- Email: [email protected]
Online measuring of quality changes of banana slabs during convective drying
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
Contents lists available at ScienceDirect
Engineering in Agriculture, Environment and Food journal homepage: www.elsevier.com/locate/eaef
Online measuring of quality changes of banana slabs during convective drying Esmaeel Seyedabadia, Mehdi Khojastehpourb,∗, Mohammad Hossein Abbaspour-Fardb a b
Department of Agronomy, Faculty of Agriculture, University of Zabol, Zabol, Iran Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Color Computer vision system Hot air drying Shrinkage
Color and shrinkage are two main quality attributes of dried banana. In this study, a hot air dryer equipped with a computer vision system (CVS) was employed for online monitoring of quality changes of banana during drying. Banana slices were dried at temperatures of 80, 90 and 100 °C and air velocities of 1.0 and 1.5 m/s. Drying took place entirely in the falling rate period. The results showed that the system could successfully measure and monitor the color and shrinkage evolutions of banana during drying. Air temperature had significant effect on drying time. For all temperatures, drying followed by decrease of the lightness (L*) and increase in the redness (a*) of banana samples but with no clear tendency of the yellowness (b*). It was found the total color difference (ΔE*) has a direct relation with drying temperature. Shrinkage showed almost a linear correlation with moisture ratio. Drying at higher temperatures led to more color changes but less shrinkage. Air velocity in the studied range had no significant effect on both color change and sample shrinkage.
1. Introduction High moisture content of ripe banana makes it to be very perishable and subject to fast deterioration after harvesting. This causes serious economic losses as a result of reduction in weight and quality. Drying is one of the oldest methods for the preservation of agricultural products through reducing their moisture content. Besides the preservation, drying adds value to the dried products. Banana chips also as valueadded produce, have a crispy and delicate taste consumed as light meals such as breakfast cereals (Doymaz, 2010). Nowadays, different drying techniques are applied in order to reduce waste and spoilage of fruits and vegetables and to extend their shelf life. Among them hot air drying under forced convection is the most popular and efficient drying technique applied for drying of products such as banana (Izli and Isik, 2015). During drying of banana, its shape, volume and surface area exhibit considerable changes simultaneously with loss of moisture (Mayor and Sereno, 2004; Pan et al., 2008). Removing water from the fruit cells during drying makes their viscoelastic matrix to contract into the space previously occupied by the water (Aguilera, 2003). Such cell wall disruption subsequently affects the diffusing distance of moisture which transports from inside to the outside. This phenomenon (stated as shrinkage) also has effect on final quality of the dried fruit and its acceptability by the consumer (Mayor and Sereno, 2004). Therefore ∗
shrinkage must be included into the mathematical model for accurately prediction of moisture content during drying and to determine the correct effective diffusion coefficient (Katekawa and Silva, 2006). Shrinkage can be quantified as the ratio of the sample's volume after and before drying process. However, in some previous studies, shrinkage was simply expressed as a function of the changes of selected dimensions of fruit which directly measured by caliper or micrometer (Hatamipour and Mowla, 2003; Mariani et al., 2008; Thuwapanichayanan et al., 2011). In general, the shrinkage as the ratio of sample's volume after and before drying is more commonly applied by researchers. In this method, the Archimedes principle or displacement techniques with toluene or n-heptane as the replacement liquid are usable to determine the apparent volume of samples (Prachayawarakorn et al., 2008). Many empirical models and diffusion models have been used to describe convective drying kinetics of ripe bananas in whole form (Jannot et al., 2004; Queiroz and Nebra, 2001), and sliced form (Boudhrioua et al., 2003; Chua et al., 2000; Demirel and Turhan, 2003; Sankat et al., 1996). Some did not consider the shrinkage phenomenon and a few studies on convective drying of banana considered the shrinkage effect (De Lima et al., 2002; Karim and Hawlader, 2005; Talla et al., 2004). However, the shrinkage estimation with the aid of pycnometer, caliper or other manual instruments cannot be applicable in automatic drying control systems since they are not under online measurement condition.
Corresponding author. E-mail address: [email protected] (M. Khojastehpour).
https://doi.org/10.1016/j.eaef.2018.10.004 Received 9 November 2016; Received in revised form 28 May 2018; Accepted 21 October 2018 1881-8366/ © 2018 Asian Agricultural and Biological Engineering Association. Published by Elsevier B.V. All rights reserved.
Please cite this article as: Seyedabadi, E., Engineering in Agriculture, Environment and Food, https://doi.org/10.1016/j.eaef.2018.10.004
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
illumination and imaging chamber, drying chamber with one layer tray, digital balance with accuracy of 0.01 g (A&D) and a PID controller system. The outer surface of the dryer was thermally insulated with glass wool. The controller unit consisted of SSR relays (Solid State Module, Fotek Co.,Taiwan), inverter (ENC, 0.4 KW, China), temperature and relative humidity sensors (DHT11) and AVR (ATmega16) microcontroller board. A program was written in MATLAB to receive data from sensors and send orders to controller unit in order to prepare and control desired condition of the drying air (temperature and velocity) (Nadian et al., 2016). The computer could continuously receive sample weight changes (moisture decrease data) from the digital balance through special software (RsCom) and RS232C interface. A Nikon Coolpix P510 digital camera (Nikon Inc., Japan) was installed at height of 30 cm above the sample tray for taking pictures from samples during drying. A steady lighting condition is essential for image processing process (Khazaei et al., 2013), therefore six LED lamps (5W) were set in order to obtain a uniform artificial illumination. The camera was set to take pictures with 4608×3456 resolution at 5 min intervals and simultaneously transfer the pictures to the computer using a special Wifi SD card (Eye Fi). The drying experiments were conducted with various drying temperatures (70, 80 and 90 °C) and air velocities (1 and 1.5 m/s). Before each drying experiments, the apparatus was run for 20 min in order to achieve the steady state condition inside the drying chamber. All experimental runs were performed in triplicate. Banana samples were dried from the initial moisture content of 392% kg/kg (dry basis) to the equilibrium moisture content in which there was not any net moisture exchange between the samples and the drying air. The dimensionless moisture ratio of banana samples were calculated using the following equation (Ruhanian and Movagharnejad, 2016; Seyedabadi, 2015):
Tracking the quality of dried products during drying is a big challenge in drying technology. The common method which is tedious, laborious and unreliable is performed by trained visual inspectors (Abdullah et al., 2004). Sampling and instrumentation is a newer method that is more precise and less time consuming. This method can be automated using different instrumentation technologies such as image processing technique (Nadian et al., 2016). Moreover, contact measurements are undesirable and may interfere the drying process and even negatively affect the product final quality (Romano et al., 2012). Color of dried materials is another important attribute, since it influences consumers’ acceptability. Essentially, the first quality judgment made by consumers on foodstuffs is their visual appearance, which is usually affected by drying negatively (Zielinska and Markowski, 2012). Besides, color of dried products can be considered as a measure of the pigment nutrients as carotenoids, phenols, flavonoids, chlorophyll and betalains retentions (Devahastin and Niamnuy, 2010). The color changes of foods occur by the results of the browning reactions and pigment degradation (Aral and Bese, 2016; Nadian et al., 2015). Therefore, the color changes of fruits and vegetables can be considered as a criterion of their chemical changes. Color changes during drying were reported for banana (Boudhrioua et al., 2002; Prachayawarakorn et al., 2008) and other fruits and vegetables such as mushroom (Kotwaliwale et al., 2007), kiwifruit (Mohammadi et al., 2008), tomato (Izli and Isik, 2015), and pumpkin (Guiné and Barroca, 2012). In these studies, color of products has been analyzed in terms of CIELAB coordinates using a colorimeter before and after drying. Noticeably the surface area measured by colorimeter is rather small and considerable time is required to make the frequent evaluations of the same sample (Shahraki et al., 2014). In contrary, a computer vision system (CVS) consisting of a computer, a digital camera and a suitable program can be replaced for more accurate and faster way for tracking of color change evolution. When compared to a colorimeter, a digital camera has the advantage of acquiring a large number of images providing continuous information on different areas of the sample with no contact between the device and the drying object (Romano et al., 2012). The review of literature shows the lack of investigation on online measurement of shrinkage and color changes of banana slabs during convective drying. Therefore the objective of this study was to monitor the drying process of a thin layer of banana in an online manner with the aid of a computer vision system (CVS) in order to track the evolutions of shrinkage and color changes. The outcome of such system can be used as feedbacks to enhance the drying process by altering the drying conditions. The CVS was also utilized to analyze the effects of drying conditions (various drying temperatures and air velocities) on shrinkage and color changes of banana slices.
MR =
Mt − Me M0 − Me
(1)
where MR is the moisture ratio, Mt is the moisture content at a specific time (dry basis), M0 is the initial moisture content (dry basis) and Me is the equilibrium moisture content (dry basis). 2.3. Image processing The images of samples were processed with a special program which was written in MATLAB software by authors. The image processing program was able to continuously read the captured images during drying and measure the shrinkage and color characteristics of banana samples. The process involved six steps: 1) Dividing the image into some non-overlapping quadrilateral blocks involving both sample and background regions (Fig. 2b). 2) Converting all sub-images into binary format using Otsu's method as the threshold value (Fig. 2b(2)). 3) Applying morphological operation in order to remove noise from subimages (Fig. 2b(3)). 4) Reconstruct the processed overall binary image from processed sub images (Fig. 2c). 5) Multiplying the obtained binary image with original color image in order to distinct the region of interest (banana samples) from background (Fig. 2d). 6) Calculating the color characteristics and shrinkage of banana samples.
2. Materials and methods 2.1. Sample preparation Raw bananas (var. Cavendish) were purchased from a local market in Mashhad (Khorasan Razavi Province, Iran). They were hand peeled, and cut into 5 mm thickness slabs by a slicing machine. Only the radial orientation was used. The ascorbic acid solution (0.1g/100 ml) were used as pretreatment in order to prevent enzymatic browning reaction of banana slices (Krokida et al., 2000). The moisture content of samples before and after drying was determined by oven drying method at 105 °C for 48 h (AOAC, 1990). Banana had a mean initial moisture content of 392 ± 8% kg/kg dry basis.
2.3.1. Shrinkage measurement The online measure of the sample shrinkage based on volume changes during drying was a big challenge. As the banana samples was very thin, assuming that the shrinkage occur in a planar form is not farfetched. Therefore, in this study the shrinkage was determined via changes of sample surface area using the so-called pixel count method. Calibration of this method was carried out by 50 precision drawings which created in AutoCAD 2013 software. The differences between AutoCAD drawing areas and image processing measurements were not significant (p < 0.01). The following equation was used to measure the surface shrinkage of banana samples:
2.2. Drying experiments A thin layer drying apparatus was used to dry the banana samples. A schematic view of dryer is shown in Fig. 1. The experimental setup consisted of a centrifugal fan, air duct, electrical heating elements, 2
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
Fig. 1. The thin layer drying apparatus: 1. Fan; 2. Air duct; 3. Heating elements; 4. Straightener; 5. Temperature and relative humidity sensor; 6. Control unit; 7. Computer; 8. Camera; 9. Led lamps; 10. One layer tray; 11. Banana slab samples; 12. Digital balance; 13. Data transfer lines (dashed lines).
%Sh =
A 0 − At × 100 A0
colors of dried samples were not uniform throughout their surface, being rather brown near the central area and yellowish near the periphery. For each sample, the changes of these values during drying were monitored and compared with the initial ones prior drying. The total color difference (ΔE*) which takes into account the differences of all three color characteristics, is visible and recognizable even by inexperienced observer (Hutchings, 1999). This parameter was calculated using as follow (Imaizumi et al., 2015; Kesbi et al., 2016):
(2)
where %Sh, At and A0 are surface shrinkage (percent), surface area at time t and initial surface area of samples, respectively. 2.3.2. Color measurement and analyses The L*a*b* color space was used for color measurement of banana samples because the RGB measurements obtained from the camera are device dependent and calibration of the optical system is required (Fernandez et al., 2005; Mendoza et al., 2006). Moreover, the color perception in the L*a*b* color space is uniform and the Euclidean distance between two colors corresponds approximately to the color difference perceived by the human eye (Pedreschi et al., 2007). The written program was able to measure the color characteristics (L*, a*, b*) of samples for all area of samples and report the mean value in time intervals of 5 min. Reporting the mean value was necessary because the
ΔE* =
(L* − L *0)2 + (a * − a *0)2 + (b* − b*0)2
(3)
where L* , a * , and b* are the lightness, redness and yellowness of dried sample respectively, while L *0 , a *0 , and b*0 are the initial values of color characteristics of samples prior drying.
Fig. 2. The image processing steps for extracting banana samples from background. A) initial image taken with the camera; b) divided image into some nonoverlapping quadrilateral blocks; b1–b3: converting to binary image and removing noise for a block; c) reconstructed binary image; d) final adequate image for calculating shrinkage and color characteristics. 3
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
Fig. 3. Drying curves of banana slices at different air temperatures (80, 90, and 100 °c); a) for air velocity of 1 m/s, b) for air velocity of 1.5 m/s (legend is common for both diagrams).
raw ones and it corresponds with change of L* and a* values. For all experiments, the L* value decreased during drying process. However the a* value increased moderately. This means that banana samples became darker and more reddish during drying time. The reduction of lightness may be due to the destruction of thermolabile pigments, which consequently results in the formation of dark compounds (Barreiro et al., 1997). However the increase of redness may be as the result of formation of brown pigments (Maskan, 2000). No clear tendency is seen for the value of b* in banana samples during drying. Also the changes in color parameters during drying fairly depended on drying temperature. Drying with higher temperatures led to more changes of lightness and redness. The air velocity had no significant (p < 0.05) effect on the color parameters. Fig. 5 shows the total color difference (ΔE*) of banana samples as a function of drying time. This parameter was frequently used to compare the color changes in fruit drying (Demiray and Tulek, 2015; Demirhan and Özbek, 2015; Horuz and Maskan, 2015). As can be seen in the figure, the ΔE* value increased with the drying time. For all drying temperatures, the rate of change of ΔE* was high during the early stages of drying as demonstrated by the steep curves during these periods, and then decreased in the later stages. It seems that this phenomenon was related with samples moisture content. In other words, the high moisture content in the early stages of drying has a main role in the Maillard reaction which corresponds to the creation of color compounds such as melanodins. It was found that the total color difference has a direct relation with drying air temperature. Namely the banana samples dried in higher temperatures experience more color changes. Air velocity had no effect on total color difference. As seen in Fig. 4 the rate of change of a* during drying was lower than that of L*, for all experiments. Therefore, a* had little impact on total color change and L* was the major parameter in the evolution of color during banana slabs drying. This leads to appropriate linear relations between total color difference (ΔE*) and decrease in lightness (ΔL*) during drying (Table 1). Shrinkage of banana slabs versus moisture ratio at different drying conditions is shown in Fig. 6. Drying of banana at 80, 90 and 100 °C from moisture ratio of 1.00 to 0.04 followed by increasing the percentage of shrinkage from 0 to 40 ± 3, 34 ± 4, and 31 ± 4 for air velocity of 1 m/s and 38 ± 4, 34 ± 3, and 32 ± 2 for air velocity of 1.5 m/s, respectively. Therefore it can be concluded the shrinkage of banana decreased by increase of drying temperature. The reason may be that drying at higher temperatures leads to break the internal tissues and to create the larger pore sizes that influence shrinkage of banana slabs. Moreover it can be explained by the fact that in higher temperatures, the lower moisture content of the external surface induces a rubbery-glass transition and results formation of rigid crust at the outer
2.4. Statistical analysis The data were statistically analyzed using a 3 × 2 factorial design (three drying temperatures and two air velocities). Analysis of variance (ANOVA) was performed in order to explain the effects of drying temperature and air velocity (p < 0.05) on the sample shrinkage and color parameters of banana slices at the end of drying process. A statistical program SPSS v20 (IBM Inc, US) was used to perform all statistical calculations. 3. Results and discussions The variation of moisture ratio (Mr) versus drying time for the samples dried at different air temperatures is shown in Fig. 3. As shown in the figure, the moisture content of banana slices decreased exponentially with increase of drying time. Actually drying was in falling rate which is typical for drying of agricultural products with porous structure (Pakowski and Adamski, 2007; Srikiatden and Roberts, 2006). The drying curves involves two distinct falling rate sets which can be seen as linear and nonlinear part of the drying curves. In the linear sections, the moisture evaporation takes place near the banana surface. As the drying continues, the dry patch surface occurs while the internal area is still wet. This phenomenon leads to take a longer time for transferring internal moisture to surface and hence provides a rapid drop in the drying rate which occurs in the second falling rate sections (Thuwapanichayanan et al., 2011). These falling rate sets were also reported in some of early studies for banana drying (Prachayawarakorn et al., 2008; Sankat et al., 1996). Drying of banana at higher temperatures led to considerable reduction in drying time. The reason may be explained by increasing the movement of water molecules at higher temperatures or by the formation of large pores inside banana structure which facilitate the moisture transfer. The air velocity in the studied range had no significant (p < 0.05) effect on drying time of banana slice. This finding is in agreement with the results of drying eggplant (Akpinar and Bicer, 2005) and potato (Hassini et al., 2007). 3.1. Color results In the present study, the color of banana was estimated using L∗, a* and b* parameters. ΔE* was calculated for measuring color differences and tracking color changes during drying. The initial values of L*, a* and b* for banana samples before drying were: 70.39 ± 2.23, −0.68 ± 0.42 and 24.76 ± 1.95, respectively. Fig. 4 shows the variation of color characteristics during drying of banana. The color of dried bananas seems to be quite different from that of 4
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
Fig. 4. Changes of color parameters: l* (lightness), a* (redness), and b* (yellowness) for banana samples during different drying air temperatures; a) T = 80 °c, b) T = 90 °c, c) T = 100 °c (legend is common for all diagrams).
Fig. 5. Total color difference (e*) of banana samples during different drying air temperatures; a) T = 80 °c, b) T = 90 °c, c) T = 100 °c (legend is common for all diagrams).
curves are linear. Therefore they can be expressed by simple form; y = Ax+B that A and B were obtained for each temperature by linear regression. No meaningful relations were found for values of A and B with air velocities. So it can be found that air velocity has no significant effect on shrinkage and the average values of A and B were selected as
layer that helps limiting the size of samples. Similar results have been reported by other researchers (Horuz and Maskan, 2015; Mayor and Sereno, 2004; Prachayawarakorn et al., 2008). Also banana shrinkage did not depend on air velocity. It was found that for all experiments the overall trends of shrinkage 5
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
Table 1 Linear fits between total color difference (ΔE*) and changes of lightness (ΔL*) for various temperatures. T = 80 °C
T = 90 °C 2
Equation
R
E* = -1.083(ΔL*)+0.239
0.986
T = 100 °C 2
Equation
R
E* = -1.045(ΔL*)-0.046
0.998
Equation
R2
E* = -1.014(ΔL*)-0.185
0.999
Fig. 6. Shrinkage percent of banana slabs for different drying conditions; a) for air velocity of 1 m/s, b) for air velocity of 1.5 m/s (legend is common for both diagrams).
Table 2 Linear relations of banana shrinkage (Sh) and moisture ratio (Mr) for various drying temperatures. T = 80 °C
T = 90 °C
T = 100 °C
Equation
R2
Equation
R2
Equation
R2
Sh = −41.612Mr + 39.4
0.99
Sh = −36.581Mr + 33.6
0.98
Sh = −34.557Mr + 30.9
0.96
the desired parameters. Table 2 shows the linear fits of shrinkage vs. moisture ratio for different drying conditions. The high values of R2 confirm the validity of correlations for banana shrinkages during drying. Other researchers reported linear correlation between shrinkage and moisture content of apple (Sturm et al., 2014), potato (Al-Muhtaseb et al., 2004) and carrot (Hatamipour and Mowla, 2002).
Aguilera, J.M., 2003. Drying and dried products under the microscope. Food Sci. Technol. Int. 9 (3), 137–143. Akpinar, E.K., Bicer, Y., 2005. Modelling of the drying of eggplants in thin‐layers. Int. J. Food Sci. Technol. 40 (3), 273–281. Al-Muhtaseb, A., McMinn, W., Magee, T., 2004. Shrinkage, density and porosity variations during the convective drying of potato starch gel. In: 14th International Drying Symposium (IDS), São Paulo, Brazil, pp. 1604–1611. AOAC, 1990. Official Methods of Analysis, fifteenth ed. Association of Official Analytical Chemists, Arlington, VA. Aral, S., Bese, A.V., 2016. Convective drying of hawthorn fruit (Crataegus spp.): effect of experimental parameters on drying kinetics, color, shrinkage, and rehydration capacity. Food Chem. 210, 577–584. Barreiro, J., Milano, M., Sandoval, A., 1997. Kinetics of colour change of double concentrated tomato paste during thermal treatment. J. Food Eng. 33 (3), 359–371. Boudhrioua, N., Giampaoli, P., Bonazzi, C., 2003. Changes in aromatic components of banana during ripening and air-drying. LWT - Food Sci. Technol. (LebensmittelWissenschaft -Technol.) 36 (6), 633–642. Boudhrioua, N., Michon, C., Cuvelier, G., Bonazzi, C., 2002. Influence of ripeness and air temperature on changes in banana texture during drying. J. Food Eng. 55 (2), 115–121. Chua, K., Mujumdar, A., Chou, S., Hawlader, M., Ho, J., 2000. Convective drying of banana, guava and potato pieces: effect of cyclical variations of air temperature on drying kinetics and color change. Dry. Technol. 18 (4–5), 907–936. De Lima, A., Queiroz, M., Nebra, S., 2002. Simultaneous moisture transport and shrinkage during drying of solids with ellipsoidal configuration. Chem. Eng. 86 (1), 85–93. Demiray, E., Tulek, Y., 2015. Color degradation kinetics of carrot (Daucus carota L.) slices during hot air drying. J. Food Process. Preserv. 39 (6), 800–805. Demirel, D., Turhan, M., 2003. Air-drying behavior of dwarf cavendish and Gros Michel banana slices. J. Food Eng. 59 (1), 1–11. Demirhan, E., Özbek, B., 2015. Color change kinetics of tea leaves during microwave drying. Int. J. Food Eng. 11 (2), 255–263. Devahastin, S., Niamnuy, C., 2010. Invited review: modelling quality changes of fruits and vegetables during drying: a review. Int. J. Food Sci. Technol. 45 (9), 1755–1767. Doymaz, İ., 2010. Evaluation of mathematical models for prediction of thin-layer drying of banana slices. Int. J. Food Prop. 13 (3), 486–497. Fernandez, L., Castillero, C., Aguilera, J., 2005. An application of image analysis to dehydration of apple discs. J. Food Eng. 67 (1), 185–193. Guiné, R.P., Barroca, M.J., 2012. Effect of drying treatments on texture and color of vegetables (pumpkin and green pepper). Food Bioprod. Process. 90 (1), 58–63. Hassini, L., Azzouz, S., Peczalski, R., Belghith, A., 2007. Estimation of potato moisture
4. Conclusion Computer vision system was employed for online measurement of quality attributes of banana slabs during convective drying with different conditions. Drying time was found to be significantly affected by drying temperature. Banana shrinkage was almost linearly related to moisture ratio and was dependent on temperature and independent from air velocities. Lightness (L*) was the major parameter in banana color that was well correlated with total color difference (ΔE*) during drying. The appropriate fits were presented for shrinkage and total color changes during drying. Acknowledgments This work was financially supported by Ferdowsi University of Mashhad (Project No. 38200). The authors would like to thank Dr. Mahmood Reza Golzarian and Dr. Mohammad Hossein Nadian for technical assistance. References Abdullah, M., Guan, L., Lim, K., Karim, A., 2004. The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. J. Food Eng. 61 (1), 125–135.
6
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
2016. Optimal pretreatment determination of kiwifruit drying via online monitoring. J. Sci. Food Agric (in press). Nadian, M.H., Rafiee, S., Aghbashlo, M., Hosseinpour, S., Mohtasebi, S.S., 2015. Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food Bioprod. Process. 94, 263–274. Pakowski, Z., Adamski, A., 2007. The comparison of two models of convective drying of shrinking materials using apple tissue as an example. Dry. Technol. 25 (7–8), 1139–1147. Pan, Z., Shih, C., McHugh, T.H., Hirschberg, E., 2008. Study of banana dehydration using sequential infrared radiation heating and freeze-drying. LWT - Food Sci. Technol. (Lebensmittel-Wissenschaft -Technol.) 41 (10), 1944–1951. Pedreschi, F., Leon, J., Mery, D., Moyano, P., Pedreschi, R., Kaack, K., Granby, K., 2007. Color development and acrylamide content of pre-dried potato chips. J. Food Eng. 79 (3), 786–793. Prachayawarakorn, S., Tia, W., Plyto, N., Soponronnarit, S., 2008. Drying kinetics and quality attributes of low-fat banana slices dried at high temperature. J. Food Eng. 85 (4), 509–517. Queiroz, M., Nebra, S., 2001. Theoretical and experimental analysis of the drying kinetics of bananas. J. Food Eng. 47 (2), 127–132. Romano, G., Argyropoulos, D., Nagle, M., Khan, M., Müller, J., 2012. Combination of digital images and laser light to predict moisture content and color of bell pepper simultaneously during drying. J. Food Eng. 109 (3), 438–448. Ruhanian, S., Movagharnejad, K., 2016. Mathematical modeling and experimental analysis of potato thin-layer drying in an infrared-convective dryer. Eng. Agric. Environ. Food. 9 (1), 84–91. Sankat, C.K., Castaigne, F., Maharaj, R., 1996. The air drying behaviour of fresh and osmotically dehydrated banana slices. Int. J. Food Sci. Technol. 31 (2), 123–135. Seyedabadi, E., 2015. Drying kinetics modelling of basil in microwave dryer. Agric. Commun. 3 (4), 37–44. Shahraki, M.H., Mashkour, M., Garmakhany, A.D., 2014. Development and application of a computer vision system for the measurement of the colour of Iranian sweet bread. Qual. Assur. Saf. Crop Foods 6 (1), 33–40. Srikiatden, J., Roberts, J.S., 2006. Measuring moisture diffusivity of potato and carrot (core and cortex) during convective hot air and isothermal drying. J. Food Eng. 74 (1), 143–152. Sturm, B., Vega, A.-M.N., Hofacker, W.C., 2014. Influence of process control strategies on drying kinetics, colour and shrinkage of air dried apples. Appl. Therm. Eng. 62 (2), 455–460. Talla, A., Puiggali, J.-R., Jomaa, W., Jannot, Y., 2004. Shrinkage and density evolution during drying of tropical fruits: application to banana. J. Food Eng. 64 (1), 103–109. Thuwapanichayanan, R., Prachayawarakorn, S., Kunwisawa, J., Soponronnarit, S., 2011. Determination of effective moisture diffusivity and assessment of quality attributes of banana slices during drying. LWT - Food Sci. Technol. (Lebensmittel-Wissenschaft -Technol.) 44 (6), 1502–1510. Zielinska, M., Markowski, M., 2012. Color characteristics of carrots: effect of drying and rehydration. Int. J. Food Prop. 15 (2), 450–466.
diffusivity from convective drying kinetics with correction for shrinkage. J. Food Eng. 79 (1), 47–56. Hatamipour, M., Mowla, D., 2002. Shrinkage of carrots during drying in an inert medium fluidized bed. J. Food Eng. 55 (3), 247–252. Hatamipour, M., Mowla, D., 2003. Correlations for shrinkage, density and diffusivity for drying of maize and green peas in a fluidized bed with energy Carrier. J. Food Eng. 59 (2), 221–227. Horuz, E., Maskan, M., 2015. Hot air and microwave drying of pomegranate (Punica granatum L.) arils. J. Food Sci. Technol. 52 (1), 285–293. Hutchings, J.B., 1999. Food Color and Appearance. Aspen Publishers. Imaizumi, T., Orikasa, T., Morifusa, S., Van Man, L., Muramatsu, Y., Koide, S., Uchino, T., Tanaka, F., Hamanaka, D., Tagawa, A., 2015. Effects of solution spraying during hot air drying on drying rate, surface hardening and browning of fresh-cut Japanese pear. Eng. Agric. Environ. Food. 8 (1), 1–6. Izli, N., Isik, E., 2015. Color and microstructure properties of tomatoes dried by microwave, convective, and microwave-convective methods. Int. J. Food Prop. 18 (2), 241–249. Jannot, Y., Talla, A., Nganhou, J., Puiggali, J.-R., 2004. Modeling of banana convective drying by the drying characteristic curve (DCC) method. Dry. Technol. 22 (8), 1949–1968. Karim, M.A., Hawlader, M.N.A., 2005. Drying characteristics of banana: theoretical modelling and experimental validation. J. Food Eng. 70 (1), 35–45. Katekawa, M., Silva, M., 2006. A review of drying models including shrinkage effects. Dry. Technol. 24 (1), 5–20. Kesbi, O.M., Sadeghi, M., Mireei, S.A., 2016. Quality assessment and modeling of microwave-convective drying of lemon slices. Eng. Agric. Environ. Food 9 (3), 216–223. Khazaei, N.B., Tavakoli, T., Ghassemian, H., Khoshtaghaza, M.H., Banakar, A., 2013. Applied machine vision and artificial neural network for modeling and controlling of the grape drying process. Comput. Electron. Agric. 98, 205–213. Kotwaliwale, N., Bakane, P., Verma, A., 2007. Changes in textural and optical properties of oyster mushroom during hot air drying. J. Food Eng. 78 (4), 1207–1211. Krokida, M., Kiranoudis, C., Maroulis, Z., Marinos-Kouris, D., 2000. Effect of pretreatment on color of dehydrated products. Dry. Technol. 18 (6), 1239–1250. Mariani, V.C., De Lima, A.G.B., dos Santos Coelho, L., 2008. Apparent thermal diffusivity estimation of the banana during drying using inverse method. J. Food Eng. 85 (4), 569–579. Maskan, M., 2000. Microwave/air and microwave finish drying of banana. J. Food Eng. 44 (2), 71–78. Mayor, L., Sereno, A., 2004. Modelling shrinkage during convective drying of food materials: a review. J. Food Eng. 61 (3), 373–386. Mendoza, F., Dejmek, P., Aguilera, J.M., 2006. Calibrated color measurements of agricultural foods using image analysis. Postharvest Biol. Technol. 41 (3), 285–295. Mohammadi, A., Rafiee, S., Emam-Djomeh, Z., Keyhani, A., 2008. Kinetic models for colour changes in kiwifruit slices during hot air drying. World J. Agric. Sci. 4 (3), 376–383. Nadian, M.H., Abbaspour‐Fard, M.H., Sadrnia, H., Golzarian, M.R., Tabasizadeh, M.,
7
Contents lists available at ScienceDirect
Engineering in Agriculture, Environment and Food journal homepage: www.elsevier.com/locate/eaef
Online measuring of quality changes of banana slabs during convective drying Esmaeel Seyedabadia, Mehdi Khojastehpourb,∗, Mohammad Hossein Abbaspour-Fardb a b
Department of Agronomy, Faculty of Agriculture, University of Zabol, Zabol, Iran Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Color Computer vision system Hot air drying Shrinkage
Color and shrinkage are two main quality attributes of dried banana. In this study, a hot air dryer equipped with a computer vision system (CVS) was employed for online monitoring of quality changes of banana during drying. Banana slices were dried at temperatures of 80, 90 and 100 °C and air velocities of 1.0 and 1.5 m/s. Drying took place entirely in the falling rate period. The results showed that the system could successfully measure and monitor the color and shrinkage evolutions of banana during drying. Air temperature had significant effect on drying time. For all temperatures, drying followed by decrease of the lightness (L*) and increase in the redness (a*) of banana samples but with no clear tendency of the yellowness (b*). It was found the total color difference (ΔE*) has a direct relation with drying temperature. Shrinkage showed almost a linear correlation with moisture ratio. Drying at higher temperatures led to more color changes but less shrinkage. Air velocity in the studied range had no significant effect on both color change and sample shrinkage.
1. Introduction High moisture content of ripe banana makes it to be very perishable and subject to fast deterioration after harvesting. This causes serious economic losses as a result of reduction in weight and quality. Drying is one of the oldest methods for the preservation of agricultural products through reducing their moisture content. Besides the preservation, drying adds value to the dried products. Banana chips also as valueadded produce, have a crispy and delicate taste consumed as light meals such as breakfast cereals (Doymaz, 2010). Nowadays, different drying techniques are applied in order to reduce waste and spoilage of fruits and vegetables and to extend their shelf life. Among them hot air drying under forced convection is the most popular and efficient drying technique applied for drying of products such as banana (Izli and Isik, 2015). During drying of banana, its shape, volume and surface area exhibit considerable changes simultaneously with loss of moisture (Mayor and Sereno, 2004; Pan et al., 2008). Removing water from the fruit cells during drying makes their viscoelastic matrix to contract into the space previously occupied by the water (Aguilera, 2003). Such cell wall disruption subsequently affects the diffusing distance of moisture which transports from inside to the outside. This phenomenon (stated as shrinkage) also has effect on final quality of the dried fruit and its acceptability by the consumer (Mayor and Sereno, 2004). Therefore ∗
shrinkage must be included into the mathematical model for accurately prediction of moisture content during drying and to determine the correct effective diffusion coefficient (Katekawa and Silva, 2006). Shrinkage can be quantified as the ratio of the sample's volume after and before drying process. However, in some previous studies, shrinkage was simply expressed as a function of the changes of selected dimensions of fruit which directly measured by caliper or micrometer (Hatamipour and Mowla, 2003; Mariani et al., 2008; Thuwapanichayanan et al., 2011). In general, the shrinkage as the ratio of sample's volume after and before drying is more commonly applied by researchers. In this method, the Archimedes principle or displacement techniques with toluene or n-heptane as the replacement liquid are usable to determine the apparent volume of samples (Prachayawarakorn et al., 2008). Many empirical models and diffusion models have been used to describe convective drying kinetics of ripe bananas in whole form (Jannot et al., 2004; Queiroz and Nebra, 2001), and sliced form (Boudhrioua et al., 2003; Chua et al., 2000; Demirel and Turhan, 2003; Sankat et al., 1996). Some did not consider the shrinkage phenomenon and a few studies on convective drying of banana considered the shrinkage effect (De Lima et al., 2002; Karim and Hawlader, 2005; Talla et al., 2004). However, the shrinkage estimation with the aid of pycnometer, caliper or other manual instruments cannot be applicable in automatic drying control systems since they are not under online measurement condition.
Corresponding author. E-mail address: [email protected] (M. Khojastehpour).
https://doi.org/10.1016/j.eaef.2018.10.004 Received 9 November 2016; Received in revised form 28 May 2018; Accepted 21 October 2018 1881-8366/ © 2018 Asian Agricultural and Biological Engineering Association. Published by Elsevier B.V. All rights reserved.
Please cite this article as: Seyedabadi, E., Engineering in Agriculture, Environment and Food, https://doi.org/10.1016/j.eaef.2018.10.004
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
illumination and imaging chamber, drying chamber with one layer tray, digital balance with accuracy of 0.01 g (A&D) and a PID controller system. The outer surface of the dryer was thermally insulated with glass wool. The controller unit consisted of SSR relays (Solid State Module, Fotek Co.,Taiwan), inverter (ENC, 0.4 KW, China), temperature and relative humidity sensors (DHT11) and AVR (ATmega16) microcontroller board. A program was written in MATLAB to receive data from sensors and send orders to controller unit in order to prepare and control desired condition of the drying air (temperature and velocity) (Nadian et al., 2016). The computer could continuously receive sample weight changes (moisture decrease data) from the digital balance through special software (RsCom) and RS232C interface. A Nikon Coolpix P510 digital camera (Nikon Inc., Japan) was installed at height of 30 cm above the sample tray for taking pictures from samples during drying. A steady lighting condition is essential for image processing process (Khazaei et al., 2013), therefore six LED lamps (5W) were set in order to obtain a uniform artificial illumination. The camera was set to take pictures with 4608×3456 resolution at 5 min intervals and simultaneously transfer the pictures to the computer using a special Wifi SD card (Eye Fi). The drying experiments were conducted with various drying temperatures (70, 80 and 90 °C) and air velocities (1 and 1.5 m/s). Before each drying experiments, the apparatus was run for 20 min in order to achieve the steady state condition inside the drying chamber. All experimental runs were performed in triplicate. Banana samples were dried from the initial moisture content of 392% kg/kg (dry basis) to the equilibrium moisture content in which there was not any net moisture exchange between the samples and the drying air. The dimensionless moisture ratio of banana samples were calculated using the following equation (Ruhanian and Movagharnejad, 2016; Seyedabadi, 2015):
Tracking the quality of dried products during drying is a big challenge in drying technology. The common method which is tedious, laborious and unreliable is performed by trained visual inspectors (Abdullah et al., 2004). Sampling and instrumentation is a newer method that is more precise and less time consuming. This method can be automated using different instrumentation technologies such as image processing technique (Nadian et al., 2016). Moreover, contact measurements are undesirable and may interfere the drying process and even negatively affect the product final quality (Romano et al., 2012). Color of dried materials is another important attribute, since it influences consumers’ acceptability. Essentially, the first quality judgment made by consumers on foodstuffs is their visual appearance, which is usually affected by drying negatively (Zielinska and Markowski, 2012). Besides, color of dried products can be considered as a measure of the pigment nutrients as carotenoids, phenols, flavonoids, chlorophyll and betalains retentions (Devahastin and Niamnuy, 2010). The color changes of foods occur by the results of the browning reactions and pigment degradation (Aral and Bese, 2016; Nadian et al., 2015). Therefore, the color changes of fruits and vegetables can be considered as a criterion of their chemical changes. Color changes during drying were reported for banana (Boudhrioua et al., 2002; Prachayawarakorn et al., 2008) and other fruits and vegetables such as mushroom (Kotwaliwale et al., 2007), kiwifruit (Mohammadi et al., 2008), tomato (Izli and Isik, 2015), and pumpkin (Guiné and Barroca, 2012). In these studies, color of products has been analyzed in terms of CIELAB coordinates using a colorimeter before and after drying. Noticeably the surface area measured by colorimeter is rather small and considerable time is required to make the frequent evaluations of the same sample (Shahraki et al., 2014). In contrary, a computer vision system (CVS) consisting of a computer, a digital camera and a suitable program can be replaced for more accurate and faster way for tracking of color change evolution. When compared to a colorimeter, a digital camera has the advantage of acquiring a large number of images providing continuous information on different areas of the sample with no contact between the device and the drying object (Romano et al., 2012). The review of literature shows the lack of investigation on online measurement of shrinkage and color changes of banana slabs during convective drying. Therefore the objective of this study was to monitor the drying process of a thin layer of banana in an online manner with the aid of a computer vision system (CVS) in order to track the evolutions of shrinkage and color changes. The outcome of such system can be used as feedbacks to enhance the drying process by altering the drying conditions. The CVS was also utilized to analyze the effects of drying conditions (various drying temperatures and air velocities) on shrinkage and color changes of banana slices.
MR =
Mt − Me M0 − Me
(1)
where MR is the moisture ratio, Mt is the moisture content at a specific time (dry basis), M0 is the initial moisture content (dry basis) and Me is the equilibrium moisture content (dry basis). 2.3. Image processing The images of samples were processed with a special program which was written in MATLAB software by authors. The image processing program was able to continuously read the captured images during drying and measure the shrinkage and color characteristics of banana samples. The process involved six steps: 1) Dividing the image into some non-overlapping quadrilateral blocks involving both sample and background regions (Fig. 2b). 2) Converting all sub-images into binary format using Otsu's method as the threshold value (Fig. 2b(2)). 3) Applying morphological operation in order to remove noise from subimages (Fig. 2b(3)). 4) Reconstruct the processed overall binary image from processed sub images (Fig. 2c). 5) Multiplying the obtained binary image with original color image in order to distinct the region of interest (banana samples) from background (Fig. 2d). 6) Calculating the color characteristics and shrinkage of banana samples.
2. Materials and methods 2.1. Sample preparation Raw bananas (var. Cavendish) were purchased from a local market in Mashhad (Khorasan Razavi Province, Iran). They were hand peeled, and cut into 5 mm thickness slabs by a slicing machine. Only the radial orientation was used. The ascorbic acid solution (0.1g/100 ml) were used as pretreatment in order to prevent enzymatic browning reaction of banana slices (Krokida et al., 2000). The moisture content of samples before and after drying was determined by oven drying method at 105 °C for 48 h (AOAC, 1990). Banana had a mean initial moisture content of 392 ± 8% kg/kg dry basis.
2.3.1. Shrinkage measurement The online measure of the sample shrinkage based on volume changes during drying was a big challenge. As the banana samples was very thin, assuming that the shrinkage occur in a planar form is not farfetched. Therefore, in this study the shrinkage was determined via changes of sample surface area using the so-called pixel count method. Calibration of this method was carried out by 50 precision drawings which created in AutoCAD 2013 software. The differences between AutoCAD drawing areas and image processing measurements were not significant (p < 0.01). The following equation was used to measure the surface shrinkage of banana samples:
2.2. Drying experiments A thin layer drying apparatus was used to dry the banana samples. A schematic view of dryer is shown in Fig. 1. The experimental setup consisted of a centrifugal fan, air duct, electrical heating elements, 2
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
Fig. 1. The thin layer drying apparatus: 1. Fan; 2. Air duct; 3. Heating elements; 4. Straightener; 5. Temperature and relative humidity sensor; 6. Control unit; 7. Computer; 8. Camera; 9. Led lamps; 10. One layer tray; 11. Banana slab samples; 12. Digital balance; 13. Data transfer lines (dashed lines).
%Sh =
A 0 − At × 100 A0
colors of dried samples were not uniform throughout their surface, being rather brown near the central area and yellowish near the periphery. For each sample, the changes of these values during drying were monitored and compared with the initial ones prior drying. The total color difference (ΔE*) which takes into account the differences of all three color characteristics, is visible and recognizable even by inexperienced observer (Hutchings, 1999). This parameter was calculated using as follow (Imaizumi et al., 2015; Kesbi et al., 2016):
(2)
where %Sh, At and A0 are surface shrinkage (percent), surface area at time t and initial surface area of samples, respectively. 2.3.2. Color measurement and analyses The L*a*b* color space was used for color measurement of banana samples because the RGB measurements obtained from the camera are device dependent and calibration of the optical system is required (Fernandez et al., 2005; Mendoza et al., 2006). Moreover, the color perception in the L*a*b* color space is uniform and the Euclidean distance between two colors corresponds approximately to the color difference perceived by the human eye (Pedreschi et al., 2007). The written program was able to measure the color characteristics (L*, a*, b*) of samples for all area of samples and report the mean value in time intervals of 5 min. Reporting the mean value was necessary because the
ΔE* =
(L* − L *0)2 + (a * − a *0)2 + (b* − b*0)2
(3)
where L* , a * , and b* are the lightness, redness and yellowness of dried sample respectively, while L *0 , a *0 , and b*0 are the initial values of color characteristics of samples prior drying.
Fig. 2. The image processing steps for extracting banana samples from background. A) initial image taken with the camera; b) divided image into some nonoverlapping quadrilateral blocks; b1–b3: converting to binary image and removing noise for a block; c) reconstructed binary image; d) final adequate image for calculating shrinkage and color characteristics. 3
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
Fig. 3. Drying curves of banana slices at different air temperatures (80, 90, and 100 °c); a) for air velocity of 1 m/s, b) for air velocity of 1.5 m/s (legend is common for both diagrams).
raw ones and it corresponds with change of L* and a* values. For all experiments, the L* value decreased during drying process. However the a* value increased moderately. This means that banana samples became darker and more reddish during drying time. The reduction of lightness may be due to the destruction of thermolabile pigments, which consequently results in the formation of dark compounds (Barreiro et al., 1997). However the increase of redness may be as the result of formation of brown pigments (Maskan, 2000). No clear tendency is seen for the value of b* in banana samples during drying. Also the changes in color parameters during drying fairly depended on drying temperature. Drying with higher temperatures led to more changes of lightness and redness. The air velocity had no significant (p < 0.05) effect on the color parameters. Fig. 5 shows the total color difference (ΔE*) of banana samples as a function of drying time. This parameter was frequently used to compare the color changes in fruit drying (Demiray and Tulek, 2015; Demirhan and Özbek, 2015; Horuz and Maskan, 2015). As can be seen in the figure, the ΔE* value increased with the drying time. For all drying temperatures, the rate of change of ΔE* was high during the early stages of drying as demonstrated by the steep curves during these periods, and then decreased in the later stages. It seems that this phenomenon was related with samples moisture content. In other words, the high moisture content in the early stages of drying has a main role in the Maillard reaction which corresponds to the creation of color compounds such as melanodins. It was found that the total color difference has a direct relation with drying air temperature. Namely the banana samples dried in higher temperatures experience more color changes. Air velocity had no effect on total color difference. As seen in Fig. 4 the rate of change of a* during drying was lower than that of L*, for all experiments. Therefore, a* had little impact on total color change and L* was the major parameter in the evolution of color during banana slabs drying. This leads to appropriate linear relations between total color difference (ΔE*) and decrease in lightness (ΔL*) during drying (Table 1). Shrinkage of banana slabs versus moisture ratio at different drying conditions is shown in Fig. 6. Drying of banana at 80, 90 and 100 °C from moisture ratio of 1.00 to 0.04 followed by increasing the percentage of shrinkage from 0 to 40 ± 3, 34 ± 4, and 31 ± 4 for air velocity of 1 m/s and 38 ± 4, 34 ± 3, and 32 ± 2 for air velocity of 1.5 m/s, respectively. Therefore it can be concluded the shrinkage of banana decreased by increase of drying temperature. The reason may be that drying at higher temperatures leads to break the internal tissues and to create the larger pore sizes that influence shrinkage of banana slabs. Moreover it can be explained by the fact that in higher temperatures, the lower moisture content of the external surface induces a rubbery-glass transition and results formation of rigid crust at the outer
2.4. Statistical analysis The data were statistically analyzed using a 3 × 2 factorial design (three drying temperatures and two air velocities). Analysis of variance (ANOVA) was performed in order to explain the effects of drying temperature and air velocity (p < 0.05) on the sample shrinkage and color parameters of banana slices at the end of drying process. A statistical program SPSS v20 (IBM Inc, US) was used to perform all statistical calculations. 3. Results and discussions The variation of moisture ratio (Mr) versus drying time for the samples dried at different air temperatures is shown in Fig. 3. As shown in the figure, the moisture content of banana slices decreased exponentially with increase of drying time. Actually drying was in falling rate which is typical for drying of agricultural products with porous structure (Pakowski and Adamski, 2007; Srikiatden and Roberts, 2006). The drying curves involves two distinct falling rate sets which can be seen as linear and nonlinear part of the drying curves. In the linear sections, the moisture evaporation takes place near the banana surface. As the drying continues, the dry patch surface occurs while the internal area is still wet. This phenomenon leads to take a longer time for transferring internal moisture to surface and hence provides a rapid drop in the drying rate which occurs in the second falling rate sections (Thuwapanichayanan et al., 2011). These falling rate sets were also reported in some of early studies for banana drying (Prachayawarakorn et al., 2008; Sankat et al., 1996). Drying of banana at higher temperatures led to considerable reduction in drying time. The reason may be explained by increasing the movement of water molecules at higher temperatures or by the formation of large pores inside banana structure which facilitate the moisture transfer. The air velocity in the studied range had no significant (p < 0.05) effect on drying time of banana slice. This finding is in agreement with the results of drying eggplant (Akpinar and Bicer, 2005) and potato (Hassini et al., 2007). 3.1. Color results In the present study, the color of banana was estimated using L∗, a* and b* parameters. ΔE* was calculated for measuring color differences and tracking color changes during drying. The initial values of L*, a* and b* for banana samples before drying were: 70.39 ± 2.23, −0.68 ± 0.42 and 24.76 ± 1.95, respectively. Fig. 4 shows the variation of color characteristics during drying of banana. The color of dried bananas seems to be quite different from that of 4
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
Fig. 4. Changes of color parameters: l* (lightness), a* (redness), and b* (yellowness) for banana samples during different drying air temperatures; a) T = 80 °c, b) T = 90 °c, c) T = 100 °c (legend is common for all diagrams).
Fig. 5. Total color difference (e*) of banana samples during different drying air temperatures; a) T = 80 °c, b) T = 90 °c, c) T = 100 °c (legend is common for all diagrams).
curves are linear. Therefore they can be expressed by simple form; y = Ax+B that A and B were obtained for each temperature by linear regression. No meaningful relations were found for values of A and B with air velocities. So it can be found that air velocity has no significant effect on shrinkage and the average values of A and B were selected as
layer that helps limiting the size of samples. Similar results have been reported by other researchers (Horuz and Maskan, 2015; Mayor and Sereno, 2004; Prachayawarakorn et al., 2008). Also banana shrinkage did not depend on air velocity. It was found that for all experiments the overall trends of shrinkage 5
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
Table 1 Linear fits between total color difference (ΔE*) and changes of lightness (ΔL*) for various temperatures. T = 80 °C
T = 90 °C 2
Equation
R
E* = -1.083(ΔL*)+0.239
0.986
T = 100 °C 2
Equation
R
E* = -1.045(ΔL*)-0.046
0.998
Equation
R2
E* = -1.014(ΔL*)-0.185
0.999
Fig. 6. Shrinkage percent of banana slabs for different drying conditions; a) for air velocity of 1 m/s, b) for air velocity of 1.5 m/s (legend is common for both diagrams).
Table 2 Linear relations of banana shrinkage (Sh) and moisture ratio (Mr) for various drying temperatures. T = 80 °C
T = 90 °C
T = 100 °C
Equation
R2
Equation
R2
Equation
R2
Sh = −41.612Mr + 39.4
0.99
Sh = −36.581Mr + 33.6
0.98
Sh = −34.557Mr + 30.9
0.96
the desired parameters. Table 2 shows the linear fits of shrinkage vs. moisture ratio for different drying conditions. The high values of R2 confirm the validity of correlations for banana shrinkages during drying. Other researchers reported linear correlation between shrinkage and moisture content of apple (Sturm et al., 2014), potato (Al-Muhtaseb et al., 2004) and carrot (Hatamipour and Mowla, 2002).
Aguilera, J.M., 2003. Drying and dried products under the microscope. Food Sci. Technol. Int. 9 (3), 137–143. Akpinar, E.K., Bicer, Y., 2005. Modelling of the drying of eggplants in thin‐layers. Int. J. Food Sci. Technol. 40 (3), 273–281. Al-Muhtaseb, A., McMinn, W., Magee, T., 2004. Shrinkage, density and porosity variations during the convective drying of potato starch gel. In: 14th International Drying Symposium (IDS), São Paulo, Brazil, pp. 1604–1611. AOAC, 1990. Official Methods of Analysis, fifteenth ed. Association of Official Analytical Chemists, Arlington, VA. Aral, S., Bese, A.V., 2016. Convective drying of hawthorn fruit (Crataegus spp.): effect of experimental parameters on drying kinetics, color, shrinkage, and rehydration capacity. Food Chem. 210, 577–584. Barreiro, J., Milano, M., Sandoval, A., 1997. Kinetics of colour change of double concentrated tomato paste during thermal treatment. J. Food Eng. 33 (3), 359–371. Boudhrioua, N., Giampaoli, P., Bonazzi, C., 2003. Changes in aromatic components of banana during ripening and air-drying. LWT - Food Sci. Technol. (LebensmittelWissenschaft -Technol.) 36 (6), 633–642. Boudhrioua, N., Michon, C., Cuvelier, G., Bonazzi, C., 2002. Influence of ripeness and air temperature on changes in banana texture during drying. J. Food Eng. 55 (2), 115–121. Chua, K., Mujumdar, A., Chou, S., Hawlader, M., Ho, J., 2000. Convective drying of banana, guava and potato pieces: effect of cyclical variations of air temperature on drying kinetics and color change. Dry. Technol. 18 (4–5), 907–936. De Lima, A., Queiroz, M., Nebra, S., 2002. Simultaneous moisture transport and shrinkage during drying of solids with ellipsoidal configuration. Chem. Eng. 86 (1), 85–93. Demiray, E., Tulek, Y., 2015. Color degradation kinetics of carrot (Daucus carota L.) slices during hot air drying. J. Food Process. Preserv. 39 (6), 800–805. Demirel, D., Turhan, M., 2003. Air-drying behavior of dwarf cavendish and Gros Michel banana slices. J. Food Eng. 59 (1), 1–11. Demirhan, E., Özbek, B., 2015. Color change kinetics of tea leaves during microwave drying. Int. J. Food Eng. 11 (2), 255–263. Devahastin, S., Niamnuy, C., 2010. Invited review: modelling quality changes of fruits and vegetables during drying: a review. Int. J. Food Sci. Technol. 45 (9), 1755–1767. Doymaz, İ., 2010. Evaluation of mathematical models for prediction of thin-layer drying of banana slices. Int. J. Food Prop. 13 (3), 486–497. Fernandez, L., Castillero, C., Aguilera, J., 2005. An application of image analysis to dehydration of apple discs. J. Food Eng. 67 (1), 185–193. Guiné, R.P., Barroca, M.J., 2012. Effect of drying treatments on texture and color of vegetables (pumpkin and green pepper). Food Bioprod. Process. 90 (1), 58–63. Hassini, L., Azzouz, S., Peczalski, R., Belghith, A., 2007. Estimation of potato moisture
4. Conclusion Computer vision system was employed for online measurement of quality attributes of banana slabs during convective drying with different conditions. Drying time was found to be significantly affected by drying temperature. Banana shrinkage was almost linearly related to moisture ratio and was dependent on temperature and independent from air velocities. Lightness (L*) was the major parameter in banana color that was well correlated with total color difference (ΔE*) during drying. The appropriate fits were presented for shrinkage and total color changes during drying. Acknowledgments This work was financially supported by Ferdowsi University of Mashhad (Project No. 38200). The authors would like to thank Dr. Mahmood Reza Golzarian and Dr. Mohammad Hossein Nadian for technical assistance. References Abdullah, M., Guan, L., Lim, K., Karim, A., 2004. The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. J. Food Eng. 61 (1), 125–135.
6
Engineering in Agriculture, Environment and Food xxx (xxxx) xxx–xxx
E. Seyedabadi et al.
2016. Optimal pretreatment determination of kiwifruit drying via online monitoring. J. Sci. Food Agric (in press). Nadian, M.H., Rafiee, S., Aghbashlo, M., Hosseinpour, S., Mohtasebi, S.S., 2015. Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food Bioprod. Process. 94, 263–274. Pakowski, Z., Adamski, A., 2007. The comparison of two models of convective drying of shrinking materials using apple tissue as an example. Dry. Technol. 25 (7–8), 1139–1147. Pan, Z., Shih, C., McHugh, T.H., Hirschberg, E., 2008. Study of banana dehydration using sequential infrared radiation heating and freeze-drying. LWT - Food Sci. Technol. (Lebensmittel-Wissenschaft -Technol.) 41 (10), 1944–1951. Pedreschi, F., Leon, J., Mery, D., Moyano, P., Pedreschi, R., Kaack, K., Granby, K., 2007. Color development and acrylamide content of pre-dried potato chips. J. Food Eng. 79 (3), 786–793. Prachayawarakorn, S., Tia, W., Plyto, N., Soponronnarit, S., 2008. Drying kinetics and quality attributes of low-fat banana slices dried at high temperature. J. Food Eng. 85 (4), 509–517. Queiroz, M., Nebra, S., 2001. Theoretical and experimental analysis of the drying kinetics of bananas. J. Food Eng. 47 (2), 127–132. Romano, G., Argyropoulos, D., Nagle, M., Khan, M., Müller, J., 2012. Combination of digital images and laser light to predict moisture content and color of bell pepper simultaneously during drying. J. Food Eng. 109 (3), 438–448. Ruhanian, S., Movagharnejad, K., 2016. Mathematical modeling and experimental analysis of potato thin-layer drying in an infrared-convective dryer. Eng. Agric. Environ. Food. 9 (1), 84–91. Sankat, C.K., Castaigne, F., Maharaj, R., 1996. The air drying behaviour of fresh and osmotically dehydrated banana slices. Int. J. Food Sci. Technol. 31 (2), 123–135. Seyedabadi, E., 2015. Drying kinetics modelling of basil in microwave dryer. Agric. Commun. 3 (4), 37–44. Shahraki, M.H., Mashkour, M., Garmakhany, A.D., 2014. Development and application of a computer vision system for the measurement of the colour of Iranian sweet bread. Qual. Assur. Saf. Crop Foods 6 (1), 33–40. Srikiatden, J., Roberts, J.S., 2006. Measuring moisture diffusivity of potato and carrot (core and cortex) during convective hot air and isothermal drying. J. Food Eng. 74 (1), 143–152. Sturm, B., Vega, A.-M.N., Hofacker, W.C., 2014. Influence of process control strategies on drying kinetics, colour and shrinkage of air dried apples. Appl. Therm. Eng. 62 (2), 455–460. Talla, A., Puiggali, J.-R., Jomaa, W., Jannot, Y., 2004. Shrinkage and density evolution during drying of tropical fruits: application to banana. J. Food Eng. 64 (1), 103–109. Thuwapanichayanan, R., Prachayawarakorn, S., Kunwisawa, J., Soponronnarit, S., 2011. Determination of effective moisture diffusivity and assessment of quality attributes of banana slices during drying. LWT - Food Sci. Technol. (Lebensmittel-Wissenschaft -Technol.) 44 (6), 1502–1510. Zielinska, M., Markowski, M., 2012. Color characteristics of carrots: effect of drying and rehydration. Int. J. Food Prop. 15 (2), 450–466.
diffusivity from convective drying kinetics with correction for shrinkage. J. Food Eng. 79 (1), 47–56. Hatamipour, M., Mowla, D., 2002. Shrinkage of carrots during drying in an inert medium fluidized bed. J. Food Eng. 55 (3), 247–252. Hatamipour, M., Mowla, D., 2003. Correlations for shrinkage, density and diffusivity for drying of maize and green peas in a fluidized bed with energy Carrier. J. Food Eng. 59 (2), 221–227. Horuz, E., Maskan, M., 2015. Hot air and microwave drying of pomegranate (Punica granatum L.) arils. J. Food Sci. Technol. 52 (1), 285–293. Hutchings, J.B., 1999. Food Color and Appearance. Aspen Publishers. Imaizumi, T., Orikasa, T., Morifusa, S., Van Man, L., Muramatsu, Y., Koide, S., Uchino, T., Tanaka, F., Hamanaka, D., Tagawa, A., 2015. Effects of solution spraying during hot air drying on drying rate, surface hardening and browning of fresh-cut Japanese pear. Eng. Agric. Environ. Food. 8 (1), 1–6. Izli, N., Isik, E., 2015. Color and microstructure properties of tomatoes dried by microwave, convective, and microwave-convective methods. Int. J. Food Prop. 18 (2), 241–249. Jannot, Y., Talla, A., Nganhou, J., Puiggali, J.-R., 2004. Modeling of banana convective drying by the drying characteristic curve (DCC) method. Dry. Technol. 22 (8), 1949–1968. Karim, M.A., Hawlader, M.N.A., 2005. Drying characteristics of banana: theoretical modelling and experimental validation. J. Food Eng. 70 (1), 35–45. Katekawa, M., Silva, M., 2006. A review of drying models including shrinkage effects. Dry. Technol. 24 (1), 5–20. Kesbi, O.M., Sadeghi, M., Mireei, S.A., 2016. Quality assessment and modeling of microwave-convective drying of lemon slices. Eng. Agric. Environ. Food 9 (3), 216–223. Khazaei, N.B., Tavakoli, T., Ghassemian, H., Khoshtaghaza, M.H., Banakar, A., 2013. Applied machine vision and artificial neural network for modeling and controlling of the grape drying process. Comput. Electron. Agric. 98, 205–213. Kotwaliwale, N., Bakane, P., Verma, A., 2007. Changes in textural and optical properties of oyster mushroom during hot air drying. J. Food Eng. 78 (4), 1207–1211. Krokida, M., Kiranoudis, C., Maroulis, Z., Marinos-Kouris, D., 2000. Effect of pretreatment on color of dehydrated products. Dry. Technol. 18 (6), 1239–1250. Mariani, V.C., De Lima, A.G.B., dos Santos Coelho, L., 2008. Apparent thermal diffusivity estimation of the banana during drying using inverse method. J. Food Eng. 85 (4), 569–579. Maskan, M., 2000. Microwave/air and microwave finish drying of banana. J. Food Eng. 44 (2), 71–78. Mayor, L., Sereno, A., 2004. Modelling shrinkage during convective drying of food materials: a review. J. Food Eng. 61 (3), 373–386. Mendoza, F., Dejmek, P., Aguilera, J.M., 2006. Calibrated color measurements of agricultural foods using image analysis. Postharvest Biol. Technol. 41 (3), 285–295. Mohammadi, A., Rafiee, S., Emam-Djomeh, Z., Keyhani, A., 2008. Kinetic models for colour changes in kiwifruit slices during hot air drying. World J. Agric. Sci. 4 (3), 376–383. Nadian, M.H., Abbaspour‐Fard, M.H., Sadrnia, H., Golzarian, M.R., Tabasizadeh, M.,
7