Optimization of image analysis techniques for quality assessment of whole-wheat breads made with fat replacer

Abstract

The cellular structure of healthy food products, with added dietary fiber and low in calories, is an important factor that contributes to the assessment of quality, which can be quantified by image analysis of visual texture. This study seeks to compare image analysis techniques (binarization using Otsu’s method and the default ImageJ algorithm, a variation of the iterative intermeans method) for quantification of differences in the crumb structure of breads made with different percentages of whole-wheat flour and fat replacer, and discuss the behavior of the parameters number of cells, mean cell area, cell density, and circularity using response surface methodology. Comparative analysis of the results achieved with the Otsu and default ImageJ algorithms showed a significant difference between the studied parameters. The Otsu method demonstrated the crumb structure of the analyzed breads more reliably than the default ImageJ algorithm, and is thus the most suitable in terms of structural representation of the crumb texture.

Full text

Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015 133
Food Science and Technology ISSN 0101-2061
OI:Dhttp://dx.doi.org/10.1590/1678-457X.6560
1 Introduction
e food industry is aware of growing consumer demand for
healthy products (Hellyeretal., 2012), with added dietary ber
(Vuholmetal., 2014) and low in calories (Heiman & Lowengart,
2014). Including whole grains in one’s diet, for instance, can
positively impact health and lifestyle.
Wheat is a unique grain because of its functional components.
Only wheat our can form a viscoelastic dough with rheological
properties (Goesaertetal., 2005). However, combining nutritional
value and organoleptic quality in bread recipes is a challenge.
Fat intake is scientically linked to increased risk of developing
chronic noncommunicable diseases, which aects morbidity
and mortality rates (Brasil, 2005). One way of reducing calories
in food products is the use of fat replacers (Zahnetal., 2010).
Enzymatically modied starches have functional benets and
can be used specically as a fat substitute in bread (Scheueretal.,
2014).
e cellular crumb structure of cereal-based products, such
as bread, is an important contributing factor to their textural
properties (Zghaletal., 1999) and to the determination and
quantication of sensory quality (Mondal & Datta, 2008).
erefore, knowledge of the structure of breads may help predict
many of their quality-related properties (Ozkoc et al., 2009),
particularly in whole-wheat breads, which are known to be
less appealing to consumers in terms of quality than are breads
made with rened wheat our (Oro, 2013).
e eects of relative variation in components or processing
parameters of a food product can be evaluated using response
surface methodology (Collaretal., 2007; Flanderetal., 2007).
Furthermore, the structural elements of food products such as
breads (Kihlberget al., 2004), starch (Wuetal., 2012), fruits
and vegetables (Qing-Guoet al., 2006) can be quantied by
image analysis (Jackman & Sun, 2013), contributing to the
understanding of quality-related properties such as texture
(Qing-Guoetal., 2006).
In the search for improved bread formulations, image
processing is a useful tool to investigate, approximate and predict
many properties, such as texture (Pourfarzadetal., 2013), by
assessing cell size, cell size distribution, number of cells per
unit area, cell wall thickness, void fraction and shape factor
(Gonzales-Barron & Butler, 2006). e accuracy of a digital
image analysis system for crumb grain measurement can be
evaluated based on its capability to predict crumb density from
directly computed structural parameters (Zghaletal., 1999),
Optimization of image analysis techniques for quality assessment
of whole-wheat breads made with fat replacer
Patrícia Matos SCHEUER1*, Jorge Augusto Sandoval FERREIRA2, Bruna MATTIONI1,
Martha Zavariz de MIRANDA3, Alicia de FRANCISCO1
a
Received 11 Nov., 2014
Accepted 25 Feb., 2015
1Cereal Laboratory, Federal University of Santa Catarina – UFSC, Florianópolis, SC, Brazil
2Federal Institute of Santa Catarina, Campus Florianópolis-Continente, Florianópolis, SC, Brazil
3Grain Quality Laboratory, Brazilian Corporation of Agricultural Research – Embrapa Wheat, Passo Fundo, RS, Brazil
*Corresponding author: patriciamatosscheuer@gmail.com
Abstract
e cellular structure of healthy food products, with added dietary ber and low in calories, is an important factor that contributes
to the assessment of quality, which can be quantied by image analysis of visual texture. is study seeks to compare image
analysis techniques (binarization using Otsu’s method and the default ImageJ algorithm, a variation of the iterative intermeans
method) for quantication of dierences in the crumb structure of breads made with dierent percentages of whole-wheat our
and fat replacer, and discuss the behavior of the parameters number of cells, mean cell area, cell density, and circularity using
response surface methodology. Comparative analysis of the results achieved with the Otsu and default ImageJ algorithms showed
a signicant dierence between the studied parameters. e Otsu method demonstrated the crumb structure of the analyzed
breads more reliably than the default ImageJ algorithm, and is thus the most suitable in terms of structural representation of
the crumb texture.
Keywords: Otsu and default algorithms; binarization; ImageJ.
Practical Application: e structural elements of food products such as breads can be quantied by image analysis, contributing
to the understanding of quality-related properties such as texture, because to prepare bread with whole-wheat our (whole grain
milled) and fat-replacer (enzymatically modied corn starch) is a challenge. To quantify the dierences in crumb structure
features of 14 dierent types of whole-wheat breads made with fat replacer, was used two dierent image thresholding techniques
(binarization using Otsu’s method and the default ImageJ algorithm), using free sowares (GIMP and ImageJ).

Image analysis algorithms in whole breads
Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015134
such as number of cells, mean cell area, cell density, circularity,
and minimum and maximum cell area. Crumb cell structure
can be commonly described by the parameters mean cell area
and cell density, and quantied by image analysis systems to
determine the visual texture (Scanlon & Zghal, 2001), which,
alongside mouthfeel perception, is signicantly impacted by the
cell size of the crumb (Skendietal., 2010).
Although a standard procedure does not yet exist, several
studies have used image analysis to assess bread loaves made
with rened wheat our (Zghaletal., 1999; Gonzales-Barron
& Butler, 2006) and some made using whole grains, as soy
(Lodi & Vodovotz, 2008), β-glucan (Skendietal., 2010), chia
(Farrera-Rebolloetal., 2012), amaranth (Sanz-Penella etal.,
2013) and whole wheat (Torrietal., 2013).
One of the most common analytical actions performed
on any image is descriptive analysis, which seeks to assess the
relationships between the color and/or textural features of the
image, find distinct patterns in the image, determine the presence
of dierent types of objects, or study the statistical distribution
of the pixels and dierent clusters related to the dierent
phenomena appearing in the image (Prats-Montalbánet al.,
2011). e success of the chosen analysis technique depends on
the structure of interest occupying a range of grey levels distinct
from that of the background (Ridler & Calvard, 1978). Several
approaches are available for this purpose: an image acquisition
method that reveals more information or is less confounded by
background noise (Prats-Montalbán al., 2011); homogenization
of lighting to eliminate irregularities; hither resolution; better
sample preparation to ensure minimum data corruption; and
the search for a better image segmentation algorithm that can
provide a more accurate region of interest (Jackman & Sun,
2013). e term segmentation refers to the isolation of one or
various objects from the background of an image to enable feature
characterization of the image (Prats-Montalbánetal., 2011).
Many automated thresholding algorithms are available
(Gonzales-Barron & Butler, 2006), including Otsu’s method
and the default ImageJ algorithm, a variation of the iterative
intermeans method; both provide robust and ecient data
partitioning (Otsu, 1979; Herbert, 2014). e aim of this study
was to compare two dierent image thresholding techniques
and quantify the dierences in crumb structure features of
14dierent types of whole-wheat breads made with fat replacer,
as a means of proposing simple procedures for quantication of
structural changes (viewed from a response surface methodology
perspective) that could have an eect on bread quality.
2 Materials and methods
2.1 Material
Commercially rened wheat our and whole-wheat our for
bread making, milled from wheat harvested in 2012 and stored
at -18 ºC, were provided by Cooperativa Agrária Agroindustrial,
Guarapuava, state of Paraná, Brazil. A representative sample of
whole-wheat our (reconstituted whole-wheat our) and white
wheat our (rened our), both from the same batch of grain, was
used to ensure standardization of variable parameters. Dierent
blends of whole wheat our (WF) and rened wheat our (RF)
were prepared and encoded: 95.35WF (95.35%WF+4.65%RF);
85WF (85%WF+15%RF); 60WF (60%WF+40%RF); 35WF
(35%WF+65%RF); and 24.64WF (24.64%WF+75.36%RF).
ese levels were determined by means of response surface
methodology, as described in the Methods section.
e enzymatically modied cornstarch used in this study as
a fat replacer (FR) was provided by Dutch Starches International,
Netherlands, and is commercially available as Selectamyx C 150.
2.2 Bread samples
Bread loaves were baked and randomly selected in duplicate
according to the optimized straight-dough bread-making method
(10-10B) (American Association of Cereal Chemists, 2000), with
a 60 min fermentation, using the following formulation: wheat
our (100%), sucrose (6%), instant active dry yeast (1.8%), sodium
chloride (1.5%), fat (3%), and tap water (Flanderetal., 2007).
Instead of fat, the aforementioned fat replacer was used in gel form
according to manufacturer instructions. e amount of water
used corresponded to 86% of the water absorption content as
determined by Farinograph analysis (method adapted from Seyer
& Gélinas, 2009). e ingredients were mixed at speed setting
2for 6 min (Flanderetal., 2007; Oro, 2013) in a commercial
mixer (RPD 25, Líder, Brazil). e temperature of the dough
was kept at 28-29 °C aer mixing. Subsequently, 500-g pieces of
dough were placed in a proong cabinet (CFC20, Perfecta, Brazil)
at 30 °C and 85% relative humidity for 35 min. e dough was
then kneaded once, fermented for 17 min, kneaded again, and
fermented for a further 8 min. e dough was sheeted manually,
placed into a rectangular mold (9.5 cm x 20 cm x 4.5 cm), and
fermented for 24 min. Finally, the dough was baked in a revolving
oven (Ventile, Líder, Brazil) for 24 min at 180 °C. Aer cooling
for 1 hour at room temperature on metal racks, 1-cm-thick
longitudinal and cross-sections of each loaf were sliced and
scanned for crumb cell analysis (Pourfarzadetal., 2013).
2.3 Experimental design
Using the rotatable central composite design of response
surface methodology, with the percentage of whole wheat our
(%WF) and the percentage of fat replacer (%FR) as independent
variables, 14 dierent loaves were baked (Table1).
e rotatable central composite design yielded 14 experiments,
as follows: four factorial treatments, in which the two factors
were %WF (%whole wheat our) and %FR (% fat replacer),
each with two levels coded to -1 and +1; four axial treatments
including minimum and maximum level of each factor coded
as -α and +α, where α=(2
2
)
1/4
= 1.414; and one central treatment
repeated six times, to estimate the pure experimental error and
calculate the reproducibility of the method, in which all factors
are coded as zero (Table1).
e second-order regression model is represented by
Equation 1 (Montgomery, 1991).
z = b0+b1x+b11(x)2+b2y+b22(y)2+b12xy (1)
Where:
z = estimated results for the response variables (mean cell
area and cell density); x = %WF; y = %FR; b0, b1, b11, b2, b22,
b12=estimates of the regression coecients

Scheueretal.
Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015 135
value and computing averages for the pixels at or below the
threshold and for those above. e averages of those two values
are computed, the threshold is incremented and the process
repeated until the threshold is higher than the composite
average (threshold = (average background + average objects)/2)
(Gonzales-Barron & Butler, 2006).
e following crumb grain properties were extracted and
calculated: number of cells (objects); mean cell area (mm2); cell
density (cells/mm
2
), circularity, and maximum and minimum cell
area (mm
2
) (Esteller & Lannes, 2005). Cell density was calculated
by dividing the number of cells by the mean cell area. Cell shape
was analyzed using a shape factor to measure circularity.
e cell size frequency distribution (Sturges, 1926) was also
calculated for both methods (Otsu and default). According to
Polakietal. (2010), the cell distribution reveals information
about the crumb structure of the bread. Cells can be classied
as small (area < 4 mm2), medium (4-8 mm2) or large (> 8 mm2),
as all cell sizes coexist in the matrix.
2.5 Analysis of results
Analysis of variance (ANOVA) and Tukey’s test (signicance
level p≤0.05) were used to determine the signicance of the
data. All statistical analyses and graphical representations were
performed in STATISTICA 7.0®.
3 Results and discussion
Bread samples 9 to 14 comprised the central points treatments,
and are thus represented by a single image.
Image analysis was performed on each of three scanned
bread slices to provide a more detailed view of the bread texture,
as shown in Figure1.
Figure 2 shows the binary images processed by the two
thresholding techniques (Otsu and default). Figures 2a, b show
a basic gure constructed from circles. Figures 2c, d show bread
sample 5 (24.64%WF+1.6%FR) and Figures 2e, f show bread
sample 6 (95.35%WF+1.6%FR), the loaves baked with the lowest
and highest whole-wheat our content respectively.
For the basic image constructed from circles (Figures 2a, b),
neither visual nor statistical dierences were found between the
thresholding techniques; all parameters were identical with both
algorithms: number of cells (three objects), mean cell area (29.946
mm2), cell density (0.100 cells/mm2), and circularity (0.918).
For bread samples 5 and 6 (Figures 2c, d) and (Figures2e, f),
respectively), dierences between the thresholding techniques
are apparent. Visually, a larger black area is seen in the image
generated by the Otsu method (Figures 2c, e) as compared
with the default algorithm (Figures 2d, f). As described by
Gonzales-Barron & Butler (2006), in similar applications of
dierent thresholding instances, the Otsu method overestimated
the void fraction, whereas the default algorithm appeared to
underestimate this fraction. Statistically signicant dierences
(p≤0.05) are shown in Table2.
As Table2 shows, signicant dierences (p≤0.05) between the
Otsu and default ImageJ methods were found for the parameters
2.4 Scanning resolution settings
Images of three slices were acquired using the atbed scanner
of an all-in-one printer (DCP-7065DN Monochrome Laser
Multi-Function Copier, Brother, Japan). Brightness and contrast
were set to the default soware values (zero) for all samples. e
images were saved as bitmap les at a resolution of 300 dots per
inch (dpi) in the red-green-blue (RGB) color space. Measurements
were obtained in pixels and converted into millimeters by using
known length values. A single 40mm x40mm square eld of view
was evaluated in each image. e free GNU Image Manipulation
Program (GIMP) soware, version 2.6, was used to obtain the
two measurements of interest. Aer cropping, the images were
pre-processed and converted to 8-bit greyscale. Pre-processing
(levels adjustment), segmentation (thresholding) and crumb
grain measurements (extraction of parameters) were carried
out in the ImageJ-based Fiji 1.46soware package.
Segmentation was performed manually, by binarization of
greyscale images into black-and-white images using the Otsu
and default ImageJ algorithms.
Otsu’s thresholding algorithm is a uniformity-oriented
method, wherein uniformity is computed as a measure within
each region independent of the surroundings and the optimal
threshold is determined by minimizing the intraclass variance
of the segmented region (Sahooetal., 1988), i.e., it maximizes
between-class variance’ e Otsu method (Otsu, 1979) has
been shown to yield adequate, consistent binary images (high
degree of uniformity) in terms of performance and thresholding
characteristics of the analyzed gures (Gonzales-Barron &
Butler, 2006).
e default ImageJ algorithm is formally known as the
iterative intermeans method, a variation of the IsoData algorithm
(Ridler & Calvard, 1978). e procedure divides the image
into object and background by obtaining an initial threshold
Tab le 1. Bread composition.
Loaf Treatment
Independent variables
%WF %FR
Real value Encoded
value Real value Encoded
value
1
Factorial
35.00 -1 0.60 -1
2 35.00 -1 2.60 +1
3 85.00 +1 0.60 -1
4 85.00 +1 2.60 +1
5
Axial
24.64 - α 1.60 0
6 95.35 +α 1.60 0
7 60.00 0 0.18 -α
8 60.00 0 3.00 +α
9
Central
60.00 0 1.60 0
10 60.00 0 1.60 0
11 60.00 0 1.60 0
12 60.00 0 1.60 0
13 60.00 0 1.60 0
14 60.00 0 1.60 0
WF: whole-wheat our; FR: fat replacer (enzymatically modied corn starch).

Image analysis algorithms in whole breads
Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015136
default algorithm, there was no signicant dierence in number
of cells across any of the samples (p≤0.05) (Table2).
With both algorithms, bread sample 5 had a similar (p≤0.05)
mean cell area (Otsu: 0.497 mm
2
; default: 0.532 mm
2
) than
samples 1, 3 and 9 and a larger mean cell area (p≤0.05) than
number of cells, mean cell area, cell density, and circularity. In
Otsu-analyzed images, bread sample 1 (35%WF+0.6%FR) had a
similar number of cells (752.67 objects, p≤0.05) as compared to
samples 2, 5, 6, 7 and 8, and a greater number of cells (p≤0.05)
than samples 3, 4 and 9 (Table2). In images analyzed with the
Figure 1. Digital images (40 x 40 mm crumb area) of bread samples. BS: bread sample; WF: whole wheat our: FR: fat replacer; BS 9 = 9 to 14.

Scheueretal.
Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015 137
50-60%whole-wheat our and 1.5% fat replacer. With both
methods, the best percentage of fat replacer is about 1.5%.
Statistically, Figure3a (Otsu’s method) corroborates the
literature, in that mean cell area decreased with increasing
percentage of whole-wheat our, regardless of the percentage
of fat replacer used, which can be contextualized by the fact
that ber disrupts the gluten–starch matrix and restricts and
forces gas cells to expand in a particular dimension (Collaretal.,
2006). is may have been compounded by the action of the fat
replacer, as studies have indicated that the size of a hydrocolloid
can inuence its distribution within the gluten matrix and,
therefore, inuence pore size (Mandalaetal., 2007). Dierent
hydrocolloids have been included in the formulation of partially
baked breads to improving quality parameters (specic volume,
rmness, moisture, shelf life) (Bárcenasetal., 2009) as well as
samples 2, 6 and 7 (Table2). With Otsu’s method, sample 5 had
a mean cell area similar (p≤0.05) to that of sample 4 and larger
(p≤0.05) than that of sample 8. By other hand, with the default
algorithm, sample 5 had a mean cell area larger (p≤0.05) to that
of sample 4 and similar (p≤0.05) than that of sample 8 (Table2).
e behavior of the mean cell area parameter is shown in
response surface plots (Figures 3a, b), Otsu and default methods
respectively) of the eect of whole-wheat our and fat replacer
content, as the cell distribution reveals information about the
crumb structure (Polakietal., 2010).
With Otsu’s method (Figure 3a), the greatest mean cell
areas were found in samples baked with low percentages of
whole-wheat our (about 20%) and fat replacer (about 1.5%).
With the default method (Figure3b), the highest mean cell
area values were observed in samples baked with approximately
Figure 2. Binary images thresholded using the Otsu and default ImageJ algorithms.

Image analysis algorithms in whole breads
Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015138
by whole-wheat our and fat replacer content, with the lowest
cell density values found with approximately 40-50%whole
wheat our and 2% fat replacer. ese results run counter to
the mean cell area results found with both Otsu’s (Figure3a)
and the default (Figure3d) methods.
Using Otsu’s method (Table2), no dierences (p≤0.05) in
cell circularity were observed among the bread samples, which
is consistent with the ndings of Rosell & Santos (2010) in loaf
formulations containing hydrocolloids. However, using the
default algorithm, dierences in circularity (p≤0.05) were found:
bread sample 6 (0.835) had a higher value than samples 5 (0.807)
and 9 (0.810); sample 5 (0.807) had a lower circularity (p≤0.05)
than sample 2 (0.833). With both algorithms, circularity values
exceeded 0.8, thus approaching a perfect circle.
e response surface plots shown in Figures 3e, f (Otsu’s and
default algorithms respectively) demonstrate that fat replacer
content had a signicant inuence on pore circularity; namely,
circularity varied more with changes in fat replacer content than
with whole-wheat our content.
Regarding the percentage of cells in each cell area range in
each sample (Table3), both in images analyzed with Otsu’s method
and with the default algorithm, most cells were characterized
as small (area < 0.1 mm2). In bread sample 6, 84-85% of cells
were classied as small, versus 91-97% of cells in the other
samples (Table 3). Furthermore, sample 6 is the only one in
which maximum cell area was in the 3.2 ┤6.4 mm2 range. is
is justied by the fact that sample 6 had a higher percentage of
whole-wheat our (95.35%WF), as the presence of ber hinders
to replace fat. As gas cells expand, the density of the dough is
reduced (Scanlon & Zghal, 2001).
Regarding cell density, in images analyzed with Otsu’s algorithm,
bread sample 6 had the highest density (4896.98 cells/mm
2
) (p≤0.05)
among all samples. As sample 6 had the highest whole-wheat
our content (95.35%WF+1.6%FR), this can be explained by
the fact that fibers can dilute and interrupt the gluten–starch
matrix, thus causing a restriction in gas retention, as reported
in whole-oat bread by Polakietal. (2010). In images analyzed
with the default ImageJ algorithm, sample 6 had a cell density
similar (4702.27 cells/mm2, p≤0.05) to that of sample 7 and
higher (p≤0.05) than that of all others (Table2).
Both with Otsu’s method and with the default ImageJ algorithm
(Table2), the highest cell density and lowest mean cell area values
were found with the highest whole-wheat our content, which
is consistent with the results obtained by Farrera-Rebolloetal.
(2012) in breads made with chia our and by Hruskovaetal.
(2012) in whole-grain breads.
e response surface plots shown in Figures 3c, d (Otsu’s
and default algorithms respectively) show a similar behavior in
cell density with percent changes in whole-wheat our and fat
replacer content. Cell density strongly inuences the mechanical
properties of bread crumb, thus allowing comparison of the
analyzed food with other raw materials and processing conditions
(Scanlon & Zghal, 2001).
Both in images analyzed with Otsu’s method (Figure3c) and
with the default algorithm (Figure3d), cell density was inuenced
Tab le 2. Image analysis parameters of bread loaves.
Otsu algorithm
Bread samples Number of cells
(objects)
Mean cell area
(mm2)
Cell density
(cells/mm2)Circularity
1 752.67a ± 84.23 0.331ab ± 0.059 2348.95bc ± 687.62 0.824a ± 0.002
2 689.00ab ± 87.93 0.285b ± 0.082 2496.81bc ± 410.10 0.837a ± 0.003
3 563.33b ± 40.13 0.346ab ± 0.029 1630.19bc ± 76.87 0.842a ± 0.006
4 562.00b ± 13.89 0.325ab ± 0.042 1747.91bc ± 220.57 0.828a ± 0.010
5 589.67ab ± 73.64 0.497a ± 0.062 1187.65c ± 96.42 0.821a ± 0.006
6 682.33ab ± 40.50 0.141c ± 0.016 4896.98a ± 668.48 0.832a ± 0.004
7 603.33ab ± 47.06 0.212b ± 0.058 3012.09b ± 979.34 0.837a ± 0.022
8 628.67ab ± 72.06 0.298b ± 0.114 2280.47bc ± 727.22 0.834a ± 0.003
9 to 14 568.33b ± 72.50 0.323ab ± 0.058 1782.57bc ± 266.18 0.815a ± 0.009
Default ImageJ algorithm
Bread samples Number of cells
(objects)
Mean cell area
(mm2)
Cell density
(cells/mm2)Circularity
1 793.67a ± 91.55 0.412abc ± 0.090 2009.59b ± 629.48 0.821abc ± 0.000
2 726.33a ± 100.08 0.332bcd ± 0.060 2246.91b ± 561.95 0.833ab ± 0.005
3 577.00a ± 48.59 0.397abcd ± 0.020 1455.88b ± 145.37 0.827abc ± 0.006
4 580.00a ± 23.52 0.327bc ± 0.056 1812.40b ± 338.38 0.826abc ± 0.002
5 625.67a ± 73.21 0.532a ± 0.070 1197.23b ± 259.88 0.807c ± 0.007
6 712.33a ± 23.96 0.159d ± 0.045 4702.27a ± 1223.48 0.835a ± 0.004
7 702.67a ± 72.50 0.244cd ± 0.072 3147.27ab ± 1340.16 0.826abc ± 0.019
8 759.00a ± 135.50 0.434abc ± 0.024 1740.99b ± 225.71 0.819abc ± 0.003
9 to 14 610.67a ± 86.94 0.505ab ± 0.118 1260.02b ± 352.71 0.810bc ± 0.010
Means ± standard deviations in the same column followed by a dierent letter are signicantly dierent (p≤0.05). Bread sample 9 actually represents samples 9 to 14.

Scheueretal.
Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015 139
content between bread samples (1 vs. 2; 3 vs. 4; 7, 8 and 9-14)
while maintaining the same percentage of whole-wheat our
(Table1) did not yield relevant dierences in relative number of
cells in each cell area range. is probably demonstrates that the
properties of whole-wheat our predominate over those of the
fat replacer, which should act as shortening. Breads made with
white our and low fat content (0.1 and 0.3% of our weight)
resulted in so loaves with a larger volume (Moulineyetal., 2011).
is same study showed that loaf volume rises progressively
the formation of air bubbles in the viscoelastic gluten network.
is is consistent with earlier studies (Pomeranzetal., 1977)
showing that addition of fibrous materials to wheat our weakens
the crumb cell structure due to dilution of gluten protein network.
Dubois (1978) emphasized that the gas retention of the dough is
impaired largely by water-insoluble fractions, thereby changing
the texture and appearance of the baked product.
As shown in Table3, with both of the studied methods (Otsu’s
and the default ImageJ algorithm), relative variation of fat replacer
Figure 3. Eect of whole-wheat our and fat replacer content on the mean cell area ((a) and (b)), cell density ((c) and (d)), circularity ((e) and
(f)) of bread crumb, as analyzed with the Otsu and default ImageJ algorithms. WF: whole-wheat our; FR: fat replacer.

Image analysis algorithms in whole breads
Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015140
small dierences and relative variation in the number of cells
for ranges from 0.8 to 50.2 mm2.
Table 4 shows the minimum and maximum cell area values
found in each bread samples using the two methods. e
minimum cell area value was identical (0.007 mm2) in all bread
samples and in both image analysis algorithms. e maximum
cell area value did not dier signicantly (p≤0.05) between
the Otsu and default methods. However, the results of samples
6and 1 bear stressing. Bread sample 6, which was made with
95.35%WF, had a maximum cell area value of approximately
5mm
2
, whereas all other samples had maximum values ranging
from 31 to 72 mm2. Bread sample 1 had a maximum cell area
of 31.096 mm2 with Otsu’s method and 71.245 mm2 with the
default ImageJ algorithm.
with increasing levels of shortening up to about 2% of our
weight. us, one important function of fat in bread-making is
stabilization of gas bubbles in the dough, increasing gas retention
in the oven (Goesaertetal., 2005), as added fat prevents binding
of the native our lipids to the gluten network, thereby stabilizing
proteins during heating of the dough (Moulineyetal., 2011).
Regarding the distribution of the dierent frequency
ranges, only samples 6 (7%) and 7 (2 to 3%) had cells with a
mean cell area in the 0.1┤0.2 mm
2
range, as determined by both
methods (Table3). Table3 shows that no sample had pores in
the 0.2 ┤0.4 mm2 range and that only sample 6 had pores in
the 0.4 ┤0.8 mm2 range, for both methods (Otsu’s and default).
Comparison of Otsu’s method and the default algorithm revealed
Tab le 3. Cell quantity in each bread sample.
Cell area
(mm2)Method
Cell quantity (%)
Bread sample
123456789
┤ 0.1 Otsu 94 96 97 95 93 85 94 94 92
Default 97 94 97 94 91 84 92 93 92
0.1 ┤0.2 Otsu 7 2
Default 7 3
0.2 ┤0.4 Otsu
Default
0.4 ┤0.8 Otsu 6
Default 6
0.8 ┤1.6 Otsu 411431233
Default 1 3 3 2 2 3 3
1.6 ┤3.2 Otsu 111111122
Default 1 1 1 2 4 0.4 1 2 3
3.2 ┤6.4 Otsu 1 1 1 2 0.3 1 2
Default 1 1 1 2 0.3 1 1 1
6.4 ┤12.8 Otsu 0.1 0.1 0.2 0.5 0.7 0.3 0.7
Default 0.4 0.3 0.2 0.2 1 0.8 0.6 0.5
12.8 ┤25.6 Otsu 0.1 0.7 0.3 0.3
Default 0.1 0.5 0.3 0.1 0.7
25.6 ┤50.2 Otsu 0.4 0.1 0.2 0.3 0.3 0.2 0.1 0.2
Default 0.4 0.1 0.3 0.2 0.3 0.2 0.4
Tab le 4. Minimum and maximum cell area.
Bread sample Minimum cell area (mm2) Maximum cell area (mm2)
Otsu Default Otsu Default
1 0.007a ± 0.000 0.007a ± 0.000 31.096a ± 12.707 71.245a ± 52.213
2 0.007a ± 0.000 0.007a ± 0.000 55.852a ± 7.557 51.290a ± 11.039
3 0.007a ± 0.000 0.007a ± 0.000 53.111a ± 0.000 51.842a ± 0.000
4 0.007a ± 0.000 0.007a ± 0.000 58.236a ± 0.710 42.929a ± 18.341
5 0.007a ± 0.000 0.007a ± 0.000 53.519a ± 12.924 46.918a ± 16.268
6 0.007a ± 0.000 0.007a ± 0.000 5.622a ± 0.066 5.562a ± 0.019
7 0.007a ± 0.000 0.007a ± 0.000 39.148a ± 16.847 36.866a ± 16.020
8 0.007a ± 0.000 0.007a ± 0.000 72.320a ± 72.242 56.437a ± 14.282
9 to 14 0.007a ± 0.000 0.007a ± 0.000 46.381a ± 4.612 36.400a ± 1.971
Means ± standard deviations in the same column followed by a dierent letter are signicantly dierent (p≤0.05). Bread sample 9 actually represents samples 9 to 14.

Scheueretal.
Food Sci. Technol, Campinas, 35(1): 133-142, Jan.-Mar. 2015 141
4 Conclusion
Comparison of the parameters of interest in the studied
bread samples using the two chosen algorithms showed that
Otsu’s method yielded a greater number of cells in the generated
images; the number of cells parameter was signicantly dierent;
the mean cell area decreased with increasing whole-wheat our
content, regardless of the percentage of fat replacer used; and
that the highest cell density parameter was found in sample 6,
which was formulated with the highest relative whole-wheat
our content (95.35%WF+1.6%FR).
e results of image analysis with the chosen binarization
algorithms may be used to investigate, approximate and predict
dierent properties of breads.
In short, Otsu’s method provided a more reliable representation
of the crumb structure of bread, and is thus the most suitable in
terms of structural representation of the crumb texture.
Acknowledgements
e authors thank Fundação de Amparo à Pesquisa e Inovação
do Estado de Santa Catarina (FAPESC) for nancial support.
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