<i>In vivo</i> prediction of intramuscular fat in pigs using computed tomography

doi:10.4236/ojas.2013.34048

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Vol.3, No.4, 321-325 (2013) Open Journal of Animal Sciences

http://dx.doi.org/10.4236/ojas.2013.34048

In vivo prediction of intramuscular fat in pigs using

computed tomography

Jørgen Kongsro*, Eli Gjerlaug-Enger

Norsvin, Furnesveien, Hamar, Norway; *Corresponding Author: [email protected]

Received 14 August 2013; revised 21 September 2013; accepted 5 October 2013

bution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

cited.

ABSTRACT

One hundred and four pure-bred Norwegian

Duroc boars were CT (computed tomography)

scanned to predict the in vivo intramuscular fat

percentage in the loin. The animals were slaugh-

tered and the loin was cut commercially. A mus-

cle sample of the m. Longissimus dorsi was

sampled and analyzed by the use of near-infra-

red spectroscopy. Data from CT images were

collected using an in-house MATLAB script.

Calibration models were made using PLS (p artial

least square) regression, containing independ-

ent data from CT images and dependent data

from near-infrared spectroscopy. The data set

used for calibration was a subset of 72 animals.

The calibration models were validated using a

subset of 32 animals. Scaling of independent

data and filtering using median filtering were

tested to improve predictions. The results showed

that CT is not a feasible method for in vivo pre-

diction of intramuscular content in swine.

Keywords: Intramuscular Fat; Computed

Tomography; PLS Regression; Calibration;

Validation

1. BACKGROUND

Intramuscular fat (IMF) in meat is an important trait

due to the impact on sensory quality and acceptance of

pork meat [1]. IMF is generally determined by extracting

IMF from muscle [2] or by spectroscopic methods [3].

However, these methods require meat samples collected

post mortem. In order to select for meat quality traits in

breeding, development of in vivo methods is of great

importance. Progeny and sib-testing programs have made

it possible to select for these traits, but these are costly in

terms of time and money [4]. Since its introduction to

animal sciences in the early eighties [5], computed to-

mography (CT) has been used to estimate and predict the

body composition of farmed animals. In recent years,

several studies have examined the use of CT to predict

intramuscular fat and fatty acid composition using CT

[1,6]. Both studies concluded that CT could be used to

predict the IMF content post mortem in carcasses or meat

samples. For meat, several studies have examined the use

of CT to predict the salt content in pork meat [7,8]. The

results showed that CT can also be used to predict salt

content of processed meat samples, especially when in-

cluding scans at several different energy levels [7]. For in

vivo studies, no results to the authors’ knowledge have

been published for breeding pigs using CT, but several

studies have been published using ultrasound. For beef,

Harada and Kumazaki [9] found in 1980 a good rela-

tionship between ultrasound estimates and marbling

scores of cross sectional areas of loin. Newcom et al. [4]

showed that estimation of in tramuscular fat percen tage in

the live pig using real-time ultrasound was feasible. In

vivo CT images may have a lot of noise due to b reathing

and muscle contractions etc. In order to remove noise,

some filtering techniques like median filtering [10] have

shown to reduce “salt and pepper” noise which may be

observed by in vivo CT images [11].

In this study, the objective was to construct an in vivo

calibration model to predict the IMF percentage in live

breeding pigs using CT at different energy levels and

using filtering to remove noise.

2. METHODS

2.1. Animal and Carcass Samples

One hundred and four pure-bred Norwegian Duroc

boars (Figure 1) were sampled from the Norsvin nucleus

population in Norway in the period from October 2012 to

January 2013. The boars were located at the Norsvin

Delta testing station for boars described by Gerlaug-

Enger et al. [12] and Aasmundstad et al. [13]. The pigs

J. Kongsro, E. Gjerlaug-Enger / Open Journal of Animal Sciences 3 (2013) 321-325

322

Figure 1. Norsvin duroc boar.

were CT scanned at 120 kg at the end of the testing pe-

riod. The pigs were slaughtered after the end of the test

period at a commercial abattoir in Norway. The carcasses

were transported and the loins were cut commercially at

the Norwegian Meat and Poultry Research Centre pilot

plant. The meat quality measurements were also per-

formed at the Norwegian Meat and Poultry Research

Centre.

2.2. Meat Quality Measurements

Intramuscular fat was measured using the method

presented by Gjerlaug-Enger [3]. The FOSS FoodScan

Tm near-infrared spectrophotometer (FOSS, Denmark)

was used for the determination of intramuscular fat (Fig-

ure 2).

2.3. Computed Tomography and Image

Analysis

The pigs were herded to individual pens and sedated

described by [13]. The CT scanner was a GE Healthcare

LightSpeed VCT 32 multi-slice. A meat quality scan was

performed on the loin area of the pig, using soft tissue

protocol for reconstruction of X-ray signals to images to

enhance the image quality of soft tissue. 5 mm slice

thickness was used and a dynamic current (mA), maxi-

mizing the mA to get as good image quality as possible.

The pigs were scanned twice using two different energy

levels; 80 kV and 140 kV (Figures 3 and 4). The pixel

spacing was 0.933 × 0.933 mm.

The images were analyzed using MATLAB [10] and

the Image Processing Toolbox [10]. The CT images were

downloaded as DICOM images and imported into MAT-

LAB using an in-house script. A landmark was set in the

coronal direction locating the last rib, and 10 images

were sampled from the last rib and backwards according

to the same meat quality sample described by Gjerlaug-

Enger [3]. The thickness of the sample was 5 mm times

10 images, which equals the 5 cm sample described by

Gjerlaug-Enger [3]. A squared region of interest was

chosen from the loin area (Figure 5) in each of the im-

Figure 2. FOSS FoodScan.

Figure 3. CT image of mid-section of pig. 80 kV energy level.

Figure 4. CT image of mid-section of pig. 140 kV energy level.

ages to cover as much of the loin area as possible. The

regions of interest were stored as image stacks of region

of interest × 10 slices for each animal. The images were

also filtered using the medfilt 2 function in MATLAB to

remove any salt- and pepper-type of noise. Both non-

filtered and filtered images were subject for the calibra-

tion study. The stacks were analyzed transforming them

into histograms (Figure 6) by counting pixels in the en-

tire stack. The histogram was further analyzed in the

calibration/validation study.

2.4. Calibration and Validation

The data set of 104 animals were separated into a cali-

J. Kongsro, E. Gjerlaug-Enger / Open Journal of Animal Sciences 3 (2013) 321-325 323

Figure 5. Region of interest (ROI) sampled from m. longissi-

ums dorsi at last rib.

Figure 6. Histogram sampled at 140 kV. Contrast samples with

different IMF levels (low = red, blue = medium and green =

high).

bration set and a validation set, where 72 animals was

used for calibration, and 32 was used for validation. The

sets were selected randomly using a random permutation

function randperm in MATLAB. The calibration of IMF

using CT images was performed using PLS regression

and the plsregress function in the MATLAB Statistical

Toolbox [10] and Several calibrations were made using

both scaled (mean centered and standardized) data using

the zscore function in MATLAB and non-scaled data.

The scaling was done to adjust for any variations in scale

in the data. Both non-filtered and median-filtered images

were subject for calibration. The independent data sets

(X) was split into 3 groups ; 80 kV energy level (X1), 140

kV energy level (X2), all with filtered and scaled d ata as

sub-groups, and all energy levels and filters as the last

group (X3). The X3 data set was made using unfolding

all the histograms from of the energy levels and filtering

methods, similar to the calibration made by Johansen et

al. [14]. The calibrations were validated using fu ll leave-

one-out cross validation. The number of PLS compo-

nents were selected based on the lowest cross-validated

calibration error; RMSECV. The models were validated

using the beta vectors from the cross-validated calibra-

tion models on the validation data set. The models were

evaluated based on its explained variance (R square) and

prediction error (RMSEP).

3. RESULTS AND DISCUSSION

The sample mean IMF was 1.78 percent and the stan-

dard deviation was 0.44. The lowest value was 0.44 per-

cent and the highest value was 3.23. An IMF level of 1.8

in boars, equals 2.4 in females and 3.0 in castrates (Nors-

vin meat quality database). This difference in level of

IMF between sexes are related to average IMF level in

the population, and higher levels gives lager differences

between sexes. The sample mean reflect the level low

level of IMF in the pig population, and is a bit higher

than the average expected value in the mixed-breed

slaughter pig. Selection based on leanness in the last

decades has led to a low level of IMF in pork meat for

many modern big breeds, which is a concern for the in-

dustry. There is a high unfavorable genetic correlation

between leanness and IMF [15], and a single focus on

leanness will eventually lead to a low level of IMF in

pork meat. Norsvin Duroc is selected for slaughter pig

efficiency and leanness in an broad breeding goal with

balanced selection for meat quality in 17 years. There-

fore, the IMF level is almost at the original level, only

0.5 lower, while the leanness has been improved with 7

percentage points in this period. The relatively low level

of IMF and small variation in boars, compared to ordi-

nary slaughter pigs, may also lead to difficulties making

good calibration models on samples selected from the

population.

The calibration errors showed that pred iction using the

cross-validated calibration error (RMSECV) was feasible

(Ta bl es 1 - 3 ). The smallest RMSECV achieved for the

unfolded data set X3 from the non-scaled data. This is

due to the amount of information that is present in all the

histograms and the degree of variation covered by the

PLS method. The results show that scaling does not seem

to have a significant effect on the calibration. This is due

to that the histograms have the same level and variation,

and scaling may have a negative effect on the calibration

by inflating errors hidden in smaller values in the histo-

gram. The histograms are also a size of the number of

pixels present at each CT value, and by scaling the inter-

pretation of these CT values might be diff icult. The effect

of scaling vs. non-scaling on CT values was also shown

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324

Tab le 1. Results from multivariate calibration of IMF content

in pig loin using CT; 80 kV energy level. Non-filtered (1) and

median filtered images (2); non-scaled (a) and scaled (b).

RMSECV R2 RMSEP R2 comp

Model 1

a 0.37 0.25 0.51 0.01 1

b 0.37 0.28 0.49 0.00 1

Model 2

a 0.36 0.31 0.52 0.00 1

b 0.36 0.30 0.48 0.00 1

Table 2. Results from multivariate calibration of IMF content

in pig loin using CT; 140 kV energy level.

RMSECV R2 RMSEP R2 comp

Model 1

a 0.36 0.32 0.45 0.08 2

a 0.31 0.48 0.46 0.08 2

Model 2

a 0.35 0.33 0.53 0.00 2

a 0.34 0.39 0.48 0.02 2

Table 3. Results from multivariate calibration of IMF content

in pig loin using CT; all energy levels and filters.

RMSECV R2 RMSEP R2 comp

a 0.30 0.53 0.50 0.02 1

b 0.33 0.41 0.48 0.18 1

by Johansen et al. [14] in calibration of soft tissues in

lamb carcasses, where similar results were found. The

number of components used in the PLS model was also

very low, as the RMSECV values increased with in-

creasing number of PLS components. By adding more

components, better calibration results could be achieved,

but the risk of overfitting a prediction model was highly

significant.

With respect to prediction, none of the models were

considered to be feasible. The best prediction model was

achieved by scaling with an RMSEP of 0.48 and a R

squared of 0.18. The prediction errors had the same level

as found by Font-i-Furno ls et al. [1] in prediction of IMF

in post mortem pork loins, but the explained variance (R

squared) was significantly lower. This might be due to

the variation in the sample, our sample set had a lower

variance (std. dev. of 0.44 vs 1.15). However, prediction

errors similar or close to the standard deviation of the

sample set is not acceptable for the prediction models,

and the models in this study should be rejected based on

these results. In vivo images seem to be more “noisy”

and have a lower image quality compared post mortem

images. Blood flow, dynamic water content in muscle,

respiratory motion and muscle contractions may all lead

to more noise and artifacts in the images. Improving im-

age quality by using anesthesia rather than sedation dur-

ing CT scanning may reduce motion artifacts. However,

in a breeding perspective, anesthesia cannot be used due

high costs related to withhold ing of non-selected animals

that is to be sent to abattoir. Median filtering did not

seem to improve the quality of images, thus the salt and

pepper noise does not seem to be the main issue for in

vivo CT images of pigs using the protocols described in

this study. The soft tissue protocol of the reconstruction

of X-ray signals to CT images in the CT scanner hard-

ware may have an effect on the results, however previ-

ously unpublished trials have shown that standard proto-

cols does not seem to improve the results of predictions

of IMF. Therefore the results show that the models used

in this study does not accurately predict the in vivo IMF

percentage of loin muscles using CT at different energy

levels and by median filtering to remove noise.

4. CONCLUSION

Computed tomography has proven to have good po-

tential to measure the intramuscular fat in post mortem

meat samples or cuts. However, in vivo results in this

paper show that the image quality is poorer and the pre-

diction results using PLS regression show ed that the pre-

diction errors were close to the standard deviation, mak-

ing the models less feasible for practical use. The use of

anesthesia instead of sedation during CT scanning may

improve image quality, however, from a breeding point a

view, the cost of withholding non-selected animals be-

fore sending them to abattoir due to regulations using

anesthesia on farmed animals is too high to be sustain-

able.

5. LIST OF ABBRE VIATIONS

IMF, intramuscular fat; CT, computed tomography;

PLS, partial least square regression; ROI, region of in-

terest.

6. ACKNOWLEDGEMENTS

The authors acknowledges support from the Norwegian Research

Council, project n o 210637/O10 and the Nortura meat cooperative.

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