling from 160 to 25 °C at 10 °C/min. The resulting endothermic peak was integrated to identify and quantify thermal parameters (Tonset, Tpeak, Tend, and Δh). Potato amylose was used as a reference. A standard pan containing 5 mg of potato amylose and 45 µL of LPL solution was analyzed under the same conditions, and the enthalpy of the complex melting peak was integrated. The amylose content of fonio samples was expressed as the ratio between the enthalpy of fonio-LPL complex melting and that of the potato amylose reference, multiplied by 100.

Lipid content was determined through quantification with acid hydrolysis, following the AOAC 2005.03 method, using the Soxtec extractor. Protein content was determined from nitrogen measured using the Kjeldahl method (NF EN ISO 20483), applying a nitrogen-to-protein conversion factor of 6.25. All analyses (protein, lipid, starch, and amylose) were performed in triplicate, and results were expressed on a dry-matter basis (per 100 g db).

## 2.3. Differential scanning calorimetry

Thermal transitions associated with starch gelatinization, melting and amylose-lipid complexes transition were determined using a DSC 7 instrument (PerkinElmer, Norwalk, USA) calibrated with indium as standard. Starch flour was weighed in stainless steel pans and deionised water was added using a micropipette. The amount of water was adjusted to obtain a water content X ranging from 0.3 to 3 kg kg⁻¹ db. The total weight of dry matter, noted *m*, of each sample was approximately 10 mg. The pans were hermetically sealed and allowed to stabilize at 4 °C for at least 8–24 h before analysis. The pans were then heated from 20 °C to 160 °C at a constant rate (noted *r*) of 10 °C/min, with an empty sealed pan as reference. The measurements were tripled for each water content tested. In total, 51 DSC thermograms were recorded. A blank thermogram (empty pans in reference and sample ovens) was recorded daily. The heat flow (mW) of sample pans minus the variation of heat flow of the blank during heating was recorded using the Pyris Thermal Analysis Software (PerkinElmer, Norwalk, USA).

## 2.4. Phase diagram modelling

The total thermal transition was the sum of the three thermal transitions (G, M1 and Cx). Thermograms were recorded as heat flow signals over time $\varphi_{DSC}(t)$, expressed in mW. First, a baseline was subtracted from the heat flow to simplify the process of curve fitting. Secondly, the DSC signal was converted into specific heat flow $\varphi(t)$ in W/g as

$$ \varphi(t) = \frac{\varphi_{DSC}(t)}{1000 \cdot (m/1000)} = \frac{\varphi_{DSC}(t)}{m} \qquad (1) $$

Given the linear heating ramp $r = dT/dt$, the energy absorbed by the sample, was reformulated as:

$$ \Delta h = \int_{T_0}^{T_f} \varphi(t) \, dt = \int_{T_0}^{T_f} \frac{\varphi(T)}{r} \, dT \qquad (2) $$

This transformation allows all thermal quantities to be expressed and modeled as functions of temperature. The function $\bar{\varphi}(T) = \varphi(T)/r$, expressed in J g⁻¹ K⁻¹ represents the specific enthalpy per degree Celsius, i.e., the rate of change enthalpy with respect to temperature:

$$ \bar{\varphi}(T) = \frac{d \Delta h}{dT} \qquad (3) $$

It represents the amount of energy absorbed per gram and per Kelvin during thermal transitions. Its integral yields the change in specific enthalpy absorbed over a temperature interval:

$$ \Delta h = \int_{T_0}^{T_f} \bar{\varphi}(T) \, dT \qquad (4) $$

Prior to curve fitting, a baseline was subtracted from each DSC thermogram, after which the signal was normalised according to sample mass. These pre-processed data were subsequently used for the modeling and deconvolution steps described below, in line with the methodology outlined by Lefèvre et al. (2021). The following sections describe the mathematical formulation and fitting procedures applied to DSC data.

### 2.4.1. Modelling the DSC peaks

The distribution function of specific enthalpy per degree Celsius $\bar{\varphi}(T)$ were analyzed using a deconvolution procedure with three Gaussian functions corresponding to the main thermal transitions ($i = G, M1$, and Cx), following the approach of Lefèvre et al. (2021) with slight modifications.

$$ \bar{\varphi}(T) = \sum_{i}^{3} \frac{\Delta h_i}{\Delta T_i \sqrt{2\pi}} \exp\left(-\frac{1}{2}\left(\frac{T-T_i}{\Delta T_i}\right)^2\right) \qquad (5) $$

where $T_i$ (°C) is the peak temperature at maximum transition $i$; $\Delta T_i$ (°C) controls the width of the peak $i$; $\Delta h_i$ is the change enthalpy variation of peak $i$, in J g⁻¹; it corresponds to the area under the Gaussian curve $i$. The total specific enthalpy of gelatinization for a given sample, denoted $\Delta h_{total}$, corresponds to the cumulative specific enthalpy at the end of the process (i.e., $T \to +\infty$). Therefore

$$ \Delta h_{total} = \lim_{T \to +\infty} \Delta h(T) = \sum_{i}^{3} \Delta h_i \qquad (6) $$

This value reflects the total energy absorbed by the sample during starch transitions. It may be lower than the specific enthalpy change of fonio starch under excess-water conditions if the transitions are incomplete due low water availability. Parameters from Eq. (5) depend on the water content $X$ in the starch-water mixture.

### 2.4.2. Peak temperatures

The Flory–Huggins equation (Flory, 1953) was used to describe the relation between $T_i$ and the volume fraction of the water ($\phi$) in the starch-water mixture:

$$ \frac{1}{T_i} - \frac{1}{T_{i,0}} = \frac{R}{\Delta h_{i,0}} \frac{v_g}{v_w} (\phi - \chi_i \phi^2) \qquad (7) $$

where $\Delta h_{i,0}$ (J mol⁻¹) is the change in the molar enthalpy of peak $i$ per repeating unit (anhydroglucose); $v_g/v_w$ is the ratio of the molar volume of the repeating unit ($v_g = 105.0 \times 10^{-6} \text{m}^3\text{mol}^{-1}$) to the molar volume of the water ($v_w = 18.1 \times 10^{-6} \text{m}^3\text{mol}^{-1}$) and, therefore, $v_g/v_w = 5.8$; $R$ is the gas constant (8.31 J mol⁻¹ K⁻¹); $T_{i,0}$ (K) is the peak temperature of transition $i$ for the pure polymer and $\chi_i$ is the Flory–Huggins interaction parameter for peak $i$. To calculate $\phi$, the density of water was taken to be 1000 kg m⁻³ and the density of starch was attributed an average value of
1500 kg m⁻³ (Cruz-Orea et al., 2002). Therefore, φ was expressed as a function of water content X:

$$ \frac{1}{\phi} = 1 + \frac{1}{1.5X} \qquad (8) $$

### 2.4.3. Peak widths

$\Delta T_i$ changes with water content and was empirically related to X as follows:

$$ \frac{\Delta T_i - \Delta T_i^\infty}{\Delta T_i^0 - \Delta T_i^\infty} = \exp\left(-\frac{X}{\gamma_i}\right) \qquad (9) $$

where $\Delta T_i^0$ (°C) and $\Delta T_i^\infty$ (°C) are the values of $\Delta T_i$, at zero and excess water contents, respectively; $\gamma_i$ is the rate parameter of the correlations. This relationship can represent either an increase or a decrease of $\Delta T_i$ with X. $\Delta T_{Cx}$ appears shows no clear or significant variation, so $\Delta T_{Cx}$ was fixed at constant value $\Delta T_{Cx}^\infty = \Delta T_{Cx}^0$.

### 2.4.4. Amplitude of the peak area

As water content increases, $\Delta h_G$ rises and $\Delta h_{M1}$ decreases. Thus, enthalpy changes as a function of water content were modeled using a Weibull function as follows:

$$ \frac{\Delta h_i - \Delta h_i^\infty}{\Delta h_i^0 - \Delta h_i^\infty} = \exp \left[ - \left( \frac{X}{\zeta_i} \right)^{\beta_i} \right] \qquad (10) $$

where $\Delta h_i^0$ is the change in enthalpy under zero water content, while $\Delta h_i^\infty$ denotes the asymptotic value under excess-water conditions. The parameters $\zeta_i > 0$ and $\beta_i > 0$ correspond to the scale and shape parameters, respectively. $\Delta h_{Cx}$ appears shows no clear or significant variation, so $\Delta h_{Cx}$ was fixed at constant value $\Delta h_{Cx}^\infty = \Delta h_{Cx}^0$.

### 2.4.5. Degree of starch conversion

The degree of starch conversion $\tau(T)$ was defined as the ratio between the cumulative specific enthalpy and the specific enthalpy change of fonio starch under excess-water conditions ($\Delta h^\infty$):

$$ \tau(T) = \frac{\Delta h(T)}{\Delta h^\infty} \qquad (11) $$

Substituting Eq. (5) into Eq. (4) and noting that the integral of a Gaussian functions yields an error functions, it follows that:

$$ \tau(T) = \frac{1}{\Delta h^\infty} \sum_i^3 \Delta h_i \times \frac{1}{2} \left( 1 + \text{erf} \left( \frac{T - T_i}{\Delta T_i \sqrt{2}} \right) \right) \qquad (12) $$

The final expression of the degree of starch conversion is therefore:

$$ \tau(T \to +\infty) = \frac{\sum_{i}^{3} \Delta h_i}{\Delta h^\infty} \qquad (13) $$

From Eq. (13), it also follows that the specific enthalpy change in water-excess conditions corresponds to

$$ \Delta h^\infty = \sum_i^3 \Delta h_i^\infty \qquad (14) $$

### 2.4.6. Parameter identification

For parameter identification, 13 distinct water content values (X), each with three repetitions (n = 39 thermograms), were assigned to the training set (Table 1). Parameter identification followed a two-step procedure. In the first step, the three so-called primary parameters ($T_i$, $\Delta T_i$ and $\Delta h_i$) of each DSC peak deconvolution model were determined by fitting the specific enthalpy per degree Celsius $\bar{\varphi}(T)$ to Eq. (5) for each water content X. All fittings were performed using the Curve Fitting Toolbox (MATLAB R2021b) with the Levenberg-Marquardt algorithm. Particular attention was paid to the initialization of model parameters to

<caption>Table 1</caption>

Experimental conditions of the dataset used for model training and validation. Starch samples with different water content (X) were analyzed by DSC from 20 °C to 160 °C.

<table><thead><tr><th>X (kg kg<sup>-1</sup> db)</th><th>Training set</th><th>Validation set</th></tr></thead><tbody><tr><td>0.30</td><td>×</td><td>—</td></tr><tr><td>0.40</td><td>×</td><td>—</td></tr><tr><td>0.50</td><td>×</td><td>—</td></tr><tr><td>0.60</td><td>×</td><td>—</td></tr><tr><td>0.62</td><td>—</td><td>×</td></tr><tr><td>0.80</td><td>×</td><td>—</td></tr><tr><td>1.00</td><td>×</td><td>×</td></tr><tr><td>1.20</td><td>×</td><td>—</td></tr><tr><td>1.50</td><td>×</td><td>—</td></tr><tr><td>1.60</td><td>×</td><td>—</td></tr><tr><td>1.70</td><td>—</td><td>×</td></tr><tr><td>1.80</td><td>×</td><td>—</td></tr><tr><td>2.00</td><td>×</td><td>—</td></tr><tr><td>2.50</td><td>×</td><td>—</td></tr><tr><td>3.00</td><td>×</td><td>×</td></tr></tbody></table>

×: condition included in the dataset (measured in triplicate).

—: not included.

ensure convergence and avoid non-physical solutions. Because DSC thermograms showed overlapping peaks depending on water content, G and M1 peaks at high moisture and M1 and Cx peaks at low moisture, the initial values of primary parameters were adjusted accordingly before each fit. For the Gaussian deconvolution, the typical initialization ranges were: $T_G = 70–75$ °C; $T_{M1} = 85–120$ °C; $T_{Cx} = 105–150$ °C, $\Delta T_i = 1–8$ °C, and $\Delta h_i = 1–3$ J g⁻¹, depending on the water content of the sample. At high water contents (X ≈ 3.0 kg kg⁻¹ db), initialization was set near [70, 85, 105 °C] for (G, M1, Cx), whereas at lower water contents (X ≤ 0.5 kg kg⁻¹ db), it was shifted toward [73, 110, 145 °C] to account for peak displacement and merging. These empirical initialization values were derived from visual inspection of representative thermograms and applied consistently across all datasets.

In the second step, the so-called secondary parameters were determined. A total of ten secondary parameters were identified per peak: ($T_{i,0}$, $\Delta h_{i,0}$, $\chi_i$), ($\Delta T_i^0$, $\Delta T_i^\infty$, $\gamma_i$) and ($\Delta h_i^0$, $\Delta h_i^\infty$, $\zeta_i$, $\beta_i$) obtained by sequentially fitting $T_i$, $\Delta T_i$ and $\Delta h_i$ to X, using Eqs. (7), (9) and (10), respectively. All fittings were carried out using the Curve Fitting Toolbox (MATLAB R2021b) with the Levenberg-Marquardt algorithm. Model performance was evaluated using RMSE and R² statistics, as reported in Section 2.4.8.

### 2.4.7. Model validation

The validation dataset, comprising four values of X (Table 1), each with three measurements, obtained from nominally identical samples prepared independently under the same experimental conditions as those used for parameter identification. The model parameters obtained from the training set were applied without modification to generate predictions for the validation thermograms. This approach ensured that the validation dataset (24 % of the total data) comprised thermograms excluded from the training process (76 %), thereby allowing a robust assessment of the model's predictive capability.

### 2.4.8. Statistical methods

The root mean square error (RMSE) was computed between experimental DSC thermograms and model predictions. Secondary parameters were estimated with their 95 % confidence intervals; R² values and RMSE were expressed in the same units as the primary parameters. All statistical analyses were performed in MATLAB R2021b.
# 3. Results and discussion

## 3.1. Characterization of fonio flour

Fonio flour contained 87.3 ± 1.9 % total starch (db), confirming starch as the dominant carbohydrate component. Its amylose content (30.9 ± 0.2 %) was substantially higher than the values reported for conventional cereals starches such as rice (14.6 – 28.7 %; Kong et al., 2015), pearl millet (13.6 – 18.1 %; Sandhu & Siroha, 2017), wheat in conventional varieties (4.0 – 24.1 %; Wang et al., 2018, 14.5 – 21.0 %; Zhang & Zhang, 2019) and maize (8.3 – 27.4 %; Uarrota et al., 2013). This distinct amylose proportion likely contributes to fonio starch's unique thermal transition behavior. Lipid content (0.5 ± 0.1 %) was lower than previously reported values (0.4 – 1.9 %; Ballogou, Soumagnou, et al., 2013), potentially reflecting methodological differences in analysis. Moisture content (10.3 ± 0.05 %) indicated proper flour stabilization prior to thermal characterization.

## 3.2. Measurement of the DSC peaks

The DSC thermograms (Fig. 1) exhibited water-content-dependent transitions, revealing distinct endotherms associated with starch gelatinization and melting. Under excess-water conditions (X > 1.5 kg/kg db), a dominant gelatinization endotherm (G) was observed, followed by a well-defined peak (Cx) corresponding to the dissociation of amylose-lipid complexes. At intermediate water content (0.8 ≤ X ≤ 1.5 kg/kg db), an additional transition (M1) emerged between G and Cx, indicative of the sequential dissociation of crystalline domains with varying thermal stability. The total gelatinization enthalpy of fonio starch in excess water (15.0 ± 1.9 J g⁻¹) significantly exceeded values reported for wheat (7.1–9.9 J g⁻¹; Wang et al., 2018) and millets (6.6–10.8 J g⁻¹; Yang et al., 2019), aligning with the upper range for rice (12–17 J g⁻¹; Kong et al., 2015; Briffaz et al., 2013). Notably, this value surpasses previous fonio data (9.1–12.0 J g⁻¹; Deriu et al., 2022; Bassey et al., 2023), suggesting a higher degree of crystalline organization and a

greater proportion of well-ordered double helices in our sample. This observation correlates strongly with the high amylose content (30.9 %) of our fonio starch, which exceeds typical values for wheat (21.9–24.1 %), millets (28–29 %), rice (16.4–28.7 %), and even fonio (21.4 %; Bassey et al., 2023). Amylose-lipid complexation begins between 60 and 90 °C after amylose leaching during gelatinization and is strongly influenced by moisture content (Lee et al., 2020; Liu et al., 2024). Deconvolution results indicate that T<sub>Cx</sub> spans from 95 to 149 °C, with a dry-limit extrapolated value of 251 °C (Flory–Huggins model). The high-temperature Cx peak (~105 °C) is attributed to the high proportion of unsaturated fatty acids (mainly linoleic and oleic) that stabilize V-type amylose-lipid complexes in fonio starch (Ballogou, Sagbo, et al., 2013; Cervantes-Ramírez et al., 2020; Yoshitomi & Kawai, 2023).

Temperature influences both the formation and dissociation of V-type amylose-lipid complexes. During heating, moderate temperatures promote the leaching and complexation of amylose chains, whereas excessive heating leads to partial disruption of these ordered structures (Lee et al., 2020; Liu et al., 2024; Tufvesson et al., 2003). The persistence of the high-temperature Cx transition in fonio starch thus reflects the remarkable thermal stability of these V-type crystalline polymorphs.

In water-limited conditions (X < 0.8 kg/kg db), both G and Cx shifted to significantly higher temperatures due to restricted molecular mobility. Notably, the enthalpy of G increased with water content, reflecting enhanced plasticization of the amorphous regions. In contrast, Cx enthalpy showed no clear change with water content. When the moisture content decreased, the endotherm G decreased progressively and became almost imperceptible (Fig. 2), reflecting the critical role of water in initiating gelatinization. Under these restricted hydration conditions, M1 became more pronounced and shifted to higher temperatures, partially replacing the G transition. This trend suggests a progressive merging of transitions as granule mobility decreases, a behavior also observed in millet and rice starches (Briffaz et al., 2013; Shi et al., 2023).

The deconvolution method reveals the presence of the M1 peak even at high water contents, where it overlaps with the G and Cx peaks. This behavior has also been reported for the starch of several pulses by Lefèvre et al. (2021). The deconvolution procedure proved highly effective, as across the full range of water contents the RMSE between experimental and predicted specific enthalpy per degree Celsius ranged from 0.005 to 0.019 J g⁻¹ K⁻¹, corresponding to relative mean deviations between 3 % and 0.5 %. Moreover, the three peaks G, M1, and Cx are clearly well identified (Fig. 2).

Under excess-water conditions, fonio starch exhibited a total enthalpy change of 15.0 ± 1.6 J g⁻¹, partitioned into G (7.1 ± 0.9 J g⁻¹), M1 (6.6 ± 0.5 J g⁻¹), and Cx (1.3 ± 0.2 J g⁻¹) (Table 2). The substantial contribution of M1 (≈47 %) contrasts sharply with most cereal (Briffaz et al., 2013; Sandhu & Siroha, 2017; Zhong & Sun, 2005), potato (Carlstedt et al., 2015; Donovan, 1979), pulse starches (Lefèvre et al., 2021) where M1 is typically absent or negligible in excess-water condition. This suggests a crystalline structure, possibly influenced by the starch's relatively high amylose content (30.9 %) as well as the distribution of amylopectin chain lengths. Amylose may contribute to starch granule integrity, while crystalline order is mainly governed by amylopectin. These networks tend to restrict water penetration and may even prevent full hydration, even when water is available in excess (Shi et al., 2023).

The persistence of the M1 endotherm in fonio, as revealed by deconvolution, provides strong evidence of heterogeneous crystalline stability. Although direct structural confirmation via X-ray diffraction was beyond the scope of this study, the observed thermal complexity strongly suggests the coexistence of crystallites with distinct melting points. This behaviour is consistent with a C-type polymorphism involving A- and B-type crystallites, as reported in other cereals (Guo et al., 2020; Xu et al., 2020; Li et al., 2022; S. Wang & Copeland, 2013; Yang et al., 2019).

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Endothermic transitions recorded by Differential Scanning Calorimetry (DSC) on fonio flour mixture with varying water contents. Peak G, M1 and Cx endotherms refer to the endotherms of starch gelatinization, starch melting and amylose-lipid complexes dissociation, respectively. X denote the water content on a dry basis (kg/kg db).</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** DSC thermograms of fonio starch at various water contents, showing model fitting for parameter identification. Transitions were deconvoluted into G, M1, and Cx peaks, with root mean square error (RMSE) values indicating the fitting accuracy in $\bar{\phi}$. X denotes the water content on a dry basis (kg kg$^{-1}$, bd).</figcaption></figure>

### 3.3. Parameters identification

Fig. 3 presents the evolution of the primary Gaussian parameters ($T_i$, $\Delta T_i$, $\Delta h_i$) as a function of water content. The experimental values (dots) correspond to the primary parameters obtained from the deconvolution of each DSC thermogram, while the solid lines represent the model predictions based on Eqs. (7), (9) and (10). The corresponding coefficients of determination ($R^2$) and root mean square errors (RMSE) quantify the goodness of fit. The dispersion of the primary parameters was very low, with 95 % confidence intervals ranging from $\pm 0.01$ to $\pm 0.3$ °C for peak temperatures, $\pm 0.1$ to $\pm 0.5$ °C for widths, and $\pm 0.1$ to $\pm 0.3$ J g$^{-1}$ for enthalpies, representing less than 5 % of the corresponding mean values. These intervals are smaller than the symbols plotted in Fig. 3, which is why no error bars were displayed. These narrow confidence intervals confirm that the fitted parameters are numerically stable and that the Gaussian deconvolution provides

consistent and physically coherent results across the full range of water contents. The peak temperature associated with G endotherm ($T_G$) was found to vary significantly with water content, aligning reasonably well with the predictions of the Flory-Huggins model (Eq. (7)). When the hydration level was high ($X > 1.5$ kg kg$^{-1}$ db i.e., $>0.70$ m$^3$ m$^{-3}$), $T_G$ tended to stabilize around 73 °C. This suggests that, beyond a certain point, the presence of sufficient water ensures full plasticization and hydration of the amorphous starch regions, leading to gelatinization at a fairly constant temperature (Fig. 3). On the other hand, when the moisture content dropped below $0.6$ kg kg$^{-1}$ db (i.e., $\phi > 0.47$ m$^3$ m$^{-3}$), $T_G$ rose steeply, reaching up to $T_{G,0} = 141 \pm 10$ °C in nearly dry conditions (Table 2). This trend echoes earlier observations by Donovan (1979) and more recently by Lefèvre et al. (2021), who all noted that $T_G$ tends to remain stable once a critical moisture level is surpassed, but rises noticeably when water becomes scarce likely due to reduced molecular mobility and incomplete gelatinization. For fonio starch, the
<table><caption>**Table 2**

Fitted secondary parameter values (mean ± 95 % CI in parentheses) for the equations describing peak temperature ($T_i$), peak width ($\Delta T_i$), and peak area amplitude ($\Delta h_i$) as a function of water content, used to model the cumulative degree of starch conversion (Eq. (12)) for gelatinization ($i = G$), amylose melting ($i = M1$), and amylose–lipid complex formation ($i = Cx$) peaks. Specific enthalpy change in water-excess conditions is $\Delta h^\circ = \sum_{i=1}^3 \Delta h_i^\circ$</caption><thead><tr><th rowspan="2">Peak</th><th colspan="3">Peak temperatures (Eq. (7))</th><th colspan="3">Peak widths (Eq. (9))<sup>a</sup></th><th colspan="3">Peak area amplitudes (Eq. (10))<sup>b</sup></th></tr><tr><th>T<sub>i,0</sub> (°C)</th><th>Δh<sub>i,0</sub> (kJmol<sup>-1</sup>)</th><th>χ<sub>i</sub></th><th>ΔT<sub>i</sub><sup>0</sup> (°C)</th><th>ΔT<sub>i</sub><sup>∞</sup> (°C)</th><th>γ<sub>i</sub></th><th>Δh<sub>i</sub><sup>0</sup> (Jg<sup>-1</sup>)</th><th>Δh<sub>i</sub><sup>∞</sup> (Jg<sup>-1</sup>)</th><th>ζ<sub>i</sub></th></tr></thead><tbody><tr><td>G</td><td>141 (10)</td><td>32.9 (4.9)</td><td>0.77</td><td>26.8 (0.02)</td><td>2.2 (0.3)</td><td>0.27</td><td>0.05 (0.05)</td><td>7.1 (0.9)</td><td>1.3 (0.1)</td><td>1.5 (0.1)</td></tr><tr><td>M1</td><td>247 (25)</td><td>21.2 (3.3)</td><td>0.60</td><td>17.6 (0.05)</td><td>4.2 (1.2)</td><td>0.84</td><td>0.28 (0.28)</td><td>6.6 (0.5)</td><td>0.5 (0.2)</td><td>1.0 (0.3)</td></tr><tr><td>Cx</td><td>251 (28)</td><td>26.4 (5.6)</td><td>0.59</td><td>4.7 (0.07)</td><td>4.7 (0.5)</td><td>×</td><td>1.3</td><td>1.3 (0.2)</td><td>×</td><td>×</td></tr></tbody></table>

(a) ΔT<sub>M2</sub> is fixed at a constant value ΔT<sub>M2</sub><sup>0</sup> = ΔT<sub>M1</sub><sup>0</sup> + ΔT<sub>M2</sub><sup>∞</sup>.

(b) Δh<sub>Cx</sub> is fixed at a constant value Δh<sub>Cx</sub><sup>0</sup> = Δh<sub>M1</sub><sup>0</sup> + Δh<sub>Cx</sub><sup>∞</sup>.

(c) $\sum_{i=1}^3 \Delta h_i^\circ = 15.0 (1.6) (Jg^{-1})$.

peak temperatures observed fall within the range reported for other starches such as millet, rice, and maize (67.9–74.4 °C; Briffaz et al., 2013; Annor et al., 2014; Xing et al., 2017b; Cervantes-Ramírez et al., 2020), while lower values (62.8–66.0 °C) are typical of wheat and lentil starches (Lefèvre et al., 2021; Wang et al., 2014). The elevated $T_G$ values observed for fonio reflect granule stability and resistance to gelatinization (Cooke & Gidley, 1992), likely influenced by hydrophobic compounds such as fatty acids that hinder water diffusion into granules (Arik Kibar et al., 2014; Cervantes-Ramírez et al., 2020) (see Fig. 4).

The Flory-Huggins interaction parameter ($\chi_G$) for the G endotherm was estimated at 0.77 ± 0.02 (Table 2), indicating less favorable water-starch interactions compared to lentil starch (0.71 ± 0.02) under similar hydration conditions (Lefèvre et al., 2021). These differences can be explained by structural features: fonio starch granules are smaller (~10 µm) than those of lentil (~20 µm; Irving & Jideani, 1997) and contain more amylose (~30 %) with longer amylopectin chains (DP 13–24), which stabilize crystallites and restrict hydration. The persistence of the M1 endotherm also suggests the coexistence of A- and B-type crystallites (C-type polymorphism; Nahar et al., 2024). These combined factors likely contribute to the higher $\chi_G$ and greater thermal resistance of fonio starch.

The dry melting temperature of fonio starch crystallites was found to be quite high (247 ± 25 °C), well above that of other cereal starches, 149 °C for rice (Briffaz et al., 2013), 181 °C for wheat, and 187 °C for maize (Donovan & Mapes, 1980). Yet, its molar enthalpy per repeating unit (21.2 ± 3.3 kJ mol<sup>-1</sup>) is less than half that of maize and wheat (52.7–57.7 kJ mol<sup>-1</sup>), suggesting smaller or less well-ordered crystallites. This unusual combination of elevated melting temperature but moderate enthalpy indicates a dense yet imperfect crystalline arrangement.

The high peak temperature of Cx ($T_{Cx,0} = 251 \pm 28$ °C) under dry conditions and the relatively low enthalpy per repeating unit (26.4 ± 5.6 kJ mol<sup>-1</sup>) suggest the formation of sparse but ultra-stable domains, consistent with amylose-lipid complexes. The application of the Flor-y-Huggins model to M1 and Cx is relevant, with $R^2$ values of at least 0.95, and similar trends of $T_{M1}$ and $T_{Cx}$ as a function of water content. The hierarchy $\chi_G > \chi_{M1} \approx \chi_{Cx}$ indicates that gelatinization domains (G) are the least compatible with water in fonio starch, whereas M1 crystallites and Cx amylose-lipid complexes exhibit comparable water affinities. This similarity suggests that both structures have a balanced hydrophilic–hydrophobic interaction profile with moderate hydration tolerance and high thermal stability. From an industrial perspective, the triphasic behaviour of fonio starch could offer benefits in terms of thermally stable food systems (Chakraborty et al., 2022) and moisture-responsive edible or biodegradable films (Santhosh et al., 2024).

For $\Delta T_i$, a good agreement was obtained for G and M1 ($R^2 > 0.91$), whereas Cx remained nearly constant (4.7 ± 0.5 °C). The latter was not significantly different from a constant value of 4.7 ± 0.5 °C. The width of the G and M1 peaks ($\Delta T_i$) decreased with increasing water content (Fig. 3), consistent with the plasticizing effect of water, which promotes granule swelling and more uniform melting of crystalline domains (Donovan, 1979; Lefèvre et al., 2021).

The change in specific enthalpy ($\Delta h_i$), reflecting the energy required for starch structural transitions, was strongly influenced by water content (Fig. 3) for G and M1. The Weibull correlation accurately described the evolution of $\Delta h_i$ with X for these transitions, with $R^2 > 0.70$ and RMSE $\le 1.3$ J g<sup>-1</sup>. These trends are consistent with previous studies (Shi et al., 2023; Wang et al., 2014). For gelatinization, $\Delta h_G$ was essentially zero under dry conditions, but increased to 7.1 ± 0.9 J g<sup>-1</sup> in excess-water condition (Table 2). This value is consistent with data reported for wheat (7.1 – 9.9 J g<sup>-1</sup>) and various millets (6.6 – 10.8 J g<sup>-1</sup>) (Wang et al., 2018; Yang et al., 2019). For the M1 endotherm, $\Delta h_{M1}$ was high in the dry state (17.9 ± 5.3 J g<sup>-1</sup>) but fell to 6.6 ± 0.5 J g<sup>-1</sup> with hydration. This suggests that water weakens or disrupts ordered crystalline domains, reducing the energy needed for melting. In contrast, $\Delta$
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Parameters of the Gaussian functions for fonio starch, plotted against water volume fraction or water content. Primary parameters (dots) represent value from peak deconvolution, while solid lines are model fits to Eq. (7), (9) and (10). The coefficient of determination (R²) and the root mean square error (RMSE) assess fitting performance. φ and X denotes, respectively, the water volume fraction (m³ m⁻³) and the water content on a dry basis (kg kg⁻¹ bd).</figcaption></figure>

$h_{Cx}$ remained constant (1.3 ± 0.2 J g⁻¹) across all water content range. Similarly, Deriu et al. (2022) reported an Cx endotherm in fonio starch with a low specific enthalpy change (0.91 ± 0.01 J g⁻¹). This stability supports the view that amylose-lipid complexes are resistant to hydration, as also noted by Santhosh et al. (2024).

### 3.4. Model validation

Fig. 4 presents DSC thermograms of fonio starch at different water contents, comparing experimental measurements (three repetition) with model predictions. Overall, the agreement between predicted and measured DSC thermograms is very good across the entire temperature range, with the model accurately reproducing the onset, peak position, and general shape of the transitions G, M1 and Cx. At high water contents (X = 3.0 kg kg⁻¹ and X = 1.7 kg kg⁻¹), the predicted curves match closely the experimental replicates in both amplitude and profile, indicating strong predictive robustness. At lower water contents (X = 1.0 kg

kg⁻¹ and X = 0.62 kg kg⁻¹), the model still captures the main thermal features but tends to slightly underestimate the intensity of the G peak. At these lower hydration levels, the M1 and Cx transitions also tend to merge progressively, resulting in partially overlapping peaks that are more difficult to resolve experimentally. These effects explain the slight deviations between measured and predicted profiles under water-limited conditions, rather than a true failure of the model. It is worth noting that the DSC signal is integrated over the temperature range to obtain transition enthalpies. Therefore, minor deviations in peak position or shape only have a limited impact on the calculated energetic parameters. Overall, these results confirm that the proposed model reliably predicts the thermodynamic behaviour of fonio starch. It reproduces the phase diagram and the evolution of G, M1 and Cx transitions as a function of conversion degree, temperature and water content.
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Comparison between experimental and predicted by Differential Scanning Calorimetry (DSC) thermograms of fonio starch at different water contents. Experimental data (dots, *n* = 3) and model predictions (solid lines) are shown for model validation. *X* denotes the water content on a dry basis (kg·kg⁻¹ db).</figcaption></figure>

**3.5. Starch conversion diagram**

The simulated phase diagram of the fonio starch– water system (Fig. 5a) delineates three distinct states as a function of temperature and water content: native, partially gelatinised, and fully gelatinised. The native state persists below 69 °C regardless of water content, corresponding to intact crystalline lamellae. At intermediate water contents (0.8 ≤ *X* ≤ 1.5 kg·kg⁻¹ db), gelatinization and melting transitions occur sequentially, yielding partial conversion curves comparable to those reported for pulse starches by Lefèvre et al. (2021). At 100 °C complete conversion is not achieved; the Cx endotherm – accounting for approximately 10 % of total enthalpy – remains unconverted under the tested thermal–moisture conditions (Fig. 5b). Such incomplete conversion under limited hydration is consistent with reports that dissociation of amylose–lipid complexes (Cx) requires higher temperatures (e.g., 115 °C to achieve full conversion) and occurs over a wider temperature range when water availability is restricted. From a processing standpoint, this thermos–moisture dependence has direct implications for fonio-based food systems. During steaming, where temperatures typically remain near 100 °C, part of the starch likely retains semi-crystalline domains, contributing to the firm texture and reduced glycaemic response observed in cooked fonio. Conversely, processes involving higher temperature–moisture inputs (e.g., film formation, drum drying, or high-moisture extrusion) would promote complete gelatinization and dissociation of amylose–lipid complexes, thereby improving flexibility and digestibility. The resulting conversion diagram thus serves as a valuable predictive tool to optimise hydrothermal

processing and tailor the functional properties of fonio-based foods.

**4. Conclusions**

The DSC deconvolution and modeling approach developed here enabled precise identification of three transitions in fonio starch – gelatinization (G), crystallite melting (M1), and amylose–lipid complex dissociation (Cx) – over a wide range of water contents. The method resolved overlapping peaks with high accuracy, and the peak temperatures of G, M1, and Cx were successfully modeled with the Flory–Huggins equation, as previously applied to melting transitions. Compared to major cereal starches such as rice, maize, wheat, and millet, fonio exhibits a markedly higher amylose content (30.9 %), a substantial M1 enthalpy change even under excess-water conditions (43 % of total), and a persistent Cx endotherm attributable to hydration-resistant amylose–lipid complexes. These characteristics are rarely reported in the thermogram analysis of cereals, where the M1 transition is often negligible in hydrated systems and the Cx transition is seldom observed. Such behavior suggests a dense crystalline structure with mixed A/B-type polymorphism, promoting high thermal stability but limiting complete gelatinization. Fonio’s gelatinization peak temperature under excess water (≈73 °C) exceeds that of wheat or pulses, reflecting reduced water–starch compatibility and higher energy requirements for structural disruption. The high thermal stability of Cx domains, linked to its unsaturated fatty acid profile, further differentiates fonio from conventional cereals and aligns it with certain millet starches.
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** (a) Simulated state diagram of the fonio starch-water system showing isoconversion lines (τ) as a function of temperature (T) and water content. Regions corresponding to native, partial, and full starch conversion are indicated. (b) Simulated starch conversion (τ) as a function of water content at three temperatures. X denotes the water content on a dry basis (kg·kg⁻¹ db).</figcaption></figure>

The complete starch conversion diagram produced in this work provides a detailed map of thermal-moisture transitions, from native to fully gelatinised states. This tool allows accurate prediction of conversion degree under processing conditions, providing a basis for optimizing cooking, particularly steaming and industrial applications for this high-amylose cereal.

However, this study is limited to *in vitro* thermal analysis and does not include direct structural confirmation of crystallinity, for example by X-ray diffraction, nor an assessment of starch digestibility. Future work should address these aspects to better connect thermal transitions with crystalline organization and nutritional functionality. Furthermore, the strong thermal stability and amylose-lipid complexation behaviour observed here imply that fonio starch could be investigated further as an ingredient in formulations of resistant starch and low-glycaemic foods.

### CRediT authorship contribution statement

**Paloma Babadoudou:** Writing – original draft, Methodology, Investigation, Formal analysis. **Romain Domingo:** Supervision, Methodology, Investigation. **Nadia Fanou Fogny:** Writing – review & editing, Project administration, Funding acquisition, Conceptualization. **Comlan Evariste Simon Mitchikpe:** Writing – review & editing, Project administration, Funding acquisition, Conceptualization. **Christian Mestres:** Writing – review & editing, Validation, Methodology, Formal analysis. **Philippe Bohuon:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project

administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors do not have any conflict of interest to declare.

### Acknowledgements

We gratefully acknowledge the support of the ANR-21-PEA2-0006 Biovalor project, which funded this study as part of the PhD research of Charmide B. Paloma Babadoudou.

### Data availability

Data will be made available on request.

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