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RESEARCH ARTICLE
Rapid on-line detection and grading of
wooden breast myopathy in chicken fillets by
near-infrared spectroscopy
Jens Petter Wold
1
*, Eva Veiseth-Kent
1
, Vibeke Høst
1
, Atle Løvland
2
1Nofima AS, Norwegian Institute for Food and Fisheries Research, Muninbakken 9–13, Breivika, Tromsø,
Norway, 2Nortura SA, Lørenveien 37, Oslo, Norway
*jens.petter.wold@nofima.no
Abstract
The main objective of this work was to develop a method for rapid and non-destructive
detection and grading of wooden breast (WB) syndrome in chicken breast fillets. Near-infra-
red (NIR) spectroscopy was chosen as detection method, and an industrial NIR scanner
was applied and tested for large scale on-line detection of the syndrome. Two approaches
were evaluated for discrimination of WB fillets: 1) Linear discriminant analysis based on NIR
spectra only, and 2) a regression model for protein was made based on NIR spectra and the
estimated concentrations of protein were used for discrimination. A sample set of 197 fillets
was used for training and calibration. A test set was recorded under industrial conditions
and contained spectra from 79 fillets. The classification methods obtained 99.5–100% cor-
rect classification of the calibration set and 100% correct classification of the test set. The
NIR scanner was then installed in a commercial chicken processing plant and could detect
incidence rates of WB in large batches of fillets. Examples of incidence are shown for three
broiler flocks where a high number of fillets (9063, 6330 and 10483) were effectively mea-
sured. Prevalence of WB of 0.1%, 6.6% and 8.5% were estimated for these flocks based on
the complete sample volumes. Such an on-line system can be used to alleviate the chal-
lenges WB represents to the poultry meat industry. It enables automatic quality sorting of
chicken fillets to different product categories. Manual laborious grading can be avoided. Inci-
dences of WB from different farms and flocks can be tracked and information can be used to
understand and point out main causes for WB in the chicken production. This knowledge
can be used to improve the production procedures and reduce today’s extensive occurrence
of WB.
Introduction
During the last five years the muscle syndrome wooden breast (WB) has become a serious
challenge to the poultry meat industry worldwide. WB is a term for abnormal muscle tissue in
the chicken breast, a myopathy, which makes the breasts appear as pale, hard and out-bulging
[1]. Since the appearance of this meat is unpleasant and the functional properties are impaired,
PLOS ONE | DOI:10.1371/journal.pone.0173384 March 9, 2017 1 / 16
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OPEN ACCESS
Citation: Wold JP, Veiseth-Kent E, Høst V, Løvland
A (2017) Rapid on-line detection and grading of
wooden breast myopathy in chicken fillets by near-
infrared spectroscopy. PLoS ONE 12(3):
e0173384. doi:10.1371/journal.pone.0173384
Editor: George-John Nychas, Agricultural
University of Athens, GREECE
Received: November 23, 2016
Accepted: February 20, 2017
Published: March 9, 2017
Copyright: ©2017 Wold et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This work was partially funded by the
Norwegian Agricultural Food Research Foundation
through the projects Food Imaging (project
number 225347/F40) and Impact of protein
composition for predictable food quality (224820/
F40) and by the Norwegian company Nortura SA.
The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
severe cases of WB fillets are downgraded and used to manufacture less valuable products.
With typical incidences of 5–10% of WB in markets with heavy broilers this represents signifi-
cant economic losses for the poultry industry [2]. The causes for WB are still not clear, but are
most likely multifactorial where an important part is related to the fast growth of modern
broiler chickens [3]. With this emerging problem, it is important to find and develop methods
and strategies that can alleviate the situation in the poultry industry.
Some introductory work has been done to identify non-invasive biomarkers based on mass
spectroscopy for diagnostic purposes in live birds [4]. Such markers can contribute to under-
stand the biochemical processes leading to tissue hardening. It might also be used as a trait for
animal selection in breeding work. For effective handling of the problem in the poultry pro-
cessing industry, however, there is a need for rapid on-line detection and grading of WB for
automatic quality grading and sorting. Maybe more important, such a method would also be a
valuable tool for mapping incidences of WB at a large scale and backtrack them in the produc-
tion chain in order detect and remove causes of WB connected to production conditions.
There are some specific features of WB muscle tissue compared to normal muscle tissue
that should enable rapid detection and grading. WB tissue has significantly higher moisture
content and a significantly lower protein content. Soglia et al. [5] reported mean protein and
moisture contents for WB tissue of 21.6% and 77.26%, respectively, compared to 24.65% and
73.78%, respectively for normal tissue. There were also significant higher concentrations of fat
and collagen in WB tissue, but the actual differences were rather small (0.22% and 0.27%,
respectively). The same researchers also measured water holding capacity by the use of low
field NMR and reported that there is a significantly higher share of loosely bound water in the
WB tissue, probably due to muscle fiber degeneration [5]. The characteristic pale color of the
WB tissue due to the degenerative processes is not a unique marker for WB since normal fillets
can also have a similar pale color. The hard texture of the WB meat, however, is notably differ-
ent from normal breast meat and is today the decisive marker used in the industry for detec-
tion by manual palpation.
A good candidate method for real-time and large scale detection of WB is near-infrared
spectroscopy (NIR). NIR is well suited for on-line grading and sorting of complex foods and is
widely used in the food industry for determination of typically fat, water, protein and carbohy-
drates in products such as meat, fish, cereals and fruits [6]. Studies show that NIR spectroscopy
can be used to determine fat, water and protein in chicken breast meat. Good accuracy has
been obtained for samples of homogenized muscle, while NIR on intact breasts yielded poorer
results with regard to average crude chemical composition [7]. The main reason for this poorer
result was probably that the NIR spectra were collected in reflection mode on the surface of
the breasts and did not capture internal sample heterogeneity. NIR technology has later been
developed to allow improved measurements on intact heterogeneous food products by use of
spectral imaging in combination with so-called interaction measurements. Interaction enables
optical probing of about the upper 2 cm of the samples, which means that more representative
spectral measurements can be obtained. This technical approach made it possible to design
on-line NIR systems for moisture determination in very heterogeneous samples such as dried
salted cod [8] and fat and color in salmon fillets [9]. An illustrative example is an application
where the edible food content in live crabs is measured through the dark brown shell (cara-
pace) [10]. NIR systems like this can also be used to determine fat content in trimmings of beef
and pork [11] and it is demonstrated that this on-line method offers a good basis for real-time
optimization of food processes based on automatic sorting of different qualities [12].
The objective of this work was to elucidate if an NIR imaging system can be used to detect
and grade wooden breast syndrome in chicken fillets in a process line. Two approaches were
tested for discrimination of wooden breast fillets: 1) Linear discriminant analysis based on
Rapid detection of wooden breast syndrome
PLOS ONE | DOI:10.1371/journal.pone.0173384 March 9, 2017 2 / 16
Competing interests: There are no patents,
products in development or marketed products to
declare. Atle Løvland’s employment by Nortura SA
does not alter the authors’ adherence to all PLOS
ONE policies on sharing data and materials.
NIR spectra only, and 2) a regression model for protein content was developed based on NIR
spectra and the estimated concentrations of protein were used for discrimination. We also
included microscopy of some selected normal and abnormal chicken breasts to verify the pres-
ence of wooden breast and to compare with NIR measurements. All NIR measurements were
done in an industrial environment on chicken breasts passing on a conveyor belt at a relevant
speed. The NIR system was finally tested in a real process for massive quality monitoring to
log the occurrence of wooden breast in different broiler flocks.
Materials and methods
Chicken fillets
A total of 197 skinless breast fillets (M.pectoralis major) were taken out for analysis during
three days in a commercial chicken processing facility. Average live bird weight at this plant
was 1.8 kg. The fresh and chilled fillets were taken directly from the processing line approxi-
mately 3 hours after CO
2
-stunning, bleeding and slaughter of the birds. The two first days, 154
fillets were sampled with the aim of spanning all kind of normal quality variation in size, color
and texture. The fillets came from different flocks and farms represented these two days. WB
fillets were not sampled, but when normal fillets among the 154 had possible symptoms of
WB, this was noted in the experimental log. Each fillet was scanned with the NIR system, and
subsequently color and pH were measured. This procedure took about 5 minutes per fillet.
The fillets were then packed in plastic bags and stored over night at 4˚C, before fat, water and
protein content for the whole breast was determined for 99 of these fillets (randomly selected)
by low field nuclear magnetic resonance (NMR).
On day three, 15 normal, 15 moderate WB and 13 severe WB fillets were picked out of the
processing line. The samples were classified by an experienced veterinarian based on visual
inspection and palpation of consistency (normal, hard and very hard). Breast fillets with hard
consistency and limited distribution of very hard parts were classified as moderate WB, while
fillets with extensive areas of very hard consistency, were considered severe WB. WB fillets
were also very pale, but no chicken breasts had significant amounts of serous fluid at the sur-
face described from some studies in markets with higher slaughter weights. The samples were
measured the same way as the first 154 samples. In addition, some muscle samples were
excised for histological evaluation (see below for details). Fat, moisture and protein were deter-
mined the following day in the outer 1-cm layer of the breast fillet.
Industrial testing was performed about one year after the sampling described above. During
these trials 55 normal fillets and and 24 wooden breast were again classified by a trained veteri-
nary. Of the WB fillets, 16 were moderate and 8 were severe WB according to characteristics
described above. They were scanned with the NIR scanner and no further analyses were done
one these. They were used as test set for discrimination models developed based on samples
from day 1–3.
NIR measurements
The on-line NIR system was a QVision500 (TOMRA Sorting Solutions, Leuven, Belgium), an
industrial hyperspectral imaging scanner designed for on-line measurement of fat in meat on
conveyor belts. It was installed in the chicken processing hall at Nortura Hærland (Hærland,
Norway). During the first part of the work it was equipped with a conveyor belt, but was off-
line, i.e. it was not integrated in the commercial processing line. This allowed more flexibility
when conducting the investigation. During the second part of the work, the scanner was
installed above the actual processing line.
Rapid detection of wooden breast syndrome
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The NIR instrument was based on interactance measurements where the light was trans-
mitted into the meat and then back scattered to the surface. Optical sampling depth in the
chicken fillets was approximately 20 mm. Each NIR measurement took less than 1 sec. The
scanner was placed 30 cm above the conveyor belt so there was no physical contact between
samples and the instrument. The scanner collected hyperspectral images of 15 wavelengths
between 760 and 1040 nm with a spectral resolution of 20 nm. The output per sample scan was
an image of the conveyor belt with the sample. Size of the image was 60 pixels in the direction
perpendicular to belt movement and 200 pixels in the direction of belt movement. Each pixel
represented a spatial area of about 7 mm ×5 mm across and along the conveyor direction,
respectively. The imaging capability of the used system was in this work used mainly for effec-
tive sampling, to obtain a representative mean spectrum from each fillet.
Each fillet was scanned three times with skin side of the breast facing the sensor. The repli-
cate measurements were used to test for reproducibility.
Determination of protein, moisture and fat
Of the 154 fillets from day 1 and 2, 99 fillets were thoroughly homogenized and two parallels
of 6 g were subjected to fat and moisture determination. The average values of the parallels
were used in the further work. Fat and moisture content were determined by low field proton
nuclear magnetic resonance (NMR), using a Maran Ultra Resonance 0.5 tesla (Oxford Instru-
ments, UK) equipped with a gradient probe. The method used was “The one-shot method”
developed by Anvendt Teknologi AS (Harstad, Norway) [13]. Operating temperature of the
magnet was 40˚C and the samples were heated up to this temperature before measurement to
ensure that the fat was in liquid form. The weights of all meat samples were measured and cali-
bration was done against a reference meat sample of known weight containing 14.3% fat
(SMRD 2000 Matrix Meat Reference Material, National food Administration, Uppsala, Swe-
den). Protein for each sample was determined as 100%—(fat% + moisture%) since water, fat
and protein make up approximately 100% of the tissue weight.
For the fillets from day three (15 normal and 28 WB) only the outer 1-cm layer of muscle
tissue was used for fat, moisture and protein determination. This was done to study in more
detail the tissue affected by wooden breast syndrome.
pH and color
Color was measured as Labvalues on the surface of the breast fillets at three locations; ros-
tral part (thick part of fillet), middle and caudal part, with a Minolta CR-400 chromameter
(Konica Minolta Sensing, Inc., Osaka, Japan). Each site of measurement covered about 1 cm
2
of the fillet surface. The values from the three sites were averaged before further use. The
instrument was calibrated once every morning with a white reference following the instru-
ment. Lis a measure for lightness, aexpresses degree of redness (or green when values are
negative), while bexpresses yellowness (or blue when values are negative). This kind of color
space is commonly used for food color measurements.
pH was measured with a Knick Portamess 911 pH (Knick Elektronische Messgera¨te,
GmbH & Co. KG, Berlin, Germany) with the electrode InLab1Solids electrode 51343153
(Mettler Toledo, Switzerland). This was an insertion probe that was inserted about 1 cm into
the rostral part of the fillet.
Classification and calibration of NIR spectra
Linear discriminant analysis. From each sample scan we obtained an average intensity
NIR spectrum (T). This spectrum was converted to an absorption spectrum (log10(1/T)) to
Rapid detection of wooden breast syndrome
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make the data more linear. To remove some of the spectral variation connected to e.g. sample
distance from scanner, light scattering etc., standard normal variate (SNV) was used to nor-
malize the data [14].
A discriminant function was made based on linear discriminant analysis (LDA) [15]. Since
the variables (absorption values at different wavelengths) in the NIR spectra are highly covari-
ant, we used the score values from a partial least squares regression (PLSR) [16] of the NIR
spectra on to the two classes; normal and WB. These score values are orthogonal to each other
and well suited as input variables in LDA.
One NIR scan from each sample from day 1–3 were used to establish a discriminant func-
tion. This function was first validated by full cross validation to determine the optimal number
of PLS factors to use in the function. It was then validated on the test set of 79 samples obtained
under fully industrial conditions one year later.
Regression model
Partial least squares regression was used to make a calibration between NIR spectra and pro-
tein and moisture concentration. Full cross validation was applied to determine the optimal
number of PLS factors and to evaluate the model’s predictive ability. The prediction error was
estimated by the root mean square error of cross validation (RMSECV) where ŷ
i
is the pre-
dicted value from the cross validation, y
i
is the reference value and idenotes the samples from
1 to N.
RMSECV ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
NX
N
I¼1ðyi^
yi
sÞ
The calibration was made based on the average of the three spectra from each of the 99 ref-
erenced samples from day one and two. The average spectra were used to establish a best possi-
ble match between spectral data and chemistry. This calibration was then applied to predict
protein in the samples from day three to see if it was possible to discriminate WB from normal
fillets based on these values. Protein values were predicted for each of the three scans per sam-
ple. The regression model was also validated on the test set of the 79 samples obtained under
industrial conditions. The main aim of the calibration model was not necessarily to obtain a
best possible prediction of protein, but to obtain a good discrimination of WB from normal
fillets.
One-way analysis of variance (ANOVA) was performed to analyze group differences
between normal and WB. Two-way ANOVA was used to check for differences between repli-
cate NIR scans. If the p-value was less than 0.05 the differences were considered significant.
The software The Unscrambler ver. 9.8 (CAMO Software AS, Oslo, Norway) was used for
regression analysis. LDA as well as all image processing of multispectral images; sample seg-
mentation, spectral extraction and spectral pre-processing were carried out by the use of
MATLAB version 7.10 (The MathWorks Inc., Natic, MA).
Histological evaluation
Samples for histological evaluation were taken from 10 normal, 10 moderate WB and 10 severe
WB fillets. Samples for transverse sections were excised from the outer layer of the rostral
region, fixed in formalin, and paraffin embedded. Sections were cut (5 μm thickness) perpen-
dicular to the muscle fiber direction and stained using a standard haematoxylin and eosin
stain. Histological evaluations were performed using a light microscope.
Rapid detection of wooden breast syndrome
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On-line testing in industrial line
The classification model based on the protein calibration was implemented in the NIR scanner
so it could detect WB fillets among chicken fillets passing on a conveyor belt. The regression
model was chosen before the LDA discriminant function because it was interesting to monitor
also variation in protein. The scanner was installed in a commercial processing line where the
frequency of fillets was up to 3.5 per sec. Two parallel lines with fillets, separated by about 15
cm, were scanned simultaneously (Fig 1). With the scanner we collected protein values for
batches of fillets belonging to different broiler flocks, and the values were automatically written
to file. We collected data for 66 flocks from different farms and typical number of fillets per
batch was between 6.000–18.000. About 8.500 fillets could typically be scanned per hour
depending on the frequency of fillets on the line. The position of scanning in the line was after
the point were the most severe cases of WB were removed manually by the workers. That
means that the cases of WB that were scanned in these trials were mostly of moderate condi-
tion. Nortura SA gave permission to this field study.
Results and discussion
Fat, water and protein, color and pH
Table 1 summarizes the approximate composition (fat, water and protein) of the fillets. Note
that measurements on normal and WB fillets on day three were done on the outer 1 cm of the
breast to study the region mostly affected by WB. This is also the layer that will have the great-
est impact on the NIR spectra. Water and protein levels for normal fillets day 3 were similar to
corresponding values for the 99 fillets sampled day 1 and 2. Fat content was significantly differ-
ent by 0.25%-points. This means that approximate composition of the outer 1-cm tissue layer
in normal fillets was the same as for the whole fillet as measured days 1 and 2. The WB fillets
had significantly lower protein content compared to the normal fillets, mean differences of
4.6% and 5.1% for moderate and severe WB, respectively. Similar differences (3.8% and 4.3%,
respectively) were found for moisture. Soglia et al. [5] found the same systematic changes in
approximate composition between normal and WB tissue, but they found smaller differences,
probably because they measured the whole fillets and not the outer 1-cm layer in the most
affected part of the fillet, as in this study.
Fig 1. NIR system installed in production line. Illuminating line crosses the entire conveyor belt. All
passing fillets were measured.
doi:10.1371/journal.pone.0173384.g001
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There was a clear tendency that the WB fillets had high L-values, meaning that they were
pale. They also had high bvalues indicating yellowness. Although significant differences in
L, aand bbetween severe WB and normal fillets, the color variation could not be used to
distinguish WB from normal, since many normal fillets were also quite pale.
There were no differences in pH between the groups on day 3, but average pH day 3 was
higher than average pH day 1 and 2. We are not sure of the reason for this. Fillets day 3 were
sampled during a shorter time span compared to fillets day 1 and 2, which were collected over
two full days. The difference might therefore be ascribed to a flock effect.
Histological characterization
In order to verify the presence of wooden breast in the selected fillets on day 3, histological
evaluations of 10 normal and 20 WB fillets were performed. Fig 2 shows representative light
microscopy images of cross sections from normal (a), moderate (b) and severe WB fillets (c).
In the normal fillets, the classical structure of skeletal muscle can be seen, with tightly packed
polygonal muscle fibers of relatively even diameter. Moreover, each muscle fiber is surrounded
by a thin layer of endomysium, and bundles of muscle fibers are surrounded by a slightly
thicker layer of perimysium. In the WB fillets, on the contrary, various signs of myopathy
could be seen. Specifically, the muscle fibers in the WB fillets are rounded and appear to be
more variable in size. Many of the muscle fibers also show signs of degeneration and infiltra-
tion of inflammatory cells, and these features increase in incidence from moderate to severe
WB fillets. In addition, the endo- and perimysium layers have thickened. These findings are in
consistence with earlier reports on myopathy and wooden breast in the pectoralis major mus-
cle of broilers [1,17–18], and thus confirms the WB phenotypes in the selected fillets.
NIR spectra
Fig 3 shows SNV corrected NIR spectra from a normal and two WB fillets of different severity,
the same samples as shown in Fig 2. The main absorption peak for moisture in this spectral
region is at about 980 nm. A close look at the spectra reveals a systematic shift of the peak
towards shorter wavelengths with the severity of WB. This shift was quite prominent and con-
stituted the main variation in the spectra from the chicken fillets. The spectral changes can
most likely be attributed to two phenomena: 1) Protein has an absorption peak at about 1020
Table 1. Approximate chemical composition, color and pH in normal breast muscle and wooden breast muscle.
Normal day 1&2 Normal day 3 Moderate WB Severe WB
Whole fillet Upper 1 cm Upper 1 cm Upper 1 cm
(n = 99) (n = 15) (n = 15) (n = 13)
Moisture % 74.9 ±0.86 75.3 ±0.66 79.1 ±1.49˚*79.6 ±1.49˚*
Protein % 23.5 ±0.89 23.5 ±0.64 18.9 ±1.22˚*18.4 ±1.47˚*
Fat % 1.6 ±0.62 1.25 ±0.50˚ 1.8 ±0.53 2.0 ±0.67˚*
(n = 154)
L*56.10 ±3.70 52.7 ±2.68˚ 60.3 ±1.7 59.8 ±2.3˚*
a*2.97 ±1.32 2.46 ±0.62 2.34 ±0.91 4.56 ±2.9*
b*7.41 ±2.92 5.19 ±1.22 8.84 ±1.48*10.52 ±1.85˚*
pH 5.99 ±0.12 6.3 ±0.10˚ 6.3 ±0.16˚ 6.3 ±0.09˚
Average value ±standard deviation. Values in shaded fields are from outer 1-cm layer of breast fillets.
*indicates significant different from group mean value of normal fillets day 3.
˚ indicates significant different from group mean value of normal fillets day 1&2.
doi:10.1371/journal.pone.0173384.t001
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Fig 2. Morphologic structures in chicken breast muscle. Normal (A), moderate (B) and severe WB (C)
(M. pectoralis major). D = degenerating fibers; E = endomysium; P = perimysium.
doi:10.1371/journal.pone.0173384.g002
Rapid detection of wooden breast syndrome
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nm (not directly discernable in the spectra). A lower protein concentration and a correspond-
ing increase in moisture could contribute to the observed spectral shift. This corresponds with
the systematic differences in water and protein in WB tissue as described above. 2) It is well
known that a shift in the water peak at 980 nm does occur when the water molecules are more
or less bound to other molecules [19]. More loosely bound water creates a shift towards shorter
wavelengths. It has been reported that there is significantly more loosely bound water in WB
compared to normal fillets [5] and it is likely that it will create a shift as observed. The same
characteristic shift in the absorption peak at 980 nm has been proposed as a method to detect
human breast cancer due to a larger share of less bound water compared to normal breast tis-
sue [20]. Thus, both the effects are probably in action and emphasize the differences between
normal and WB muscle tissue.
Classification
Linear discriminant analysis. PLSR score values for all samples measured day 1–3 are
shown in Fig 4. Component 1 expressed 54% of the variation in the NIR spectra, separating
normal and WB fillets quite well. The score values of component 1 correlated closely with the
protein content of the samples (R = 0.90). A cross validated model based on 3 PLSR compo-
nents gave 99.5% correct classification of the 197 samples. All 28 WB fillets were correctly
classified and only one normal fillet was classified as WB. This is a good result taking into con-
sideration that there was a gradual change in muscle tissue from normal to WB. In a system
Fig 3. Typical NIR spectra from chicken fillets. Normal fillet (blue), moderate WB (green) and severe WB (red)
fillets. Spectra were measured on samples A, B and C, respectively, shown in Fig 2.
doi:10.1371/journal.pone.0173384.g003
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with gradual change it will always be some misclassification close to the decision line. The dis-
criminant function did also work very well on the test set recorded under industrial conditions
one year later. All samples were correctly classified, indicating that the NIR spectra contained
systematic and clear differences between the groups of fillets.
Regression analysis. Table 2 summarizes cross validated calibration results for water and
protein in chicken fillets based on the NIR spectra from normal fillets collected day one and
two. The rather low correlations obtained were reasonable since the range in e.g. protein was
short (20.5–25.3%). Prediction errors (RMSECV), however, were quite low and indicated
that protein and moisture could be estimated with an accuracy of approximately ±0.57%
and ±0.58%, respectively. This result is comparable with what was obtained by the use of
reflectance NIR on intact chicken breasts [7].
The regression model obtained for protein (Fig 5A) was used to estimate protein content in
the samples from day 3 (Fig 5B). The normal fillets got estimated protein values above about
Fig 4. PLSR score plot for NIR spectra. Score values for PLSR components 1 and 2 for normal fillets from days 1
and 2 (blue), normal fillets day 3 (red), moderate WB (green) and severe WB (magenta).
doi:10.1371/journal.pone.0173384.g004
Table 2. Calibration results for chemical constituents in whole chicken fillets based on NIR imaging
scanner.
# LV
a
R
b
RMSECV
c
(%)
Protein 4 0.76 0.57
Moisture 6 0.67 0.58
a
# LV—number of latent variables in the PLS regression model.
b
R—Correlation coefficient.
c
RMSECV—Root mean square error of cross validation.
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22% and lied along the target regression line. All WB samples got protein estimates below
22%. Estimated protein values for many of the WB fillets were slightly higher than the mea-
sured protein concentration in these fillets. There are different reasons for this: 1) Protein was
measured in the upper 1 cm layer of the breast, while the NIR system did probably measure
deeper than 1 cm, and the spectra were affected by more normal tissue deeper than 1 cm with
higher protein concentration. 2) The calibration model was not calibrated with samples of
such low protein values, meaning that the model was extrapolating and larger deviations from
the true protein content could be expected. 3) Spectral shifts due to loosely bound water were
not included in the regression model.
Since the regression model did not include WB samples, we can more strictly say that the
WB fillets were discriminated by outlier detection. This approach is in line with established
methodology within statistical process control: The normal variation of samples or process
conditions are modeled, and the models are used to detect deviations from the normal [21].
We did try to include all samples from day 1–3 in the same regression model (normal and
WB), but this approach did not work well with regard to classification. The main point is that
the protein model did clearly separate between normal and WB fillets. A linear discriminant
analysis of the predicted protein values for the groups of normal and WB resulted in an opti-
mal decision limit at 21.9%. The model for moisture could also separate between normal and
WB fillets, but not as clear as the protein model.
The model did also work very well on the 79 test samples from the industrial trial (Fig 5C).
Again normal fillets got protein values above 22% and WB below. Note that there were a few
samples in the calibration set (Fig 5A) with protein content less than 21.9%. Four out of these
samples were listed as “possible WB” in the experimental log during the experimental work.
Fig 5. NIR estimated protein in chicken fillets. a) Cross validated predicted versus measured protein in normal chicken fillets. b) Estimated protein in
samples from day 3. Normal (blue), moderate WB (green) and severe WB (red). c) Estimated protein in test samples. Normal (blue), moderate WB (green),
severe WB (red). The dashed red line at 21.9% indicates the decision line between normal and WB.
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Rapid detection of wooden breast syndrome
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In both test sets (Fig 5Band 5C) there was a tendency that the severe WB had lower esti-
mated protein levels compared to the moderate WB group, but there were no significant differ-
ence between the two groups.
Fig 5B shows that the replicate NIR scans resulted in very similar protein predictions. There
was no significant difference between the replicate protein estimates, indicating good repro-
ducibility of the NIR measurements.
The results show that both LDA and the calibration model for protein are very well suited
for classification of WB from normal fillets. The very high percentage of correct classification
obtained in this study is rather optimistic. As noted above, there is a gradual change in muscle
properties from normal to WB. This means that in the region of the decision line there will be
miss-classifications in a grey zone. In a practical industrial setting, different decision limits can
be chosen according to needs and experience. Two limits can for instance be used to separate
the fillets into high, medium and poor quality.
An advantage with the classification model based on protein values is that it gives additional
information. Monitoring the protein content can be of interest in the poultry industry. This
method is also intuitively easy to understand for operators of such a system. Adjusting the
decision line in a LDA model is more complex and less intuitive.
An important reason for the good results is connected to the measurement mode of the
NIR spectra. It seems that for severe cases of WB a thicker part of the breast muscle is affected
by myopathy. An efficient grading based on NIR will therefore require that the light penetrates
rather deep into the muscle, as it did in this study. Reflectance measurements at the surface
might work to separate WB from normal muscle, but would most likely be a less accurate
method. Degree of severity can also be defined by how much of the fillet surface that is
affected. Some fillets are affected on mainly the thick rostral part while on other fillets, the
complete breast fillet is affected. This difference can be captured by the NIR system used in
this study since the entire fillet is measured, not only a limited region.
Industrial on-line measurement trials
Fig 6 shows estimated protein values for all fillets from one flock of birds from farm A (approx-
imately 9063 fillets). Only 8 samples were below the chosen threshold of 21.9%. This was a
flock with very low incidence of fillets with protein estimates typical for WB.
Fig 7 shows similar recordings from two other farms, flock B (6330 fillets) and C (10483 fil-
lets). In those cases the incidence of breast fillets with low protein estimates, indicating WB,
Fig 6. NIR estimated protein in flock A. Red horizontal line indicates the chosen threshold of 21.9% protein.
doi:10.1371/journal.pone.0173384.g006
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was considerable higher. The x-axis in the plots count the numbers of fillets and do also indi-
cate sequence of measurement. The patterns indicate that there were higher incidences of WB
in certain parts of the batch, maybe coinciding with certain houses at the farms or other pro-
duction factors.
Fig 8 illustrates the protein distribution in fillets from the three farms as histograms. Most
values were centered around 24%. Farm A had a more or less Gaussian shape around 24%,
while farm B and C had bigger tails towards lower protein values. Farm B and C had preva-
lences of 6.6% and 8.5% of protein estimates below 21.9%, respectively, which were the highest
incidences of WB in the 66 flocks that were screened in this period.
Conclusion
The results shows that on-line interactance NIR is a well working, practical and useful tool for
detection and grading of WB syndrome. Low protein content is clearly characteristic for WB,
which is shown in this study and also by others [5]. At this point it is not clear if the main vari-
ation in the NIR spectra are attributed to differences in protein and water, or to the amount of
less bound water. The two effects would probably co-vary, but it should be further studied in
order to understand the system in the best possible way.
An on-line system as presented here can be used to alleviate the challenges wooden breast
represent to the poultry meat industry. It can be used for two important tasks: 1) It is possible
to automatically sort chicken fillets of different quality to different product categories. Manual
grading and removal of WB can then be avoided and replaced by a much more rapid and
objective system. 2) It is possible to track incidences of WB in detail from different farms and
use this crucial information to understand and point out main causes for WB in chicken pro-
duction. This knowledge can be used to improve the production procedures and reduce
today’s extensive occurrence of WB.
Fig 7. NIR estimated protein in flock B (upper panel) and C.
doi:10.1371/journal.pone.0173384.g007
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Supporting information
S1 File. Spectroscopic and reference data for all samples.
(XLSX)
S2 File. Description of supplementary data.
(DOCX)
Acknowledgments
This work was partially funded by the Norwegian Agricultural Food Research Foundation
through the projects Food Imaging (project number 225347/F40) and Impact of protein compo-
sition for predictable food quality (224820/F40) and by the Norwegian company Nortura SA.
Karen Wahlstrøm Sanden and Bjørg Narum are thanked for skilled technical assistance. We
are grateful to Dr. Ragni Ofstad, Dr. Ingrid Måge and Dr. Ingunn Berget for valuable
discussions.
Author Contributions
Conceptualization: JPW EVK AL.
Data curation: JPW VH AL.
Formal analysis: JPW.
Funding acquisition: JPW.
Investigation: JPW VH AL.
Fig 8. Histograms showing distribution of estimated protein in the broiler flocks A, B and C.
doi:10.1371/journal.pone.0173384.g008
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Methodology: JPW.
Project administration: JPW.
Resources: JPW AL VH.
Software: JPW.
Supervision: JPW.
Validation: JPW AL VH.
Visualization: JPW AL VH.
Writing – original draft: JPW.
Writing – review & editing: AL EVK VH.
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