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Engineering in Agriculture, Environment and Food

journal homepage: www.elsevier.com/locate/eaef

Online measuring of quality changes of banana slabs during convectivedrying

Esmaeel Seyedabadia, Mehdi Khojastehpourb,∗, Mohammad Hossein Abbaspour-Fardb

a Department of Agronomy, Faculty of Agriculture, University of Zabol, Zabol, IranbDepartment of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

A R T I C L E I N F O

Keywords:ColorComputer vision systemHot air dryingShrinkage

A B S T R A C T

Color and shrinkage are two main quality attributes of dried banana. In this study, a hot air dryer equipped witha 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 tookplace entirely in the falling rate period. The results showed that the system could successfully measure andmonitor the color and shrinkage evolutions of banana during drying. Air temperature had significant effect ondrying 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 moistureratio. Drying at higher temperatures led to more color changes but less shrinkage. Air velocity in the studiedrange had no significant effect on both color change and sample shrinkage.

1. Introduction

High moisture content of ripe banana makes it to be very perishableand subject to fast deterioration after harvesting. This causes seriouseconomic losses as a result of reduction in weight and quality. Drying isone of the oldest methods for the preservation of agricultural productsthrough reducing their moisture content. Besides the preservation,drying adds value to the dried products. Banana chips also as value-added produce, have a crispy and delicate taste consumed as light mealssuch as breakfast cereals (Doymaz, 2010).

Nowadays, different drying techniques are applied in order to re-duce waste and spoilage of fruits and vegetables and to extend theirshelf life. Among them hot air drying under forced convection is themost popular and efficient drying technique applied for drying of pro-ducts such as banana (Izli and Isik, 2015).

During drying of banana, its shape, volume and surface area exhibitconsiderable changes simultaneously with loss of moisture (Mayor andSereno, 2004; Pan et al., 2008). Removing water from the fruit cellsduring drying makes their viscoelastic matrix to contract into the spacepreviously occupied by the water (Aguilera, 2003). Such cell wall dis-ruption subsequently affects the diffusing distance of moisture whichtransports from inside to the outside. This phenomenon (stated asshrinkage) also has effect on final quality of the dried fruit and its ac-ceptability by the consumer (Mayor and Sereno, 2004). Therefore

shrinkage must be included into the mathematical model for accuratelyprediction of moisture content during drying and to determine thecorrect effective diffusion coefficient (Katekawa and Silva, 2006).

Shrinkage can be quantified as the ratio of the sample's volume afterand before drying process. However, in some previous studies,shrinkage was simply expressed as a function of the changes of selecteddimensions 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 ratioof sample's volume after and before drying is more commonly appliedby researchers. In this method, the Archimedes principle or displace-ment techniques with toluene or n-heptane as the replacement liquidare usable to determine the apparent volume of samples(Prachayawarakorn et al., 2008). Many empirical models and diffusionmodels have been used to describe convective drying kinetics of ripebananas in whole form (Jannot et al., 2004; Queiroz and Nebra, 2001),and sliced form (Boudhrioua et al., 2003; Chua et al., 2000; Demireland Turhan, 2003; Sankat et al., 1996). Some did not consider theshrinkage phenomenon and a few studies on convective drying of ba-nana considered the shrinkage effect (De Lima et al., 2002; Karim andHawlader, 2005; Talla et al., 2004). However, the shrinkage estimationwith the aid of pycnometer, caliper or other manual instruments cannotbe applicable in automatic drying control systems since they are notunder online measurement condition.

https://doi.org/10.1016/j.eaef.2018.10.004Received 9 November 2016; Received in revised form 28 May 2018; Accepted 21 October 2018

∗ Corresponding author.E-mail address: mkhpour@um.ac.ir (M. Khojastehpour).

Engineering in Agriculture, Environment and Food 12 (2019) 111–117

Available online 23 October 20181881-8366/ © 2018 Asian Agricultural and Biological Engineering Association. Published by Elsevier B.V. All rights reserved.

T

Tracking the quality of dried products during drying is a big chal-lenge in drying technology. The common method which is tedious, la-borious and unreliable is performed by trained visual inspectors(Abdullah et al., 2004). Sampling and instrumentation is a newermethod that is more precise and less time consuming. This method canbe automated using different instrumentation technologies such asimage processing technique (Nadian et al., 2016). Moreover, contactmeasurements are undesirable and may interfere the drying process andeven negatively affect the product final quality (Romano et al., 2012).

Color of dried materials is another important attribute, since it in-fluences consumers’ acceptability. Essentially, the first quality judg-ment made by consumers on foodstuffs is their visual appearance,which is usually affected by drying negatively (Zielinska andMarkowski, 2012). Besides, color of dried products can be considered asa 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 reac-tions and pigment degradation (Aral and Bese, 2016; Nadian et al.,2015). Therefore, the color changes of fruits and vegetables can beconsidered as a criterion of their chemical changes.

Color changes during drying were reported for banana (Boudhriouaet al., 2002; Prachayawarakorn et al., 2008) and other fruits and ve-getables 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 beenanalyzed in terms of CIELAB coordinates using a colorimeter before andafter drying. Noticeably the surface area measured by colorimeter israther small and considerable time is required to make the frequentevaluations of the same sample (Shahraki et al., 2014). In contrary, acomputer vision system (CVS) consisting of a computer, a digitalcamera and a suitable program can be replaced for more accurate andfaster way for tracking of color change evolution. When compared to acolorimeter, a digital camera has the advantage of acquiring a largenumber of images providing continuous information on different areasof 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 onlinemeasurement of shrinkage and color changes of banana slabs duringconvective drying. Therefore the objective of this study was to monitorthe drying process of a thin layer of banana in an online manner withthe aid of a computer vision system (CVS) in order to track the evolu-tions of shrinkage and color changes. The outcome of such system canbe used as feedbacks to enhance the drying process by altering thedrying conditions. The CVS was also utilized to analyze the effects ofdrying conditions (various drying temperatures and air velocities) onshrinkage and color changes of banana slices.

2. Materials and methods

2.1. Sample preparation

Raw bananas (var. Cavendish) were purchased from a local marketin Mashhad (Khorasan Razavi Province, Iran). They were hand peeled,and cut into 5mm thickness slabs by a slicing machine. Only the radialorientation was used. The ascorbic acid solution (0.1g/100ml) wereused as pretreatment in order to prevent enzymatic browning reactionof banana slices (Krokida et al., 2000). The moisture content of samplesbefore and after drying was determined by oven drying method at105 °C for 48 h (AOAC, 1990). Banana had a mean initial moisturecontent of 392 ± 8% kg/kg dry basis.

2.2. Drying experiments

A thin layer drying apparatus was used to dry the banana samples. Aschematic view of dryer is shown in Fig. 1. The experimental setupconsisted of a centrifugal fan, air duct, electrical heating elements,

illumination and imaging chamber, drying chamber with one layer tray,digital balance with accuracy of 0.01 g (A&D) and a PID controllersystem. The outer surface of the dryer was thermally insulated withglass wool. The controller unit consisted of SSR relays (Solid StateModule, Fotek Co.,Taiwan), inverter (ENC, 0.4 KW, China), tempera-ture and relative humidity sensors (DHT11) and AVR (ATmega16)microcontroller board. A program was written in MATLAB to receivedata from sensors and send orders to controller unit in order to prepareand control desired condition of the drying air (temperature and velo-city) (Nadian et al., 2016). The computer could continuously receivesample weight changes (moisture decrease data) from the digital bal-ance through special software (RsCom) and RS232C interface.

A Nikon Coolpix P510 digital camera (Nikon Inc., Japan) was in-stalled at height of 30 cm above the sample tray for taking pictures fromsamples during drying. A steady lighting condition is essential for imageprocessing process (Khazaei et al., 2013), therefore six LED lamps (5W)were set in order to obtain a uniform artificial illumination. The camerawas set to take pictures with 4608×3456 resolution at 5min intervalsand simultaneously transfer the pictures to the computer using a specialWifi SD card (Eye Fi).

The drying experiments were conducted with various drying tem-peratures (70, 80 and 90 °C) and air velocities (1 and 1.5 m/s). Beforeeach drying experiments, the apparatus was run for 20min in order toachieve the steady state condition inside the drying chamber. All ex-perimental runs were performed in triplicate. Banana samples weredried from the initial moisture content of 392% kg/kg (dry basis) to theequilibrium moisture content in which there was not any net moistureexchange between the samples and the drying air. The dimensionlessmoisture ratio of banana samples were calculated using the followingequation (Ruhanian and Movagharnejad, 2016; Seyedabadi, 2015):

=−

−

MR M MM M

t e

0 e (1)

where MR is the moisture ratio, Mt is the moisture content at a specifictime (dry basis), M0 is the initial moisture content (dry basis) and Me isthe equilibrium moisture content (dry basis).

2.3. Image processing

The images of samples were processed with a special program whichwas written in MATLAB software by authors. The image processingprogram was able to continuously read the captured images duringdrying and measure the shrinkage and color characteristics of bananasamples. The process involved six steps: 1) Dividing the image intosome non-overlapping quadrilateral blocks involving both sample andbackground regions (Fig. 2b). 2) Converting all sub-images into binaryformat using Otsu's method as the threshold value (Fig. 2b(2)). 3) Ap-plying morphological operation in order to remove noise from sub-images (Fig. 2b(3)). 4) Reconstruct the processed overall binary imagefrom processed sub images (Fig. 2c). 5) Multiplying the obtained binaryimage with original color image in order to distinct the region of in-terest (banana samples) from background (Fig. 2d). 6) Calculating thecolor characteristics and shrinkage of banana samples.

2.3.1. Shrinkage measurementThe online measure of the sample shrinkage based on volume

changes during drying was a big challenge. As the banana samples wasvery thin, assuming that the shrinkage occur in a planar form is notfarfetched. Therefore, in this study the shrinkage was determined viachanges of sample surface area using the so-called pixel count method.Calibration of this method was carried out by 50 precision drawingswhich created in AutoCAD 2013 software. The differences betweenAutoCAD drawing areas and image processing measurements were notsignificant (p < 0.01). The following equation was used to measure thesurface shrinkage of banana samples:

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=−

×%Sh A AA

1000 t

0 (2)

where %Sh, At and A0 are surface shrinkage (percent), surface area attime t and initial surface area of samples, respectively.

2.3.2. Color measurement and analysesThe L*a*b* color space was used for color measurement of banana

samples because the RGB measurements obtained from the camera aredevice dependent and calibration of the optical system is required(Fernandez et al., 2005; Mendoza et al., 2006). Moreover, the colorperception in the L*a*b* color space is uniform and the Euclideandistance between two colors corresponds approximately to the colordifference perceived by the human eye (Pedreschi et al., 2007). Thewritten program was able to measure the color characteristics (L*, a*,b*) of samples for all area of samples and report the mean value in timeintervals of 5 min. Reporting the mean value was necessary because the

colors of dried samples were not uniform throughout their surface,being rather brown near the central area and yellowish near the per-iphery. For each sample, the changes of these values during drying weremonitored and compared with the initial ones prior drying. The totalcolor difference (ΔE*) which takes into account the differences of allthree color characteristics, is visible and recognizable even by in-experienced observer (Hutchings, 1999). This parameter was calculatedusing as follow (Imaizumi et al., 2015; Kesbi et al., 2016):

= − + − + −ΔE (L L ) (a a ) (b b )* *0* 2 *

0* 2 *

0* 2 (3)

where L*, a*, and b* are the lightness, redness and yellowness of driedsample respectively, while L0

* , a0* , and b0

* are the initial values of colorcharacteristics of samples prior drying.

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).

Fig. 2. The image processing steps for extracting banana samples from background. A) initial image taken with the camera; b) divided image into some non-overlapping quadrilateral blocks; b1–b3: converting to binary image and removing noise for a block; c) reconstructed binary image; d) final adequate image forcalculating shrinkage and color characteristics.

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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 dryingtemperature and air velocity (p < 0.05) on the sample shrinkage andcolor parameters of banana slices at the end of drying process. A sta-tistical program SPSS v20 (IBM Inc, US) was used to perform all sta-tistical calculations.

3. Results and discussions

The variation of moisture ratio (Mr) versus drying time for thesamples dried at different air temperatures is shown in Fig. 3. As shownin the figure, the moisture content of banana slices decreased ex-ponentially with increase of drying time. Actually drying was in fallingrate which is typical for drying of agricultural products with porousstructure (Pakowski and Adamski, 2007; Srikiatden and Roberts, 2006).The drying curves involves two distinct falling rate sets which can beseen as linear and nonlinear part of the drying curves. In the linearsections, the moisture evaporation takes place near the banana surface.As the drying continues, the dry patch surface occurs while the internalarea is still wet. This phenomenon leads to take a longer time fortransferring internal moisture to surface and hence provides a rapiddrop in the drying rate which occurs in the second falling rate sections(Thuwapanichayanan et al., 2011). These falling rate sets were alsoreported in some of early studies for banana drying (Prachayawarakornet al., 2008; Sankat et al., 1996).

Drying of banana at higher temperatures led to considerable re-duction in drying time. The reason may be explained by increasing themovement of water molecules at higher temperatures or by the for-mation of large pores inside banana structure which facilitate themoisture transfer. The air velocity in the studied range had no sig-nificant (p < 0.05) effect on drying time of banana slice. This finding isin 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 differencesand 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 thevariation of color characteristics during drying of banana.

The color of dried bananas seems to be quite different from that of

raw ones and it corresponds with change of L* and a* values. For allexperiments, the L* value decreased during drying process. Howeverthe a* value increased moderately. This means that banana samplesbecame darker and more reddish during drying time. The reduction oflightness 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 theresult of formation of brown pigments (Maskan, 2000). No clear ten-dency is seen for the value of b* in banana samples during drying. Alsothe changes in color parameters during drying fairly depended ondrying temperature. Drying with higher temperatures led to morechanges 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 afunction of drying time. This parameter was frequently used to comparethe color changes in fruit drying (Demiray and Tulek, 2015; Demirhanand Özbek, 2015; Horuz and Maskan, 2015). As can be seen in thefigure, the ΔE* value increased with the drying time. For all dryingtemperatures, the rate of change of ΔE* was high during the early stagesof drying as demonstrated by the steep curves during these periods, andthen decreased in the later stages. It seems that this phenomenon wasrelated with samples moisture content. In other words, the highmoisture content in the early stages of drying has a main role in theMaillard reaction which corresponds to the creation of color com-pounds such as melanodins. It was found that the total color differencehas a direct relation with drying air temperature. Namely the bananasamples 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 lowerthan that of L*, for all experiments. Therefore, a* had little impact ontotal color change and L* was the major parameter in the evolution ofcolor during banana slabs drying. This leads to appropriate linear re-lations between total color difference (ΔE*) and decrease in lightness(ΔL*) during drying (Table 1).

Shrinkage of banana slabs versus moisture ratio at different dryingconditions is shown in Fig. 6. Drying of banana at 80, 90 and 100 °Cfrom moisture ratio of 1.00 to 0.04 followed by increasing the per-centage of shrinkage from 0 to 40 ± 3, 34 ± 4, and 31 ± 4 for airvelocity of 1m/s and 38 ± 4, 34 ± 3, and 32 ± 2 for air velocity of1.5 m/s, respectively. Therefore it can be concluded the shrinkage ofbanana decreased by increase of drying temperature. The reason maybe that drying at higher temperatures leads to break the internal tissuesand to create the larger pore sizes that influence shrinkage of bananaslabs. Moreover it can be explained by the fact that in higher tem-peratures, the lower moisture content of the external surface induces arubbery-glass transition and results formation of rigid crust at the outer

Fig. 3. Drying curves of banana slices at different air temperatures (80, 90, and 100 °c); a) for air velocity of 1m/s, b) for air velocity of 1.5 m/s (legend is commonfor both diagrams).

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layer that helps limiting the size of samples. Similar results have beenreported by other researchers (Horuz and Maskan, 2015; Mayor andSereno, 2004; Prachayawarakorn et al., 2008). Also banana shrinkagedid not depend on air velocity.

It was found that for all experiments the overall trends of shrinkage

curves are linear. Therefore they can be expressed by simple form;y = Ax+B that A and B were obtained for each temperature by linearregression. No meaningful relations were found for values of A and Bwith air velocities. So it can be found that air velocity has no significanteffect on shrinkage and the average values of A and B were selected as

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 alldiagrams).

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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 duringdrying. Other researchers reported linear correlation between shrinkageand moisture content of apple (Sturm et al., 2014), potato (Al-Muhtasebet al., 2004) and carrot (Hatamipour and Mowla, 2002).

4. Conclusion

Computer vision system was employed for online measurement ofquality attributes of banana slabs during convective drying with dif-ferent conditions. Drying time was found to be significantly affected bydrying temperature. Banana shrinkage was almost linearly related tomoisture ratio and was dependent on temperature and independentfrom air velocities. Lightness (L*) was the major parameter in bananacolor that was well correlated with total color difference (ΔE*) duringdrying. The appropriate fits were presented for shrinkage and totalcolor changes during drying.

Acknowledgments

This work was financially supported by Ferdowsi University ofMashhad (Project No. 38200). The authors would like to thank Dr.Mahmood Reza Golzarian and Dr. Mohammad Hossein Nadian fortechnical assistance.

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Equation R2 Equation R2 Equation R2

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Equation R2 Equation R2 Equation R2

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