Physical properties of yellow passion fruit seeds (Passiflora edulis) during the drying process

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Physical properties of yellow passion fruit seeds (Passiflora edulis) during the drying process Marcos Eduardo Viana de Araujo*, Eloiny Guimarães Barbosa, Augusto Cesar Laviola de Oliveira, Raquel Santana Milagres, Francisco de Assis de Carvalho Pinto, Paulo Cesar Corrêa Federal University of Viçosa, Department of Agricultural Engineering, Avenida Peter Henry Rolfs, s/n, Campus Universitário, Viçosa, Minas Gerais, 36570-900, Brazil

A R T I C LE I N FO

A B S T R A C T

Keywords: Digital image processing Gravimetric properties Size and shape Volumetric contraction

The reuse potential of passion fruit seeds to obtain functional ingredients can find many industrial applications. This requires knowledge of their physical properties for the design of conveyors and equipment. The objective of this study was to determine the physical properties of passion fruit seeds during drying and to evaluate the feasibility of using digital images to determine the dimensional properties of seeds in comparison to caliper. Passion fruit seeds with initial moisture content, in dry basis (d.b.), of 0.402 (decimal) were subjected to drying in a cylindrical tray dryer at 45 ± 2 °C to different moisture content levels and were characterized by their physical properties. Unit specific mass, mass of one thousand seeds, and seed size and shape were evaluated. The characteristic dimensions of the seeds were obtained by caliper and digital image processing. The digital image processing method was satisfactory to obtain the characteristic dimensions of passion fruit seeds. The dimensional properties did not show significant difference (P ≤ 0.05) during the drying process, indicating low contraction of the characteristic seed dimensions. The seeds presented volumetric contraction of approximately 4 %, which was represented by the Bala & Woods adapted model. The mass of one thousand seeds showed a reduction of approximately 21 % with the decrease of moisture content. The unit specific mass ranged from 1121 to 1022 kg m-3 for moisture contents of 0.402 and 0.047 (decimal, d.b.), respectively. These results indicate that the separation of passion fruit seeds as a function of moisture content should be performed by densimetric methods.

1. Introduction Brazil is the world's largest producer and consumer of fresh, processed yellow passion fruit (Passiflora edulis Sims - Passifloraceae), accounting for 50 and 60 % of total world production, respectively (Oliveira et al., 2016). The manufacture of concentrated juice has the most important economic impact for the yellow passion fruit market, as world demand is continuously growing (Cerqueira-Silva et al., 2014). Juice production results in a large amount of waste, such as seeds that represent between 4–12 % of the total fruit mass and have about 30 % oil. Despite current efforts to reuse passion fruit industrial waste, large quantities of seeds are still underused (Oliveira et al., 2017). The oil found in passion fruit seeds has a high content of unsaturated fatty acids, especially linoleic acid, as well as tocopherols, carotenoids and phenolic compounds, known to have antioxidant activity (Ferreira et al., 2011). Thus, the potential for reusing passion fruit seeds to obtain functional ingredients could find various applications in the food, pharmaceutical and cosmetic industries, enabling the

conversion of an agro-industrial residue into value-added products (Malacrida and Jorge, 2012). Despite the disadvantages, conventional mechanical press and liquid solvent-based extractions are the main methods used to extract oil from passion fruit seeds (Oliveira et al., 2013). However, several extraction techniques have been used and developed for seed oil extraction (Pereira et al., 2017). For the extraction of oil to occur satisfactorily, it is extremely important that the seeds have been previously submitted to the drying process to reduce the moisture content of the samples (Rodrígues-Rojo et al., 2012). It is emphasized that before the extraction process, the seeds need to be transported, separated, cleaned, and if necessary, stored (Rodrígues-Rojo et al., 2012). Seed quality is a key factor in obtaining quality products. Araujo et al. (2018) report that seed quality is directly governed by their physical characteristics. Thus, the reduction of seed moisture content during drying can directly influence their physical properties and quality (Aviara et al., 2013). Knowledge of these properties is

⁎ Corresponding author at: Department of Agricultural Engineering, Federal University of Viçosa, Avenida Peter Henry Rolfs, s/n, Viçosa, Minas Gerais, 36570-900, Brazil. E-mail address: [email protected] (M.E.V. de Araujo).

https://doi.org/10.1016/j.scienta.2019.109032 Received 12 September 2019; Received in revised form 7 November 2019; Accepted 12 November 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Marcos Eduardo Viana de Araujo, et al., Scientia Horticulturae, https://doi.org/10.1016/j.scienta.2019.109032

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fundamental for cost reduction, correct conservation and for equipment sizing and operation in the main post-harvest operations of agricultural products (Ramashia et al., 2018). Some seeds may be difficult to separate, requiring knowledge not only of dimensional properties, but also of gravimetric properties as a function of moisture content (Garnayak et al., 2008). Over the years, several authors have determined the variation of physical properties of agricultural products as a function of moisture content, and overall reported direct changes with humidity (Izli, 2015; Gely and Pagano, 2017). In addition, they sought alternative methodologies for determining these properties. Isaza et al. (2018) proposed an image processing methodology to determine the physical properties of castor beans for the development of more efficient oil extraction machines. Mirzabe et al. (2017) applied a method based on digital image processing to investigate the effect of moisture content on the gravimetric and frictional properties of cucumber seeds and grains. Araujo et al. (2018) used image processing to determine the physical properties of sesame seeds harvested at different stages of maturity and thirds in the plant. In general, the authors report that the determination of the physical properties of agricultural products using images is equivalent to the determination by other methods. However, although the physical characteristics are extremely important in the production chain of seed oil, there are no studies in the literature about such characteristics for yellow passion fruit seeds. Therefore, this study aimed to (i) determine and model the variation of the physical properties of yellow passion fruit seeds during drying; (ii) compare the methods of obtaining the characteristic dimensions, digital image processing, and caliper.

Fig. 1. Characteristic dimensions of passion fruit seeds: (L) length, (W) width and (T) thickness.

replications. The characteristic dimensions, length (L), width (W) and thickness (T) of seeds (Fig. 1) for each moisture content were obtained in two ways: directly using a Mitutoyo 500-174B digital caliper with a resolution of 0.01 mm (São Paulo, Brazil) and by the digital image processing method. After determining the characteristic dimensions during the drying process, the seed volume and surface area were determined according to Jain and Bal (1997), according to Eq.s 1 and 2. The sphericity, circularity, projected area and geometric diameter were determined as proposed by Mohsenin (1986), according to Eqs. 3–6, respectively.

πWTL2

V=

1

(

)

(1)

(2L − (WT ) )

(2)

6 2L − (WT ) 2 2. material and methods

1

π (WT ) 2 L2

As =

2.1. Obtaining the raw material This study was conducted at the Laboratory of Physical Properties and Quality Assessment of Agricultural Products, at the Centro Nacional de Treinamento em Armazenagem (CENTREINAR), located on the campus of the Federal University of Viçosa (UFV), Viçosa – Minas Gerais (20°45′00″ S, 42°56′15″ W; 643 m a.s.l.), Brazil. Seeds extracted from the ripe fruits of yellow passion fruit (Passiflora edulis Sims Passifloraceae) from cultivar BRS GA1, obtained from the local market of Viçosa – state of Minas Gerais, Brazil, were used. Fruits were screened and selected for color uniformity (totally yellow), size (6–7 cm in length) and mass (110–130 g).

1 2

1

⎡ (LWT ) 3 ⎤ S= ⎢ ⎥ × 100 L ⎦ ⎣

(3)

W C = ⎡ ⎤ × 100 ⎣L⎦

(4)

πLW 4

(5)

Ap = Dg =

2.2. Sample preparation

1 (LWT ) 3

(6)

Where,

The fruits were washed, dried and cut in half. The pulp was removed and the seeds were removed manually using tweezers. Subsequently, the seeds were cleaned with the aid of a microfiber cloth for total pulp elimination. The cleaned seeds were subjected to drying in a GrainMan 6623 cylindrical tray dryer (Seedburo, Illinois, USA) at 45 ± 2 °C to different moisture content levels. The reduction of moisture content was monitored by gravimetric method (mass loss), knowing the initial moisture content of the product. For this monitoring a Marte® AY220 analytical balance with precision of 0.0001 g (Minas Gerais, Brazil) was used. The moisture contents of the samples were determined by the standard greenhouse method at 105 ± 2 °C for 24 h in four replications (Brasil, 2009) and expressed in dry basis (d.b.). During drying, for each moisture content obtained, the samples were homogenized and characterized by determining their physical properties.

L – Length, mm; W – Width, mm; T – Thickness, mm; V – Unit volume, mm3; As – Surface area, mm2; S – Sphericity, %; C – Circularity, %; Ap – Projected area, mm2; Dg – Geometric diameter, mm.

The volumetric contraction index (Ψ) of passion fruit seeds during drying was determined by the relationship between the volume for each moisture content (V) and the initial volume (V0), according to Eq. 7.

Ψ=

V V0

(7)

The experimental data of the unit volumetric contraction index were adjusted to some mathematical models commonly used to predict the volumetric contraction of agricultural products. The mathematical models selected for the adjustment of the experimental data were the models of Bala and Woods (1984), Corrêa et al. (2004); Rahman (1995),

2.3. Dimensional properties To determine the dimensional properties of passion fruit seeds, 20 randomly selected seeds were used to compose each of the four 2

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performed using the OTSU method to automatically obtain the threshold. To fill in possible seed failures, two filters per mask were implemented, one minimum and one maximum. The image negative was obtained and a scan was performed on the image to detect the objects (seeds). The matrices corresponding to each seed were separated, thus obtaining an image for each seed. Subsequently, the seed contours were detected for descriptors. The determination of the characteristic dimensions was performed using some form descriptors. To obtain the seed length, the descriptor of the smallest circumscribed circle was used. This descriptor allows the diameter of the smallest circle surrounding the seed to be obtained. For the determination of the seed width, the adjusted ellipse descriptor was used, this descriptor provides the diameter of the major axis and of the major semi-axis of the ellipse that best fits the seed, the value of the largest semi-axis corresponds to the characteristic dimension W. In determining seed thickness, the smallest circumscribed rectangle descriptor was used, which gives the width and height of the smallest rectangle surrounding the seed, the height of this rectangle corresponds to the characteristic dimension T. Finally, the data obtained by the algorithm were exported to a data analysis software. It is noteworthy that this work is the first report that uses the above methods to obtain the characteristic dimensions of seeds.

linear, exponential and quadratic, according to Eqs. 8–13, respectively.

Ψ = 1 − β0 {1 − exp[− β1 (U0 − U ]} Ψ=

1 β0 + β1 exp (U )

(8)

(9)

Ψ = 1 + β0 (U − U0)

(10)

Ψ = β0 + β1 U

(11)

Ψ = β0 exp (β1 U )

(12)

Ψ = β0 + β1 U + β2 U 2

(13)

Where, U – Moisture content, decimal d.b.; U0 – Initial moisture content, decimal d.b.; and β0, β1, β2 – Constant coefficients that depend on the product.

For the adjustment of the mathematical models, the nonlinear regression analysis was performed by the Gauss-Newton method, using the STATISTICA 8.0® computer program. The models were selected considering the significance of the regression coefficients by the t-test, adopting the 5 % probability level, the magnitude of the determination coefficient (R2), adjusted determination coefficient (R2adj), the magnitude of the relative mean error (P) and the standard deviation of the estimate (SE), in addition to verifying the behavior of the waste distribution. P value less than 10 % was considered as one of the criteria for model selection, according to Mohapatra and Rao (2005). Eqs. 14 and 15, respectively calculated P and SE.

P=

100 n

SE =

n

∑ i=1

|Yi − Yˆi | Yi

2.3.2. Comparison of methods for obtaining characteristic dimensions For the comparison between the methods of obtaining the characteristic dimensions of the seeds, a variation of the Bland-Altman graph (Bland and Altman, 1995) was adopted, where the values obtained by the reference method (caliper) are plotted on the X axis, and the values obtained by the suggested method (digital image) are plotted on the Y axis. This variation is widely used for method evaluation when a reference method is used (Bahar et al., 2017). For this, a linear regression model (Eq. 11) is fitted to the data to obtain the statistical parameter β1. This parameter indicates the degree of slope of the adjusted line between the data obtained by the two methodologies, where a value of 1 represents full agreement between the methods, provided that β0 is equal to zero. In addition, the analysis of variance between the data obtained by both methods was performed. Obtaining the characteristic dimensions by digital image processing is considered satisfactory if the parameter β1 is close to 1, β0 is equal to zero, and the analysis of variance does not show significant difference (P ≤ 0.05) between the data.

(14)

n ∑i = 1 (Yi − Yˆi )2

GLR

(15)

Where, Yi – Observed value; Yˆi – Estimated value; n – Number of observed data; and GLR – Degrees of freedom of the residue.

2.3.1. Image processing configuration For the acquisition of the images an expanded polystyrene platform (25 mm) built in step shape was used. The 20 seeds of each repetition were arranged in a row. A Canon SX30IS camera with a resolution of 14.1 Mega pixels and 35 times optical zoom, positioned at 0.15 m from the seed line was used to capture the images. Images were captured in an environment with only one light source (12 W LED lamp) near the platform to avoid shadow formation. A 5 × 5 mm dark colored square was placed next to the seed line to automatically obtain the spatial resolution of the image. To obtain the three characteristic dimensions of the seeds, two images for each repetition were obtained. The first image was captured as a top view of the seeds and allowed to obtain the L and W dimensions. The second image was captured as a front view of the seeds and allowed to obtain the T dimension. The seeds were positioned to remain in the center of the camera's field of view. Image processing to obtain the characteristic dimensions was performed using the free software Jupyter, available on the Anaconda® platform. An algorithm was developed in the Python programming language, and the steps for obtaining the characteristic dimensions can be seen in Fig. 2. The original image (RGB) was transformed into a monochrome image and the higher contrast band was used. A 5 × 5 mask smoothing filter (low pass) was used to remove noise from the image capture process. To obtain the binary image, a segmentation was

2.4. Gravimetric properties 2.4.1. Unit specific mass The unit specific mass of passion fruit seeds during drying was determined using the volume complementation methodology proposed by Moreira et al. (1985) and described in ASTM D 792 (1991), using sunflower oil as fluid. To determine this property, 25 mL glass pycnometers were used. Specific mass is defined as the ratio of mass to volume occupied by a given product. This concept applied to the mass and volume of only one seed determines the unit specific mass, which was obtained through Eq. 16.

ρu =

m V

(16)

Where, ρu – Unit specific mass, kg m-3; m – Mass, kg; e V – Volume, m-3.

2.4.2. Mass of one thousand seeds The mass of one thousand seeds was determined using the counting method (eight repetitions of 100 seeds) with mass determination in analytical balance accurate to 0.0001 g according to the methodology 3

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Fig. 2. Algorithm for obtaining the characteristic dimensions L, W and T of passion fruit seeds by the digital image processing technique.

prescribed in the Rules for Seed Analysis – RAS (Brasil, 2009). The mass of one thousand seeds was determined by multiplying by ten the average of the eight sub samples.

obtaining and processing images, as well as the operation of the caliper. It is observed that the methodology of obtaining the characteristic dimensions by image processing does not tend to overestimate or underestimate the values when compared to the reference method, but presents a random distribution of data for the analyzed range. The ANOVA results showed no significant difference (P ≤ 0.05) between the data obtained by the two methods. Thus, it can be said that the methods are equivalent, and that image processing can be used as a satisfactory tool to obtain the characteristic dimensions of passion fruit seed.

2.5. Data analysis Data were analyzed by analysis of variance and regression using statistical analysis software STATISTICA 8.0® and SAEG 9.1. The regression models were chosen based on the magnitude and significance of R2, the significance of the regression coefficients and the verification of the fit of the statistical model to the experimental data.

3.2. Dimensional properties

3. Results and discussion

As there was no difference between the methods used to obtain the characteristic dimensions, the results presented below are based on data obtained by digital image processing. The ANOVA results showed no significant effect (P ≤ 0.05) of moisture content on the dimensional properties of passion fruit seeds. The average properties for each moisture content and their respective deviations can be seen in Table 2. These results will be extremely important for the passion fruit seed processing industry. This will increase the efficiency of cleaning, drying and transporting passion fruit seeds, especially in the oil production industry. Although not presenting significant difference between the moisture contents, the volume, the surface area and the geometric diameter of the seeds have a decreasing tendency with the drying advance. Due to the combined effect between the presence of empty spaces inside the seeds and the small contraction of their dimensions, the remaining properties remain practically constant. These results show that the contraction of passion fruit seeds does not accompany the reduction of their mass during drying. This behavior corroborates those observed for pistachio grains and nuts (Razavi et al., 2007), paddy rice (Reddy and Chakraverty, 2004), pumpkin seeds (Paksoy and Aydin, 2004), among others. Digital image processing allows, besides obtaining the characteristic dimensions, to obtain the value referring to the projected area and the seed roundness using shape descriptors. Table 2 shows the projected area and circularity values obtained directly from the image, and the values obtained by the equations proposed by Mohsenin (1986). Note that the projected area obtained by the equation is larger than that obtained directly from the image by the shape descriptor (contour area). This is due to the equation proposed to obtain this property, assuming that the product is a perfect spheroid, overestimating the values for grains and seeds with low circularity. The mean error found between the areas was 17 %. The same behavior is observed for circularity, since this property can be given as the ratio between the

3.1. Comparison of methods for obtaining characteristic dimensions Table 1 shows the coefficients of the linear regression models for the characteristic dimensions of passion fruit seeds during the drying process obtained by caliper and image processing. All adjustments presented high values of R2 demonstrating the high degree of correlation between the data obtained by both methods. It is also noted that the values of parameter β1 were close to 1, demonstrating a high degree of agreement between the methods (Bland and Altman, 1995). The t-test did not show significance for the parameter β0, indicating that the null hypothesis must be accepted and β0 is equal to 0. This indicates that there is no displacement of the data fitting line in relation to the origin. This shows that the method of obtaining the characteristic dimensions by image processing proposed in this study is similar to the standard method using digital caliper. Figs. 3A, 3B and 3C show the correspondence of the average of the characteristic dimensions obtained by the two methods. Forty data were used regarding the average of the 20 seeds of each repetition. It is noteworthy that the variation found between the methodologies may be associated with both random and non-random errors, related to both Table 1 Coefficients of linear regression models adjusted for the characteristic dimensions of passion fruit seeds, obtained by caliper and digital image processing. Characteristic dimension

β0

β1

R2

Length (L) Width (W) Thickness (T)

−0.0062 −0.0111 0.0002

1.0008++ 1.0023++ 0.9986++

0.9786** 0.9871** 0.9886**

++ Significant at 1 % probability by t-test; ** Significant at 1 % probability by F test.

4

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Fig. 3. Correspondence of the average of the characteristic dimensions: length (A), width (B) and thickness (C) of passion fruit seeds obtained by caliper and image.

projected area and the area of the smallest circumscribed circle to the seed, for this property the mean error was 10 %. This can be evidenced in Fig. 4. These results demonstrate the advantage of using digital image processing, rather than approximate equations. The low variation in the physical properties of passion fruit seeds during drying indicates that separation by size and shape from moisture content can be difficult. However, other separation methods such as ventilation and density may be analyzed. The physical characterization of passion fruit seeds has not been previously evidenced in the literature and this information will surely be extremely important for the industry of these seeds (Goneli et al., 2011; Araujo et al., 2018). Table 3 presents the values of the coefficients of R2, R2adj, P, SE and the trend of residue distribution for the models used to evaluate the unit volumetric contraction of passion fruit seeds during drying. In addition, the values of the parameters β0, β1, β2 for each model are informed, when applied. (2015), these statistical parameters are of great importance in mathematical modeling, especially when models with different numbers of estimated parameters are used. However, these parameters should not be analyzed separately in the evaluation of nonlinear models

Fig. 4. Evidence of overestimation of the projected area by approximate calculations for passion fruit seed.

(Kashaninejad et al., 2007). According to Avhad and Marchetti (2016), P and SE are considered strong helpers in the choice of mathematical models. The model fits well with experimental data if the P value is less than 10 % and the standard error of estimate is low (Costa et al., 2015). In addition, to ensure that the model satisfactorily describes the phenomenon, it is

Table 2 Average values and respective standard deviation of the dimensional properties of passion fruit seeds, unit volume (V), surface area (As), geometric diameter (Dg), Sphericity (S), projected area (Ap) and circularity (C) as a function of moisture content. Moisture content

V

As

Dg

S

Ap (Mohsenin, 1986)

Ap (Image)

C (Mohsenin, 1986)

C (Image)

(decimal, d.b.)

(mm3)

(mm2)

(mm)

(%)

(mm2)

(mm2)

(%)

(%)

0.402 0.387 0.270 0.241 0.213 0.189 0.149 0.112 0.070 0.047

22.31 22.27 21.89 21.79 21.81 21.62 21.66 21.50 21.56 21.44

± ± ± ± ± ± ± ± ± ±

0.41 1.03 0.62 0.93 0.45 0.39 0.58 0.32 0.90 1.23

43.84 44.02 43.44 43.56 43.32 43.17 43.23 43.06 42.99 42.95

± ± ± ± ± ± ± ± ± ±

0.66 1.37 0.68 1.05 0.56 0.54 0.79 0.50 1.01 1.51

4.06 4.07 4.04 4.04 4.04 4.03 4.03 4.02 4.02 4.02

± ± ± ± ± ± ± ± ± ±

0.03 0.06 0.03 0.05 0.03 0.03 0.04 0.02 0.05 0.07

56.44 55.62 55.87 55.69 55.93 55.56 55.58 55.41 55.91 55.43

± ± ± ± ± ± ± ± ± ±

0.49 0.20 0.69 0.72 0.28 0.29 0.21 0.75 0.67 0.53

5

26.31 27.06 26.32 26.50 26.21 26.52 26.72 26.42 26.06 26.35

± ± ± ± ± ± ± ± ± ±

0.73 0.90 0.29 0.69 0.13 0.39 0.54 0.40 0.62 0.90

22.81 22.34 22.57 23.19 22.93 22.57 22.88 22.98 23.34 22.74

± ± ± ± ± ± ± ± ± ±

0.82 0.53 0.24 0.20 0.35 0.50 0.46 0.91 0.99 0.97

64.58 64.36 63.73 64.13 64.00 64.15 64.59 63.76 64.09 63.80

± ± ± ± ± ± ± ± ± ±

1.01 0.76 1.67 1.60 0.78 0.43 0.43 1.37 1.44 1.09

57.44 58.70 57.75 57.83 58.11 58.30 58.04 58.42 57.79 57.32

± ± ± ± ± ± ± ± ± ±

1.35 1.28 0.64 0.49 1.87 1.00 0.65 0.61 1.25 0.44

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Table 3 Estimated parameters of the models of unit volumetric contraction of passion fruit seeds adjusted to the experimental values. Model

(8) (9) (10) (11) (12) (13)

Model Parameters

Statistical Parameters

β0

β1

β2

R²

R2adj

0.05635 1.13677 −0.11848 0.95374 0.95393 0.96051

3.17054 −0.09068 – 0.10880 0.11141 0.02858

– – – – – 0.1756

0.9757 0.9644 0.9360 0.9461 0.9482 0.9760

0.9726 0.9600 0.9360 0.9394 0.9417 0.9691

Waste Distribution

SE

P

0.0022 0.0027 0.0034 0.0033 0.0033 0.0024

0.1714 0.2154 0.2563 0.2672 0.2624 0.1705

Random Biased Biased Biased Biased Random

All models presented high values of R2 and R2adj. According to Cano-higuita et al.

necessary to perform the dispersion analysis of the residues. The residual values should be close to zero, and not have geometric shapes, indicating the randomness of the model. If dispersion is considered biased, the model is considered inadequate (Corrêa et al., 2014). The Bala & Woods adapted and quadratic models presented the highest values of R2 and R2adj, and the lowest magnitudes of P and SE. It is also verified that only these models presented a non-biased distribution of the residues, which can satisfactorily represent the phenomenon under study. Thus, the Bala & Woods adapted model was chosen to represent the volumetric contraction of passion fruit seeds. This model is considered one of the most used to describe the volumetric contraction of agricultural products (Resende et al., 2005; Ribeiro et al., 2005; Corrêa et al., 2006). Fig. 5 shows the correspondence of the observed and estimated data by the Bala & Woods adapted model. The high degree of correlation between observed and estimated data can be observed. The values observed and estimated by the model can be observed in Fig. 6 It is noted that the passion fruit seeds showed a volumetric reduction of only 4 % in relation to the initial volume for the moisture content ranging between 0.402 and 0.047 (decimal, d.b.), allowing this contraction to be overlooked in drying process modeling (Goneli et al., 2011).

Fig. 6. Observed and estimated values, by the Bala & Woods adapted model, of the unit volumetric contraction of passion fruit seeds. Table 4 Coefficients of nonlinear regression models adjusted for the gravimetric properties of passion fruit seeds as a function of moisture content.

3.3. Gravimetric properties The ANOVA results showed a significant effect (P ≤ 0.05) of moisture content on the gravimetric properties of passion fruit seeds. Table 4 shows the coefficients of the best-fit nonlinear regression models for mass of one thousand seeds and specific massof passion fruit seeds as a function of moisture content. For these, the quadratic model (Eq. 13) presented the best fit. It is noted that all the adjustments presented high values of R2 and, therefore, satisfactorily represent the studied phenomena. According to Cano-higuita et al. (2015), this statistical parameter is of fundamental importance because it describes the

Gravimetric property

β0

β1

β2

R2

Mass of one thousand seeds Unit specific mass

27.4931 1013.8008

6.2635+ 95.5178+

31.3041+ 425.8140+

0.9877** 0.9791**

+ Significant at 5 % probability by t-test; ** Significant at 1 % probability by F test.

Fig. 7. Observed and estimated values of the mass of one thousand passion fruit seeds during drying.

Fig. 5. Correspondence of the observed and estimated values by the Bala & Woods adapted model for volumetric contraction of passion fruit seeds. 6

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contraction of the characteristic dimensions. Passion fruit seeds showed a volumetric contraction of approximately 4 % of the initial volume, allowing this contraction to be neglected in drying process modeling. The Bala & Woods adapted model was chosen to represent the volumetric contraction of passion fruit seeds. The mass of one thousand seeds presented a nonlinear reduction of approximately 21 % with the decrease of moisture content, showing that the loss of mass of passion fruit seeds during drying is more expressive than its volumetric variation. The unit specific mass ranged from 1121 to 1022 kg m-3 for moisture contents of 0.402 and 0.047 (decimal, d.b.), respectively. These results indicate that the separation of passion fruit seeds according to moisture content should be done by densimetric methods and not by size and shape. Declarations of interest Fig. 8. Observed and estimated values of the unit specific mass of passion fruit seeds during drying.

None. Declaration

degree of correlation between the adjusted data and the estimated data. The authors certify that there is no conflict of interest. Also this article is not being evaluated nor has it been submitted to any other Journal, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder.

3.3.1. Mass of one thousand seeds The variation of the mass of one thousand seeds as a function of moisture content can be observed in Fig. 7. There is a nonlinear decrease of this property during drying. The mass of one thousand passion fruit seeds reduced from 35.2–27.9 g (P ≤ 0.05), with moisture content ranging from 0.402 to 0.047 (decimal, d.b.). This result is associated with product water loss during drying, and is reported for several products such as jatropha seeds (Garnayak et al., 2008), pine nuts (Gharibzahedi et al., 2010), safflower seeds (Shakeri and Khodabakhshian, 2011), pumpkin seeds (Aviara et al., 2013), among others. Knowledge of this property is fundamental in cleaning, separation and pneumatic transport processes, as it directly influences seed acceleration, affecting the aerodynamic drag force exerted on the particle and its terminal velocity (Solomon and Zewdu, 2009).

Acknowledgments The authors thank CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for their scholarships. References Araujo, M.E.V., Barbosa, E.G., Gomes, F.A., Teixeira, I.R., Lisboa, C.F., Araujo, R.S.L., Corrêa, P.C., 2018. Physical properties of sesame seeds harvested at different maturation stages and thirds of the plant. Chil. J. Agr. Res. 79, 495–502. Araujo, W.D., Goneli, A.L.D., Orlando, R.C., Martins, E.A.S., Hartman Filho, C.P., 2015. Propriedades físicas dos frutos de amendoim durante a secagem. Rev. Caatinga 28, 170–180. ASTM D 792, 1991. Standard test methods for density and specific gravity (relative density) of plastics by displacement. Philadelphia. Avhad, M.R., Marchetti, J.M., 2016. Mathematical modelling of the drying kinetics of Hass avocado seeds. Ind. Crop. Prod. 91, 76–87. Aviara, N.A., Power, P.P., Abbas, T., 2013. Moisture-dependent physical properties of Moringa oleifera seed relevant in bulk handling and mechanical processing. Ind. Crop. Prod. 42, 96–104. Bahar, B., Tuncel, A.F., Holmes, E.W., Homles, D.T., 2017. An interactive website for analytical method comparison and bias estimation. Clin. Biochem. 50, 1025–1029. Bala, B.K., Woods, J.L., 1984. Simulation of deep bed malt drying. J. Agr. Eng. Res. 30, 235–244. Bland, J.M., Altman, D.G., 1995. Comparing methods of measurement: why plotting difference against standard method is misleading. Lancet 346, 1085–1087. Brasil, 2009. Regras Para Análise De Sementes. Ministério da Agricultura, Pecuária e Abastecimento/ Secretaria de Defesa Agropecuária, Brasília-DF, Brasil. Cano-Higuita, D.M., Villa-Vélez, H.A., Telis-Romero, J., Váquiro, H.A., Telis, V.R.N., 2015. Influence of alternative drying aids on water sorption of spray dried mango mix powders: a thermodynamic approach. Food Bioprod. Process. 93, 19–28. Cerqueira-Silva, C.B.M., Jesus, O.N., Santos, E.S.L., Corrêa, R.X., Souza, A.P., 2014. Genetic breeding and diversity of the genus Passiflora: progress and perspectives in molecular and genetic studies. Int. J. Mol. Sci. 15, 14122–14152. Corrêa, P.C., Botelho, F.M., Botelho, S.C.C., Goneli, A.L.D., 2014. Isotermas de sorção de água de frutos de Coffea canefora. Rev. Bras. Eng. Agr. Ambient. 18, 1047–1052. Corrêa, P.C., Ribeiro, D.M., Resende, O., Afonso Júnior, P.C., Goneli, A.L.D., 2004. Mathematical modeling for representation of coffee berry volumetric shinkage. International Drying Symposium, 14., 2004. CD ROM), São Paulo. Anais… São Paulo. Corrêa, P.C., Ribeiro, D.M., Resende, O., Botelho, F.M., 2006. Determinação e modelagem das propriedades físicas e da contração volumétrica do trigo, durante a secagem. Rev. Bras. Eng. Agr. Ambient. 10, 665–670. Costa, J.M.G., Silva, E.K., Hijo, A.A.C.T., Azevedo, V.M., Borges, S.V., 2015. Physical and thermal stability of spray-dried swiss cheese bioaroma powder. Dry. Technol. 33, 346–354. Ferreira, B.S., Almeida, C.G., Faza, L.P., Almeida, A., Diniz, C.G., Silva, V.L., Grazul, R.M., Hyaric, M.L., 2011. Comparative properties of Amazonian oils obtained by different extraction methods. Molecules 16, 5875–5885.

3.3.2. Unit specific mass Fig. 8 shows the experimental and estimated values of the unit specific mass of passion fruit seeds for the different moisture contents. Nonlinear reduction occurs during the drying process. Experimental values ranged from 1121 to 1022 kg m-3 (P ≤ 0.05) for moisture content ranging from 0.402 to 0.047 (decimal, d.b.). The reduction in unit specific mass directly affects aerodynamic properties and pressure drop as air circulates in the seed bed (Shahbazi, 2015). Despite the low variation of this property, the results indicate that class separation using ventilation and densimetric methods would be possible (Khodabakhshian et al., 2018). The results found are contrary to those observed for most agricultural products, which generally have increased unit specific mass with decreasing moisture content (Mansouri et al., 2017; Munder et al., 2017). However, this behavior has been previously evidenced for agricultural products that present part or all of the stiffened integument, such as paddy rice (Zareiforoush et al., 2009), sunflower seeds (Figueiredo et al., 2011) and peanut fruits (Araujo et al., 2015). The unit specific mass reduction indicates that the mass reduction during drying is more expressive than the volumetric contraction of the product. 4. Conclusions The digital image processing method was satisfactory to obtain the characteristic dimensions of passion fruit seeds, besides providing more reliable values of properties that were previously obtained by approximations, such as projected area and circularity. The dimensional properties of passion fruit seeds showed no significant difference (P ≤ 0.05) during the drying process, indicating low 7

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