Food Bioprocess Technol (2015) 8:409–419
DOI 10.1007/s11947-014-1410-y

ORIGINAL PAPER

Visualization of Gluten, Starch, and Butter in Pie Pastryby Fluorescence Fingerprint Imaging
Mito Kokawa & Naoto Yokoya & Hiroko Ashida & Junichi Sugiyama &Mizuki Tsuta & Masatoshi Yoshimura & Kaori Fujita & Mario Shibata

Received: 15 May 2014 / Accepted: 15 September 2014 / Published online: 25 September 2014
# The Author(s) 2014. This article is published with open access at Springerlink.com

Abstract The distribution of starches, proteins, and fat in
baked foods determine their texture and palatability, and there
is a great demand for techniques to visualize the distributionsof these constituents. In this study, the distributions of gluten,
starch, and butter in pie pastry were visualized without any
staining, by using the fluorescence fingerprint (FF). The FF,
also known as the excitation–emission matrix (EEM), is a setof fluorescence spectra acquired at consecutive excitation
wavelengths. Fluorescence images of the sample were acquired with excitation and emission wavelengths in the rangesof 270–320 and 350–420 nm, respectively, at 10-nm increments. The FFs of each pixel were unmixed into the FFs andabundances of five constituents, gluten, starch, butter, ferulic
acid, and the microscope slide, by using the least squares
method coupled with constraints of non-negativity, full additivity, and quantum restraint on the abundances of the slide
glass. The calculated abundances of butter, starch, and gluten

at each pixel were converted to shades of red (R), green (G),and blue (B), respectively, and RGB images showing the
distribution of these three constituents was composited. The
composited images showed high correspondence with theimages acquired with the conventional staining method.
Furthermore, the ratio of gluten, starch, and butter in short
pastry was calculated from the abundance images. The calculated ratio was 16.6:37.6:45.8, which was very close to theactual ratio of 12.7:38.8:48.5, and further proved the accuracyof this imaging method.

M. Kokawa : M. Shibata
Research Fellow of Japan Society for the Promotion of Science,
5-3-1 Koujimachi, Chiyoda-ward, Tokyo 102-0083, Japan

Starches, proteins, and fat are important elements of the microstructure of baked foods (Cauvain and Young 2006). Bychanging the distribution of these three constituents, a broad
range of foods with completely different textures can beproduced, ranging from a soft brioche-type bread to a crispy
pie. Because the microstructure of foods determines its texture, appearance, taste perception, and stability of the final
product (Autio and Salmenkallio-Marttila 2001), developing
techniques to study the microstructure of foods is very
important.
However, visualizing the distribution of three or moreconstituents in microscale is difficult, especially in samples
where the structure is easily destructed by procedures such as
staining or substitution of water. Sowmya et al. (2009) and
Watanabe et al. (2002) have observed the structure of cakecrumbs and fat-containing dough, respectively, with scanningelectron microscopy (SEM); however, they have reported thatfat globules could not be observed with this method.

M. Kokawa : M. Yoshimura
Analytical Science Division, National Food Research Institute,
National Agriculture and Food Research Organization, 2-1-12
Kan-nondai, Tsukuba, Ibaraki 305-8642, Japan
N. Yokoya
Department of Advanced Interdisciplinary Studies, The University ofTokyo, 4-6-1 Komaba, Meguro-ward, Tokyo 153-0041, Japan
H. Ashida
Research and Development Center, Fuji Oil Co., Ltd, 4-3 Kinunodai,
Tsukubamirai, Ibaraki 300-2497, Japan
J. Sugiyama (*) : M. Tsuta : K. Fujita : M. Shibata
Food Engineering Division, National Food Research Institute,
National Agriculture and Food Research Organization, 2-1-12
Kan-nondai, Tsukuba, Ibaraki 305-8642, Japane-mail: sugiyama@affrc.go.jp

Keywords Fluorescence fingerprint . Autofluorescence .
Hyperspectral imaging . Spectral unmixing . Nonnegativity

Introduction

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Confocal scanning lazar microscopy (CSLM) is a popularmethod to visualize the gluten and starch matrix in breaddough (Durrenberger et al. 2001; Lynch et al. 2009;
Peighambardoust et al. 2006; Upadhyay et al. 2012).
However, visualizing fat along with these two constituents isdifficult. Li et al. (2004) have visualized fat and gluten
proteins in bread dough using a combination of fluorescencestains and CSLM; however, the two components were stainedand visualized separately. Moreno and Bouchon (2013)
have also used CSLM to visualize the gluten matrix
and absorbed oil in fried foods, although by incorporating fluorescence stains into the sample during the mixing
process. Therefore, this method is not suitable for measuring
actual products.
Although staining can be used to visualize specific constituents in a complex mixture (Autio and Salmenkallio-Marttila
2001, it becomes more difficult as the number of target constituents becomes larger. In addition, the results could markedly vary depending on the selection of stains and staining
conditions such as concentration, solvent, and staining time.
Therefore, nondestructive methods that do not need astaining process have been developed. Hyperspectral imaging(HSI) is a technology that integrates conventional imaging
and spectroscopy to obtain both spatial information and spectral information from a sample (Gowen et al. 2007).
Hyperspectral images are composed of multiple wavebands
for each spatial position of the target studies. Information suchas the internal structure or the distribution of specific constituents can be obtained by analyzing the spectroscopic data.
Another advantage of HSI, apart from its nondestructive quality, is that multiple constituents can be visualized from a single
set of data, because a spectrum can provide information about
multiple constituents.
Recently, studies have been performed using the fluorescence fingerprint (FF) as the spectroscopic data in HSI tovisualize the distribution of gluten and starch in bread dough(Kokawa et al. 2011; Kokawa et al. 2012. The FF, also known
as the excitation–emission matrix (EEM), is a set of fluorescence spectra acquired at consecutive excitation wavelengths,
creating a three-dimensional diagram (Jiang et al. 2010. The
pattern of this diagram is unique for every constituent, rather
like a fingerprint. The FF has an advantage over conventional
fluorescence spectra because it includes emission bands excited at many different excitation wavelengths, making it
possible to make fine distinctions between constituents thatdiffer only slightly in their fluorescence characteristics(Sikorska et al. 2008).
This study aims to visualize the distribution of fat, in
addition to gluten and starch, using the FF in imaging. Piepastry was chosen as the observation target. Pie pastry is madefrom wheat, water, and butter, and the fat is incorporated inrelatively large clumps to provide the typical crunchy texture.
There exist many types of pie pastries that differ in their

Food Bioprocess Technol (2015) 8:409–419

structure and texture, depending on the manufacturingmethods. We focused on two typical types of pie pastry, puff
pastry and short pastry. Puff pastry is made by layering wheat
flour dough and butter, so that when the butter melts in thebaking process, the remaining dough forms thin crisp
layers. On the other hand, short pastry is named after
its “short” texture, which means that the food forms small
crumbles in the mouth when bitten into. This is because thebutter is mixed into the wheat flour, inhibiting the development of gluten.
Although these structures can be estimated from the
manufacturing method, there are no studies that we know ofwhich have actually visualized them. Therefore, the visualization of these structures would clearly show the strong link
between the food structure and their known textures.

Materials and Methods
Sample Preparation
Two types of pie pastry were made, puff pastry and short
pastry. Table 1 shows the composition of the ingredients forthe two pastry doughs.
For the puff pastry, the first five ingredients were mixed in adough mixer (Kenmix Aicoh Premier, KMM770, Kenwood
Limited, Havant, United Kingdom) for 1 min at low speed and6 min at medium speed. The temperature at the end of mixing
was 23–24 °C. The total amount of flour used for the doughwas 2,000 g, yielding dough of approximately 3,200 g. One
thousand grams of the dough was rolled to a thickness of 3–
4 cm and stored at 5 °C for 15–20 h. After resting, the doughwas wrapped around 602 g of butter and rolled to a thicknessof 5 mm with a sheeter. The dough sheet was folded in three,turned around 90°, and then folded in four. After a 30-min rest
at −7 to −8 °C, the dough was folded in three, turned around90°, and folded in three again (total 108 layers). After resting
again at −7 to −8 °C for 30 min, the dough was rolled to a
thickness of 2.5 mm.
Table 1 Ingredients for the two pastry doughs. The ingredients for puff
pastry and short pastry are shown in baker’s percentage which expresses
each ingredient in parts per hundred as a ratio of the ingredient’s mass tothe total flour’s mass

Strong flour
Weak flour
Salt
Shortening
Water
ButterPuff pastry

Short pastry

70
30
1
7
53
97

70
30
1
–
40
85

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For the short pastry, refrigerated butter was cut into approximately 1-cm3-sized pieces and mixed with the strong andweak types of flour and salt in a mixer (Kenmix Aicoh
Premier, KMM770, Kenwood Limited) with a beating attachment until the butter particles were 2–3 mm in size. Water wasadded and the mixture was lightly kneaded into dough. The
total amount of flour used for the short pastry was 1,000 g.
Gluten was extracted from the flour using the method byMacritchie (1985). Fractionation was performed with 50 gstrong flour, 50 g weak flour, and 65 g of pure water. The
flour used was taken from the same batches as those used forthe pie pastry. The flour and water were cooled to 4 °C before
mixing with a pin mixer (National MFG., Nebraska, USA) at
20 °C for 60 s. The dough temperature at the end of mixing
was 18.1 °C. The dough was soaked in pure water for 60 min
to strengthen the gluten connectivity and then kneaded withpure water to separate the insoluble protein fraction (gluten)from starch granules and other soluble substances. The bowl
was cooled while washing off the starch granules, because it
has been reported that the gluten yield is higher when thetemperature is kept low (Macritchie 1985).
Both the pastry doughs and the extracted gluten were cut
into approximately 1-cm3-sized pieces, embedded in a freeze
embedding agent (3 % CMC embedding medium, iTec
Science, Ibaraki, Japan) and frozen immediately in the coolingbath using hexane as the cooling medium in a cold trap (Eyela
UT-2000, Tokyo Rikakikai Co. Ltd, Tokyo, Japan).
When the samples were completely frozen, they weresliced to 10-μm-thick slices using a cryotome (CM-1900,
Leica) fixed with a Surgipath DH80HS blade (Leica). The
thin slices were mounted on a slide glass (S-8215 and S-9901,
Matsunami Glass Ind., Ltd., Osaka, Japan) and kept at −20 °C
until observation.
FF Imaging System
The FF imaging system was constructed as reported earlier(Kokawa et al. 2012; Kokawa et al. 2013b). The imaging
system consisted mainly of a xenon lamp (MAX302, Asahi
Spectra), two sets of band-pass filters (HQBP filter and M.C.
filter, Asahi Spectra), and a monochrome charged coupleddevice (CCD) camera (ORCA-ER-1394, Hamamatsu
Photonics, Shizuoka, Japan). The xenon lamp was equippedwith a built-in UV mirror module, which restricted the wavelength range of light to a band of 250–385 nm. Light from thexenon lamp was transmitted through a band-pass filter toacquire the light of a specific wavelength, which was irradiated onto the sample through a rod lens (RLQL80-05, Asahi
Spectra). Fluorescence from the sample was collected throughan objective lens (NUV M Plan Apo, CVI Melles Griot Co.,
Ltd., Tokyo, Japan), transmitted through another band-passfilter, and the fluorescence image was acquired using the CCD
camera. The two sets of band-pass filters were set in a filter

411

wheel, and by rotating the wheel, fluorescence images ofdifferent excitation and emission wavelengths can beacquired.
The excitation and emission band-pass filters ranged from
270 to 330 nm and 350 to 420 nm, respectively, at 10-nm
intervals. The excitation band-pass filters were coupled to two
short-pass filters (XUVC260 and SU350, Asahi Spectra),which cut off unwanted light that passed through the bandpass filters. Fluorescence images of the sample were acquired
at 53 combinations of the 7 excitation and 8 emission wavelengths. Three wavelength conditions (excitation/emission=
320/350, 330/350, 330/360) were omitted because the excitation and emission were close, causing reflective light to transmit. The 53 combinations of excitation and emission wavelengths make up the FF of this data. Therefore, the acquired
dataset of 53 fluorescence images could also be viewed as theFFs of all the pixels in the image.
The imaging system was constructed on the top of anantivibration table (HMX-0605, Nippon Boushin Industry
Co., Ltd., Shizuoka, Japan), and the sample was mounted on
an XYZ-stage composed of a Z-axis motorized stage (MMU60 V and QT-ADL1, Chuo Precision Industrial Co., Ltd,Tokyo, Japan) and a manual X–Y axis stage (Sigma Koki
Co., Ltd, Tokyo, Japan). The Z-axis stage was essential toadjust the sample height to match the focal plane, whichchanged slightly for each emission wavelength.
The xenon lamp, filter wheels, Z-axis, and CCD camera
were controlled from a personal computer using system development software (LabVIEW 8.2, National Instruments
Corporation, Texas, USA).
Calibration of Imaging System
The lightning system (xenon lamp, UV mirror module, bandpass filters) and the observation system (objective lens, bandpass filters, CCD camera) were not constant regarding the
wavelength of light, i.e., in some wavelengths the light wasstronger or the CCD sensitivity was higher than in otherwavelengths. In order to acquire an accurate FF of the sample,
these distortions needed correcting.
The intensity of the excitation light was made uniform byadjusting the “light intensity (LI)” parameter of the xenon
light source. The built-in mirror module allowed the LI to be
adjusted from 5 to 100 % of the original intensity. The powerof the light was measured with a power meter (NOVA2, Ophir
Optronics Solutions Ltd., Jerusalem) for wavelengths of 260–
330 nm and LI of 10–100 % (10 % intervals). Because thelight power was lowest with light of 260 nm, the LI for theother wavelengths were adjusted to make them equal to thelight power of 260 nm at LI 100 %.
The sensitivity of the observation system was made uniform by adjusting the exposure time of the camera. A whitereflection standard (Spectralon SRM-99, LabSphere, Inc.,

412

New Hampshire, USA) was captured with the camera using
emission filters from 350 to 420 nm. The white reflection
standard shows over 99 % reflectance in the measured wavelength range. The white reflection standard image was acquired at exposure times of 10, 50, 100, and 500 ms, and theaverage luminance values of the images were plotted against
the exposure times. Assuming a linear correlation between the
exposure time and the luminance value, the relation between
these parameters were calculated for each emission wavelength. Because 10 s was the longest exposure time available
for the camera, the exposure time for the wavelength with thelowest sensitivity (350 nm) was set to 10 s. The exposure time
for the other wavelengths was set to obtain the same luminance values as those obtained at 350 nm.

Acquisition of FF Images and Image Registration
The FF images of the two types of pie pastry and gluten wereacquired using the imaging system described above. The
fluorescence images were acquired in the order of the emission wavelength, with those of the combination of the longest
emission and excitation wavelengths being the first. This wasdone to minimize the effect of UV radiation on the sample.
The images were acquired with a binning of 2×2 (Nasibov
et al. 2010) and saved as tagged image file format (tiff)
images.
Dark image data were acquired in the same exposure times
as the samples by turning off the light source and covering theobjective lens with an aluminum foil. The dark images weresubtracted from the corresponding fluorescence images toremove dark noise (Sugiyama 1999; Tsuta et al. 2002).
The fluorescence images acquired with the FF imaging
system were imported into a computer and analyzed withversatile numerical analysis software (MATLAB R2012b,
The MathWorks, Inc., MA, USA).
The fluorescence images needed position aligning because
the images tended to slightly shift when the emission filter waschanged (Kokawa et al. 2012), possibly due to color aberration of the optical system or a slight angle difference between
each band-pass filter and the filter wheel. The fluorescence
images were difficult to align because different constituents
showed fluorescence depending on the emission wavelength.
Therefore, an objective micrometer (NOB1, Sankei Co., ltd,
Tokyo, Japan) was measured in advance using the sameemission wavelengths. The shift between the emission wavelengths was calculated from the micrometer images and thefluorescence images of the samples were roughly aligned
according to the calculated shift. Fine alignment was performed by using the image registration tool in MATLAB,
which allowed the images to be aligned to an accuracy of
one tenth of a pixel. The fringe parts that were out of view insome of the fluorescence images were deleted.

Food Bioprocess Technol (2015) 8:409–419

Spectral Unmixing
Spectral unmixing is a collective term for methods to decompose the observed spectra into a collection of constituent
spectra, or endmembers, and a set of corresponding fractions,or abundances, which indicate the proportion of eachendmember present in the pixel (Keshava and Mustard2002). In this research, the endmembers would be the spectraof gluten, starch, and butter. Based on the preliminary analysis, the spectra of ferulic acid (fluorescent constituent
contained in the wheat aleurone layer) and the microscopeslide was added to the endmember library. By unmixing theFFs of each pixel in the pie pastry images, the content of these
five constituents at each pixel can be calculated, and a distribution map for each constituent can be obtained.
Many spectral unmixing techniques based on the linear
mixing model have been developed in recent decades(Boardman 1994; Keshava 2003; Nascimento and Bioucas
Dias 2005a). The constrained least squares method was usedin this study (Heinz and Chein 2001).
This method requires the endmembers to be known andassumes that the spectra of all the pixels in the sample can be
expressed as a mixture of these endmembers. If appropriate
endmembers can be acquired, least squares inversion is performed with two physical constraints, all abundances areequal to or larger than zero (nonnegativity), and the sum of
the abundance fractions at one pixel is less than unity. This
corresponds to solving the following problem:
1
argmin y−Dα222mX
subject to α ≥ 0;αi ≤ 1

ð1Þ

i¼1

in which the square of the Euclidean norm is minimized, andwhere y∈ℝL is the observed spectra, and D∈ℝL×M and α∈ℝL
are the endmember and abundance matrices, respectively. L is
the number of combinations of excitation and emission wavelengths, and M is the number of endmembers. This can beformulated asargmin 
1 T T
α D Dα−yT Dα2ð2Þsubject to Gα ≤ h



T
T
M M
; 1∈ℝM and h ¼ 0T 1
whereMG ¼ ½ −I 1  I∈ℝ. This is solved by optimization methods for qua0∈ℝdratic programming, such as active-set (Gill et al. 1984) andinterior-point (Gondzio 1997) methods.

Food Bioprocess Technol (2015) 8:409–419

413

In this study, an additional constraint was applied to theabundances of the microscope slide to be either 0 (the slide iscovered with the sample) or 1 (there is no sample on the slide).
This quantum restraint on the abundances of the microscopeslide altered formula (1) to




2

1
y−Dα2 þ 1 λ αslide −U αslide − 1
2 2
2
2

ð3Þ

0; jx
1; jx

<
≥

0
0

Results and Discussions
Endmember FFs

where U(x) is a step function that is expressed as
U ðxÞ ¼

holes in the pie pastry image were selected for the starch andmicroscope slide, respectively. For the butter, the butter layer
area in the puff pastry was used. The FFs of all the pixels in theselected area were averaged to obtain the endmember spectra.

ð4Þ

The second term is added only to the slide data. This
optimization becomes the following minimization:
8
>
>
>
>
>
>
>
>
<

 
 

1 T T1 T
α D D þ λC α−yT Dα þy y22
αi ≤ 1=2
 
 
  T


1 T T1  Targminα D D þ λC α− y D þ λbT α þ
y yþλ
>
α
>
>
2
2
>
>
>
>
αi > 1=2>:
subject to Gα ≤hð5Þwhere b∈ℝM is unity for bslide and zero for other components,
and C=bbT. In order to solve this problem, formula (1)(without quantum regularization) was optimized first, followed by either of the problems in (5) depending on the αi value
first obtained by the optimization of (1).
Because the abundances can be calculated unambiguously
from the endmembers, choosing them is an important step.
The endmember spectra of gluten, starch, butter, ferulic acid,and microscope slide were calculated by the following
methods.
Gluten and ferulic acid show characteristic FFs, and therefore, the endmember spectra were extractable from the pie
pastry data by an unsupervised extraction method, vertex
component analysis (VCA) (Nascimento and Bioucas Dias
2005b). VCA extracts the candidates for endmembers by
projecting the data onto the subspaces determined by dimensionality reduction algorithms and choosing the vertices of thedata group. The candidate endmember FFs were compared to
the FFs of the extracted gluten and manually selected aleurone
fragments in the pie pastry, and the candidate with the largest
cosine similarity was chosen as the endmember of gluten andferulic acid, respectively.
Starch, butter, and the microscope slide show only weakfluorescence and their endmember FFs needed to be chosen
manually. Small areas corresponding to starch granules and air

Figure 1 shows the endmember FFs of gluten, starch,butter, ferulic acid, and the microscope slide. Gluten
shows the typical fluorescence pattern of aromatic aminoacids, mainly tryptophan, which has a peak at excitation andemission wavelengths of 280 and 350 nm, respectively(Schulman 1985).
The fluorescence of ferulic acid in the aleurone layers ofwheat kernel has been indicated by others (Irving et al. 1989).
Ferulic acid has a fluorescence peak at excitation and emission
wavelengths of 350 and 430 nm, respectively. Although thiswavelength combination is beyond the measurement range
used in this study, it is possible to observe the fluorescenceband in the longer wavelengths of the FF.
The microscope slide shows weak fluorescence with peakintensity at excitation and emission wavelengths of 320 and
380 nm, respectively. This fluorescence could be due to somechemical treatment on the surface of the microscope slide, oralternately, because of weak reflected or diffused light (where
the exCitation and emission wavelengths are equal) that has
passed through the band-pass filters. The ideal band-pass filter
completely cuts out light outside the designed wavelength
band; however, the actual filters may pass a small fraction oflight at other wavelengths. As a result, a small portion of thelight transmitted through the excitation band-pass filter may
directly pass thorough the emission band-pass filter after
being reflected by the microscope slide. The latter explanationis convincing, because the same fluorescence pattern can beobserved with higher intensities in the FFs of starch and butter.
Starch and butter show fluorescence patterns that seem likethe combination of microscope slide fluorescence and aromatic amino acids. Amylose and amylopectin in starch are notfluorescent, but starch granules are known to have a thinprotein membrane on their surface (Yoshino et al. 2005) andthis may the reason for the fluorescence peak similar to that ofgluten. Butter is known to contain fluorophors such as carotenoids; fluorescence from these constituents can be measured
with a fluorescence spectrophotometer if the sample is thickenough (approximately 2 mm thickness). This fluorescence
becomes very weak when the sample is thinly sliced and ishardly detectable with the FF imaging system. However, aslight difference between the FFs of starch and butter atemission wavelength of 420 nm, which enables the discrimination of the two constituents, may be due to carotenoids.

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Food Bioprocess Technol (2015) 8:409–419

Fig. 1 Fluorescence fingerprints (FFs) of the five endmember constituents. The FFs of a gluten, b starch, c butter, d ferulic acid, and e microscope slide.
Colors denote fluorescence intensity (arbitrary units)

Visualization by the Constrained Least Squares Methodwith the Quantum Restraint

Since the FFs of starch and butter are similar to thelinear combination of the FFs of microscope slide andaromatic amino acids, the normal constrained leastsquares algorithm unmixes the FF of a pixel containing
a pure constituent into the FFs of several other constituents. Figure 2 shows preliminary results suffering fromthis problem. Starch is distributed “thinly” over theareas shown with red arrows, which is unrealistic because starch exists in granular form. As shown in later
results, this area corresponds to the microscope slide, but
the FFs of microscope slide were incorrectly unmixed into
starch and butter.

The quantum restraint was applied to the abundances of
microscope slide to overcome the problem explained above.
Figure 3 shows the abundance images of ferulic acid, butter,starch, gluten, and the microscope slide in the short pastry,acquired by unmixing the FFs with the quantum restraint on
the microscope slide. Abundances for the microscope slide areshown in black (abundance=0.0) and white (abundance=1.0),
and those for the other constituents are shown in continuous
values between 0.0 and 1.0. Compared to Fig. 2, theFig. 2 Abundance images acquired in preliminary calculations. Abundance images of a starch, c butter, and e microscope slide in short pastry.
The grayscale corresponds to an abundance of 1.0 (white) to 0.0 (black).

The scale bar shows 100 μm. In areas shown with red arrows, starch
exists in “thin layers,” which is unrealistic since starch exists in granularform

Food Bioprocess Technol (2015) 8:409–419

415

Fig. 3 Abundance images
calculated with the quantum
restraint (short pastry).
Abundance images of a gluten, b
starch, c butter, d ferulic acid, ande microscope slide in short pastry.
The grayscale corresponds to anabundance of 1.0 (white) to 0.0(black). The scale bar shows
100 μm. Compared to Fig. 2, thestarch is shown in sharp granularform and there is a clear
distinction between the
microscope slide and sample

constituents are clearly separated from each other, and theabundance image of starch shows granular features. Figure 4
shows the abundance images of the same constituents in the
puff pastry. The image shows a band of butter sandwiched
between two layers of the wheat dough.
The two sets of images show distinct differences between
the structures of the short and puff pastry. Apart from theobvious difference in the distribution of butter (mixed into
the wheat dough or existing in layers), there is a large difference in the morphology of gluten. The gluten in the short
pastry is observed in small and large clumps, whereas those in
the puff pastry form a net-like structure, spread in the directionof dough extension (parallel to the layers of the dough andbutter). The net-like structure of gluten in the puff pastry is

presumed to be due to the mixing of dough (6 min at themedium speed) in the absence of fat. On the other hand, theflour for the short pastry is directly mixed to fat, which is
known to inhibit the formation of gluten (Figoni 2008). The
small clumps of gluten in the short pastry are presumably
aggregations of the protein fraction existing in the flour
particles.
The size distributions of the microscope slide areas are also
very different. The short pastry shows many large voids
compared to the small airspaces seen in the puff pastry. The
puff pastry is rolled many times during its preparation, which
would eliminate large voids. Consequently, the short pastry is
mixed only roughly, which leaves or even incorporates air inthe dough.

416

Food Bioprocess Technol (2015) 8:409–419

Fig. 4 Abundance images
calculated with the quantum
restraint (puff pastry). Abundance
images of a gluten, b starch, cbutter, d ferulic acid, and e
microscope slide in short pastry.
The grayscale corresponds to anabundance of 1.0 (white) to 0.0(black). The scale bar shows
100 μm. The layer of butter issandwiched between two layersof dough

The texture of puff pastry is expressed as “flakiness,”referring to the number of layers in the baked product(Figoni 2008). The development of gluten structure with good
extensibility is critical in achieving this “flakiness,” since thedough layers need to stay intact during the baking process totrap in the steam that puffs up the pastry (Cauvain and Young2006). On the other hand, short pastry are characterized bytheir “tenderness” or “shortness” which is achieved by lubricating gluten proteins with fat and preventing them fromhydrating and forming structure (Figoni 2008; Ghotra et al.
2002). With either manufacturing method, the optimum distribution of the gluten proteins and fat is the key to achieving
the desired texture. The abundance images of gluten andbutter clearly show these distributions.

Furthermore, the distribution of ferulic acid which iscontained in the aleurone layers of wheat kernel could
become critical when working with whole wheat flour,
which contains more traces of the aleurone and pericarp than white flour. Although there are no reports on
the structure of pie pastry made with whole wheat that
we know of, Autio (2006)) have reported that solids
from the germ and aleurone layer prevent gluten formation and gas cell structure both physically andchemically. Gluten formation is an important factor inwheat products, and methods to visualize gluten andother constituents simultaneously, and without any
staining would contribute greatly to the research on
cereal foods.

Food Bioprocess Technol (2015) 8:409–419

417

Fig. 5 Comparison of the image calculated from the fluorescence fingerprint (FF) data and the stained image (puff pastry) a Composite imageof the abundance images of butter (red), starch (green), and gluten (blue)in puff pastry, and b stained image of the same sample. Protein is stained
blue and fat is stained orange. The scale bar shows 100 μm

Image Composition and Validation by Stained Image

Compared to the stained image, the FF image seems
to show a large quantity of butter. Therefore, the total
quantity of gluten, starch, and butter calculated fromthe abundance image was compared to the value calculated from the ratio of the ingredients used for the
pastry.
In the short pastry, 85 g of butter was mixed to
141 g of wheat flour dough. Around 20 % of the wheat
flour dough is gluten, and the rest, starch (Kokawa
et al. 2013a). This means that the weight ratio ofgluten, starch, and flour is 12:50:38. Because the fluorescence intensity would be proportional to the volumeof these constituents, the specific gravity of 1.1, 1.5,
and 0.91 for gluten, starch, and butter, respectively, wasused to convert the weight ratio to volume ratio. Thisafforded a volume ratio of 13.1:38.5:48.4 for gluten, starch,and butter.
In contrast, the ratio calculated from the abundance matrix
was 16.6:37.6:45.8 for gluten, starch, and butter. This is very
close to the ratio calculated from the ingredients of the pie
pasty, and seems to support the accuracy of the imaging
method.

In order to validate the analysis results, both the samples were
stained with a dual stain method, which stains gluten and fat inblue and orange, respectively. Figure 5 shows the composite
image of the puff pastry with butter, starch, and gluten abundances shown in red, green, and blue, respectively, and thecorresponding stained image.
The red band of butter is visualized distinctly in both theimages, and some large starch granules can also be distinguished in both the images. The gluten strands running in thehorizontal direction can also be seen in the stained image.
Figure 6 shows the composite image of the short pastrywith butter, starch, and gluten abundances shown in red,green, and blue, respectively and the corresponding stained
image.
In both the FF and stained images, large aggregates ofgluten can be observed. The positions of the starch granules
and fat in the analyzed image largely correspond with those of
the stained image. Although the FFs of butter and starch weredifficult to distinguish by visual judgment, it was possible toaccurately obtain their abundances.

Fig. 6 Comparison of the image calculated from the fluorescence fingerprint (FF) data and the stained image (short pastry) a Composite imageof the abundance images of butter (red), starch (green), and gluten (blue)in short pastry, and b stained image of the same sample. Protein is stained
blue and fat is stained orange. The scale bar shows 100 μm

418Conclusions
In this study, FF imaging was combined with spectral
unmixing methods to visualize the distribution of three constituents: gluten, starch, and butter. As a result, it was possible
to visualize two more constituents: ferulic acid and the microscope slide. The results of this study are very significant
because they indicate that a broad range of constituents, eventhose that show low levels of fluorescence such as starch andbutter, can be visualized with FF imaging, if the rightconstraints are applied.
Imaging with autofluorescence is seemingly restrictedcompared to near-infrared (NIR) or infrared (IR) imaging
because only fluorescent samples can be visualized with autofluorescence. However, this study showed that many constituents that are categorized to be measured by vibrational
information (measured by NIR and IR), such as starch and fat,can actually be measured by fluorescence.
Open Access This article is distributed under the terms of the Creative
Commons Attribution License which permits any use, distribution, andreproduction in any medium, provided the original author(s) and thesource are credited.

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