_ Food Science and Technology Research, 21 (2), 187 192, 2015 Copyright © 2015, Japanese Society for Food Science and Technology doi: 10.3136/fstr.21.187 http://www.jsfst.or.jp
Original paper Measurement of Residual Bran Distribution on Milled Rice Using Fluorescence Fingerprint-derived Imaging Wei-Tung Chen and Yan-Fu Kuo* Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 106, Taiwan, R.O.C. Received September 15, 2014 ; Accepted January 5, 2015 Rice is typically consumed after milling, a process of removing the husk and bran layers on rice surface. The degree of bran residue remaining on milled rice directly affects the rice quality. This work proposed to detect the bran residue on a single rice grain using fluorescence fingerprint-derived imaging nondestructively. In the experiment, combinations of fluorescence excitation and emission wavelengths that could effectively distinguish the bran and endosperm pixels were identified through fluorescence fingerprint (FF) spectroscopy. Fluorescence images of milled rice samples at these wavelengths were acquired. A support vector machine classifier was then developed to predict the bran residue on rice grains using the FF-derived images as the inputs. It was demonstrated that the proposed method could observe the distribution of bran residue and could predict the percentage of bran residue on milled rice grains with an error of 3.54%. Keywords: excitation emission matrix (EEM), fluorescence fingerprint (FF), sparse linear discriminant analysis (SLDA), support vector machine (SVM), surface lipid content (SLC), degree of milling (DOM)
Introduction
rice bran residue from regular digital images using an adaptive-
Rice (Oryza sativa L.) is a critical staple food for human
network-based fuzzy inference system. Chen and Kuo (2014a)
population. It is typically consumed after milling, a process that
demonstrated the use of hyperspectral imaging to measure the bran
removes the husk and bran layers from rice surface. The degree of
distribution on milled rice surface.
milling (DOM) determines the taste and quality of rice. It is crucial
Fluorescence fingerprint (FF) spectroscopy, also known as
to rapidly and nondestructively evaluate the amount of bran residue
excitation-emission matrix spectroscopy, is a technique that
on rice surface in the field. This work developed an approach to
simultaneously collects a range of excitation spectra at various
detect the bran residue of a single rice grain using fluorescence
emission wavelengths (Bieroza et al., 2012). Combining all the
fingerprint-derived imaging (FFDI).
emission and excitation information as an FF makes it possible to
Nondestructive approaches have been proposed to rapidly
observe subtle discrepancies among organic matters (Chen et al.,
assess the bran residue. Chen et al. (1997) determined the DOM on
2003; Zhou et al., 2013). As a highly sensitive and selective
3 rice cultivars by using visible and near-infrared spectroscopy. Liu
inspection approach, FF spectroscopy was applied to discriminate
et al. (1998) quantified the percentage of the bran layer residue on
wines of different varieties (Yin et al, 2008), predict the buckwheat
a single rice grain based on machine vision and image analysis.
flour ratio in noodles (Shibata et al, 2011), determine the chemical
Gangidi et al. (2002) evaluated DOM variation by using Fourier
changes in olive oil (Tena et al., 2012), and detect the aflatoxin in
transform infrared spectroscopy. Shiddiq et al. (2011) estimated
corn grains (Fujita et al., 2013; Hruska et al., 2014).
*To whom correspondence should be addressed.
E-mail:
[email protected]
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W.-T. Chen & Y.-F. Kuo
In recent years, FF spectroscopy has been extended to FF
plastic bags and then were stored in an oven at 25℃ to maintain
imaging, an approach that acquires an FF for each pixel in an
their moisture content before further treatment. The dried rice seeds
image. FF imaging has been applied in the field of food and
were hulled in a commercial paddy separator (THM-1, Long-Good;
agriculture to access the starch distribution in dough (Kokawa et
Kaohsiung, Taiwan) to remove the husker and to obtain brown
al., 2013), visualize the structural changes in soybean seeds during
rice. The brown rice was further treated to obtain grain samples
germination (Tsuta et al., 2007), and identify the viable bacteria
using a laboratory mill (McGill No. 2, Rapsco; Brookshire, TX,
distribution on pork surface (Nishino et al., 2013).
USA). The mill consists of a cutter bar, a 2-kg milling weight, and
FF imaging acquires dozens or hundreds of contiguous narrow-
a lever arm. The weight was placed on the lever arm approximately
band fluorescence images. This could be a drawback for on-line
20 cm away from the saddle center (Andrews et al., 1992; Pan et
applications because the time and cost for acquiring the abundant
al., 2007). The brown rice was milled for 40 s. Approximate 50 g
images can be considerable. Studies in the past performed principal
of grains was milled in a batch. Broken kernels were removed from
component analysis (PCA) for wavelength selection (Tsuta et al.,
head rice after milling.
2007) or reduced the wavebands for image acquisition (Kokawa et
Excitation and emission wavelength selection
Optimal
al., 2011) to limit the number of wavelengths. However, PCA is a
combinations of λex and λem for distinguishing bran and endosperm
post-process technique and does not eliminate the images needed
were determined. In the process, 10 powdered bran and endosperm
to be acquired. In the other hand, reducing the waveband range
samples, respectively, were prepared. See Chen and Kuo (2014b)
blindly may cause the loss of essential information and reduce the
for the details of powdered sample preparation. The FFs of the
estimation accuracy.
powdered samples were acquired by using a fluorimeter
In this study, sparse linear discriminant analysis (SLDA;
(FlexStation3, Molecular Device; Sunnyvale, CA, USA). The λex
Clemmensen et al., 2011) was applied to select a set of key
ranged from 250 to 450 nm in an increment of 10 nm, and the λem
wavelengths for FF imaging. The FF images at the selected
ranged from 300 to 550 nm in an increment of 10 nm. The ranges
wavelengths, also referred to as FF-derived images, were then
of λex and λem were selected because the FFDI system (with a built-
acquired and applied to discriminate bran and endosperm pixels on
in UV mirror module) developed in this study only possessed the
milled rice surface. Hence, the accuracy of the FF imaging can be
capability to provide UV excitation. Note that expanding the ranges
retained, while the images to be acquired can be reduced.
of λex and λem to visible light may help in identifying more key
The specific objectives of this research were to (1) collect FFs
wavelengths for bran and endosperm discrimination.
on bran and endosperm powders of rice grains, (2) determine a few
SLDA was applied to select combinations of λex and λem based
optimal combinations of excitation wavelengths (λex) and emission
on the FFs. SLDA is a shrinkage technique that imposes constraints
wavelengths (λem) to be used in FFDI, (3) develop an FFDI system
in linear discriminant analysis to achieve attribute sparseness. In
and a classifier to identify the bran residue on milled rice surface,
the beginning of the SLDA process, all the combinations of λex and
(4) and evaluate the performance of the proposed approach by
λem in the FFs were included in the attribute set. Constraints were
correlating the results with a chemical analysis method.
then applied to remove the combinations from the attribute set until there was only one combination retained. SLDA was practiced
Materials and Methods
because it reaches sparseness effectively and is computationally
Rice sample preparation Rice samples of a Japanese cultivar,
efficient. The calculation of SLDA was performed by using
Tai Keng Number 9, were dried to moisture content of
MATLAB (The MathWorks; Boston, MA, USA). The selected λex
approximately 10% using the procedure and equipment described
and λem were applied to determine the central wavelengths for the
in the work by Chen (1993). The rice samples were sealed in thick
band-pass filters in an FFDI system.
Fig. 1. Fluorescence fingerprint-derived imaging system.
Measuring Rice Bran Residue Using Fluorescence Fingerprint-derived Imaging
Fluorescence fingerprint-derived imaging system
An FFDI
system was developed (Fig. 1). The system mainly comprised a xenon lamp (MAX-303 with built-in UV mirror module, Asahi
189
were referred to as endosperm pixels. The bran and endosperm pixels were presented in gray and white, respectively. Image registration
An image registration algorithm (Lucas
Spectra; Tokyo, Japan), a quartz light guide, a collimator, a
and Kanade, 1981) was applied to transform the FF-derived and
microscope (BXFM, Olympus; Tokyo, Japan), a 2X objective lens
bran images of the same rice grain into one coordinate system. This
(PLN UIS2, Olympus; Tokyo, Japan), a monochrome digital
transformation was a necessary preliminary step because the
camera (ORCA-Flash2.8, Hamamatsu Photonics; Shizuoka, Japan),
corresponding pixels in the 2 images needed to be identified as the
2 sets of filter wheels, narrow-band band-pass filters (XBPA series,
training samples in the subsequent classifier development. During
Asahi Spectra, Tokyo, Japan), and a mirror module. The FFDI
image registration, 6 control points corresponding to common
system provided excitation light at specific λex and collected images
features in the 2 images were manually selected. Spatial
at specific λem by using the band-pass filters. The mirror module
transformations were then applied to the bran images to rescale and
was composed of 3 mirrors (3 × 3 cm each), and was utilized to
align them with the FF-derived images.
provide more uniform illumination to the objects being studied
Pixel classifier development and performance evaluation
(Chen and Kuo, 2014b). The FFDI system was enclosed in a dark
soft-margin support vector machine (SVM) with a radial basis
box to prevent stray light.
function kernel (Hsu et al., 2003) was implemented to discriminate
Rice bran-staining and micrograph image acquisition
A
A dye-
bran and endosperm pixels on rice surface. The inputs to the model
staining procedure was applied to rice grains to enable the bran
were fluorescence intensities of the pixels in the FF-derived
residue to be observed by using optical microscopy. The bran
images. The classifier was developed using LIBSVM (Chang and
residue forms a covalent bond with Sudan black B (CAS number
Lin, 2011). The margin and kernel parameters of the classifier were
4197-25-5; MP Biomedicals, Aurora, OH, USA) and appears dark
identified by using grid-search (Chang and Lin, 2011) and 10-fold
blue (Ogawa et al., 2002; Sun, 2008; Wood et al., 2012). In the
cross-validation (CV).
dye-staining, the milled rice grains were soaked in a solution
The developed classifier was then applied to predict bran pixels
consisting of 0.3% Sudan black B and 70% ethanol for 10 min.
on rice surface. The performance of the classifier was evaluated by
Then the grains were rinsed in 70% ethanol for 30 s. The images of
comparing the predicted bran residue content (BRC) to measured
the stained rice grains served as references for evaluating the
BRC. In this study, the BRC is defined as the percentage of rice
performance of the bran residue detection by using the FFDI
surface area covered with bran. The BRC error, defined as the
system. The images of the stained rice grains were acquired by
measured BRC subtracted from the predicted BRC, was also
using the same microscope of the FFDI system and a digital camera
calculated.
(EOS 450D, Canon; Tokyo, Japan). Pixels on the rice surface covered with and without bran
Results and Discussion
residue were automatically identified by using image processing
Excitation and emission wavelength selection Figure 2 display
algorithms. The process included foreground segmentation by
the FFs of the powdered bran and endosperm samples. It can be
using grabCut algorithm (Rother et al., 2004) and binarization by
observed that the fluorescence characteristics of the 2 matters are
using Otsu’s thresholding (Otsu, 1979). The details of the
distinct. The FF of the bran demonstrated 2 fluorescent peaks,
algorithm implementation are stated in Chen and Kuo (2013). The
whereas the FF of the endosperm presented one peak. The
outcome binary images were referred to as bran images, in which
fluorescence intensity of the peak centered at λex = 360 nm and
the pixels covered with bran residue were referred to as bran pixels.
λem = 440 nm for the bran was stronger than that for the endosperm.
By contrast, the pixels that were not covered with bran residue
The FFs also exhibited scattered lights on the top right corner of
Fig. 2. Fluorescence fingerprints of powdered (a) bran and (b) endosperm samples.
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W.-T. Chen & Y.-F. Kuo
Fig. 3. Excitation (Ex) and emission (Em) wavelength selection by using sparse linear discriminant analysis.
the figure (Shibata et al., 2011). The scattered lights were excluded from being used in the subsequent analysis.
Fig. 4. Fluorescence fingerprint-derived images at λ ex/λ em of (a) 290/436, (b) 340/436, and (c) 360/436 nm. (d) Stained image and (e) binarized image of (d). The red crosses indicate selected control points for image registration.
Prediction of rice bran residue A training dataset composed of 200 bran and endosperm pixels, respectively, was formed for
SLDA was applied to select optimal combinations of λex and
developing the pixel classifier. The training samples were selected
λem from the FFs. Figure 3 illustrates the SLDA shrinkage process
evenly from 5 rice grains. The region of interest for the training
for the 10 most critical excitation and emission conditions. Ten-
sample selection was the middle lateral surface of the grains. The
fold CV was performed to select appropriate number of λex/λem
superior region (i.e., edge) of the grains was avoided because the
combinations to be applied to the FFDI system. The results
excitation light to the region could be nonuniform. During the
indicated that including 3 combinations in the SLDA model gave
selection, the bran images were regarded as the references for
no CV error. The 3 most effective λ ex/λ em combinations for
determining pixel type (e.g., bran or endosperm). The fluorescence
discriminating bran and endosperm were 290/430, 340/430, and
intensities in the corresponding FF-derived images were identified.
360/440 nm.
The pixel types and the fluorescence intensities together were
FF-derived image acquisition and image registration
In the
FFDI system, a band-pass filter with central wavelength at 436 nm,
collected as the training samples. The developed SVM model was applied to classify pixels on rice surface and to predict the BRC.
rather than the 430 and 440 nm derived by SLDA, was utilized to
Figure 5 illustrates the micrograph, bran, and predicted image
provide emission. The purpose of the replacement was to reduce
of the 5 rice grains. In the predicted images, the bran pixels were
the cost of the system setup. The full width half magnitude of the
presented as black dots and were plotted on the top of the
band-pass filters is 10 nm; thus, the alternation of the emission filter
fluorescence image collected at λex/λem of 290/436 nm. The figure
should affect the experiment result at a minimal level.
demonstrates that the bran and endosperm pixels can be reasonably
Figures 4(a), 4(b), and 4(c) display the FF-derived images of a
distinguished using the proposed approach. However, the model
rice grain collected at λex/λem of 290/436, 340/436, and 360/436 nm.
did not effectively identify the bran residue distributed on the
The FF-derived images were acquired using an exposure time of
superior margin of the grains. Some endosperm pixels in these
1 s and an incident angle of approximately 55°. The images
regions were misclassified as bran pixels. The rice surface is
demonstrate that a higher level of fluorescence intensity was
curved. The error may result from the nonuniformity of the
observed in the area of bran residue along the lateral surface of the
excitation illumination provided to the rice surface. Note that the
grain. This is consistent to the results of the FF analysis (Fig. 2).
mirror module utilized in this study made an advance in
Figures 4(d) and 4(e) present the micrograph and bran images of
illumination uniformity, as compared with the performance of the
the identical rice grain displayed in Figs. 4(a), 4(b), and 4(c). The
FFDI system without the mirror module in a previous study (Chen
bran distribution in the dye-stained image (Fig. 4d) was readily
and Kuo, 2014b). A mirror module design that can achieve better
differentiable. In the subsequent bran image (Fig. 4e), the bran and
illumination uniformity on curved object surface may further
endosperm pixels were represented in gray and white. It was
improve the performance of the FFDI system.
observed that the pattern of bran distribution on the fluorescence
Table 1 shows the measured BRC, predicted BRC, and BRC
images were similar to the pattern of bran distribution on the bran
errors of the rice samples. Here the measured BRC of the sample
image. Image registration was performed on the FF-derived and
rice grains was calculated based on the bran images in Fig. 5. The
bran images. The red crosses in Fig. 4 indicate selected control
BRC ranged from 2.66% to 11.97%. The BRC root mean squared
points.
error (RMSE) was 3.54%.
Measuring Rice Bran Residue Using Fluorescence Fingerprint-derived Imaging
191 Table 1 . Measured BRC, predicted BRC, and BRC error.
Sample number 1 2 3 4 5
Measured BRC (%) 12 . 32 9 . 23 6 . 04 5 . 87 4 . 22
Predicted BRC (%) 8 . 55 11 . 97 10 . 67 10 . 00 2 . 66
BRC error (%) 3 . 77 _2 . 74 _4 . 63 _4 . 13 1 . 56
Fluorescence excitation-emission matrix regional integration to quantify spectra for dissolved organic matter. Environ. Sci. Technol., 37, 57015710. Chen, W.-T. and Kuo, Y.-F. (2013). Measurement of degree of milling for rice using hyperspectral imaging. Proceedings of the 2013 ASABE Annual International Meeting, Kansas City, July 21-24.
Fig. 5. Stained, binarized, and predicted images demonstrating bran distribution on rice grains.
Chen, W.-T. and Kuo, Y.-F. (2014a). Observation and measurement of residual bran on milled rice using hyperspectral imaging. Cereal Chem., 91, 566-571. Chen, W.-T. and Kuo, Y.-F. (2014b). Detecting bran residue distribution on
Conclusion This study proposed a procedure to measure bran residue distributed on the surface of milled rice grains using FFDI. In the work, the FFs of powdered bran and endosperm samples were acquired. Analysis demonstrated that the fluorescence characteristics of the bran and endosperm samples were distinct. There optimal
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Fujita, K., Sugiyama, J., Tsuta, M., Shibata, M., Kokawa, M., Onda, H.,
290/436, 340/436, and 360/436 nm. An SVM classifier was
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Gangidi, R.R., Proctor, A., and Meullenet, J.F. (2002). Milled rice surface
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lipid measurement by diffuse reflectance Fourier transform infrared
rapidly and nondestructively identify the bran residue distribution on milled rice surface.
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