Combining fruit pulp and rice protein agglomerated with collagen to potencialize it as a functional food: particle characterization, pulp formulation and sensory analysis

Abstract

In this study, agglomeration process was applied in concentrated rice protein (RP) powder using hydrolyzed collagen (HC) as binder to improve wetting time and flowability, aiming at its application in the food industry, namely for fruit pulp supplementation. Fruit pulps from acerola (Malpighia emarginata), cashew (Anarcadium occidentale), guava (Psidium guajava), soursop (Annona muricate), passion fruit (Passiflora edulis) and mandarin (Citrus reticulata) replaced in 1–5% (w/w) by RP or RP agglomerated with collagen were evaluated in terms of viscosity/color and sensory attributes. The addition of RP led to changes in the color of the pulps analyzed, resulting in a red and yellowish color. Viscosity analysis showed that the agglomeration process increased RP dispersion as a function of collagen concentration. The percentage of concentrated RP and RP agglomerated with collagen was limited to 1–3% in order to generate acceptance levels higher than 80%, which is similar to the acceptance rate of pulps without any addition (control—NA).

Electronic supplementary material

The online version of this article (10.1007/s13197-020-04892-7) contains supplementary material, which is available to authorized users.

Introduction

Fruit pulp is the most basic food product obtained from fresh fruit processing, in which fruit waste is minimized given its perishability, promoting commercialization to distant regions, as they can be stored cold, ensuring consumption during the off-season, besides maintaining the flavor of fruits and their nutritional value (Silva et al. 2015a; Silva and Abud 2017). The constant need of diversifying the fruit pulp industry has resulted in various researches on different formulations to improve the nutritional quality of this product with protein supplementation, which bring new opportunities (Silva et al. 2019).

Functional foods present physiological and/or metabolic effects capable of helping in the prevention or treatment of diseases, besides their basic nutritional properties (Silva et al. 2017). Under a nutritional point of view, the increase of food rich in vegetal protein, when used in appropriate combinations, can provide adequate amounts of amino acids to maintain a good human health (Day 2013).

In this context, rice proteins (RP) are of great interest, especially in the food industry, as result of their nutritional value (rich in essential amino acids), nutraceutical and hypoallergenic properties, as well as due to their antioxidant, antihypertensive, anticancer and anti-obesity properties (Amagliani et al. 2019; Saunders 1990; Helm and Burks 1996). In addition, better results in important properties such as the protein digestibility and biological value of rice showed potential in comparison with other major cereals (i.e., corn, wheat, and barley) (Amagliani et al. 2017; Juliano 1994). Thus, RP can be an interesting substitute for soy protein and proteins derived from other cereals or even legumes. However, the low solubility of RP is a limiting factor for their application in the food industry, as they are largely composed by glutelin (Amagliani et al. 2019).

In the food industry, specifically, agglomeration is employed in the production of food powders, which can reconstitute rapidly when added in liquids. This process is also used to change the handling properties of powders, such as flowability, appearance and powder formation (Schubert 1987; Dacanal and Menegalli 2010). Agglomeration in fluidized beds is carried out by the atomization of a liquid binder over particles in movement, so that the gas used in the fluidization of particles can also lead to the evaporation of the binder, thus, drying the material (Tardos et al. 1997; Pont et al. 2001; Iveson et al. 2001). The most appropriate combination between the binder and powder to be agglomerated is essential to maintain the nutritional value of foods.

Hydrolyzed collagen (HC) is a product of enzymatic hydrolysis of the collagen tissue in mammals, consisting of a combination of amino acids and bioactive peptides which, when consumed, enable the easy absorption in bloodstream and distribution in the tissues. HC is an edible product used in dietary supplements, being considered safe by regulatory agencies (Walrand et al. 2008), which can be potentially used as a binder in the agglomeration of RP because it promotes the particle size increasing acting as a suitable binder (Andreola et al. 2016). Also, the use of HC solution as binder allows the non-use of binder agents like carbohydrates in the production of a protein powder. Although carbohydrates are commonly used in the agglomeration of food powders, these substances can affect the nutritional value of strictly protein powders. Therefore, the use of HC solution as a binder is interesting since it acts as a binder and can add nutritional value and functional properties to the agglomerated product.

With this in mind, the present study developed different formulations of fruit pulps fortified with concentrated RP in natura or agglomerated with HC and analyzing particle size and color/viscosity/sensory properties of the formulations to evaluate the product’s acceptance.

Material and methods

Selection and characterization of fruit pulps

The physicochemical characterization of the pulps assessed acidity (titration), pH (TECNAL TEC-5 digital pHgameter), total soluble solids (°Brix) (Hanna Instruments HI96801 digital refractometer), total solids (gravimetry) and vitamin C (titrimetry), according to the minimum standards established in the legislation on fruits pulps (Standards of Identity and Quality, PIQ’s) (Brazil 2000) as described by Silva et al. (2015b) and Silva and Abud (2017), following the analytical norms of IAL (2005) and AOAC (2002), except for total sugars, which was determined according to the DNS method at 540 nm (Shimadzu Multipurpose UV-1280 UV–Vis Spectrophotometer) (Miller 1959). The fruits (acerola, cashew, guava, soursop, passion fruit and mandarin) were selected according to their total soluble solids content and acidity, as the combination of sweetness and acidity can better represent the flavor of fruits, with the proportion between total soluble solids (°Brix) and total acidity (g/100 g) constituting the Ratio, as detailed in the results found by Silva et al. (2019). Thermotolerant coliforms at 45 °C were counted using the most probable number method (BAM 2001; Blodgett 2010).

Agglomeration process of concentrated rice protein powder

Commercial concentrated RP powder (Conventional Oryzatein 80, Axiom Foods, USA and generously granted by Gramkow, Brazil) was used as raw material for the agglomeration experiments. An aqueous solution of HC (Gelita, Brazil) at room temperature (± 27 °C) was used as liquid binder.

Experiments were performed in a rotating-pulsed fluidized bed. The fluidized bed chamber has a conical base (0.075 m inlet air diameter and 0.15 m height), above which there is a cylindrical column (0.15 m diameter and 0.60 m height). Adequate air distribution was ensured by using a perforated plate with orifices of 0.001 mm diameter. To promote the fluidizing air pulsation, a rotating disk with an opening (60°) was installed bellow the perforated plate. The fluidizing airflow was supplied by an air blower (WEG, 7.5 HP), which was connected to a frequency inverter (WEG, CFW 08) to regulate the speed of the blower. The airflow rate is calculated by using an orifice plate, measuring the pressure drop across the plate and the pressure upstream of the plate. The air temperature was maintained by an electrical heater, controlled by a PID controller (Novus, N1200) and monitored by thermoresistances (Novus, Pt-100). A peristaltic pump (Cole Parmer, Master-flex L/S) was used to deliver the liquid binder to a bi-fluid spray nozzle (Spraying Systems, SU12A). Compressed air provided by a compressor (1.1 kW, Schulz) was used to perform the binder atomization. More details of the equipment are described by Andreola et al. (2016) and by Nascimento et al. (2019).

The concentrated protein powder was submitted to agglomeration process using HC as a binder. The agglomeration process consisted in the atomization of the hydrolyzed solution over the particle bed of concentrated RP, which remained in constant movement due to the passage of hot air. The agglomeration experiments were performed according to a full factorial design with three replicates at the central point condition, totaling 11 experimental runs. The operating independent variables were: fluidizing inlet air temperature (65, 75 and 85 °C), binder flow rate (1.5, 2.0 and 2.5 mL/min) and binder concentration (10%, 20% and 30% w/w). The response variables (dependent variables) were: median particle size (D₅₀) and process yield (Yld). The operating conditions which remained fixed during the agglomeration process were as follows: 0.4 kg of raw material, 300 mm height of the atomizing nozzle in relation to the bed base, pulse rate of 7 Hz and 10 Psi of atomization air pressure. The fluidization velocity of 0.47 m/s was gradually increased (at a rate of 0.05 m/s) every 10 min until reaching 0.62 m/s. The total addition of 125 mL of binder was considered as the standard for the final agglomeration process. After agglomeration, the material was dried until reaching a moisture content equal or less than of the raw material (approximately 4.6%, on wet basis).

Process yield (Yld) was calculated by the ratio between the remaining sample mass in the equipment (m_final) and the sample mass introduced into the equipment (m_initial), on dry basis, according to Eq. (1). The mass fraction lost by incoming fines and the mass fraction of large lumps or incrustation (m_inc) were deduced from the initial mass.

$$Yld\left(\%\right)=\frac{m_{\mathit{final}}}{m_{\mathit{initial}}}\times 100$$ 1

Characterization of raw and agglomerated RP

The median particle size, Carr index (CI), wetting time, moisture and protein content (PC) of the raw and agglomerated RP were determined, with particle size being monitored using an in-line spatial filter velocimetry probe (SFV) (Parsum IPP70, Chemnitz, Germany). Subsequently, the median particle size in volume (D_50v) was reported. For the raw material, the particle size was measured by laser diffraction (Mastersizer 2000, Malvern Instruments, Malvern, UK). Powder flow characteristics were predicted by the calculation of CI values, as described by Turchiuli et al. (2005). The wetting time was measured as the time required for the complete wetting and immersion of 3 g of the sample when placed on water surface (60 mL at 27 °C), according to Hogekamp and Schubert (2003). The moisture content (X_w.b.) of the samples was measured every 10 min using a halogen moisture analyzer (HR83, Mettler Toledo). PC was evaluated by the Kjeldahl method (AOAC 1997), and the nitrogen conversion factor used was of 5.95 (Juliano 1994). All measurements were performed in triplicate, and statistical analysis was made by Tukey’s test, at a confidence level of 95% (p < 0.05) using the same calculator described in the details of the sensory analysis.

Color analysis

In this work, CIELAB space was used for representing the color of the samples, as carried out by Silva et al. (2019), using a MINOLTA digital colorimeter CR-400 and determining the parameters a*, b* and L*.

In order to determine how close or far from the standard (NA) the formulation is, the 2D notation Δa* vs Δb* was used to verify the change in color, with the 1D representation of DL* being used to characterize how clear or dark the sample was in relation to the standard.

The total difference of cooler (ΔE*) can be calculated by Eq. (2):

$$\mathrm{\Delta }{E}^{*}=\sqrt{\mathrm{\Delta }{L}^{*2}\mathrm{\Delta }{a}^{*2}\mathrm{\Delta }{b}^{*2}}$$ 2

Viscosity analysis

The apparent viscosity analysis of the formulations proposed was determined according to the shear rate, as a function of shear stress (τ) and deformation rate (γ), measured in a Brookfiel DV-II+Pro viscometer, with a coaxial cylinder assembly, at shear rates ranging between 5 and 68 s⁻¹. The formulations were prepared and remained at rest for 1 h in the fridge at 4 °C before the measurements. The sample was transferred to the outer cylinder and the rotational speed of the internal cylinder, immersed in the formulation, varied within rates pre-established in the device (14.7–200 rpm), resulting in different amounts of shear rates. The corresponding stresses were determined by converting the readings.

The dependence of shear stress on shear rate was described by the Ostwald–de Waele (or the Power law) and Herschel–Bulkley models (Chhabra and Richardson 1999; Silva et al. 2019). In the models K is the consistency index and n the flow behavior index. The value of n is used to characterize the fluid as Newtonian (n = 1), pseudoplastic (n < 1) or shear-thickening (n > 1) (Toneli et al. 2005). The guava and soursop pulps were diluted 5×.

Sensory analysis

The sensory analysis was carried out in alternate days (one flavor per day, to avoid fatigue of tasters), with the juices prepared from the pulp formulations. The formulations were prepared according to the following composition: 100 g of pulp +150 mL of mineral water +25 g of sugar.

Thus, the sensory analysis consisted of randomly serving the samples frozen at temperatures between 7 ± 1 °C, identified by three random numbers, using between 25 and 30 tasters who were regular/potential consumers of fruits and fruit pulps. A certain profile of tasters was required, such as a good health and appetite, concentration, at least medium sensitivity, the ability of reproducing the results and, mainly, good will (Teixeira 2009). The parameters analyzed were flavor, aroma, color, smell and appearance.

The sensory analysis was carried out using a hedonic scale, which identified how much the tasters liked the product. The scores varied between 1 and 9, with 1 being the score for “dislike extremely” and 9 for “like extremely”. From these scores, it was possible to calculate the acceptance index (AI) or acceptance rate of the product, according to Eq. (3).

$$AI\left(\%\right)=100.\frac{Averagescore}{Higherscore}$$ 3

Statistical analysis and ethics approval

The statistical significance between the samples was evaluated using Tukey’s test, using an online statistical calculator ASTATSA^® (http://astatsa.com/OneWay_Anova_with_TukeyHSD/), with statistical significance of 95% (p < 0.05). The project was approved by the Ethics Committee of the Federal University of Alagoas, under CAAE: 02843118.5.0000.5013 on 23/11/2019.

Formulations and target audience

Initially, fruit pulp formulations with concentrated RP were tested (which had not been submitted to agglomeration process—raw concentrated RP, denominated RP, as pointed out in section “Agglomeration process of concentrated rice protein powder”) and concentrated RP with 10%, 20% and 30% of HC solution as binder, denominated RP(10), RP(20) and RP(30), respectively (represented by the experiments 3, 9 and 7 of the factorial design of the fluidization process), added in 5% (w/w), with fruit pulps, mineral water and sugar. The target audience consisted of a young-adult public aged between 18 and 30 years old.

Subsequently, the best concentration between 1% and 5% (w/w) was analyzed when compared to the pulp without any additions (NA). Therefore, in this second run were tested formulations of pulps with 1%, 3% and 5% of raw concentrated pulp (RP) and comparing them with pulp formulations with 1%, 3% and 5% of concentrated RP agglomerated with 30% of HC solution (RP(30)), in relation to the standard (NA). The second run of experiments was carried out under the same conditions and parameters of the first run.

Results and discussion

Physicochemical characterization

The physicochemical and microbiological parameters (Table S1—Supplementary Information) were in according to the Brazilian Legislation (Brazil 2000, 2001). For the microbiological analysis, the pulps and concentrated protein extracts were characterized for thermotolerant coliforms according to the Brazilian Legislation, exhibiting values < 3 NMP/mL or g of product (Brazil 2001). The ratio between sweetness and acidity, was of 7, 14.1, 15.2, 9.3, 4.1 and 26.3 for acerola, cashew, guava, soursop, passion fruit and mandarin, respectively, having obtained a wide interval which proved to be adequate for the present study, as discussed by Silva et al. (2019).

Characterization of raw and agglomerated RP

Figure 1 compares the PC, wetting time and flowability of raw and agglomerated RP. For each characterization analysis, the mean values were shown, with different letters representing significantly different values at a 95% confidence level. The PC of the raw material was of 77.1 ± 0.2%, shown in Fig. 1a as a dashed line. The operating conditions together with the binder used resulted in granules with PC similar or higher than those of the raw RP.

Fig. 1.

Characterization of raw material and agglomerated concentrated RP. (A) Protein content, (B) wetting time and (C) Carr index. Statistical analysis at a level of 95% of confidence (p < 0.05) must be read as: (A) no significant differences were found between the experiments. (B) a, b, c and d are significantly different, and cd, between c and d, has no statistical difference with them. (C) a and ab have no significant differences between them but have with c; abc is between ab and c and has no significant difference between them but has with a. The dashed line represents the PC of the raw concentrated RP

Figure 1b displays the results of wetting time for the RP granules and raw material. RP granules presented an enhancement of instant properties, which was characterized by the shorter wetting time when compared with the raw material. Larger particles presented high wettability and dispersed easily, as visually observed. The raw RP took 140 s to reach complete wetting and dispersing. In contrast, the complete wetting and dispersing of granules was achieved in less than 40 s for all almost operating conditions, excepted for run 6 (wetting time: 112 s). The higher wetting time observed for the granules produced in run 6 can be related to significant amounts of small lumps, as previously discussed. Furthermore, it was also observed that a lower binder concentration and lower binder flow rate levels (run 2 and run 1) resulted in shorter wetting times.

Figure 1c shows the CI values used to classify powder flowability. The CI value for raw RP was 25.3 ± 1.2%, while for the agglomerated RP it was less than 20%. This observation conveys that the flowability level changed from fair to good or very good.

A higher temperature combined with lower binder flow rates resulted in granules with very good flowability (run 2 and run 6). These operating conditions presented a more favorable thermal balance during the spray phase, as previously discussed. As a result, strong granules with low moisture content and very good flowability were produced.

The increase in powder flowability and reduced wetting time was also described in other agglomeration studies (Machado et al. 2014; Dacanal and Menegalli 2010; Rayo et al. 2015). In general, the process produced a high protein powder with improved flowability and instantization properties.

Figure 2 shows the moisture content of the agglomerated powders. It is observed that the raw material presented a moisture content of 4.60% w.b. (dashed line) and the moisture content of the agglomerated at the end of the process powders varied from 3.10% and 4.15%.

Fig. 2.

Moisture content (A), median particle size and process yield (B) of the raw and agglomerated concentrated RP powders. Statistical analysis at a level of 95% of confidence (p < 0.05) must be read as: (A) a and b are statistically different and ab is between a and b but has no significant difference between them. (B) Letters above the columns are with respect to the particle size and below the line with the process yield. For Particle size, a, b and c are significant different, abc is between ab and c and has no significant differences between them or with b. On the other hand, for Process yield, a and b are significantly different and ab, between a and b, has no significant difference with them. Dashed line in (A) represents the moisture content of the raw concentrated RP

The characteristic sizes for the raw material, measured by laser diffraction, were as follows: D_10v - 9.55 ± 0.16 μm, D_50v - 54.2 ± 0.98 μm and D_90v - 148.5 ± 9.68 μm. As seen in Fig. 3, the median particle size of the agglomerated powders and the process yield varied from 187.4 to 341.7 μm and from 31.4% to 69.7%, respectively. In general, higher process yields and particle size were obtained at higher binder concentrations.

Fig. 3.

Formulations of fruit pulps with concentrated RP. RP—raw concentrated rice protein. Agglomerated RP formulations with collagen are represented by 10%, 20% and 30%

Therefore, the conditions that provided higher process yields, significant increase of particle size, as well as satisfactory results for PC, flowability and wetting time were those performed in runs 5, 7 and 8, wherein a higher binder concentration was used. Moreover, the central point results were also satisfactory. Thus, agglomerated powders obtained in runs 7 and 9 (central point), as well as the agglomerated powder obtained in run 3, which exhibited a lower particle size, were chosen for the formulations used in the sensory analyses. The moisture content and the median particle size of the agglomerated powders obtained at the experimental conditions of runs 3, 7 and 9 were of 4.13%, 4.15% and 3.99% and 187.4, 311.7 and 250.1 μm, respectively.

Color analysis

Figure 3 illustrates the pulps with and without the addition of concentrated RP. For the characterization of the color parameter, the CIELAB system was used, which identified that the yellow–red order is the following: passion fruit–cashew–soursop–mandarin–acerola–guava. This finding can be visually observed in Fig. 3 and numerically verified in Fig. S1 (Supplementary Information).

It was noted that most of the formulations became more reddish (Δa > 0) and yellowish (Δb > 0). Regarding lightness (L), the formulations resulted in clearer pulps (ΔL > 0), except for acerola and passion fruit pulps. For the difference in total color (ΔE), results ranging from 4 to 12 were verified, which, according to the scale defined by Limbo and Piergiovanni (2006), there are noticeable differences between the color of the formulations and the standard (no addition of concentrated protein—NA) (data numerically detailed in Table S2—Supplementary Information and presented graphically in Fig. 4).

Fig. 4.

Color of the formulations. RP—raw concentrated rice protein. Agglomerated RP formulations with collagen are represented by 10%, 20% and 30%

By observing Fig. 4 and analyzing parameters a* and b* (red and yellow features, respectively), it can be observed that in most formulations, there was an increase in yellow perception, except for cashew fruit pulps, which increased this perception for the formulations of RP agglomerated with collagen and a reduction for the formulation with raw concentrated RP.

On the other hand, greater variations were observed for red color, with a greater increase in red perception for all formulations of cashew, soursop and mandarin pulps. As for acerola pulps, an increase in red perception was verified for RP and agglomerated RP formulations with 10% and 20% of collagen, while the formulation with agglomerated RP with 30% of collagen showed a decrease in this perception in relation to the control (NA). For the guava pulp, an increase in red perception was only observed for RP agglomerated with 20% collagen, while the remaining formulations exhibited a reduction in red color intensity. Finally, passion fruit presented an increase in red perception in the pulps with concentrated RP and agglomerated RP with 10% and 30% of collagen, while no change in this perception was verified for the pulp with the addition of agglomerated RP with 20% of collagen.

Regarding lightness, in general, the formulations resulted in clearer pulps after the addition of concentrated RP. The exceptions to this case include the fruit pulps of acerola and passion fruit, and some formulations of mandarin, as observed in Fig. S2 (Supplementary Information).

The parameter difference in total color (∆E) ranged as follows: acerola between 4.7 and 6.4; cashew from 4.1 to 4.6; guava from 1.7 to 4.1; soursop from 8.8 to 11.8; passion fruit between 4.9 and 6.9 and mandarin from 0.7 to 4.2. This parameter is an indication of the total difference in color between the standard and the samples, being extremely valuable for industrial control, as it can determine the changes in color tolerated by consumers (Tiano 2009).

Several reactions can occur when a fruit pulp is submitted to heating or some additive is added, for example, pigment destruction (carotenoids and chlorophylls), because oxidation reactions are carried out. In addition, even natural products have components that can alter significantly the colour of the fruit products as a result of these conjoint of reactions between their components (Lozano and Ibarz 1997; Ávila and Silva 1999; Granato et al. 2011).

Viscosity analysis

The pulp (control sample), with no addition of RP (NA), exhibited a lower viscosity profile. Table 1 shows the parameters of the models used (power law and Herschel–Bulkley model). The flow behavior index (n) varied among the different formulations, though the general pseudoplasticity behavior (n < 1) prevailed. According to Bezerra et al. (2013), most fruit pulps are expected to exhibit a pseudoplastic behavior. The shear-thickening (dilatant) behavior (n > 1) observed for the mandarin pulp is a common characteristic of citrus products, as pointed out by Guazelli (2015).

Table 1.

Parameters for the viscosity models as a function of shear stress. NA—pulp without any additives, RP—raw concentrated rice protein and the percentages are the concentration of binder in solution with respect to the RP agglomerated with collagen

Flavor/formulation		Power law			Herschel–Bulkley
		K (mPa s)	n	R2	K (mPa s)	n	η (mPa s)	R2
Acerola	NAd	2656	0.35	0.961	–	–	–	–
	RPa	6727	0.13	0.945	–	–	–	–
	10%b	5954	0.16	0.939	–	–	–	–
	20%c	5196	0.15	0.946	–	–	–	–
	30%c	4996	0.20	0.940	–	–	–	–
Cashew	NAb	571.0	0.40	0.985	1211	0.28	−0.84	0.998
	RPa	761.9	0.41	0.972	2684	0.22	−2.39	0.985
	10%a	755.6	0.45	0.964	2411	0.22	−3.07	0.977
	20%b	653.7	0.47	0.965	2404	0.24	−2.33	0.982
	30%b	641.0	0.45	0.966	1991	0.26	−1.79	0.978
Guava	NAc	604.7	0.35	0.985	240.4	0.52	0.55	0.999
	RPa	911.2	0.39	0.979	395.1	0.55	0.83	0.992
	10%a	852.8	0.41	0.983	345.5	0.49	0.52	0.994
	20%ab	829.1	0.40	0.978	332.8	0.57	0.81	0.992
	30%b	725.2	0.40	0.978	288.1	0.58	0.72	0.994
Soursop	NAb	139.4	0.53	0.984	86.7	0.62	0.10	0.997
	RPa	185.8	0.53	0.962	44.0	0.84	0.31	0.992
	10%a	206.5	0.60	0.961	45.6	0.88	0.30	0.989
	20%a	193.9	0.57	0.965	57.5	0.82	0.32	0.989
	30%a	190.2	0.58	0.965	58.8	0.83	0.31	0.990
Passion fruit	NAb	309.7	0.42	0.982	148.6	0.56	0.26	0.999
	RPa	474.4	0.40	0.987	248.1	0.53	0.36	0.999
	10%a	461.1	0.42	0.986	230.1	0.55	0.38	0.999
	20%a	463.6	0.41	0.980	177.5	0.59	0.47	0.999
	30%a	436.2	0.42	0.979	154.6	0.62	0.48	0.998
Mandarin	NAc	1.11	1.30	0.946	1.46	1.25	−0.01	0.998
	RPb	2.63	1.11	0.882	0.95	1.36	0.01	0.998
	10%a	5.57	0.93	0.884	0.66	1.43	0.02	0.997
	20%b	2.60	1.10	0.853	0.47	1.52	0.01	0.998
	30%c	1.21	1.30	0.977	1.03	1.34	0.00	0.999

Statistical analysis at a level of 95% of confidence (p < 0.05). Different letters a, b, c, d and e indicate that the samples had significantly differences in the consistency index (K) of the power law model. a and b are statistically different, and ab is between a and b but has no significant difference between them

Sensory analysis

In the first step, 5% in mass of each formulation was added. The formulations consisted of raw concentrated RP and RP agglomerated with collagen (in concentrations of 10%, 20% and 30%) as a binder, denominated RP(10), RP(20) and RP(30), respectively.

Following the statistical analysis of the data collected, an acceptance rate of 70% was observed in the formulations as shown in Fig. S3 (Supplementary Information). This acceptance level can be considered the limit to consider a product, in study, as acceptable based on its sensory properties (Bastos et al. 2014). Therefore, the commercialization of the products tested would be considered too uncertain, given the high variability observed in the acceptance tests, mainly when considering the different profiles of consumers.

It was possible to observe that the formulations differed significantly when compared to the control (NA). This can be due to the vegetal proteins present, which are usually associated to bitter, acid or astringent characteristics, mainly regarding concentrated/isolated plant-derived proteins, constituting a great problem to the acceptance of products from these proteins. This problem is a result of the presence of phytocomponents, volatile compounds or even components derived from lipid degradation during the extraction and drying process, or from compounds linked to vegetal proteins (Drewnowski and Gomez-Carneros 2000; Silva et al. 2019).

Figures S3 and 5 show the AI calculated based on the general evaluation of the product (juices produced from the pulps studied), which is intimately connected to the flavor attribute for this product type, as demonstrated by Silva et al. (2019). Nevertheless, other attributes were also analyzed (aroma, odor, color and appearance) and are described in Supplementary Information.

Based on the results from the first round of tests, it was possible to conclude that the formulations with raw concentrated RP and RP agglomerated with 30% of collagen showed greater sensory acceptance. Therefore, a second round of tests was carried out aimed at reducing the sensory sensitivity of these products and consequently increasing the overall assessment of the product. Thus, lower concentrations of RP and RP with 30% of collagen were analyzed (1%, 3% and 5%).

Figure 5 shows that the acceptance of the product decreases with the increase of protein concentration, with the highest acceptance level being observed for a concentration of 1% in both formulations. No significant difference was observed between concentrations of 1% and 3%, though this difference was significant when comparing these concentrations with a concentration of 5% (p < 0.05).

Fig. 5.

Sensory analysis of the juices with different concentrations of concentrated RP. NA—control—pulp with no additives, RP—raw concentrated rice protein. RP(30) is concentrated rice protein agglomerated with 30% of collagen. Statistical analysis at a level of 95% of confidence (p < 0.05) must be read as follows: a, b and c are different statistically. ab is between a and b but has no significant difference with them, but differs with respect to bc and c. bc is between b and c but has no significant difference with them, but differs with respect to a and ab. All statistical analyses were made between RP and RP(30) formulations compared with NA, or between the formulations with different concentrations (1%, 3% and 5%) for RP or RP(30), i.e., RP was not compared with RP(30) formulations

The goal of increasing the overall sensory evaluation of the product was reached, as it can be observed that, in most cases, the sensory AI was significantly higher than 70% (reaching values up to 90%) for concentrations of 1% and 3%, resulting in a potentially marketable product.

Tukey’s test showed no significant difference between the samples with different concentrations of RP for fruit pulps of guava and passion fruit. It is also possible to observe that, in most cases, for the samples added with RP agglomerated with collagen, no significant difference was observed between concentrations from 1% to 3%, nor for concentrations between 3% and 5%, though there was a significant difference for concentrations between 1% and 5%.

It can be observed that for a concentration of 1%, for both formulations and for the different fruit pulps analyzed, no significant difference was observed in relation to the control (NA), which shows that this concentration has the greatest market potential. Similarly, for concentrations of 5%, overall, a significantly negative difference was observed in relation to the control (NA), for a confidence level of 95% (p < 0.05).

In general, the six fruit pulps studied exhibited AI ranging from liked very much to liked slightly. A possibility of increasing this AI even more would lie on verifying the acceptance of the product for specific consumers, such as those who use some type of protein supplement or practice physical activity, aiming at benefitting from the functional properties of proteins, as described by Silva et al. (2019).

Similar concentrations of protein extract were used in other food products, having showed a negative sensitivity, as in the work carried out by Eiki et al. (2015), who studied ice cream prepared from soy milk and supplemented with chia and Psyllium in concentrations between 1% and 2% in mass, having obtained acceptance levels of 65%, 80% and 83% for chia, Psyllium and the control, respectively. Goudarzi et al. (2015) prepared an apple juice containing whey protein in concentrations of 1–5% in mass, and having verified a significant sensitivity in the overall evaluation of the process for concentrations higher than 1%, with the results being of slightly liked for concentrations of 1% and very dislike for concentrations of 5%. In turn, Uliana et al. (2012) observed that drinks prepared with soybean extract and mulberry juice varied between indifferent and slightly liked. These results of AI when using only 5% of vegetal protein sources revealed a certain difficulty in associating vegetal proteins with fruit pulps, preventing greater sensory acceptance of the product. This difficulty is directly connected to sensory changes caused by this association.

Conclusion

Fruit pulps (six flavors) replaced in 1–5% by raw concentrated RP or agglomerated with collagen were evaluated in terms viscosity/color and sensory terms in the form of juices. The agglomeration process increased the dispersion capacity of concentrated RP. The addition of RP presented a change in the color of the pulps analyzed, resulting in a red and yellowish color. There was a decrease in the flow consistency index (K) when compared to the pulp added with RP and those with RP agglomerated with collagen. Percentages of RP or RP agglomerated with collagen between 1% and 3% showed acceptance rates higher than 80%, similar to the acceptance rate of pulps with no additives (control—NA). However, this value decreased to approximately 70% when concentrations increased to 5% (p < 0.05).

Supplementary electronic material

Data S1 ^{(320.5KB, docx)}

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Compliance with ethical standards

Conflict of interest

Authors declare that there is no conflict of interest.

Credit author statement

C.E.F. Silva and A.K.S. Abud performed the conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, roles/writing—original draft and writing—review & editing (viscosity, color and sensory analysis). I.C.C. Silva, R.B.O. Cerqueira, N.P. Andrade, F. Claudino da Silva, F.P. Andrade participated in the methodology, data curation and formal analysis (sensory analysis). K. Andreola and O.P. Taranto worked in the methodology, data curation and formal analysis (agglomeration process).