foods

Article
Developing an Automatic Color Determination
Procedure for the Quality Assessment of Mangos
( Mangifera indica ) Using a CCD Camera and
Color Standards
Khanitta Ratprakhon 1,2 , W erner Neubauer 3 , Katharina Riehn 1 , Jan Fritsche 1
and Sascha Rohn 2 , 4 , *
1 Department of Nutrition and Home Economics, Faculty of Life Sciences, University of Applied Sciences
Hamburg, Ulmenliet 20, 20133 Hambur g, Germany; [email protected] (K.R.);
[email protected] (K.R.); [email protected] (J.F .)
2 Institute for Food Chemistry , Hambur g School of Food Science, Universität Hamburg, Grindelallee 117,
20146 Hamburg, Germany
3 win ing.-Büro W erner Neubauer , Paradiesweg 4, 96148 Baunach, Germany; [email protected]
4 Department of Food Chemistry and Analysis, Institute of Food T echnology and Food Chemistry ,
T echnische Universität Berlin, TIB 4 / 3-1, Gustav-Meyer-Allee 25, 13355 Berlin, Germany
* Correspondence: [email protected] g.de or [email protected] ; T el.: + 49-40-42838-7979
Received: 15 October 2020; Accepted: 19 November 2020; Published: 21 November 2020
     
  

Abstract:
Color is one of the key sensory characteristics in the evaluation of the quality of
mangos ( Mangifera indica ) especially with r egard to determining the optimal level of ripeness.
However , an objective color determination of entir e fruits can be a challenging task. Conventional
evaluation methods such as colorimetric or spectr ophotometric procedur es are primarily limited
to a homogenous distribution of the color . Accor dingly , a direct assessment of the mango quality
with r egard to color r equires mor e pronounced color determination pr ocedures. In this study , the
color of the peel and the pulp of the mango cultivars “Nam Dokmai”, “Mahachanok”, and “Kent”
was evaluated and categorized into various levels of ripeness using a char ge-coupled device (CCD)
camera in combination with a computer vision system and color standar ds. The color evaluation
pr ocess is based on a transformation of the RGB (red, gr een, and blue) color space values into the
HSI (hue, saturation, and intensity) color system and the Natural Color Standard (NCS). The r esults
showed that for pulp color codes, 0560-Y20R and 0560-Y40R can be used as appr opriate indicators
for the ripeness of the cultivars “Nam Dokmai” and “Mahachanok”. The peels of these two mango
cultivars pr esent two distinct colors (1050-Y40R and 1060-Y40R), which can be used to determine
the fruit maturity during the post-ripening pr ocess. However , in the case of the cultivar “Kent”,
peel color detection was not an applicable appr oach for determining ripeness; thus, the determination
of the pulp color with the color code 0550-Y20R gave pr omising results.
Keywords: mango color; CCD camera; computer vision system; NCS color standar d
1. Introduction
Color is an important factor for the quality assessment of fr esh produce, as it can be used to
estimate the ripeness of fruits and vegetables. A survey of the existing literatur e revealed, for example,
that Khairunniza-Bejo et al. (2014) used color to determine internal attributes, like sweetness [
1
].
Jha et al. (2007), as well as Nambi et al. (2015), used color to study internal properties, such as fruit
maturity [ 2 , 3 ].
Foods 2020 , 9 , 1709; doi:10.3390 / foods9111709 www .mdpi.com / journal / foods

Foods 2020 , 9 , 1709 2 of 19
Accor ding to Nagle et al. (2016), chlorophylls, car otenoids, and anthocyanins are r esponsible for
the gr een, yellow , and r ed colors of fruit and vegetables, respectively , also repr esenting the key pigments
in mango ( Mangifera indica ) maturation [
4
]. Hence, these natural pigments might be applicable for
the visual color assessment of mangos. In addition, changes in mango peel and pulp color reflect
their maturity development during the post-ripening process [
5
,
6
]. T ypically , mangos ar e regar ded
as fully ripe when they change to a juicy and soft textur e, with a sweet taste and rich ar oma, and
fully developed coloring depending on cultivars [
7
]. This behavior can also be found in many other
(climacteric) fruits, such as blueberries, wher e color significantly changes from slightly gr een to deep
purple tones [
8
]. For color measurement, spectr ophotometry is a technique widely used to determine
pr oduce color by measuring the spectral distribution of transmittance using a sample’s reflection [
9
].
Being traditionally non-destructive instr uments, colorimeters are extensively used in the fruit industry
for measuring the color of fruits [
10
]. In this context, uniform measur ements achieved with the so-called
Commission Internationale d’Eclairage (CIE) L*a*b* (CIELAB) color space, as standardization and
visual assessment, can be very pr ecise [
11
]. Generally , CIE L*a*b* (CIELAB) is the most complete color
model, being used conventionally for describing colors visible to the human eye. It was developed
for this specific purpose by the Commission Internationale d’Eclairage (CIE). However , ther e are
further models that seem to be easier to handle and seem to be mor e intuitive when color changes.
Some color models and methods can also be beneficial, when measurements and evaluations have
to be carried out dir ectly on-site (e.g., for monitoring fr uit ripeness). Another important aspect to
consider is that the entir e fruit needs to be characterized [
12
]. In addition, a number of fruits must
be assessed so that they can be repr esentative of the entire field. These aspects can be limited when
using colorimeters and CIELAB [
12
]. However , using colorimetric measurements and CIELAB is often
limited with r egard to sampling ar ea compared to the size of the fr uit [
13
]. Consequently , modified
two-dimensional color imaging is r equired to over come this limitation. Even mor e easy to apply ,
photons r eflected from the fruit’s skin can be detected and converted to electric signals, e.g., by a
char ge-coupled device (CCD) camera [
12
]. However , alternative techniques and models have to be
developed and tested in or der to determine the heterogeneous color distribution of mangos, especially
when the uneven shape of the mango fruit has to be consider ed as well and measurements should be
done with easy-to-use equipment.
The importance of computer vision systems in the quality assessment of food has steadily incr eased
in r ecent years. The biggest advantage, when compared to traditional methods, is that each pixel of the
entir e detected surface is included in the analysis, resulting in complex color modeling appr oaches
assisted by multivariate statistical methods [
2
]. Image capturing and image pr ocessing are the main
components of machine vision systems. Due to their low noise levels, high sensitivity , and great
dynamic range [
14
], CCD cameras seem pr omising for mango color evaluation. According to most of
the literatur e surveyed, computer vision systems are mostly used for automatic mango fruit grading
based on color systems [ 4 , 15 – 19 ].
The pr e-processing pixel analysis used in this work for converting input images into output
images is based on a transformation of RGB values into the HSI color system that defines hue (H),
saturation (S), and intensity (I). The RGB color space is a thr ee-dimensional color system that constructs
all colors fr om the combination of the colors red, green, and blue. Similarly to the CIELAB color
space, it is possible to analyze fruit ripeness thr ough RGB values. This system can be regar ded as a
cubic model, wher e red , gr een , and blue are the key colors of the RGB color space on the di ff er ent axes.
This model has serious disadvantages when someone wants to perform di ff er ent types of processing of
the images such as enhancement, segmentation, or classification. T o reduce such limitations, alternative
thr ee-dimensional models have been developed that separate the color from the lighting information.
For example, the HSI space further considers hue , saturation , and intensity of a color . This model has
been described as suitable for food technological resear ch questions such as the evaluation of the quality
of fruits [
14
,
20
,
21
]. W ith appr opriate calibration, it is possible to process images with changes in light,
as the ambient colors can be di ff er entiated from one another by the color tone component and certain

Foods 2020 , 9 , 1709 3 of 19
calibration sets (e.g., with color car ds), with which external influencing factors can be minimized [
14
].
The use of such r eferences is easy to handle and the influence of even mor e complex conditions such as
lighting changes in the field or camera-dependent parameters can be r educed. Comparatively , CIELAB
encounters the out-of-gamut pr oblem. It depends on the shape of the 3D color gamut and which color
space (CIELAB vs. RGB / HSI) is easier to use and assess (e.g., for estimating thresholds). Consequently ,
CIELAB is not necessarily superior to HSI and vice versa. When cylinder coor dinates are needed,
HSI seems to be easier [
14
]. However , this model has been designed to match human intuition [
20
].
It seems to also have advantages in image processing, such as color image enhancement, segmentation,
fusion, color -based object detection, recognition, and tra ffi c signal detection [ 21 ].
As in the RGB system (estimated with a CCD camera), a digital image in the HSI space also consists
of values of hue, saturation, and intensity / brightness. RGB and HSI values can be converted into each
value by data conversion. However , initially , before the RGB-to-HSI conversion, it is necessary to
normalize the R, G, and B components of a pixel in a color image fr om the range of (0, 255) to (0, 1),
which can be performed automatically by fr ee as well as commercially available softwar e [ 22 ].
The HSI color system is commonly used in the food industry , with hue defining perceived color ,
saturation measuring color density , and intensity repr esenting color brightness or illuminance [
14
].
The stabilization of illuminance values is of particular importance for obtaining precise r esults.
Because of the many possible variants, the application of the HSI color system is comparable to the
human visual per ception of food surfaces [
23
]. Blasco et al. (2007) also used this color space to study
quality defects of citrus fruits [
24
]. Similarly , Abdullah et al. (2006) converted RGB values into HSI
values for the classification of starfruits ( A verrhoa carambola ) into four levels of maturity [ 25 ].
In this work, the idea and concept of developing the computer vision method wer e derived from
the German national standar d DIN EN 60350, which sets the standards for measuring the performance
of particular household electric cooking appliances such as ranges, ovens, steam ovens, and grills [
26
].
It describes the color brown for determining the degr ee of food browning using a computer vision
system. However , there is still a lack of studies connecting color evaluation to a standar d color system
or other well-known color mappings.
Developed in Sweden, the Natural Color Standard System
®
(NCS) has continuously become
the international color standar d most commonly used in the food industry . It classifies colors into
six types, labeling them as elementary colors, namely whiteness (W), blackness (S), redness (R),
yellowness (Y), gr eenness (G), and blueness (B), all of which are per ceived by the human sense of
color . Color is specified by three main parameters expr essed in per centages: blackness, chr omaticness,
and hue. Whiteness is determined by subtracting the sum of blackness and chr omaticness from
100% [
27
]. The application of a color standar d ensures incr eased stability , consistency , and applicability
in measuring systems [
28
]. A display of color can be pr esented by using a color map or color code
standar d. Instead of only indicating a color value, this method allows for a direct color detection.
This study aimed at developing a visual RGB / HSI-based measur ement and quantification method
for the automated color assessment of the peel and / or the pulp of mangos measur ed at various
stages of maturity . Mor eover , the color spectrum of mango ripeness was coded by means of color
standar ds in order to simplify the evaluation. In mor e detail, the study aimed at pr oviding a
compr ehensive color evaluation of mangos using a computer vision system for both homogeneous-
and heter ogeneous-colored mangos using a new method that combines the application of a color
system and a color code standar d. The mango cultivar “Nam Dokmai” was selected to r epresent a
homogenously color ed mango, as its peel color changes from light gr een-yellow to gold-yellow , while
the mango cultivars “Mahachanok” and “Kent” r epresent heter ogeneously colored varieties, pr oviding
a mix of r ed and green colors on their surface.

Foods 2020 , 9 , 1709 4 of 19
2. Materials and Methods
2.1. Samples
The mango cultivars “Nam Dokmai” (NDM) and “Mahachanok” (MHC) used in this experiment
wer e harvested 110 days (for NDM) or 98–100 days (MHC) after full bloom, respectively . They wer e sent
immediately fr om Thailand to Germany by airfreight, while the cv . “Kent”, which can be purchased
at the wholesale market in Hambur g, Germany , were initially transported fr om Peru to Hambur g,
Germany , by ship. All samples were allowed to post-ripen in a climacteric chamber (T yp SB222 / 500,
300 L, W eiss Umwelttechnik GmbH, Reiskirchen, Germany) with temperatur e, humidity , and ethylene
gas r egulated at 30
◦
C, 90% RH, and 20 ppm, r espectively . The ripening time ranged from 0 to 70 h
for NDM and MHC and fr om 0 to 60 h for “Kent” mangos. The color of the samples changed from
light gr een-yellow to gold-yellow in the cases of NDM and MHC, while the green peel color of “Kent”
r emained the same throughout the ripening period. Each batch of ripening NDM and MHC mangos
was evaluated every 12 h. Duplicate measur ements of the peel and pulp color on each side of several
samples, all at a first glance of a comparable ripening stage, wer e taken simultaneously . MHC fruits
wer e measured in batches of five mangos. Peel and pulp color were then used to determine ripeness.
After measuring peel color , the mangos wer e halved along the seed and cut lengthways in slices for
evaluating the pulp color (Figur e 1 ).
Food s 2020 , 9 , x FO R P E ER R EVIE W 4 of 19

Th e mango c ul tiv ar s “ N am Dokm ai ” ( NDM) and “ Ma h acha no k ” (M HC) use d in th is exp e riment
were harve sted 1 10 d ays ( f or NDM) o r 98 – 100 d ays ( MHC) af ter f ul l b loom, re s pec tively. Th ey were
sent imm edi ately from T haila n d to Germ any by airfr eig ht , wh i le th e cv . “ K ent ” , which can be
purcha sed at th e who lesal e market in H a mb urg , Ger many, we re i nitiall y tran s po rted from Peru to
Ha mb ur g, Ge rmany, by shi p. A ll s ample s were allowe d to po st - ripe n in a cl imact eric ch amber (Typ
SB22 2/500, 300 L, We is s U mw elttec hnik Gm bH, Re iskirchen , Ger many) with t emp era ture , humid ity,
and eth yle ne ga s r egu l ated at 30 ° C, 90% RH, and 20 pp m, respe ct ively . Th e r ipening time ra nge d
from 0 to 70 h for NDM an d MHC and from 0 to 60 h for “ Kent ” mangos . Th e color o f th e s amples
changed from li ght g reen - y ell ow to go ld - yel lo w in th e cas e s of ND M and MHC, while th e g re en peel
color o f “ Ken t ” rem ai n ed t he s ame th roughout th e r i pen ing pe rio d. E ach batch of r ipenin g N DM and
MHC m ango s wa s ev alu ated every 12 h. Dupl ica te m ea s urement s of th e pee l an d pulp color on ea ch
si de o f seve ra l s ampl es, all at a f ir st g la nce o f a com p ara ble r ipenin g sta ge, wer e tak en
si mu ltaneo us ly . M HC fruits wer e me asu red in batch es of fi ve man g os. Pee l and p ul p color wer e th en
used to deter mine ripene s s. After me asuring pee l col or, th e m ang os we re ha lve d a lon g th e s e ed a nd
cut lengthw ay s in slice s fo r eva luating the pulp color (F i gure 1).

Figure 1. S l ic e s of “ Nam Dokmai ” m ang o for eval uating the pu lp co lo r in the co m pu ter vi si o n syst em.
2.2. Digital Co lo r M eas uring Sy s tem
Th e ima ge p rocess ing syst em was sup plied by w in ing . - B ü ro W erner Neu b auer (B au n ach ,
Germ any). T he sys tem co nsis ts o f a CC D camer a pur chased from I DS Im aging Dev elopm ent Sys tem s
Gm bH (Ober sul m , Germ an y) , a l ig ht - ti ght mea surin g ch amber an d a l ig ht uni t ( Fi g ure 2). Th e camer a
was con nect e d to a PC v ia USB interf ace and con tro ll e d by im ag e pro ce ss i ng softw ar e called
WinFood Eva l , version 1.2 1 (wi n in g - B ü r o Werner N e ubauer, B au n ach, Germ an y) . Th e pro g r am wa s
made espec iall y fo r th e ev alua tion o f fo od. In th is c ase, it w as appli ed to au to m atic ally pe rfo rm th e
following fu n ct ions: sy stem ca li br ation, im ag e reco rd ing (corr ect io n of th e brigh tn ess) , con ver si on of
RGB into HSI , di v ision into color c l as s es, a nd color ev alua tion .
Th e CC D cam era h ad a re so lu tion of 12 80 × 10 24 pixe ls , with e ach p ix el h avi ng a co lor re sol ution
of 3 × 8 bit s ( RGB) . Th e f ram era te o f th e ca mer a w as 1 0 fr ames/s . T he l ig ht in g s y stem wa s co mp osed
of 16 fluores c ent la mp s (e ac h la mp 36 W ). Th e light color numb er w as 965, which means it had a colo r
tem perature of 6500 K and a c olo r ren de ring index (C RI) of >9 0 %. A CRI va lue of 90 % or gre ater (CRI
> 90%) is nec ess ar y to pro duce h ig h - q u ality co lor im ag e s. Th e l ig h ting ch amber dimens ions ( w × d ×
h ) were 140 c m × 90 cm × 190 cm . A d iff usi on d isk w as m ount ed 50 cm ben e ath th e l amps. Th e l arge
chamb er can genera te an e ven and smo ot h stream of li ght in th e m iddle of th e m ea s uri n g area . It was
also nece ss ary to ma inta in th e l ig ht acc ura c y re q ui re d to mea sure colors in ade quate re sol uti on and
to meet re q uirem ent s speci fi ed in accor d ance with th e Germ an n atio nal st andard DI N E N 60 350 [ 26].
Th e con trast was c learer when th e imag e intensity was not at its maximum se tt ing, i. e ., wh en only
half the m axim um brightn ess w as us ed.
Th e camer a was mo unted 120 cm above th e test sam ples an d in serted into th e diffu si on d i sk.
Th e mea surin g area w as 49 cm × 35 cm . I mage captur i ng to ok p la ce in th e c lose d chamb er (F i g ure 2).

Figure 1.
Slices of “Nam Dokmai” mango for evaluating the pulp color in the computer vision system.
2.2. Digital Color Measuring System
The image pr ocessing system was supplied by win ing.-Bür o W erner Neubauer (Baunach,
Germany). The system consists of a CCD camera pur chased from IDS Imaging Development
Systems GmbH (Obersulm, Germany), a light-tight measuring chamber and a light unit (Figure 2 ).
The camera was connected to a PC via USB interface and contr olled by image processing softwar e
called W inFoodEval, version 1.21 (win ing -Bür o W erner Neubauer , Baunach, Germany). The program
was made especially for the evaluation of food. In this case, it was applied to automatically perform
the following functions: system calibration, image recor ding (corr ection of the brightness), conversion
of RGB into HSI, division into color classes, and color evaluation.
The CCD camera had a resolution of 1280
×
1024 pixels, with each pixel having a color resolution
of 3 × 8 bits (RGB). The framerate of the camera was 10 frames / s. The lighting system was composed
of 16 fluor escent lamps (each lamp 36 W). The light color number was 965, which means it had a color
temperatur e of 6500 K and a color rendering index (CRI) of > 90%. A CRI value of 90% or gr eater (CRI
> 90%) is necessary to pr oduce high-quality color images. The lighting chamber dimensions ( w
×
d
×
h )
wer e 140 cm
×
90 cm
×
190 cm. A di ff usion disk was mounted 50 cm beneath the lamps. The large
chamber can generate an even and smooth stream of light in the middle of the measuring ar ea. It was
also necessary to maintain the light accuracy r equired to measur e colors in adequate resolution and to
meet r equirements specified in accor dance with the German national standard DIN EN 60350 [
26
].

Foods 2020 , 9 , 1709 5 of 19
The contrast was clear er when the image intensity was not at its maximum setting, i.e., when only half
the maximum brightness was used.
Food s 2020 , 9 , x FO R P E ER R EVIE W 5 of 19

Figure 2. S che m ati c il lustration of the co m pu ter vi s io n syst em se tup incl u di ng the imag e acquisiti on
chamber for the c o lo r measurem ent o f m ang o slic e s. CC D: C har g e - co up le d de vi c e camera
2.3. Ima ge Ana lysis an d Co lo r Code Sta nd a r d
Th e ma in tas k o f th e W in FoodEv al sof tw ar e w as to map th e me asured RGB values from t he
camer a to cr ea t e a speci fic color chart designed for mango me asuremen t pur po ses, inc lu d ing th e
colors m ai n ly impo rtant fo r mango ev alua tion . Th i s c olor ch ar t ha s th ree di me nsions : one f or hue
(color ), one f or chrom atic ness (ra tio of color) , an d th e th ird on e for bl ackn e ss (r atio o f b la c k) .
Howev er, in order to com ply with th e NCS, th e co lo r chart wa s c onv erted and la be led with rega r d
to hue ( H), c hrom aticnes s (S — s atur atio n ), and wh iteness (I — inten si ty) . It shou l d be not ed th at th is
is an ad apted HS I co lor sp ace th at i s oft en used in i m ag e pro cess in g.
Fi g ure 3 pres ent s a f lowch ar t br iefly i llu strating th e c olor me asure men t pro cess . First , a man g o
image i s c ap tured w ith th e CC D ca mera. Secon dly , th e ima ge ta ken in th e prev ious step i s
au to mat ically recogn iz e d as being a typ i cal mango for m. Next, all b la c k an d re d colors in th e i mage
ar e identi fi e d and separ ate d from th e ot her color s, as only th e la tt er ar e impo rtan t for man go r i pen ess
evaluati on. A fter th at, th e image i s con vert ed from th e camer a - o rig in atin g an d - depen d ent RGB
values to H S I val u es and fi n ally into NCS col o r co des with th e com merc ially avail abl e sof tw ar e
WinFood Eva l . F inally , a histogr am an d pse udo - c olor ima ge are cre ated aut om atic ally by th e
softwar e. In t his stu dy , th e sys tem ’s m ain fu nct ion is t he color me asuremen t of m ango s ur fa c es (peel
and p ul p ). Ho we ver, it i s alr ea dy u sed in f ood ind ustry for ot her app l ica tion s (e.g., brow ning o f bread
surface s) [ 22]. To ada pt th e sys tem to m an gos, it w as c alibrated in a way th at s up po rts th e r an ge s o f
color, br ig ht n ess, and s atur ation that ar e approp ri ate for t his s tudy .
Figure 3. Flowc har t for m ang o co lo r proce ss in g using a co m pu ter vi so n syst em with CC D camera and
outpu t. RG B : re d, gr een, an d blue co lo r s pac e; HSI: hu e, sat ur ati on, and in tensi ty co lo r sy st em; NCS :
Natural Color Standard .
After th e mat hem atic al con version o f th e RGB va lu e s, HSI va lu e s ca n be si mp ly t ra ns formed t o
NCS color co des. As show n exem pl aril y in F igure 4, th e co lor cod e NC S S 20 10 - G4 0Y is den ot ed by
a h ue of 60% green b asic c olor and 40% ye ll ow ( “G 40Y” ). Bl ackne ss is at 20% an d chrom atic ness is
at 10% ( “20 10”). Accord in gl y, whit ene s s can be c al c u la ted in th is e xample as 70 % ( 100% − 20 % − 10%
= 70 % ). Th is e xample illust r ates a co lor w ith wea k satu ra tion.

Figure 2.
Schematic illustration of the computer vision system setup including the image acquisition
chamber for the color measurement of mango slices. CCD: Charge-coupled device camera
The camera was mounted 120 cm above the test samples and inserted into the di ff usion disk.
The measuring ar ea was 49 cm
×
35 cm. Image capturing took place in the closed chamber (Figur e 2 ).
2.3. Image Analysis and Color Code Standard
The main task of the W inFoodEval softwar e was to map the measured RGB values fr om the
camera to cr eate a specific color chart designed for mango measurement purposes, including the colors
mainly important for mango evaluation. This color chart has three dimensions: one for hue (color),
one for chr omaticness (ratio of color), and the third one for blackness (ratio of black). However , in
or der to comply with the NCS, the color chart was converted and labeled with regar d to hue (H),
chr omaticness (S—saturation), and whiteness (I—intensity). It should be noted that this is an adapted
HSI color space that is often used in image pr ocessing.
Figur e 3 presents a flowchart briefly illustrating the color measur ement process. First, a mango
image is captur ed with the CCD camera. Secondly , the image taken in the previous step is automatically
r ecognized as being a typical mango form. Next, all black and red colors in the image ar e identified
and separated fr om the other colors, as only the latter are important for mango ripeness evaluation.
After that, the image is converted from the camera-originating and -dependent RGB values to HSI
values and finally into NCS color codes with the commercially available softwar e W inFoodEval. Finally ,
a histogram and pseudo-color image are cr eated automatically by the softwar e. In this study , the
system’s main function is the color measurement of mango surfaces (peel and pulp). However , it is
alr eady used in food industry for other applications (e.g., browning of br ead surfaces) [
22
]. T o adapt
the system to mangos, it was calibrated in a way that supports the ranges of color , brightness, and
saturation that ar e appropriate for this study .
Food s 2020 , 9 , x FO R P E ER R EVIE W 5 of 19

Figure 2. S che m ati c il lustration of the co m pu ter vi s io n syst em se tup incl u di ng the imag e acquisiti on
chamber for the c o lo r measurem ent o f m ang o slic e s. CC D: C har g e - co up le d de vi c e camera
2.3. Ima ge Ana lysis an d Co lo r Code Sta nd a r d
Th e ma in tas k o f th e W in FoodEv al sof tw ar e w as to map th e me asured RGB values from t he
camer a to cr ea t e a speci fic color chart designed for mango me asuremen t pur po ses, inc lu d ing th e
colors m ai n ly impo rtant fo r mango ev alua tion . Th i s c olor ch ar t ha s th ree di me nsions : one f or hue
(color ), one f or chrom atic ness (ra tio of color) , an d th e th ird on e for bl ackn e ss (r atio o f b la c k) .
Howev er, in order to com ply with th e NCS, th e co lo r chart wa s c onv erted and la be led with rega r d
to hue ( H), c hrom aticnes s (S — s atur atio n ), and wh iteness (I — inten si ty) . It shou l d be not ed th at th is
is an ad apted HS I co lor sp ace th at i s oft en used in i m ag e pro cess in g.
Fi g ure 3 pres ent s a f lowch ar t br iefly i llu strating th e c olor me asure men t pro cess . First , a man g o
image i s c ap tured w ith th e CC D ca mera. Secon dly , th e ima ge ta ken in th e prev ious step i s
au to mat ically recogn iz e d as being a typ i cal mango for m. Next, all b la c k an d re d colors in th e i mage
ar e identi fi e d and separ ate d from th e ot her color s, as only th e la tt er ar e impo rtan t for man go r i pen ess
evaluati on. A fter th at, th e image i s con vert ed from th e camer a - o rig in atin g an d - depen d ent RGB
values to H S I val u es and fi n ally into NCS col o r co des with th e com merc ially avail abl e sof tw ar e
WinFood Eva l . F inally , a histogr am an d pse udo - c olor ima ge are cre ated aut om atic ally by th e
softwar e. In t his stu dy , th e sys tem ’s m ain fu nct ion is t he color me asuremen t of m ango s ur fa c es (peel
and p ul p ). Ho we ver, it i s alr ea dy u sed in f ood ind ustry for ot her app l ica tion s (e.g., brow ning o f bread
surface s) [ 22]. To ada pt th e sys tem to m an gos, it w as c alibrated in a way th at s up po rts th e r an ge s o f
color, br ig ht n ess, and s atur ation that ar e approp ri ate for t his s tudy .
Figure 3. Flowc har t for m ang o co lo r proce ss in g using a co m pu ter vi so n syst em with CC D camera and
outpu t. RG B : re d, gr een, an d blue co lo r s pac e; HSI: hu e, sat ur ati on, and in tensi ty co lo r sy st em; NCS :
Natural Color Standard .
After th e mat hem atic al con version o f th e RGB va lu e s, HSI va lu e s ca n be si mp ly t ra ns formed t o
NCS color co des. As show n exem pl aril y in F igure 4, th e co lor cod e NC S S 20 10 - G4 0Y is den ot ed by
a h ue of 60% green b asic c olor and 40% ye ll ow ( “G 40Y” ). Bl ackne ss is at 20% an d chrom atic ness is
at 10% ( “20 10”). Accord in gl y, whit ene s s can be c al c u la ted in th is e xample as 70 % ( 100% − 20 % − 10%
= 70 % ). Th is e xample illust r ates a co lor w ith wea k satu ra tion.

Figure 3.
Flowchart for mango color pr ocessing using a computer vison system with CCD camera and
output. RGB: red, gr een, and blue color space; HSI: hue, saturation, and intensity color system; NCS:
Natural Color Standard.

Foods 2020 , 9 , 1709 6 of 19
After the mathematical conversion of the RGB values, HSI values can be simply transformed to
NCS color codes. As shown exemplarily in Figur e 4 , the color code NCS S 2010-G40Y is denoted by a
hue of 60% gr een basic color and 40% yellow (“G 40Y”). Blackness is at 20% and chromaticness is at
10% (“20 10”). Accordingly , whiteness can be calculated in this example as 70% (100%
−
20%
−
10% =
70%). This example illustrates a color with weak saturation.
Food s 2020 , 9 , x FO R P E ER R EVIE W 6 of 19

Th e who le sy stem def ines 16 color sh ad es in th e ra n g e of green (G) to yellow (Y ) and re d ( R ). S o ,
fi n al co des c an be G, G1 0Y, G20 Y, G 30Y, G40 Y, G 50Y, G60 Y, G 70Y, G80 Y, G 90Y, Y, Y10R, Y2 0R, Y 30R,
Y40R, Y 50R. Th ere can be eig ht step s i n sa tur ation ( 10, 15, 20, 30, 40, 50, 60, 80) and fo ur s tep s in
brightn ess (5, 10, 2 0, 40) , le adi n g to a to tal of 5 12 colo r com binat io ns (1 6 × 8 × 4).

Figure 4. Relat io n of the Nat ur al Color Sta ndard ( NCS ) c ol or co de with the m od i fie d HSI (hu e,
sat ur ati on, and intensi ty) co lo r s ystem .
2.4. Co lo r Ca li bratio n
Th e sys tem i s ini t ially ca l ibra ted us ing a c olo r char t with an ap pro pria te s el ect ion of tar g et
colors: i d eall y , all 51 2 sh ade com bination s ( 16 × 4 × 8) h ave to be c alib ra ted. Th e ca l ibra tion also allows
for th e corr e ct ion of influ encing en do genous f acto rs s uch as di ff er ent light source s ( e.g ., when
apply ing suc h a sy stem d i rect ly in th e ( mango) f ie ld). Howev e r, based on th e ap pro pria te N CS col or
chart, wh ich i s ori gi n ally d efi ne d by th e NCS color st and ar d o rg an ization, th e mo st relev ant color s
typ ica l ly use d fo r m ango evaluati on (Figur e 5) we re app li ed for a so - ca ll ed sho rt - check ca li b ra tion.
For th at p urp ose, th e came ra pro v ides th e res ul ts as a com bin atio n of R, G, an d B v alues . I n a v ery
si mp le w ay , t his co lor mo d el is c ubic an d only re fl ect s th e color of t he sa mp le . Co nseq uently , it needs
to be con vert ed to HSI v alues in or de r to gi ve info rmation abo ut hue and c hrom aticnes s. Th e
con version o f RGB va lu e s t o HSI va lues and th e integ ra tion o f th e NCS colo r co d es i s th en ca l culated
by th e Win Fo odEv al so ftware .
For cor rect in g an uneven il l umin ation i n th e ima ge acquisition c hamb er, th e color ca li br ation
was per form ed us ing r efe rence stan da rd s amples . I n th is stud y, th e brow n color st andar d with
know n value s of re fl ect ed li ght ( si m ila r to intensity ) was appl ie d for th e sy s tem cal ibr ati on. As
specif ied in t he Germ an n ational stand ar d DI N EN 60350, th e c alibration mo d el con s is ted o f th e 15
color refe renc es wh ich are used to c alibr ate th e intens i ty of th e brow n color o f sma ll ca kes . Exp e rience
has shown th at when th e brow n colors were u sed f or calibr ation , th e green co lors wer e ca lib ra te d
au to mat ically . Howev er , t he valid ation of th e g reen colors , com mo nly know n as a short check or
color check, was addi t ion ally ca rrie d o ut to ensu re th at th e co lor s were w ithin th e ra n ge of ± 5% . In
th e fol low ing , all re su lts p resented wer e in th is ra n g e. Co nse qu en tly, f urther labeling o f st an dard
devia t ion w as om itted .
For th is so - c alled color ch e ck, a va li d ate d color ch ar t from th e N atu ral Co lo r St and ar d Sys te m ®
was u sed as a reference . T he color ref ere nces were se l ect ed from co lors t hat are typ ically inc luded in
th e mango co lor ra ng e. Th e fo ll ow ing were th e f ive color s requi r ed for th e col or check : S 10 20 G, S
1040 Y 60R, S 0530 Y, S 051 5 G 20 Y , and S 2020 G 80 Y . Each b lock i s 4. 5 × 4.6 6 cm (F i gure 5) an d wa s
directl y appl i ed in th e ch amber of th e com put er vi si on s ystem setup. Th i s b asic va li d atio n was
perform ed be fore e ve ry me asuremen t.

Figure 4.
Relation of the Natural Color Standard (NCS) color code with the modified HSI (hue,
saturation, and intensity) color system.
The whole system defines 16 color shades in the range of gr een (G) to yellow (Y) and red (R).
So, final codes can be G, G10Y , G20Y , G30Y , G40Y , G50Y , G60Y , G70Y , G80Y , G90Y , Y , Y10R, Y20R,
Y30R, Y40R, Y50R. Ther e can be eight steps in saturation (10, 15, 20, 30, 40, 50, 60, 80) and four steps in
brightness (5, 10, 20, 40), leading to a total of 512 color combinations (16 × 8 × 4).
2.4. Color Calibration
The system is initially calibrated using a color chart with an appr opriate selection of target colors:
ideally , all 512 shade combinations (16
×
4
×
8) have to be calibrated. The calibration also allows for the
corr ection of influencing endogenous factors such as di ff erent light sour ces (e.g., when applying such a
system dir ectly in the (mango) field). However , based on the appropriate NCS color chart, which is
originally defined by the NCS color standar d organization, the most r elevant colors typically used for
mango evaluation (Figur e 5 ) were applied for a so-called short-check calibration. For that purpose, the
camera pr ovides the results as a combination of R, G, and B values. In a very simple way , this color
model is cubic and only r eflects the color of the sample. Consequently , it needs to be converted to HSI
values in or der to give information about hue and chromaticness. The conversion of RGB values to
HSI values and the integration of the NCS color codes is then calculated by the W inFoodEval software.
Food s 2020 , 9 , x FO R P E ER R EVIE W 7 of 19

Figure 5. NCS col or chart use d for t he co lo r chec k for mang o eval uation .
2.5. Ima ge Re c ognition an d C ol or Code Cal c ulatio n
After th e b asi c ca li br ation, th e meas ure men t was per formed by pl aci n g th e m an go s ample s i nt o
th e im ag e ac qu i s ition ch am ber ( Fi g ure 2). Th e s oftw ar e tak es th e RGB v alue s f rom th e c ame ra and
con vert s th e m into co lor , s atur ation , a n d bri ghtn ess a nd tak es th e ca l ibra tion d at a into accou nt . Th is
meas urement sy stem pro vi des a hi gh - re solu t ion ima g e ( 1280 × 1024 p ixels o f co lor, s atur atio n, and
brightn ess ) o f th e mango s amples . A ll meas urement pre - re quis ite s were set in accord ance w ith th e
Germ an st an dard DIN E N 6035 0.
After th e so f tw ar e to ol h ad reco gni zed every s ing le man go/m an go slice, it au to mat icall y
sea rch ed for c olors th at wer e not in th e d efi ne d co lor r ange. O ut - of - ra nge co lors t hat ar e to o da rk ar e
represent ed b y bl ack spo ts or red co lors .
Th e rem ai n in g co lors ( Fi g ure s 6 and 7, denot ed as C OLO R) were used fo r con version into th e
HSI color spa ce. Th e re la ti onship bet we en RGB and HSI va lu e s w as c al c ulated in th e followi ng way :
mo re th an on e hun dred R GB va lu e s, w hich served as th e re ferenc e, we re ob tai ned from th e color
meas urement when us ing th e CC D camera. After plot ting th es e values , all of th em exhi bited a
non li ne ar be havior . To ge t th e H SI v alues, a li n ea r interp ola t ion was app li ed to c alcu la te th e H SI
value bet wee n tw o point s f rom the no nl i near c urve.
Th e res ul ts w ere exp r esse d as a f ull *.x lsx d ata f il e as e xem pla r il y sh own in F igure 6. As th is d at e
fi l e is ver y co mp lex, it is ex pla in ed in mo re det ail : t he pixel va lu e s an d d is trib utio n of bl ack, re d, and
remain ing co lors ar e di spl ay ed at th e t op , respect iv ely (F i gur e 7) . As alre ady exp lained ab ov e, all
bla ck and red color s in th e i mage are ide nt if ie d and se para ted f rom th e ot her co lo rs, as on ly th e l atter
ar e impo rtant for man go r i pen es s eva luation. A ll pix el v alues ( Fi g ure 7, PIX EL ) are c al c ulate d as a
percen tage ( % ) of the t ot al man go s ur face.

Figure 5. NCS color chart used for the color check for mango evaluation.

Foods 2020 , 9 , 1709 7 of 19
For corr ecting an uneven illumination in the image acquisition chamber , the color calibration was
performed using r eference standar d samples. In this study , the brown color standar d with known
values of r eflected light (similar to intensity) was applied for the system calibration. As specified in the
German national standar d DIN EN 60350, the calibration model consisted of the 15 color refer ences
which ar e used to calibrate the intensity of the brown color of small cakes. Experience has shown
that when the br own colors were used for calibration, the gr een colors were calibrated automatically .
However , the validation of the green colors, commonly known as a short check or color check, was
additionally carried out to ensur e that the colors were within the range of
±
5%. In the following, all
r esults presented wer e in this range. Consequently , further labeling of standard deviation was omitted.
For this so-called color check, a validated color chart from the Natural Color Standar d System
®
was used as a r eference. The color refer ences were selected fr om colors that are typically included in
the mango color range. The following wer e the five colors requir ed for the color check: S 1020 G, S 1040
Y60R, S 0530 Y , S 0515 G20Y , and S 2020 G80Y . Each block is 4.5
×
4.66 cm (Figur e 5 ) and was directly
applied in the chamber of the computer vision system setup. This basic validation was performed
befor e every measurement.
2.5. Image Recognition and Color Code Calculation
After the basic calibration, the measurement was performed by placing the mango samples
into the image acquisition chamber (Figur e 2 ). The software takes the RGB values fr om the camera
and converts them into color , saturation, and brightness and takes the calibration data into account.
This measur ement system provides a high-r esolution image (1280
×
1024 pixels of color , saturation,
and brightness) of the mango samples. All measurement pr e-requisites wer e set in accordance with
the German standar d DIN EN 60350.
After the softwar e tool had recognized every single mango / mango slice, it automatically sear ched
for colors that wer e not in the defined color range. Out-of-range colors that are too dark ar e repr esented
by black spots or r ed colors.
The r emaining colors (Figures 6 and 7 , denoted as COLOR) were used for conversion into the
HSI color space. The r elationship between RGB and HSI values was calculated in the following
way: more than one hundr ed RGB values, which served as the refer ence, wer e obtained from the
color measur ement when using the CCD camera. After plotting these values, all of them exhibited a
nonlinear behavior . T o get the HSI values, a linear interpolation was applied to calculate the HSI value
between two points fr om the nonlinear curve.
The r esults were expr essed as a full *.xlsx data file as exemplarily shown in Figure 6 . As this date
file is very complex, it is explained in mor e detail: the pixel values and distribution of black, red, and
r emaining colors are displayed at the top, respectively (Figur e 7 ). As already explained above, all
black and r ed colors in the image are identified and separated fr om the other colors, as only the latter
ar e important for mango ripeness evaluation. All pixel values (Figur e 7 , PIXEL) ar e calculated as a
per centage (%) of the total mango surface.

Foods 2020 , 9 , 1709 8 of 19
Food s 2020 , 9 , x FO R P E ER R EVIE W 8 of 19

Figure 6. Exemp lary d ata fi le as provi de d by the so ftware WinFoodE val .

Figure 6. Exemplary data file as provided by the software W inFoodEval.

Foods 2020 , 9 , 1709 9 of 19
Food s 2020 , 9 , x FO R P E ER R EVIE W 9 of 19

Th e next data set to be interp reted is a li st of th e co l ors (Fig ure 7) . As men tion ed above in t he
description o f th e co lor tran sf orm ation, th ere c an be 16 sha des in th e r ange o f gre en to yellow ( Fi g ure
7, CO LO R), e ig ht step s in s atur ation ( Fi g ure 7, S ATU R ATI ON), and fou r s tep s in brightn ess (Fi gure
7, BRIGHT N ESS).

Figure 7. Mang o co lo r result s as provid e d by the so ftware WinFoodE val . List of co lo r tone s and co d es
acc ordi ng to th e HS I cl as si fi ca ti on. ( T hi s is a zoom from Figu re 6) .
Th e pixe l per cent ag es fo r CO LOR, S ATURATI O N, an d B RIGHT N ESS ( Fi g ure 7) o f an im ag e ar e
fu rth er d is p l ay ed in th e f orm of histog ra ms (F i gur e 8a – c ). Th e hi stograms use d in th is stud y were
describe d as f oll ows .
• CO LOR h is to gra m ( 16 step s) (Figur e 8a),
• SATU RA TI O N hist ogr am ( eig ht step s), (F i gure 8 b ),
• BRIGHT NE S S hi stogr am ( fou r step s) ( F ig ur e 8c).
A fou rth h i stogram i llu strates all r eleva nt NCS color code numb er s r esu lting fro m a
meas urement (m axi m ally 512 colo rs) ( Fig ure 8d ). Th e X - ax is repres en ts th e colo r n umb er abbre via te d
from th e NCS colo r co de, w hil e th e Y - axis shows th e p ercent ag e o f pixels . For vi s ualiz ation p u rpo ses,
not every co l or code is pr e sented here , i. e., on ly relev ant code n um ber s ar e d is p l ay ed ( F igure 8d).

Figure 7.
Mango color r esults as provided by the softwar e W inFoodEval. List of color tones and codes
according to the HSI classification. (This is a zoom from Figur e 6 ).
The next dataset to be interpr eted is a list of the colors (Figure 7 ). As mentioned above in the
description of the color transformation, there can be 16 shades in the range of gr een to yellow (Figur e 7 ,
COLOR), eight steps in saturation (Figur e 7 , SA TURA TION), and four steps in brightness (Figure 7 ,
BRIGHTNESS).
The pixel per centages for COLOR, SA TURA TION, and BRIGHTNESS (Figure 7 ) of an image ar e
further displayed in the form of histograms (Figur e 8 a–c). The histograms used in this study were
described as follows.
• COLOR histogram (16 steps) (Figur e 8 a),
• SA TURA TION histogram (eight steps), (Figur e 8 b),
• BRIGHTNESS histogram (four steps) (Figur e 8 c).

Foods 2020 , 9 , 1709 10 of 19
A fourth histogram illustrates all r elevant NCS color code numbers resulting fr om a measur ement
(maximally 512 colors) (Figur e 8 d). The X-axis r epresents the color number abbr eviated from the NCS
color code, while the Y -axis shows the percentage of pixels. For visualization purposes, not every color
code is pr esented here, i.e., only r elevant code numbers are displayed (Figur e 8 d).
The r elevant code numbers are listed in Figur e 9 . All codes are given with full abbr eviations, pixel
values, and distribution.
Food s 2020 , 9 , x FO R P E ER R EVIE W 10 of 19

Figure 8 . Mang o co lo r result s as provi de d by the so ftware Wi nF oo dEv al as hist ogr am s , ( a ) co lo r, ( b )
sat ur ati on, ( c ) brigh tness ac c ordi ng to the H SI cl a ssifi cat i on, ( d ) his togram of co lo r co de s according
to NCS cl a ssificatio n. Al l p ixel val ues w ere c alc ulated a s a p ercentage ( % ) of th e total m ang o sur fac e.
(This i s a zoom from F igu re 6) .
Th e rel evant code numb er s ar e l is ted in Figur e 9. A ll codes ar e giv en with full abbrev ia tion s ,
pixel v alues , and d is tr ibuti on.
Taki n g all in formation to geth er (ex em pla r il y show n in Fi g ure 8 a – c ), th e colo r pro vid ing t he
highest h ue, s atur ation, an d brightn es s was 43.7 % , 50 .9 %, 69. 2% , re sul t ing in th e color code Y 40R (H ),
50 (S) , and 10 (I), re spect iv ely . Con sequ ent ly , the col or code of thi s exemp l ar y samp le c an be la be led
as th e NC S c olor cod e 1050 - Y 40R. Th i s correspo nds to color numb er 36 7 (Figu r e 9) w ith its value
being 22.7 % o f th e com plet e mea sur e d mango ar e a.
Pseudo - co lor or f alse co lor images ar e al so gener ated in ad diti on to th e dat a f ile( s) . A ps eudo -
color im ag e r epresent s th e mango’ s s ur f ace as th e 16 c olor code s (Fi gure 6). Th e p seudo - colo r i mages
of this stud y ar e shown in S upplemen t ary M aterial s ( Fi g ures S 1 – S 6).
2.6. Data A nalysis
Th e com par iso n of r ipenes s betw een dif ferent m ango cu ltivars i s h ar d ly p oss ibl e, bec ause col or
formation i s cul tiv ar d epe ndent . Some cul tiv ar s rem ai n green , w hil e ot hers ch ange from gr een to
yel lo w d uri n g matu ra tion . Th erefor e, th e matur ation pro gress of th e ind ivid ual mango c ul tiv ar s wa s
evaluated in t his st udy. As men tioned ab ov e, a co lor check wa s c arried o ut to e nsure th at th e color
determ i nat io n i s w ithin th e r ange of ± 5% . Va lues high er than 5% i ndica te a shi f t of the pixe l value o f
a co lor.

Figure 8.
Mango color results as pr ovided by the software W inFoodEval as histograms, (
a
) color ,
(
b
) saturation, (
c
) brightness according to the HSI classification, (
d
) histogram of color codes accor ding
to NCS classification. All pixel values were calculated as a per centage (%) of the total mango surface.
(This is a zoom from Figur e 6 ).
T aking all information together (exemplarily shown in Figure 8 a–c), the color providing the
highest hue, saturation, and brightness was 43.7%, 50.9%, 69.2%, resulting in the color code Y40R (H),
50 (S), and 10 (I), respectively . Consequently , the color code of this exemplary sample can be labeled as
the NCS color code 1050-Y40R. This corr esponds to color number 367 (Figure 9 ) with its value being
22.7% of the complete measur ed mango area.
Pseudo-color or false color images ar e also generated in addition to the data file(s). A pseudo-color
image r epresents the mango’s surface as the 16 color codes (Figur e 6 ). The pseudo-color images of this
study ar e shown in Supplementary Materials (Figures S1–S6).

Foods 2020 , 9 , 1709 11 of 19
Food s 2020 , 9 , x FO R P E ER R EVIE W 11 of 19

Figure 9 . Mang o co lo r re sult s a s provided by the so ftwar e WinFood Ev a l . Color co de s acc ordi ng to
NCS cl as si f ic at io n. All co d es a re giv en with f ull abbrev iati o ns, pixe l val ues , and distributi on. (Thi s i s
a zoom from Fi gu re 6) .
3. R esu lt s
Bas e d on th e NCS co lor co de ca lculat io n descr ibed i n Section 2.5, th e si x mo st dom ina nt co l or
codes were id ent if ie d and s elected f or th e man go eva l ua tion . The se colo rs were o bserved dur i ng the
po st - ripen ing pro cess of th e mangos , tak ing 70 h for cv . “ Na m D okma i ” ( NDM ) an d cv .
“ M aha ch ano k ” (M HC), an d 60 h for cv . “ Kent ” . E ach typ e of man g o wa s mo n itored in tw o se para te
images for pe el and pu lp c olor. Th e res u lts ar e d is pl ayed in th e fo llo wing f ig u res ( Fi g ures 10 – 15) and
were plot ted in tw o dimen si ons , in whi ch th e X - axis present s th e r ipening d ur ation up to 70 h and
th e Y - ax is i s t he percen ta g e of pixe ls of th e correspo n ding color co des. Th e si x mo st dom in an t color
codes (wi th t heir co lor) ar e disp l ay ed as a le gend on th e ri ght s ide .
3.1. Co lo r Dete rmination of Man gos fro m the c v. “ Nam D okmai ” an d De ve lo pmen t du ri ng Post - Ri pe ni ng
Init iall y , p ul p color s of ND M wer e eva luated. As sho wn in Fi g ure 10, m ost p ul p color s of ND M
clu ster ed in a sm all r ang e of p ixels. Co nseq uently , t hose v alues f all w ithin th e ca li br ation o f ± 5% .
Howev er, co l ors 05 60 - Y2 0R an d 05 60 - Y40R ch ange d si gn ific antl y, du e to th e change s dur ing th e
ripening pro c ess. Th e se tw o color s h ad r ela tive ly l ar g e values at th e beginn ing an d at th e end of th e

Figure 9.
Mango color results as pr ovided by the software W inFoodEval. Color codes according to
NCS classification. All codes ar e given with full abbreviations, pixel values, and distribution. (This is a
zoom from Figur e 6 ).
2.6. Data Analysis
The comparison of ripeness between di ff er ent mango cultivars is hardly possible, because color
formation is cultivar dependent. Some cultivars r emain green, while others change from gr een to
yellow during maturation. Ther efore, the maturation pr ogress of the individual mango cultivars was
evaluated in this study . As mentioned above, a color check was carried out to ensure that the color
determination is within the range of
±
5%. V alues higher than 5% indicate a shift of the pixel value of
a color .
3. Results
Based on the NCS color code calculation described in Section 2.5 , the six most dominant color
codes wer e identified and selected for the mango evaluation. These colors were observed during the
post-ripening pr ocess of the mangos, taking 70 h for cv . “Nam Dokmai” (NDM) and cv . “Mahachanok”
(MHC), and 60 h for cv . “Kent”. Each type of mango was monitored in two separate images for peel
and pulp color . The results ar e displayed in the following figur es (Figures 10 – 15 ) and wer e plotted in

Foods 2020 , 9 , 1709 12 of 19
two dimensions, in which the X-axis pr esents the ripening duration up to 70 h and the Y -axis is the
per centage of pixels of the corresponding color codes. The six most dominant color codes (with their
color) ar e displayed as a legend on the right side.
Food s 2020 , 9 , x FO R P E ER R EVIE W 12 of 19

ripening pro c ess com p ar ed to th e rem aining co lors . Th e pro gre ss of th e matur ation pro ce ss of th e
mangos c an b e determ ined from th e incr ea se in th e pe rcent ag e p ixe l va lu e of a ch aract eris t ic co lor. It
was fou nd th at aft er ha lf of th e ripen i ng period (36 h) , th e pre vious ly dom i nant color co de was
gra d u ally r ed uced in fa vo r of anoth er c ode, wh ich i ndivid ually c an be associ at ed with in cr ea s ing
ripenes s (f or NDM: ye ll ow ) . Howev er , i n th e c as e o f any trans itio n of a co lor c ode, on ly th e value o f
percen tage p i xel w as use d for the m atur ity i nd ex.
Th e color 05 60 - Y 20R, con sist ing of 20% redness and 80 % yellown ess, d ecre as e d with a long er
ripening time fr om 27% to 4.9% of to tal pixels . Th e re dness inc reas ed and th e ov erall sh ade be came
0560 - Y 30R an d gradu ally c hanged to 05 60 - Y4 0R. Th e re were incre asing va lues of 05 60 - Y4 0R from
9.2% to 29. 1% with longer r ipening time . Th is color co nt ai ns 40 % re dness and 60 % yel lown ess . As a
result , the re d ness incre as e d and the ent i re sh ade shi fted clo se to an ora n ge - y el lo wish tone.

Figure 10. Dev el opm ent of the si x m os t do m inati ng pu lp co lo rs of c v. “ Na m Dokm ai ” m a ng os durin g
the 70 h po st - ri pening proces s , repr es ente d a s NCS co lo r co de s.
After pu lp col or evaluat ion, th e peel color of NDM w as analy ze d . Fi g ure 11 pres en ts th e changes
of pee l color s of NDM tak in g pl ace d uri n g th e po st - r ipening pro ces s . H ere, onl y t he colo r 1050 - Y40R,
which h as a r edness : ye ll o wness r atio o f 40:60, show ed a con tinuo usly i ncre as e d pixe l int en s ity from
22.1 % to 49. 1% . Th e rem aining color s sh owed on ly s light di fferenc e s and can al m ost be neg lected. As
th e color cod e ha s a clos e rel ation o f ye ll ow and re d colors, an inc rea s e in its p i xel intensity by 27%
result ed in th e eq u al com bi nation o f red ness and ye llow ness, i .e. , 1050 - Y4 0R ( re present ing an oran ge
to ne) .

Figure 11. Dev el opm ent of th e s ix m os t do m inati ng peel co l or s of cv. “ Na m Dokm ai ” m ang os during
the 70 h po st - ri pening proces s , presente d a s NCS co lo r cod es .

Figure 10.
Development of the six most dominating pulp colors of cv . “Nam Dokmai” mangos during
the 70 h post-ripening process, r epresented as NCS color codes.
Food s 2020 , 9 , x FO R P E ER R EVIE W 12 of 19

ripening pro c ess com p ar ed to th e rem aining co lors . Th e pro gre ss of th e matur ation pro ce ss of th e
mangos c an b e determ ined from th e incr ea se in th e pe rcent ag e p ixe l va lu e of a ch aract eris t ic co lor. It
was fou nd th at aft er ha lf of th e ripen i ng period (36 h) , th e pre vious ly dom i nant color co de was
gra d u ally r ed uced in fa vo r of anoth er c ode, wh ich i ndivid ually c an be associ at ed with in cr ea s ing
ripenes s (f or NDM: ye ll ow ) . Howev er , i n th e c as e o f any trans itio n of a co lor c ode, on ly th e value o f
percen tage p i xel w as use d for the m atur ity i nd ex.
Th e color 05 60 - Y 20R, con sist ing of 20% redness and 80 % yellown ess, d ecre as e d with a long er
ripening time fr om 27% to 4.9% of to tal pixels . Th e re dness inc reas ed and th e ov erall sh ade be came
0560 - Y 30R an d gradu ally c hanged to 05 60 - Y4 0R. Th e re were incre asing va lues of 05 60 - Y4 0R from
9.2% to 29. 1% with longer r ipening time . Th is color co nt ai ns 40 % re dness and 60 % yel lown ess . As a
result , the re d ness incre as e d and the ent i re sh ade shi fted clo se to an ora n ge - y el lo wish tone.

Figure 10. Dev el opm ent of the si x m os t do m inati ng pu lp co lo rs of c v. “ Na m Dokm ai ” m a ng os durin g
the 70 h po st - ri pening proces s , repr es ente d a s NCS co lo r co de s.
After pu lp col or evaluat ion, th e peel color of NDM w as analy ze d . Fi g ure 11 pres en ts th e changes
of pee l color s of NDM tak in g pl ace d uri n g th e po st - r ipening pro ces s . H ere, onl y t he colo r 1050 - Y40R,
which h as a r edness : ye ll o wness r atio o f 40:60, show ed a con tinuo usly i ncre as e d pixe l int en s ity from
22.1 % to 49. 1% . Th e rem aining color s sh owed on ly s light di fferenc e s and can al m ost be neg lected. As
th e color cod e ha s a clos e rel ation o f ye ll ow and re d colors, an inc rea s e in its p i xel intensity by 27%
result ed in th e eq u al com bi nation o f red ness and ye llow ness, i .e. , 1050 - Y4 0R ( re present ing an oran ge
to ne) .

Figure 11. Dev el opm ent of th e s ix m os t do m inati ng peel co l or s of cv. “ Na m Dokm ai ” m ang os during
the 70 h po st - ri pening proces s , presente d a s NCS co lo r cod es .

Figure 11.
Development of the six most dominating peel colors of cv . “Nam Dokmai” mangos during
the 70 h post-ripening process, pr esented as NCS color codes.
Food s 2020 , 9 , x FO R P E ER R EVIE W 13 of 19

3.2. Co lo r Dete rmination of Man gos fro m t he c v. “ Ma hac hanok ” an d Co lo r Develo pm e nt du rin g P ost -
Ri pe ning
Si mi l ar l y to NDM man go s, th e matur ity of MHC p u lp can be al s o char acteri zed by th e col ors
0560 - Y 20R an d 0560 - Y 40R , as shown in F ig ur e 12. Th e fi r st one drop ped f rom 44. 5% to 4%, whe rea s
th e la tt er incr ea se d from 2% to 47 . 9% . Pri nc ipally , th ese res ul ts were si m ilar to th ose of th e NDM
mangos , but with la r ger v alues for th e i ncreas e in an d reduction o f pixel intens i ty. As a con se qu enc e,
redness of th e code Y 20R became Y 30R , mean ing a to tal incre as e in redn ess by 10%. Fu rth er mo re,
redness con ti nuous ly incre as ed to Y4 0R, as a res ul t o f th e al r ea d y m ent ioned pixe l int ens ity inc rea s e
from 2% to 47. 9%. Co lor 0560 - Y4 0R con si sts o f 40% r edness and 60% ye ll own e s s. Accord ing l y, th e
pulp sh ad e of MHC tu rned i nt o a n or an ge - ye ll ow is h to ne.
Fi g ures 12 an d 13 show t he pulp and peel colo r of MHC d uri n g th e po st - r i pen ing pro ce ss .
Ob vious ly , it can be se en t hat th e co lor 1060 - Y 40R in creased from 0.2% to 42. 6% aft er 60 h an d th en
fu rth er decr e as ed to 23. 9% afterw ar ds . As i ts va lu e c hanged rem arkabl y com pa red to oth er c olors, it
main ly deter mines peel c olor. Desp ite th e la r ge f luct ua tion, th e peak shows t he inc rease in pixe l
intensity bein g e quiva lent to th e co lor Y50R. It c an be interp reted t hat th e re d uc tion of yello wness
drop ped fro m 6 0% to 50%. There fore, such a r educt ion i mp li e s th e trans ition in to ora nge sh ad es.

Figure 12. Dev el opm ent of th e si x m os t do m inati ng pu lp co lo r s of cv. “ Ma hachan ok ” m a ng os during
the 70 h po st - ri pening proces s , d epic te d as N CS c ol or co de s .

Figure 13. De v el opm ent o f th e s ix m os t do m inati ng peel c ol or s of cv . “ Mah achanok ” m ang os during
the 70 h po st - ri pening proces s , d epic te d a s N CS c ol or co de s .

Figure 12.
Development of the six most dominating pulp colors of cv . “Mahachanok” mangos during
the 70 h post-ripening process, depicted as NCS color codes.

Foods 2020 , 9 , 1709 13 of 19
Food s 2020 , 9 , x FO R P E ER R EVIE W 13 of 19

3.2. Co lo r Dete rmination of Man gos fro m t he c v. “ Ma hac hanok ” an d Co lo r Develo pm e nt du rin g P ost -
Ri pe ning
Si mi l ar l y to NDM man go s, th e matur ity of MHC p u lp can be al s o char acteri zed by th e col ors
0560 - Y 20R an d 0560 - Y 40R , as shown in F ig ur e 12. Th e fi r st one drop ped f rom 44. 5% to 4%, whe rea s
th e la tt er incr ea se d from 2% to 47 . 9% . Pri nc ipally , th ese res ul ts were si m ilar to th ose of th e NDM
mangos , but with la r ger v alues for th e i ncreas e in an d reduction o f pixel intens i ty. As a con se qu enc e,
redness of th e code Y 20R became Y 30R , mean ing a to tal incre as e in redn ess by 10%. Fu rth er mo re,
redness con ti nuous ly incre as ed to Y4 0R, as a res ul t o f th e al r ea d y m ent ioned pixe l int ens ity inc rea s e
from 2% to 47. 9%. Co lor 0560 - Y4 0R con si sts o f 40% r edness and 60% ye ll own e s s. Accord ing l y, th e
pulp sh ad e of MHC tu rned i nt o a n or an ge - ye ll ow is h to ne.
Fi g ures 12 an d 13 show t he pulp and peel colo r of MHC d uri n g th e po st - r i pen ing pro ce ss .
Ob vious ly , it can be se en t hat th e co lor 1060 - Y 40R in creased from 0.2% to 42. 6% aft er 60 h an d th en
fu rth er decr e as ed to 23. 9% afterw ar ds . As i ts va lu e c hanged rem arkabl y com pa red to oth er c olors, it
main ly deter mines peel c olor. Desp ite th e la r ge f luct ua tion, th e peak shows t he inc rease in pixe l
intensity bein g e quiva lent to th e co lor Y50R. It c an be interp reted t hat th e re d uc tion of yello wness
drop ped fro m 6 0% to 50%. There fore, such a r educt ion i mp li e s th e trans ition in to ora nge sh ad es.

Figure 12. Dev el opm ent of th e si x m os t do m inati ng pu lp co lo r s of cv. “ Ma hachan ok ” m a ng os during
the 70 h po st - ri pening proces s , d epic te d as N CS c ol or co de s .

Figure 13. De v el opm ent o f th e s ix m os t do m inati ng peel c ol or s of cv . “ Mah achanok ” m ang os during
the 70 h po st - ri pening proces s , d epic te d a s N CS c ol or co de s .

Figure 13.
Development of the six most dominating peel colors of cv . “Mahachanok” mangos during
the 70 h post-ripening process, depicted as NCS color codes.
Food s 2020 , 9 , x FO R P E ER R EVIE W 14 of 19

3.3. Co lo r Dete rmination of Man gos fro m the c v. “ Ke nt ” and De ve lo pment du rin g Pos t - Ri peni ng
Th e pulp of c v . “ Kent ” als o pro vided a close si m ilar i ty in co lor to nes to th e ot h er cu ltivars. A s
il l ustr ated in Fi g ure 14, th e col or c ode 0550 - Y 20R, c onsistin g of 20% redne ss an d 80 % y el lo wness,
incre as ed co nt inu ou sl y fr om 17. 9% to 36 .8 % du ring th e first 48 h and th en d rop ped gr ad uall y to
22.7 % afterw ar ds . H enc e, i t is re leva nt f or th e determ ina tion of th e key pu lp colo r of cv . “ Kent ” . The
color cod e 0560 - Y4 0R de cr ea se d and 0560 - Y 40R w as r educed to 05 50 - Y 20R. Th i s i ll u strated a d ecrea s e
in redne ss an d an inc rease in yellown e s s from 60 % t o 80 % , le ad in g to a mo re y ell ow is h to ne . Th e
remain ing col ors u n derwe nt only sl ig ht change s and c an be t here f ore almo st ne gl ect ed.

Figure 14. Dev el opm ent of the four m os t do m inati ng pu lp co lo r s of c v. “ K ent ” m ang os during the 70
h pos t - ripenin g proces s , de pi ct ed as NCS co lo r c o de s .
In con trast to th e ot her cul ti vars, th e peel color to ne of “ Kent ” i s q ui te different, as i t is dom in ate d
by gre en co lo rs (Figur e 15) . How ever, th ose si x dom in ating colo rs r em ai ne d unc hanged and h ad no
dis tinct ion in th e ripening pro cess f rom 0 to 60 h. It ca n be con clu d ed th at th is m eth od could hard ly
detec t th e di ff er ences o f “ Kent ” mang o peel color , as colo r ch an ge i s not th at intens e wi th out
chang ing th e to ne si gn ific an tly.

Figure 15. Dev el opm ent of th e s ix m os t do m inati ng peel co l or s of cv. “ Ken t ” m ang os duri ng the 70 h
post - ripening proces s , de pic t ed as NCS co lo r c od e s.
4. Di sc ussio n
Co lor i s one of th e key se nsory ch aract eris tic s in th e evalu ation o f th e q uali ty of man g os an d
bo th th e s urfa ce and th e p u lp co lor can b e u sed to asse ss th e de gree of r ipenes s of th e fr ui t. Ho wev er,
color chan ges , detec table w ith th e naked eye or s imple camer a sy stem s, are often not very mea ningfu l
due to th e hi gh si m ilari ty bet ween colo rs in si mp le c olor sp aces. F urtherm ore, with si mp le , camer a -
based syst ems, on ly indi v i dual po ints o n th e surf ace c an be c aptur ed.

Figure 14.
Development of the four most dominating pulp colors of cv . “Kent” mangos during the 70 h
post-ripening process, depicted as NCS color codes.
Food s 2020 , 9 , x FO R P E ER R EVIE W 14 of 19

3.3. Co lo r Dete rmination of Man gos fro m the c v. “ Ke nt ” and De ve lo pment du rin g Pos t - Ri peni ng
Th e pulp of c v . “ Kent ” als o pro vided a close si m ilar i ty in co lor to nes to th e ot h er cu ltivars. A s
il l ustr ated in Fi g ure 14, th e col or c ode 0550 - Y 20R, c onsistin g of 20% redne ss an d 80 % y el lo wness,
incre as ed co nt inu ou sl y fr om 17. 9% to 36 .8 % du ring th e first 48 h and th en d rop ped gr ad uall y to
22.7 % afterw ar ds . H enc e, i t is re leva nt f or th e determ ina tion of th e key pu lp colo r of cv . “ Kent ” . The
color cod e 0560 - Y4 0R de cr ea se d and 0560 - Y 40R w as r educed to 05 50 - Y 20R. Th i s i ll u strated a d ecrea s e
in redne ss an d an inc rease in yellown e s s from 60 % t o 80 % , le ad in g to a mo re y ell ow is h to ne . Th e
remain ing col ors u n derwe nt only sl ig ht change s and c an be t here f ore almo st ne gl ect ed.

Figure 14. Dev el opm ent of the four m os t do m inati ng pu lp co lo r s of c v. “ K ent ” m ang os during the 70
h pos t - ripenin g proces s , de pi ct ed as NCS co lo r c o de s .
In con trast to th e ot her cul ti vars, th e peel color to ne of “ Kent ” i s q ui te different, as i t is dom in ate d
by gre en co lo rs (Figur e 15) . How ever, th ose si x dom in ating colo rs r em ai ne d unc hanged and h ad no
dis tinct ion in th e ripening pro cess f rom 0 to 60 h. It ca n be con clu d ed th at th is m eth od could hard ly
detec t th e di ff er ences o f “ Kent ” mang o peel color , as colo r ch an ge i s not th at intens e wi th out
chang ing th e to ne si gn ific an tly.

Figure 15. Dev el opm ent of th e s ix m os t do m inati ng peel co l or s of cv. “ Ken t ” m ang os duri ng the 70 h
post - ripening proces s , de pic t ed as NCS co lo r c od e s.
4. Di sc ussio n
Co lor i s one of th e key se nsory ch aract eris tic s in th e evalu ation o f th e q uali ty of man g os an d
bo th th e s urfa ce and th e p u lp co lor can b e u sed to asse ss th e de gree of r ipenes s of th e fr ui t. Ho wev er,
color chan ges , detec table w ith th e naked eye or s imple camer a sy stem s, are often not very mea ningfu l
due to th e hi gh si m ilari ty bet ween colo rs in si mp le c olor sp aces. F urtherm ore, with si mp le , camer a -
based syst ems, on ly indi v i dual po ints o n th e surf ace c an be c aptur ed.

Figure 15.
Development of the six most dominating peel colors of cv . “Kent” mangos during the 70 h
post-ripening process, depicted as NCS color codes.
3.1. Color Determination of Mangos fr om the cv . “Nam Dokmai” and Development during Post-Ripening
Initially , pulp colors of NDM were evaluated. As shown in Figure 10 , most pulp colors of NDM
cluster ed in a small range of pixels. Consequently , those values fall within the calibration of
±
5%.
However , colors 0560-Y20R and 0560-Y40R changed significantly , due to the changes during the
ripening pr ocess. These two colors had relatively lar ge values at the beginning and at the end of the

Foods 2020 , 9 , 1709 14 of 19
ripening pr ocess compared to the r emaining colors. The progr ess of the maturation pr ocess of the
mangos can be determined fr om the increase in the per centage pixel value of a characteristic color .
It was found that after half of the ripening period (36 h), the previously dominant color code was
gradually r educed in favor of another code, which individually can be associated with incr easing
ripeness (for NDM: yellow). However , in the case of any transition of a color code, only the value of
per centage pixel was used for the maturity index.
The color 0560-Y20R, consisting of 20% r edness and 80% yellowness, decreased with a longer
ripening time fr om 27% to 4.9% of total pixels. The redness incr eased and the overall shade became
0560-Y30R and gradually changed to 0560-Y40R. There wer e incr easing values of 0560-Y40R from 9.2%
to 29.1% with longer ripening time. This color contains 40% r edness and 60% yellowness. As a result,
the r edness increased and the entir e shade shifted close to an orange-yellowish tone.
After pulp color evaluation, the peel color of NDM was analyzed. Figure 11 pr esents the changes
of peel colors of NDM taking place during the post-ripening pr ocess. Here, only the color 1050-Y40R,
which has a r edness:yellowness ratio of 40:60, showed a continuously increased pixel intensity fr om
22.1% to 49.1%. The remaining colors showed only slight di ff er ences and can almost be neglected.
As the color code has a close relation of yellow and r ed colors, an increase in its pixel intensity by
27% r esulted in the equal combination of redness and yellowness, i.e., 1050-Y40R (repr esenting an
orange tone).
3.2. Color Determination of Mangos from the cv . “Mahachanok” and Color Development during Post-Ripening
Similarly to NDM mangos, the maturity of MHC pulp can be also characterized by the colors
0560-Y20R and 0560-Y40R, as shown in Figur e 12 . The first one dropped fr om 44.5% to 4%, whereas the
latter incr eased from 2% to 47.9%. Principally , these results wer e similar to those of the NDM mangos,
but with lar ger values for the increase in and r eduction of pixel intensity . As a consequence, redness
of the code Y20R became Y30R, meaning a total increase in r edness by 10%. Furthermore, redness
continuously incr eased to Y40R, as a result of the alr eady mentioned pixel intensity increase fr om 2%
to 47.9%. Color 0560-Y40R consists of 40% r edness and 60% yellowness. Accordingly , the pulp shade
of MHC turned into an orange-yellowish tone.
Figur es 12 and 13 show the pulp and peel color of MHC during the post-ripening process.
Obviously , it can be seen that the color 1060-Y40R increased fr om 0.2% to 42.6% after 60 h and then
further decr eased to 23.9% afterwards. As its value changed remarkably comp ared to other colors,
it mainly determines peel color . Despite the large fluctuation, the peak shows the incr ease in pixel
intensity being equivalent to the color Y50R. It can be interpr eted that the reduction of yellowness
dr opped from 60% to 50%. Therefor e, such a reduction implies the transition into orange shades.
3.3. Color Determination of Mangos fr om the cv . “Kent” and Development during Post-Ripening
The pulp of cv . “Kent” also provided a close similarity in color tones to the other cultivars.
As illustrated in Figur e 14 , the color code 0550-Y20R, consisting of 20% redness and 80% yellowness,
incr eased continuously from 17.9% to 36.8% during the first 48 h and then dr opped gradually to
22.7% afterwar ds. Hence, it is relevant for the determination of the key pulp color of cv . “Kent”.
The color code 0560-Y40R decr eased and 0560-Y40R was reduced to 0550-Y20R. This illustrated a
decr ease in redness and an incr ease in yellowness from 60% to 80%, leading to a mor e yellowish tone.
The r emaining colors underwent only slight changes and can be therefor e almost neglected.
In contrast to the other cultivars, the peel color tone of “Kent” is quite di ff er ent, as it is dominated
by gr een colors (Figure 15 ). However , those six dominating colors remained unchanged and had no
distinction in the ripening pr ocess from 0 to 60 h. It can be concluded that this method could hardly
detect the di ff er ences of “Kent” mango peel color , as color change is not that intense without changing
the tone significantly .

Foods 2020 , 9 , 1709 15 of 19
4. Discussion
Color is one of the key sensory characteristics in the evaluation of the quality of mangos and both
the surface and the pulp color can be used to assess the degr ee of ripeness of the fruit. However , color
changes, detectable with the naked eye or simple camera systems, are often not very meaningful due
to the high similarity between colors in simple color spaces. Furthermore, with simple, camera-based
systems, only individual points on the surface can be captur ed.
Modern computer vision systems enable the inspection of entire fr uits on a pixel-based level. Each
pixel can be consider ed as a sample of an original image; mor e samples typically provide mor e accurate
r epresentations of the original. Due to the tri-chromatic theory , all colors that humans can recognize
as an image are a combination of the so-called primary colors r ed, green, and blue. When digitally
captur ed, pixel-based RGB values are converted into a suitable color model and the corr esponding
color of an item can be r ead directly .
A color model is the specification of a thr ee-dimensional coordinate system and of one subspace
of this system in which each color is repr esented by a unique point. When this model is associated
with a pr ecise description of how the components are to be interpr eted (viewing conditions, etc.), the
r esulting set of colors is called “color space”. The goal of a color model is to facilitate the specification
of colors in a standar dized way . However , the choice of a suitable space for color repr esentation
r emains a challenge for scientists resear ching color image processing. Blasco et al. (2007) studied
the automatic external defect detection of citrus fr uit, e.g., oranges and mandarins, using an image
pr ocessing technique. In total, 635 fruits were tested based on an HSI color space. In that study , they
compar ed five color spaces for the assessment of external fruit defects. As a result, they concluded that
the HSI system was highly appr opriate for detecting the defects [
24
]. Similarly , Abdullah et al. (2006)
also used the HSI color space for automatic grading of starfruits by color and shape using a computer
vision system. They successfully developed a method for categorizing the fruit into di ff er ent maturity
levels. In that study , RGB values were also converted into an HSI system, but the H component was
mainly used to classify starfruit into four maturity categories (unripe, underripe, ripe, overripe) [ 25 ].
The Natural Color Standard System
®
has continuously been the international color standar d
most commonly used in the food industry . Unfortunately , ther e is a lack of literature describing
the application of the NCS for evaluating mango ripeness. Instead, only color systems such as hue,
saturation, brightness (HSB) [
29
], HSI [
15
,
24
,
30
], and CIE L*a*b* [
4
,
17
,
31
] ar e described and employed
for the ripeness measur ement of mangos and some other fruits. The present study is one of the first to
apply the internationally r ecognized NCS code to fresh fruits and thus forms the basis for subsequent
work in this ar ea.
For the individual valuation of the maturity of di ff erent mango cultivars, characteristic values for
assessing the color change of the surface and pulp must be determined. By plotting the r esults in a way
that allows for three di ff er ent colors to be assessed, the color changes that characterize ripening can be
determined objectively . These color values can then be integrated into a multifactorial quality and
maturity index that allows for an objective assessment of the ripeness of the fruits. It has to be noted in
this context, that the maturity index does not depend solely on the fruit color . In addition, firmness,
acidity , and total soluble solid, for instance, dry matter , are also used to evaluate fruit maturity [ 32 ].
Such an index is particularly useful for optimal post-harvest tr eatment, as the degree of ripeness
of tr ee-ripened mangos can di ff er significantly depending on the position of the fruit on the tree and
other influencing factors. A suitable sorting and gr ouping of the fruits, based on their maturity level,
ar e therefor e crucial for optimal post-ripening of the fruits [ 28 ].
Kienzle et al. (2012) studied the maturity of NDM mango using five parameters, such as titratable
acidity (T A), CIE hue angle of the mesocarp, chlorophyll b content, total soluble solids (TSSs), and
dry matter (DM), to detect the ripeness. Principle component and cluster analysis were used for
data analysis. The r esults showed that T A and hue angle were the two most significant attributes
describing maturity of NDM, followed by chlor ophyll b and TSSs [
33
]. Lebrun et al. (2008) determined
the volatiles of mango cultivars “Cogshall”, “Kent”, and “Keitt” using an electronic nose and gas

Foods 2020 , 9 , 1709 16 of 19
chr omatography in order to di ff er entiate the harvest maturities and ripening stages in di ff erent sizes.
Mor eover , the e ff ect of harvest maturity on mango flavor was studied. The use of an e-nose appr oach
was able to distinguish among the volatiles of the five size categories of “Keitt” mangos, while “Kent”
mangos wer e categorized between three of the five sizes. In addition, the e-nose could also di ff erentiate
between the volatiles of ripe “Kent” and “Keitt” mangos [
32
]. Suwansichon et al. (2012) analyzed
nine mango cultivars, including NDM, by means of sensory evaluation for determining the stage of
ripeness. T wenty flavor attributes and eleven textur e attributes were evaluated by a trained panel.
The r esults showed that the attributes could be used to describe the variations in the degree of ripeness
in terms of both taste and textur e characteristics between the samples [
34
]. However , all these methods
ar e invasive and need trained sensory panelists for performing the analyses.
The assessment of the surface color o ff ers various advantages in this context: it is a non-invasive
method that can be carried out immediately after the harvest in order to make an initial assessment of
the degr ee of ripeness. As in other sensory examinations, the surface color is traditionally assessed
by appr opriately trained and experienced panelists. However , these methods can sometimes be very
err or-pr one: In addition to the observers’ individual assessment standar ds, there ar e also external
factors such as time of day , incidence of light, and light intensity that can influence the color assessment.
Furthermor e, the significance of conventional imaging inspection methods, such as colorimetric or
spectr ophotometric procedur es, is limited, since only a few individual measuring points can be
r ecorded. Khairunniza-Bejo et al. (2014) reviewed the limitation of conventional color measuring
methods with a colorimeter and spectr ophotometer . The authors concluded that such methods only
give a single average r eading over only one spot of a sample, but not all surface pixels available.
Further , the use of these methods showed, due to low spatial resolution, a limited color sensing capacity .
In addition, some appr oaches are not applicable with non-homogeneous color [ 1 ].
In contrast, modern automated computer vision systems may allow for a standardized and
r eproducible assessment of the surface color and might have the potential to be used in commer cial
industry as a rapid test method for fruit sorting. The pr esent study showed that the use of CCD cameras
could successfully be applied for evaluating the color and the ripeness of mangos. Mor e specifically ,
the observed changes in pixel intensity according to the commonly applied color codes can indicate the
degr ee of fruit ripeness. The peel color of NDM and MHC di ff ered slightly . NDM can be described by
the color 1050-Y40R, while MHC is mainly characterized by 1060-Y40R. They have distinct saturation
levels of 50% and 60%, respectively . V
á
squez-Caicedo et al. (2004) noted that the peel color of NDM
did not r eally change during ripening at room temperatur e, when measuring with a colorimeter [
35
].
In the pr esent study , however , changes in peel color of NDM wer e observed, when using a computer
vision system under defined ripening conditions. It should be noted that the r esults of the present
experiment wer e also based on a calibration system which used the color green (consisting of five
di ff er ent tones) as a refer ence with a tolerance range set to
±
5%. Therefor e, the measured data wer e
always in the acceptable range, indicating a valid and accurate measur ement.
The codes 0560-Y20R and 0560-Y40R wer e the two most relevant codes for observing the maturity
of both NDM and MHC fruits with r egard to their pulp colors. The pixel intensity reduction of the code
0560-Y20R, which has 20% r edness and 80% yellowness, means that the yellow color is reduced within
the yellow-orange zone, which ultimately indicates the ripeness. Similarly , an increase in the pr esence
of the code 0560-Y40R (40% r edness and 60% yellowness) also indicated ripeness in both NDM and
MHC mangos. V
á
squez-Caicedo et al. (2005) found that, due to the accumulation of
β
-car otene, the
pulp of NDM and MHC developed a yellow-orange color . The same pattern was also found in the
pr esent study [
36
]. Nandi et al. (2012) also discovered similar r esults in which the same color pattern
was observed at di ff er ent levels of ripeness, particularly along the fruit’s apex region [ 16 ].
Based on the abovementioned results, the computer vision system has pr oven to be a suitable
methodology for estimating the pulp and peel colors of the mango cv . “Nam Dokmai” and
“Mahachanok”, as well as the pulp colors of cv . “Kent”. Peel color changes of cv . “Kent” could not
be assessed because the fruits kept their rich gr een color during the entire post-ripening period and

Foods 2020 , 9 , 1709 17 of 19
developed no detectable color gradients. Sivakumar et al. (2011) also postulated that skin color is not
always applicable for evaluating the maturity index of mangos that have a green surface color even
when fully matur e [
37
]. The restrictions when assessing the surface color must also be consider ed
when assessing the ripeness of other fruits, especially those with a high pr oportion of green in the skin
color . In addition, only fruits that show a significant change in color during ripening can be assessed
on the basis of the surface color .
Inevitably , there ar e inconsistencies in fruit maturity . Mangos at di ff erent stages of ripening
can lead to significant measur ement fluctuations. Consequently , the present study tried to minimize
the interindividual fruit maturity status by a parallel measur ement of at least several samples of
a batch in parallel. At the beginning of the experiments with MHC, five samples were measur ed
(
Figur es S1 and S2
). During the experiments, it was realized that thr ee samples might already be
enough for evaluating the other mango varieties. Such a number should be used to repr esent a full
sample of each ripening hour for compensating color fluctuations. In order to identify peel colors
mor e accurately , the same fruit sample should be measured thr oughout the whole duration of the
ripening period. However , this is not applicable for pulp color measurement, as it must be cut for the
measur ements and cannot be measured again afterwar ds.
5. Conclusions
This study focused on the automated color measur ement of mango peel and pulp using a computer
vision system and color standar ds. Namely , “Nam Dokmai”, “Mahachanok”, and “Kent” mango
varieties wer e investigated in this study . Peel and pulp colors repr esent the change in pigments that
occurs during mango ripening and can therefor e be used to identify di ff er ent levels of mango ripeness.
A combined method using both a computer vision system and the color-coded index can pr ecisely
pr edict the ripeness stage of mangos with a color pixel range precision of
±
5%. However , the method
seems to be less suitable for samples with no gradient color change over di ff erent maturity stages (in
this case: cv . “Kent” (peel)).
Corr elations with the traditional parameters for evaluating ripeness, such as firmness, acidity ,
total soluble solids, and further parameters, have to be made in futur e studies to get a clearer view of
mango quality and biochemical mechanisms behind post-ripening processes during transport and
storage in stor es, shops, or under household conditions.
Supplementary Materials:
T he f o ll ow i ng a r e av a il ab l e on l in e a t ht tp : // ww w .m dp i .c o m / 23 04 - 81 5 8 / 9 / 1 1 / 17 09 / s 1 , Fi gu r e
S 1: Peel color and pseudo-color images of “Mahachanok” (MHC) mango depending on ripening duration; Figure
S2: Pulp color and pseudo-color images of “Mahachanok” (MHC) mango depending on ripening duration; Figur e
S3: Peel color and pseudo-color images of “Nam Dokmai” (NDM) mango depending on ripening duration; Figure
S4: Pulp color and pseudo-color images of “Nam Dokmai” (NDM) mango depending on ripening duration; Figure
S5: Peel color and pseudo-color images of “Kent” mango depending on ripening duration; Figur e S6: Pulp color
and pseudo-color images of “Kent” mango depending on ripening duration.
Author Contributions:
Conceptualization, K.R. (Khanitta Ratprakhon); methodology , K.R. (Khanitta Ratprakhon)
and W .N.; formal analysis, K.R. (Khanitta Ratprakhon); writing—original draft preparation, K.R. (Khanitta
Ratprakhon); writing—review and editing, S.R., K.R. (Katharina Riehn), and J.F .; supervision, S.R., K.R. (Katharina
Riehn), and J.F .; project administration, K.R. (Katharina Riehn). All authors have read and agr eed to the published
version of the manuscript.
Funding: This resear ch received no external funding.
Acknowledgments:
The mango cv . “Kent” was provided by Fr esh Factory GmbH & Co. KG, Hamburg, Germany .
Khanitta Ratprakhon received a Ph.D. scholarship in the Royal Thai Government Scholarship Pr ogram that was
kindly provided by the Government of Thailand.
Conflicts of Interest: The authors declare no conflict of interest.

Foods 2020 , 9 , 1709 18 of 19
References
1.
Khairunniza-Bejo, S.; Kamaruddin, S. Determination of Chokanan mang o sweetness ( Mangifera indica ) using
non-destructive image pr ocessing technique. Aust. J. Crop Sci. 2014 , 8 , 475–480.
2.
Jha, S.N.; Chopra, S.; Kingsly , A.R.P . Modeling of color values for nondestructive evaluation of maturity of
mango. J. Food Eng. 2007 , 78 , 22–26. [ CrossRef ]
3.
Nambi, V .E.; Thangavel, K.; Jesudas, D.M. Scientific classification of ripening period and development of
colour grade chart for Indian mangos ( Mangifera indica L.) using multivariate cluster analysis. Sci. Hortic.
2015 , 193 , 90–98. [ CrossRef ]
4. Nagle, M.; Intani, K.; Romano, G.; Mahayothee, B.; Sardsud, V .; Müller , J. Determination of surface color of
‘all yellow’ mango cultivars using computer vision. Int. J. Agric. Biol. Eng. 2016 , 9 , 42–50.
5.
Malevski, Y .; Brito, L.; G
ó
me, Z.; Peleg, M.; Silberg, M. External color as maturity index of mango. J. Food Sci.
1977 , 42 , 1316–1318. [ CrossRef ]
6.
Brecht, J.K.; Y ahia, E.M. (Eds.) Harvesting and Postharvesting T echnology of Mango. In Handbook of Mango
Fruit. Production, Postharvest Science, Processing T echnology and Nutrition ; W iley: Oxfor d, UK, 2018.
7.
Delgado, C.H.O. Mango. In V alorization of Fruit Processing By-Pr oducts ; Academic Press: London, UK;
San Diego, CA, USA, 2019.
8.
Castrej
ó
n, A.D.R.; Eichholz, I.; Rohn, S.; Kroh, L.W .; Huyskens-Keil, S. Phenolic profile and antioxidant
activity of highbush blueberry ( V accinium corymbosum L.) during fruit maturation and ripening. Food Chem.
2008 , 109 , 564–572. [ CrossRef ]
9.
Pathare, P .B.; Opara, U.L.; Al-Said, F .A.-J. Colour Measurement and Analysis in Fr esh and Processed Foods.
A Review . Food. Bioproc. T echnol. 2013 , 6 , 36–60. [ Cr ossRef ]
10.
Hobson, G.E.; Adams, P .; Dixon, T .J. Assessing the colour of tomato fruit during ripening. J. Sci. Food Agric.
1983 , 34 , 286–292. [ CrossRef ]
11.
Camelo, A.F .L.; G
ó
mez, P .A. Comparison of color indexes for tomato ripening. Hortic. Bras.
2004
, 22 , 534–537.
[ CrossRef ]
12.
Li, B.; Lecourt, J.; Bishop, G. Advances in Non-Destructive Early Assessment of Fr uit Ripeness towards
Defining Optimal T ime of Harvest and Y ield Prediction—A Review . Plants 2018 , 7 , 3. [ CrossRef ]
13.
Y am, K.L.; Papadakis, S.E. A simple digital imaging method for measuring and analyzing color of food
surfaces. J. Food Eng. 2004 , 61 , 137–142. [ CrossRef ]
14.
Sun, D.W . (Ed.) Computer V ision T echnology for Food Quality Evaluation , 2nd ed.; Elsevier / Academic Press:
London, UK, 2016.
15.
Ilagan, L.C.; T uringan, J.V .; Aranas, A.K.; Ignacio, E.D.; Rasay , E.C. (Eds.) Grading of Carabao Mango
Using Image Processing. In Proceedings of the Second International Confer ence on Electrical, Electronics,
Computer Engineering and their Applications (EECEA2015), Manila, Philippines, 28 January 2015.
16.
Nandi, C.S.; T udu, B.; Koley , C.A. Machine V ision-Based Maturity Prediction System for Sorting of Harvested
Mangos. IEEE T rans. Inst. Meas. 2014 , 63 , 1722–1730. [ Cr ossRef ]
17.
Pandey , R.; Gamit, N.; Naik, S. Non-destructive quality grading of mango ( Mangifera indica L) based on
CIELab colour model and size. In Proceedings of the International Confer ence on Advanced Communication
Control and Computing T echnologies (ICACCCT), Ramanathapuram, India, 8–10 May 2014; pp. 1246–1251.
18.
S
á
ad, F .S.A.; Ibrahim, M.F .; Shaka ff , A.M.; Zakaria, A.; Abdullah, M.Z. Shape and weight grading of mangos
using visible imaging. Comput. Electron. Agric. 2015 , 115 , 51–56. [ CrossRef ]
19.
V yas, A.M.; T alati, B.; Nail, S. Quality Inspection and Classification of Mangos using Color and Size Features.
Int. J. Comput. Appl. 2014 , 98 , 1–5.
20.
Jayashree, R.A. RGB to HSI color space conversion via MACT algorithm. In Proceedings of the International
Conference on Communication and Signal Pr ocessing, Melmaruvathur , India, 3–5 April 2013.
21.
Chien, C.L.; T seng, D.C. Color enhancement with exact HSI color model. Int. J. Inn. Comp. Inf. Cont.
2011
, 7 ,
6691–6710.
22.
Gonzalez, R.C.; W oods, R.E.; Eddins, S.L. (Eds.) ; Digital Image Processing Using MA TLAB ; Prentice- Hall:
Upper Saddle River , NJ, USA, 2004.
23.
Y ang, X.Q.; Feng, Y .; Liu, H.C. An object extraction method based on HSI color mode. Opt. T ech.
2006
, 32 ,
290–292.

Foods 2020 , 9 , 1709 19 of 19
24.
Blasco, J.; Aleixos, N.; Molt
ó
, E. Computer vision detection of peel defects in citrus by means of a r egion
oriented segmentation algorithm. J. Food. Eng. 2007 , 81 , 535–543. [ CrossRef ]
25.
Abdullah, M.Z.; Mohamad-Saleh, J.; Fathinul-Syahir , A.S.; Mohd-Azemi, B.M.N. Discrimination and
classification of fresh-cut starfr uits ( A verrhoa carambola L.) using automated machine vision system. J. Food
Eng. 2006 , 76 , 506–523. [ CrossRef ]
26.
DIN EN 60350. Household Electric Cooking Appliances—Part 1: Ranges, Ovens, Steam Ovens and Grills—Methods
for Measuring Performance ; Beuth: Berlin, Germany , 2018.
27.
Lübbe, E. Farbempfindung, Farbbeschreibung und Farbmessung. Eine Formel für die Farbsättigung ; Springer:
W iesbaden, Germany , 2013.
28.
Kilcast, D. (Ed.) Instrumental Assessment of Food Sensory Quality . A Practical Guide ; W oodhead: Oxfor d,
UK, 2013.
29.
Khairunniza-Bejo, S.; Kamarudin, S. Chokanan Mango Swe etness Determination Using HSB Color Space.
In Proceedings of the Third International Confer ence on Computational Intelligence, Modelling and
Simulation (CIMSiM), Langkawi, Malaysia, 20–22 September 2011; pp. 216–221.
30.
Sirisathitkul, Y .; Thumpen, N.; Puangtong, W . Automated Chokun Orange Maturity Sorting by Color
Grading. W alailak J. Sci. T echnol. 2006 , 3 , 195–205. [ Cr ossRef ]
31.
Balaban, M.O.; Aparicio, J.; Zotarelli, M.; Sims, C. Quantifying nonhomogeneous colors in agricultural
materials. Part II. Comparison of machine vision and sensory panel evaluations. J. Food Sci.
2008
, 73 , 438–442.
[ CrossRef ]
32.
Lebrun, M.; Plotto, A.; Goodner , K.; Ducamp, M.-N.; Baldwin, E. Discrimination of mango fruit maturity
by volatiles using the electronic nose and gas chr omatography . Postharv . Biol. T echnol.
2008
, 48 , 122–131.
[ CrossRef ]
33.
Kienzle, S.; Sruamsiri, P .; Carle, R.; Sirisakulwat, S.; Spr eer , W .; Neidhart, S. Harvest maturity detection for
‘Nam Dokmai #4’ mango fruit ( Mangifera indica L.) in consideration of long supply chains. Postharv . Biol.
T echnol. 2012 , 72 , 64–75. [ CrossRef ]
34.
Suwansichon, S.; Chambers, E.; Kongpensook, V .; Oupadissakoon, C. Sensory Lexicon for Mango as A ff ected
by Cultivars and Stages of Ripeness. J. Sens. Stud. 2012 , 27 , 148–160. [ CrossRef ]
35.
V
á
squez-Caicedo, A.L.; Neidhart, S.; Carle, R. Postharvest Ripening Behavior of Nine Thai Mango Cultivars
and thier Suitability for Industrial Application. Acta Hort. 2004 , 645 , 617–625. [ CrossRef ]
36.
V
á
squez-Caicedo, A.L.; Sruamsiri, P .; Carle, R.; Neidhart, S. Accumulation of all-trans-
β
-Carotene and its
9-cis and 13-cis Stereoisomers during Postharvest Ripening of Nine Thai Mango Cultivars. J. Agric. Food
Chem. 2005 , 53 , 4827–4835. [ CrossRef ] [ PubMed ]
37.
Sivakumar , D.; V an Deventer , F .; Francois, T .; Leon, A.; Polenta, G.A.; Korsten, L. Combination of
1-methylcyclopropene tr eatment and contr olled atmosphere storage r etains overall fruit quality and bioactive
compounds in mango. J. Sci. Food Agric. 2012 , 92 , 821–830. [ CrossRef ]
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article distributed under the terms and conditions of the Cr eative Commons Attribution
(CC BY) license (http: // creativecommons.or g / licenses / by / 4.0 / ).

Why institutions use Plag.ai for originality review, entry 93

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