# Impact of the physico-chemical properties of commercial pea proteins concentrate and isolate on the quality of meat analogs

Gabriel Le Flem, Francine Fayolle, Aurélie Lagorce, Vanessa Jury

► To cite this version:

Gabriel Le Flem, Francine Fayolle, Aurélie Lagorce, Vanessa Jury. Impact of the physico-chemical properties of commercial pea proteins concentrate and isolate on the quality of meat analogs. LWT - Food Science and Technology, 2026, 239, pp.118935. 10.1016/j.lwt.2025.118935. hal-05426654

HAL Id: hal-05426654

https://hal.science/hal-05426654v1

Submitted on 19 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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

Distributed under a Creative Commons Attribution 4.0 International License
# Impact of the physico-chemical properties of commercial pea proteins concentrate and isolate on the quality of meat analogs

Gabriel Le Flem<sup>a,b</sup>, Francine Fayolle<sup>a</sup>, Aurélie Lagorce<sup>b</sup>, Vanessa Jury<sup>a,*</sup>

## ARTICLE INFO

**Keywords:**

Pea proteins

High moisture extrusion cooking

Fractionation

Powder flow

Plant based meat analog

## ABSTRACT

Plant proteins extracted from soys and peas, have been identified as potential functional ingredients for plant-based meat analogs produced by high moisture extrusion cooking (HMEC). However, the extraction process (dry for concentrate and wet for isolate) can have an impact on the primary structure of the proteins, affecting their technical and functional properties. This study aims to compare the impact of two fractionation process on pea proteins (structure, fractions, flowing index) and their ability to create a network, which can mimic the fiber structure found in meat, within moisture extrusion. Pea protein concentrates (PPC) obtained by dry fractionation, and pea protein isolate (PPI) by isoelectric precipitation were characterized in terms of functional and flow properties. Results showed that PPI have a higher aggregation, lower solubility and water-holding capacity than PPC, and no thermal event was highlighted by differential scanning calorimetry, indicating proteins denaturation of PPI. Denaturation of proteins before the heating step in extruder barrel contrasts with the current model described in the literature.

However, treating with HMEC, PPC is less suitable than PPI where better results in term of flow properties (respectively with flowability indexes of 27.33 and 56.17) and anisotropic structure. In that way, it appears that proteins concentration (55 % and 70 % respectively for PPC and PPI) is the most important parameter for creating a meat like texture in the conditions of the experiment.

## 1. Introduction

The global population is projected to reach 9.7 billion by 2050, leading to a significant increase in food and protein demand (Tilman & Clark, 2014). While a transition from animal-based to plant-based protein sources is already underway in many countries, expanding the variety of available protein sources remains a major challenge. Pea proteins are increasingly considered a promising alternative to soy, which currently dominates the plant-based protein market. In addition to their favourable environmental impact, peas are less allergenic than soy (Codreanu-Morel et al., 2019), making them attractive for wider food applications.

Pea proteins are available in different forms depending on the fractionation method applied. Two main processing routes exist: dry fractionation, used to produce protein concentrates (PPC), and wet fractionation, yielding protein isolates (PPI). Dry fractionation is a milder, solvent-free mechanical process, such as turboseparation, which separates starch, fibers, and proteins based on particle size and density.

This method provides multi-component ingredients containing 50%–75 % protein (Schutyser et al., 2015), with lower environmental and energetic costs. In contrast, wet fractionation involves extraction, solubilization, and precipitation in aqueous media, often accompanied by drastic pH changes and heat treatments, which result in highly purified isolates (80–94 % protein, dry basis). However, these harsh conditions can denature proteins, alter their composition, and impair techno-functional properties critical to food structuring (Dijkstra et al., 2003; Kornet et al., 2020).

These differences are particularly important in the context of High Moisture Extrusion Cooking (HMEC), the most widely used method to produce plant-based meat analogs (PBMA) with a fibrous texture similar to muscle meat (Elzerman et al., 2021; Starowicz et al., 2022). HMEC is performed using a twin-screw extruder, where proteins undergo a complex transformation: denaturation, unfolding, dissociation, and chain alignment under controlled thermal and mechanical conditions (Schmid et al., 2022). These transformations enable the formation of an anisotropic, meat-like structure. However, excessive covalent

<footnote><sup>a</sup> Oniris, Université de Nantes, CNRS, GEPEA, UMR CNRS 6144, 44000, Nantes, France</footnote>

<footnote><sup>b</sup> Université Bourgogne Franche-Comté, Institut Agro, Université Bourgogne, INRAE, UMR PAM 1517, 21000, Dijon, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: vanessa.jury@oniris-nantes.fr (V. Jury).</footnote>
cross-linking can hinder chain mobility, increase viscosity, and reduce homogeneity (Rouilly et al., 2006; Verbeek & van den Berg, 2010). The balance between sufficient alignment and limited aggregation is therefore critical for optimal structuring. Through shear forces during mixing, gluten undergoes aggregation and elongation. The network formation occurs through hydrogen bonds, disulfide bridges, and hydrophobic interactions.

The performance of protein ingredients in HMEC depends not only on protein content, but also on native structure, residual components (fibers, starch, lipids), and functional properties. While isolates offer high protein levels, the intense processing they undergo may negatively affect their extrusion performance. Concentrates, despite their lower purity, may retain more native functionality and bring additional structuring effects due to their multi-phase nature. Recent studies have even suggested that a multi-phase system can be beneficial for creating anisotropic structures (van der Sman & van der Goot, 2023), challenging the conventional preference for isolates in PBMA production.

Several techniques are used to assess the fibrous structure of meat analogs, including mechanical testing (tensile strength, cutting force, texture profile analysis), spectroscopy, and imaging (Chiang et al., 2019; Osen et al., 2014; Pietsch et al., 2019; Schreuders et al., 2021). However, many gaps remain between instrumental data and sensory perception. Additionally, it has been observed that fibrousness is often better appreciated through tearing or bending than slicing (Nisov et al., 2022; Ramos Diaz et al., 2022).

Despite the growing number of studies on HMEC and the use of pea protein isolates, to our knowledge, no study has directly compared protein concentrates (PPC) and isolates (PPI) in terms of their ability to form fibrous structures via HMEC. This comparison is essential to evaluate whether some more sustainable and less processed ingredients could offer similar performances, or even improved them.

The objective of the present study was to investigate whether the type of fractionation process (dry vs wet) impacts the physico-chemical properties of pea proteins and their ability to form fibrous textures through HMEC. To accomplish this, two commercially available protein ingredients were selected: a dry-fractionated organic PPC and a widely used wet-fractionated PPI. Both were used in the formulation of PBMAs processed under identical HMEC conditions, and their functional, structural, and mechanical properties were systematically compared.

# 2. Materials and methods

## 2.1. Raw materials

Two types of pea proteins are used in this study, both were extracted from yellow peas: Pea Protein Isolate (PPI) Nutralys® F85M (Nisov et al., 2022) obtained by wet extraction from Roquette Frères S.A (Lestrem, France) and Pea Protein Concentrate (PPC) obtained by dry extraction supplied by UFAB (France). To help the formation of a meat analog texture, isolated wheat gluten (WG), provided by Louis François (France), salt, and sodium bicarbonate were also added to the formulation. Blends of biopolymers containing gluten are frequently employed for their ability to form a network (Cornet et al., 2021; V. Pietsch et al., 2017). Chemical composition based on supplier's specifications and internal measurement of protein and moisture (Section 2.4.1), is displayed in Table 1.

## 2.2. Formulation

Three formulations for HMEC were investigated in this study (F-PPI, F-PPC and F-PITC), all based on a mixture of pea proteins and gluten. Proportions of these ingredients were the same in F-PPI formulation and F-PPC formulation as described in Table 2. These formulations were based on commercial meat analogs list of ingredients, to enhance protein structuration at each extrusion trial. An additional trial named F-PITC, was formulated in order to reproduce the same amount of protein as in F-PPC, but using isolate as source of pea protein. In that formulation, pea isolate was mixed with pea starch and pea fibers to achieve the same chemical composition as pea concentrate. This F-PITC formulation will help highlight the impact of proteins structure on wet extrusion ability to form meat analog texture.

## 2.3. High moisture extrusion cooking process

### 2.3.1. High moisture extrusion parameters

Extrusion was performed using a co-rotating intermeshing twin screw extruder (Rheomex PTW 16/40, Thermo Fisher Scientific, Karlsruhe, Germany) with a ratio L/D of 40, a screw diameter of 16 mm and a barrel length of 640 mm. The barrel is segmented into 10 temperature-controlled zones heated by an electric cartridge system and cooled with water (Fig. 1). Tested parameters are described in Table 3.

Extrusion parameters targeted were 1.5 kg/h powder feed rate, 55 % moisture (g/100g), 500 rpm screw speed and a cooling die temperature of 70 °C at the beginning and 30 °C at the end. Configurations of the die were 2 mm height and 600 mm length (same as Guyony et al., 2022). After extrusion, extrudates were packed in plastic vacuum bag and frozen at -20 °C before further analysis. For each extrusion trial with the same formulation, among the different extrusion conditions achieved, only 2 are presented in this study: A, the first tangible sample and B, the sample with the most fibrous structure: Table 4 summarises the extrusion conditions for each sample studied.

### 2.3.2. Extruder response

The extruder responses including motor torque, temperature, and pressure at the end of the barrel, were recorded with Polylab software (Thermo Fisher Scientific, Karlsruhe, Germany).

The specific mechanical energy (SME) is a measure of the work input from the driving motor to the raw material during the extrusion process and is used to quantify the characteristics of the process (Godavarti & Karwe, 1997). The SME (kJ/kg) was calculated with Equation (1) (Fang

<table><caption>**Table 2**

Powder formulation for extrusion and their protein content (g/100g wet basis) (*supplier information) (NA = no data available).</caption><thead><tr><th rowspan="2">Raw materials</th><th colspan="3">Wet extrusion formulation</th></tr><tr><th>F-PITC</th><th>F-PPC</th><th>F-PPI</th></tr></thead><tbody><tr><td>Pea Protein Isolate</td><td>37.2</td><td>0.0</td><td>69.0</td></tr><tr><td>Pea Protein Concentrate</td><td>0.0</td><td>69.0</td><td>0.0</td></tr><tr><td>Wheat Gluten</td><td>29.2</td><td>29.2</td><td>29.2</td></tr><tr><td>Salt</td><td>1.3</td><td>1.3</td><td>1.3</td></tr><tr><td>Sodium bicarbonate</td><td>0.5</td><td>0.5</td><td>0.5</td></tr><tr><td>Pea starch</td><td>18.8</td><td>0.0</td><td>0.0</td></tr><tr><td>Pea Fiber</td><td>13.0</td><td>0.0</td><td>0.0</td></tr><tr><td><strong>Protein content</strong></td><td><strong>55,6</strong></td><td><strong>57,5</strong></td><td><strong>78,5</strong></td></tr></tbody></table>

<table><caption>**Table 1**

Chemical composition of the raw materials (g/100g wet basis).</caption><thead><tr><th></th><th>Name, Supplier</th><th>Fractionation type</th><th>Protein (N x 6.25)</th><th>Lipid*</th><th>Carbohydrates*</th><th>Fiber*</th><th>Ash*</th><th>Moisture</th></tr></thead><tbody><tr><td>PPC</td><td>Protep 55 UFAB</td><td>Dry</td><td>49.90</td><td>4.70</td><td>30.00</td><td>21.00</td><td>5.00</td><td>7.77 ± 0.00</td></tr><tr><td>PPI</td><td>Nutralys F85M Roquette</td><td>Wet</td><td>80.30</td><td>9.00</td><td>NA</td><td>1.00</td><td>5.00</td><td>6.58 ± 0.01</td></tr><tr><td>WG</td><td>Gluten Louis Francois</td><td>NA</td><td>79.00</td><td>5.00</td><td>10.2</td><td>1.00</td><td>NA</td><td>7.60 ± 0.01</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>Fig. 1. High moisture extrusion cooking setup.</figcaption></figure>

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

Operational extruder parameters used in the study.</caption><thead><tr><td>Extrusion Parameters</td><td>Values</td></tr></thead><tbody><tr><td>screw speed</td><td>500 rpm</td></tr><tr><td>screw diameter</td><td>16 mm</td></tr><tr><td>screw length</td><td>640 mm</td></tr><tr><td>feed rate</td><td>3–3.5 kg/h</td></tr><tr><td>barrel temperature</td><td>50, 70, 100, 125, 135, 145, 148, 155, 155, 155 °C</td></tr><tr><td>flowrate</td><td>1.5 kg/h (0.7 kg/h for PPC)</td></tr><tr><td>liquid dosing</td><td>1.5–2 kg/h</td></tr><tr><td>cooling die configuration</td><td>2 mm height, 600 mm length, 5 mm width</td></tr><tr><td>cooling die temperature</td><td>70 to 30 °C</td></tr></tbody></table>

et al., 2014).

$$SME = \frac{2\pi \times n \times T}{Q} \qquad \text{Equation 1}$$

with screw-speed *n* (rpm), motor torque *T* (N.m) and mass flow rate *Q* (g/min).

### 2.3.3. Surface obstruction

Powder enters the sheath through a circular opening 2.5 cm in diameter. If the powder flow is not optimal, this surface will be obstructed, as shown in Fig. 2. Once the surface is clogged, the likelihood of the sheath clogging followed by the shutdown of the extrusion process increases due to flow disruption. To monitor this indicator, a camera (JAI, København, Denmark) was placed above this surface. A capture is made every 5 s by Moticam software, and the images are processed by ImageJ software (Wayne Rasband). Equation (2) was used to determine the percentage of the surface obstructed.

$$\text{obstruction surface (\%)} = \frac{\text{powder area}}{\text{total area}} \qquad \text{Equation 2}$$

## 2.4. Analytical methods

### 2.4.1. Protein and moisture content

The protein content was measured with Dumas method and a nitrogen analyzer FP828 (Leco, St Joseph, Michigan, USA). Approximately 0.02 g of protein matter were weighed. The measured nitrogen content was converted to crude protein using Jones conversion factor of 6.25. Moisture content was determined gravimetrically by drying 5 g of raw matter at 105 °C for 16 h. Each measurement was made in triplicate.

### 2.4.2. Protein fraction

Protein fractions were identified from protein suspensions at 1 % (prepared the day before – 350 rpm; 7 °C) by electrophoresis in three different conditions: native, SDS (Sodium Dodecyl Sulfate) and SDS-DTT (SDS and Dithiothreitol). Migration was performed using Invitrogen Novex Wedge Well 8–16 % Tris-Glycine Gel (ThermoFischer Scientific, Waltham, USA) as migration gel with 0.3 % Tris, 1.4 % glycine buffer for native gel. For SDS and SDS-DTT gels 0.1 % SDS were added to the native buffer.

Sample were prepared as follow: diluting by half the protein suspension on the different sample buffers composed of (i) 12.5 % Tris HCl 0.5 mol/L pH 6.8, 10 % glycerol, 0.05 % bromophenol blue for native condition; (ii) 12.5 % Tris HCl 0.5 mol/L pH 6.8, 10 % glycerol, 0.05 % bromophenol blue, 2 % SDS for SDS condition; and (iii) 12.5 % Tris HCl 0.5 mol/L pH 6.8, 10 % glycerol, 0.05 % bromophenol blue, 2 % SDS, 2 % DTT for SDS-DTT condition (reagents were purchased at Merck - Darmstadt, Germany). For this last condition only, protein suspensions mixed with SDS-DTT sample buffer were heated at 95 °C during 10 min to allow DTT to break disulfuric bonds.

The Invitrogen NativeMark Unstained Protein Standard Protocol molecular weight scale (Thermo Fischer Scientific, Waltham, USA) and Sigma Marker S8445 (Merk, Darmstadt, Germany) were used at a rate of 10 µL per well for native and denaturant conditions (SDS and SDS-DTT) respectively. Exactly 100 V and 35 mA was applied on a single gel. Revelation was performed applying Coomassie blue for pea isolate and concentrate gels and was rinsed with water. Revelation protocol used for extrudates was different since no bands appeared clearly with

<figure><img src="image_3.png" /><figcaption>Fig. 2. Picture example of surface obstruction.</figcaption></figure>

<table><caption>**Table 4**

Extrusion process parameters for each sample.</caption><thead><tr><td>Sample</td><td>Powder flow (kg/h)</td><td>Water flow (kg/h)</td><td>Water content (wb%)</td><td>Screw speed (rpm)</td><td>T1</td><td>T2</td><td>T3</td><td>T4</td><td>T5</td><td>T6</td><td>T7</td><td>T8</td><td>T9</td><td>T10</td></tr></thead><tbody><tr><td>PPI_A</td><td>1,5</td><td>1,80</td><td>58</td><td>500</td><td>50</td><td>70</td><td>90</td><td>100</td><td>115</td><td>125</td><td>135</td><td>135</td><td>135</td><td>135</td></tr><tr><td>PPI_B</td><td>1,5</td><td>1,56</td><td>54</td><td>500</td><td>50</td><td>70</td><td>100</td><td>125</td><td>135</td><td>145</td><td>148</td><td>155</td><td>155</td><td>155</td></tr><tr><td>PPC_A</td><td>0,7</td><td>0,90</td><td>60</td><td>500</td><td>50</td><td>70</td><td>90</td><td>100</td><td>115</td><td>120</td><td>125</td><td>130</td><td>130</td><td>130</td></tr><tr><td>PPC_B</td><td>0,7</td><td>0,90</td><td>60</td><td>700</td><td>50</td><td>70</td><td>90</td><td>100</td><td>115</td><td>120</td><td>125</td><td>130</td><td>135</td><td>135</td></tr><tr><td>PITC_A</td><td>1,5</td><td>1,81</td><td>58</td><td>500</td><td>50</td><td>70</td><td>90</td><td>100</td><td>115</td><td>115</td><td>115</td><td>130</td><td>130</td><td>130</td></tr><tr><td>PITC_B</td><td>1,5</td><td>1,85</td><td>58</td><td>500</td><td>50</td><td>70</td><td>100</td><td>125</td><td>135</td><td>145</td><td>148</td><td>155</td><td>155</td><td>155</td></tr></tbody></table>
Coomassie blue. Therfore, Pierce™ color silver stain kit (Thermo Fischer Scientific, USA) was used to reveal gel with proteins suspensions from extrudates PPI-A and PPI-B. This specific revelation protocol allows highlighting bands containing less than 1 ng of protein, according to supplier information.

### 2.4.3. Protein solubility

Protein solubility was determined in compliance to the procedure used by Tiong et al. (2024) with moderate modifications. Pea proteins solutions at 3.2 % (g/100g) of raw matter, were stirred for 2 h at 20 °C in the following buffered: (i) For pH 2 to 4: 0.1 mol/L citric acid and 0.1 mol/L sodium citrate. (ii) For pH 5 to 8: 0.1 mol/L dibasic sodium phosphate and 0.1 mol/L monobasic sodium phosphate. (iii) For pH 9 to 11: 0.1 mol/L sodium carbonate, 0.1 mol/L sodium bicarbonate.

The non-soluble fraction was separated by centrifugation at 6000g for 15 min at ambient temperature. Protein content in the supernatant was measured by Dumas method described in section 2.4.1. Protein solubility (%) was determined by calculating the amount of protein in the supernatant relative to the amount of protein in the initial dispersion (Eq. (3)). Each measurement was made 3 times.

$$ \text{Protein solubility (\%)} = \frac{\text{protein content in supernatant (\%)}}{\text{Protein in initial dispersion (\%)}} \times 100\% $$

Equation 3

### 2.4.4. Thermal properties

Protein suspensions at 5 % (g/100g) of proteins were prepared the day before analysis (stirred at 7 °C and 350 rpm overnight). Roughly 500 mg of these suspensions were introduced in a micro-DSC device Setaram MicroDSC III (Caluire-et-Cuire, France) with linear temperature profile of 0.5 °C/min from 20 °C to 100 °C. Experiment were conducted in triplicate for both PPC and PPI. The data treatment software associated with the device and used for the peak integration is SETSOFT2000 v.1.2. The parameters measured on the thermograms are: peak temperature (T<sub>p</sub>), peak onset (T<sub>on</sub>), peak offset (T<sub>off</sub>) temperatures and peak enthalpy (through integration).

### 2.5. Powder characterization

#### 2.5.1. Particle size

Particle size distributions of PPC, PPI and WG raw materials were determined by laser diffraction using a Malvern Mastersizer 3000 with an Aero S dispersion unit (Malvern Instruments, Worcestershire, UK) with the setup described in Table 5.

For each sample, approximately 7 g of powder were used. The criteria selected are d[4,3], d10, d50, and d90 expressed in micrometers. Criterion d[4,3] represents the mean diameter over the volume distribution, and d10, d50, and d90 represent particle size respectively at 10 %, 50 %, and 90 % in the cumulative size distribution curve. Experiments were conducted in triplicate.

#### 2.5.2. Flow properties

The flow properties of the powders were evaluated using a Powder Characteristic Tester (Powder Pro A1, Bettersize Instruments Ltd., Dong, China) based on the flowability and floodability measurements,

<table><caption>**Table 5**
Particle size measurement parameters.</caption><thead><tr><td>Parameters</td><td>Values</td></tr></thead><tbody><tr><td>Air pressure</td><td>320 kPa</td></tr><tr><td>Vibration rate</td><td>59 %</td></tr><tr><td>Hopper opening</td><td>2.5 %</td></tr><tr><td>Particle refractive index</td><td>1.53</td></tr><tr><td>Particle absorption index</td><td>0.1</td></tr><tr><td>Dispersant refractive index</td><td>1</td></tr><tr><td>Scattering model</td><td>Mie</td></tr></tbody></table>

known as Carr's index (initially developed by Carr, 1965) and recently used and described by Laurent et al. (2022). Measurements were done for the angle of repose, compressibility, angle of spatula, and uniformity (the latter was determined from the particle size distribution). The flowability indices were subsequently computed using the data from these four measurements and described in Equation (4).

$$ i_{\text{flowability}} = i_{\text{angle of repose}} + i_{\text{angle of spatula}} + i_{\text{compressibility}} + i_{\text{uniformity}} \quad \text{Equation 4} $$

The overall value was assessed on a scale spanning from 0 to 100. A flowability index nearing 100 means that the powder exhibits easy flow, while an index approaching 0 means extremely poor flow. The floodability index, also rated from 0 to 100, was determined based on the flowability index value, angle of fall, angle of difference, and dispersibility measurements. This calculation is defined by Equation (5).

$$ i_{\text{floodability}} = i_{\text{flowability}} + i_{\text{angle of fall}} + i_{\text{angle of difference}} + i_{\text{dispersibility}} \quad \text{Equation 5} $$

Floodability serves as an indicator of a powder's stability, reflecting its susceptibility to dusting and/or flushing. A high floodability index implies that the powder is likely to flush readily, whereas a low index indicates that the powder is less prone to flushing.

### 2.6. Fiber texture characterization

#### 2.6.1. Radius of curvature

When there is enough fibration (sample B) the extrudate is pre-cut with a scalpel, approximately 1.5 mm on the side and manually opened in the direction of flow (longitudinal). The inner face with the fibers is scanned (Scaner V600 Epson Suwa, Nagano, Japan). The radius of curvature K is defined by Equation (6), with a the minor axis of the ellipse and b the major axis (Fig. 3). ImageJ software was then used to perform 10 measurements on each extrudate.

$$ K = \frac{b^2}{2a} \quad \text{Equation 6} $$

#### 2.6.2. Elongation strength at break

All mechanical tests to assess the textural properties of extrudates were performed using a Lloyd TA1 (Ametek Inc., Berwyn, Pennsylvania, USAs) with a pizza tensile rig (Fig. 4). This device includes two four-pronged attachments. The upper clamp attaches directly to the force transducer, and the other clamp to the base of the texture analyzer. The rectangular-shaped sample is positioned on the holders using the clamps. The tensile force and extension to break the sample are measured and used as an indication of extrudate strength.

This probe was chosen over a classic grip for tearing because the average thickness (~5 mm) and rigidity of our samples caused them to slip in the grips. Furthermore, this module, thanks to the tearing it causes in the direction of material flow, also allows visual analysis of the extrudate strips.

The analysis was carried out on cold extruded strip samples after cold storage at -20 °C and thawing at room temperature (23 ± 2 °C) for 1 h. Strips of about 10 cm in length were cut and placed on the mobile by inserting them on the peaks with a 5 cm gap between clamp. The elongation parameter was at a speed of 1 mm/s with a breakaway stop.

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Diagram and calculation of bending radius angle.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Pictures of extrudates after texture analysis - elongation with pizza tensile rig.</figcaption></figure>

## 2.7. Statistical analysis

All experiments were performed at least in triplicate and the results were expressed as means ± standard deviation (SD). Statistical analysis were evaluated by 1-way-ANOVA using Tukey's test with a significance level of p < 0.05 using a PC-based software (Statgraphics®, Manu- gistics, Inc., Rockville, MD).

# 3. Result and discussions

## 3.1. Characterization of protein ingredients

### 3.1.1. Particle size distribution

The average particle size diameter of food powders is a crucial parameter that significantly influences the practicality and suitability of the powder for use in the extrusion process (McGuire et al., 2022). The relation between particle size distribution and suitability in extrusion is better known for low moisture extrusion than HMEC. By considering only the flow in the powder feeder, it is possible to determine a preferred particle size distribution, to obtain a constant powder flow in HMEC and avoid clogging phenomena. Table 6 shows the particle size distribution

<caption>**Table 6**</caption>

Pea protein powder sample size distributions and mean diameters (d[4; 3]) (n = 8) Note: Data are expressed as mean ± standard deviation (n = 8). For each powder, samples with the same letter within the same row are not statistically different (one-way ANOVA) followed by Tukey's test (p > 0.05). Note: Data are expressed as mean ± standard deviation (n = 3). For each powder, samples with the same letter within the same row are not statistically different (one-way ANOVA) followed by Tukey's test (p < 0.05). Green means "not good" and orange means "bad" for flowability indexes, grey means "can flow" for the floodability index.

<table><thead><tr><th></th><th>PPI</th><th>PPC</th></tr></thead><tbody><tr><td>d<sub>10</sub> (μm)</td><td>41.4 ± 0.65 <sup>a</sup></td><td>2.3 ± 0.10 <sup>b</sup></td></tr><tr><td>d<sub>50</sub> (μm)</td><td>138.0 ± 2.77 <sup>a</sup></td><td>8.5 ± 0.30 <sup>b</sup></td></tr><tr><td>d<sub>90</sub> (μm)</td><td>262.0 ± 7.57 <sup>a</sup></td><td>21.9 ± 0.72 <sup>b</sup></td></tr><tr><td>D<sub>[4;3]</sub> (μm)</td><td>147.0 ± 2.13 <sup>a</sup></td><td>11.0 ± 0.90 <sup>b</sup></td></tr><tr><td>Span</td><td>1.6 ± 0.06</td><td>2.3 ± 0.06</td></tr></tbody></table>

(d10, d50, and d90), mean particle size (d[4; 3]) and span of PPI and PPC. The average particle sizes for PPI from the same supplier than Osen et al. (2014), are similar for d₁₀ (40.8 and 41.4), in the same range for d₅₀ (157.7 and 138.0) and thinner for our PPI (344.5 and 262.0). The results obtained for PPC are in line with the literature, as the turboshear process separates proteins from starch according to particle size. It can be seen on Fig. 5 that powder distribution of PPC is bimodal which is common for PPC (Pelgrom et al., 2013), whereas for PPI, the particle size distributions is more uniform and larger. The particle size was significantly different between powders (p ≤ 0.05).

From the particle number distribution curve in Fig. 6, PPC particles displayed mostly size below 1 µm, which would correspond to proteins (Rivera et al., 2022). The bi-modality of the volume density curve would then be explained mainly by the pea starch present in the concentrate (30 %) as well as the crushed fiber parts of the cell (21 %). In fact, Tyler and Panchuk (1982) affirmed that the milling process can effectively separate protein bodies and various cellular components, yielding flour particles with distinct compositions. In starch-rich legumes such as peas, the cotyledon cells present a structural composition where starch granules (±20 µm) are intricately interwoven within a matrix of protein bodies (<3 µm). These protein bodies are enveloped by a cell wall rich in dietary fiber.

According to Rivera (2022) and Pelgrom et al. (2013), fine particles as PPC exhibits increased surface areas, causing stronger cohesive forces that, in turn, leads to decreased flowability. McGuire et al. (2022) hypothesize that in extrusion the energy increase in smaller particles is due to heightened surface area, intensifying inter-particle friction and increasing resistance to flow. To the knowledge of the authors, there is no particle size distribution represented in density numbers in PPI, only volume representations. However, this additional information provides a better understanding of the properties of powder.

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Particle size distribution in volume density.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 6. Particle size distribution in number density.</figcaption></figure>

### 3.1.2. Flow properties of powders

The values of pea powders flowability parameters and indices are presented in Table 7 in which the differences between the fractionation of pea can be observed.

According to Carr's classification, powders with an Angle of Repose (AoR) less than 40° are expected to have good flow properties, while those exceeding 45° may have poor flow characteristics. The angle of repose for PPI (38,6°) is lower than the one for PPC (45.9°).

PPI powder flows more easily, which can be explained by their larger particle size distribution than for PPC (Table 6). Several studies (Pelgrom et al., 2013; Rivera et al., 2022) have demonstrated the significant importance of particle size distribution on the cohesiveness of powders.

It also explains the compressibility, PPC (45.38) is more than twice as compressible as PPI (18.68). Carić and Milanović (2002) observed that a wide range of particle sizes enables smaller particles to fill the inter-granular space between the larger ones, leading to a more compact and tightly packed arrangement, which could explain the much higher compressibility of PPC. According to Carr's (1965) flow table, powders with compressibility values below 25 % are considered "good flowing powders". Here, regarding to this parameter, PPC is subject to "flow problems". In addition, the results concerning the spatula angle suggest poor fluidity for the two pea powders (AoS >60°).

Cohesion is the most discriminating measure between isolate (50.7) and concentrate (99.6). According to Carr's tables, cohesion between 30 and 55 is considered a "poor" flow index, and cohesion above 55 as "very poor". It could be explained by the lower protein ratio for PPC which leads to more cohesion (Pelgrom et al., 2013) and also by the fractionation process of PPI resulting in the separation of the globulin and albumin proteins of pea fractions. Indeed, these fractions showed

<caption>**Table 7**</caption>

Values of pea protein powders flowability, floodability and surface obstruction parameters.

<table><thead><tr><th></th><th>PPI</th><th>PPC</th></tr></thead><tbody><tr><td>Angle of repose (AoR)</td><td>38.6 ± 3.74<sup>a</sup></td><td>45.9 ± 1.08<sup>b</sup></td></tr><tr><td>Angle of collapse (AoF)</td><td>33.0 ± 1.57<sup>a</sup></td><td>36.1 ± 0.36<sup>b</sup></td></tr><tr><td>Angle of difference (AoD)</td><td>5.6 ± 3.21<sup>a</sup></td><td>9.8 ± 0.78<sup>a</sup></td></tr><tr><td>Flat plate angle (AoS)</td><td>61.5 ± 0.93<sup>a</sup></td><td>72.85 ± 2.16<sup>b</sup></td></tr><tr><td>ρ<sub>aerated</sub> (kg/cm<sup>3</sup>)</td><td>0.41 ± 0.00<sup>a</sup></td><td>0.25 ± 0.00<sup>b</sup></td></tr><tr><td>ρ<sub>packed</sub> (kg/cm<sup>3</sup>)</td><td>0.51 ± 0.00<sup>a</sup></td><td>0.47 ± 0.03<sup>a</sup></td></tr><tr><td>Compressibility (C)</td><td>18.68 ± 1.14<sup>a</sup></td><td>45.38 ± 4.30<sup>b</sup></td></tr><tr><td>Uniformity (U)</td><td>3.87 ± 0.02<sup>a</sup></td><td>4.47 ± 0.70<sup>b</sup></td></tr><tr><td>Dispersibility (D) %</td><td>10.00 ± 0.00<sup>a</sup></td><td>10.00 ± 0.00<sup>a</sup></td></tr><tr><td>Cohesion</td><td>50.7 ± 0.00<sup>a</sup></td><td>99.6 ± 0.00<sup>b</sup></td></tr><tr><td>Flowability index (cohesion calcul)</td><td>56.17 ± 0.76<sup>a</sup></td><td>27.33 ± 2.88<sup>b</sup></td></tr><tr><td>Floodability index</td><td>44.66 ± 4.25<sup>a</sup></td><td>40.33 ± 2.02<sup>a</sup></td></tr><tr><td>Obstruction Surface %</td><td>0.00 ± 0.00<sup>a</sup></td><td>27.00 ± 0.00<sup>b</sup></td></tr></tbody></table>

differing functionalities, as albumin fractions resulted in a stiff cohesive interfacial layer (Kornet et al., 2022).

PPI had a flowability index of 56, corresponding to the "not good" fluidity category requiring bridge-breaking measures. In the case of PPC, the powder had a flow index of 27, falling into the "bad" flow category associated with a significant possibility of bridge formation, in the classification of Carr.

The floodability index was determined by measuring the index of flowability value, the angle of fall, the angle of difference and the dispersibility. The AoF values of PPC and PPI, 36.1° and 33.0° respectively, are both below 39° and fell into the fairly high floodability level. Regarding dispersibility, the values for both powders are similar and present a value of 10%, which is consider as less floodable according to Carr (1965). Thus, there is no significant differences in floodability indices, PPC and PPI were found to be similar with values of 40.33 and 44.66 respectively. Carr (1965) estimates that an index between 40 and 59 falls into the "tends to flow" category, which is associated with a potential need for flow prevention measures.

In the extruder inlet, the obstruction surface is visible for PPC (27%) but not for PPI, which is consistent with the flow index results, but not with those for floodability. Indeed, PPC agglomerates at the outlet of the powder feeder and at the inlet to the extruder sheath (Fig. 2) while PPI flows smoothly. This flowing characterizations, allow anticipating clogging phenomena in the extrusion process, and will be interesting for the use of new raw materials.

### 3.1.3. Protein solubility

Assessing protein solubility across varying pH levels is mandatory before going deeper in its functional characteristics like gelation, foaming capacity, water holding capacity, which is for the latter a key parameter for protein texturization (Samard & Ryu, 2019).

The protein solubility patterns of PPC and PPI for pH ranging from 2 to 11 are presented in Fig. 7. As expected, protein solubility decreases at the isoelectric point (pH 4-5) following a U-curve shape already reported by many authors (Liu et al., 2010; Osen et al., 2014; Schutyser et al., 2015). PPC outstands with 4 times higher protein solubility at pH 7 compared to PPI. The lower protein solubility of PPI at this neutral pH may be due to extraction process which uses isoelectric precipitation and subsequent neutralization. Dijkstra et al. (2003) reported this phenomenon and described the impact of extraction process on protein structure. Such process used for commercial PPI causes protein denaturation and thereby influences their techno-functional properties such as protein solubility. Adebiyi and Aluko (2011) as well as Taherian et al. (2011) also highlight in their works this loss of solubility on commercial PPI and measured a maximum solubility of 20% between pH 2 and 9. In addition, since the initial pH of PPI and PPC (5% (g/100g) solutions in water stirred for 15 min) were 7.5 and 6.8 respectively, it means that for extrusion process, solubility of proteins is not at its maximum, 22% and

<figure><img src="image_8.png" /><figcaption>Fig. 7. Protein solubility (%) of PPC and PPI as a function of pH (n = 3).</figcaption></figure>
84 % for PPI and PPC respectively. Therefore, sodium bicarbonate was added to the base extrusion formulation in order to increase the pH in the extruder.

Since NaCl is also added in the extrusion formulation, a study of solubility as a function of pH in presence of NaCl at different concentrations of NaCl (0; 1; 1.5 %) was carried out. Results obtained indicates that there is no significant impact of NaCl concentration on proteins solubility. The results can be found in the additional data section of this article.

However, two authors has reported that having a too high proteins solubility level was often a disadvantage to obtain an anisotropic texture and fibration: Nisov et al. (2022) in HMEC and Geerts et al. (2017) with shear cell technology. In this solubility test, the stirring time is 2 h, whereas the residence time of the mixture during extrusion is closer to 3 min.

The hydrophobicity of pea globular proteins, which causes their insolubility and therefore their heterogeneity in the mixture, could be a factor favouring anisotropic structuring. Indeed, it has long been accepted that two immiscible phases are necessary for fibration in extrusion (Akdogan, 1999; Cheftel et al., 1992). More recently van der Sman and van der Goot's (2023) theory suggests that the two phases could be a protein-rich phase and a water-rich phase. This would explain the good texturing of PPI despite its low solubility.

### 3.1.4. Protein denaturation state

Micro Differential Scanning Calorimetry (µDSC) subjects samples to thermal treatment, assessing native protein status and measuring denaturation temperature. Fig. 8 shows the thermogram obtained for PPI and PPC protein solutions. PPC shows two endothermic peaks: a first one between 43 °C and 63 °C displays a maximum at 52.83 °C, that can be identified as pea starch (Shand et al., 2007). The second one between 64 °C and 98 °C with a maximum at 77.86 °C is well known in literature (Rodriguez & Beyrer, 2023) as globulin identification. On the contrary the absence of the peak for PPI witnesses of the denature state of proteins and can be correlated with the low solubility seen in Fig. 7. This loss of native protein structure can be explained by the process used to extract these pea proteins. Several solutions at extreme pH are used to isolate the proteins from pea seeds. This extraction process is not without consequences for the structure of the proteins, which are in a denatured state in PPI powder and thus lose some of their properties, such as solubility (Rodriguez & Beyrer, 2023), as discussed in part 3.1.3.

### 3.1.5. Proteins fraction

Electrophoresis were performed in order to go further in the understanding of the PPI and PPC protein structure and fractions. Fig. 9, A)

<figure><img src="image_9.png" /><figcaption>Fig. 8. Differential scanning calorimetry thermograms of PPC and PPI suspension (5 % g/100g proteins) - 1st heating at 0.5 °C/min.</figcaption></figure>

displays electrophoresis carried out under native condition for PPI and PPC protein solutions. For PPI, a large blue trail can be observed without any representative protein bands. It can also be seen that part of the sample is carried off at the start of the electrophoresis gel, which means that the PPI proteins are aggregated and cannot migrate within the gel. This is in line with the previous results obtained on µDSC and solubility. Regarding PPC, the profile obtained is different. The two main proteins fractions, legumin (11S–300–400 kDa) and vicilin (7S–150–180 kDa) (Dziuba et al., 2014; Lam et al., 2018) can be observed in the native condition. This highlights that the process used for PPC production allows to keep the native state of such protein, which agrees with µDSC results.

Regarding the electrophoresis conducted under reducing conditions, PPC displays conventional profile where proteins, such as convicilin (~70 kDa), legumin (~60 kDa), vicilin (~50 kDa) as well as vicilin subunits, can be clearly identified. On the contrary, for PPI, almost the same profile as in native condition is observed: a blue trail along the electrophoresis gel, without allowing a clear identification of protein fraction. The use of SDS in electrophoresis allowed in particular breaking non-covalent interactions, such as hydrogen bonds and hydrophobic interaction. For PPI used in this study, the electrophoresis conditions did not allow to separate the proteins correctly, and thus forbade their identification.

Legumin is composed of α-chain (~40 kDa) and β-chains (~20 kDa) which are joined by disulphide bonds into a complex. Using SDS + DTT on electrophoresis allows highlighting these subunits and bands close to 45 kDa and 20 kDa. They can be observed in Fig. 9, B) for PPC. Regarding PPI, blue trail is still obtained, but bands corresponding to vicilin are visible. However, legumin fraction are still difficult to identify which can be due to their poor solubility in PPI as reported in the review of Lam et al. (2018).

Altogether, results on electrophoresis performed in three conditions on PPI and PPC clearly show the impact of extraction process on protein structure and fractions, with is a starting point for understanding protein fibration during extrusion. Consequently, the model proposed for proteins structural evolution using commercial isolates, during wet extrusion needs to be reconsidered in the light of these results and those found in the literature on the structure of protein isolates.

## 3.2. Extruder response

SME (Equation (1)) quantifies the mechanical energy transmitted to the extrudate, independently of the scale. The fundamental concept of SME involves measuring the energy entering the extrusion system per mass unit in the form of motor work. This energy is transferred to the extrudate through viscous dissipation, primarily converted into heat within the extruder. Other reactions may occur, such as starch gelatinization or protein denaturation, and a portion is lost through the extruder barrel wall. The effects of formulation and cooking temperature on the SME are shown in Fig. 10. SME values in this article are generally higher than those found in the literature (Fang et al., 2014; Guyony et al., 2022; Osen et al., 2014; Pöri et al., 2023).

As discussed in section 2.5.2, PPC powders have flowing problems, therefore, it was necessary to lower the flowrate used: 0.7 kg/h instead of 1.5 kg/h, in order to maintain a small clogging surface. A high value of 27 % obstruction area is obtained for F-PPC (Table 7), SME is a function of flow rate according to Equation (1). It can be assumed that at equivalent flow rates (1.5 kg/h), the SME values for F-PPC would have at least doubled. SME values for F-PPI are relatively constant regardless of barrel temperature. The SME for F-PPC and F-PITC values increases by more than four times between 100 and 135 °C. According to the results, it seems that the presence of polysaccharides has a huge impact on SME increase. It could also be hypothesizing that the protein denaturation needed for F-PPC requires more energy from the extruder than F-PPI, since proteins seems to be completely denaturated in the latter, and thus lead to an increase in the SME value. Results displayed in this study are
<figure><img src="image_10.png" /><figcaption>**Fig. 9.** Pea protein profiles of PPI and PPC under A) non-reducing (Native), B) reducing (SDS) and denaturing/reducing, (SDS + DTT) conditions. Ø:empty well; M: markers.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 10.** Effects of formulations (F-PITC, F-PPC, F-PPI) and cooking temperature (on the last barrel) on SME and pressure at 55 % moisture content (g/100g).</figcaption></figure>

in line with those of Osen et al. (2014) were SME value for PPI was close to 800 kJ/kg.

Below the denaturation temperature of proteins, their functional attributes, such as protein solubility, exert an important influence on the extruder responses, particularly in the initial phase of the protein-melt creation (Osen et al., 2014). This consideration is crucial in processing to mitigate the risk of extruder barrel clogging or water going backward. Conversely, at temperatures higher than the denaturation temperature of proteins, the thermal properties, which significantly affect the protein functional attributes, become less relevance concerning process performances.

### 3.3. Extrudates analysis

#### 3.3.1. Radius of curvature

The radius of curvature is used here as an innovative method to quantify visually inner fibration of the extrudates. It was designed following the same principle that the angle of fibration developed by Guyony et al. (2022). The point is to be more accurate when protein ratio is low (~50 %). In this case, the fibration is not very developed and the V-shape in the extrudate inner layer, characteristics of fiber structure, can be difficult to identify. According to Guyony et al. (2022), a decrease of fibration angle suggests an extreme fibration in the flow

direction or longitudinal fibers. Table 8 summarises the extrusion conditions for each sample studied.

It can be identified from Table 8 that Ex-F-PPI_B has a better fiber structure. Ex-F-PPC_B was set at different parameters (135 °C, 60 % moisture content, 700 rpm and 0.7 kg/h) to avoid clogging. The three samples can be compared as protein rate and flows properties of powders are information that can be isolated from the trials. There is no significant difference between bend radius of Ex-F-PPC_B and Ex-F-PITC_B. This is due to a similar amount of protein in the formulation, 78.5 % for F-PPI and more than 55 % for F-PPC and F-PITC. However, process conditions between F-PPC and F-PITC were not the same and the influence of the powder flow is not negligible (Akdogan, 1999).

#### 3.3.2. Proteins association during fibration

Electrophoresis were performed on extrudates made with F-PPI formulation (Fig. 11) since their show the best fibration (Table 8). In order to go deeper in the understanding of fibration mechanism during extrusion process, two conditions were chosen, Ex-PPI_A and Ex-PPI_B which correspond to a sample without and with apparent fibrous texture respectively.

Native electrophoresis performed on the two extrudates (Fig. 11, A) revealed a long trail along the electrophoresis gel for both extrusion conditions in which no band can be identified. Looking at reducing condition, proteins such as convicilin, legumin, vicilin can be observed on both extrudates without any difference between them. The distinction between native and reducing conditions is really significant and highlights (i) the disruption of PPI aggregate during extrusion process, thank to high shear, temperature and pressure and (ii) the formation of non-covalent bonds during extrusion process between proteins. Similar observation can be made on reducing and denaturing conditions (SDS + DTT) with similar protein fraction, and with the addition of legumin α band. However, seeing the importance of bands between 36 kDa and 24 kDa, it is difficult to identify the legumin β. It is also worthy to note that gluten is present in the formulation of this two extrudates. Gluten proteins, gliadin and glutenin also appeared in the electrophoresis gel, with a same order of molecular mass, i.e, 30–80 kDa for gliadins and 67–80 kDa and 36–44 kDa for high and low molecular weight glutenin (Feillet, 2000). Presence of these proteins, which are used in this work to help the fibration during extrusion process, contribute to increase the number and the concentration of proteins in each band, compared to PPI alone.

Regarding the three electrophoresis conditions, comparison between the two extrudates, Ex-PPI-A and Ex-PPI-B with visible difference in fibration texture, displayed the same profile. Consequently, it does not allow a better understanding of the fibration mechanism and of the bonds which could be created during this process. This work needs to be completed by other assays such as Ellman method in order to verify if
<caption>**Table 8**</caption>

Table of average radius of curvature and their standard deviation (n = 9) Note: Data are expressed as mean standard deviation (n = 9). For each extrudate sample with the same letter are not statistically different (one-way ANOVA) followed by Tukey's test (p < 0.05).

<table><thead><tr><th>Extrudate Samples</th><th>Ex-F-PPC_B</th><th>Ex-F-PPI_B</th><th>Ex-F-PITC_B</th></tr></thead><tbody><tr><td>Radius of curvature</td><td>5.6 ± 1.1 a</td><td>3.3 ± 0.5 b</td><td>4.8 ± 2.0 a</td></tr><tr><td>Photo of a strip</td><td></td><td></td><td></td></tr></tbody></table>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 11.** PPI extrudates profiles under A) non-reducing (Native), B) reducing (SDS) and denaturing/reducing, (SDS + DTT) conditions. Ø: empty well; M: markers.</figcaption></figure>

protein thiols are accessible or not, and if they are engaged in disulphide bonds. Raman spectroscopy could also be performed, expecting a change in the spectra when the fibration looks better.

### 3.3.3. Tensile stress

The effects of pea fractionation on extrudate texture properties are shown in Fig. 12 with the area under each curve. Ex-PPC_B and Ex-PITC_B share the same elongation profile as a function of applied force. This translates visually into the extrudate breaking into two parts (Fig. 4). However, the breaking point of Ex-PPC_B is twice higher (10 N) than the one of Ex-PITC_B (5 N). It could be explained by the difference in powder flow. As for Ex-PPI_B, the shape of the curve is not at all the same and is showing scratches at the module attachment points. The breaking point is not clearly identified but the maximal force applied is 12 N. One interpretation could be that the protein concentration plays a role in the structuration and elasticity of the extrudate.

<figure><img src="image_16.png" /><figcaption>**Fig. 12.** Tensile test of PPI, PPC and PITC extrudates on the Pizza Force module with an elongation of 1 mm/s 50N loadcell n = 5.</figcaption></figure>

Furthermore, starch and fiber, which represent half of F-PPC and F-PITC formulation, affect the textural properties of extrudate, with less internal fiber structure, making them brittler. To obtain better textural properties Deng et al. (2023), added between 10 and 20 % of insoluble fibers with soy isolate to produce PBMA with a ternary system. They conclude that proteins have to remain is the continuous phase in order to form an anisotropic structure. The protein content of F-PITC and F-PPC formulations may be at the limit to remain in the continuous phase.

An increase in SME concurrently amplifies tensile stress, hardness, and chewiness in the final product, as demonstrated by Chen et al. (2010) and Maung et al. (2021). Conversely, an inverse relationship between SME and texturization degree has been identified, indicating that elevated SME values are associated with a suboptimal fibrous structure (Fang et al., 2014). Our results are more in line with this second relationship since a greater SME is observed for extrudates with low tensile stress (Ex-PPC and Ex-PITC).

As already mentioned, feed rate of PPC was inferior to the one of PITC and PPI. Ex-PPI_A and Ex-PPI_B have almost the same elongation profile and therefore the same area under the curve. This is quite logical given that the extrusion conditions between the two were very similar (4 % difference in water content and 20 °C at the end of the barrel). Ex-PPC_A was useable on the rig tensile modulus of the texturometer, even though the sample was soft and not plasticized on its surface. This explains why it has the smallest area under the curve value. Between Ex-PPC_A and Ex-PPC_B, there is a variation in the screw speed of 200 rpm and 5 °C, resulting in an increase by a factor of 10 of the area under the tensile test curve. On the other hand, PITC_A and PITC_B only show a difference of a factor of 2 with a difference of 20 °C between the two conditions. Freezing and thawing may have an influence on the tensile test. Van der Sman and van der Goot (2023) theorised that ice crystals start forming in the water-rich areas created by syneresis during solidification because the elastic protein-rich areas inhibit ice nucleation. As freezing progresses, the growth of ice crystals further increases the water-rich phase at the expense of the protein-rich phase, which in turn amplifies the formation of a fibrous structure initially started by
syneresis. If this theory proves correct, freezing/thawing the extrudate would reinforce the differences in fibration measured, several non-thawing tensil tests just after extrusion should be investigated. The final problem to be considered with this method is that the samples are not pre-cut into dog bones shapes, to centralise the elongation forces in the middle of the sample. This was chosen to avoid damaging the lateral anistropic structure of the extrudate. However, the downside of this choice is that the breaking point is no longer centred and is most often located at the clamps.

## 4. Conclusion

This study investigated how pea protein fractionation, specifically dry fractionation (PPC) versus wet extraction (PPI), affects the functional and structural performance of proteins during HMEC, with the aim of evaluating their suitability for producing PBMA.

Our results demonstrate that the fractionation method significantly influences protein solubility, with PPI exhibiting much lower solubility than PPC due to its more intensive processing. However, protein concentration, rather than solubility or native structure, was found to be the primary driver of fibration capacity. Despite its denatured state even before processing, PPI led to extrudates with superior fibrous texture and mechanical integrity. In contrast, PPC, with a lower protein content and higher levels of starch and fiber, produced brittler extrudates with less pronounced internal fiber structure, as confirmed by tensile testing and structural analysis.

The initial structure of the proteins, whether native or not, did not appear to be critical for fibration, as both materials underwent significant denaturation during extrusion. However, the higher content of non-protein components in PPC altered the melt behavior, increasing the SME but negatively impacting the structural quality of the extrudates. Experimental validation with PITC further supported these findings. This trade-off between energy input and product quality highlights the importance of ingredient composition and matrix interactions during HMEC.

PPC also presented specific challenges in extrusion feeding due to its narrower particle size distribution, which reduced flow continuity and accuracy. To address this, we developed a predictive method based on powder rheology that effectively anticipates feeding behavior and offers practical value for researchers and industry users of HMEC.

An important and unexpected finding was that the commercial PPI used in this study did not keep its native protein structure, despite being produced via wet extraction. This challenges the assumptions of several mechanistic models in the literature, which commonly presume the involvement of native globular proteins in structure formation. Further investigation is needed to clarify the role of denatured or partially unfolded proteins in HMEC.

In conclusion, while both PPC and PPI can produce fibrous extrudates, only the isolate consistently delivered high-quality textures under the tested conditions. Nonetheless, dry-fractionated PPC remains a promising alternative due to its milder processing and lower environmental impact, providing that its limitations in formulation and processing are addressed. Future research should explore how to enhance PPC-based formulations by enrichment, blending, or particle size adjustment, including the use of hybrid fractionation strategies (combining dry separation with mild aqueous processing) to increase protein concentration and improve performance in meat analog applications.

### CRediT authorship contribution statement

**Gabriel Le Flem:** Writing – original draft, Validation, Resources, Methodology, Investigation, Conceptualization. **Francine Fayolle:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **Aurélie Lagorce:** Writing – review & editing, Validation, Supervision, Methodology, Funding acquisition,

**Conceptualization. Vanessa Jury:** Writing – review & editing, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

### Declaration of competing interest

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

### Acknowledgments

This work was supported by Carnot Qualimenst® with funding from ANR (#20 CARN 0026 01) and obtained under the 2021–2026 State-Region Plan (CPER). Financial support from the Ministère de l'Agri-culture et de la Souveraineté Alimentaire (MASA) of the French Government and the Pays de la Loire Regional Council are gratefully acknowledged.

### Data availability

Data will be made available on request.

### References

Adebiyi, A. P., & Aluko, R. E. (2011). Functional properties of protein fractions obtained from commercial yellow field pea (Pisum sativum L.) seed protein isolate. *Food Chemistry, 128*(4), 902–908. https://doi.org/10.1016/j.foodchem.2011.03.116

Akdogan, H. (1999). High moisture food extrusion. *International Journal of Food Science and Technology, 34*(3), 195–207. https://doi.org/10.1046/j.1365-2621.1999.00256.x

Carić, M., & Milanović, S. (2002). MILK POWDERS | physical and functional properties of milk powders. In H. Roginski (Ed.), *Encyclopedia of dairy sciences* (pp. 1874–1880). Elsevier. https://doi.org/10.1016/B0-12-227235-8/00316-3.

Carr. (1965). Evaluating flow properties of solids. *Chemical Engineering Journal, 72*, 163–168.

Cheftel, J., Kitagawa, M., & Queguiner, C. (1992). New protein texturization processes by extrusion cooking at high moisture levels. *Food Reviews International, 8*, 235–275. https://doi.org/10.1080/87559129209540940

Chen, F. L., Wei, Y. M., Zhang, B., & Ojokoh, A. O. (2010). System parameters and product properties response of soybean protein extruded at wide moisture range. *Journal of Food Engineering, 96*(2), 208–213. https://doi.org/10.1016/j.jfoodeng.2009.07.014

Chiang, J. H., Loveday, S. M., Hardacre, A. K., & Parker, M. E. (2019). Effects of soy protein to wheat gluten ratio on the physicochemical properties of extruded meat analogues. *Food Structure, 19*. https://doi.org/10.1016/j.foostr.2018.11.002. undefined-undefined.

Codreanu-Morel, F., Morisset, M., Cordebar, V., Larré, C., & Denery-Papini, S. (2019). L'allergie au pois. *Revue Française d'Allergologie, 59*(3), 162–165. https://doi.org/10.1016/j.reval.2019.02.197

Cornet, S. H. V., Bühler, J. M., Gonçalves, R., Bruins, M. E., van der Sman, R. G. M., & van der Goot, A. J. (2021). Apparent universality of leguminous proteins in swelling and fibre formation when mixed with gluten. *Food Hydrocolloids, 120*, Article 106788. https://doi.org/10.1016/j.foodhyd.2021.106788

Deng, Q., Wang, Z., Fu, L., He, Z., Zeng, M., Qin, F., & Chen, J. (2023). High-moisture extrusion of soy protein : Effects of insoluble dietary fiber on anisotropic extrudates. *Food Hydrocolloids, 141*, Article 108688. https://doi.org/10.1016/j.foodhyd.2023.108688

Dijkstra, D. S., Linnemann, A. R., & van Boekel, T. A. J. S. (2003). Towards sustainable production of protein-rich foods : Appraisal of eight crops for Western Europe. PART II: Analysis of the technological aspects of the production chain. *Critical Reviews in Food Science and Nutrition, 43*(5), 481–506. https://doi.org/10.1080/10408690390246332

Dziuba, J., Szerszunowicz, I., Nałęcz, D., & Dziuba, M. (2014). Proteomic analysis of albumin and globulin fractions of pea (Pisum sativum L.) seeds. Acta Scientiarum Polonorum. *Technologia Alimentaria, 13*(2), 181–190. https://doi.org/10.17306/j.afs.2014.2.7

Elzerman, J. E., Keulemans, L., Sap, R., & Luning, P. A. (2021). Situational appropriateness of meat products, meat substitutes and meat alternatives as perceived by Dutch consumers. *Food Quality and Preference, 88*, Article 104108. https://doi.org/10.1016/j.foodqual.2020.104108

Fang, Y., Zhang, B., & Wei, Y. (2014). Effects of the specific mechanical energy on the physicochemical properties of texturized soy protein during high-moisture extrusion cooking. *Journal of Food Engineering, 121*, 32–38. https://doi.org/10.1016/J.JFOODENG.2013.08.002

Feillet, P. (2000). *Le grain de blé : Composition et utilisation* (p. 308). Editions INRA. http://hal.inrae.fr/hal-02843056.
Geerts, M. E. J., Nikiforidis, C. V., van der Goot, A. J., & van der Padt, A. (2017). Protein nativity explains emulsifying properties of aqueous extracted protein components from yellow pea. *Food Structure*, **14**, 104–111. https://doi.org/10.1016/j.fostr.2017.09.001

Godavarti, S., & Karwe, M. (1997). Determination of specific mechanical energy distribution on a twin-screw extruder. *Journal of Agricultural Engineering Research*, **67** (4), 277-287. https://doi.org/10.1006/jaer.1997.0172

Guyony, V., Fayolle, F., & Jury, V. (2022). Die dimensions impact on fibrous plant protein formation during high moisture extrusion. *Appl. Food Res., 2*(2), Article 100228. https://doi.org/10.1016/j.afres.2022.100228

Kornet, C., Venema, P., Nijssje, J., van der Linden, E., van der Goot, & Meinders, M. (2020). Yellow pea aqueous fractionation increases the specific volume fraction and viscosity of its dispersions. *Food Hydrocolloids*, **99**, 105332. https://doi.org/10.1016/j.foodhyd.2019.105332.

Kornet, R., Yang, J., Venema, P., van der Linden, E., & Sagis, L. M. C. (2022). Optimizing pea protein fractionation to yield protein fractions with a high foaming and emulsifying capacity. *Food Hydrocolloids*, **126**, Article 107456. https://doi.org/10.1016/j.foodhyd.2021.107456

Lam, A. C. Y., Can Karaca, A., Tyler, R. T., & Nickerson, M. T. (2018). Pea protein isolates : Structure, extraction, and functionality. *Food Reviews International*, **34**(2), 126-147. https://doi.org/10.1080/87559129.2016.1242135

Laurent, S., Jury, V., de Lamballerie, M., & Fayolle, F. (2022). Effect of two defatting processes on the physicochemical and flow properties of Hermetia illucens and Tenebrio molitor larvae powders. *Journal of Food Processing and Preservation*, **46**(10). https://doi.org/10.1111/jfpp.16853

Liu, S., Elmer, C., Low, N. H., & Nickerson, M. T. (2010). Effect of pH on the functional behaviour of pea protein isolate-gum Arabic complexes. *Food Research International*, **43**(2), 489-495. https://doi.org/10.1016/j.foodres.2009.07.022

Maung, T.-T., Gu, B.-Y., & Ryu, G.-H. (2021). Influence of extrusion process parameters on specific mechanical energy and physical properties of high-moisture meat analog. *International Journal of Food Engineering*, **17**(2), 149-157. https://doi.org/10.1515/ijfe-2020-0042

McGuire, C., Siliveru, K., Ambrose, K., & Alavi, S. (2022). Food powder flow in extrusion : Role of particle size and composition. *Processes*, **10**(1). https://doi.org/10.3390/pr10010178. Article 1.

Nisov, A., Nikinmaa, M., Nordlund, E., & Sozer, N. (2022). Effect of pH and temperature on fibrous structure formation of plant proteins during high-moisture extrusion processing. *Food Research International*, Article 111089. https://doi.org/10.1016/j.foodres.2022.111089

Osen, R., Toelstede, S., Wild, F., Eisner, P., & Schweiggert-Weisz, U. (2014). High moisture extrusion cooking of pea protein isolates : Raw material characteristics, extruder responses, and texture properties. *Journal of Food Engineering*, **127**, 67-74. https://doi.org/10.1016/j.jfoodeng.2013.11.023

Pelgrom, P. J. M., Vissers, A. M., Boom, R. M., & Schutyser, M. A. I. (2013). Dry fractionation for production of functional pea protein concentrates. *Food Research International*, **53**(1), 232-239. https://doi.org/10.1016/j.foodres.2013.05.004

Pietsch, V. L., Bühler, J. M., Karbstein, H. P., & Emin, M. A. (2019). High moisture extrusion of soy protein concentrate : Influence of thermomechanical treatment on protein-protein interactions and rheological properties. *Journal of Food Engineering*, **251**, 11-18. https://doi.org/10.1016/j.jfoodeng.2019.01.001

Pietsch, V., Emin, M., & Schuchmann, H. (2017). Process conditions influencing wheat gluten polymerization during high moisture extrusion of meat analog products. *Journal of Food Engineering*, **198**, 28-35. https://doi.org/10.1016/J.JFOODENG.2016.10.027

Pöri, P., Aisala, H., Liu, J., Lille, M., & Sozer, N. (2023). Structure, texture, and sensory properties of plant-meat hybrids produced by high-moisture extrusion. *LWT*, **173**, Article 114345. https://doi.org/10.1016/j.lwt.2022.114345

Ramos Diaz, J. M., Kantanen, K., Edelmann, J. M., Suhonen, H., Sontag-Strohm, T., Jouppila, K., & Piironen, V. (2022). Fibrous meat analogues containing oat fiber concentrate and pea protein isolate : Mechanical and physicochemical characterization. *Innovative Food Science & Emerging Technologies*, **77**, Article 102954. https://doi.org/10.1016/j.ifset.2022.102954

Rivera, J., Siliveru, K., & Li, Y. (2022). A comprehensive review on pulse protein fractionation and extraction : Processes, functionality, and food applications. *Critical Reviews in Food Science and Nutrition*, **1-23**. https://doi.org/10.1080/10408398.2022.2139223

Rodriguez, Y., & Beyrer, M. (2023). Impact of native pea proteins on the gelation properties of pea protein isolates. *Food Structure*, **37**, Article 100340. https://doi.org/10.1016/j.fostr.2023.100340

Rouilly, A., Mériaux, A., Geneau, C., Silvestre, F., & Rigal, L. (2006). Film extrusion of sunflower protein isolate. *Polymer Engineering & Science*, **46**(11), 1635-1640. https://doi.org/10.1002/pen.20634

Samard, S., & Ryu, G.-H. (2019). Physicochemical and functional characteristics of plant protein-based meat analogs. *Journal of Food Processing and Preservation*, **43**(10), Article e14123. https://doi.org/10.1111/jfpp.14123

Schmid, E.-M., Farahnaky, A., Adhikari, B., & Torley, P. J. (2022). High moisture extrusion cooking of meat analogs : A review of mechanisms of protein texturization. *Comprehensive Reviews in Food Science and Food Safety*, **21**(6), 4573-4609. https://doi.org/10.1111/1541-4337.13030

Schreuders, F. K. G., Schlangen, M., Kyriakopoulou, K., Boom, R. M., & van der Goot, A. J. (2021). Texture methods for evaluating meat and meat analogue structures : A review. *Food Control*, **127**, Article 108103. https://doi.org/10.1016/j.foodcont.2021.108103

Schutyser, M. A. I., Pelgrom, P. J. M., Van Der Goot, A. J., & Boom, R. M. (2015). Dry fractionation for sustainable production of functional legume protein concentrates. *Trends in Food Science & Technology*, **45**(2), 327-335. https://doi.org/10.1016/j.tifs.2015.04.013

Shand, P. J., Ya, H., Pietrasik, Z., & Wanasundara, P. K. J. P. D. (2007). Physicochemical and textural properties of heat-induced pea protein isolate gels. *Food Chemistry*, **102** (4), 1119-1130. https://doi.org/10.1016/j.foodchem.2006.06.060

Starowicz, M., Poznar, K. K., & Zieliński, H. (2022). What are the main sensory attributes that determine the acceptance of meat alternatives? *Current Opinion in Food Science*, Article 100924. https://doi.org/10.1016/j.cofs.2022.100924

Taherian, A. R., Mondor, M., Labranche, J., Drolet, H., Ippersiel, D., & Lamarche, F. (2011). Comparative study of functional properties of commercial and membrane processed yellow pea protein isolates. *Food Research International*, **44**(8), 2505-2514. https://doi.org/10.1016/j.foodres.2011.01.030

Tilman, D., & Clark, M. (2014). Global diets link environmental sustainability and human health. *Nature*, **515**(7528), 518-522. https://doi.org/10.1038/nature13959.

Tiong, A. Y. J., Crawford, S., Jones, N. C., McKinley, G. H., Batchelor, W., & van 't Hag, L. (2024). Pea and soy protein isolate fractal gels : The role of protein composition, structure and solubility on their gelation behaviour. *Food Structure*, **40**, Article 100374. https://doi.org/10.1016/j.fostr.2024.100374

Tyler, R. T., & Panchuk, B. D. (1982). Effect of seed moisture content on the air classification of field peas and faba beans. *Cereal Chemistry*, **(59)**, 31-33.

van der Sman, R. G. M., & van der Goot, A. J. (2023). Hypotheses concerning structuring of extruded meat analogs. *Current Research in Food Science*, **6**, Article 100510. https://doi.org/10.1016/j.crfs.2023.100510

Verbeek, C. J. R., & van den Berg, L. E. (2010). Extrusion processing and properties of protein-based thermoplastics. *Macromolecular Materials and Engineering*, **295**(1), 10-21. https://doi.org/10.1002/mame.200900167