Visible–near-infrared spectroscopy to predict water-holding capacity
in normal and pale broiler breast meat
D. Samuel,* B. Park,*1 M. Sohn,* and L. Wicker†
*USDA, ARS, Russell Research Center, Athens, GA 30605; and †The University of Georgia,
Department of Food Science and Technology, Athens 30602
ABSTRACT Visible–near-infrared spectroscopy was
examined as a tool for rapidly determining water-holding capacity (WHC) in broiler breast meat. Reflectance
measurements for 85 breast filets were recorded over
the 400 to 2,498 nm wavelength range at 0.5-nm intervals and 32 scans. Chemometric analysis was performed utilizing Savitzky-Golay derivative processing
and multiplicative scatter correction. Both partial least
squares regression and discriminant analysis were used
to develop calibration models tested by cross-validation. Partial least squares regression modeling resulted
in coefficients of determinants of 0.72, 0.67, and 0.62
for WHC, pH, and lightness (L*) values, respectively.
The mean spectra of samples categorized as either high
or low WHC, pH, and L* showed significant differences between absorption peaks between 400 to 800 nm
and between 1,400 to 2,500 nm (associated with heme
pigments and water absorption, respectively). The results showed potential use of visible–near-infrared spectroscopy as a predictor of WHC in pale broiler breast
meat.
Key words: visible–near-infrared spectroscopy, water-holding capacity, broiler breast meat,
meat quality, chemometrics
2011 Poultry Science 90:914–921
doi:10.3382/ps.2010-01116
INTRODUCTION
1981; kruggel et al., 1981; Lanza, 1983); fat, moisture, and protein contents (Renden et al., 1986; Valdes
and Summers, 1986; Isaksson et al., 1995; Berzaghi et
al., 2005; kadim et al., 2005); fat depths and softness
(Swatland, 1995); and meat texture and tenderness
(Lyon et al., 2001; Park et al., 2001; Liu et al., 2003;
Muellenet et al., 2004). Near-infrared spectroscopy has
also been used to evaluate pigment content in meat
(Mitsumoto et al., 1991; Chen and Massie, 1993; Chen
et al., 1994, 1996a,b). In addition, Cozzolino and Murray (2004) used NIRS to correctly classify 80% of meat
samples according to the muscle species (beef, lamb,
pork, or chicken). Muellenet et al. (2004) suggested
that near-infrared reflectance could be used to predict
poultry meat texture and to classify muscles according
to tenderness levels. Park et al. (1998) used NIRS to
predict Warner-Bratzler shear force values of beef longissimus steaks.
More recent applications of NIRS involved the determination of water binding characteristics, waterholding capacity (WHC), and drip loss in fresh pork.
Near-infrared spectroscopy analysis at 24 h postmortem was used to predict drip loss in pork 24 h after
slaughter (Forrest et al., 2000) and was used to predict
intramuscular fat and drip loss (Geesink et al., 2003;
Hoving-Bolink et al., 2005) with varying predictive accuracy. Their results indicated that NIRS does enable
Spectroscopy has become a desirable method for analyzing qualitative characteristics in food as a result
of a decrease in instrument prices, an improvement in
equipment design, and an improvement in data analysis methodology such as chemometrics. The main advantages of using spectroscopic measurements include
rapid data acquisition with minimum sample preparation, the possibilities for simultaneous determination of
several quality parameters, and the ability to replace
expensive and slower reference technique (Brøndum
et al., 2000a). Monin (1998) considered near-infrared
spectroscopy (NIRS) to be one of the most promising
of these techniques for large-scale meat quality evaluation.
Several applications of NIRS as a qualitative and
quantitative measurement tool have been applied to
the meat industry. Research involving NIRS and meat
include fat and moisture contents in emulsions of meat
products (Ben-Gera and Norris, 1968; Iwamoto et al.,
©2011 Poultry Science Association Inc.
Received September 10, 2010.
Accepted January 2, 2011.
1 Corresponding author: [email protected]
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WATER-HOLDING CAPACITY IN BROILER BREAST MEAT
the classification of pork longissimus muscles with a
superior or inferior WHC.
Water-holding capacity, or the ability of meat to retain water, is influenced by several factors. Those factors
include production of lactic acid and loss of adenosine
triphosphate, which influences rate and extent of pH
decline, protein oxidation, and changes in cell structure
associated with proteolytic enzyme activity (Huff-Lonergan and Lonergan, 2005). Belitz et al. (2004) states
that approximately 5% of the water found in muscle is
bound by hydrophilic groups on the proteins and the
other 95% is held by capillary forces between the thick
and thin filaments. Some of the water in muscle is also
found in free form and will be expelled during even the
mildest form of applications.
Previous research has identified the occurrence of
pale, soft, and exudative meat as a leading cause of
reduced WHC in the poultry industry. Occurrences
ranging from 5 to 50% have been reported in commercial processing plants (Barbut, 1996, 1997; McCurdy et
al., 1996; Owens et al., 2000; Woelfel and Sams, 2001;
Woelfel et al., 2002). Pale, soft, and exudative meat
results from a rapid postmortem pH decline at warm
carcass temperatures and leads to protein denaturation
causing a pale color and a decrease in WHC translating
into excessive yield losses (Alvarado and Sams, 2003).
The objective of this study was to evaluate the role of
lightness (L*) values and pH on WHC of broiler breast
muscle and to use visible (Vis)–NIRS as a predictor of
WHC.
MATERIALS AND METHODS
Sample Collection
Boneless, skinless broiler breast meat was obtained
from a local commercial processing plant. Samples were
subjectively preselected and preclassified into pale and
normal categories. The samples were put on ice and
taken to the Russell Research Center (Athens, GA) for
Vis–NIRS measurement and to the University of Georgia (Athens) for WHC analysis. The left breast portion
was used to determine the WHC of the breast sample
and the right breast portion was used to determine L*
values and Vis–NIR spectra. Samples with a pH value
<5.6 and an L* value >60 were classified as pale birds.
Excessive fat and connective tissue were avoided to
minimize sampling errors.
Color and pH Measurements
The pH measurements were acquired through use of a
spear tip Hannah pH meter (Hannah Instruments, Van
Nuys, CA) designed for meat samples. The L* values
were determined on the dorsal surface of the pectoralis
major using a Minolta Chroma Meter (CR-310, Minolta , Osaka, Japan). All color and pH measurements
were taken 24 h postmortem.
915
WHC
The WHC was determined as described by Barbut
(1993) with some minor modifications. Frozen samples
were thawed overnight at 4°C. All skin and visible fat
were removed from the breast meat. Approximately 75
to 100 g of the medial portion of the breast meat was
chopped for approximately 60 s in a small chopper to
mince meat. A 10-g aliquot of the chopped muscle was
mixed with 16 mL of 0.6 M NaCl and then incubated
for 30 min at 4°C. Afterward, samples were centrifuged
at 7,000 × g at 4°C for 15 min and the excess fluid was
decanted. The WHC was defined as the portion of fluid
retained by the sample. Measurements were done in
triplicate assays.
Vis–NIRS
Samples for Vis–NIRS analysis were taken from the
anterior portion of broiler breast meat and cores with
a 38 mm diameter and 10 mm depth were collected for
Vis–NIRS in the quartz optical cell as described earlier
(Park, 1998; Liu et al., 2004). The raw core samples
from broiler breast meat were scanned using a scanning monochromator (XDS, Foss North America, Eden
Prairie, MN) and the spectral data were collected using
Vision Spectral Analysis software for Windows (Foss
NIRSystems Inc., Laurel, MD). Reflectance measurements were recorded over the 400 to 2,498 nm wavelength range at 0.5-nm intervals and 32 scans, giving
4,200 data points. A total of 85 cored breast samples
was used (Park et al., 2001). Each of the samples was
scanned 3 times and the triplicate spectra were averaged.
Data Processing and Chemometric Analysis
Chemometric analysis was performed using Matlab
software (versopm 7.01, The MathWorks Inc., Natick,
MA) with PLS Toolbox (version 4.0, Eigenvector Research Inc., Manson, WA) and Unscrambler software
(version 9.8, Camo, Trondheim, Norway). SavitzkyGolay first or second derivative processing (third polynomial and 15-point convolution interval) followed by
multiplicative scatter correction (MSC) were used for
preprocessing of the spectral data. All data were mean
centered before analysis.
Partial least squares (PLS) regression with full
leave-one-out cross-validation was performed to develop calibration models. Outlier detection was based
on the plot of leverage versus student residuals, where
samples with over +2 or less than −2 were removed
for 99% confidence. Model performance was reported
as a multiple coefficient for determination, root mean
squared error of calibration, and root mean squared
error of cross-validation (RMSECV). Evaluation of
the model performance was tested by cross-validation.
Earlier research involving NIRS (Valdes and Summers,
1986) separated samples into a calibration and a pre-
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Samuel et al.
RESULTS AND DISCUSSION
rameter was based on the threshold for pale, soft, and
exudative meat described above. The small windows
represent the second derivative spectra in the regions
of 400 to 600 nm and 1,300 to 2,000 nm, which show
distinct differences between the 2 spectra. Second derivative processing is useful for separating overlapping
bands or distinguishing differences between spectra and
gives the maximum amplitude as the most negative intensity value (Hruschka, 1987).
The samples with high (>15%) and low (<13%)
WHC (Figure 2) show definitive differences in the spectra at absorbance peaks between 400 and 800 nm and
between 1,400 to 2,500 nm. The spectra of the samples
with high (>5.65) and low (<5.55) pH (Figure 3) were
similar to the WHC spectra (Figure 2) in their absorption pattern. From the second derivative spectra, it was
observed that the absorbance of high WHC (or high
pH) samples was higher than that of the low WHC (or
low pH) samples at 440 and 560 nm but lower at 492,
1,450, and 1,900 nm. The 440, 560, and 492 nm bands
have been assigned to deoxymyoglobin, oxymyoglobin,
and metmyoglobin, respectively (Liu and Chen, 2000).
The bands of 1,450 and 1,900 nm are attributed to
water (Osborne et al., 1993). For the spectra of L* samples (Figure 4), the absorption pattern was different
from the other 2 parameters. The samples with high
L* values (>68) had lower intensity than the samples
with low L* values (<66) at 440 and 560 nm, but higher at 492, 1,450, and 1,910 nm. The results indicate
that these 3 quality parameters of the breast meats are
closely related to myoglobin and water absorption.
Isengard (1995) stated that water gives signals at approximately 1,450 (1,460) and 1,950 (1,910) nm wavelengths that are usually stronger than those of other
components. However, these signals from water can
hide signals from other components in the spectra,
such as proteins, which are usually found around 1,500
The data in Table 1 show the range, mean, and SD
for WHC, pH, and L* values of the samples analyzed
by Vis–NIRS in evaluating its use as a rapid method for
detecting pale birds indicative of low WHC. The WHC
ranged from 0.09 to 0.26 with a mean value of 0.167
and an SD of 0.04. The pH values ranged from 5.0 to
6.2 with an average pH of 5.6 and an SD of 0.16. The
L* values were as low as 59.6 and as high as 71.7, with
a mean of 65.6 and an SD of 2.33 (Table 1). Pale, soft,
and exudative meat is defined as broiler breast with pH
<5.61, WHC <14%, or L* >67.
Figure 1 shows the Vis–NIRS raw spectra for the
total 85 samples used in this study. Differences in scatter effects among the samples were observed over the
entire range, especially in the region of 1,400 to 2,400
nm. The 2 broad peaks around 1,450 and 1,900 nm are
related to water absorption. Moisture accounts for approximately 76% of broiler breast meat.
Figures 2, 3, and 4 show differences in mean spectra
of high and low values of WHC, pH, and L*, respectively. Grouping of high and low value samples for each pa-
Figure 1. Visible–near-infrared raw spectra of broiler breast meat
samples (n = 85).
Table 1. Mean reference values of broiler breast meat samples
(n = 85) used for visible–near-infrared analysis
Parameter1
WHC
pH
L* (d)
Range
Mean
SD
0.09–0.26
5.0–6.2
59.6–71.7
0.17
5.6
65.6
0.04
0.16
2.33
1WHC = water-holding capacity; L* (d) = lightness value on dorsal
side of muscle.
diction set. However, because of advancements in technology, the software of the newer instruments has made
it possible to estimate prediction accuracy through the
use of cross-validation. Cross-validation uses the same
sample set for both calibration and prediction. Each
sample is removed from the calibration set and used for
prediction until all samples have been used for prediction (Prevolnik et al., 2004).
For discriminant analysis, Martens’ uncertainty regression in the Unscrambler software was used to develop calibration models. The Martens’ uncertainty
technique eliminates those spectral variables that do
not contribute to the PLS regression model to simplify
the final model and make it more reliable (Martens
and Martens, 2000; Westad and Martens, 2000). The
high value samples (>0.15 for WHC, >5.65 for pH, >68
for L*) were arbitrarily ascribed a value of 1.0 for the
dummy values and the low value samples (<0.13 for
WHC, <5.55 for pH, <66 for L*) were ascribed a value
of 0; following model development using calibration set
and prediction on a cross-validation set, those samples
with a cross-validated value ≥0.5 were identified as being of the high value sample. All samples with a crossvalidated value <0.5 were identified as being of the low
value sample.
WATER-HOLDING CAPACITY IN BROILER BREAST MEAT
917
Figure 2. Mean spectra of broiler breast samples with low (<13%) and high (>15%) water-holding capacity (WHC). Water-holding capacity
values based on corresponding pH values and L* (lightness) values combined. D2 = second derivative; R = reflectance.
Figure 3. Mean spectra of broiler breast samples with low pH (<5.55) and high pH (>5.65). Mean value of pH 5.6 was used to segregate
samples based on pH. The small windows inserted represent the second derivative spectra in the range of 400 to 600 nm and 1,280 to 2,000 nm.
D2 = second derivative; R = reflectance.
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Samuel et al.
Table 2. Results of partial least squares regression models for water-holding capacity (WHC), pH, and lightness (L*) value of broiler
breast meat samples using visible–near-infrared spectroscopy1
Parameter
n
LV
R2
RMSEC
RMSECV
Wavelengths with high loadings
WHC
pH
L*
77
80
81
2
2
3
0.719
0.674
0.617
0.019
0.070
1.27
0.028
0.10
1.54
440, 556, 580, 1,384
440, 556, 580, 1,384
425, 456, 568, 1,140, 1,396, 1,875
1n = number of samples used for the calibration model after removing outliers; LV = latent variables; RMSEC = root mean squared error of calibration; RMSECV = root mean squared error of cross-validation.
(1,485) nm and above approximately 2,000 (2,055) nm
(Brøndum et al., 2000b). From spectra obtained from
intact chicken muscles, Cozzolino et al. (1996) identified bands at 422 and 552 nm related to myoglobin
and at 1,936 nm related to water. Absorption bands at
430 and 574 nm were associated with hemoglobin and
oxyhemoglobin, respectively (Mitsumoto et al., 1991).
Cozzolino and Murray (2004) showed absorption bands
at 540 and 580 nm that were associated with both myoglobin and oxymyoglobin whereas Cozzolino and Murray (2002) showed an absorption band at 762 nm related to the oxidation of myoglobin or deoxymyoglobin.
Table 2 shows the statistical results of PLS regression
models developed for determining 3 quality parameters
of broiler breast meat using Vis–NIRS. The second derivative followed by MSC was used as preprocessing of
the data for WHC and pH analysis, whereas the first
derivative followed by MSC was used for L* analysis.
For WHC, 8 samples were detected as outliers. The
calibration model resulted in an R2 of 0.719 and RMSECV of 0.019 using 2 latent variables (LV). The LV
are the common orthogonal structures in which PLS
project the spectral data after centering it and are used
to describe the maximum covariance between the spectral information and the references (Brøndum et al.,
2000b). The prediction error (RMSECV) of the crossvalidation set was 0.028. High loadings for the model
were observed at the wavelengths between 400 and 600
nm, previously associated with heme pigments (Mitsumoto et al., 1991; Cozzolino et al., 1996; Cozzolino and
Murray, 2002, 2004) and at 1,384 nm, which is associated with fat.
For pH, 5 samples were detected as outliers. The
model resulted in an R2 of 0.674 and RMSECV of 0.10
using 2 LV. The loading plots were very similar to the
WHC loading plots in that the wavelengths relating
Figure 4. Mean spectra of broiler breast samples with low lightness (L*; <66) and high L* (>68). D2 = second derivative; R = reflectance.
WATER-HOLDING CAPACITY IN BROILER BREAST MEAT
919
For L* value, 4 samples were removed from the data
set as outliers. The model required 3 LV and resulted
in an R2 of 0.617 and RMSECV of 1.54. The loadings
showed influence by fewer wavelengths associated with
the heme pigments (456, 568 nm) and additional wavelengths between 1,100 and 1,900 nm, most likely associated with water and other proximate compositions.
Figure 5 shows the scatter plots of measured versus
predicted value of the Vis–NIRS model for WHC, pH,
and L*.
The determination coefficients of 0.80 and higher
in previous research have demonstrated the ability
of NIRS to predict the chemical composition of meat
from several species (pork, beef, lamb, and poultry).
However, less research has been conducted on the use
of NIRS as a tool for assessing poultry meat quality.
The most common quality attributes studied have been
WHC and color. In addition, pH has been studied, but
less frequently. Determination coefficients for the quality attributes were lower than those found in research
involving NIRS as a tool for assessing chemical composition (i.e., fat, moisture, protein) of meat. A determination coefficient of 0.55 was the highest reported
for WHC whereas a range from 0.64 to 0.85 has been
reported for the color parameter L* for pork and beef
(Brøndum et al., 2000b; Geesink et al., 2003; Leroy et
al., 2003; Liu et al., 2003; Meulemans et al., 2003). The
differences in the current research and others are in the
species of meat studied and the methodology used for
measuring WHC. Previous research examined drip loss
in pork whereas the current research evaluated the water binding capacity in chicken, which possibly accounts
for the higher coefficient of determination reported in
the current study. Determinant coefficients for WHC
were found to be higher than those of previous research
whereas the determinant coefficients for L* were within
Figure 5. Scatter plots of partial least squares models for water-holding capacity (WHC), pH, and lightness (L*) values of broiler
breast meat by visible–near-infrared spectroscopy.
to respiratory pigments (oxyglobin, myoglobin, and deoxymyoglobin) and water had the greatest influence in
developing the calibration models. This could be related to the positive correlation (R = 0.53) between the
reference values of the 2 parameters, pH and WHC.
Figure 6. Cross-validation result of broiler breast samples with
partial least squares regression-based classification model for pH. The
samples over 0.5 indicate high pH (>5.65) and the samples under 0.5
indicate low pH (<5.55). LV = latent variables; RMSEC = root mean
squared error of calibration; RMSECV = root mean squared error of
cross-validation.
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Samuel et al.
Table 3. Summary of samples used for discriminant analysis and regression results
Sample set2
Parameter1
WHC
pH
L*
High (code = 1)
Low (code = 0)
Total
Recognition
accuracy3 (%)
56
34
8
9
11
45
65
45
53
91
100
94
1WHC
= water-holding capacity; L* = lightness value.
1 means high value of parameter (>0.15 for WHC; >5.65 for pH; >68 for L*) and code 0 means low value
of parameter (<0.13 for WHC; <5.55 for pH; <66 for L*).
2Recognition accuracy (%) is based on cross-validated set.
2Code
the range of the previously reported studies. Although
the determinant coefficients were consistent with previous research, the relative percentage difference (RPD)
values were lower for the quality parameters than the
ones determined in studies evaluating the ability of
NIRS as a predictor of meat chemical composition. The
RPD is defined as the ratio of the SE of performance
to the SD of the reference data (Williams, 1987). The
RPD for the current study were 2.1, 2.3, and 1.8 for
WHC, pH, and L* variables, respectively. These values were similar to those reported by Muellenet et al.
(2004). The RPD values were associated with the second derivative of the reflectance spectra and ranged
from 1.89 to 2.23 for maximum shear force and total
shear energy. The study evaluated the use of NIRS as a
predictor for the quality parameter of texture in poultry breast meat (Muellenet et al., 2004).
The use of the selected wavelength region produced
lower accuracy than using the entire region for all 3
parameters (data not shown). For the WHC and pH
models using NIR region (1,100–2,498 nm), the number of LV was increased to 4 and the multiple coefficient for determination was decreased. No significant
changes were found in RMSECV values. For L* model
using visible region only (400–700 nm), the number of
LV was increased to 5 and the multiple coefficient for
determination was decreased. The RMSECV was not
changed. The results indicate that the multiple variables from visible and NIR regions are required for developing accurate models for 3 parameters, even though
the visible region has information for color and the NIR
region has more information for meat components such
as moisture, protein, and fat, which can influence WHC
and pH values.
Table 3 shows the summary of samples used for discriminant analysis and the results for each parameter.
The entire spectral range (400–2,498 nm) was used for
analysis. For WHC, 3 LV were required for the model
and cross-validation showed that 6/65 samples were
wrongly identified, resulting in the recognition accuracy
of 91%. For the pH, the recognition accuracy was 100%
using 4 LV model. No wrongly identified samples were
found. For L*, the recognition accuracy was 94% using
2 LV model and 3 misclassified samples were found.
Figure 6 shows the cross-validation result of discriminant model for pH, which showed the best result from
Table 3. The low pH samples and high pH samples were
clearly separated. This result indicates the importance
of pH in determining the WHC of broiler meat and its
influence in using Vis–NIRS as a predictor of WHC.
Offer and Knight (1988) found that pH was one of
the main determinants of WHC. As the pH reaches approximately 5.2 to 5.5, the isoelectric point of myosin,
the distance between the thick filaments is drastically
reduced. The decline in pH induces lateral shrinkage
of the myofibril leading to expulsion of water from the
myofibrillar into the extramyofibrillar spaces of the
muscle cells (Huff-Lonergan and Lonergan, 2005). HuffLonergan and Lonergan (2005) stated that it is thus
likely that the gradual mobilization of water from the
intramyofibrillar spaces to the extramyofibrillar spaces
may be responsible for some drip loss.
Consequently, the PLS regression models produced
insufficient accuracy for quantitative analysis for all 3
parameters. However, PLS-based discriminant analysis
showed that Vis–NIR spectroscopy had the potential
to classify the breast meats into high or low groups
according to the value with prediction accuracy over
90%.
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