Next Article in Journal
Artificial Jellyfish Optimization with Deep-Learning-Driven Decision Support System for Energy Management in Smart Cities
Next Article in Special Issue
Nutritive Profile of Canned Goat Meat Food with Added Carrot
Previous Article in Journal
Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data
Previous Article in Special Issue
Advances in the Characterization of Usnea barbata (L.) Weber ex F.H. Wigg from Călimani Mountains, Romania
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Development and Characterization of a Low-Fat Mayonnaise Salad Dressing Based on Arthrospira platensis Protein Concentrate and Sodium Alginate

Departamento de Ingeniería Química, Ambiental y Alimentos, Universidad de las Américas Puebla, San Andrés Cholula, Puebla 72810, Mexico
Food Analysis Laboratory, Intema S.A. de C.V., 31 Sur 2901, Col. Santa Cruz Los Ángeles, Puebla 72400, Mexico
Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Prague, Czech Republic
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(15), 7456;
Received: 25 June 2022 / Revised: 8 July 2022 / Accepted: 18 July 2022 / Published: 25 July 2022
(This article belongs to the Special Issue Plants, Lichens, Fungi and Algae Ingredients for Nutrition and Health)



Featured Application

This work presents an alternative approach to produce low-fat mayonnaises with good acceptability and using clean label ingredients, useful for developing new formulations in this type of products.


The food industry is constantly reformulating different foods to fulfill the demands of the consumers (natural ingredients and good sensory quality). The present work aimed to produce low-fat mayonnaises using 30.0, 22.5, and 15.0% oil, 1% soy protein isolate (SPI) or spirulina (Arthrospira platensis) protein concentrate (SPC), and 2% sodium alginate. The physical properties (thermal stability, rheological behavior, and particle size), the sensory attributes (appearance, texture, taste, and acceptability), the purchase probability, and amino acid availability (after a simulated digestion) were evaluated. The mayonnaises demonstrated good thermal stability (>90%) using 22.5 and 15% oil, all products showed shear-thinning behavior and a consistency index of 20–66 Pa·s. The reduction of oil from 30 to 15% increased the particle size from 6–9 µm to 10–38 µm. The most acceptable product was the formulated with SPI and 22.5% oil (8.3 of acceptability and 79% of purchase probability). Finally, the addition of proteins improved the total essential amino acids compared to a commercial product (28 and 5 mg/25 g, respectively). In summary, it was possible to obtain well accepted products with high purchase probability using low concentrations of oil and vegetable proteins.

Graphical Abstract

1. Introduction

In recent decades, the demand for healthier foods has risen, boosting the interest of the food industry to reformulate a wide variety of products. This reformulation includes reducing a specific component (such as sugar or fat content) or the use of naturally derived ingredients [1], but without compromising the food safety and the sensory quality. Fulfilling the latter requirements is especially challenging in food products where fat is the primary component, for example, in ice creams, vegetable spreads (i.e., peanut butter), and salad dressings (i.e., mayonnaise), because the fat plays a crucial role as structuring agent, contributing to the development of texture [2].
Mayonnaise is a worldwide-consumed seasoning used in several types of foods, for instance, sandwiches, salads, hamburgers, hot dogs, seafood, and others. The traditional formulation contains approximately 70–80% of oil, vinegar, and spices; however, the consumers usually perceive its high oil content as potentially unhealthy [3]. This conception led to the elaboration of low-fat mayonnaise, which contains 20–40% oil, this has been possible by using thickening agents such as modified starches or gums to develop texture and physical stability in the product [4,5,6]. However, the use of the latter ingredients might have some sensory disadvantages. For instance, the texture or taste of the product could be negatively affected [7,8], and some of these ingredients (modified starches) could be perceived as potentially harmful [9].
One alternative to overcome the negative texture impact of reducing the fat content in mayonnaises is the use of Emulsion-Filled Gels (EFG). The EFGs are gel matrices in which emulsified oil droplets replace the water phase of the gel. These systems are stable against creaming, flocculation, and coalescence, have a gel-like behavior, and they can be easily produced with a wide range of biopolymers such as gums (pectin, tragacanth gum, gellan gum, alginates, carrageenan) and proteins [10,11]. To date, the application of EFGs in mayonnaise has been demonstrated to mimic the sensory properties (texture and mouthfeel) of the regular product using egg yolk (as emulsifier), tragacanth gum, and sodium alginate (as a thickening and gelling agents) [12,13], fulfilling the actual demands of the consumers (low-fat and naturally derived ingredients).
There are many sources for obtaining natural derived biopolymers, for instance, cereals, tubers, oilseeds, and algae. These latter are comprised by macroalgae and microalgae, where the first one is a common source of sodium alginate; this biopolymer is used as thickening and gelling agent, and one of the most important characteristics of this compound is the irreversible gelation when is combined with ionic salts, for example, calcium chloride [14]. In the case of microalgae, the Arthrospira platensis, commonly known as spirulina, is nowadays an important raw material to extract high valuable ingredients, because it contains unsaturated fatty acids (omega 3), pigments (phycocyanin), and a high concentration of proteins (40–50%) [15,16] with promising applications due to its emulsion capacity (approximately 60%) and stability (ranging from 80–100%) [17,18,19,20].
In our previous work [21], it was observed that the consumers of mayonnaise (in Mexico) perceived the proteins as natural ingredients and have good acceptability, hence, it was decided to produce mayonnaises using egg yolk, vegetable proteins, and sodium alginate. Therefore, the present study aimed to exploit the techno-functionality (i.e., emulsifying properties and water and oil holding capacities) of the A. platensis protein in combination with sodium alginate to produce low-fat mayonnaises (using an EFG approach) with comparable physical and sensory characteristics than a commercial low-fat mayonnaise.

2. Materials and Methods

Microalgae (A. platensis) was purchased from Galtec Algae Technologies (Guadalupe, Mexico). Soy protein isolate containing 91.9% w/w of protein was purchased from Food Technologist Trading S.A de C.V. (Atizapan de Zaragoza, Mexico). Soybean oil, salt, sugar, vinegar, and low-fat commercial mayonnaise (41% of total fat) used as reference were purchased from local groceries in Puebla, Mexico. Sodium alginate and calcium chloride were obtained from Sigma-Aldrich Co. (St. Louis, MO, USA) and J.T Baker (Phillipsburg, NJ, USA), respectively. All reagents used for the analyses (amino acid mix, o-phthaldehyde (OPA) and 9-fluorenylmethylchloroformate (FMOC)) were analytical grade obtained from Sigma-Aldrich Co. (St. Louis, MO, USA).

2.1. Spirulina Conditioning and Protein Extraction

The biomass (A. platensis) was freeze-dried, grind, and sieved using a 300 µm mesh. The freeze-dried spirulina was defatted twice with n-hexane at room temperature (for 30 min), at a ratio of 1:5 (powder: solvent w/v), then the n-hexane was decanted, and the remaining solvent was allowed to evaporate overnight at room temperature.
To obtain the Spirulina Protein Concentrate (SPC), the defatted biomass was subjected to alkaline solubilization for the protein extraction at pH 12 using a NaOH solution at 0.05 N in a ratio of 1:10 (w/v) for 1 h (at 25 °C). Afterwards, the sample was centrifuged (4500 rpm, 4 °C, 10 min), and the pH of the supernatant was adjusted to 3.5 to precipitate the proteins. The precipitate was collected by centrifugation (at the same conditions), rinsed with distilled water, and centrifuged again [17]. The protein concentrates were freeze-dried at −50 °C for approximately 48 h and stored in sealed flasks.

2.2. Protein Concentrates Characterization

2.2.1. Protein Content

The protein content was determined spectrophotometrically using the Total Protein Kit, Micro Lowry, Peterson’s Modification (Sigma-Aldrich Co., St. Louis, MO, USA). This method was selected to avoid the interaction of the polyphenolic compounds of the spirulina. First, 500 μL of solubilized protein (at pH 13 to promote full solubilization) was mixed with 50 μL a deoxycholate solution (0.15%) and followed by 50 μL of trichloroacetic acid (72%), the sample was allowed to stand for 10 min after the addition of each reagent. These solutions promote the protein precipitation, which was further collected by centrifugation (5000 rpm at 25 °C for 5 min). The protein pellet was dissolved with 500 μL of Lowry’s reagent and 500 μL of water. After 20 min, 250 μL of Folin Ciocalteau reagent was added and allowed to react for 30 min. Finally, 200 µL of the samples were transferred to a 96 wells microplate and the absorbance was measured using a UV-Vis Multiskan Sky Microplate (Thermo Fischer Scientific, Walthman, MA, USA) spectrophotometer at 720 nm. A solution of bovine serum albumin (0–1000 µg/mL) was used as standard following the same procedure. The protein content was calculated using Equation (1) and expressed as a percentage (%).
Protein   content   ( % ) = P s S w × 100 ,
where PS is the total protein quantified in the sample and SW is the initial sample weight.

2.2.2. Water Holding Capacity (WHC) and Oil Holding Capacity (OHC)

Water and oil holding capacities (WHC, OHC, respectively) were measured by weighing 0.25 g of protein in a 50 mL Falcon tube. Then, 10 mL of distilled water or oil was added, and the sample was stirred using a vortex for 10 s every 5 min (for 30 min). Afterwards, the samples were centrifuged (at 4500 rpm, 4 °C, for 10 min), the supernatants were discarded, and the WHC and OHC were calculated by weight difference as is shown in Equation (2) [22].
WHC   or   OHC = Final   weight Initial   weight Initial   weight ,

2.3. Mayonnaise Preparation

The continuous phase, constituted by an aqueous solution added with water, vinegar, sugar, salt, and protein (soy or spirulina), was homogenized (Ultraturrax (IKA, Staufen, Germany) with the dispersed phase (egg yolk and oil) at 7500 rpm (60 s). A double emulsion was prepared by adding a solution of sodium alginate (2%) to the primary emulsion at a 1:1 ratio. Finally, for gelation of sodium alginate in the secondary emulsion, 1 mL of CaCl2 (0.5 % w/v) was dropped in constant stirring at 7500 rpm [13]. Finally, we obtained nine different formulations of low-fat mayonnaise (Table 1).

2.4. Mayonnaise Characterization

2.4.1. Stability Analysis

The thermal stability of the samples was studied by pouring approximately 20 g of mayonnaise into a sealed Falcon flask (50 mL of capacity) and heating them in a water bath (85 °C) for 30 min. Afterwards, the samples were centrifuged at 4000 rpm for 10 min to induce phase separation and the stability was calculated with Equation (3) [23].
Mayonnaise   stability   ( ES ) = Remaining   emulsified   layer   ( cm ) Initial   emulsified   layer   ( cm ) × 100

2.4.2. Particle Size Analysis

The particle size was measured using dynamic light scattering with a Bluewave Nanotrac Wave II (Microtrac Inc., Montgomeryville, PA, USA) analyzer. All samples were diluted 1:100 (v/v) with distilled water and homogenized with a Vortex at full speed for 15 s. The refractive index was set at 1.47 for oil droplets and 1.33 for water [24], the results were expressed as mean particle size (μm) and the span was calculated using Equation (4).
Span = d 90 d 10 d 50 ,
where d90, d50, and d10 represents the diameter of the 90%, 50%, and 10% of all particles measured.

2.4.3. Rheological Characterization

The viscosity was determined at 25 °C using a Brookfield Viscometer DV II (AMETEK Inc., Devon-Berwyn, PA, USA) equipped with an LV-3 spindle at 0.25, 0.5, 1, 2, 5, 10 and 16.67 s−1 for 45 s. The data were fitted to the power law model [25], this mathematical model allows a simple and easy interpretation of the flow behavior of a non-Newtonian fluid. This process was done using Microsoft Office Excel 365 solver to obtain the consistency index (K) and power law index (n) using the following expression.
σ = K γ n ,
afterwards, the loss of consistency index (ΔK) was calculated using Equation (6) to assess the effect of oil concentration and the vegetable proteins in the loss of viscosity of the systems.
Δ K = | K 30 K i | ,
where K30 is the consistency index of the sample with 30% oil and Ki is the consistency index of the sample.

2.4.4. Sensory Analysis

A total of 50 non-trained panelists were recruited in “Universidad De Las Américas Puebla”. The samples were spread on a wheat cookie and labeled using three random digits. The sensory evaluation of the mayonnaises was studied using a structured scale from 1 to 10 (1 = lowest score and 10 = highest score) for assessing the general appearance, taste, texture, and acceptability. Furthermore, the panelists were asked for their purchase intention (possible answers: Yes/No) for each sample after its evaluation; the results were expressed as percentage (%) [26].

2.4.5. Amino Acid Bioavailability

To investigate the nutritional enhancement of proteins (bioavailability of amino acids) in the low-fat mayonnaise, the products with the best purchase intention with one of each vegetable protein (SPI and SPC) were selected for an in vitro gastric digestion simulation (GDS). First, artificial saliva (AS) and gastric juices (GJ) were prepared to mimic the buccal and gastric environment. The AS was prepared by solubilization of α-amylase (0.2 mg/mL) in phosphate buffer at 20 mM (pH 7) and GJ consisted of pepsin enzyme in HCL solution (0.1 M) at 3.2 mg/mL. The digestion in the mouth was simulated by homogenizing mayonnaise (25 g) and 10 mL of AS using a magnetic stirrer at 150 rpm for 3 min (at 37 °C). Afterward, the gastric digestion was simulated by adjusting the pH to 2.0 using concentrated HCl (4 M) and adding 15 mL of GJ using the same stirring conditions as above; this digestion was done for 60 min. The conditions of the experiment were selected according to the study of Rui et al. [27], which selected the appropriate food to digestive juices ratios based on the human physiology (food to AS = 2.5:1; food to GJ = 1.5:1) [28].
The sample collection was done after the buccal digestion (time 1) and gastric digestion (time 2), whereas the fresh sample was considered as time 0. For each sample, the amino acid profile was determined (time 0, 1, and 2) with the Agilent amino acid analysis protocol [29] in an Agilent 1290 Infinity II (Agilent Technologies Inc., Santa Clara, CA, USA) HPLC coupled with a diode array detector (DAD). The column used was a ZORBAX Eclipse Plus C18 (Agilent Technologies Inc., Santa Clara, CA, USA), the mobile phase A was a 10 mM of Na2HPO4:10 mM Na2B4O7 (pH 8.4) buffer, and the mobile phase B was a solution of acetonitrile: methanol: water (45:45:10). The column temperature was at 40 °C, and the flow rate was 1.5 mL/min. The total time of the assay was 16 min, with 98% of mobile phase A (at 0.35 min), 100% of mobile phase B at 13.5 min, and 98% of mobile phase A used at 16 min. Amino acids (primary and secondary) were derivatized using o-phthalaldehyde (OPA) and 9-fluorenylmethyl chloroformate (FMOC). The amino acids detection was done at 263 and 338 nm and quantified using norvaline (internal standard) and a calibration curve (amino acid mix).

2.5. Statistical Analysis

All experiments were done in triplicate (n = 3). The data was expressed as mean with standard deviation, median with interquartile ranges, and percentage where appropriate. The statistical differences were analyzed using ANOVA (p < 0.05). Afterward, a pairwise t-test was used to determine the sample’s mean differences (p < 0.05). The relationship between mayonnaise properties and the sensory scores was determined by a correlation coefficient analysis and expressed as Correlation Index (CI). All statistical analyses were done in Microsoft Office Excel 365 using the open-source Real Statistics Resource Pack [30]. Artwork (Figures) was processed in Python 3.9 using Matplotlib (v. 3.4.2) library in Spyder (v. 5.1.1) IDE.

3. Results and Discussion

3.1. Protein Concentrates Characterization and Techno-Functional Properties

The content of protein (dry basis) found in SPC was 66.1 ± 1.5%, this concentration was slightly lower to others reports on A. platensis protein concentrates (69–75%) [19,31]. The difference in protein concentration may be due to the biomass growth conditions or the protein extraction process, in this regard, the use of pretreatments (sonication or high pressures) for cell disruption as used in [19,32] and [33] could increase the protein concentration up to 73–85% in the concentrates.
The WHC and OHC properties of SPC were 0.49 ± 0.029 g/g and 3.25 ± 0.218 g/g, respectively. The WHC observed for the SPC was very lower than previous reports (3–5 g/g) [17,20] but similar than the values found by Bleakley and Hayer [32]. In the case of the OHC, it was slightly higher than the reported by Benelhadj et al. (2.5 g/g) [17] but lower than the OHC of the SPC in [20,32], these studies demonstrated around 6–8 g/g, this enhanced OHC could be attributed to a partial unfolding of the protein structure due to the sonication pretreatment for the protein extraction.
For soy protein (control) the WHC (18.77 ± 1.091 g/g) and OHC (1.48 ± 0.126 g/g) were significantly higher and lower (p < 0.05), respectively, compared to the results of SPC. In related works [34,35,36], the OHC of soy protein (1.0–5.5 g/g) agrees with the results of this study. In contrast, the WHC determined herein was higher than previous studies (3.5–6.9 g/g) [35,36,37]. The difference in WHC may be due to the processing of the protein or drying methods.

3.2. Mayonnaise Characterization

3.2.1. Stability Analysis

The physical stability of the mayonnaise is an important quality attribute, but also thermal stability for applications in hot dishes such as hamburgers, hot dogs, and grilled sandwiches, among others. The low-fat mayonnaises showed excellent physical stability (100% of stability and any syneresis was observed) during storage (30 days) at 4 °C. This result was consistent with previous reports of similar systems; for example, low-fat mayonnaises developed with A. platensis and Dunaliella proteins and starch showed a low reduction of G’ and G’’ during 60 days of storage, which was indicative of a physically stable network [38].
In the case of the thermal stability (Figure 1), this property was negatively affected by the increase in the oil concentration (p < 0.05), for instance, the stability was in the range of 81–90% at 30% of oil, where the highest value was found in D’, E’, and F’ systems. The mayonnaise formulated with 15% oil showed similar stability and presented the best stability (>94%) compared to all systems elaborated. Finally, as was expected, the commercial product did not show instability against high temperature (due to the high content of stabilizers), and it was significantly higher (p < 0.05) than all our samples.
Mayonnaises are physically stable emulsions due to the high viscosity of the system; however, the stability could be affected by ingredient interactions (proteins and polysaccharides). For instance, pea protein used in the low-fat mayonnaises decreased the product’s thermal stability (62%) [39]. In contrast, using starches or cereals flour has enhanced thermal stability (~99%) [7,40]. The products’ stability in this study agrees with gelled emulsions with sodium alginate (previously reported), which demonstrated ~95% stability at pH 2 and 4 [41]. Moreover, Yang et al. [42] and Li et al. [12] developed very similar mayonnaises to the present study (containing sodium alginate) with good thermal stability due to minor microstructural changes (observed by Confocal Laser Scanning Microscopy). However, these results cannot be compared with those obtained in the present work. A possible synergistic mechanism among the polysaccharides and proteins in our products probably helped produce systems with good thermal stability. In this regard, sodium alginate could increase the stability due to the irreversible gelation of the continuous phase, preventing the mobility of oil droplets [13,43]. Meanwhile, the WHC and the OHC could favor the interactions between the protein hydrophilic residues with the hydrogel and the hydrophobic protein surface with the oil.

3.2.2. Particle Size Analysis

The particle size in the samples increased when the oil concentration decreased and was significantly higher than the commercial product (p < 0.05) (Figure 2). All samples with 30% oil (A, D, and D’) demonstrated a mean particle size in a range of 6–9 μm; however, the presence of protein showed a slight reduction in particle size (6–7 μm). In samples with lower oil content (22.5%), there was an increase in the particle size for A, B, C, D, E, and F (~10 μm), while the systems with SPC (D’, E,’ and F’) showed the twice size compared to the samples with 30% oil. In the case of the mayonnaises with the lowest content of oil (15%), D’, E,’ and F’ showed smaller particle sizes (12 μm) in comparison to the rest of the samples where the particles increased up to 30 μm. Two possible mechanisms for this increase in particle size could occur. First, A, B, and C behavior could be due to the absence of a second emulsifier to help reduce the interfacial tension and reach smaller droplet sizes in emulsions [44]. In the case of D, E, and F, the SPI could contribute to a larger droplet size because the mayonnaise pH is close to this protein’s isoelectric point (4–4.5). Hence, when the net charge is near or equal to zero (at the isoelectric point of the protein) leads to poor emulsifying activity and the generation of larger droplet size [45].
Our products showed larger particle sizes than related studies of low-fat mayonnaises. For example, Sun et al. [46] developed mayonnaises with microparticulate Whey Protein Isolate (M-WPI), and the systems had approximately 2 µm in diameter. The difference in the particle size could be because the M-WPI have a tiny particle size (~8 µm), contributing to reducing the lipid droplets size in their products [46]. Nevertheless, the particle size determined in the samples with more than 22.5% oil showed comparable results to a previous report of low-fat mayonnaise (2–12 µm) elaborated with modified starches [7].
The span measurement indicates the width of the particle size distribution. Higher span values indicate more heterogeneous particle sizes within the system. The lowest span values (Figure 2) were found for the commercial low-fat mayonnaise and C (~0.9). In contrast, the rest of the samples showed values of 1.1–1.6, and the highest span (span = 2.7) was recorded in F’. The particle size distribution (Figure 3) in commercial mayonnaise and the samples with 22.5 and 30% oil (A, B, D, E, D’, and E’) was monomodal; on the contrary, the mayonnaises with 15% oil content (C, F, and F’) showed a bimodal or multimodal distribution. Usually, systems with high viscosity and homogeneous particle size distribution (monomodal) have been related to better stability. In contrast, multimodal distributions are usually observed in low viscosity and unstable systems [47]. In our low-fat mayonnaises, only the latter behavior was detected (higher viscosity for monomodal systems), and even though the particle size distribution (multimodal) could induce instability, the presence of the sodium alginate probably helped to prevent the oil droplet movement and their further coalescence in the products [10].

3.2.3. Rheological Characterization

Regarding the rheological behavior, the low-fat mayonnaises fit well with the power law model (r2 = 0.998–0.999 and RMSE = 0.284–1.442). All samples showed a power law index in a range of 0.28–0.33 (Table 2), indicating a shear-thinning fluid (pseudoplastic fluid) behavior (power law index < 1). The results herein agree with previous reports of low-fat mayonnaises where this parameter was 0.28–0.52 [25,48].
On the other hand, the consistency index (K) represents the viscosity of the sample; the highest K values were found in samples with 30% oil (66–35 Pa·sn), while the minimum values (20–35 Pa·sn) were determined in the mayonnaises elaborated containing 15% oil (Table 2). Uribe-Wandurraga et al. [38] prepared low-fat mayonnaises (30% oil) using Chlorella, Spirulina, and Dunaliella microalgae, demonstrating higher K (74–94.5 Pa·sn) than those found in this work, probably because these products also contained 4% starch, which increased the viscosity of the product. However, the K values of our samples agree with previous reports of low-fat mayonnaises (K = 31–49 Pa·sn) elaborated with different gums such as xanthan gum, guar gum, and corn-dextran [25,48]. Like the behavior observed herein, Park et al. [8] also determined that the consistency index of low-fat mayonnaise can reduce from 90–51 and 87–23 Pa·sn when the oil content is reduced from 54 to 38%. This could be explained by the role of oil particle-particle interactions in developing the viscosity of mayonnaise [49]. Hence, if the oil content is low, the number of oil particle-particle interactions will be reduced, and the system’s viscosity will be lower.
It was found that the use of vegetable proteins, the WHC, and the OHC showed a negative correlation (CI = −0.936, −0.732, and −0.571, respectively) with the ΔK (loss of consistency index), indicating that proteins helped to mitigate the loss of viscosity. For instance, the reduction of oil content (from 30 to 15%) in samples with SPI (D, E, and F) and SPC (D’, E,’ and F’) results in minor losses of consistency index (ΔK = 3–15 Pa·sn) in comparison to samples containing only sodium alginate (ΔK = 31–45 Pa·sn). This was probably because the techno-functional properties of proteins might increase the number and strength of inter and intramolecular interactions [50,51].
The viscosity showed to be positively affected by the oil content (p < 0.05); as can be seen in Figure 4, the viscosity of the systems showed to reduce at higher shear stress; moreover, the viscosity at zero shear rate tends to reduce with reduced oil concentrations. The highest viscosities at zero shear rate were obtained in samples A (175 Pa·s) and D’ (125 Pa·s), while the lowest values were found in the D (~100 Pa·s). In summary, the samples A, D’ and E’ were demonstrated to have similar rheological characteristics (consistency index, viscosity, and power law index) to the commercial product.

3.2.4. Sensory Analysis

The results obtained from the sensory evaluation of the low-fat mayonnaises are shown in Figure 5; in addition, the relationship between the sensory scores and the products’ characteristics (oil concentration, presence of protein, and viscosity) was determined using a correlation matrix (Figure 6). It is noteworthy that sample A was not included in the correlation matrix because it was detected as an outlier for the correlation between K with the taste score and the acceptability of the product (the full correlation matrix can be consulted in Figure S1).
The appearance of all mayonnaises containing SPC (D’, E,’ and F’) was significantly lower (p < 0.05) in comparison to the rest of the samples, probably due to the green color of the SPC (Figure 7); therefore, proteins with creamy-like color such as from amaranth, oat, or rice could be considered for further reformulations. The rest of the products demonstrated identical evaluations (p > 0.05) compared to the commercial product. This sensory attribute was demonstrated to positively correlate with the oil concentration, the WHC, and the consistency index (+0.29, +0.49, and +0.47, respectively). This result probably because these factors enhance the thickness of the system and mimic the appearance of the mayonnaise. In contrast, the presence of proteins had a negative correlation (CI = −0.46) with the appearance, and the same trend was found for the OHC (CI = −0.84). In this regard, the negative effect of proteins and the OHC could result from the low scoring in the mayonnaises containing SPC.
The texture of the samples B, D, D’, E, E’, and F’ demonstrated similar scoring to the commercial product (p > 0.05). Conversely, the A, C, and F scored significantly lower than the commercial mayonnaise (p < 0.05). The main contributor to desired texture was the oil concentration (CI = +0.58), followed by the OHC of proteins (CI = +0.15). The K was negatively correlated with this sensory attribute (CI = −0.42), suggesting that even if the hydrogel formed provided similar viscosity to a commercial low-fat mayonnaise, using only sodium alginate did not mimic the lubricity and the smooth mouthfeel expected for this product [52]. However, the techno-functionality of the proteins could develop these textural properties [53].
All samples containing SPC (D’, E’, and F’) generally showed an acceptability score higher than 7. However, these values were significantly lower than the commercial mayonnaise (p < 0.05).
One of the decisive factors was the product’s appearance; nevertheless, the panelists commented that these samples with SPC could be directed toward producing flavored mayonnaises, such as jalapeño, coriander, and avocado taste. The other samples showed similar acceptability with the commercial mayonnaise (p > 0.05), except for the A sample. The most correlated characteristic with the acceptability was the appearance and the taste scores (CI = +0.78 and +0.75, respectively). Additionally, WHC and the consistency index (K) (CI = +0.49–+0.63, respectively) contributed to the acceptability mainly because they enhanced the products’ appearance and taste. In addition, the texture score showed an inverse relation with the acceptability; this could suggest that the consumers are more interested in the product’s appearance and taste than the texture.
The sensory evaluation of our products demonstrated better results compared to similar formulations. For example, Yang et al. [42] developed mayonnaises with an emulsion-filled gel approach using sodium alginate and 30% oil; these products showed acceptability around 4.9–6.9. On the other hand, comparable acceptability values (7–8.5) in microparticulate low-fat mayonnaises (20–40% oil reduction) using whey protein isolate are reported by [46].
Finally, the E, C, D, and the D’ showed similar or higher purchase (>65%) intention in comparison to the commercial low-fat mayonnaise (68%), indicating that these selected products have promising opportunities in the market (Figure 8). In contrast, the rest of the samples showed a lower probability of being purchased by the consumers.

3.2.5. Amino Acid Bioavailability

For evaluating the essential amino acids (EAA) provided by the proteins used, it was decided to study the bioavailability of EAA in the sample with higher purchase intention containing soy protein (sample E) and SPC (sample D’). Furthermore, these were compared to the EAA bioavailability in the commercial product (Table 3). Control samples (not subjected to digestion) type E (soy) contained all essential amino acids; in the case of sample D’ (spirulina), only His was not detected. The bioavailability profile showed a nutritional improvement compared to commercial samples, which lacked four essential amino acids (His, Phe, Ile, and Lys). The total EAA (∑ EAA) in low-fat mayonnaises showed around 21 mg/25 g, which overcomes (p < 0.05) to the ∑ EAA of commercial products (14.645 ± 0.465 mg/25 g).
After the mouth digestion (time 1), there was an increase in the concentration of Val, Trp, Phe, Ile, and Lys in the D’ sample. Similarly, sample E slightly increased Trp, Phe, Ile, and Leu, but the His was not detected in this sample. Probably the amylase and environment conditions (pH) promoted hydrogel hydrolysis (artificial saliva) formed by sodium alginate gum, releasing the protein and thus increasing its availability [27,54]. In this step, the highest ∑ EAA found was in D’ (25.132 ± 2.181 mg/25 g), followed by E (19.684 ± 1.929 mg/25 g), remaining the lowest in the commercial low-fat mayonnaise (15.352 ± 0.601 mg/25 g).
After gastric digestion (time 2), all EAA increased, standing out samples E and D’ (made with plant proteins). Only three EAA (Thr, Val, Trp) remained in commercial samples, while E and D’ samples were complete (except for His). The ∑ EAA of the commercial product (5.562 ± 0.966 mg/25 g) was significantly lower (p < 0.05) in comparison to our low-fat mayonnaises (∑ EAA = 28–29 mg/25 g). The increase of EAAs after gastric digestion could be explained by the disruption of the peptide bonds induced by the pepsin activity (proteolytic), provoking the release of a higher amount of EAAs from the low-fat mayonnaise [55,56].
This digestion phase is essential because es followed by the ileal digestion and absorption of amino acids; hence, our products could provide a higher amount of available EAAs. Even though the concentrations of EAAs in the mayonnaises did not fulfill the FAO requirements [57], the formulations proposed herein could slightly contribute to EAA intake in the diet with approximately one tablespoon.

4. Conclusions

The present study demonstrated that the emulsion-filled gels could be used to formulate low-fat mayonnaises. These products showed good physical stability, similar rheological characteristics to commercial products, and an increase in bioavailability of essential amino acid content (after buccal and gastric digestion). Moreover, it was possible to obtain well-accepted products with vegetable proteins and low oil concentration. Specifically, the purchase intention demonstrated that new products could be developed with the following formulations: soy protein isolate with 22.5–30% oil or A. platensis protein concentrate with 30% oil. Further research could be conducted on using different proteins with high WHC and creamy-like color. These could enhance the taste and appearance, which were the most relevant attributes for the final acceptability of the product.

Supplementary Materials

The following supporting information can be downloaded at: Figure S1. Correlation matrix of all low-fat mayonnaise (including sample A) characteristics and sensory attributes. Table S1. Amino acid profile of low-fat mayonnaises.

Author Contributions

Conceptualization, D.B.-A. and J.M.-O.; methodology, D.B.-A., J.M.-O. and L.R.-O.; validation, D.B.-A. and M.R.-R.; formal analysis, D.B.-A. and M.R.-R.; investigation, J.M.-O. and L.R.-O.; resources, D.B.-A. and M.R.-R.; data curation, D.B.-A. and J.M.-O.; writing—original draft preparation, J.M.-O. and D.B.-A.; writing—review and editing, D.B.-A. and M.R.-R.; visualization, J.M.-O.; supervision, D.B.-A. and M.R.-R.; project administration, D.B.-A.; funding acquisition, D.B.-A. All authors have read and agreed to the published version of the manuscript.


This research and the APC was funded by “Consejo Nacional de Ciencia y Tecnología” CONACyT, grant number FOINS 4950.

Institutional Review Board Statement

The Research and Ethics Committee from Health Sciences Department in the “Universidad de Las Americas Puebla” approved the protocol for the sensory evaluation conducted in this work on 4 May 2021 (document number P-001).

Informed Consent Statement

Informed consent was obtained from all participants at the beginning of the sensory evaluation.

Data Availability Statement

Not applicable.


The authors acknowledge to “Universidad De Las Americas Puebla (UDLAP)” and “Consejo Nacional de Ciencia y Tecnología (CONACyT)” for the scholarships granted to J. Metri Ojeda and the access to laboratories, as well as the funds granted to the project FOINS 4950.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Maruyama, S.; Streletskaya, N.A.; Lim, J. Clean label: Why this ingredient but not that one? Food Qual. Prefer. 2020, 87, 104062. [Google Scholar] [CrossRef]
  2. Taslikh, M.; Mollakhalili-Meybodi, N.; Alizadeh, A.M.; Mousavi, M.-M.; Nayebzadeh, K.; Mortazavian, A.M. Mayonnaise main ingredients influence on its structure as an emulsion. J. Food Sci. Technol. 2021, 59, 2108–2116. [Google Scholar] [CrossRef]
  3. Mirzanajafi-Zanjani, M.; Yousefi, M.; Ehsani, A. Challenges and approaches for production of a healthy and functional mayonnaise sauce. Food Sci. Nutr. 2019, 7, 2471–2484. [Google Scholar] [CrossRef]
  4. Teklehaimanot, W.H.; Duodu, K.G.; Emmambux, M.N. Maize and teff starches modified with stearic acid as potential fat replacer in low calorie mayonnaise-type emulsions. Starch-Stärke 2013, 65, 773–781. [Google Scholar] [CrossRef][Green Version]
  5. Agyei-Amponsah, J.; Macakova, L.; DeKock, H.L.; Emmambux, M.N. Effect of Substituting Sunflower Oil with Starch-Based Fat Replacers on Sensory Profile, Tribology, and Rheology of Reduced-Fat Mayonnaise-Type Emulsions. Starch-Stärke 2021, 73, 2000092. [Google Scholar] [CrossRef]
  6. Su, H.; Lien, C.; Lee, T.; Ho, J. Development of Low-fat Mayonnaise Containing Polysaccharide Gums as Functional Ingredi-ents. J. Sci. Food Agric. 2010, 90, 806–812. [Google Scholar] [CrossRef]
  7. Bajaj, R.; Singh, N.; Kaur, A. Properties of octenyl succinic anhydride (OSA) modified starches and their application in low fat mayonnaise. Int. J. Biol. Macromol. 2019, 131, 147–157. [Google Scholar] [CrossRef]
  8. Park, J.J.; Olawuyi, I.; Lee, W.Y. Characteristics of low-fat mayonnaise using different modified arrowroot starches as fat replacer. Int. J. Biol. Macromol. 2020, 153, 215–223. [Google Scholar] [CrossRef]
  9. Varela, P.; Fiszman, S. Exploring consumers’ knowledge and perceptions of hydrocolloids used as food additives and ingredients. Food Hydrocoll. 2013, 30, 477–484. [Google Scholar] [CrossRef]
  10. Farjami, T.; Madadlou, A. An overview on preparation of emulsion-filled gels and emulsion particulate gels. Trends Food Sci. Technol. 2019, 86, 85–94. [Google Scholar] [CrossRef]
  11. Geremias-Andrade, I.M.; Souki, N.P.; Moraes, I.C.; Pinho, S.C. Rheology of Emulsion-Filled Gels Applied to the Development of Food Materials. Gels 2016, 2, 22. [Google Scholar] [CrossRef]
  12. Li, A.; Gong, T.; Hou, Y.; Yang, X.; Guo, Y. Alginate-stabilized thixotropic emulsion gels and their applications in fabrication of low-fat mayonnaise alternatives. Int. J. Biol. Macromol. 2019, 146, 821–831. [Google Scholar] [CrossRef]
  13. Yang, X.; Gong, T.; Lu, Y.-H.; Li, A.; Sun, L.; Guo, Y. Compatibility of sodium alginate and konjac glucomannan and their applications in fabricating low-fat mayonnaise-like emulsion gels. Carbohydr. Polym. 2020, 229, 115468. [Google Scholar] [CrossRef]
  14. Benslima, A.; Sellimi, S.; Hamdi, M.; Nasri, R.; Jridi, M.; Cot, D.; Li, S.; Nasri, M.; Zouari, N. The brown seaweed Cystoseira schiffneri as a source of sodium alginate: Chemical and structural characterization, and antioxidant activities. Food Biosci. 2020, 40, 100873. [Google Scholar] [CrossRef]
  15. Ramírez-Rodrigues, M.; Estrada-Beristain, C.; Metri-Ojeda, J.; Pérez-Alva, A.; Baigts-Allende, D. Spirulina platensis Protein as Sustainable Ingredient for Nutritional Food Products Development. Sustainability 2021, 13, 6849. [Google Scholar] [CrossRef]
  16. Torres-Tiji, Y.; Fields, F.J.; Mayfield, S.P. Microalgae as a future food source. Biotechnol. Adv. 2020, 41, 107536. [Google Scholar] [CrossRef] [PubMed]
  17. Benelhadj, S.; Gharsallaoui, A.; Degraeve, P.; Attia, H.; Ghorbel, D. Effect of pH on the functional properties of Arthrospira (Spirulina) platensis protein isolate. Food Chem. 2016, 194, 1056–1063. [Google Scholar] [CrossRef]
  18. Böcker, L.; Bertsch, P.; Wenner, D.; Teixeira, S.; Bergfreund, J.; Eder, S.; Fischer, P.; Mathys, A. Effect of Arthrospira platensis microalgae protein purification on emulsification mechanism and efficiency. J. Colloid Interface Sci. 2020, 584, 344–353. [Google Scholar] [CrossRef]
  19. Menegotto, A.L.L.; de Souza, L.E.S.; Colla, L.M.; Costa, J.A.V.; Sehn, E.; Bittencourt, P.R.S.; Flores, L.D.M.; Canan, C.; Colla, E. Investigation of techno-functional and physicochemical properties of Spirulina platensis protein concentrate for food enrichment. LWT 2019, 114, 108267. [Google Scholar] [CrossRef]
  20. Yücetepe, A.; Saroğlu, Ö.; Özçelik, B. Response surface optimization of ultrasound-assisted protein extraction from Spirulina platensis: Investigation of the effect of extraction conditions on techno-functional properties of protein concentrates. J. Food Sci. Technol. 2019, 56, 3282–3292. [Google Scholar] [CrossRef] [PubMed]
  21. Ojeda, J.M.; Rodrigues, M.R.; Allende, D.B. Study of the perception and the acceptability of mayonnaise ingredients among Mexican consumers and its global preference. Rev. Española De Nutr. Hum. Y Dietética 2022, 26, 1620. [Google Scholar] [CrossRef]
  22. Stone, A.K.; Avarmenko, N.A.; Warkentin, T.D.; Nickerson, M.T. Functional properties of protein isolates from different pea cultivars. Food Sci. Biotechnol. 2015, 24, 827–833. [Google Scholar] [CrossRef]
  23. Nourbehesht, N.; Shekarchizadeh, H.; Soltanizadeh, N. Investigation of stability, consistency, and oil oxidation of emulsion filled gel prepared by inulin and rice bran oil using ultrasonic radiation. Ultrason. Sonochemistry 2018, 42, 585–593. [Google Scholar] [CrossRef]
  24. Xu, W.; Xiong, Y.; Li, Z.; Luo, D.; Wang, Z.; Sun, Y.; Shah, B.R. Stability, microstructural and rheological properties of complex prebiotic emulsion stabilized by sodium caseinate with inulin and konjac glucomannan. Food Hydrocoll. 2020, 105, 105772. [Google Scholar] [CrossRef]
  25. Kumar, Y.; Roy, S.; Devra, A.; Dhiman, A.; Prabhakar, P.K. Ultrasonication of mayonnaise formulated with xanthan and guar gums: Rheological modeling, effects on optical properties and emulsion stability. LWT 2021, 149, 111632. [Google Scholar] [CrossRef]
  26. Sukkwai, S.; Chonpracha, P.; Kijroongrojana, K.; Prinyawiwatkul, W. Influences of a natural colourant on colour and salty taste perception, liking, emotion and purchase intent: A case of mayonnaise-based dipping sauces. Int. J. Food Sci. Technol. 2017, 52, 2256–2264. [Google Scholar] [CrossRef]
  27. Rui, X.; Zhang, Q.; Huang, J.; Li, W.; Chen, X.; Jiang, M.; Dong, M. Does lactic fermentation influence soy yogurt protein digestibility: A comparative study between soymilk and soy yogurt at different pH. J. Sci. Food Agric. 2018, 99, 861–867. [Google Scholar] [CrossRef]
  28. Shim, S.-M.; Choi, M.-H.; Park, S.-H.; Gu, Y.-U.; Oh, J.-M.; Kim, S.; Kim, H.-Y.; Kim, G.-H.; Lee, Y. Assessing the digestibility of genetically modified soybean: Physiologically based in vitro digestion and fermentation model. Food Res. Int. 2010, 43, 40–45. [Google Scholar] [CrossRef]
  29. Matsushita, K. Automatic Precolumn Derivatization of Amino Acids and Analysis by Fast LC Using the Agilent 1290 Infinity LC System. Agil. Tech. Note 2010, 5990, 1–4. [Google Scholar]
  30. Zaiontz, C. Real Statistics Software. Available online: (accessed on 5 February 2022).
  31. Safi, C.; Ursu, A.V.; Laroche, C.; Zebib, B.; Merah, O.; Pontalier, P.-Y.; Vaca-Garcia, C. Aqueous extraction of proteins from microalgae: Effect of different cell disruption methods. Algal Res. 2014, 3, 61–65. [Google Scholar] [CrossRef][Green Version]
  32. Bleakley, S.; Hayes, M. Functional and Bioactive Properties of Protein Extracts Generated from Spirulina platensis and Isochrysis galbana T-Iso. Appl. Sci. 2021, 11, 3964. [Google Scholar] [CrossRef]
  33. Lozober, H.S.; Okun, Z.; Shpigelman, A. The impact of high-pressure homogenization on thermal gelation of Arthrospira platensis (Spirulina) protein concentrate. Innov. Food Sci. Emerg. Technol. 2021, 74, 102857. [Google Scholar] [CrossRef]
  34. Stone, A.K.; Karalash, A.; Tyler, R.T.; Warkentin, T.D.; Nickerson, M.T. Functional attributes of pea protein isolates prepared using different extraction methods and cultivars. Food Res. Int. 2015, 76, 31–38. [Google Scholar] [CrossRef]
  35. Alfaro-Diaz, A.; Urías-Silvas, J.; Loarca-Piña, G.; Gaytan-Martínez, M.; Prado-Ramirez, R.; Mojica, L. Techno-functional properties of thermally treated black bean protein concentrate generated through ultrafiltration process. LWT 2020, 136, 110296. [Google Scholar] [CrossRef]
  36. Yu, D.; Zhao, Y.; Li, T.; Li, D.; Chen, S.; Wu, N.; Jiang, L.; Wang, L. Effect of electrochemical modification on the structural characteristics and emulsion storage stability of soy protein isolate. Process. Biochem. 2018, 75, 166–172. [Google Scholar] [CrossRef]
  37. Bühler, J.M.; Dekkers, B.L.; Bruins, M.E.; van der Goot, A.J. Modifying Faba Bean Protein Concentrate Using Dry Heat to Increase Water Holding Capacity. Foods 2020, 9, 1077. [Google Scholar] [CrossRef]
  38. Uribe-Wandurraga, Z.N.; Martínez-Sánchez, I.; Savall, C.; García-Segovia, P.; Martínez-Monzó, J. Microalgae fortification of low-fat oil-in-water food emulsions: An evaluation of the physicochemical and rheological properties. J. Food Sci. Technol. 2020, 58, 3701–3711. [Google Scholar] [CrossRef]
  39. Shen, Y.; Babu, K.S.; Amamcharla, J.; Li, Y. Emulsifying properties of pea protein/guar gum conjugates and mayonnaise application. Int. J. Food Sci. Technol. 2022, 57, 3955–3966. [Google Scholar] [CrossRef]
  40. Carcelli, A.; Crisafulli, G.; Carini, E.; Vittadini, E. Can a Physically Modified Corn Flour Be Used as Fat Replacer in a May-onnaise? Eur. Food Res. Technol. 2020, 246, 2493–2503. [Google Scholar] [CrossRef]
  41. Sato, A.C.; Moraes, K.; Cunha, R. Development of gelled emulsions with improved oxidative and pH stability. Food Hydrocoll. 2012, 34, 184–192. [Google Scholar] [CrossRef]
  42. Yang, X.; Li, A.; Yu, W.; Li, X.; Sun, L.; Xue, J.; Guo, Y. Structuring Oil-in-Water Emulsion by Forming Egg Yolk/Alginate Complexes: Their Potential Application in Fabricating Low-Fat Mayonnaise-like Emulsion Gels and Redispersible Solid Emul-sions. Int. J. Biol. Macromol. 2020, 147, 595–606. [Google Scholar] [CrossRef] [PubMed]
  43. Li, J.; Wang, Y.; Jin, W.; Zhou, B.; Li, B. Application of micronized konjac gel for fat analogue in mayonnaise. Food Hydrocoll. 2014, 35, 375–382. [Google Scholar] [CrossRef]
  44. Chen, H.; Mao, L.; Hou, Z.; Yuan, F.; Gao, Y. Roles of additional emulsifiers in the structures of emulsion gels and stability of vitamin E. Food Hydrocoll. 2020, 99, 105372. [Google Scholar] [CrossRef]
  45. Zhong, Y.; Xiang, X.; Wang, X.; Zhang, Y.; Hu, M.; Chen, T.; Liu, C. Fabrication and characterization of oil-in-water emulsions stabilized by macadamia protein isolate/chitosan hydrochloride composite polymers. Food Hydrocoll. 2020, 103, 105655. [Google Scholar] [CrossRef]
  46. Sun, C.; Liu, R.; Liang, B.; Wu, T.; Sui, W.; Zhang, M. Microparticulated whey protein-pectin complex: A texture-controllable gel for low-fat mayonnaise. Food Res. Int. 2018, 108, 151–160. [Google Scholar] [CrossRef]
  47. Drozłowska, E.; Bartkowiak, A.; Łopusiewicz, Ł. Characterization of Flaxseed Oil Bimodal Emulsions Prepared with Flaxseed Oil Cake Extract Applied as a Natural Emulsifying Agent. Polymers 2020, 12, 2207. [Google Scholar] [CrossRef]
  48. Schädle, C.N.; Bader-Mittermaier, S.; Sanahuja, S. Characterization of Reduced-Fat Mayonnaise and Comparison of Sensory Perception, Rheological, Tribological, and Textural Analyses. Foods 2022, 11, 806. [Google Scholar] [CrossRef]
  49. Katsaros, G.; Tsoukala, M.; Giannoglou, M.; Taoukis, P. Effect of storage on the rheological and viscoelastic properties of mayonnaise emulsions of different oil droplet size. Heliyon 2020, 6, e05788. [Google Scholar] [CrossRef]
  50. León, O.; Soto, D.; López, D.; Muñoz-Bonilla, A.; Fernández-García, M. Fat-Replacer Properties of Oxidized Cassava Starch Using Hydrogen Peroxide/Sodium Bicarbonate Redox System in Mayonnaise Formulation and Its Stability. Starch-Stärke 2019, 71, 1900112. [Google Scholar] [CrossRef]
  51. Chen, B.; Cai, Y.; Liu, T.; Huang, L.; Deng, X.; Zhao, Q.; Zhao, M. Improvements in physicochemical and emulsifying properties of insoluble soybean fiber by physical-chemical treatments. Food Hydrocoll. 2019, 93, 167–175. [Google Scholar] [CrossRef]
  52. Du, M.; Lu, W.; Zhang, Y.; Mata, A.; Fang, Y. Natural polymer-sourced interpenetrating network hydrogels: Fabrication, properties, mechanism and food applications. Trends Food Sci. Technol. 2021, 116, 342–356. [Google Scholar] [CrossRef]
  53. Wang, Y.; Jiao, A.; Qiu, C.; Liu, Q.; Yang, Y.; Bian, S.; Zeng, F.; Jin, Z. A combined enzymatic and ionic cross-linking strategy for pea protein/sodium alginate double-network hydrogel with excellent mechanical properties and freeze-thaw stability. Food Hydrocoll. 2022, 131, 107737. [Google Scholar] [CrossRef]
  54. Zhu, Y.; Marin, L.; Xiao, Y.; Gillies, E.; Siqueira, W. pH-Sensitive Chitosan Nanoparticles for Salivary Protein Delivery. Nanomaterials 2021, 11, 1028. [Google Scholar] [CrossRef] [PubMed]
  55. Fang, M.; Xiong, S.; Hu, Y.; Yin, T.; You, J. In vitro pepsin digestion of silver carp (Hypophthalmichthys molitrix) surimi gels after cross-linking by Microbial Transglutaminase (MTGase). Food Hydrocoll. 2019, 95, 152–160. [Google Scholar] [CrossRef]
  56. Ketnawa, S.; Ogawa, Y. Evaluation of protein digestibility of fermented soybeans and changes in biochemical characteristics of digested fractions. J. Funct. Foods 2018, 52, 640–647. [Google Scholar] [CrossRef]
  57. World Health Organization. Protein and Amino Acids Requirement in Human Nutrition; WHO Technical Report Series; World Health Organization: Geneva, Switzerland, 2002; p. 935. [Google Scholar]
Figure 1. Thermal stability of low-fat mayonnaises. A, B, C = sodium alginate low fat mayonnaises with 30, 22.5 and 15% oil. D, E, F = low fat mayonnaises with 30, 22.5 and 15% oil and soy protein isolate. D’, E’, F’ = low fat mayonnaises with 30, 22.5% oil and spirulina protein concentrate. Different superscript letters indicate significant difference (α = 0.05).
Figure 1. Thermal stability of low-fat mayonnaises. A, B, C = sodium alginate low fat mayonnaises with 30, 22.5 and 15% oil. D, E, F = low fat mayonnaises with 30, 22.5 and 15% oil and soy protein isolate. D’, E’, F’ = low fat mayonnaises with 30, 22.5% oil and spirulina protein concentrate. Different superscript letters indicate significant difference (α = 0.05).
Applsci 12 07456 g001
Figure 2. Mean particle size (color bars) and span (gray bars) of the low-fat mayonnaises. A, B, C = sodium alginate low fat mayonnaise with 30, 22.5 and 15% oil. D, E, F = low fat mayonnaises with 30, 22.5 and 15% oil and soy protein isolate. D’, E’, F’ = low fat mayonnaises with 30, 22.5 and 15% oil and spirulina protein concentrate. The different superscript letter indicates a significant difference (p < 0.05).
Figure 2. Mean particle size (color bars) and span (gray bars) of the low-fat mayonnaises. A, B, C = sodium alginate low fat mayonnaise with 30, 22.5 and 15% oil. D, E, F = low fat mayonnaises with 30, 22.5 and 15% oil and soy protein isolate. D’, E’, F’ = low fat mayonnaises with 30, 22.5 and 15% oil and spirulina protein concentrate. The different superscript letter indicates a significant difference (p < 0.05).
Applsci 12 07456 g002
Figure 3. Particle size distribution of different mayonnaises. (A) Sodium alginate low-fat mayonnaises (A, B, C = 30, 22.5, 15% oil, respectively). (B) Low-fat mayonnaises with soy protein isolate (D, E, F = 30, 22.5, 15% oil, respectively). (C) Low-fat mayonnaises with spirulina protein concentrate (D’, E’, F’ = 30, 22.5, 15 oil%, respectively). (D) Commercial low-fat mayonnaise.
Figure 3. Particle size distribution of different mayonnaises. (A) Sodium alginate low-fat mayonnaises (A, B, C = 30, 22.5, 15% oil, respectively). (B) Low-fat mayonnaises with soy protein isolate (D, E, F = 30, 22.5, 15% oil, respectively). (C) Low-fat mayonnaises with spirulina protein concentrate (D’, E’, F’ = 30, 22.5, 15 oil%, respectively). (D) Commercial low-fat mayonnaise.
Applsci 12 07456 g003
Figure 4. The viscosity of different low-fat mayonnaises at different strain stress. (A) Sodium alginate low-fat mayonnaises (A, B, C = 30, 22.5, 15% oil, respectively). (B) Low-fat mayonnaises with soy protein isolate (D, E, F = 30, 22.5, 15% oil, respectively). (C) Low-fat mayonnaises with spirulina protein concentrate (D’, E’, F’ = 30, 22.5, 15% oil, respectively). (D) Commercial low-fat mayonnaise.
Figure 4. The viscosity of different low-fat mayonnaises at different strain stress. (A) Sodium alginate low-fat mayonnaises (A, B, C = 30, 22.5, 15% oil, respectively). (B) Low-fat mayonnaises with soy protein isolate (D, E, F = 30, 22.5, 15% oil, respectively). (C) Low-fat mayonnaises with spirulina protein concentrate (D’, E’, F’ = 30, 22.5, 15% oil, respectively). (D) Commercial low-fat mayonnaise.
Applsci 12 07456 g004
Figure 5. Structured scale results for sensory evaluation of low-fat mayonnaises. Dotted lines represent the arithmetic mean, and the solid yellow line indicates the median. Different letter in the same panel indicates a significant difference (p < 0.05).
Figure 5. Structured scale results for sensory evaluation of low-fat mayonnaises. Dotted lines represent the arithmetic mean, and the solid yellow line indicates the median. Different letter in the same panel indicates a significant difference (p < 0.05).
Applsci 12 07456 g005
Figure 6. Correlation matrix of low-fat mayonnaise characteristics and sensory attributes.
Figure 6. Correlation matrix of low-fat mayonnaise characteristics and sensory attributes.
Applsci 12 07456 g006
Figure 7. Low-fat mayonnaises obtained with 30%, 22.5%, and 15% oil (panels (A), (B), and (C), respectively). 1 = A, B, and C samples. 2 = D, E, and F samples. 3 = D’, E’, and F’ samples.
Figure 7. Low-fat mayonnaises obtained with 30%, 22.5%, and 15% oil (panels (A), (B), and (C), respectively). 1 = A, B, and C samples. 2 = D, E, and F samples. 3 = D’, E’, and F’ samples.
Applsci 12 07456 g007
Figure 8. Purchase intention of different low-fat mayonnaise formulations.
Figure 8. Purchase intention of different low-fat mayonnaise formulations.
Applsci 12 07456 g008
Table 1. The concentration of the ingredients in the low-fat mayonnaise.
Table 1. The concentration of the ingredients in the low-fat mayonnaise.
Aqueous Phase
SampleOil (%)Egg Yolk (%)Water (%)Vinegar (%)Sugar (%)Salt (%)Protein (Type; %)Sodium Alginate (%)
D30.010214.521.5SPI; 130
E22.510254.521.5SPI; 134
F15.010294.521.5SPI; 138
D’30.010214.521.5SPC; 130
E’22.510254.521.5SPC; 134
F’15.010294.521.5SPC; 138
SPI = soy protein isolate. SPC = spirulina protein concentrate.
Table 2. Rheological behavior of low-fat mayonnaise samples using different oil concentrations (%).
Table 2. Rheological behavior of low-fat mayonnaise samples using different oil concentrations (%).
Mayonnaise SampleK (Pa·sn)ΔKnRMSEr2
K (consistency index), n (power law index), RMSE (Root Mean Square Error), r2 (Coefficient of Determination).
Table 3. Essential amino acid profile for E, D’ and Commercial low-fat mayonnaises.
Table 3. Essential amino acid profile for E, D’ and Commercial low-fat mayonnaises.
Amino Acid
(mg/25 g Product)
Time 0
HisND1.740 ± 0.068 aND
Thr2.613 ± 0.096 a2.266 ± 0.059 b2.191 ± 0.157 b
Val1.566 ± 0.098 a1.102 ± 0.043 b0.656 ± 0.076 c
Met0.774 ± 0.155 a0.621 ± 0.131 a0.511 ± 0.054 a
Trp8.149 ± 1.478 ab7.533 ± 0.535 b9.278 ± 0.106 a
Phe2.120 ± 0.177 b2.537 ± 0.083 aND
Ile1.142 ± 0.300 a1.059 ± 0.048 aND
Leu3.400 ± 0.091 a2.699 ± 0.187 b2.007 ± 0.069 c
Lys1.729 ± 0.108 a1.506 ± 0.170 aND
∑ EAA21.496 ± 1.203 a21.066 ± 1.328 a14.645 ± 0.464 b
Time 1
Thr2.451 ± 0.162 a2.217 ± 0.212 a1.520 ± 0.033 b
Val1.302 ± 0.071 a1.032 ± 0.077 b0.709 ± 0.004 c
Met0.914 ± 0.149 a0.753 ± 0.024 a0.628 ± 0.005 b
Trp10.624 ± 0.095 a7.731 ± 0.997 b10.966 ± 0.492 a
Phe3.029 ± 0.190 a2.435 ± 0.021 bND
Ile1.331 ± 0.107 a1.186 ± 0.038 aND
Leu3.176 ± 0.478 a2.825 ± 0.463 a1.527 ± 0.064 b
Lys2.303 ± 0.930 a1.501 ± 0.093 aND
∑ EAA25.132 ± 2.181 a19.684 ± 1.929 b15.352 ± 0.601 c
Time 2
Thr3.219 ± 0.164 a3.038 ± 0.131 a2.481 ± 0.499 a
Val1.326 ± 0.141 a1.274 ± 0.115 a0.610 ± 0.166 b
Met0.850 ± 0.146 a0.779 ± 0.024 aND
Trp13.222 ± 0.048 a13.354 ± 1.291 a2.471 ± 0.301 b
Phe3.587 ± 0.630 a3.113 ± 0.352 aND
Ile1.355 ± 0.140 a1.094 ± 0.187 aND
Leu3.714 ± 0.235 a3.324 ± 0.065 aND
Lys1.992 ± 0.191 a2.076 ± 0.105 aND
∑ EAA29.265 ± 1.695 a28.052 ± 2.270 a5.562 ± 0.966 b
ND = Not Detected. E = mayonnaise with soy protein isolate and 22.5% oil. D’ = mayonnaise with spirulina protein concentrate and 30% oil. ∑ EAA = sum of Essential Amino Acids. Different letters in the same row indicates significant difference among samples (p < 0.05).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Metri-Ojeda, J.; Ramírez-Rodrigues, M.; Rosas-Ordoñez, L.; Baigts-Allende, D. Development and Characterization of a Low-Fat Mayonnaise Salad Dressing Based on Arthrospira platensis Protein Concentrate and Sodium Alginate. Appl. Sci. 2022, 12, 7456.

AMA Style

Metri-Ojeda J, Ramírez-Rodrigues M, Rosas-Ordoñez L, Baigts-Allende D. Development and Characterization of a Low-Fat Mayonnaise Salad Dressing Based on Arthrospira platensis Protein Concentrate and Sodium Alginate. Applied Sciences. 2022; 12(15):7456.

Chicago/Turabian Style

Metri-Ojeda, Jorge, Milena Ramírez-Rodrigues, Lizbeth Rosas-Ordoñez, and Diana Baigts-Allende. 2022. "Development and Characterization of a Low-Fat Mayonnaise Salad Dressing Based on Arthrospira platensis Protein Concentrate and Sodium Alginate" Applied Sciences 12, no. 15: 7456.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop