www.elsevier.com/locate/foodqual

Food Quality and Preference 16 (2005) 471–478

Correlation between visual and instrumental colour measurementsof orange juice dilutions: effect of the background q

Antonio J. Melendez-Martınez, Isabel M. Vicario, Francisco J. Heredia *

Area de Nutricion y Bromatologıa, Facultad de Farmacia, Universidad de Sevilla, Sevilla 41012, Espana

Received 25 April 2004; received in revised form 23 July 2004; accepted 23 September 2004

Available online 8 December 2004

Abstract

The colour of 21 ultrafrozen orange juice samples was measured by spectroradiometry. Each sample was diluted with distilled

water to obtain eight levels of concentration (10%, 25%, 40%, 50%, 60%, 75%, 90% and 100% of juice). Measurements were made

against both a white and a black background. The results showed that the colour parameters which best correlated with orange juice

concentration were different depending on the background used. The correlation between sensory and instrumental measurements

was also evaluated. The mixtures were classified by 12 panellists in two cabins, one with white walls and the other one with black

walls. These visual assessments revealed that the usage of a cabin with black walls led to better classifications of the samples as a

function of their concentration.

� 2004 Elsevier Ltd. All rights reserved.

Keywords: Background; Colour; Orange juice; Partial least squares (PLS); Reflectance measurement; Spectroradiometry; Tristimulus Colorimetry;

Visual analysis

1. Introduction

Colour of foods dramatically influences consumers�preferences (Calvo, Salvador, & Fiszman, 2001; Hutch-ings, 1994). In this sense, some studies revealed that col-

our of citric beverages in general, are related to the

consumer�s perception of flavour, sweetness and other

characteristics in relation to the quality of these prod-

ucts (Huggart, Petrus, & Buslig, 1977, 1979; Tepper,

1993). Thus, implementation of instrumental measure-

ment of colour within the orange juice industry is very

important for quality control purposes and must beencouraged. In the United States, the colour of citrus

0950-3293/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.foodqual.2004.09.003

q The results of this study were partially presented at the 6th

Spanish Congress of Colour, held in Sevilla (Spain) in September 2002.* Corresponding author. Tel.: +34 95 4556495/4556761; fax: +34 95

4557017.

E-mail address: heredia@us.es (F.J. Heredia).

juices is one of the parameters that are evaluated for

the commercial classification of the product in relation

to its quality. Owing to the psychological impact of col-

our on several foods, a survey on the consumers� prefer-ences regarding the colour of orange juice was carried

out. As a result of this survey, the United States Depart-

ment of Agriculture (USDA) assigned 40 points out of a

scale of 100 points for colour, for the commercial classi-

fication of this product. Thus, a grade A orange juice

must have a colour number between 36 and 40 points,

whereas grade B orange juices are those with colour

numbers ranging from 32 to 35 points (Lee, 2001; Ste-wart, 1977).

In relation to this, Tristimulus Colorimetry has al-

ready been used for quality control purposes to estimate

the carotenoids contents of orange juice (Melendez-

Martınez, Vicario, & Heredia, 2003).

The influence of several agents such as vitamin C or

pulp on orange juice has been evaluated before (Gullett,

Francis, & Clydesdale, 1972; Lee & Chen, 1998). The

472 A.J. Melendez-Martınez et al. / Food Quality and Preference 16 (2005) 471–478

influence of orange juice concentration, among other

factors, on the colour parameters has also been reported

elsewhere (MacDougall, 1983, 2002), which may be very

interesting in order to detect adulterations of orange

juices rapidly in the industry. For this purpose, objective

measurement of colour by means of Tristimulus Colori-metry has important advantages in comparison to other

analytical methods, since it is a non-destructive tech-

nique that makes possible to obtain several parameters

in a few seconds.

The objectives of this study were twofold. First to

evaluate the validity of Tristimulus Colorimetry to de-

tect the concentration of orange juice in the mixtures.

For these purposes appropriate equations obtained bymeans of partial least squares (PLS) have been pro-

posed. Second, this work was also aimed at studying

the correlation between instrumental and visual assess-

ment of orange juice colour, in order to decide both

the best method to classify the samples as a function

of their concentration, and the best conditions to assess

colour differences between orange juices. For these pur-

poses, two backgrounds, white and black, were used forboth types of colour measurements.

2. Material and methods

2.1. Samples

Twenty-one ultrafrozen orange juice samples, sup-plied by the industry, were analysed. Each sample was

diluted with distilled water to obtain eight levels of con-

centration (10%, 25%, 40%, 50%, 60%, 75%, 90% and

100% of juice). In the industry, the juices are cooled

and immediately frozen using liquid nitrogen in an

industrial freezing tunnel. The samples analysed in this

study were kept at �21 �C until its analysis in the labo-

ratory. Thawing was carried out at room temperature(23 �C) for 24 h.

This type of orange juice was chosen for the experi-

ment owing to its inherent deep orange colour. As a

result of successive dilutions different orange juice solu-

tions, which simulate the colour of paler orange juices,

were obtained. Colour simulation was due to both scat-

tering and pigment reduction.

2.2. Colour measurement

Reflectance spectra were obtained by means of a CAS

140 B spectroradiometer (Instrument Systems, Munich,

Germany) with an external incandescent lamp, equipped

with a Top 100 telescope optical probe (Instrument Sys-

tems, Munich, Germany) and a Tamron zoom mod. SP

23A (Tamron USA, Inc., Commack, NY, USA).A plastic cuvette (475 · 350 · 10 mm) was used for

the measurements. Blank measurements were made with

the cuvette filled with distilled water against a reference

BaSO4 pressed plate (USRS-99-010, Labsphere Inc.,

North Sutton, NH, USA), which was also used as white

background. The samples were measured against a white

background and a black background, since some studies

have shown that sometimes, reflectance measurementsagainst a black background (Huang, Francis, & Clydes-

dale, 1970a, 1970b; Melendez-Martınez & Heredia,

2002) are better correlated with visual assessment of col-

our and other parameters. A round shaped plastic piece

with homogeneous black colour was used as black back-

ground. All the instrumental measurements were carried

out in a dim ambient illumination to avoid possible

interferences from other external sources. In additionto this, the cuvettes were placed inside a cabin with grey

walls to which the external illumination source of the

spectroradiometer was attached. The zoom, to which

the probe was attached, was held at a fixed distance of

50 cm in a straight line from the sample. As far as geo-

metry of presentation, 45� incident illumination was

used throughout the experiment.

The spectroradiometer was set to take three consecu-tive measurements of each sample, so colour coordinates

obtained were averages of three measurements.

The whole visible spectrum (380–770 nm) was re-

corded (Dk = 1 nm) and Illuminant D65 and 10� Ob-

server were considered as references. The colour

parameters corresponding to the uniform colour space

CIELAB (CIE, 1978) were obtained directly from the

apparatus. Within the uniform space CIELAB, two col-our coordinates, a* and b*, as well as a psychometric

index of lightness, L*, are defined. a* takes positive

values for reddish colours and negative values for the

greenish ones, whereas b* takes positive values for

yellowish colours and negative values for the bluish

ones. L* is an approximate measurement of luminosity,

which is the property according to which each colour

can be considered as equivalent to a member of the greyscale, between black and white, taking values within the

range 0–100.

From the uniform colour space, the psychological

parameters of chroma (C�ab) and hue (hab) are defined:

C�ab ¼ ½ða�Þ2 þ ðb�Þ2�1=2; hab ¼ arctanðb�=a�Þ

Chroma (C�ab) is used to determine the degree of dif-

ference of a hue in comparison to a grey colour with

the same lightness, and is considered the quantitative

attribute of colourfulness. Hue (hab) is the attribute

according to which colours have been traditionally

defined as reddish, greenish, etc and is used to definethe difference of a colour with reference to a grey

colour with the same lightness. This attribute is related

to the differences in absorbance at different wave-

lengths and is considered the qualitative attribute of

colour.

A.J. Melendez-Martınez et al. / Food Quality and Preference 16 (2005) 471–478 473

Colour differences, which are very important to eval-

uate relationships between visual and numerical analyses

(Melgosa, Hita, Poza, Alman, & Berns, 1997), are calcu-

lated as the Euclidean distance between two points in the

three-dimensional space defined by L*, a* and b*:

DE�ab ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðDL�Þ2 þ ðDa�Þ2 þ ðDb�Þ2

q

2.3. Visual assessment of colour

Visual analyses of the samples were carried out within

a well-illuminated room provided with two cabins

(70 · 70 · 55 cm), one of them with white walls and

the other one with black walls. Further over-head artifi-

cial illumination by means of fluorescent bulbs was sup-plied in each cabin to promote reflection of light by the

samples. A series of eight cylindrical flasks (4 · 10 cm),

made of transparent glass, was placed in each cabin.

Each series contained the different levels of concentra-

tion studied, which were placed randomly. Each bottle

was filled with 100 ml of the corresponding solution,

to avoid that different volumes might affect the percep-

tion of colour. The flasks were carefully positioned sothat the distance from the panellists was very similar

to that between the probe and the cuvette containing

the samples analysed by spectroradiometry. Before each

visual analysis all the bottles were vigorously shaken to

avoid pulp sedimentation.

Twelve panellists, with normal colour vision and

experience in both visual assessment of colour and in

the Tristimulus Colorimetry field, took part in the exper-iment. All of them were asked to classify the two series

of bottles in increasing order of coloration. Further-

more, each dilution had to be assigned a score between

1 and 8 according to its coloration.

Table 1

Means and standard deviations for the colour coordinates of the orange jui

Concentration L* a*

White background

10% 88.97 ± 2.25 2.30 ± 1.49

25% 81.54 ± 1.79 8.58 ± 1.11

40% 77.53 ± 1.17 14.23 ± 1.14

50% 75.99 ± 1.12 16.68 ± 1.40

60% 74.77 ± 1.00 19.27 ± 1.50

75% 73.32 ± 0.86 22.19 ± 1.12

90% 72.66 ± 0.92 23.59 ± 1.36

100% 71.91 ± 1.23 24.68 ± 1.70

Black background

10% 46.86 ± 1.80 �1.80 ± 0.79

25% 54.30 ± 1.10 1.19 ± 0.70

40% 58.13 ± 1.14 5.12 ± 0.85

50% 59.82 ± 1.07 7.67 ± 1.05

60% 61.08 ± 0.92 10.21 ± 0.93

75% 62.49 ± 1.10 13.00 ± 0.87

90% 63.05 ± 1.10 14.51 ± 0.97

100% 63.36 ± 1.18 15.86 ± 1.22

3. Results and discussion

Means and standard deviations for the colour param-

eters as a function of the concentration of orange juice

and background are shown in Table 1.

After calculating the means of the colour parameters,these were plotted versus concentration (Fig. 1). In the

case of L*, a* and hab, curves could be fitted well with

polynomial equations of second degree, independently

from the background used for the measurements.

Regarding b* and C�ab, a better fit was achieved with pol-

ynomial equations of third degree (Table 2). The behav-

iour of the colour parameters as a function of

concentration was similar independently of the back-ground, except in the case of L*. Considering the white

background, the least concentrated solutions showed the

highest values of L*, whereas the contrary was observed

when the black background was used. These findings

suggest that, as a consequence of the level of dilution,

the least concentrated solutions suffered a loss of scatter-

ing power, being the amount of reflected light with black

background considerably lower, so instrumentally theyappeared to be the darkest ones. The path length of

the cuvette (1 cm) did contribute to these results, since

the spectroradiometer was not measuring at infinite

thickness. In relation to the behaviour of L* as a func-

tion of concentration, some differences with that re-

ported in a previous study (MacDougall, 1983) were

found, which was mainly due to the use of different

instrumentation and path lengths. As for hue, changesin reflectance throughout the visible spectrum as a result

of dilution are responsible for its changes from orange

to yellow (MacDougall, 1983).

Calculation of the simple regression coefficients be-

tween the colour parameters and concentration revealed

ce solutions analysed

b* C�ab hab

45.71 ± 5.97 45.78 ± 6.04 87.21 ± 1.31

71.68 ± 10.53 72.20 ± 10.55 83.09 ± 0.77

80.76 ± 3.61 82.01 ± 3.56 79.99 ± 0.89

80.85 ± 4.70 82.57 ± 4.70 78.33 ± 0.95

80.44 ± 3.88 82.73 ± 3.83 76.51 ± 1.09

80.10 ± 3.53 83.13 ± 3.36 74.49 ± 1.04

79.18 ± 3.35 82.63 ± 3.20 73.38 ± 1.13

78.09 ± 3.37 81.91 ± 3.39 72.45 ± 1.15

20.47 ± 3.81 20.56 ± 3.82 95.13 ± 1.82

42.56 ± 4.45 42.58 ± 4.44 88.35 ± 0.96

52.83 ± 4.11 53.09 ± 4.08 84.43 ± 1.02

57.16 ± 3.68 57.68 ± 3.62 82.32 ± 1.18

60.03 ± 3.89 60.90 ± 3.80 80.30 ± 1.13

63.09 ± 3.89 64.43 ± 3.77 78.31 ± 1.13

64.10 ± 3.71 65.73 ± 3.60 77.21 ± 1.10

64.62 ± 3.44 66.55 ± 3.37 76.19 ± 1.21

Concentration

L*

40

50

60

70

80

90

100

0 20 40 60 80 100 120

Concentration

a*

-4

0

4

8

12

16

20

24

28

0 20 40 60 80 100 120

Concentration

b*

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120Concentration

C* ab

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100 120

Concentration

h ab

70

74

78

82

86

90

94

98

0 20 40 60 80 100 120

WHITE BACKGROUNDBLACK BACKGROUND

Fig. 1. Diagrams of L*, a*, b*, C�ab and hab vs orange juice concentration in black and white backgrounds. Lines were calculated according to the

equations showed in Table 2.

474 A.J. Melendez-Martınez et al. / Food Quality and Preference 16 (2005) 471–478

that all the relationships were significant (p < 0.05) ex-cept those corresponding to b* and C�

ab for white back-

ground (Table 3). Regression coefficients for L* were

virtually identical, although with opposite signs (nega-

tive for white background, positive for the black one)

due to the differences in behaviour mentioned above.

ANOVA test for repeated measurements (a = 0.05)

was applied between consecutive concentrations in order

to test whether there were significant differences (Table

Table 2

Fitting equations between the colour parameters and concentration

Parameter White background

L* y = 92.442�0.46x + 0.003x2

a* y = �2.447 + 0.502x �0.002x2

b* y = 21.897 + 2.892x �0.044x2 + 2.085e�4x3

C�ab y = 21.692 + 2.903x �0.0432 + 2.036e�4x3

hab y = 89.958 � 0.298x + 0.001x2

4). Significant differences were found in all the casesfor a* and hab, independently the background used,

and for L* when the white background was used. Taking

into account the results of the test, it can be said that the

black background allowed a better differentiation of

consecutive dilutions by means of the colour para

meters. However, when the white background was used,

values of b* and C�ab that were not significantly different

between consecutive dilutions were obtained.

Black background

y = 43.252 + 0.468x � 0.003x2

y = �5.295 + 0.308x �9.507e�4x2

y = 2.34 + 2.118x � 0.025x2 + 1.05e�4x3

y = 2.684 + 2.082x �0.024x2 + 9.99e�5x2

y = 98.801 � 0.443x + 0.002x2

b*

40

60

80

100

10%25%40%

Table 4

Results of the ANOVA test for repeated measurements (a = 0.05)

between consecutive concentrations

100–90 90–75 75–60 60–50 50–40 40–25 25–10

White background

L* 0.003 0.000 0.000 0.000 0.000 0.000 0.000

a* 0.000 0.000 0.000 0.000 0.000 0.000 0.000

b* 0.040 0.000 0.361 0.225 0.846 0.000 0.000

C�ab 0.154 0.007 0.316 0.632 0.276 0.000 0.000

hab 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Black background

L* 0.112 0.006 0.000 0.000 0.000 0.000 0.000

a* 0.000 0.000 0.000 0.000 0.000 0.000 0.000

b* 0.243 0.005 0.000 0.000 0.000 0.000 0.000

C�ab 0.081 0.001 0.000 0.000 0.000 0.000 0.000

hab 0.000 0.000 0.000 0.000 0.000 0.000 0.000

p values in italics denote significant differences.

Table 3

Simple regression coefficients (r*) and significance levels (p) between

the colour parameters and concentration

Parameter White background r* (p) Black background r* (p)

L* �0.92 (0.001) 0.91 (0.002)

a* 0.97 (0.000) 0.99 (0.000)

b* 0.65 (0.080) 0.88 (0.004)

C�ab 0.71 (0.048) 0.89 (0.003)

hab �0.98 (0.000) �0.96 (0.000)

A.J. Melendez-Martınez et al. / Food Quality and Preference 16 (2005) 471–478 475

Discriminant analyses were also carried out. Percent-

ages of correct classification of the samples when eachcolour parameter was considered as independent varia-

ble are shown in Table 5. The results also indicated that

the usage of the black background allowed a better clas-

sification of the samples, being always the percentages of

correct classification over 50%. For the black back-

ground, the colour parameters that allowed better and

worse classifications were a* and L*, respectively. For

the white background, they were hab and C�ab, in this

order.

Taking into consideration both regression simple

analysis and discriminant analysis, it can be observed

that the parameters that lead to the best classifications

of the samples were those better-correlated with concen-

tration. In this sense two linear equations are proposed

in order to calculate easily the concentration of the sam-

ples as a function of those colour parameters. However,

Table 5

Percentages of correct classification of dilutions as a function of colour

parameters

White background Black background

L* 63% 56%

a* 67% 84%

b* 37% 57%

C�ab 32% 59%

hab 72% 70%

a better fitting could be achieved by polynomial equa-

tions of second degree:

Black background a� ¼ 3:14þ 0:202c

White background hab ¼ 87:143� 0:159c

where c is the concentration of the samples expressed as

percentage v/v.

Location of the samples within the (a*b*) diagram as

a function of their concentration and the background is

shown in Figs. 2 and 3. For both backgrounds, the least

concentrated samples were clearly separated between

them, although the samples with the highest concentra-tions (75%, 90% and 100%) could not be differentiated

easily.

Multiple regression analyses by means of partial least

squares (PLS) were carried out in order to calculate the

level of concentration of the samples from the colour

parameters. For this purpose, due to the tridimensional

nature of colour, on one hand the scalar coordinates L*,

a*, b* and, on the other hand, L* and the angular co-ordinates (hab and C�

ab) were considered together. Nor-

mally, the regression coefficients obtained by means of

these models do not differ much with respect to those

obtained by classic multiple regression analysis,

although PLS is considered a more robust statistical

method.

White background

c ¼ 4:489L� þ 0:007C�ab � 10:917hab; R2 ¼ 0:990

c ¼ �19:913L� � 5:712a� � 3:816b�; R2 ¼ 0:991

a*

0

20

-10 0 10 20

50%60%75%90%100%

Fig. 2. Diagram of the scores of the orange juice diluted samples

within the (a*b*) plane. White background.

a*

b*

0

20

40

60

80

100

-10 0 10 20

10%25%40%50%60%75%90%100%

Fig. 3. Diagram of the scores of the orange juice diluted samples

within the (a*b*) plane. Black background.

476 A.J. Melendez-Martınez et al. / Food Quality and Preference 16 (2005) 471–478

Black background

c ¼ �14:97L� þ 0:969C�ab � 15:36hab; R2 ¼ 0:994

c ¼ 55:673L� � 2:283a� � 18:007b�; R2 ¼ 0:987

Averages of the colour differences (DEab) between

consecutive dilutions of the samples are summarised in

Table 6. It was observed that, in general, the usage of

black background led to higher colour differences, whichwas in agreement with the findings of other authors

(Huang, Francis, & Clydesdale, 1970b; Gullett et al.,

1972). The small path length of the cuvette may contrib-

ute to these results.

In order to compare the instrumental and the visual

analyses, 12 panellists took part in the visual assessment

of the colour of the different mixtures considered. Each

of them was asked to sort the two series of bottles con-taining the different dilutions of orange juice in increas-

ing order of coloration.

Eight completely correct visual analyses (67%) were

achieved when the cabin with black walls was used.

The most concentrated dilutions (75%, 90% and 100%)

Table 6

Means and standard deviations of colour differences between dilutions

White background Black background

100–90 2.71 ± 1.73 2.44 ± 1.44

100–75 4.02 ± 1.62 3.95 ± 1.48

90–75 1.99 ± 0.91 2.43 ± 0.99

75–60 3.72 ± 1.18 4.56 ± 1.20

60–50 3.25 ± 1.13 4.16 ± 1.25

50–40 3.47 ± 1.51 5.38 ± 1.37

40–25 11.68 ± 7.99 11.72 ± 2.29

25–10 30.53 ± 5.76 23.53 ± 4.87

were not correctly arranged in the remaining four anal-

yses. One of the panellists transposed the dilutions at

75% and 100%, another one transposed the dilutions

at 90% and 100% and two of them transposed the dilu-

tions at 75% and 90%.

Results obtained in the cabin with white walls werequalitatively and quantitatively different. The percent-

age of correct classifications was dramatically lower

(33%). The dilutions transposed were also the most con-

centrated: 5 out of the 8 incorrect analyses were due to

the transposition of the samples at 90% and 100%. Dilu-

tions at 75% and 90% were transposed by two panellists,

and one of them transposed the three most concentrated

orange juices among them.It can be observed that colour differences (DE�

ab) be-

tween the transposed pairs of dilutions ranged from

1.99 to 4.02 CIELAB units (Table 6). In all the cases

DE�ab was not only over the range 0.38–0.73 CIELAB

units, which has been considered as the threshold for dis-

tinguishing between different colours, but also over the

considered suprathreshold (1.75 CIELAB units) (Mel-

gosa et al., 1997; Melgosa, Perez, Yebra, Huertas, &Hita, 2001). Nevertheless, from a industrial point of

view, it has been considered that the ranges of colour dif-

ferences 1.1–2.8 and 2.8–5.6 CIELAB units correspond

with rigorous and normal colour tolerances, respectively,

whereas colour differences over 5.6 CIELAB units ought

to be easily distinguished (Lozano, 1978; Melgosa et al.,

2001). Curiously, all the panellists were able to distin-

guish the pairs of solutions 75–60%, 60–50% and 50–40%, in spite of the fact that their corresponding colour

differences considering the white background (3.72, 3.25

and 3.47 CIELAB units, respectively) were higher than

those corresponding to the pair 100–75% (4.02 and

3.95 CIELAB units, for white and black background,

respectively) (Table 6). Furthermore, the results of this

study indicated that there is not a direct correlation be-

tween the magnitude of the colour differences and thenumber of errors. Thus, the most transposed pairs in

the cabin with white backgrounds were 100–90% and

90–75% in this order, being their colour differences 2.71

and 1.99 CIELAB units, respectively; that is, the pair

with a higher DE�ab was transposed more times that the

pair with a lower colour difference.

The best results obtained with the cabin with black

backgrounds could be explained due to the higher corre-lations between the colour parameters and juice concen-

tration (Table 3). The low correlation between chroma

C�ab and concentration when the white background is

used, could explain the results obtained by the panellist

when the visuals analyses were carried out in the cabin

with white walls. These findings indicate that there are

important differences in the visual assessment of orange

juice colour depending on the background used, which isin agreement with the results obtained by Huang et al.

(1970a, 1970b) in their studies on other foodstuffs.

Table 8

Simple regression coefficients between the colour parameters and the

panellists� scores

White background Black background

L* �0.91 0.91

a* 0.97 0.99

b* 0.65 0.88

C�ab 0.71 0.89

hab �0.98 �0.96

Table 7

Average panellists� scores (P)

Concentration White cabin Black cabin

10 12 12

25 24 24

40 36 36

50 48 48

60 60 60

75 75 76

90 88 83

100 89 93

P

L*

40

50

60

70

80

90

100

0 20 40 60 80 100

WHITE BACKGROUNDBLACK BACKGROUND

Fig. 4. Diagram L* vs average panellists� scores (P).

A.J. Melendez-Martınez et al. / Food Quality and Preference 16 (2005) 471–478 477

Correlations between instrumental and visual data,

considering the mean of the panellists�scores (P) (Table7), were evaluated by multiple regression analysis by

means of PLS:

White background

P ¼ 13:022L� þ 1:488C�ab � 16:705hab; R2 ¼ 0:997

P ¼ �5:525L� þ 1:74a� � 1:702b�; R2 ¼ 0:992

Black background

P ¼ �0:386L� � 3:11C�ab � 12:16hab; R2 ¼ 0:999

P ¼ 9:677L� þ 4:046a� � 3:479b�; R2 ¼ 0:995

Independently of the cabin used, R2 values were

higher than those obtained in the correlations betweenthe colour parameters and the concentration. Simple

correlation coefficients between P and the colour para-

meters (Table 8) are virtually identical to those obtained

between the colour parameters and concentration (Table

3).

The diagram of L* vs the panellists� scores (Fig. 4) wassimilar to the plot of L* against concentration (Fig. 1).

Lines on the diagrams were calculated according to theequations y = 99.485 � 1.066x + 0.016x2 � 8.068e�5x3

and y = 37.366 + 0.963x � 0.013x2 + 6.236e�5x3, for

white and black background, respectively. Instrumental

measurements against black background and at 10 mm

thickness would lead to inconsistent interpretations of

the correlation between instrumental and visual meas-

urements. Misleading instrumental assessment of the

colour of translucent samples have been reported before(MacDougall, 1983; Rummens, 1970), which indicates

that sometimes instrumental designs fail to measure what

the human eye perceives.

4. Conclusions

The results of this study show that objective measure-

ment of colour by spectroradiometry, especially using a

black background, can be used to detect dilutions of or-

ange juice rapidly. It has been also demonstrated that

there is a good correlation between the instrumental

and visual assessment of orange juice colour, althoughthe most concentrated dilutions can not be correctly

sorted. Despite of some inconsistent results between L*

and visual assessment in black background, probably

due to the path length used, the application of the

instrumental measurement of colour in the industry is

recommended. Dilutions of orange juice can be accu-

rately and rapidly detected by equations which relate

colour parameters and concentration. This is veryadvantageous for quality control purposes in the indus-

try, since the most concentrated solutions can not be

easily distinguished by means of visual assessments as

shown in this study. Therefore, the use of instrumental

measurement of colour by means of Tristimulus Colori-

metry should be encouraged rather than visual analysis,

which may be used in an auxiliary way. In this case, cab-

ins with black walls should be used.

Acknowledgments

We gratefully acknowledge CCH Zumos Vitafresh

(Almonte, Spain) for supplying the samples analysed.

We also wish to thank the panellists who took part in

this study.

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