2. Norwegian University of Life Sciences
Department of Chemistry, Biotechnology and Food Science
PHILOSOPHIAE DOCTOR THESIS 2008:1
Reliable prediction and determination of Norwegian
lamb carcass composition and value
Pålitelig bestemmelse av sammensetningen i norske lammeslakt og verdi nedskåret vare
Jørgen Kongsro
ISBN 978-82-575-0798-5
ISSN 1503-1667

4. TABLE OF CONTENTS
PREFACE................................................................................................................ii
SUMMARY ............................................................................................................iii
OPPSUMMERING (Summary in Norwegian) ..............................................................iv
LIST OF PAPERS .....................................................................................................v
Background and motivation.........................................................................................1
Dissection, cutting and value of cuts from lamb carcasses ................................................5
Classification of lamb and sheep carcasses; the EUROP classification system.....................8
Measuring systems for lamb carcass composition .........................................................11
Multivariate calibration.............................................................................................22
Main results of papers I-V and future perspectives. .......................................................28
References..............................................................................................................31
PAPERS I - V

5. PREFACE
This work was sponsored by grant 162188 of the Norwegian Research Council, as a part of a
Ph. D. study program. The Ph.D. study is a part of a research project at Animalia – Norwegian
Meat Research Centre, which among other activities is also devoted to optimizing
classification and grading of Norwegian lamb carcasses. The main area of activity for
Animalia is to conduct generic work funded by a farmer Research and Development levy. The
classification and grading system in Norway is supervised by Animalia, but the system is
owned by Nortura BA. Nortura BA has served as an industry partner in this project, and has
provided the sampled carcasses from different abattoirs located in southern Norway.
I would like to thank my supervisors at Norwegian University of Life Sciences, Prof. Are
Aastveit, Associate Prof. Knut Kvaal and last but not least, my main supervisor, Prof. Bjørg
Egelandsdal, who’s scientific and administrative skills, experience and valuable opinions have
guided me through this work to a higher academic level. Morten Røe at Animalia is
acknowledged for his practical and universal skills concerning the meat industry, carcass
classification and dissection, and for providing data and advice, and guiding me through this
work on a pragmatic level. The butchers at Animala are acknowledged for their skills in
dissection of carcasses, and for showing me the art of cutting and dissection of carcasses. Tor
Arne Ruud, Dr. Ole Alvseike and Per Berg are acknowledged for their support and help
during start-up of the project. Dr. Mohamed Kheir Omer Abdella is gratefully acknowledged
for his editing support. I would also like to thank the Norwegian Research Council for
funding this work (grant 162188).
I would also like to thank my family and friends, and especially my wife Tone for her love,
support and motivation during this work.

6. SUMMARY
The main objective of this work was to study prediction and determination of Norwegian
lamb carcass composition with different techniques spanning from subjective appraisal to
computer-intensive methods. There is an increasing demand, both from farmers and
processors of meats, for a more objective and reliable system for prediction of muscle (lean
meat), fat, bone and value of a lamb carcass. When introducing new technologies for
determination of lamb carcass composition, the reference method used for calibration must be
precise and reliable. The precision and reliability of the current dissection reference for lamb
carcass classification and grading has never been quantified. A poor reference method will not
benefit even the most optimal system for prediction and determination of lamb carcasses. To
help achieve reliable systems, the uncertainty or errors in the reference method and measuring
systems needs to be quantified. Using proper calibration methods for the measuring systems,
the uncertainty and modeling power can be determined for lamb carcasses.
The results of the work presented in this thesis show that the current classification system
using subjective appraisal (EUROP) is reliable; however the accuracy with respect to carcass
composition, especially for lean meat or muscle and carcass value, is poor. The reference
method used for determining lamb carcass composition with respect to lamb carcass
classification and grading is precise and reliable for carcass composition. For the composition
and yield of sub-primal cuts, the reliability varied, and was especially poor for the breast cut.
Further attention is needed for jointing and cutting of sub-primals to achieve even higher
precision and reliability of the reference method. As an alternative to butcher or manual
dissection, Computer Tomography (CT) showed promising results with respect to prediction
of lamb carcass composition. This method is nicknamed “virtual dissection”. By utilizing the
spectroscopic features of CT histograms of tissue density estimates, the composition of a lamb
could be modeled and validated using multivariate calibration. The precision and reliability of
virtual dissection was higher than for butcher dissection, and the running costs are much
lower, even though fixed costs of CT equipment is somewhat high. When summarizing all the
different techniques for lamb carcass composition used in this work, it seems like the most
precise and reliable system at the present time for prediction of lamb carcass composition and
value, is on-line optical probing of carcass side calibrated against Computer Tomography
(CT) virtual dissection.

7. OPPSUMMERING (Summary in Norwegian)
Hovedmålet med dette arbeidet var å studere måling og prediksjon av sammensetningen
(kjøtt, fett og bein) av norske lammeslakt ved bruk av forskjellige måleteknikker som strekker
seg fra subjektiv visuell bedømming til data-intensive instrumentelle metoder. Det er et
konstant ønske, både fra produsenter og foredlingsledd av kjøtt, om et mer objektivt og
pålitelig system for prediksjon av kjøtt, fett, bein og fastsettelse av verdi i et lammeslakt. Når
man introduserer og kalibrerer nye teknikker for bestemmelse av sammensetningen, er man
helt avhengig av en presis og pålitelig referansemetode. Nøyaktigheten til dagens
referansemetode, nedskjæring av slakt, har aldri blitt kvantifisert. Et optimalt system for
bestemmelse av sammensetningen i lammeslakt vil ikke kunne dra nytte av en god
måleteknikk når referansemetoden ikke er tilstrekkelig god nok. For å oppnå en høy
pålitelighet av et system, må usikkerheten eller feilen i referansemetoden kunne oppgis. Ved å
kombinere en god referansemetode med en god kalibrering av målesystemer, vil man kunne
kvantifisere usikkerheten og forklaringsgraden til målesystemer for bestemmelse av
kroppsinnhold i lammeslakt.
Resultatene i denne avhandlingen viste at det nåværende klassifiseringssystemets (EUROP)
bruk av subjektiv bedømming er pålitelig, men nøyaktigheten for prediksjon av
sammensetningen i lammeslakt, spesielt for muskelvev og fastsettelse av verdi, er ikke god
nok. Nedskjæring av slakt ved bruk av et panel av kjøttskjærere, viste seg å være akseptabel
som referansemetode for å bestemme sammensetningen av lammeslakt. Resultatene var noe
varierende for utbytte av stykningsdeler og innhold av kjøtt, fett og bein i stykningsdelene.
Skjærepanelet hadde store problemer med nedskjæring av bryststykket. Ytterligere
oppmerksomhet må rettes mot presisjon ved stykking av slakt, spesielt for bryststykket, for å
oppnå enda høyere nøyaktighet i referansemetoden nedskjæring av slakt. Resultatene har vist
at datatomografi (CT) er et godt alternativ til nedskjæring av slakt, og CT var både mer presis
og mer pålitelig enn nedskjæring av slakt. Ved å utnytte de spektroskopiske egenskapene til
pikselverdier i CT-bilder, og koble data mot nedskjæring, kan man estimere og studere
sammensetningen i lammeslakt ved bruk av multivariat kalibrering. De faste kostnadene (CT-
skanner og utstyr) er noe høy, mens driftskostnadene på sikt er mye lavere enn ved
nedskjæring. Evalueringen av forskjellige teknikker for å predikere sammensetningen i norske
lammeslakt viste at det mest presise og pålitelige systemet ved nåværende tidspunkt, synes å
være ”on-line” optisk probemåling av sidetykkelse kalibrert mot CT.

8. LIST OF PAPERS
I. J. Johansen, A.H. Aastveit, B. Egelandsdal, K. Kvaal and M. Røe (2006). Validation
of the EUROP system for lamb classification in Norway; repeatability and accuracy of
visual assessment and prediction of lamb carcass composition. Meat Science 74: 497-
509.
II. J. Kongsro, B. Egelandsdal, K. Kvaal, M. Røe, A.H. Aastveit (2008). The reference
butcher panel’s precision and reliability of dissection for calibration of lamb carcass
classification in Norway. Animal, Submitted manuscript.
III. J. Johansen, B. Egelandsdal, M. Røe, K. Kvaal and A.H. Aastveit (2007). Calibration
models for lamb carcass composition analysis using Computerized Tomography (CT)
imaging. Chemometrics and Intelligent Laboratory Systems 87: 303-311.
IV. J. Kongsro, M. Røe, A.H. Aastveit, K. Kvaal and B. Egelandsdal (2007). Virtual
dissection of lamb carcasses using computer tomography (CT) and its correlation to
manual dissection. Journal of Food Engineering, In Press, Accepted Manuscript.
V. J. Kongsro, M. Røe, K. Kvaal, A.H. Aastveit and B. Egelandsdal (2007). Prediction of
fat, muscle and value in Norwegian lamb carcasses using EUROP classification,
carcass shape and length measurements, visible light reflectance and computer
tomography (CT). Meat Science, Submitted manuscript.
Note: The author J. Johansen has changed his name as from 12th of July 2007 to J. Kongsro.

10. 1
Background and motivation
Grading and classification of farmed animal carcasses and determination of carcass value are
the basis for the economical interface between the farmers and abattoirs in Norway. It is
critical to have an accurate and reliable determination of carcass quality and its value. The
definitions of accuracy and reliability are not always equal between different fields of
science. Accuracy is defined, from a technical and general perspective, to be an
approximation to a certain expected value (Hofer et al., 2005). Esbensen (2000) defined
accuracy as faithfulness of a method, i.e. how close the measured values is to the actual or
true values. Accuracy has to be seen in relation to precision, which indicates how close
together or how repeatable the results are (information about measurement error). Reliability
is defined as to express a degree of confidence that a part or system will successfully function
in a certain environment during a specified time period (Juran and Gryna, 1988). This means
to minimize uncertainty or doubt about the validity of the measurement method or experiment
(Martens and Martens, 2001), expressed as experimental error. For prediction of lamb carcass
composition and value, accuracy is defined as the relationship or closeness between the actual
and predicted value for the lamb carcass tissues and value, and is expressed as explained
variance (R2
) and prediction error (RMSEP). Precision of measurements is the degree to
which measurements show the same or similar results, and is expressed as the ratio between
standard deviation of the difference between two repeated measurements and the mean value
of the measure (expressed as coefficient of variation, CV %). Reliability is expressed as the
correlation (Pearson’s r) between repeated measurements.
The major motivation behind this work was to characterize and predict lamb carcass
composition and value using a range of technologies, spanning from simple, univariate
carcass weighing, to computer intensive Computer Tomography (CT). It is crucial to know
what is measured, its relation to carcass composition and value and the accuracy and
reliability of the measurement. Another important feature of the measurements is how it can
be applied in abattoirs. Is one type of technology more relevant in small scale abattoirs in
comparison to larger ones? What is most crucial, speed, cost or accuracy?
For sheep, the classification system in Norway is under constant debate with respect to
accuracy and reliability. Sometimes, the sheep farmers are not satisfied with the current
classification system, and complain that their animals are not correctly assessed (i.g. obtain

11. 2
too low classification scores) compared to other farmers in other parts of the country. An
example from the US, shows that some cattle producers are reluctant to market cattle on a
carcass merit system because of subjective grading (Savell and Cross, 1991). The sheep
farmers in Norway seems to be less reluctant as the farmers in the US, however, the same
problem prevails here also for both sheep and cattle farmers. Sometimes, the meat processors
argue that the current system does not reflect the real value of the carcass, and the payment to
farmers does not correspond to the yield obtained from different classes of carcasses. Another
Norwegian example which highlights the disparity between classification and yield is the
abattoirs reluctance towards cutting carcasses with high conformation class. The price level of
high conformant carcasses is too high compared to the saleable meat yield obtained from the
carcasses. The opposite situation with respect to carcass prices is the willingness to cut low
conformant carcasses due to the low price of carcasses compared to the saleable meat yield
obtained from them. This situation highlights the need to have a price system which is reliable
and reflects the value and yield obtained from the carcasses. The implications or usefulness of
any technology for prediction of lamb carcass composition will depend on the future
commitment of the sheep industry to developing a lamb price system based on carcass or
primal cut composition (Berg et al., 1997).
During the last decades, methods for measuring lamb carcass composition have moved from
subjective appraisal towards more objective and computer intensive methods. Scientifically,
the development of methods for prediction of lamb carcass composition is moving forward,
however, the application and practice in the meat industry has not kept up with the science.
The pig industry is the most advanced of the meat industries with respect to objectivity and
use of new technologies in practice (Kirton, 1989). Even though the disadvantage of using
subjective appraisal has been document in several studies (Diaz et al., 2004; Kirton, 1989;
Swatland, 1995), the lamb meat industry still applies subjective methods for prediction of
lamb carcass composition. There seems to be a huge gap between science and practice in
terms of prediction of lamb carcass composition. In Norway, the European classification
system EUROP is used for determination of lamb carcass composition. The system is based
on visual appraisal of carcass conformation and fatness, in addition to carcass weight, sex and
age. In addition to the system being based on subjective appraisal, the major concerns have
been relationship between classification and saleable meat yield, and the confounding
between conformation and fatness. The confounding is due to carcasses with thicker fat cover
tend to be judged to have better conformation (Navajas et al., 2007).

12. 3
In most cases, the national sheep population in previous studies, does not reflect the
worldwide sheep population, especially with respect to fatness (Diaz et al., 2004). The carcass
weight, breed and time of slaughter (maturity) of sheep varies between regions, i.e.
Mediterranean lambs having a carcass weight of approx. 10 kg compared to northern
European lambs (UK, Germany) of approx. 22 kg. It is difficult to have a global validity of
studies performed on carcasses sampled around the national or regional mean carcass weight.
Sampling of lean vs. fat carcass and proper validation must be taken into consideration when
addressing global prediction models which are valid both scientifically and for practical
applications in abattoirs worldwide. Building a solid experimental design for sampling will
make the modeling of measurement systems more efficient, bring focus and ensure a more
global variability. This must be the overall aim from a sampling point of view, even when it
may seem difficult in practice.
During recent years, new computer intensive and technologically advanced measurements
have become available for prediction of lamb carcass composition. However, the studies or
applications of these new emerging technologies have been too narrowly focused, or have not
been adapted for sheep (i.e. developed for pigs). When applying new technologies for
classification or prediction of lamb carcass composition, the precision of measurements in an
industry environment is of the greatest importance. In a scientifically controlled experiment,
the precision of measurements will most probably be better than in an industrial environment.
This may be one of the main reasons why science has not kept up with industry applications.
Berg et al. (1997) stated that further testing of emerging technologies in an industrial setting is
needed before adoption of specific technology to quantify lamb carcass composition can
occur. Precision studies including repeatability and reproducibility standard deviations,
preferably in an industrial environment, can help bring the gap between science and industry
closer together.
Emerging technologies which are computer and technology intensive, challenge the modeling
and analysis of measurement data. The data generated by these instruments are often complex
(i.e. spectral, image or profile data) and are characterized by being multi-component and
having many-to-many relationships. The data may also be organized not only as matrices of
rows and columns, but as multi-level matrices (i.e. 3D cubes). The basis of statistical
modeling is to separate the relevant information in a data set from the background noise. By
introducing computer intensive chemometric methods such as Partial Least Square Regression

13. 4
(PLSR) for 2-way (rows*columns) and multi-level PLSR (NPLSR) and Parallel Factor
Analysis (PARAFAC) for multi-way modeling and analysis of data, calibration and prediction
of lamb carcass composition can be carried out in a short time collecting relevant information
from the complete spectrum of complex instrument data. Meat science, like other food
sciences, draws on a wealth of disciplines from chemistry and physics, mathematics and
statistics, to biology, genetics, medicine, microbiology, agriculture, technology and
environmental science, and even further to the cognitive sciences like sensory and consumer
analysis and psychology as well as to other social disciplines like economy (Munck et al.,
1998). Such a wide field of sciences increases the need for the establishment of basic
principles for multivariate data analysis. Chemometric methods can contribute to food and
meat science with new more flexible data programs which display the exploratory results in
cognitively accessible graphical data interfaces.
The aim of the project was to evaluate state of art technologies for grading and classification
of lamb carcasses, and to study the accuracy and reliability of the different technologies for
prediction of lamb carcass composition and value. New approaches for calibration and data
analysis are also addressed to achieve robust prediction models of carcass tissues like fat and
muscle, and the value or yield of products derived from lamb carcasses.

14. 5
Dissection, cutting and value of cuts from lamb carcasses
The main tissues of a lamb carcass are (proportion average; decreasing order) muscle, bone
and fat. Dissection of carcasses is defined as separation of the different tissues in carcasses
where the main purpose is scientific analysis, such as anatomical studies. Cutting of carcasses
is defined as separation of carcass tissues performed by a butcher with respect to producing
meat for consumption and to maximize profit. Dissection is performed in controlled scientific
environments; while cutting is performed in industrial environments. Lamb cutting in Norway
is based on three primal cuts; legs, side and forepart, and their respective five sub-primals;
legs, loin, side, shoulder and breast (Fig. 1). The five sub-primal cuts are cut into retail
products such as filets, steaks, manufacturing meats, fat and bone. In addition, residual tissues
like glands are removed, as waste, at time of cutting. The leg (proximal pelvic limb) may be
cut long or short, with or without the sirloin (Swatland, 2000). The mid-part (lumbar region)
of the carcass is divided into loin and flank or side (Fig. 1). The shoulder (proximal thoracic
limb) is removed to contain the large anterior (forepart) bones (Os scapula, humerus, ulna and
radius), leaving the anterior ribs and cervical and anterior thoracic vertebrae as a breast with
neck (Swatland, 2000) (Fig. 1). The Norwegian dissection of lambs is based on guidelines
supervised by Gunnar Malmfors, SLU, Sweden, exemplified in a Swedish Master Thesis
(Einarsdottir, 1998) and the EAAP standard described by (Fisher and de Boer, 1994).

15. 6
1
2
3
4
5
Figure 1. Norwegian sub-primal cuts; lamb carcass. Shoulder (proximal thoracic limb, 1),
breast (neck and anterior thorax, 2), side (lumbar, ventral side, 3), loin (lumbar, dorsal side, 4)
and leg (proximal pelvic limb, 5). Surrounding pictures: Different retail products derived from
lamb carcass primal cuts.
The loin and the leg for all livestock animals are in average higher priced compared to the
side, shoulder and breast. This is due to the high content of tender and lean muscle i.e. M.
longissimus dorsi in loin and M. semimembranosus in leg. In Norway, there are some
exceptions, i.e. during Christmas where the side of pig and lamb is highly appreciated. The
retail products derived from lamb leg and loin are roast, filets and lean manufacturing meats.
The side is mostly used for rolls and cold cuts, and the largest retail products from shoulder
and breast are stew meat with bone (for sheep and cabbage stew, which is a Norwegian
tradition) and manufacturing meats with higher fat content compared to leg and loin.
When dissection is used as a reference method for grading, classification or breeding traits,
one must be able to quantify the size of the error and bias. Introduction of new classification
or grading methods, or maintenance of existing methods, will be compared through the
accuracy of the reference method. A large error and bias in the reference method will
eventually lead to a poorer reliability for the whole system for lamb carcass classification and
grading. For dissection of pig carcasses, the accuracy of dissection was high, although

16. 7
significantly different dissection results were found between butchers with respect to lean
meat percentage (Nissen et al., 2006). The dissection of ruminants like sheep is more complex
compared to non-ruminants such as pig, due to differences in level of subcutaneous fat (higher
proportion in pig carcasses). An international reference method for lamb carcass
measurements and dissection procedures was presented in 1994 (Fisher and de Boer, 1994),
where the approach was to describe carcass form and size, and quantify carcass composition.
The reference method involved four stages of operation: Measurement of carcass dimensions,
preparation of half carcass to a defined standard, carcass jointing and tissue separation. All
stages were defined so that it could be implemented by all research groups in this international
reference exercise. However, the authors stated that it was probably too costly to carry out
studies on carcass composition involving a large number of animals. In Norway, the tradition
has been to dissect carcasses to produce saleable products (commercial dissection).
Commercial dissection is based on separation of saleable retail products (lean muscle,
manufacturing meats, fat and bone) rather than complete anatomical dissection. The main
advantage of commercial cutting is that the dissected parts produced are saleable (industry
products; steaks, filets, manufacturing meats etc) after dissection, which makes the operation
less expensive, and the cutting trials can involve a larger number of animals. The
disadvantage of commercial cutting is that the procedure is difficult to harmonize between
countries, since commercial industry products may vary in shape, size and fat/lean ratio
between countries. Complete anatomical dissection is regarded to be the theoretical value of
carcass components, while commercial dissection is the economic value of the carcass
components, reflected by i.e. saleable meat yield.

17. 8
Classification of lamb and sheep carcasses; the EUROP classification
system
Grading is defined as a single measurement or set of measurements sampled from carcasses
to assign or estimate the amount or value of meat, fat and bone obtained from carcasses.
Classification is defined as sorting or classifying carcasses into groups or meat trade classes
which reflect the value and allow sorting of carcasses for further processing of fresh meat
merchandising, and transfer information back to the farmers (Kvame, 2005).
Classification of sheep and lamb has been carried out systematically in Norway since 1931
performed by trained operators or assessors. Category (age and sex), carcass weight,
conformation and fatness have formed the basis for classification. In 1996, the European
classification system EUROP was introduced in Norway. EUROP is very similar to previous
classification systems in Norway, based on a subjective assessment of category, conformation
and fatness, in addition to carcass weight. However, like any other subjective system, the
system has its weaknesses with respect to accuracy and reliability within and between
operators or assessors. The reference method used for the EUROP system is based on
quantified expertise according to EU commission standards (Commission Regulation (EC) No
823/98, 1998; Commission Regulation (EEC) No 461/93, 1993), but has never been validated
with respect to fat content, saleable or lean meat yield. For cattle, it was stated that the EC or
EU plan for grading and classification had two main disadvantages: it is subjective, and the
carcass characteristics that determine value are not recorded accurately enough. There is no
lack of demand for the recording of carcass values to be objectivized (Augustini et al., 1994).
This situation is also valid for sheep. For cattle, the inclusion of conformation in the EUROP
system was done to make the classification system more acceptable to meat trades concerned
than because of the additional accuracy of the yield information provided (Colomer-Rocher et
al., 1980). Little evidence supports the use of conformation as a classification factor for
predicting meat yield in sheep (Kirton, 1989).
The EUROP system is based on visual appraisal of carcass conformation and fat cover laid
down by the EU Commission (Commission Regulation (EC) No 823/98, 1998; Commission
Regulation (EEC) No 461/93, 1993) (Fig. 2).

18. 9
Figure 2. Visual appraisal of lamb carcass
conformation and fat using EUROP
classification system.
Table 1. EUROP classification system; conformation class E-U-R-O-P and fat class 5-4-3-2-
1, with +/- for each class. Numerical discrete scale from 1 to 15 for each class with +/-.
Conformation + E - + U - + R - + O - + P -
Scale 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
Fat + 5 - + 4 - + 3 - + 2 - + 1 -
The system is based on 5 main classes, both for conformation and fat cover, with the
possibility of extending +/- for each class, making the total number of classes 15 (Tab. 1).
Conformation is classified using the letter E-U-R-O-P, where E is the most convex
conformation group (Fig. 3). Fat cover is classified 1-2-3-4-5, where 5 is the highest fat cover
(Fig. 3). In some cases with extreme conformation, an additional S has been added (S-
EUROP), i.e. for Belgian blue cattle and callipygian gene sheep.
Figure 3. Left: EUROP conformation classification of lamb carcasses. Carcass with convex
shape (U+) vs. a carcass with concave shapes (P). Right: EUROP fat classification of lamb
carcasses. Carcasses with low fat cover (1) vs. a carcass with high fat cover (5).

19. 10
From a scientific perspective, one of the shortcomings of the EUROP classification system is
that conformation tends to be confounded with fatness, i.g. conformation tends to be
correlated with fatness (Navajas et al., 2007). It is difficult to obtain lean, high conformant
carcasses in sheep population, even though some callipygian gene sheep have shown to yield
lean and high conformant carcasses. In general, any improvement in conformation will
inevitably lead to increased fatness and lead to a lower proportions (%) of lean meat.
One of the main objectives of the EUROP classification system for sheep is to improve
market transparency in the sheep meat sector; (Council Regulation (EEC) No 2137/92, 1992).
In order to improve the market transparency, a more objective, accurate and reliable
classification standard is needed, based on the direct relationship between the amount of lean
meat and fat content, and the value of saleable meat obtained from the lamb carcasses.

20. 11
Measuring systems for lamb carcass composition
Measurement systems for lamb carcass composition must be based on robust predictions that
explain highest possible carcass and meat variation, and provides the lowest possible
prediction error. Berg et al. (1997) stated that determination of carcass yield and composition
must be determined by instrument means that can be monitored, standardized, and regulated
(Berg et al., 1997). One of the best established and accepted sheep carcass grading systems is
that in New Zealand, which is the largest international trader of sheep meat products (Kirton,
1989). The system is based on objective carcass weighing and fat classes specified
subjectively or objectively by grading rule (GR) total tissue thickness in the region of the 12th
rib, 11 cm from the dorsal mid-line. The GR is assessed by a metal ruler or grading probe.
Due to high chain speed, the bulk of New Zealand sheep carcasses are classified subjectively
for fatness, however improvements are being made to measure fatness electronically on-line
at chain speed at least as accurately, preferably more accurately, than the subjective
measurement (Kirton, 1989). Recent advances of on-line carcass grading in New Zealand
involve i.e. Video Image Analysis (VIA) and visible light reflectance probing with frames for
classification of lamb carcasses (Chandraratne et al., 2006; Hopkins et al., 1995; Kirton et al.,
1995). New marketing initiatives have been introduced, involving payment of farmers based
directly on the assessment of carcass value using ultrasound, Computer Tomography (CT) or
Video Image Anaysis (VIA) (Jopson et al., 2005).
Objective systems for prediction of lamb carcass composition have developed from easily
obtainable carcass measures such as specific gravity or the ratio of the density of a given
substance, to the density of water (H2O) (Barton and Kirton, 1956), carcass weight, backfat
thickness, kidney fat weight and sub-primal weight (Judge et al., 1966), towards more
advanced and computer – and equipment intensive measurements using Bioelectrical
Impedance (BIA) or Computer Tomography (CT) (Berg et al., 1994; Lambe et al., 2006).
Visual scores and linear carcass measurements
Kempster et al. (1986) exemplified linear measurements, visual scores and the proportions of
tissues in primal or sub-primal cuts as predictors of carcass composition (Kempster et al.,
1986b). The result from this study outlines the importance of breed differences, especially in a
highly diverse population of sheep. The methods are based on subjective appraisal of the
carcasses similar to the EUROP system. The results showed that there was a considerable bias

21. 12
(predicted vs. actual lean percentage) when applying an overall (global) prediction to
individual breeds. No significant sex differences were found. Joints and combination of joints
with high predictive precision tended to have predictions that were robust to differences
between breeds. The convex and concave shapes of carcass conformation can be assessed
more detailed or objective than the EU Commission guidelines for the industry. Unpublished
trials for scientific use have been tested in Norway using a more detailed assessment of
conformation across the entire carcass. Linear shape and size measurements of conformation
from the unpublished Norwegian trial are shown in Figure 4 (from paper #5); utilizing the
convex and concave shapes on a carcass more objectively using i.e. rulers and measuring
tapes.
Figure 4. EUROP advanced carcass shape (white or gray L1-L4, R1
and F1-F2) and length / width (black) measurements based on the
detailed rules laid down by the EU commission concerning the
classification of ovine animals. In addition, carcass length from 1st
anterior rib to carcass steel hook was measured (from paper #5).
Video image analysis (VIA)
Video image analysis (VIA) is a fast and automatic method to assess the shape, length and
color of carcass surfaces. The technology is based on objective and computed assessment of
carcass shapes, lengths and surface color from digital images captured by a charge-coupled
device (CCD) camera on-line (Fig. 5) (Hopkins et al., 2004; Newman, 1987; Stanford et al.,
1998; Swatland, 1995). In a comparison study, a video image analysis system developed by
Meat and Livestock Australia, VIAScan®, was compared to hot carcass weight (HCW) and
tissue depth at grading rule (GR) site (thickness over the 12th
rib, 11 cm from the midline),

22. 13
with respect to prediction of lean meat yield (Hopkins et al., 2004). A greater prediction
accuracy (R2
=0.52) was achieved by the VIAScan® system compared to HCW and GR
(R2
=0.41). The VIAScan® system offered a workable method for predicting lean meat yield
automatically. The video image device Lamb Vision System (LVS), accounted for 50-54% of
the observed variation in boxed carcass value, compared to traditional HCW based value
assessment which accounted for 25-33% of the variation in boxed carcass value (Brady et al.,
2003). The LVS assessed individual lamb carcass value more accurately than the traditional
HCW assessment. Interestingly, the LVS was found to be highly accurate with respect to
prediction of lamb fabrication yields, with a repeatability of 0.98 (Cunha et al., 2004). For
beef carcasses, it was found that VIA was equally accurate to the EUROP classification scores
plus HCW in predicting saleable and primal yield (Allen, 2003). In a Norwegian trial using
the E+V vision system VSS2000 for lamb carcasses, it was found that VSS2000 compared
well with EUROP conformation scores (Berg et al., 2001). The repeatability was higher for
VSS2000 compared to trained operators for EUROP scores. In EU member states, new
technologies presented for carcass classification must be approved according to EU
Commission standards (Commission Regulation (EC) No 1215/2003, 2003). An annex was
added to this regulation in 2003, setting conditions and minimum requirements for
authorisation of automated grading techniques for beef. This annex is also valid for lamb,
since the requirements are equal, in practice. These requirements are based on prediction of
EUROP grading or classification scores, and not weight or yield of meat and sub-primals. The
prediction of EUROP scores will be a prediction of a prediction, since EUROP is a method
for predicting market value. This cannot be considered an optimal solution in practice, and
raises the following question: What is the actual reference; EUROP scores or weight / yield?
The common practice in some countries have been to meet the requirements of the EU
commission for EUROP grading and classification towards farmers, and use the VIA systems
for predicting saleable meat yield within the company for process control. The main concern
from the EU Commission is that saleable meat yield is difficult to standardize and to
harmonize between the member states. For now, it seems like harmonization is favoured in
contrast to higher accuracy and estimation of yields by using VIA and other automatic
technologies. In Norway, the VSS2000 system has not yet passed the requirements for
prediction of EUROP scores. The use of the system for on-line prediction of primal cut and
saleable meat yield has not yet been fully utilized in Norway, however, the system have
shown to be very accurate (Berg et al., 2001). The trend in Europe seems to shift towards the
same marketing initiatives involving payment of farmers based directly on the assessment of

23. 14
carcass value by VIA in New Zealand (Jopson et al., 2005). In New Zealand, one of the
largest meat processors has recently installed VIA systems in all of its sheep plants, and the
other meat companies are working on similar systems (Jopson et al., 2005). Despite VIA’s
recent popularity in the meat industry, the main future challenge for VIA systems, however, is
to introduce a new reference or payment system based on saleable meat yield or the value of
the carcass directly. The experience so far has been that this is a rather slow process where the
changes will be gradual.
Figure 5. Video Image Analysis. CCD image of lamb carcass.
Visible light reflectance probing
Visible light reflectance probing is a spectroscopic method which utilizes the reflectance of
visible light from different types of tissues. The probe is inserted into i.e. the loin of a carcass,
and a profile of the loin, from back-fat to the body cavity (costa) is measured (Fig. 6). The
probe is an evolution of the manual caliper used to perform length and width measurements.
The data generated for industrial use from the probe are fat and muscle thickness. The tip of
the probe contains a light-emitting diode followed by a light detection device (Berg et al.,
1997). Muscle and fat tissue reflects the light differently, and this difference is used to
measure muscle and fat depth at the probe site. Optical probes are considered to be invasive,
although penetration damage is minimal (Swatland et al., 1994). Optical probing is currently
used in Norway and other European countries for grading of pig carcasses by measuring
backfat and m. longissimus thickness. Recent advances of the probe provide the color and
level of marbling in the muscle. The color can be related to meat quality attributes, and is
currently used in Norway to identify Pale Soft Exudative (PSE) meat on pigs. However, it has
recently been questioned in the Norwegian pork meat industry how increased marbling (intra

24. 15
muscular fat) impacts the measurements. This concern may be excessive, since the “noise”
from marbling can be modeled statistically and may not compromise the accuracy of
measurements. In New Zealand and Australia, lamb and sheep carcasses are graded using
grading probes, measurements of back-fat in the same fashion as pig carcasses in Europe.
Probing by using GR or other back-fat measures is considered to be more robust and accurate
compared to visual appraisal using the EUROP system (Kempster et al., 1986a). Probe
measurement of backfat thickness between the 12th
and 13th
rib provided a superior method
compared to visual assessment for prediction of lean content in lamb carcasses (Jones et al.,
1992). In Europe (including Norway), there has been a major concern using probing for sheep
and cattle, due to large variation in breeds and crossbreeds, and damaged subcutaneous fat
cover during slaughter and hide-pulling (Augustini et al., 1994; Kirton, 1989). In Iceland,
probe measurements (ICEMEAT probe) of backfat and side thickness has proven to be
successful (Einarsdottir, 1998), probably due to a very homogenous population of sheep
(Icelandic sheep breed). In Iceland and New Zealand, no major concerns have been raised
concerning damaged subcutaneous fat during slaughter (Kirton, 1989), however there are
some concerns due to positioning and operation of the probe at high chain speed.
Figure 6. Visible light reflectance probe (Hennessy Grading Probe®). Measurement of lamb
side and backfat thickness assessed by the author J. Kongsro. Reflectance profile from
Hennessy Grading Probe®, from backfat to body cavity. Reflectance peaks (white) at back-fat
and costa (high fat).
The repeatability of probe measurements is highly dependent on the operator of the equipment
(Olsen et al., 2007). Robotics or support frames can increase the repeatability of
measurements by visible light reflectance probing (Swatland et al., 1994). The cost of
equipment is also an issue; however, the price of visible reflectance probes is relatively low.
Robotics and support frames will also increase cost; however, increased repeatability will pay
off over time. Stanford et al. (1998) found that the increased accuracy of optical probing
compared to manual GR measurements of back-fat, was likely due to improvements in the
accuracy of prediction of carcass composition of cold as compared to warm carcasses. The
reason for the improvement in accuracy and repeatability of cold vs. warm carcasses may be

25. 16
errors caused by fat bubbles in subcutaneous fat when the hide is removed from warm
carcasses. During chilling of carcasses, the fat bubbles are reduced significantly and the
subcutaneous fat layer obtains a more even shape and thickness. The effect of fat bubbling on
subjective appraisal or VIA has, however, not been documented. Information on meat color
and quality from GP is an additional advantage. When measuring meat color, time post
mortem is of great importance. Measurements of color 24 hours post mortem and 7 days post
mortem are different (Linares et al., 2007). The accuracy of probes can probably be improved
by increasing the number of measuring sites, sampling from several anatomical positions
along the carcass. However, the penetration damage may increase by adding probing sites,
and may be too invasive in practice. The operation at high chain speed may also be an issue
when introducing several measuring sites.
Total Body Electrical Conductivity (TOBEC) and Bioelectrical Impedance (BIA)
Total Body Electrical Conductivity (TOBEC) and Bioelectrical Impedance (BIA) are methods
which utilize the transfer of an electrical current through biological material like a lamb
carcass. Lean tissue is much more conductive than fat and bone tissue due to the high
concentration of water and electrolytes in the tissue (Stanford et al., 1998). A fat lamb carcass
should impede the transmission of electrical current to a larger extent than a lean lamb (Berg
et al., 1996). Using this difference between tissues in electrical conductivity or impedance, the
carcass composition can be predicted. Berg et al. (1996) also found that individual electronic
methodologies tested in their study were moderate predictors of proportional carcass lean
(Berg et al., 1996). Another study reported that the impedance method is not suitable for the
prediction of carcass composition, neither in lambs of similar weight nor in heterogeneous
animals (Altmann et al., 2005). For TOBEC, is was found that the research approach using
electromagnetic scanning was not a reliable tool for predicting body composition of live
lambs (Wishmeyer et al., 1996). Overall, it seems that methods using transfer of an electrical
current through a lamb carcass need to be further developed to achieve higher accuracy and
reliability.
Computer Tomography (CT)
Computer Tomography was introduced for medical diagnostics in the 1970’s (Hounsfield,
1973), for which G. N. Hounsfield and A.M. Cormack received the Nobel Price in Medicine
in 1979. The method is computer intensive, and the principle is based on X-ray attenuation
through an object, where an X-ray source and detectors rotate 360o
around the object (Fig. 7).

26. 17
For sheep, CT has primarily been used for selection of breeding traits (Kvame, 2005) and
prediction of lamb carcass tissue weights (Junkuszew and Ringdorfer, 2005; Lambe et al.,
2003).
Figure 7. Left: Computer Tomography (CT) scanner. Lamb carcass subject for assessment.
Right: CT Tomogram Image. Image sampled from mid-part of carcass (11th
rib).
X-ray images are generated during rotation of the X-ray tube, and data recovered from the X-
ray detectors are reconstructed by a computer to form a tomogram or CT image of the entire
object, both internally and externally (Fig. 7). A set of CT images from a set of trans-sectional
images or spiral scanning can be used to generate 3D images or volumes of the object
subjected for study. Different tissues produce different degrees of X-ray attenuation,
reflecting their density, thickness and atomic number (Harvey and Blomley, 2005). Lower
density tissues will appear more transparent than higher density tissues to X-rays. Air is
transparent to X-rays, and will appear black, while bone, due to its high mineral content, is
not very transparent, and appears white in CT images. In radiographic terms, the transparency
of X-rays is often called radiodensity, and is quantified in Hounsfield Units (HU), where the
X-ray attenuation of distilled water is used as a Hounsfield scale reference (HU=0). The
images generated from CT can be analyzed using the HU value of each pixel. CT images can
be organized according to spectroscopic profiles using the histogram of pixels, where the
intensity of pixels can be visualized according to the respective CT value (HU) (Fig. 8). Fat
tissue has a lower density compared to muscle tissue, and much lower density than bone
tissue. To get a better separation of tissues with respect to radiodensity, contrasting agents can
be added via feeding pre-slaughter or via blood vessels (i.e. for segmentation of internal
organs using iodine).

27. 18
-200 -100 0 100 200
0
2000
4000
6000
8000
10000
12000
CT value (HU)
Frequencypixels
Figure 8. CT histogram pixels from 120 lambs (left) (samples from paper III). Soft tissue
region from HU value -120 to 120. The first, smaller peak was identified as fat tissue, the
second, larger peak identified as muscle tissue (right).
The CT histograms can be decomposed using two strategies: (1) utilize a priori knowledge or
windowing of CT values (Kalender, 2005) reflecting the CT values of fat, muscle and bone
tissue, or (2) through calibration of CT histograms against a known reference such as
commercial or full dissection (Dobrowolski et al., 2004). If the a priori knowledge is robust
and globally valid for new samples, the computation is both fast and efficient. If there are
differences in CT value windows or radiodensity for the same tissue (i.e. muscle) between and
within populations of lambs, the predictions will be less accurate using windowing. A pixel
will represent the mean value of the area covered by the pixel, and the pixel may sometimes
(i.e. border pixels between two types of tissues) represent an average of two tissues, making
discrimination between the tissues difficult. This mixed pixel distribution is called the partial
volume effect (Lim et al., 2006). It is therefore of great importance to perform calibrations by
using representative samples of the actual carcass population which CT is meant to predict.
Using the calibration strategy, the CT values are calibrated against real data sampled from the
actual population you want to model. The calibration is performed using the spectroscopic
approach, where the CT histogram is treated as a spectrum, and can be modeled using
multivariate calibration. Regression coefficients can be estimated from calibration, and can be
used as window levels or models for further prediction of carcass tissues. The disadvantage of
calibration, is that the reference method used (dissection) is often inaccurate and have poor
repeatability due to butcher or operator error, as shown for pig carcass dissection (Nissen et
al., 2006).

28. 19
By using stereological methods such as the Cavalieri principle (Russ, 2002), unbiased
estimates of the tissue volumes can be obtained (Fig. 9). The CT images are organized in
sections based on the equipment settings and method, and the total volume of the segmented
tissue will be the area of tissue in the CT images, multiplied by the section distance.
Dissection seemed to be a choice between accuracy and number of samples; full tissue
separation vs. commercial dissection. CT can offer a combination of both, providing a high
number of “low-cost” estimates of full tissue separation. Dissection using CT is sometimes
nicknamed “virtual dissection”, where live animals or carcasses can be dissected in virtual
space using a computer. For industrial on-line use, it has been stated that CT would be too
slow, even if it is cost-effective (Stanford et al., 1998). Advances in CT technology since
1998, has proven that CT can operate during high speed in hospital environments. Single
scans of selected anatomical sites can in theory be obtained in 0.8 seconds (scan time;
protocol). High-speed dual-source computed tomography scanning (DSCT) of human hearts
have been performed with mean scan times of 8.58 seconds (Weustink et al., 2007). CT
scanners may be able to predict lamb carcass composition on-line at chain speed; it is just a
matter of designing a CT scanner for abattoir environments.
Figure 9. Cavalieri estimation and visualization of lamb carcass side using CT (left). Fat
(yellow), muscle (red) and bone (light gray) segmented using windows presented by (Kvame
et al., 2004).

29. 20
Summary of methods and economical considerations
Table 2. Summary of different methods or technologies (systems) for prediction of lamb
carcass tissues presented, with respect to explained variance and prediction error.
System (independent) Tissue reference
(dependent)
Explained
variance
RSD RMSE Reference
Live weight Muscle (kg) R2
= 0.96 (Teixeira et al., 2006)
HCW Muscle (g) R2
= 0.92 RSD = 69.94 (Diaz et al., 2004)
Leg fat (%) Carcass fat (%) R = 0.93 RSD = 1.55 (Kirton and Barton,
1962)
Loin fat (%) Carcass fat (%) R = 0.97 RSD = 1.07 (Kirton and Barton,
1962)
Specific gravity
(hind saddle)
Carcass fat trim % R2
= 0.51 (Adams et al., 1970)
Linear carcass measures Total dissected lean (%) R2
= 0.72 RMSE = 2.55 (Berg et al., 1997)
Linear carcass measures Total dissected lean (kg) R2
= 0.86 RMSE = 0.78 (Berg et al., 1997)
Linear carcass measures Muscle (%) R2
= 0.63 RSD = 1.55 (Diaz et al., 2004)
Linear carcass measures Fat (%) R2
= 0.84 RSD = 1.83 (Diaz et al., 2004)
EUROP classification Fat (%) R2
= 0.57 RSD = 2.35 (Einarsdottir, 1998)
EUROP classification Lean meat (%) R2
= 0.23 RSD = 2.54 (Einarsdottir, 1998)
GR Carcass fat (%) R2
= 0.57 -
0.58
RSD = 2.97 (Kirton et al., 1995)
Ultrasound Total dissected lean (%) R2
= 0.26 RMSE = 4.46 (Berg et al., 1996)
Ultrasound Total dissected lean (kg) R2
= 0.54 RMSE = 1.31 (Berg et al., 1996)
Ultrasound Fat (%) R2
= 0.06 -
0.41
(Olesen and Husabø,
1992)
HC Fat (%) R2
= 0.73 RSD = 2.06 (Einarsdottir, 1998)
ICEMEAT Lean meat (%) R2
= 0.28 RSD = 2.53 (Einarsdottir, 1998)
HC + EUROP Fat (%) R2
= 0.80 RSD = 1.80 (Einarsdottir, 1998)
HC + EUROP Lean meat (%) R2
= 0.38 RSD = 2.46 (Einarsdottir, 1998)
Electronic probe Carcass fat (%) R2
= 0.47 -
0.58
RSD = 2.99 -
3.48
(Kirton et al., 1995)
BIA Fat-free soft tissue (kg) R2
= 0.94 RSD = 0.43 (Jenkins et al., 1988)
BIA +
linear carcass measures
Fat-free soft tissue (kg) R2
= 0.96 RSD = 0.34 (Jenkins et al., 1988)
HCW +
VIA (color + shape)
Saleable meat yield (%) R2
= 0.71 RSD = 1.43 (Stanford et al., 1998)
VIA + HCW Saleable meat yield (%) R2
= 0.64 RMSE = 3.30 (Brady et al., 2003)
TOBEC Dissected lean (%) R2
= 0.62 RMSE = 2.97 (Berg et al., 1997)
TOBEC Dissected lean (kg) R2
= 0.83 RMSE = 0.85 (Berg et al., 1997)
CT Primal weight (kg) R2
= 0.85 -
0.98
RSD = 0.02 -
0.37
(Kvame et al., 2004)
CT Primal lean (kg) R2
= 0.80 -
0.98
RSD = 0.01 -
0.32
(Kvame et al., 2004)
CT Primal fat, subcutaneous
and intermuscular (kg)
R2
= 0.82 -
0.98
RSD = 0.004 -
0.09
(Kvame et al., 2004)
CT Fat (kg) R2
= 0.80 -
0.84
(Junkuszew and
Ringdorfer, 2005)
CT Muscle (kg) R2
= 0.63 -
0.65
(Junkuszew and
Ringdorfer, 2005)
BIA = Bioelectrical impedance
CT = Computer Tomgraphy
GR = fat thickness, grading rule site (mm)
HC = Icelandic Manual GR meter (hot
carcass)
HCW = hot carcass weight
ICEMEAT = ICEMEAT GR probe (cold
carcass)
Rack = lamb loin with ribs
RMSE = Root Mean Square Error
RSD = Residual Standard Deviation
SE = Standard Error
TOBEC = total body electrical conductivity
VIA = Video Image Analysis

30. 21
The usefulness of different measurements or methods from previous studies was compared in
table 2, with respect to explained variance (R2
) and residual standard deviation (RSD) or root
mean square error (RMSE), when available. The table spans from live or carcass weight,
subjective appraisal and linear measurements, electronic probing and bioelectrical impedance,
and finally computer tomography (CT).
The usefulness for tissue composition in weights (kg) seems to be more accurate than those
for tissue proportion in percentage. For practical purposes, the most accurate solution seem to
be to estimate the carcass tissue in weight, then, an estimate of the proportion can be obtained
as a proportion of carcass weight; tissue (kg) * carcass weight-1
(kg). The results in Table 2
show that live or carcass weight is a very good single predictor of both fat and muscle weight
in kg. The best measuring systems in Table 2 with respect to explained variance, RSD or
RMSE seem to be Computer Tomography (CT). The authors used single scans from selected
anatomical sites (Junkuszew and Ringdorfer, 2005) or sequential scanning using 50 mm
section distances, with an average of 18 images per animal (Kvame et al., 2004). By using
denser scans with smaller section distances or spiral scanning, the accuracy may be improved.
Results from spiral scanning of pig carcasses have shown that the predictions were very good
and provided a fast volumetric scanning method of the entire carcass (Dobrowolski et al.,
2004; Fuchs et al., 2003; Kalender, 1994; Romvari et al., 2006). Using tissue proportions
obtained from primals have shown to be very well correlated with carcass tissue proportion
(Kirton and Barton, 1962). However, primal dissection used as predictor of carcass
composition is a laborious process, which has little relevance in a practical setting. The error
of determining the tissue reference (i.e. by dissection) has not been quantified in any of the
previous studies. A significant error in the reference will inevitably have an effect of the
precision of the measuring method. This can be solved by repeated measurements, i.e.
estimating paired differences between repeated measurements, depending on how costly or
time consuming the measurements are (Esbensen, 2000).

31. 22
Multivariate calibration
The aim of calibration is to establish explanatory power and correlation between the different
classification, grading and measurement systems, and the “true“ quantity of muscle, fat and
bone in carcasses (Fig. 10). In addition, regression coefficients can be used to study the
impact (i.e. windowing of CT values) of the variables in the measurement system. The
different calibration models are validated using leave-one-out cross validation, test set
validation or a combination of both. The calibration models are evaluated in terms of
explained variance, prediction error and bias. The modeling is usually done by linear
regression, where the response y is the quantity of muscle, fat or bone from dissection or the
value of cuts, and Xi are the different classification, grading and measurement systems
variables i, b is the regression vectors of the i measuring system variables, and e are the
residuals. In matrix notation, the linear regression equation (1) can be written:
y = Xb + e (1)
where X=[1, x1, x2,….,xi] and b = [b0, b1,b2,…,bi]T
X
Classification
Grading
Measurement
systems
Y
Fat
Muscle
Bone
Value
Figure 10. Calibration of different measurement methods or technologies (X), and weights or
proportions (quantity) of carcass tissues (fat, muscle and bone) and value (Y).

32. 23
Table 3. Classification of data by their tensorial properties, and typical methods for data
analysis (Escandar et al., 2006). Instrument data examples, regression method and second
order advantage.
Classification Order of
data
Sample
data set
Instrument
data
Typical
method
Second
order
advantage
Univariate Zeroth-order One-way - Fat
thickness
- EUROP
fat score
OLSR No
Multivariate First-order Two-way - Set of fat
thickness
(GP
probing)
- CT
histogram
PCR, PLSR No
Higher-order
unfolded to
first-order
Two-way CT
histogram
Unfold PCR
Unfold PLSR
No
Second-order Three-way CT
histogram
PARAFAC
NPLSR
Yes
CT = Computer Tomography
GP = visible light reflectance probing
NPLSR = N-way PLSR
OLSR = Ordinary Least Squares Regression
PARAFAC = Parallel Factor Analysis
PCR = Principal Component Regression
PLSR = Partial Least Squares Regression
Many instrumental measurements produce one, two or multidimensional arrays of data. The
different dimensions of data is called the order of data (Escandar et al., 2006). The different
dimensions of data produced by classification, grading or other measurement are seen as the
components of a first-, second- or nth
-order tensor, respectively (Sanchez and Kowalski,
1987). The univariate case or zeroth
-order of data can be exemplified by fat thickness
measured at a singe site as a single vector x and total fat from a carcass in kg as a y. This is
handled by Ordinary Least Squares regression (OLSR) (Tab. 3). Univariate calibration or
modeling using estimates to predict the quantity of carcass tissues are sometimes called direct
estimation. Another example of univariate calibration can be tissue estimates from CT
scanning using windowing. In this case, single estimates (vector x) from CT scanning is
calibrated against a cutting reference y. When introducing a set of measurement variables
such as EUROP conformation and fat classes, carcass weight and several fat thicknesses
probed by GP, we enter the multivariate domain with several variables in X. This is best
handled by multivariate calibration methods such as Principal Component Regression (PCR)

33. 24
or Partial Least Square Regression (PLSR). The original sets of sampled responses within
these variables are transformed into scores by latent variable selection, and regression is
performed on these scores. Higher order data has recently been applied to a number of
different fields within analytical chemistry and food science (Andersen and Bro, 2003; Bro,
1996; Escandar et al., 2006; Huang et al., 2003). These data are provided by i.e. sampling
using multi-component instruments and cross-section images from CT. The data are
recognized by each sample providing a data array (multi-way) instead of a vector (2-way).
This multi-way data array can be handled in two different ways; either by unfolding the
higher order (I * K * L) data set to a first-order (two-way) data set by rearranging the data
across a higher order mode (IK * L) (Chiang et al., 2006). There are several advantages of
keeping the higher order data structure in the previous example, called the second-order
advantage. The second-order advantage makes it possible to utilize the multi-way structure,
like in the previous example, and extracting valuable information concerning the higher order
structure, i.e. cross section from CT images.
One of the requirements of linear regression is that the variables X should preferably be
independent or orthogonal (Martens and Martens, 2001). In measuring systems, the variables
are often correlated, and calibration and prediction may suffer from collinearity when using
OLSR. OLSR has a number of assumption, for example that the errors are independently
distributed and that the independent variables are not to strongly correlated or collinear
(Esbensen, 2000; Martens and Martens, 2001). When collinearity is high, it is almost
impossible to obtain reliable estimates of regression coefficients. It does not affect the ability
of the regression to predict the response; however, the estimates or contribution of the
individual regression coefficients bi becomes unstable. The main purpose of regression is to
seek the largest explanation of variance in y as a function of X. The obvious solution seems to
be removal of one or more of the correlated variables in X. Instead of looking at collinearity
as a problem, some multivariate calibration methods utilize the correlation between variables,
and construct a set of latent variables which are orthogonal (independent). The latent variables
are estimated as linear functions of both original input variables and the observations, and is
often called bilinear modeling (BLM) (Esbensen, 2000; Martens and Martens, 2001), as
shown in Figure 11. Principal Component Analysis (PCA) or Principal Component
Regression (PCR) and Partial Least Square Regression (PLSR) are some bilinear methods
which handle collinearity and construct a set of orthogonal latent variables called principal
components for further calibration. The goal of PCR and PLSR is to fit as much variation as

34. 25
possible using as few PCs possible (Martens and Martens, 2001). The first latent variable or
PC explains the largest amount of variation, the 2nd
the second largest, and so on. The original
variables are projected down to the PCs space, and are called loadings. The measurements or
information carried by the original variables are also compressed and projected down on the
PC space, and are called scores. Each sample has a score along each PC (Esbensen, 2000).
For each PC, we have loadings and scores which reflect the compression of the original data
structure with samples and variables (Fig. 11). The number of latent variables is always
smaller than the original data set; especially for spectroscopic studies, where the number of
variables (i.e. wavelengths) is very large. PCR focus on obtaining PCs from the X data array,
followed by regression of Y using the scores obtained from the PC. For PLSR, the modeling
of PCs is done by seeking the largest covariance between X and y or ensuring y-relevant PCs
from X (Martens and Martens, 2001). The result is that the PLSR models are simpler and
more compact models, and in most cases uses fewer PCs compared to PCR.
X t
l
=
Figure 11. Bilinear modeling. Latent variable decomposition of a data set X. Scores (t) and
loadings (l).
The performance of a multivariate calibration model is quantified by validation. The purpose
of validation is two-fold (Esbensen, 2000): (1) to make sure that the calibration model will
work in the future, on new data sets and (2) to find the optimal dimensionality of the model to
avoid under- or overfitting. The overall aim of validation is to obtain the lowest prediction
error possible using the optimal dimensionality of the model. The calibration modeling error
is defined as the Root Mean Square Error of Cross Validation (RMSECV). The cross-
validated model is tested using a separate test set, and the prediction error is found using the
Root Mean Square Error of Prediction (RMSEP).
The bilinear modeling handles first-order data structures (samples*variables). For higher-
order data structures, i.e. second-order or three way data matrices, two original input spaces of

35. 26
variables and the observations are modeled, and this is often called trilinear modeling. A set
of scores and two sets of loadings are estimated from the trilinear modeling (Fig 12). NPLSR
is PLSR for multi-way or higher order data, where trilinear modeling estimates a set of scores
and n set of loadings, where n is larger than 1. PARAFAC or Parallel Factor Analysis was
introduced in two parallel papers by (Carroll and Chang, 1970; Harshman, 1970) for
psychometric studies, and has been further developed for Chemometrics by Bro (Bro, 1997).
PARAFAC is a generalization of PCA into higher order data arrays, but is somewhat different
from the bilinear PCA (Bro, 1997). PARAFAC yields n number of loadings when there are n
modes or dimensions in the data, and often the first mode is named scores and represent the
information in samples or objects (Rinnan, 2004). The decomposition of data using
PARAFAC differs from PCA by providing unique solutions (Bro, 1997), calculating all
components simultaneously, different from PCA which calculates one component at a time.
The components in PARAFAC will represent the unique solution in X, while PCA will seek
the largest covariance in X. If the optimal number of components is selected, and the data is
trilinear or higher order in nature and a global optimum is achieved, PARAFAC is a robust
and strong tool for decomposition and modeling of multi-way data. While PCR, PLS and N-
PLS for multi-way data require reference samples for modeling (y), the uniqueness of
PARAFAC makes it able to estimate the true underlying profiles in the multi-way data set
(Khayamian, 2007). The optimal number of components can be found by different validation
techniques, like core consistency and split-half analysis (Trevisan and Poppi, 2003). If the
PARAFAC model is correct, then it is expected that the superdiagonal elements will be close
to one and the off-diagonal elements close to zero, and core consistency is achieved (Trevisan
and Poppi, 2003). In an optimal PARAFAC model, the core consistency should be as close to
100% as possible (Bro and Kiers, 2003). Another validation tool is split-half analysis. The
idea of this analysis is to divide the data set into two halves and make a PARAFAC model on
both halves. Due to the uniqueness of the PARAFAC model, one will obtain the same result
on both data sets, if the correct number of components is chosen (Christensen et al., 2005).

36. 27
X t
l1
l2
=
Figure 12. Trilinear modeling. Latent variable decomposition of a data set X. Scores (t) and
loadings (l1) for mode 1 and loading (l2) for mode 2.
Multivariate calibration methods have been successfully applied to a number of areas, but
spectroscopic measurements are typically used. In the meat industry, multivariate data
analysis can be helpful in analyzing, monitoring and modeling new measuring systems. Bro et
al. (2002) listed some main areas where multivariate data analysis can be a useful tool for
food production: visualization, optimization and calibration (Bro et al., 2002). All these areas
which can be utilized for the assessment of lamb carcass composition in relation to the
quantity of fat, muscle and bone, and the value of cuts obtained from the carcass, especially
for CT measurements sampling from cross-sections.

37. 28
Main results of papers I-V and future perspectives.
This thesis focuses on reliable prediction and determination of lamb carcass composition
using different methods or techniques.
The objective of Paper I was to study the repeatability and accuracy of the EUROP
classification system applied in Norway. The assessors were highly reliable, achieving high
correlation between repeated measurements and between assessors. There were some
differences between abattoir operators and EU commission assessors, but these differences
were within limits accepted by the EU commission. The EUROP prediction of lean meat
percentage was poor, achieving relatively high prediction error and low explained variance.
The prediction of bone and fat percentage was somewhat better, especially for fat. This
showed that EUROP does not predict lean meat in carcasses very well, but is somewhat
accetable for prediction of fat.
The precision and reliability of lamb carcass dissection as the reference method for lamb
carcass classification and grading has never been quantified. In paper II, an estimate of the
reliability and precision of the reference butcher panel used for calibration of lamb carcass
classification and grading in Norway was obtained from a sample set of Norwegian lambs.
The goal was to develop a methodical framework to study the accuracy of lamb carcass
dissection in Norway; describe and obtain estimates of the precision and reliability of the
reference dissection in Norway for calibration of lamb carcass classification. The overall
precision and reliability was acceptable (reliability > 0.80) for carcass composition traits,
however, the results for sub-primal yield and composition were somewhat poorer. The sub-
primal breast seemed to be difficult for the butchers to dissect, and needs special attention
when setting up a dissection of lamb carcasses.
In paper III, the objective was to find the best prediction model for carcass soft tissues (fat
and muscle) using Computer Tomography (CT). The digital image data from CT scanning
was organized according to histograms of CT value and anatomical direction, yielding a
multi-way data array. Two strategies of modeling were tested. The first, direct estimation was
based on a priori thresholds of fat and muscle tissue in CT images or scores from PARAFAC
modeling of the multi-way data array. The second strategy was based on multivariate
calibration using 2-way PLS or n-way NPLS against a commercial dissection reference. The

38. 29
results showed that multivariate calibration using NPLS gave the best results for fat and
muscle tissue with respect to prediction error (RMSEP). There were some biases between
measured (dissection) and predicted (CT) fat and muscle, and bias corrections proved to be
advantageous for the models.
In paper IV, the objectives were: (1) to obtain estimates of precision and reliability using
virtual dissection by CT scanning of lamb carcass, and (2) to test different equidistances or
section distances using sequential CT scanning with respect to correlation between manual
commercial and virtual dissection. The precision and reliability of virtual dissection was
higher (reliability > 0.95) compared to manual commercial dissection in paper II. Increasing
section distances gave poorer accuracy, which is an effect of poor modeling of irregular 3D
structures (i.e. bone cartilage) in carcasses. There were some biases between manual and
virtual dissection, especially for bone and muscle. This may be a combination of butcher error
and modeling by sequential scanning. Spiral scanning may solve the bias problem and
modeling of 3D structures, and may prove CT to be a more accurate reference compared to
manual commercial dissection.
In paper V, a number of different technologies for measuring carcass soft tissues (fat and
muscle) and carcass value were tested with respect to accuracy and prediction. Four
technologies were tested on the same data set, spanning from manual EUROP classification to
Computer Tomography (CT) scanning of carcasses. CT yielded the highest overall accuracy
and most unbiased predictions, both for fat and muscle tissue. Currently, CT may be too slow
and expensive for on-line, however, recent developments of CT scanners may operate at chain
speed in the near future. The chain speed at Norwegian abattoirs during lamb slaughter season
is approx. 300-400 animals per hour. The most practical solution at the time for prediction of
carcass soft tissues and value, seem to be optical probing of carcass side thickness calibrated
against a CT virtual dissection reference.
The calculation of costs when introducing new measuring systems for lamb carcass
composition needs further attention.
ƒ Does the increased accuracy and reliability of an alternative or new measuring system,
relate to the running, development and training costs?
ƒ Are some types of measuring systems more relevant for larger abattoirs than for
smaller ones?

39. 30
In this work, different technologies for prediction of carcass composition and value have been
tested, and current and alternative reference methods used for prediction have been evaluated
with respect to accuracy and reliability. The speed and cost of maintaining the current
EUROP classification system must be compared with the development costs and maintenance
of new technologies. A cost-benefit study beyond this work will determine the future
developments of technologies for prediction of carcass composition and value. In addition,
CT images, once available may provide many other relevant data in slaughter houses:
intramuscular fat, abnormal water to protein ratios of lean meat. Palatability traits such as
tenderness, juiciness etc., have not been addressed in this thesis. These traits must be
considered in future work in development of new systems for carcass evaluation. CT scanning
has proven throughout this work as the most accurate and reliable tool for prediction of
carcass composition. Spiral scanning of carcasses was not applied in this work; however it
may prove to be the best solution, covering variation in complex 3D structures (i.e. bone
cartilage) in carcasses. For future work using CT scanning, spiral scanning is therefore highly
recommended. Whether the methods or technologies presented in this thesis are dependent on
size of abattoirs or plants may be discussed, but it seems obvious that smaller plants with a
smaller turnover of carcasses and meat will not be able to benefit as much as larger plants, i.e.
fixed costs of expensive equipment. The size of plants is a major concern when trying to
harmonize the classification or grading methods between and within countries or regions. This
emphasises the need of an objective and reliable reference, in which the plants can use as a
measure. New methods or technologies needs to be measured and validated against this
reference, in order to obtain solid risk assessment, both in terms of accuracy and cost.
During this work, an application using CT as a reference method for carcass composition and
value has shown to be more accurate, cheaper and reliable compared to manual dissection
performed by butchers. In terms of measuring systems, smaller plants can to a large degree
utilize carcass weight or simple linear measures, and still obtain an accuracy close (or
sometimes better) to more computer-intensive systems like VIA and BIA. When investing in
new technology for prediction of lamb carcass composition and value; the easiest solution is
most often the best one, and it all depends on the reference method used for prediction.

40. 31
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46. Paper I

48. Validation of the EUROP system for lamb classification in
Norway; repeatability and accuracy of visual assessment and
prediction of lamb carcass composition
Jørgen Johansen a,b,*, Are H. Aastveit b
, Bjørg Egelandsdal b
, Knut Kvaal c
, Morten Røe a
a
Norwegian Meat Research Centre, P.O. Box 396, Økern, N-0513 Oslo, Norway
b
Norwegian University of Life Sciences, Department of Chemistry, Biotechnology and Food Science, N-1432 A˚ s, Norway
c
Norwegian University of Life Sciences, Department of Mathematical Sciences and Technology, N-1432 A˚ s, Norway
Received 10 October 2005; received in revised form 14 April 2006; accepted 14 April 2006
Abstract
The EUROP classification system is based on visual assessment of carcass conformation and fatness. The first objective was to test the
EUROP classification repeatability and accuracy of the national senior assessors of the system in Norway. The second objective was to
test the accuracy of the trained and certified abattoir EUROP classifiers in Norway relative to EU Commission’s supervising assessors.
The third and final objective was to test the accuracy of the EUROP classification system, as assessed by the National senior assessors,
for prediction of lean meat, fat and bone percentage and lean meat in relation to bone ratio. The results showed that the repeatability and
accuracy of the national senior assessors was good, achieving high correlations both for conformation and fatness. For the abattoir asses-
sors, there were some systematic differences compared to EU Commission’s assessors, but these differences were within limits accepted by
EU Commission. The relationship between abattoir and national senior assessors was good, with only small systematic differences. This
may suggest that there also is a systematic difference between the national senior assessors of the system and EU Commission’s assessors.
The EUROP system predicted lean meat percentage poorly (R2
= 0.407), with a prediction error for 3.027% lean. For fat and bone per-
centage, the results showed a fairly good prediction of fat percentage, but poorer for bone percentage, R2
= 0.796 and R2
= 0.450, respec-
tively. The prediction error for fat and bone percentage was 2.300% and 2.125%, respectively. Lean: bone ratio was predicted poorly
(R2
= 0.212), with a prediction error of 0.363 lean: bone ratio.
Ó 2006 Elsevier Ltd. All rights reserved.
Keywords: Lamb; Carcass; Classification; Subjective assessment; Commercial cutting
1. Introduction
Carcass classification of ruminants in Norway, as in the
European Union, is based on the EUROP carcass classifi-
cation system (Commission Regulation (EEC) No 461/93,
1993; Council Regulation (EEC) No 2137/92, 1992). The
overall aim of the EUROP classification system is to sort
carcasses according to their value for further processing
and to ensure fair payment to farmers. The EUROP classi-
fication system in Norway makes use of four carcass cate-
gories or maturity groups for sheep; mutton, yearling
mutton, lamb and suckling lamb. For ruminants in Nor-
way, EUROP classification is carried out by human assess-
ment of conformation and fat class in addition to carcass
weight. Conformation class describes carcass shape in
terms of convex or concave profiles and is intended to indi-
cate the amount of flesh (meat) in relation to bone, where
flesh or meat is regarded as the sum of fat and lean (Fisher
& Heal, 2001). Fat class describes the amount of visible fat
(subcutaneous) on the outside of the carcass (Fisher &
Heal, 2001). Carcasses are given classes from 1 to 15, where
grade 1 is PÀ for conformation class and 1À for fat class.
0309-1740/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.meatsci.2006.04.017
*
Corresponding author. Tel.: +47 2209 2246; fax: +47 2222 0016.
E-mail address: jorgen.johansen@fagkjott.no (J. Johansen).
www.elsevier.com/locate/meatsci
Meat Science 74 (2006) 497–509
MEAT
SCIENCE

49. Grade 15 is E+ conformation class and 5+ for fat class.
High value for conformation class indicates a carcass with
well to excellent rounded muscles. High value on fat class
indicates a carcass with a high degree of external fat (sub-
cutaneous), and utilizes the relationship between external
fat and total fat content of carcass.
In Norway, human assessors carry out EUROP classifi-
cation of lamb carcasses (manually) by sensory evaluation
of carcasses. Classification of ruminant carcasses is tradi-
tionally done by trained assessors because of the difficulty
of identifying appropriate instrumental methods. The Nor-
wegian Meat Research Centre (NMRC) (national senior
assessors) has been given the responsibility by the Norwe-
gian classification board (Røe, 2002) to train and certify
abattoir assessors, using EU Commission photographic
standards. Abattoir assessors are supervised after they have
finished their training and certification, and are validated
several times annually by the national senior assessors.
Certification is withdrawn from abattoir assessors if they
fail supervision and validation tests. The approval limits
for certification and validation of assessors for the EUROP
classification system are described by the EU Commission
regulation (EC) No. 1215/2003. National senior assessors
are also supervised and validated annually by the EU Com-
mission assessors. The foundation of the EUROP carcass
classification system is a 5-class system, legislated by the
EU Commission (Regulation (EEC) No 2137/92, No
461/93, 1992/1993; Commission Regulation (EEC) No
461/93, 1993; Council Regulation (EEC) No 2137/92,
1992). In Norway, EUROP carcass classification of lamb
carcasses is carried out using 15 classes (5 classes with +
and À for each class), both for conformation and fat.
The rules laid down by the EU Commission states that
absolute maximum deviation (bias) between EU Commis-
sion and abattoir assessors should not be larger than 0.3
and 0.6 for conformation and fat class, respectively (Com-
mission Regulation (EC) No 1215/2003, 2003). The slope
of a linear regression line (fitted) between EU Commission
and abattoir assessors should not deviate more than ±0.15
and 0.30 from 1 for conformation and fat class, respectively
(Commission Regulation (EC) No 1215/2003, 2003).
Pig carcasses are not classified, but graded instrumen-
tally by measuring backfat and muscle depth as a predictor
of lean meat percentage. At the same level of overall body
fat, pigs have 68% of the dissectible fat subcutaneous, while
sheep and dairy cattle have 43% and 24%, respectively
(Warriss, 2000). Beef cattle have a somewhat higher pro-
portion of subcutaneous fat than dairy cattle. The greater
proportion of subcutaneous fat in pig carcasses makes
grading using instruments measuring backfat more accu-
rate for pigs than for sheep and cattle. There is however,
a lot of interest (Allen, 2003; Allen & Finnerty, 2001; Berg,
Neary, Forrest, Thomas, & Kauffman, 1997; Cunha et al.,
2003; Du & Sun, 2004; Fisher, 1990; Garrett, Edwards,
Savell, & Tatum, 1992; Hopkins, Anderson, Morgan, &
Hall, 1995; Kempster, Chadwick, Cue, & Granley-Smith,
1986; Kirton, Mercer, & Duganzich, 1992; Stanford, Jones,
& Price, 1998; Swatland, Ananthanarayanan, & Golden-
borg, 1994), both industrially and scientifically, to look
for instrumental methods for ruminant species, i.e. optical
probes and video image analysis (VIA). It can be argued
that the use of the EUROP assessment scheme involving
training of assessors and the use of photographic standards
as reference points result in an evaluation system which is
objective in nature. However, since instrumental methods
usually are calibrated against known references for a given
set of parameters, visual assessment may be less stable due
to differences between operators plus the season-based nat-
ure of lamb slaughtering. This is a major concern, even
when assessors are well trained, supervised and calibrated
against photographic standards.
The main objectives of this study were to:
1. Study and identify the accuracy of the national senior
assessors using the EUROP classification system photo-
graphic standards for lamb.
2. Study the abattoir EUROP classification accuracy in
Norway compared with EU Commission’s assessors
using the EUROP classification system photographic
standards for lamb.
3. Compare national senior vs. abattoir assessors with
respect to EUROP classification, and study the accuracy
of the EUROP classification system for prediction of
lean meat, fat and bone percentage and lean meat in
relation to bone ratio.
The first two objectives will identify the accuracy of
visual assessment before the EUROP system is tested
against carcass composition end-points.
2. Materials and methods
2.1. Trials
Three separate trials were carried out (Table 1).
The assessors that participated in the different trials
were allocated into three levels: (1) Abattoir assessors, (2)
national senior assessors (NMRC) and (3) EU Commission
assessors (Fig. 1). The abattoir assessors were trained and
approved assessors available and working at the selected
plants during the time of the study. National senior asses-
sors were a group of three highly skilled assessors working
at the Norwegian Meat Research Centre. The EU Commis-
sion assessors were a group of four highly skilled interna-
tional assessors from Great Britain, France, Iceland and
Norway. The photographic standards of the EU Commis-
sion were used as the main reference point for lamb carcass
classification in all trials. The first trial was carried out in
autumn of 2000 to check the repeatability of the national
senior assessors. The second trial was carried out in
autumn of 2004 to validate the abattoir classification level
in Norway. The third trial was carried out in autumn of
1999 to check the accuracy of the EUROP classification
system carried out by the national senior assessors for pre-
498 J. Johansen et al. / Meat Science 74 (2006) 497–509