# Gelatin from red tilapia (Oreochromis spp.) scales: Optimization of its extraction and detailed characterization of its chemical and viscoelastic properties

Jairo Andrés Camaño Echavarría, Amine Nekkaa, Philippe Arnoux, Christelle Mathé, Céline Cakir-Kiefer, Loïc Stefan, Cédric Paris, Katalin Selmeczi, José Edgar Zapata Montoya, Laetitia Canabady-Rochelle

► To cite this version:

Jairo Andrés Camaño Echavarría, Amine Nekkaa, Philippe Arnoux, Christelle Mathé, Céline Cakir-Kiefer, et al. Gelatin from red tilapia (Oreochromis spp.) scales: Optimization of its extraction and detailed characterization of its chemical and viscoelastic properties. Applied Food Research, 2025, 5 (2), pp.101128. 10.1016/j.afres.2025.101128. hal-05224311

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# Gelatin from red tilapia (Oreochromis spp.) scales: Optimization of its extraction and detailed characterization of its chemical and viscoelastic properties

Jairo Andrés Camaño Echavarría<sup>a,b,*</sup> <sup> ID </sup>, Amine Nekkaa<sup>a</sup>, Philippe Arnoux<sup>a</sup>, Christelle Mathé<sup>a ID</sup>, Céline Cakir-Kiefer<sup>c</sup>, Loïc Stefan<sup>d ID</sup>, Cédric Paris<sup>c</sup>, Katalin Selmeczi<sup>f ID</sup>, José Edgar Zapata Montoya<sup>b</sup>, Laetitia Canabady-Rochelle<sup>a,*</sup>

## ARTICLE INFO

### Keywords:

- Fish by-products

- Collagen hydrolysis

- Hydrothermal extraction

- Gel properties

- Fractional factorial design

- Red tilapia (Oreochromis spp.) scales

## ABSTRACT

Gelatin is widely used in food and pharmaceutical industries for its functional and bioactive properties. When extracted from fish by-products, the environmental impacts of these former waste are reduced. This study aimed to optimize the gelatin extraction from red tilapia scales using a fractional factorial design and evaluate its physicochemical and viscoelastic properties. The effects of solvent-to-solid ratio (2–10 mL/g), extraction time (1–3 h), temperature (65–85 °C) and ultrasound (0–60 min) were investigated on the crude protein content (CPC), the free amino groups (FAG) and the protein extraction yield (PEY). The CPC extracted (95.4–99.2 %) was in agreement with protein isolates (>85 %) and the extraction time and temperature had a significant positive effect on FAG and PEY. Extraction conditions after optimization were: 10 mL/g solvent-to-solid ratio, 3 h, 85 °C, and without ultrasounds. Gelatin obtained under optimal condition improved highly the protein extraction and exhibited the best molecular weight distribution profile (α₁/α₂ chain ratios: 1.7) to offer suitable viscoelastic properties in term of gelling (18 °C) and melting capacities (25.8 °C). This finding highlights the important potential of such natural protein source as a tailored gel for food or cosmetic formulations.

**Abbreviations:** AA, Amino acid; Adeq, precision, Adequate precision; AAA, Aromatic amino acids; ANOVA, Analysis of variance; BCAA, Branched chain amino acids; CD, Circular dichroism spectroscopy; CPC, Crude protein content; EAA, Essential amino acids; FAG, Free amino group; FFD, Fractional factorial design; FTIR, Fourier-transform infrared spectroscopy; G', Storage modulus, G'', Loss modulus; HAA, Hydrophobic amino acids; LMWA, Low molecular weight aggregates; M, Protein marker, NCAA, Negatively charged amino acids; PCAA, Positively charged amino acids; PEY, Protein extraction yield; OPA, o-phthalaldehyde; SDS-PAGE, Sodium dodecyl sulfate polyacrylamide gel electrophoresis; TSF, tilapia scale flour; TSG, tilapia scale gelatin.

## 1. Introduction

Nowadays, aquaculture has resulted in a rapid increase in fish production with 223.2 million tons produced in 2022 (FAO, 2024). This growth is accompanied by numerous amounts of wastes (50–70 % of the total fresh fish weight, Villamil et al., 2017), which significantly impact

the environment (Martínez-Alvarez et al., 2015). Tilapia (Oreochromis spp.) is the third most widely cultured fish worldwide (Ngo et al., 2010). Yet, in fish factories, the scales, one of the main fish processing wastes, are often discarded during the filet processing (Weng & Wu, 2015). To date, approximately 50,000 tons of wastes are produced per year worldwide during the de-scaling processing, which pollutes the

<footnote><sup>a</sup> Université de Lorraine, CNRS, LRGP, F-54000 Nancy, France</footnote>

<footnote><sup>b</sup> Universidad de Antioquia, NUTEC, 050010 Medellín, Colombia</footnote>

<footnote><sup>c</sup> Université de Lorraine, INRAE, URAFPA, 54000 Nancy, France</footnote>

<footnote><sup>d</sup> Université de Lorraine, CNRS, LCPM, F-54000 Nancy, France</footnote>

<footnote><sup>e</sup> Université de Lorraine, LIBIO, F-54000 Nancy, France</footnote>

<footnote><sup>f</sup> Université de Lorraine, CNRS, L2CM, F-54000 Nancy, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: jairo-andres.camano-echavarria@univ-lorraine.fr (J.A. Camaño Echavarría), laetitia.canabady-rochelle@univ-lorraine.fr (L. Canabady-Rochelle).</footnote>
environment (Wang et al., 2020). Mainly composed of collagen protein (30–50 %) and minerals as hydroxyapatite (Ca₅(PO₄)₃OH) (30–40 %), fish scales by-products are of great interest for gelatin extraction (Ganjeh et al., 2023; Lin et al., 2020). Indeed, once extracted, gelatin could be marked as high added-value product, while solving a recurrent problem of waste disposal (Kasankala et al., 2007).

Gelatin, a fibrous protein obtained from collagen heat denaturation (Caruso et al., 2020), has numerous applications (Gong et al., 2024). For instance, this protein is widely used in the pharmaceutical field (200, 000 tons in the last decades, Alipal et al., 2021), in addition to food, cosmetic and packaging industries, due its excellent functional and biological properties (Viji et al., 2019). Besides, gelatin is often used in biotechnology and biomedicine (Huang et al., 2018), notably for culture medium, tissue engineering and bone substitutes (Al-Nimry et al., 2021). Moreover, in the past two decades, a growing interest has emerged concerning fish by-product to produce hydrolysate containing bioactive peptides due to their various health potential applications. Huang et al. (2018) found antioxidants and angiotensin I-converting enzyme inhibitor peptides from milkfish scale gelatin. Also, literature (Chen et al., 2019; Qiu et al., 2019; Sheng et al., 2022; Viji et al., 2019; Yang et al., 2019; Zhang et al., 2022) reported a high potential of gelatin extracted from grass carp scales, skipjack tuna scales, siberian sturgeon (*Acipenser baeri*) cartilages, skin and scale of sole fish, skipjack tuna bone and skin for obtaining antioxidant peptides, respectively. Gelatin can be produced from various sources, notably the pig skin and bovine hide (Tu et al., 2015). However, other sources are sought for various reasons (i.e., the outbreak of mad cow disease or Bovine Spongiform Encephalopathy but also due to its unacceptability for some religions when produced from cattle and/or pigs (Kasankala et al., 2007). Thus, new gelatin resources have been explored, notably fish by-products (i.e., fish skin, fin, bone and scale), rich in collagen (Zhang et al., 2019).

Collagen is commonly converted into gelatin by a four-step process (Wangtueai & Noomhorm, 2009): demineralization of scales, washing, extraction, and drying. The gelatin extraction includes the use of heat, acid and protease methods (pepsin and trypsin, Yu et al., 2024). Hydrothermal method involves hot water at a controlled temperature and a time to break down collagen and turn it into gelatin (Chen et al., 2019). This approach reduces chemical waste, requires fewer processing steps, and it is cost-effective and scalable. These advantages make the hydrothermal method well-suited for industrial applications (Adnan et al., 2024).

To ensure an efficient gelatin extraction, this common process has to be optimized to obtain the maximum yield of gelatin with the required physical (i.e., molecular weight distribution, solubility, gelling and melting temperatures, and viscosity) and chemical properties (i.e., crude protein content, amino acid composition and degree of hydrolysis, Montero & Acosta, 2020). Both yield and quality of gelatin depend on the extraction conditions used during the collagen treatment (Kasankala et al., 2007). Thus, several parameters should be evaluated to achieve optimal gelatin extraction process from fish by-products. One-factor-at-a-time method is often used in product/process studies. Yet, this approach lacks investigation into interaction effects between investigated parameters, resulting in time-consuming and potentially unreliable for determining optimal conditions.

Experimental design, crucial in various scientific and industrial fields, provides meaningful insights for product and process development (Rezende et al., 2018). The exploration of optimal conditions for protein extraction increased the use of statistical tools like factorial design, which assesses multiple factors and their interactions simultaneously (Montero & Acosta, 2020). While a complete factorial design is costly and time-consuming (Salleh et al., 2015), fractional factorial design (FFD) provides an effective optimization tool that reduces the number of experiments without compromising accuracy. FFD focuses on significant main effects and two-factor interactions while neglecting higher order interactions, making FFD a valuable tool for optimizing protein extraction processes with fewer experiments (Heiberger &

Holland, 2015).

To the author's knowledge, the gelatin extraction process from red tilapia scales has never been optimized to date using FFD. Therefore, this study aims to determine the optimal conditions for gelatin extraction from red tilapia scales using this former approach while studying the effects of four factors (i.e., the solvent-to-solid ratio, the extraction time, the temperature, and the ultrasound time) on three investigated responses (i.e., the crude protein content, the free amino groups content and the protein extraction yield). In addition, this study presents the physico-chemical characterization of the most interesting gelatin extracts obtained by SDS-PAGE profile, circular dichroism (CD) and Fourier transform infrared (FTIR) spectroscopies, as well as their viscoelastic properties.

## 2. Materials and methods

### 2.1. Chemicals and reagents

Chemicals and reagents were provided by Sigma-Aldrich (St Louis, MO, USA): 37 % hydrochloric acid (HCl), 97 % sulphuric acid (H₂SO₄), o-phthal dialdehyde (OPA), glycine, N,N-dimethyl-2-mercaptoethyl ammonium, sodium tetraborate decahydrate, sodium dodecyl sulfate and sodium carbonate. The NaOH anhydrous pellets, potassium sulfate and hydrogen peroxide 30 % (w/v) were purchased from VWR International (Atlanta, USA). Boric acid and methyl red were provided by Merck (Darmstadt, Germany). All reagents used in this study were analytical grade.

### 2.2. Gelatin extraction from tilapia scales

#### 2.2.1. Scale collection and preparation

Red tilapia (*Oreochromis* spp.) scales were supplied by El Gaitero SAS (Sopetrán, Colombia). The tilapia scales flour (TSF) was prepared according to the literature (Sierra-Lopera & Zapata-Montoya, 2021; Tu et al., 2015) with slight modifications. In brief, scales were washed three times with distilled water to remove the surface-adhering impurities. Then, they were dried by a convective oven (50 °C, 24 h; Thermo Scientific™, Waltham, USA) and ground with liquid nitrogen using a cutting mill (Retsch SM 300, Éragny, France) equipped with a 2 mm sieve (Restch, Éragny, France).

#### 2.2.2. Gelatin extraction process

The gelatin was extracted from the tilapia scales flour by hydrothermal extraction using ultrapure water according to the process described in Fig. 1. Hydrothermal extraction involves hot water at a controlled temperature and a controlled amount of time to break down collagen and turn it into gelatin (Chen et al., 2019). First, a demineralization step was carried out similarly to Chen et al. (2019). The tilapia scales were decalcified using 0.4 M HCl solution. Then, the demineralized scales were washed with ultrapure water (18.2 MΩ·cm⁻¹ at 25 °C). The gelatin was then extracted from demineralized scales using a hydrothermal extraction. The extraction was carried out using a 250 mL thermostatic beaker connected to a water flow and a thermostat (Haake, Karlsruhe, Germany). The demineralized scales were dissolved in water at different solvent-to-solid ratios, for different extraction time and temperature conditions according to the defined FFD (Table 1). Then, the mixture was transferred into a 500-mL beaker and cooled down until reaching room temperature. When required, the mixture was then submitted to ultrasounds (Bandelin sonopuls hd 2070, Berlin, Germany) at a 20 kHz frequency and 70 W of power for 0 or 60 min. To remove the solid residues, the extracting solutions were ultracentrifuged (Eppendorf AG, Hamburg, Germany). The collected supernatants containing the gelatin were dried at 50 °C using a convective oven for 24 h (Memmert GmbH & Co. KG, Saxony, Germany). Finally, the dried gelatin samples were ground for 30 s using a basic mill (Moulinex, Alençon, France) and stored at -20 °C until further analysis.
<figure><img src="image_3.png" /><figcaption>Fig. 1. Tilapia scales gelatin extraction process set up in this study with fractional factorial experimental design approach. Adapted from Chen et al. (2019).</figcaption></figure>

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

Responses of 2<sup>4-1</sup> fractional factorial design for tilapia scales gelatin extraction: crude protein content (%), free amino groups content (mmol Eq G/g) and protein extraction yield (%). Run 9–11: central points.

<table><thead><tr><th rowspan="3">Run</th><th colspan="4">Design matrix</th><th colspan="4">Uncoded factors</th><th colspan="3">Reponses</th></tr><tr><th colspan="4">Code factors</th><th colspan="4">Uncoded factors</th><th rowspan="2">CPC<br>%</th><th rowspan="2">FAG<br>mmol Eq G/g</th><th rowspan="2">PEY<br>%</th></tr><tr><th>A</th><th>B</th><th>C</th><th>D</th><th>A (mL/g)</th><th>B (h)</th><th>C (°C)</th><th>D (min)</th></tr></thead><tbody><tr><td>TSF</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>45.2 ± 0.8</td><td>0.01 ± 0.004</td><td></td></tr><tr><td>1</td><td>-1</td><td>-1</td><td>-1</td><td>-1</td><td>2</td><td>1</td><td>65</td><td>0</td><td>99.2 ± 0.9</td><td>0.23 ± 0.004</td><td>37.6 ± 0.1</td></tr><tr><td>2</td><td>+1</td><td>-1</td><td>-1</td><td>+1</td><td>10</td><td>1</td><td>65</td><td>60</td><td>96.9 ± 0.3</td><td>0.23 ± 0.01</td><td>41.3 ± 0.1</td></tr><tr><td>3</td><td>-1</td><td>+1</td><td>-1</td><td>+1</td><td>2</td><td>3</td><td>65</td><td>60</td><td>96.7 ± 0.4</td><td>0.25 ± 0.02</td><td>37.3 ± 0.1</td></tr><tr><td>4</td><td>+1</td><td>+1</td><td>-1</td><td>-1</td><td>10</td><td>3</td><td>65</td><td>0</td><td>97.2 ± 0.2</td><td>0.24 ± 0.02</td><td>38.7 ± 0.1</td></tr><tr><td>5</td><td>-1</td><td>-1</td><td>+1</td><td>+1</td><td>2</td><td>1</td><td>85</td><td>60</td><td>98.2 ± 1.3</td><td>0.26 ± 0.02</td><td>38.8 ± 0.4</td></tr><tr><td>6</td><td>+1</td><td>-1</td><td>+1</td><td>-1</td><td>10</td><td>1</td><td>85</td><td>0</td><td>95.4 ± 0.8</td><td>0.28 ± 0.03</td><td>48.5 ± 0.4</td></tr><tr><td>7</td><td>-1</td><td>+1</td><td>+1</td><td>-1</td><td>2</td><td>3</td><td>85</td><td>0</td><td>97.6 ± 0.8</td><td>0.28 ± 0.03</td><td>31.9 ± 0.2</td></tr><tr><td>8</td><td>+1</td><td>+1</td><td>+1</td><td>+1</td><td>10</td><td>3</td><td>85</td><td>60</td><td>97.1 ± 0.7</td><td>0.31 ± 0.04</td><td>47.2 ± 0.3</td></tr><tr><td>9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td><td>2</td><td>75</td><td>30</td><td>96.9 ± 1.5</td><td>0.26 ± 0.02</td><td>39.1 ± 0.7</td></tr><tr><td>10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td><td>2</td><td>75</td><td>30</td><td>96.7 ± 0.8</td><td>0.27 ± 0.02</td><td>39.7 ± 0.4</td></tr><tr><td>11</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6</td><td>2</td><td>75</td><td>30</td><td>95.6 ± 0.7</td><td>0.26 ± 0.02</td><td>41.2 ± 0.1</td></tr></tbody></table>

A: Solvent-to solid ratio; B: Extraction time; C: Temperature; D: Ultrasound time.

CPC: Crude protein content

FAG: Free amino group.

EqG: Equivalent glycine.

PEY: Protein extraction yield.

TSF: Tilapia scales flour.

### 2.2.3. Fractional factorial design

To optimize the extraction conditions of gelatin from tilapia scales, a 2<sup>4-1</sup> FFD was used. In a resolution IV FFD, 4 factors (A, B, C, D) are investigated, where the main effects do not consider the third order interactions and above, since insignificant (Rezende et al., 2018). In this design, the fraction generator “I”, equal to “ABCD”, was applied to obtain the factorial fraction (factor D obtained by multiplying ABC, Table 1). The 2<sup>4-1</sup> FFD applied here was commonly used to minimize the number of experiments to run without sacrificing its accuracy (Salleh et al., 2015). The following 4 factors were all studied at two levels (‘low’ -1 or ‘high’ +1): the solvent-to-solid ratio (A, 2 or 10 mL/g), the

extraction time (B, 1 or 3 h), the temperature (C, 65 or 85 °C) and the ultrasound time (D, 0 or 60 min). For estimating pure error and increasing the probability of detecting significant factors, three repetitions at the central point (runs 9, 10 and 11) were added to the experimental design matrix, leading to eleven experimental runs on total.

After carrying out this FFD, the effect of these 4 factors was investigated for each extracted gelatin sample, on the 3 followed responses: the crude protein content (CPC, %), the free amino groups content (FAG, mmol Eq G/g) and the protein extraction yield (PEY, %), all being important for gelatin production of high productivity and quality (Montero & Acosta, 2020). Each analysis was repeated to present data as
mean ± standard deviation.

## 2.3. Responses investigated

### 2.3.1. Crude protein content

The CPC was determined by Kjeldahl (AOAC 954.01; AOAC, 2000). First, TSF and the gelatin extracts were digested inside a START D Microwave Digestion System (Milestone, Milan, Italy) and the total nitrogen content was determined using a PRO—NITRO M Kjeldahl distiller (Selecta, Barcelona, Spain). CPC was estimated using a nitrogen-to-protein conversion factor of 5.95 as reported for fish scales (Huang et al., 2016).

### 2.3.2. Free amino group content

The FAG content was determined by OPA quantification similarly to Canabady-Rochelle et al. (2018). Briefly, the FAG determination in each sample was performed by mixing 200 µL of the OPA reagent with 20 µL of sample or standard solution, prepared at 1 mg/mL in ultrapure water. The 96-well microplates (NuncIon™ delta surface, Thermo scientific, Roskilde, Denmark) were used for spectroscopic measurements. The absorbance value was measured in quintuple at a wavelength of 340 nm using a spectrophotometer MRX Multiskan Go (ThermoFicher Scientific, Vantaa, Finland) equipped with a microplate reader. A calibration curve was performed with glycine diluted in ultrapure water (0–5 mM). Results were expressed as mmol equivalent glycine per gram of sample (mmol Eq G/g).

### 2.3.3. Protein extraction yield

The PEY (%) of each extracted sample, expressed by g crude protein extracted/100 g crude protein in TSF used, was estimated by the protein mass present in each gelatin extract divided by the total crude protein mass present in tilapia scales flour for the same sample (Eq. (1)) (Zhang et al., 2019).

$$ PEY (\%) = \left| \frac{M_{TSG} \times P_{TSG}}{M_{TSF} \times P_{TSF}} \right| \times 100 \quad (1) $$

Where $M_{TSG}$ is the mass (g) of TSG obtained after extraction, $M_{TSF}$ is the mass (g) of TSF used for extraction, and $P_{TSG}$ and $P_{TSF}$ are the crude protein content (%) of TSG and TSF, respectively.

### 2.3.4. Statistical analysis of experimental design

After experimental extraction, the 3 investigated responses were integrated into the experimental design to investigate the effects of the principal factors and their interactions on the responses, and determine which effects were statistically significant. All statistical analyses were performed at a significance level of 0.05 (p-value < 0.05). The responses were analyzed using the Design Expert 11 statistical software (Stat-Ease Inc, Minneapolis, USA). It enabled to determine initially their contribution percentages for each factor and their interactions in order to preselect which ones were relevant. Then, these latter ones were confirmed by ANOVA (Sepian et al., 2019). From ANOVA, a relationship between the predicted responses and the investigated factors were obtained using a first-order polynomial model (Supplementary data, Data S1, Sepian et al., 2019).

## 2.4. Physico-chemical characterization

### 2.4.1. SDS-PAGE profile

The molecular weight profile of TSF, 8 gelatin extracts (run 1–8 in FFD), 1 sample at central point (run 9) and the optimal run (10 mL/g, 3 h, 85 °C) was investigated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) according to the method reported by Tu et al. (2015) and Zhang et al. (2022), using a Mini-Protean tetra cell electrophoresis system (Bio-Rad Laboratories, Hercules, USA). The samples were prepared at 20 mg/mL in ultrapure water. Then, the

solubilized protein solutions were dissolved at a ratio of 1:4 (v/v) with sample buffer (0.5 M Tris–HCl, pH 6.8, containing 10 % (w/v) SDS, 50 % (v/v) glycerol, and 0.05 % (w/v) bromophenol blue with 0.5 % (w/v) 2-mercaptoethanol). These mixtures were heated at 100 °C for 5 min to denature proteins. Then, 10 µL of each sample (i.e., containing 40 µg of protein) or solution of molecular weight maker (range: 10 – 250 kDa; Catalog N°: 26,619, Thermo Scientific™, Vilnius, Lithuania) were loaded onto the polyacrylamide gel, made of a 7.5 % separation gel and a 5 % stacking gel (Zhang et al., 2022). The electrophoresis was conducted at a voltage of 120 V for 15 min and then, 180 V for 1 h. After electrophoresis, the gel was stained for 1 h with a solution containing 0.1 % (w/v) Coomassie blue R-250 (Sigma-Aldrich, Oakville, Canada), 7.5 % (v/v) acetic acid and 5 % (v/v) ethanol. Subsequently, the gel was destained with a solution containing 30 % (v/v) ethanol and 7.5 % (v/v) acetic acid for 1 h. Finally, densitometric analysis of Coomassie blue-stained gels was carried out using GelAnalyzer 19.1 (www.gelana lyzer.com) to quantify the relative amount of gelatin components ($α_1$, $α_2$ β and γ chains) and its low molecular weight aggregates (LMWA).

### 2.4.2. Amino acid composition

The amino acid (AA) composition was only carried out for the optimal TSG sample obtained by FFD using an UHPLC system (Thermo Fisher Scientific) set on-line with an Orbitrap ID-X Tribrid mass spectrometer (Thermo Fisher Scientific), equipped with an atmospheric pressure ionization interface. First, the sample was subjected to a complete acid hydrolysis (6 N HCl, 130 °C, 24 h) and then, dried at 65 °C under vacuum (Huang et al., 2016). Finally, 5 µL sample solution (1 g/L in ultrapure water) were injected and separated on C18 Alltima reverse phase column (150 × 2.1 mm, 5 µm; Grace/Alltech, Darmstadt, Germany) equipped with a C18 Alltima pre-column (7.5 × 2.1 mm, 5 µm) at 10 °C.

### 2.4.3. Circular dichroism (CD) spectroscopy

The CD spectra of the 10 gelatin extracts (9 gelatin extracts and optimal run) were recorded from 185 to 260 nm at 0.5 nm intervals using a MOS-450 CD spectrometer (Bio-Logic Science Instruments, France). Samples were prepared at 0.2 mg/mL in 0.1 M acetic acid, and transferred into a 2 mm-pathlength cuvette for analysis (at 25 °C as in Chen et al., 2022).

### 2.4.4. Fourier transform infrared (FTIR) spectroscopy

The infrared spectra from the gelatin extracts were recorded in ATR-FTIR spectrophotometer (ATR-FTIR Brucker ALPHA-P Spectrometer, Ettlingen, Germany) as previously reported (Cansu, 2023). A small amount of gelatin powder (approximately 50 µg), enough to cover the diamond crystal area, was placed on the spectrophotometer base, and analysis was performed. The transmittance was measured in the range of 400-4000 cm⁻¹ with a resolution of 4 cm⁻¹ and 200 scans. The wavelength corresponding to each characteristic absorption band was determined by identifying the peak with the highest intensity in each region of interest of the FTIR spectrum. All data were processed using OPUS 6.5 software (Bruker Optics GmbH, Ettlingen, Germany) and OriginPro 2023 software (Origin Lab, MA, Northampton, USA).

## 2.5. Rheology properties

Rheology measurements were performed for the most interesting gelatin extracts solely using an AR2000 rheometer (TA Instruments, New Castle, DE) operating in oscillatory mode, equipped with a Peltier plate temperature control and a Couette geometry immersed inside the sample (Giraud et al., 2020). Gelatin samples were prepared at 6.67 % (w/v) in ultrapure water and heated up using an oil bath (60 °C, 20 min), allowing a complete dissolution. Then, the samples were transferred to the rheometer ($T = 60$ °C) and the following sequence of measurements were carried out. The steps and experimental conditions used are displayed in supplementary data (Data S2). All the measurements were
repeated a minimum of three times. Collected data were treated using Origin 2023 (OriginLab Corporation), and fitted as sigmoid functions to extract the gelling and melting temperatures values, corresponding to the inflection point of the curve.

## 2.6. Statistical analysis

The analyses of amino acid composition and rheological properties were conducted three times, and all the data were presented as means ± standard deviation. The statistical analyses were one-way analysis of variance (ANOVA) and significant differences between means of samples were determined by Fisher's multiple range test. The differences at *p*-value < 0.05 were considered significant. The statistical calculations

were conducted using the software Statgraphics centurion XVI version 16.1.03 (Statgraphics Technologies Inc, Virginia, USA).

# 3. Results and discussion

## 3.1. Effect of the studied parameters on gelatin extraction

### 3.1.1. Crude protein content

The effect of the solvent-to-solid ratio (A), extraction time (B), temperature (C) and ultrasound time (D) was investigated on gelatin extraction using a FFD. The responses CPC, FAG content and PEY are presented for each extract in Table 1.

The CPC varies slightly between 95.4 and 99.2 % as a function of the

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Percentage of contribution of single factors and two-factor interactions effects on responses (A), and Pareto charts for CPC (B), FAG content (C) and PEY (D). CPC: Crude protein content; FAG: Free amino group; PEY: Protein extraction yield; A: Solvent to solid ratio; B: Extraction time; C: Temperature; D: Ultrasound time; AB: Interaction of solvent to solid ratio with extraction time; AC: Interaction of solvent to solid ratio with temperature; AD: Interaction of solvent to solid ratio with ultrasound time.</figcaption></figure>
investigated factors and is in agreement with the CPC in a protein isolate (> 85 %). Whatever the treatment, the gelatin extracts obtained show similar and even higher protein concentration compared to the literature. Zhang et al. (2019) reported similar protein values (97.8 %) in gelatin extract obtained from tilapia scales using a hydrothermal treatment (ratio 1:2 w/v, 135 °C for 90 min). Besides, for temperature comprised between 65-85 °C, protein values were reported at 87.02 ± 1.67 %, 86.9 ± 0.58 % and 88.56 ± 0.16 % for gelatin extracts obtained from grass carp skin (Kasankala et al., 2007), bighead carp scales (Wangtueai & Noomhorm, 2009), and lizardfish scales (Tu et al., 2015), respectively. Thus, the protein extraction set up here on red tilapia scales flour is an efficient process: the gelatin obtained is above 95 % of CPC for all the extracts and the mineral fraction - notably hydroxyapatite - may be mainly removed by the demineralization process. All the gelatin extracts obtained from red tilapia scales was light yellow and translucent, which is consistent with typical fish gelatin. This appearance is indicative of the gelatin's purity and suitability for food and pharmaceutical applications.

Additionally, these experimental responses were analysed by contribution percentage of each factor and their respective interactions and Pareto charts analysis (Fig. 2). In a Pareto chart, individual values of each effect are plotted in descending order by bars. As observed, an order of priorities is assigned with positive (increasing levels of factor, response increases) and negative effects (increasing levels of factor, response decreases) on investigated responses. The contribution percentage of each single factor and their two-factor interactions are presented in Fig. 2A. For CPC, when all 4 factors are compared individually, the solvent-to-solid ratio (A) shows the highest percentage of contribution (33.6 %), while the two factors interaction AB (i.e., solvent-to-solid ratio with extraction time) shows the highest percentage of contribution at 31.6 %.

Then, the Pareto charts were plotted for the CPC (Fig. 2B). From Pareto charts, the investigated factors show both positive (orange) and negative (blue) effects on the studied responses. The Pareto chart plotted for the CPC response evidences a positive effect of the interactions solvent-to-solid ratio with extraction time (AB) and with the ultrasound time (AD). On the other hand, a negative effect is observed for the solvent-to-solid ratio (A), temperature (C), their respective interaction (AC), the extraction time (B) and the ultrasound time (D). Although various involved factors have a different effect on the CPC, all the levels of factors have the potential to produce a protein isolate from red tilapia scales, maintaining a suitable CPC for gelatin production (95-99 %).

### 3.1.2. Free amino groups content

According to the gelatin extraction process applied, the FAG content varies between 0.23 and 0.31 mmol Eq G/g (Table 1). The FAG content of all gelatin extracts is higher (> 0.2 mmol Eq G/g) than that of TSF (0.012 ± 0.004 mmol Eq G/g) due to the natural hydrolysis of collagen upon protein extraction. This is in agreement with Nagarajan et al. (2012), who evidenced that upon hydrothermal process, collagen present in tilapia scales flour was partially hydrolyzed into gelatin, leading to an increased FAG content. Besides, the highest FAG content is reached after 60 min (70 W) of ultrasounds (0.31 mmol Eq G/g), probably due to the large shear forces generated from cavitation, breaking inter and intramolecular bonds of collagen (Chandrapala et al., 2011). Although the hydrolysis of collagen can be increased by ultrasounds, our results only show a slight improvement in FAG content (without and with ultrasound: 0.28 and 0.31 mmol Eq G/g, respectively).

The FAG content determined in our gelatin extracts is similar to the value reported by Tu et al. (2015) (0.17 - 0.20 mmol Eq l-leucine/g), who observed the highest degradations with ultrasound carried out at 200 W at 60 °C for 1-3 h.

According to the contribution percentage of each single factor and their two-factor interactions (Fig. 2), the FAG content is highly affected by the temperature (C) and the extraction time (B), which show the highest contribution (70.8 %, and 17.1 %, respectively). Regarding the

interactions, the solvent-to-solid ratio with temperature (AC) shows the highest percentage of contribution at 7.3 %.

Concerning the FAG response, the Pareto chart (Fig. 2C) evidences that the four single factors (solvent-to-solid ratio, extraction time, temperature and ultrasound time) in addition to the two interaction (solvent-to-solid ratio with temperature (AC) and solvent-to-solid ratio with ultrasound time (AD)) show a positive effect. The extraction time (B) and temperature (C) particularly improve the release of a high content of free amino groups. Indeed, this latter variable (C) is positively and directly correlated to the degree of hydrolysis of tilapia scales collagen into gelatin.

Our results are in accordance with the study of Tu and colleagues (Tu et al., 2015), who reported that the gelatin extraction from bighead carp scales led to a FAG content increase for longer extraction times. Likewise, Zhang et al. (2019) found that higher temperatures increased the FAG content.

### 3.1.3. Protein extraction yield

Whatever the gelatin extraction process applied, the PEY, expressed by g crude protein extracted/100 g crude protein in TSF used, varies from 31.9- 48.5 % (Table 1). The gelatin extracted at 85 °C (runs: 6 and 8) have significantly higher extraction yields as compared to those extracted at 65 °C in similar conditions (runs: 2 and 4). Similarly to our study, Huang et al. (2016) evidenced that the high temperatures used during the extraction process decomposes the matrix of tilapia scales and weakens the tight linkage between collagen and hydroxyapatite minerals, leading to the solvent penetration and the PEY increase. Several studies suggested that PEY of gelatin does not only depend on the CPC of the fish scales but also on fish species, fish parts, biological conditions (i.e., fish length and weight) and extraction procedures used (Upasen et al., 2019).

In Fig. 2A, the solvent-to-solid ratio (A) has the highest effect on the protein extraction yield (54 %), followed by the interaction of solvent-to-solid ratio with temperature (AC, 23.7 %), the temperature (C, 7.9 %) and the extraction time (B, 7.4 %). Meanwhile, according to the Pareto chart (Fig. 2D), the PEY response is positively affected by the solvent-to-solid ratio (A), the interaction of solvent-to-solid ratio with temperature (AC), the temperature (C), the ultrasound time (D), and the interaction of solvent-to-solid ratio with extraction time (AB). On the contrary, the extraction time (B) and the interactions of solvent-to-solid ratio with ultrasound time (AD) negatively affect the protein extraction yield.

Our results are in agreement with the literature: the PEY increases with the solvent-to-solid ratio, since a substrate increase (lower ratios) causes a viscosity increase, thus slowing down the mass and the heat transfers required for protein extraction, until preventing them (Xue et al., 2015; Zhang et al., 2019).

Besides, higher temperatures (C) release higher gelatin content, enhancing the protein extraction yield. Similar results were reported by Zhang et al. (2019) and Huang et al. (2016) who extracted gelatin from tilapia scales by hydrothermal treatment at 85-135 °C for 15-120 min, without previous demineralization. According to these former authors, the collagen-hydroxyapatite crosslinking was destructed upon high-temperature treatment, enhancing the gelatin extraction.

Although ultrasound treatment disrupted cellular structures and enhanced mass transfer, facilitating extraction processes, our results indicate that ultrasound did not have a statistically significant effect on gelatin extraction yield within the studied parameters. Thus, ultrasound may provide limited benefits under certain conditions. Additionally, while ultrasound can potentially improve FAG content, the associated power consumption must be considered, particularly at an industrial scale to ensure the sustainability and the cost-effectiveness of the process.
## 3.2. Statistical evaluation of experimental design

### 3.2.1. Model assumption validation

From former results (Section 3.1), we focused our attention on single factors and two-factor interactions, which have the greatest impact on responses investigated, to determine the statistically significant effects carried out by ANOVA analysis. Thus, whatever positive or negative, effects with the three lowest contribution percentages for each investigated response (last three ones shown in Figs. 2B, 2C, and 2D) are

constructed using Design-Expert® software from significant single factors and their interactions (p-value < 0.05; Eqs. (3), (4) and (5)).

Note that in these first-order polynomial models, the negative or positive coefficients correspond to positive or negative effects, with a value proportional to the observed coded effect.

$$CPC (\%) = 97.07 - 0.65A + 0.63AB \quad (3)$$

$$FAG \ content \ (mmol \ EqG / g) = 0.26 + 4.47 \times 10^{-3}A + 0.01B + 0.02C + 7.04 \times 10^{-3}AC \quad (4)$$

neglected similar to Sepian et al. (2019).

Main results of the ANOVA analysis are shown in Table 2 with the meaning and interpretation of statistical parameters used in the analysis of experimental design given in supplementary data (Data S3). For the statistically significant effect of models, the single factors and their interactions, the *p-value* of ANOVA should not exceed 0.05 at the 95 % confidence interval, explaining a significant portion of the variance. The analysis of variance provides three statistically significant models (*p-value* < 0.05) corresponding to the CPC, FAG content and PEY (Table 2). The validity of the results obtained in all variance analysis is dependent on the model assumptions: normality, constant variance (i.e., equal variance of treatments) and independence of investigated responses. In our study, the responses are normally distributed, with the same variance in each treatment and the response measurements are independent. Thus, we can conclude that the model assumptions are checked and satisfied for all three investigated responses (Supplementary data, Data S4).

### 3.2.2. Multiple regression modeling

From ANOVA data (Table 2), the multiple regression modeling enables to plot the linear relationship between the factors (A, B, C and D) and their interactions with the investigated responses (CPC, FAG, and PEY). It allows to predict responses outcome given the level of factors. ANOVA data (Table 2) provides three statistically significant models (*p-value* < 0.05) corresponding to the CPC, FAG content and PEY. Based on these experimental data, models for each response variable were

$$PEY (\%) = 40.12 + 3.75A + 1.43C + 2.49AC \quad (5)$$

With CPC: crude protein content; FAG: free amino groups; PEY: protein extraction yield; A: solvent-to-solid ratio; B: extraction time; C: temperature; AB, AC and AD: corresponding two factor interactions.

In order to evaluate the quality of fit in the obtained models, the regression coefficients (R² and adjusted R²), lack of fit and adequate precision (Adeq precision) of the resulting models were determined (Table 2). For FAG content and PEY models, the values of R² and adjusted R² are close to 1, indicating a good fitting since higher than 0.90. Thus, these two models could explain >90 % of variability of the experimental responses. On the contrary, for CPC, although the model was significant, the R² coefficients are lower than 0.9 (0.69 and 0.82), just explaining 60–80 % variability of this response. Concerning the lack of fit test, the Table 2 shows that for FAG content and PEY, this value is non-significant (*p-value* ≥ 0.05), confirming a high accuracy and reliability of these models for prediction of responses. Inversely, for the CPC, lack of fit is significant (*p-value* < 0.05), suggesting that this mathematical model does not fit experimental values with strong prediction, corroborating the results found with R² coefficients.

Besides, the Adeq precision is used as secondary check. This signal-to-noise ratio compares the range of the predicted values to the average prediction error at the experimental runs. For the three models (CPC, FAG, and PEY), Adeq precision values are higher than 4, satisfying

<table><caption>**Table 2**
ANOVA analysis of fractional experimental design of gelatin protein extraction on the three investigated responses.</caption><thead><tr><th rowspan="3">Factor</th><th colspan="2">CPC</th><th colspan="4">Response variable</th></tr><tr><th rowspan="2">p-value</th><th rowspan="2">Coefficient</th><th colspan="2">FAG content</th><th colspan="2">PEY</th></tr><tr><th>p-value</th><th>Coefficient</th><th>p-value</th><th>Coefficient</th></tr></thead><tbody><tr><td>Model</td><td><strong>0.02</strong></td><td>–</td><td><strong>2.50</strong> × 10<sup>-5</sup></td><td>–</td><td><strong>1.36</strong> × 10<sup>-3</sup></td><td>–</td></tr><tr><td>Constant</td><td>–</td><td>97.07</td><td>–</td><td>0.26</td><td>–</td><td>40.12</td></tr><tr><td>A</td><td><strong>0.02</strong></td><td>–0.65</td><td><strong>0.02</strong></td><td>4.47 × 10<sup>-3</sup></td><td><strong>5.16</strong> × 10<sup>-4</sup></td><td>3.75</td></tr><tr><td>B</td><td>–</td><td>–</td><td><strong>3.02</strong> × 10<sup>-4</sup></td><td>0.01</td><td>0.05</td><td>–1.38</td></tr><tr><td>C</td><td>0.31</td><td>–0.22</td><td><strong>5.21</strong> × 10<sup>-6</sup></td><td>0.02</td><td><strong>0.04</strong></td><td>1.43</td></tr><tr><td>D</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>AB</td><td><strong>0.02</strong></td><td>0.63</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>AC</td><td>–</td><td>–</td><td><strong>2.82</strong> × 10<sup>-3</sup></td><td>7.04 × 10<sup>-3</sup></td><td><strong>4.20</strong> × 10<sup>-3</sup></td><td>2.49</td></tr><tr><td>AD</td><td>0.09</td><td>0.40</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Lack of fit</td><td>2.88E-04</td><td>–</td><td>0.81</td><td>–</td><td>0.27</td><td>–</td></tr><tr><td>Pure error</td><td>2.67 × 10<sup>-4</sup></td><td>–</td><td>5.72 × 10<sup>-5</sup></td><td>–</td><td>2.18</td><td>–</td></tr><tr><td>R<sup>2</sup></td><td><strong>0.82</strong></td><td>–</td><td><strong>0.98</strong></td><td>–</td><td><strong>0.93</strong></td><td>–</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.69</td><td>–</td><td>0.97</td><td>–</td><td>0.88</td><td>–</td></tr><tr><td>Adeq Precision</td><td>10.14</td><td>–</td><td>28.80</td><td>–</td><td>14.38</td><td>–</td></tr></tbody></table>

A: Solvent-to solid ratio; B: Extraction time; C: Temperature; D: Ultrasound time.

CPC: Crude protein content.

FAG: Free amino group.

PEY: Protein extraction yield.

Adeq precision: Adequate precision.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** 2D contour plots of responses. (A) crude protein content (CPC, %) as a function of solvent-to-solid ratio and extraction time; (B) free amino groups content (FAG, mmol Eq G/g) and (C) protein extraction yield (PEY, %) as a function of solvent-to-solid ratio and temperature. Factors set constant: 65 °C and 0 min of ultrasound time for crude protein content; 3 h of extraction time and 0 min ultrasound time both for FAG and PEY. PC: Crude protein content; FAG: Free amino group; PEY: Protein extraction yield.</figcaption></figure>

the requirement in terms of signal-to-noise ratio (Portilha-Cunha et al., 2020). Thus, these models can be used to navigate within the design space. Yet, the FAG (28.80) and PEY (14.38) models show Adeq precision values much higher than that of CPC (10.14), highlighting once again their higher reliability. In summary, taken together the results of R², lack of fit and Adeq precision, the results indicate that the FAG content and PEY models only could be suitable to predict the responses and to optimize the gelatin extraction from red tilapia scales.

### 3.2.3. Response surface

Based on ANOVA and modeling results, the 2D contour plots were obtained using the Design Expert statistical software in order to observe the potential interaction effects between two factors. Only one interaction has a statistically significant effect for each response (Fig. 3A, 3B and 3C). For each of these panels, the interaction between two factors is presented, setting the other two factors constant. The Fig. 3A shows the effects of the solvent-to-solid ratio (A) and extraction time (B) on the CPC, at 65 °C and without ultrasounds. For low values of solvent-to-solid ratio and extraction time, the crude protein content increases slightly (red color). The effects of the solvent-to-solid ratio (A) and temperature (C) were investigated on FAG content (Fig. 3B) and PEY (Fig. 3C), both with extraction time set constant at 3 h and without ultrasounds. For high values of solvent-to-solid ratio and temperature, both FAG content and PEY increase (red color). Indeed, more collagen is turned into gelatin as a consequence of high temperatures, which is in agreement with Zhang et al. (2019).

### 3.3. Responses optimization

The multiple response optimization aims to define factor conditions that would maximize FAG content (0.31 mmol Eq G/g) and PEY (48.5 %), while keeping the CPC suitable for protein isolates (95–99 %). Indeed, the optimal extraction conditions are obtained by the desirability function of design Expert statistical software, similarly to Montero and Acosta (2020). To reach this goal, the conditions for gelatin extraction must be: 10 mL/g of solvent-to-solid ratio, 3 h of extraction time, 85 °C of temperature and no ultrasounds. The process parameter optimization was performed via response surface methodology using Design-Expert® software. The predicted response values obtained by the various models (Eqs. (3), (4) and (5)) were checked experimentally using same optimum extraction conditions (optimal run: 10 mL/g, 3 h, 85 °C, 0 min) in order to validate the three models and the reproducibility of the optimum. The Table 3 compares the predicted values with

<caption>**Table 3**</caption>

Predicted and experimental values of crude protein content, free amino groups content and protein extraction yield obtained for tilapia scales gelatin extracts.

<table><thead><tr><th>Reponses</th><th>Predicted value</th><th>95 % CI low*</th><th>95 % CI high**</th><th>Experimental value</th><th>% Relative error***</th></tr></thead><tbody><tr><td>CPC (%)</td><td>96.2</td><td>95.6</td><td>97.7</td><td>95.9 ± 0.4</td><td>0.2</td></tr><tr><td>FAG (mmol Eq G/g)</td><td>0.31</td><td>0.30</td><td>0.30</td><td>0.29 ± 0.003</td><td>6.9</td></tr><tr><td>PEY (%)</td><td>46.5</td><td>41.3</td><td>51.6</td><td>42.8 ± 0.2</td><td>8.7</td></tr></tbody></table>

Solvent-to-solid ratio: 10 mL/g; extraction time: 3 h; temperature: 85 °C, ultrasound time: 0 min.

CPC: Crude protein content.

FAG: Free amino group.

PEY: Protein extraction yield.

CI: Confidence Interval.

* 95 % CI low: Lower limit of the 95 % CI (experimental value).

** 95 % CI high: Upper limit of the 95 % CI (experimental value).

***Relative error estimated by making the absolute value of subtraction of the measured value by the real value, divided by the real value and then multiply by 100 %.

the observed ones using the relative error (%). For each analysed response, the relative error is <10 %, thus the predicted values are consistent with the experimental ones, demonstrating good agreement and confirming the reliability of the obtained models. Thus, the models are quite reliable especially for predicting FAG content and PEY of gelatin obtained from red tilapia scales. Besides, the CPC model can be considered acceptable despite its low fit.

### 3.4. Physico-chemical characterization

#### 3.4.1. SDS-PAGE profile

To observe the presence of gelatin components under different hydrothermal extraction condition, SDS-PAGE profile was obtained. Fig. 4A shows the molecular weight of TSF and gelatin extracts. Overall, the TSF and gelatin extracts present different bands, which consist of the following chains: α, α₁ (~120 kDa) and α₂ (~130 kDa), and their cross-linked β chain (~190 kDa) and γ chain (~330 kDa) (Fig. 4A), belonging to gelatin components as previously reported (Chen et al., 2016; Thuy et al., 2014). Based on the densitometric analysis of the gel, quantitative
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Physico-chemical characterization of gelatin extracts (A) SDS-PAGE (12.5 %) profile of the red tilapia scales flour (TSF) and the ten gelatin extracts from tilapia scales. Numbers define the coded level of FFD. Lane “M” represents a pre-stained molecular weight marker (ref: 26,619, Thermo Scientific™, Vilnius, Lithuania), γ: γ-chain; β: β-chain; α₁: α₁-chain; α₂: α₂-chain; LMWA: low molecular weight aggregates, Opt: optimal gelatin. (B) Amount of molecular weight constituents of gelatin extracts obtained by densitometric analysis of SDS-PAGE gels. (C) α₁/α₂ ratio of gelatin extracts.</figcaption></figure>

data of the gelatin components (α₁, α₂, β and γ chains) and LMWA (<100 kDa) are shown in Fig. 4B Notably, in all the gelatin extracts, the bands intensity of low molecular weight aggregates (i.e., LMWA <100 kDa) increases compared to TSF (Fig. 4B). This band intensity increase is associated to collagen hydrolysis into gelatin upon thermal extraction process, leading to a higher presence of smaller gelatin component due to the cleavage of inter-chain covalent crosslinks (Karim & Bhat, 2009). Moreover, gelatin extracted with higher extraction temperature (85 °C; run 5,6,7,8) present a lower band intensity of the γ, β and α chains than those produced with lower extraction temperature (65 °C; run 1,2,3,4). Similar results were reported in gelatin extraction from splendid squid skin (Sinthusamran et al., 2014).

The major components of gelatin, γ, β and α chains are present in all the gelatin extracts (Fig. 4A and 4B), except in the run 8, where γ and β chains are strongly degraded and the relative amount of LMWA increases notably. These results are in agreement with the OPA quantification since smaller protein fragments production is related to higher

FAG content. With the highest FAG content (Table 1; 0.31 mmol Eq G/g), the run 8 evidences that the highest solvent-to-solid ratio (A: 10 mL/g), extraction time (B: 3 h) and temperature (C: 85 °C) improve the degree of collagen hydrolysis in agreement with literature (Tu et al., 2015; Zhang et al., 2019). Likewise, the use of ultrasounds induces a higher degradation of γ, β and α-chains of collagen. Indeed, this aggressive treatment increases the hydrolysis of collagen-derived constituents due to the large shear force generated from cavitation phenomenon, breaking inter and intramolecular cross-links between collagen and remaining hydroxyapatite. Similarly to our SDS-PAGE results, Tu et al. (2015) showed by FAG quantification that ultrasounds concomitantly increase small-sized protein production during gelatin extraction. However, according to the experimental design results, this increase does not have any significant effects on studied responses (CPC, FAG, PEY) in the range of investigated factors.

On the other hand, it was demonstrated that functional properties of gelatin can be affected by the molecular weight distribution, notably the
$α_1/α_2$ chain ratio. Based on densitometric analysis of the gel, $α_1/α_2$ chain ratios were estimated (Fig. 4C). The $α_1/α_2$ ratio of gelatin extracts obtained by different condition ranges between 0.9–1.7, highlighting that the optimal gelatin, run 1, 8 and the central point (run 9) exhibit the highest values with 1.7, 1.4, 1.3 and 1.3, respectively. These values are similar to those found in other fish processing by-products (cod and hake skin gelatin, $α_1/α_2$ ratio: ~1, Gómez-Guillén et al., 2010), and closer to the characteristics of type I collagen ($α_1/α_2$ ratio: ~2). In relation to $α_1/α_2$ ratio, these four gelatin extracts (run 1, 8, 9 and optimal run) obtained with different extraction conditions may have some interesting dynamic viscoelastic properties, which play an important role in gelatin applications, thus these properties were further investigated.

### 3.4.2. Amino acid analysis

The AA profile was assessed only for red tilapia scales gelatin obtained in the optimal condition (10 mL/g, 3 h, 85 °C, 0 min) (Fig. 5), both as individual AA residues content (Fig. 5A) and gathered as class of AA (i.e., hydrophobic, positively charged, negatively charged, aromatic, branched chain and essential AA (Fig. 5B). High levels of glycine, glutamic acid, alanine, proline, and arginine are found, whereas methionine, tyrosine and lysine had the lowest levels (Fig.5A). Optimal gelatin exhibited a high concentration of hydrophobic amino acids, such as alanine, valine, leucine, isoleucine, and proline. These amino acids are known to affect water interactions by promoting intra and intermolecular hydrophobic interactions. At the same time, a significant content of polar and charged amino acids, including glycine, glutamic acid, and arginine, indicated the presence of hydrophilic properties. The AA balance suggests that the obtained gelatin is amphiphilic, with a slight tendency toward hydrophilicity due to the dominance of glycine and polar residues. This composition is favourable for various applications since it supports solubility in aqueous environments as well as functional properties such as gelling and emulsifying capacity.

Similar AA profile was found in type I collagen, the main collagen present in gelatin from fish scales (Nagai et al., 2004). The AA profile of the obtained optimal gelatin is quite similar to the one observed by Zhang et al. (2019) and Weng and Wu (2015) who worked on tilapia scales gelatin from *Oreochromis niloticus* and tilapia *zillii*, respectively. Similarly, Nagai et al. (2004) evidenced those AA as the most abundant ones in collagen obtained from three types of fish scales (sardine, red sea bream, japanese sea bass). Thanks to its high content of essential AA (i. e., tryptophan, lysine, histidine, phenylalanine, leucine, isoleucine, methionine, valine, threonine, Fig. 5B), tilapia scales gelatin shows interesting nutritional applications (Sierra-Lopera & Zapata-Montoya, 2021). Besides, acidic AA, negatively charged AA (glutamic and aspartic acids), hydrophobic AA (proline and alanine) and positively

charged AA (arginine, histidine, lysine) composition (Fig. 5B), widen the applications of tilapia scales gelatin to cosmetic, food and medicine (Zhang et al., 2019).

### 3.4.3. Circular dichroism spectra

The CD analysis is widely used to observe protein secondary structure. The CD signatures (Fig. 6A) of all the gelatin extracts exhibit a similar behavior with a negative peak at 196–200 nm with slight variations in ellipticity values. This suggests that the triple-helix structure of collagen is destroyed upon the gelatin thermal extraction process, and that the protein structure of gelatin extracts is characterized by random coils (Karim & Bhat, 2009). Indeed, the native collagen triple-helix structure is generally observed with a positive peak at 220–230 nm and a negative peak at around 200 nm. Yet, once partial thermal hydrolysis occurs, the positive peak disappears due to the disruption of the covalent crosslink present in collagen. Similar to our study, the CD spectra of commercial cold-water fish gelatin, bovine bone gelatin (Zhang et al., 2020) and Nile tilapia skin gelatin (Chen et al., 2022) exhibited random coils structure, generally found in denatured collagen (Tan & Chang, 2018).

### 3.4.4. Fourier transform infrared spectroscopy

The gelatin extracts obtained in runs 1, 8 and 9, and the optimal run (opt) which exhibited the most interesting properties in terms of molecular weight distribution ($α_1/α_2$ ratio) were characterized using FTIR to determine the detailed molecular changes in the secondary structure. All the investigated gelatin extracts exhibit representative FTIR absorption spectra within the 400–4000 cm⁻¹ region (Fig. 6B). According to the literature, the main absorption bands of native (undenatured) collagen from fish scale are amide A (3427 cm⁻¹), amide B (2937 cm⁻¹), amide I (1651 cm⁻¹), amide II (1545 cm⁻¹) and amide III (1244 cm⁻¹) bands (Shalaby et al., 2019) (Table 4). In our study, FTIR spectra contain the characteristic absorption bands primarily associated with the amide functional groups: amide A (≈ 3300 cm⁻¹), amide B (≈ 2930 cm⁻¹), amide I (≈ 1630 cm⁻¹), amide II (≈ 1520 cm⁻¹) and amide III (≈ 1230 cm⁻¹) (Fig. 6B, Table 4). Compared to the undenatured native collagen from fish scales, all gelatin extracts exhibited shifts to lower wave numbers of the amide functional groups (Table 4). These shifts suggest the collagen denaturation (amide A shift) and the loss of its triple-helix structure (amide I, II and III shift) during the extraction process. Indeed, these results show that the triple helix unwinds into random coils in all the gelatin extracts, as confirmed by CD measurements. Moreover, the amide A band (N–H stretching vibrations) exhibits greater peak broadening in runs 1 and 8 compared to runs 9 and opt (Fig. 6B), suggesting a greater collagen denaturation in runs 1 and 8. Similarly, when

<figure><img src="image_7.png" /><figcaption>Fig. 5. Amino acids composition of red tilapia scale gelatin obtained in optimal conditions (g/100 g of gelatin), (A) Individual AA content, (B) AA gathered as class. Hydrophobic AA (HAA): alanine, valine, isoleucine, tyrosine, phenylalanine, proline, methionine. Positively charged AA (PCAA): histidine, lysine, arginine. Negatively charged AA (NCAA): aspartic acid, glutamic acid. Aromatic AA (AAA): phenylalanine, tryptophan, tyrosine. Branched chain AA (BCAA): leucine, isoleucine, valine. Essential AA (EAA): tryptophan, lysine, histidine, phenylalanine, leucine, isoleucine, methionine, valine, threonine.</figcaption></figure>
<table><caption>**Table 4**
Fourier transform infrared spectra of red tilapia scales gelatin extracts.</caption><thead><tr><th rowspan="2">Sample</th><th colspan="4">Wavenumber (cm<sup>-1</sup>)</th></tr><tr><th>Amide A<br>N-H stretching<br>vibrations</th><th>Amide B<br>CH<sub>2</sub> stretching<br>vibrations</th><th>Amide I<br>C=O stretching of peptide<br>bonds</th><th>Amide II<br>N-H bending and C-N stretching<br>vibrations</th><th>Amide III<br>C-N stretching and N-H in-plane bending</th></tr></thead><tbody><tr><td>Native collagen*</td><td>3427</td><td>2937</td><td>1651</td><td>1545</td><td>1244</td></tr><tr><td>Run 1: 2mL/g, 1 h, 65 °C,<br>60 min</td><td>3279</td><td>2935</td><td>1629</td><td>1516</td><td>1234</td></tr><tr><td>Run 8: 10 mL/g, 3 h, 85 °C,<br>60 min</td><td>3268</td><td>2932</td><td>1632</td><td>1519</td><td>1234</td></tr><tr><td>Run 9: 6 mL/g, 2 h, 75 °C,<br>30 min</td><td>3284</td><td>2931</td><td>1631</td><td>1523</td><td>1235</td></tr><tr><td>Opt: 10 mL/g, 3 h, 85 °C,<br>0 min</td><td>3288</td><td>2937</td><td>1635</td><td>1520</td><td>1234</td></tr></tbody></table>

<figure><img src="image_8.png" /><figcaption>**Fig. 7.** Rheological properties of the most interesting gelatin extracts (Run 1, 8, 9 and optimal run). (A) gelling and (B) melting temperatures; (C) and (D) storage (G') and loss (G") moduli, respectively.</figcaption></figure>

(13.5–19.8 °C) and melting (21.8–27.2 °C) temperatures were obtained at the inflection point of the sharp decrease of the phase angle (Fig. 7A-B).

Gelatin obtained by run 1 (2 mL/g, 1 h, 65 °C, 60 min) and optimal run (10 mL/g, 3 h, 85 °C, 0 min) exhibit the best gelling and melting capacity, showing the highest G' and G'' (Fig. 7C-D) with not statistically significant differences (*p-value* >0.05), followed by run 9 (6 mL/g, 2 h, 75 °C, 30 min) and 8 (10 mL/g, 3 h, 85 °C, 60 min). The viscoelastic properties are notably affected by the physico-chemical properties of gelatin such as molecular weight distribution profile, amount of γ, β components and α₁/α₂ ratio. In fact, α₁-chain interacts better with the other constituent chains of gelatin that form the gel than α₂-chain does (Karim & Bhat, 2009). Sample 1 (2 mL/g, 1 h, 65 °C, 60 min) and optimal sample (Opt: 10 mL/g, 3 h, 85 °C, 0 min) are characterized by the highest α₁/α₂ ratio (Section 3.4.1), which may explain their high gelling and melting temperatures.

The gelling temperatures (run 1, 19.8 °C and optimal run, 18 °C) are in the same range than other studies on gelatin fish skin from cod and

hake (11–13 °C, Gómez-Guillen et al., 2002), grass carp (19.5 °C, Kasankala et al., 2007), tuna (19 °C, Montero & Acosta, 2020), rohu (18.52 °C) and common carp (17.9 °C) (Ninan et al., 2011), chemical (18.03 °C) and fermentation (18.33 °C) gelatins from Nile tilapia skin (Chen et al., 2022). Similarly, the melting temperatures of run 1 (27.2 °C) and optimal run (25.8 °C) were higher than those of squid gelatin (20 °C, Gómez-Guillen et al., 2002) and similar than those of gelatin from tuna skin (25.6 °C, Montero & Acosta, 2020), bighead carp scales (27.7 °C, Tu et al., 2015) and slightly lower than those of gelatin fish skin from Nile tilapia (28.8–29.9 °C, Chen et al., 2022), rohu (29.27 °C) and common carp (28.13 °C, Ninan et al., 2011). Even though there is no statistically significant effect of ultrasound on studied response variables, the SDS-PAGE profile analysis shows noticeable differences regarding the amount of γ, β, α-chains of the different gelatin extract when ultrasound was used, which does influence on the gelling and melting temperatures. The higher content of LMWA, associated to a high degradation of gelatin components (mainly β, α-chains) in run 8 (10 mL/g, 3 h, 85 °C, 60 min), could explain the lowest viscoelastic

<footnote>*FTIR profile of native collagen was obtained from literature data (Shalaby et al., 2019).</footnote>
properties in this gelatin. This is in agreement with Gómez-Guillen et al. (2002), who reported that squid skin gelatin offered low gelling temperature due to the higher amount of LMWA compared to other fish skin gelatins. Hence, extensive degradation of gelatin components must be avoided to obtain gelatin endowed with high viscoelastic properties from red tilapia scale for its further use in food and pharmaceutical applications.

## 4. Conclusion

The red tilapia scales can be valorized through gelatin extraction. To the author's knowledge, the gelatin extraction process from the red tilapia scales has never been optimized to date. The application of the FFD for gelatin extraction process enabled to assess the impact of numerous parameters on responses i.e., crude protein content, free amino group content and protein extraction yield. This approach optimizes results while minimizing the number of experiments required. The crude protein content, high for all extraction experiments (> 95 %), is suitable for protein isolates. During the gelatin extraction from tilapia scales, the solvent-to-solid ratio and the interaction of the solvent-to-solid ratio with temperature have the most significant effect on FAG content and PEY. Two reliable statistical models based on FFD were developed and used for the optimization of these responses. The optimization solution for multiple responses shows that a solvent-to-solid ratio of 10 mL/g, extraction time of 3 h, and temperature of 85 °C without ultrasounds, provide the maximal responses of FAG content and PEY, while crude protein content remained high. More, viscoelastic properties analysis revealed that run 1 (2 mL/g, 1 h, 65 °C, 60 min) and optimal run exhibit (10 mL/g, 3 h, 85 °C, 0 min) the highest gelling and melting capacities, in agreement with SDS-PAGE. Taken together, the optimal gelatin includes both the most optimized response in term of CPC, FAG, PEY, but also exhibits a molecular weight distribution profile that highlights β and α-chains as major components. These former chains provide appropriate viscoelastic properties, making gelatin from red tilapia scales suitable for food and pharmaceutical application or bioactive peptides production due to its content of β, α-chains and LMWA.

As a future research, gelatin extracted from red tilapia scales is a promising substrate for further enzymatic hydrolysis to generate bioactive peptides from agro-industrial by-products. The high concentration of free amino groups (FAG) and the presence of β, α-chains, as well as low-molecular-weight aggregates (LMWA), may enhance the efficiency of hydrolysis by facilitating access of enzymes to internal peptide bonds. Furthermore, evaluating the functional properties of the extracted gelatin in more detail (e.g., gel strength, water-holding capacity, foaming properties and emulsifying ability) could provide a deeper understanding of the potential applications of gelatin from red tilapia scales. This could support the development of specific applications in food and pharmaceutical formulations.

### CRediT authorship contribution statement

**Jairo Andrés Camaño Echavarría:** Writing – original draft, Software, Resources, Formal analysis, Data curation, Conceptualization.
**Amine Nekkaa:** Writing – review & editing, Software, Conceptualization.
**Philippe Arnoux:** Writing – review & editing, Methodology, Conceptualization.
**Christelle Mathé:** Writing – review & editing, Methodology.
**Céline Cakir-Kiefer:** Writing – review & editing, Methodology.
**Loïc Stefan:** Writing – review & editing, Methodology, Data curation.
**Cédric Paris:** Writing – review & editing, Methodology, Data curation.
**José Edgar Zapata Montoya:** Writing – review & editing, Supervision, Project administration, Data curation, Conceptualization.
**Laetitia Canabady-Rochelle:** Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Declaration of competing interest

The authors have no competing interests to declare that are relevant to the content of this article.

### Acknowledgements

The authors acknowledge financial support from the ANR JCJC MELISSA (2020–2024), the "Impact Biomolecules" project of the "Lorraine Université d'Excellence" (Investissements d'avenir – ANR project number 15–004) and the PHC Tassili project (20MDU917). The authors would also like to thank Aurre-Anne Balland, Tatjana Buila and Yhoan Gaviria for their technical help. The authors also thank the PhotoNS platform (L2CM laboratory, Université de Lorraine, Nancy, France) for CD measurements.

### Funding

This research is funded by the ANR JCJC MELISSA (2020–2024), the "Impact Biomolecules" project of the "Lorraine Université d'Excellence" (Investissements d'avenir – ANR project number 15–004) and the PHC Tassili project (20MDU917).

### Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.afres.2025.101128.

### Data availability

Data will be made available on request.

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