IMEQ Project Summary
The objectives for the IMEQ Project are shown below:
Overall Objective: The aim of the project is to develop a novel, integrated system for
objective measurement of eating quality so as to improve and increase the consistency of
eating quality. The proposed system will combine existing technologies, most of which are in
use individually around the global meat industry.
Specific Objectives:
1. Development of an automatic method for estimating pH and temperature
2. Development of an automatic method for improving estimation of carcase fat
3. Integrate objectives 1-2 with currently available Video Image Analysis (VIA) system
for estimation of EUROP grid and meat yield
4. Develop, refine and validate the use of Near Infrared spectroscopy and
Hyperspectral Imaging to predict sensory and nutritional meat quality at boning
5. Explore and develop the use of Near Infrared spectroscopy and Hyperspectral
Imaging to predict colour of meat at boning
6. Combine all data into a single database
Conclusions, outcomes and impact of the IMEQ project
Research was undertaken to develop and test both automated and semi-automated
technologies to predict beef carcass and meat eating quality that can be deployed in a
commercial abattoir environment. After installing these technologies in a commercial setting,
their suitability was assessed for on-line implementation, both in terms of functionality and
prediction accuracy. The main technological developments and steps towards
implementation are summarised on the following pages and grouped as they relate to the
original objectives.
Objective 1
Development of an automatic method for estimating pH and temperature
1. An autonomous robotic system was developed that can position an end-of-arm
system consistently and reliably on carcasses of variable size and shape. The robotic
system was developed to guide sensor systems to carcasses at line-speed within a
very fast cycle time of less than one minute.
2. One of the end-of-arm sensors, mounted on the robotic system, was a pH probe
including an associated (but separate) temperature probe, allowing for automatic
measurement of pH and temperature at approximately 45 minutes post-mortem on
the slaughter line at line-speed.
3. The robotic measurement of pH early post-mortem was demonstrated for the first
time. The robotic system can operate without human guidance, place probes
appropriately in a commercial abattoir environment and cope with variation in
carcass size and presentation. This system could be extended to a range of other
tasks on-line, such as tissue sampling for authenticity, or automated dissection.
4. The robotic system works reliably. However, further developments are necessary to
improve the reliability of pH probe technology, in terms of measurement precision,
calibration and cleaning procedures, for this measurement system to work reliably
when integrated with the robot.
5. If pH probe technology cannot be improved for use on a robotic system, roboticenabled tissue sampling could be implemented on-line to extract tissue samples from
the carcass for subsequent pH measurement.
6. A semi-autonomous pH processing system was developed that could gather and log
data automatically. This has the potential to extend semi-automatic monitoring of
pH at varying time-points post-mortem. This system received highly positive
feedback from industry (e.g. from abattoir QA personnel), and is currently being
developed to a product prototype by commercial partner Peacock Ltd, with the intent
of marketing this to the industry in 2014.
Objective 2
Development of an automatic method for improving estimation of carcase fat
7. The second end-of-arm sensor installed on the robotic system was a novel
ultrasound sensor with software that automatically predicts fat depth when placed in
contact with the carcass. This was incorporated to obtain a higher accuracy of
prediction of carcass fatness than the current EUROP grading system.
8. The robotic ultrasound system, after continuous design refinements throughout the
project, was accurate in estimation of carcass fat depth when data were gathered
successfully.
Link between objective 1 and objectives 4 & 5
9. It is envisaged that the robotic system developed in the IMEQ project could be
utilised further for automatic on-line Near Infrared spectroscopy (NIR) scans to
predict meat eating quality.
Objective 3
Integrate objectives 1-2 with currently available Video Image Analysis (VIA)
system for estimation of EUROP grid and meat yield
10. Prediction of carcass composition by video image analysis (VIA) has been compared
for the first time with CT-determined muscle and fat weights of the entire half
carcass in beef. The results show that the VIA system accurately predicts muscle
weight (r=0.9), but less accurately predicts fat weight (r =0.78).
11. The improved estimation of carcass composition using VIA is more accurate than
that of manual grading based on the EUROP carcass classification scheme (38% and
28% increase in accuracy using the VIA system for prediction of CT-determined
muscle weight and CT-determined fat weight, respectively, compared to using
EUROP carcass classification).
12. Correlations between manual EUROP grading and VIA grading, alongside the
correlations of VIA with CT-predicted fat and muscle weights, suggest that VIA could
replace manual grading. This would result in greater accuracy and precision in
predicting leanness and fatness of the carcass, as well as prediction of individual
primal cut weights.
13. The best predictability (r=0.97) of the VIA system was obtained for saleable meat
yield, most likely because the prediction equation of the VIA system is calibrated for
this trait.
14. To develop a next generation VIA system, a high resolution camera was tested. This
was shown to work robustly under abattoir conditions for the first time within the
IMEQ project. Due to its success, a similar camera is now being implemented in
currently-marketed VIA systems.
15. The potential of the high resolution camera to contribute towards an improved, next
generation VIA was identified. Colour parameters from this camera resulted in
improved correlations with fat class at grading compared to colour parameters from
the currently-approved VIA camera system. This indicates that carcass fatness could
be predicted more accurately using a high resolution camera system.
16. The robotic ultrasonic fat depth prediction was integrated with the VIA system to
predict EUROP grades and meat yield. To the best of our knowledge, this concept is
unique worldwide.
17. To identify the potential of this integration, ultrasonic fat depth measurements of the
carcass from a semi-automatic technology (BCF’s “Easi-scan” system) were combined
with total fat weights predicted by the VIA system, resulting in an improvement in
the prediction of fat class by 23% compared to the prediction using VIA only. This
indicates that the successful measurement of carcass fat depth, using the robotic
system for the application of the ultrasound sensors, will improve fat prediction of
the carcass above that achieved by VIA alone.
18. The comparison of existing carcass grading systems with newly developed systems in
the IMEQ project has given substantial information on the relative inaccuracy of the
present system and the opportunities to improve those systems with new and
improved technologies.
19. Because meat yield may be used within abattoir payment systems in the future,
improved accuracy of the VIA system to determine carcass composition is highly
relevant for industry. The development of new technologies (e.g. BCF’s Easi-scan)
and the testing of new systems (e.g. new camera systems) have successfully shown
that current technologies for measuring carcass composition can be enhanced.
20. The IMEQ project has provided detailed scientific insight into the challenges,
opportunities, and latest technological developments to predict carcass composition
more accurately under commercial conditions, using a range of technical approaches,
which can be implemented or further developed by the industry.
Objective 4
Develop, refine and validate the use of Near Infrared spectroscopy and
Hyperspectral Imaging to predict sensory and nutritional meat quality at
boning
21. To predict sensory and nutritional properties of beef, various advanced spectroscopic
techniques were employed, such as visible and near infra-red spectroscopy (VisNIR),
Raman spectroscopy (Raman), and hyperspectral imaging (HSI). The Raman system
used in this project exhibited very high spectral noise and was too sensitive for
practical application within an abattoir. In addition, the measurement time for the
portable Raman system used was considered too slow for useful commercial
implementation on-line in the abattoir and therefore this technology was not taken
forward.
22. The VisNIR technique is robust, showing an ability to estimate a range of sensory
panel attributes (R2 up to 0.22, 0.14, 0.46 for tenderness, juiciness and flavour of
the rump muscle, respectively), physical tenderness (R2 up to 0.52 for slice shear
force at 14 days post-mortem), pH at 48 hours post-mortem (R2 = 0.57) and also
the amount of a range of fatty acids relating to nutritional quality (R2 up to 0.71).
23. In particular, VisNIR has been shown to very accurately predict extremes of meat
quality, defined by different criteria. For example, when the target is to identify the
bottom 10% of the population for sensory-related attributes, suTch as tenderness,
juiciness and flavour, VisNIR resulted in correct predictions of 93%-100% of
samples. This could offer a new on-line method for sorting carcasses based on meat
eating quality criteria.
24. For some meat eating quality traits, such as tenderness, the integration of measures
of VisNIR (at 2 days post-mortem), Slice Shear Force (at 3 days post-mortem), pH
(45 minutes post-mortem) and EUROP fat class, improved the prediction of
tenderness in comparison with using VisNIR data alone.
25. A user-friendly application to predict meat quality traits using NIR spectral data was
developed.
26. Importantly, predictions of most meat eating quality traits by spectroscopic
technologies were more accurate when measured on rump muscle than on loin
muscle. At Scotbeef, an automated system using the developed robotic technique
would be feasible to obtain spectroscopic measurements from the rump muscle at 48
hours post mortem directly on the hind quarter (still on the hook) prior to boning at
the breakdown rail area. The spectral measurements on the loin muscle is expected
to be more difficult using an automated system and a semi-automatic system would
then be more appropriate.
27. HSI was shown to predict meat eating quality characteristics at least as accurately as
VisNIR (e.g. for tenderness of 10th rib loin muscle, R2 = 0.13 vs. 0.07 using HSI and
VisNIR respectively) and has substantial future prospects, considering the extensive
amount of image and spectral information the HSI system provides. Therefore, all
outcomes described for VisNIR could be achieved to a higher accuracy with HSI, if
suitable methods for on-line data collection and automated image and data analysis
can be developed.
Objective 5
Explore and develop the use of Near Infrared spectroscopy and Hyperspectral
Imaging to predict colour of meat at boning
28. Vis-NIR spectroscopy was able to predict colour parameters accurately, even as early
as 2 minutes after cutting of the meat. Colour parameters are usually measured at
45 minutes after cutting, to allow full blooming to occur (development of the red
colour of beef). However, only a short time of blooming would be feasible to allow
colour measurement to be implemented on-line at the point of cutting in a
commercial abattoir environment.
29. The HSI-NIR system predicted colour parameters slightly better than Vis-NIR on the
10th rib loin muscle. Further combination of visible (VIS) and NIR spectra of the HSI
technology is expected to improve the estimation of colour. In particular, the
lightness parameter L* is expected to improve when both spectra are combined
based on the results from the separate analyses of data from each spectra.
30. Vis-NIR and HSI-NIR were most accurate in predicting samples with extreme colour
values for L* (lightness), a* (redness) and b (yellowness). This demonstrates that
Vis-NIR and HSI-NIR can be used more accurately to discriminate between extreme
colour classes. Therefore, Vis-NIR and HSI-NIR have the potential to be used as a
replacement for the Minolta colorimeter to predict extreme colour classes, with the
advantage of simultaneously obtaining data to allow prediction of other sensory
characteristics, slice shear force information, fatty acid profiles, etc. from the same
measurement on the same sample.
Objective 6
Combine all data into a single database
31. A single Microsoft SQL server database has been generated to combine and integrate
the data.
32. Additionally, a secure server was installed on-site at the industry partner’s premises
(Scotbeef). Appropriate network links (wired and wireless) were installed and tested
to allow data to be uploaded from all locations within the abattoir deemed necessary.
Overall outcome
33. The IMEQ project has achieved substantial progress towards an integrated online
system for assessment of carcass and meat eating quality under abattoir conditions,
with considerable new information on the suitability (implementation, automation,
functionality, and accuracy) of the tested technologies. There is great potential for
commercialisation of the techniques developed and assessed within this project,
which is already underway for some.