Journal of Food Engineering 120 (2014) 215–222

Contents lists available at ScienceDirect

Journal of Food Engineering

journal homepage: www.elsevier .com/ locate / j foodeng

Influence of illumination on the characterization of banana ripening

0260-8774/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.jfoodeng.2013.08.005

⇑ Corresponding author at: Brazilian National Institute of Metrology, Quality andTechnology, Av. N. Senhora das Graças, 50, Duque de Caxias, RJ CEP-25250-020,Brazil. Tel.: +55 21 2679 9026, +55 21 2629 5460.

E-mail addresses: jfgomes@inmetro.gov.br (J.F.S. Gomes), fabiana@id.uff.br(F.R. Leta).

1 Tel.: +55 21 2679 9026.2 Tel.: +55 21 2629 5460.

Juliana Freitas Santos Gomes a,b,⇑, Rafaela Rezende Vieira a,1, Ivo Antônio Azara de Oliveira a,1,Fabiana Rodrigues Leta b,2

a Brazilian National Institute of Metrology, Quality and Technology, Av. N. Senhora das Graças, 50, Duque de Caxias, RJ CEP-25250-020, Brazilb Fluminense Federal University, R. Passo da Pátria, 156, Niteroi, RJ CEP-24210-240, Brazil

a r t i c l e i n f o

Article history:Received 14 July 2012Received in revised form 30 July 2013Accepted 5 August 2013Available online 13 August 2013

Keywords:IlluminationColorRipeningBananaFruitStandardizationTCCCRI

a b s t r a c t

For its commercialization, both in internal and external markets, fruits are classified according to thedegree of ripening, mainly on the basis of peel color. For a correct classification, using artificial lighting,it is necessary it provides a perception of color that is as similar as possible to natural. This paper com-pares the use of nine different commercial lamps (incandescent, fluorescent e LED) in the classification ofbanana ripening (subclasses C1 to C7 of Von Loesecke). During the analysis, it was observed that the coor-dinate of chromaticity a* of the CIELAB system was highly influenced by the use of different sources in theillumination system. The differences in color were visually perceived when a single subclass of ripeningwas observed under different lamps, mistakenly influencing the grading during packing, thus making itnecessary to standardize the illumination used in the systems of visual inspection for the commerciali-zation of fruits.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Given the great growth in fruits exports, the need for standard-ization and improvement in the quality of products has continuallygrown. Several countries have adopted rules for grading, labelingand packing, in order to standardize the quality of the productscommercialized and to meet consumer demands.

Fruit classification involves several parameters, such as ripeningstage, size, and defects. Studies have been conducted in order todefine parameters for the characterization of the ripening stagesof fruits and to standardize the classification during packing (Zouet al., 2007; Kavdir and Guyer, 2008; Dobrzanski and Rybczynski,2008; Blasco et al., 2009a,b; Davies, 2009; Mollazade et al., 2012;Sánchez et al., 2012; Sirisomboon et al., 2012). The great diversityof fruits with varying sizes, forms, textures and color emphasizesthe importance of accuracy in the illumination system project,and alerts to the difficulty of establishing an effective illuminationsystem for different products. It is worth highlighting that a goodillumination system permits to maximize the quality of the final

result and to avoid cost increases in the stage of selection priorto packaging.

The color perception phenomenon is based on the response ofthe organism to the stimulus received from an electromagneticradiation originated from a luminous source, reflected or not by acertain material, and reaching the eyes. Each material has itsown characteristics, showing different properties when perceived,such as, for example, the color. Considering that peel color is one ofthe main indicators of ripening of the fruit, the factors that influ-ence color analysis must be considered in the system of visualevaluation.

The illumination directly influences the visualization of the col-or of the object, resulting in different perceptions when it submit-ted to different luminous sources (Ling et al., 2008; Ollkonen et al.,2008). There are many light sources available in the market thatpresent very different properties. In this way, in order to standard-ize the identification of color, it is necessary to define certain char-acteristics for the luminous sources, such as the spectral powerdistribution (SPD) and the correlated color temperature (CCT)(CIE, 2004). Once knowing the spectral distribution, it is possibleto calculate the correlated color temperature, which expressesthe appearance of the color of the light emitted by the source.The CCT value increases from red to blue, and, the higher theCCT, the brighter the color tone of light, and the lower the CCT,more yellowish, thus influencing the perception of the materialcolor. Lamps with CCT below 3100 K are considered lamps of warm

Fig. 1. Experimental device: 1 – Source; 2 – Spectrocolorimeter; 3 – Background; 4– Sample; 5 – Goniophotometer; and 6 – Support.

216 J.F.S. Gomes et al. / Journal of Food Engineering 120 (2014) 215–222

color; with values above 4100 K, cold colors, and in the interval be-tween 3100 K and 4100 K, they are considered neutral or moderatecolors.

Other important indicator of lamp quality is the color renderingindex (CRI), which quantifies the fidelity with which the colors arereproduced under a particular source (CIE, 1995). CRI is calculatedon the basis of the SPD of the source and an average index is calcu-lated on the basis of the difference of color of a set of previouslyspecified samples under a reference illuminant and a test illumi-nant (Luo, 2011). The best possible performance of a luminoussource, compared to a given reference, generates a CRI = 100. ACRI greater than 80 is generally considered acceptable dependingon the requirements of the application that the lamp is to meet(LRC, 2010).

Several types of illumination have been used in systems of foodclassification. Most of the research declares the type of illumina-tion employed, although around 25% do not show this concern,not mentioning the device used. Among those who inform the illu-mination system, the most cited devices are fluorescent lamps(65%), incandescent lamps (10%), natural lighting (19%) and LEDs(2%) (Gomes and Leta, 2012). Although they do not explicitly de-clare the reason for the choice of lamp, there is a tendency forthe use of fluorescent lamps, and, in general, the choice is appar-ently due to the fact that they are usual in the illumination of inte-riors. Incandescent lamps present a high value of CRI, althoughthey present high energy consumption and their use has beenavoided. The technology of fluorescent lamps has replaced theincandescent ones, even though their characteristics vary accord-ing to the composition (amount or type of phosphor), and theypresent mercury in their composition. Nonetheless, their use hasgreatly increased, mainly with the use of compact lamps, due tothe low energy consumption and reasonable life time (Gomesand Leta, 2012).

In this way, the illumination to be used for the characterizationof a material should be selected considering the properties of thelight source and its application requires experimental tests in par-ticular cases. Mohan et al. (2005) determined in a comparativestudy that the reflectance of grains in the close infrared region pro-vided for better accuracy in the classification than the reflectanceon the visible region, thus enabling the choice of the best sourceto be used in grain classification. Manickavasagan et al. (2008)studied the influence of three types of illumination in the classifi-cation of grains (incandescent (CCT 2870 K), fluorescent ring (CCT3000 K) and fluorescent tube (CCT 4100 K)). They concluded thatfluorescent tube lamps of T8 type, normally used in interior, pres-ent better accuracy for identifying defects in grains when com-pared to incandescent halogen lamps, due to the fact that thesepresent a higher radiation emission on the infrared region, thuscorroborating the study conducted by Mohan et al. (2005).

The choice of banana as the initial focus of this research is basedon the importance of this fruit in Brazilian production, being one ofthe most consumed fruits worldwide (Embrapa, 2012). Bananasare usually harvested during the green stage of ripening; the peelcolor gradually changes from green to yellow, with brown spotsappearing on the yellow color at the end of the ripening process.Before being packaged for later commercialization, bananas areclassified, and the pack is correspondingly labeled, with some ofthe main characteristics (producer identification, group, class, sub-class, presentation, etc.) (CEAGESP, 2006).

Among the parameters evaluated in the grading of bananas, theone that offers greatest complexity is the ripening. In Brazil, for theclassification of banana ripening, the so called Von Loesecke ripen-ing scale is used, which is subdivided in seven subclasses, C1 to C7,corresponding respectively to the following stages: totally green;green with yellow traces; more green than yellow; more yellowthan green; yellow with green edges; yellow; and yellow with

brown spots. This classification is conducted through visual inspec-tion, on the basis of a set of photographs representative of the dif-ferent stages of ripening.

The evaluation of the subclass is conducted according to the vi-sual perception of the fruit color and there is no definition of tech-nical parameters regarding the different colors to be classified. Thiscomparative method is very subjective, given that visual analysisof color depends on several factors, such as illumination of theenvironment, observer, and angle of observation, thus hinderingstandardization in the classification process.

This article presents a comparison between nine different typesof illumination in the characterization of the ripening stages of theBrazilian ‘prata’ banana (Musa sapientum AAB). For that, measure-ments of spectral radiance on the banana peel were conducted, inorder to calculate the coordinates for chromaticity and luminosity,in the CIELAB color system, with variation in the source of illumi-nation, using nine types of commercial lamps. Lamps were charac-terized according to their CCT and CRI. Measurements of thecoordinates L* a* b* of each stage of banana ripening were analyzedin terms of the differences observed when individually evaluatedwith each lamp. The measurements were conducted on a spectro-colorimetric system, specially designed for this research, togetherwith the goniophotometer in the Radiometry and Photometry Lab-oratory (Laraf) of the National Institute of Metrology, Quality andTechnology (Inmetro), in Brazil. This research is performed withcollaboration with the Fluminense Federal University situated inRio de Janeiro, Brazil.

2. Methodology

An experimental device was developed in order to assess thedifferences presented in the classification of banana ripening usingnine different commercial lamps. A spectrocolorimeter was usedfor the acquisition of spectroradiometric measurements in theoptical geometry of visualization normal/45� (0�:45�), where thestandard boards and samples (‘prata’ banana) were illuminatedby the lamps placed on the goniophotometer support (Fig. 1).The standard tiles and samples were placed on a black backgroundin a way to minimize the light spread on the system. Other back-ground colors were tested and the results will be presented later.

The spectrocolorimeter uses an array detector as detection sys-tem with a wavelength interval between 380 nm and 780 nm. The

Table 1Commercial sources used in the stages of the ‘prata’ banana ripening.

Code Lamp CCT(K)

CRI

F01 High pressure tube metallic steam HPIT 400 W 3834 49.5F02 High pressure tube sodium steam VIALOX NAV-T

250 W2059 22.5

F03 High pressure ellipsoidal metallic steam HQI-EDaylight E40 400 W

5680 77.0

F04 Double envelope incandescent tube halogen 1000 W 2991 99.6F05 High pressure tube metallic steam HCI-TS 150 W/942

NDL3545 97.1

F06 Compact fluorescent Dulux T/E 42 W 3864 78.8F07 Fluorescent T5 Lumilux FQ 54 W/840 HO 4085 78.2F08 Lighting fixture 49 LED 73 W 4284 67.3F09 Fluorescent T8 FO 32 W/840 4010 83.3

J.F.S. Gomes et al. / Journal of Food Engineering 120 (2014) 215–222 217

spectrocolorimeter certified values are the following: radiance fac-tor (0.3–100%) and its uncertainty (0.51–2.02%); the uncertainty ofx, y (0.0003–0.0012) and the uncertainty of Y% (1.13–1.32%).

Each lamp was placed on the goniophotometer so that the illu-minance on the surface of the samples was around 1000 lx, after30 min of heating. Environmental conditions were monitored dur-ing measurement. Different images of the bananas were acquiredduring measurements for later analysis and comparison of results.A white tile and a black tile provide traceability to the system, andthe standard plates, in the colors red, blue and green, were used inorder to determine the accuracy of the system. The measurementswere taken with metrological rigor so as to allow the validation ofthe system and their reproducibility.

The samples used in this research were bananas of the ‘prata’cultivar (Musa sapientum AAB), obtained in ordinary markets inthe state of Rio de Janeiro, Brazil, in each of the ripening stage, fromC1 to C7 in the Von Loesecke ripening scale. The bananas werevisually classified before being measured. Values of spectral radi-ance were obtained every 4 nm, from 380 nm until 780 nm, thengathered and recorded in file for data treatment. With the acquisi-tion of the radiance spectrums by the spectrocolorimeter, the radi-ance factor and the tristimulus values XYZ were calculatedaccording to the CIE recommendation (2004). From the tristumulusvalues XYZ, the coordinates of chromaticity were calculated on thesystems CIExy and CIELAB and the difference of color DE*.

The International Commission of Illumination (CIE) mathemat-ically defined specific color spaces with the goal of standardizingcolor measurement (CIE, 2004). In the food sector, the color spacemost commonly used is CIELAB, also known as L* a* b* CIE 1976,due to the fact that it is the most uniform and closest to visual per-ception (Dobrzanski and Rybczynski, 2008). In this system, L* is themeasure of lightness (scale from 0 (black) to 100 (white)), a* is thegreen–red axis and b* is the blue–yellow. The calculations of L*, a*

and b* depend on the illuminant (source) used, according to the setof equations:

a� ¼ 500XXn

� �1s

� YYn

� �1s

" #; for

XXn

> 0:008856

andYYn

> 0:008856

b� ¼ 200YYn

� �1s

� ZZn

� �1s

" #; for

YYn

> 0:008856

andZZn> 0:008856

where X, Y, and Z describe the color stimulus considered, and Xn, Yn

and Zn describe the color stimulus of the white object specified,where it is common to use the tristimulus values of a perfect illumi-nated diffuser, by an illuminant CIE or by a source (lamp). Whentwo colors are compared in this space, the total color difference(DE*) is defined as:

DE� ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiDL�2Da�2Db�2

qwhere DE* is the square root of the sum of the squares of the differ-ences between L*, a* and b*. The lower the DE*, the lesser the differ-ence in color perception. However, in applications in the foodsector, differences up to 3.0 are considered acceptable, being thelowest color difference that can be visually distinguished (Coleman,2004).

Converting this color system to polar coordinates, are defined:

C�ab ¼ a�2 þ b�2� �1=2

hab ¼ arctanðb�=a�Þ

where C�ab represents the chroma and Hab represents the hue(angle).

With the aim of verifying the influence of illumination on theassessment of the banana peel color in each of the seven ripeningstages, nine different sources of illumination were used in the re-search, so as to add all metrological rigor in a real application.The sources used in the experiment were commercial lamps com-monly used in illumination systems of internal and external areas,and new technologies of lighting with LED. For each source, thespectral distribution was measured and the CCT and the CRI werecalculated according to Costa et al. (2011) and CIE (1995).

3. Results and discussion

Environmental conditions during the measurements were keptin the temperature of 25.5 ± 0.6 �C and relative humidity of46.9 ± 2.4%. The highest uncertainty of the measurement for radi-ance factor was 1.2%.

3.1. Analysis of lamps used in the measurement system

Each lamp was used under the same conditions in all measure-ments. The values of CCT and CRI are shown in Table 1 and thespectral distribution of the lamps (F01 to F09) is graphically pre-sented in Fig. 2. The uncertainty of the measurement for CCT variesfrom 21 K (F04) until the maximum of 174 K (F03), and the uncer-tainty of the measurement for CRI is on the maximum of 5.6, con-sidering coverage factor k = 2.

3.2. Analysis of accuracy of the measurement system

In order to evaluate accuracy of the spectrocolorimetric systemwhich was specially assembled for this research, three tiles in thecolors red, blue and green were measured. In order to allowcomparison of the results, the source F04 was used on themeasurements, since it is a halogen incandescent lamp and itsspectral distribution approximates to the illuminant A CIE. The re-sults obtained for the coordinates xy were compared with the cal-ibration certificate Lacoe Dimci 0802/2010 (maximum uncertaintydeclared of Uxy = 0.0015 and UY% = 1.25%), as shown in Table 2. It isobserved that the system showed good accuracy, considering thatthe deviations were lower than 0.0010 for x and y, and lower than1% for Y%, both lower than the uncertainty of the standardcertificate.

3.3. Analysis of the banana peel color

Firstly, the spectral radiance factor of the banana peel wasexamined under the halogen incandescent lamp (F04). The F04

Fig. 2. Spectral distribution of lamps used in the classification of ‘prata’ banana ripening.

Table 2Measured values for three standard boards using source F04.

Color Dimci certificate 0802/2010 Measured values

X y Y (%) x Y Y (%)

Red (R) 0.6494 0.3314 14.65 0.6495 0.3314 14.37Green (G) 0.3848 0.4761 18.65 0.3856 0.4764 18.34Cyan (B) 0.2955 0.3660 15.84 0.2957 0.3658 15.51

Fig. 3. CIELAB values for the ripening stages of the ‘prata’ banana with F04 source.

218 J.F.S. Gomes et al. / Journal of Food Engineering 120 (2014) 215–222

lamp shall be adopted as a reference in the comparison of the re-sults obtained with the remaining lamps due to the fact that itshowed a spectral distribution close to the illuminant A of CIE,and a color reproduction index close to 100. From the radiance fac-tor of the banana peel, values of CIELAB were calculated for eachripening stage, as presented in Fig. 2. The measurement uncer-tainty of the CIELAB values are: DL* = 0.45, Da* = 0.42 andDb* = 0.77, respectively for the coordinates L*; a* and b*, consider-ing the coverage factor k = 2.

Comparing the values obtained for L* and b*, it is observed a lin-ear increase of these values up to subclass C6, and a decrease at thebeginning of subclass C7, where the perception of brown spots onthe banana peel start. The values of L* for subclasses C5 and C6 arevery close, considering that the visual difference perceived be-tween these two stages are found in the extremities (green edgesof subclass C5) and that the measurements were obtained at thecenter of the sample, due to the high curvature of the extremities.A different behavior is observed for the value of a* during thechange of subclasses C4 to C5, with a change of sign. This is dueto fact that, in subclass C5, the green color present in the peel ofthe subclass C4 is no longer perceived, with predominance of theyellow color. The value of a* increases with ripening until subclassC7, with no juxtaposition of subclasses between their values,which is in accordance with what is visually perceived. From theresults obtained for each subclass of ripening (Fig. 3), it shall bepossible to analyze and compare the replication of results for theremaining lamps used in the study.

From the radiance factor (Gomes et al., 2013), CIELAB valueswere calculated for each subclass (C1 to C7) of ‘prata’ banana rip-ening, on the basis of the stimulus function generated by the ninedifferent sources used (F01 to F09). Upon analyses of the values ofL* a* b* (Figs. 4–6) for the seven subclasses of ripening and for eachlamp, it can be observed that the value of L* (ANOVA analysis:F(6,62) = 679, p = 0.05) (Fig. 4) is little influenced by the changein illumination in subclasses C1 to C3. This influence increases insubclasses C5, C6 and C7, for the lamps F02 and F03, presentinga relative deviation of 4% in relation to F04 lamp.

The value of b* (Fig. 5) (ANOVA analysis: F(6,62) = 152.6,p = 0.05) is more influenced by changes in illumination than thevalue of L*, considering that the values of illuminance were keptapproximately constant for each lamp. In the subclasses C1, C2and C3, the relative deviations in relation to lamp F04 were onaverage 15%, with the exception of lamps F02 and F05 (3%). Forthe subclass C4, the relative deviations were on average 10%, withexception of lamps F03 and F05 (1%). From the subclass C4, lampF02 presents a reverse behavior from the others, increasing its rel-ative deviation up to subclass C7.

Fig. 6. Evolution of the value of a* for nine different sources (F01 to F09) for each class of ripening (C1 to C7).

Fig. 4. Value of L* for nine different sources (F01 to F09) for each ripening class (C1 to C7).

Fig. 5. Value of b* for nine different sources (F01 to F09) for each ripening class (C1 to C7).

J.F.S. Gomes et al. / Journal of Food Engineering 120 (2014) 215–222 219

The value of a* (Fig. 6) (ANOVA analysis: F(6,62) = 14.3, p = 0.05)is the most influenced by the change in illumination, consideringthat it represents a higher proximity to the real perception of thepeel color, varying from green (subclass C1) to red (subclass C7with Brown spots on the peel). In all subclasses, the deviations rel-ative to the F04 lamp were high, except for subclass C4, which

showed lower deviation, especially for the sources F02 and F05.The F05 lamp was the one presenting the lowest deviation in rela-tion to F04 lamp during all stages of ripening (average of 8%). Fromsubclass C1 to C3, the influence of the F02 lamp at the value of a*

presents a reverse behavior than the one observed in all others,showing higher values of a* in relation to F04.

Fig. 7. Evolution of the value of C�ab for nine different sources (F01 to F09) for each subclass of ripening (C1 to C7).

Fig. 8. Evolution of the value of Hab for nine different sources (F01 to F09) for each subclass of ripening (C1 to C7).

Fig. 9. DE*calculated for eight sources (F04 as reference) used in the characterization of the ‘prata’ banana ripening (C1 to C7).

220 J.F.S. Gomes et al. / Journal of Food Engineering 120 (2014) 215–222

It is observed that the same ripening subclass may havedifferent interpretations depending on the type of lamp used in

evaluation. For instance, when examined under the F02 source,the banana appears to be more ripe (higher values of a*), while

Fig. 10. DE* calculated for eight sources (F03 as a reference) used in the characterization of the ‘prata’ banana ripening (C1 to C7).

J.F.S. Gomes et al. / Journal of Food Engineering 120 (2014) 215–222 221

being in subclasses of green appearance (C1 to C3). Conversely,when under sources F06 to F09, the banana presents a greenerappearance (lower values of a*) despite the fact that they are ripe(subclasses C5 to C7). These results are compatible with the CCTvalues of these lamps. F02 source has a low value of CCT (below3000 K), and, consequently, a more yellow appearance, presentinga very low CRI. Sources F06 to F09 have CCT above 4000 K, andconsequently, a more blue appearance, which can be also ob-served on the variation of parameter b*. It should be highlightedthat the influence of illumination is a factor of concern duringthe selection of fruits, considering the possibility of mistakeninterpretations regarding the real ripening stage of the fruit.

Upon analyses of the values of C�ab e Hab (Figs. 7 and 8) for theseven subclasses of ripening and for each lamp, we can observethat the chroma values (ANOVA analysis: F(6,62) = 75.01,p = 0.05) and hue values (ANOVA analysis: F(6,62) = 96.44,p = 0.05), are significantly affected by illumination (Ling et al.,2008; Ollkonen et al., 2008). The values obtained for the hue anglewere in the range of 70� to 120�, since the b* analyzed values arealways positive (yellow) and the a* range are between negativeand positive values (green to red). In all studies it is possible to ob-serve the consistency between the results obtained by the sourceF04 and F05, regardless of the subclass evaluated.

When analyzing the difference in color DE* calculated from theCIELAB values for each source (F01 to F09) in relation to the F04source (Fig. 9), high values of DE* are observed for all sources, withthe exception of source F05. It is noted that, for the sources F01 andF08, DE* increases according to the ripening of the banana, that is,the difference in color is lower in the evaluation of the green bana-na than it is with the yellow ones. With the lamps F06, F07 and F09,the color difference presented is high (DE* > 8.0), regardless of thesubclass examined.

The same analysis was conducted considering the F03 lamp as areference in the comparison of the results obtained with theremaining lamps, for presenting a spectral distribution closer tothe illuminant D65 of CIE (CCT = 5680 K) and a color reproductionindex close to 80. Firstly, the spectral radiance factor of the bananapeel was examined under the high pressure metallic steam lamp(F03). From the radiance factor of the banana peel, CIELAB valueswere calculated for each stage of ripening.

When analyzing the color difference DE* calculated from theCIELAB values for each source (F01 to F09) in relation to F03 source(Fig. 10), lower values of DE* are observed, in relation to the onesfound for F04, with the exception of F02 source, which presentedhigh values of DE*. It is observed that, overall, the differences in-crease with the ripening of the banana, that is, the color differenceis lower in the evaluation of the green banana than it is for the yel-low ones. This does not occur for the F04 and F05 sources, whichshow very constant values of DE*, regardless of the ripening sub-class. This fact is due to the technical characteristic of each lampwhich is perceived from the spectral distribution (Fig. 2). Thelamps F04 and F05 present a homogenous spectrum, different fromthe fluorescent lamps, which present peaks of emission in particu-lar wavelengths. It is also worth of highlight the similarities foundbetween sources F01 and F08, despite the differences in theirspectrums.

It is known that the lower the value of DE*, the lower the differ-ence of color perception under certain conditions. However, in thefood sector, a difference of up to 3.0 is considered acceptable (Cole-man, 2004), as the maximum color difference that can be visuallyperceived by the human eye. In this light, as the obtained resultsfor DE* were, in the most part, higher than 3.0, it is observed thatthe classification of the banana may be mistakenly conducted,depending on the type of illumination of the room duringclassification.

4. Conclusions

The need for standardization and improvement in quality of thecommercialized fruits has increased in recent years due to thegreat increase in exports. Several countries use rules for grading,labeling and packing in order to standardize the quality of theproducts and to meet consumer demands. Fruit classificationencompasses several parameters, such as ripening stage, and size.The specification of the subclass is a very important stage in clas-sification before commercialization, considering that the subclassdetermines the stage of ripening, which is a decisive factor for con-sumer choice. As peel color is one of the main indicators of ripen-ing, the factors that influence the analysis of color must bestandardized in the systems of visual inspection.

222 J.F.S. Gomes et al. / Journal of Food Engineering 120 (2014) 215–222

This article compares the use of nine different types of illumina-tion in the classification of the ripening stages of the Brazilian ‘pra-ta’ banana (C1 to C7). During the analysis, it was observed that thechromaticity coordinate a* of the CIELAB system was stronglyinfluenced by the use of different sources in the illumination sys-tem. A greener appearance was perceived with the use of lampswith higher CCT and a yellowier appearance was detected withlamps with lower CCT, thus influencing classification during pack-ing. Independently of compatibility of the results between some ofthe lamps, there is no reason why the remaining ones should notbe used in visual analysis, so long as they are taken as a standardfor the systems of measurement and classification, in a way thatthe systems of visual analysis have standardized illumination sys-tems among them or so long as they have their specification clearlyidentified.

Large color differences (DE* > 3.0) were found when the sameripening subclass was compared in different sources. The differ-ences observed justify the need for higher concern regarding thesystem of illumination used in rooms of banana classification, aswell as for other fruits and food in general.

It is known that illumination directly influences human percep-tion of color, however, it is necessary to highlight the importanceof the use of an adequate illumination system in order to ensurestandardization in the classification for commercialization of fruitsand thus to guarantee the quality of the product for consumers.

Acknowledgments

The authors acknowledge CNPq and Faperj for the financial sup-port (CNPq –563132/2010-8 and 370160/2011-8; FAPERJ – E-26/171.362/2001 E-26/103.269/2008). And also we would like tothank Cláudio Vicente Souza and Marcelo Bezerra Guedes for help-ing in the measurements.

References

Blasco, J., Aleixos, N., Gomez-Sanchıs, J., Molto, E., 2009a. Recognition andclassification of external peel damage in citrus fruits using multispectral dataand morphological features. Biosystems Engineering 103, 137–145.

Blasco, J., Cubero, S., Gomez-Sanchıs, J., Mira, P., Molto, E., 2009b. Development of amachine for the automatic sorting of pomegranate (Punicagranatum) arils basedon computer vision. Journal of Food Engineering 90, 27–34.

CEAGESP, 2006. PBMH & PIF – Programa Brasileiro para a Modernização daHorticultura & Produção Integrada de Frutas. Normas de Classificação deBanana. São Paulo, CEAGESP, 2006. (Documentos, 29).

CIE 13.3, 1995. Method of Measuring and Specifying colour rendering properties oflight sources. Publicação CIE.

CIE15.3, 2004. Colorimetry. Publicação CIE.Coleman, W.K., 2004. Comparative performance of the L* a* b* colour space and

North American colour charts for determining chipping quality in tubers ofpotato (Solanumtuberosum L.). Canadian Journal of Plant Science 84, 291–298.

Costa, C.L.M., Vieira, R.R., Alvarenga, A.P.D., Grieneisen, H.P.H., 2011. Calibration ofstandard lamps in color temperature. In: Proceedings of the 6� CongressoBrasileiro de Metrologia, Recife, Pernambuco, Brasil.

Davies, ER., 2009. The application of machine vision to food and agriculture: areview. The Imaging Science Journal 57 (4), 197–217 (21).

Dobrzanski Jr., B., Rybczynski, R., 2008. Influence of packing method on colourperception improving the appearance of fruits and vegetables. Research inAgricultural Engineering 54 (2), 97–103.

Embrapa – Empresa Brasileira de Pesquisa Agropecuária, , 2012, site <http://www.cpact.embrapa.br>, accessed in 23 de janeiro de 2012.

Gomes, J.F.S., Leta, F.R., 2012. Applications of computer vision techniques in theagriculture and food industry: a review. European Food Research andTechnology 235, 989–1000.

Gomes, J.F.S., Vieira, R.R., Leta, F.R., 2013. Colorimetric indicator for classification ofbananas during ripening. Scientia Horticulturae 150, 201–205.

Kavdir, I., Guyer, D.E., 2008. Evaluation of different pattern recognition techniquesfor apple sorting. Biosystems Engineering 99, 211–219.

Ling, Y., Vurro, M., Hurlbert, A. 2008. Surface chromaticity distributions of naturalobjects under changing illumination. In: Proceedings of the 4th EuropeanConference on Colour in Graphics, Imaging and Vision (CGIV), pp. 263–267.

LRC – Lighting Research Center, 2010. Recommendations for specifying colorproperties of light sources for retail merchandising. Alliance for Solid-StateIllumination Systems and Technologies, vol. 8, pp. 2.

Luo, M.R., 2011. The quality of light sources. Coloration Technology 127, 75–87.Manickavasagan, A., Sathya, G., Jayas, D.S., 2008. Comparison of illuminations to

identify wheat classes using monochrome images. Computers and Electronicsin Agriculture 63, 237–244.

Mohan, L.A., Karunakaran, C., Jayas, D.S., White, NDG., 2005. Classification of bulkcereals using visible and NIR reflectance characteristics. Canadian BiosystemEngineering 47, 7.7–7.14.

Mollazade, K., Omid, M., Tab, F., Mohtasebi, S., 2012. Principles and applications oflight backscattering imaging in quality evaluation of agro-food products: areview. Food and Bioprocess Technology, published online.

Ollkonen, M., Hansen, T., Gegenfurtner, KR., 2008. Color appearance for familiarobjects: effects of object shape, texture, and illumination changes. Journal ofVision 8 (5), 1–16, 13.

Sánchez, M.T., Haba, M.J., López, M.B., Novales, J.F., Varo, A.G., Marín, D.P., 2012.Non-destructive characterization and quality control of intact strawberriesbased on NIR spectral data. Journal of Food Engineering 110, 102–108.

Sirisomboon, P., Tanaka, M., Kojima, T., 2012. Evaluation of tomato texturalmechanical properties. Journal of Food Engineering 111, 618–624.

Zou, X.B., Zhao, J.W., Li, Y.X., 2007. Apple color grading based on organizationfeature parameters. Pattern Recognition Letters 28, 2046–2053.