Competition with other protein sources in the domestic market
and competition with other countries in the global market provides
adequate incentive for improving the quality of pork produced
in the United States. The challenge of that incentive has been
taken up by the major seedstock companies in the US and worldwide.
Over the last decade, pork processors have experienced increased
pressure to provide quality pork in many specialized markets.
Since there is a differential market based upon quality, the
need for quality assessment is twofold. In the short term, there
is increased need for better sorting techniques to satisfy the
growing segment of the market that demands higher quality product.
Long term, there is a need for more consistent feedback of quality
information to all segments of the pork production chain to bring
the overall level of quality up and increase market share for
pork over other protein sources.
Any factor that may influence a user's perception of the desirability
of a product could be included as a quality factor; therefore,
we must narrow our scope to those factors upon which we may want
to place first priority. In the contemporary market, food safety
is the major overriding factor for the entire food industry; however,
that is not the focus of this presentation. The focus of this
presentation is upon the factors that currently limit pork consumption
because they affect utilization at the processor level or are
a direct limiting factor on consumer demand for your product.
Water holding capacity, which affects drip loss, limits the utilization
of fresh pork in further processed products. Color of fresh meat
in retail display cases influences the consumer's decision to
purchase. In particular, consistency of color and texture may
be of great importance. Tenderness, juiciness and flavor of the
prepared product in turn influence repeat purchases of both fresh
and processed pork products.
Technology for Assessment of Pork
Quality at Line Speed
Current line speeds in plants that harvest and fabricate pork
present a major challenge to technology that has the ability to
detect the quality factors that are important to the industry.
Typical line speed in modern plants exceeds 1,000 units per hour
when operating at peak efficiency. The point in the process where
the measurement is made also limits the technologies that can
be used. For purposes of our discussion I would like to explore
both Pre-rigor, early post-mortem assessment, and Post-rigor,
assessment after carcass chilling. Pre-rigor measurements would
be made during the first 3 hours after the harvest process begins.
From a practical standpoint, these measurements would typically
be made before the carcass enters the chillers which varies from
30 to 45 minutes after the exsanguination. Post-rigor measurements
would be made in the range of 4 to 48 hours after exsanguination.
Early postmortem prediction of meat quality would be desirable
in situations where it is important to feedback quality information
to producers and in situations where carcasses could be sorted
into quality groups when going into the chill coolers.
Measurement of electrical conductivity on muscle membrane at a
low frequency may offer opportunities for detection of early post-mortem
changes. German scientists found that the dielectric loss factor
measured at 15 kHz is ten times higher in PSE than normal muscle.
However, results have been variable, and studies conducted in
Canada found that neither dielectric loss factor nor electrical
conductivity could reliably distinguish among the various Canadian
quality standards used to identify PSE/DFD in pork. Further development
appears to be required to improve its accuracy and commercial
Early results from a tetra polar electrode system being developed
at Purdue University indicate that it may be feasible to predict
the water binding capacity of pork within 15 to 45 minutes post-mortem.
Further testing of a new design of this instrument in a Danish
pork processing plant confirms the potential usefulness of this
technology either alone or in combination with other sensors.
This would allow at least one quality assessment at the time
carcasses are normally weighed and evaluated for leanness. If
this system becomes a reality, this information could easily be
included on kill sheets given to producers.
Measurement of muscle pH at 45 minutes post-mortem has proven
to be one of the more reliable methods currently available for
early detection of PSE muscle. This involves either the insertion
of a pH electrode into the muscle or the excision of a small muscle
sample and preparation of a slurry. Although newer more robust
pH measurement devices are now available, consistent, accurate,
on-line measurement of pH is difficult in the early stages of
the conversion of muscle to meat. In addition, the rate and complexity
of the metabolic changes at this time suggest that a measurement
of a single parameter at this time may be inadequate for prediction
of meat quality.
Fiber optic probes
Recent studies in the laboratories of the Danish Meat Research
Institute suggest that, early post-mortem prediction of water
holding capacity in the longissimus muscle may be possible by
measuring changes in the Near Infra-red (NIR) spectra over time.
This may be superior to a single measure to predict color at
30-45 minutes postmortem. These investigations were conducted
in an off-line area at an industrial plant, and confirm the potential
for on-line adaptation of this technology.
Fiber optic probes (FOP) have also been developed to assess pork
quality through measurement of muscle reflection or light scatter
via fiber optics. Since color changes often do not occur within
the first hour post-mortem, this technique seems to work better
after carcasses have been chilled for a few hours. This time
delay can cause problems in modern-day harvest facilities for
two reasons. Carcass identity may be lost for feedback of information
and, second, in most plants it would be difficult to sort carcasses
for quality at that time. Further work over a wider range of
wavelengths may improve the potential for this technology.
A new application of ultrasound technology has been developed
to test the hardness and softness of biological tissues. Elastography
compares an initial traditional ultrasound scan with a second
scan obtained after the tissues are slightly compressed. Scientists
at Texas A&M University reported that quantitative elastography
was not successful in detecting differences in pork quality groups;
however, qualitative elastography was able to differentiate the
elasticity differences among pork quality classes. They concluded
that elastography has the potential to be a non-intrusive method
for determining differences among pork quality groups.
Skatole or androstenone content in fat of boars could be used
to separate boars that may have odor and flavor problems when
used as fresh meat (>.24 PPM skatole:>.5 mg/g androstenone).
Some Danish pork processing plants utilize an automated chemical
analysis system to measure the skatole level in boars slaughtered
in order to reduce boar odor incidence in fresh pork.
Post rigor assessment is applicable when sorting of individual
carcass parts is of primary importance. It is much easier to
assess quality after all post-mortem changes have been completed.
In general, this allows for measurement of a specific trait,
rather than predicting what it will be after all of the changes
that occur during the conversion of muscle to meat are completed.
Traditional indicators of fresh meat quality include physiological
maturity, marbling, color of lean, texture of lean, firmness of
lean, wateriness of cut lean surfaces and firmness of fat. These
indicators are typically evaluated subjectively, making them time
consuming and subject to human error. Meat color, texture, wetness
and marbling standards exist in photographic and/or color chip
form for graders and evaluators to use in the subjective evaluation
of quality. In 1991, the Japanese developed sets of standard
color models for lean and fat based upon objective color coordinates
measured on carcasses representing the full range of colors encountered
in Japanese pork. Pigments were added to silicone resin until
proper values were achieved to match desired grade standards.
This approach still involves visual assessment; however, scientifically
developed color standard models provide objective standards for
direct comparison and matching to color scores. Purdue University
and the University of Illinois are currently involved in a collaborative
project sponsored by the National Pork Producers Council to develop
a set of Color Reference Standards for U.S. Pork. These standards
are designed to be used with both visual and objective technologies.
Pork processors that centrally package and brand fresh retail
pork cuts for domestic or export markets usually sort these products
for quality. Most utilize visual inspection for this sorting.
Subjective evaluation is effective in eliminating major quality
defects and narrowing the range of quality coming from any given
processor. However, it is difficult to overcome the inconsistencies
associated with any visual assessment, without an intensive training
program. Visual assessment can only be applied after the carcasses
have been chilled and are being fabricated. Once carcasses are
on the fabrication line it becomes very difficult for most operations
to maintain identity that would allow information to be passed
back to producers.
Several color meters are available for the objective evaluation
of color. However, color alone does not fully describe the quality
defects inherent in pork. Color meters are only effective in
assessing color after the carcasses have had time to chill and
develop their final color. They are effective in sorting meat
by color, and in assessing quality in test lots where identity
is maintained. Another limitation associated with most color
meters is that the area of meat assessed is usually limited in
size. Sometimes the result obtained is not fully representative
of the cut or carcass.
Advancements in RGB (Red, Green, Blue) color video technology
combined with newly developed computer neural network software
offer great potential for objective visual assessment of meat
quality. The application of color machine vision to meat quality
assessment is being developed in several research laboratories
around the world. Machine vision has the potential for assessment
and classification of both color and marbling. There is also
a possibility that textural properties and low water binding that
results in wetness on a cut meat surface can be detected. If
this technology is developed to its full potential it may be possible
to sort centrally packaged retail pork cuts into uniform quality/color
groups before shipping to retail.
On-line measurement of pH after most of the rigor associated chemical
and physical changes are completed currently offers the best method
of identifying carcasses that are PSE. Industrial experience
shows that when pH is measured as carcasses are moved from the
chill cooler to the fabrication line, Pale, Soft, Exudative pork
can be detected with a high degree of accuracy. When ultimate
pH (pHu) is 5.5 or lower, nearly 99% of the pork will be PSE.
When pHu is 5.65 or higher there will be almost no Pale color,
but the level of drip loss may be variable. Ultimate pH is less
accurate in detecting Red, Soft, Exudative (RSE) pork.
Utilization of pHu offers the possibility of beginning to minimize the variability in pork by identifying the very dark and very light muscles.
Meat quality meter (MQM)
The Danish Meat Research Institute developed a single wavelength
probe in the Near Infrared range. That detects color differences
in pork carcasses after rigor changes are completed. Pale pork
can be identified at line speeds using this instrument.
Optical fat/lean probes
Some optical fat-lean probes have been designed to detect color
and marbling. These probes suffer from the same problems as the
fiber optic probe when applied on the harvest line when fat depth
and lean depth is typically measured to predict composition, in
that major color changes may occur after the carcasses have been
placed in the chill room. Scientists from the Netherlands reviewed
various studies and stated that the relationship between harvest
line measurements with either the optical fat-lean probes or fiber
optic probe and ultimate meat quality attributes appeared to be
of a low to moderate nature. The utilization of these probes
after carcass chilling may offer more promise.
Prediction or measurement of tenderness presents a major challenge
because so many factors influence tenderness including the final
preparation. Several devices have been designed for on-line measurement.
One device measured the force required for a bank of needles
to penetrate to a fixed depth in the cut surface of the meat.
A more recent development is a portable device that measures
the mechanical force required to shear meat fibers. Neither of
these devices has proven effective enough for commercial application;
however, further development of the concept may be worthy of merit.
Challenges to On-line Measurement
I paraphrase the way one industry researcher summed up the challenge
"Post-rigor assessment is more accurate but provides
fewer opportunities to adjust utilization - Pre-rigor assessment
is less precise, but provides greater opportunity for sorting
carcasses for specialized markets and feedback to production segment."
The industry must continue to search for sensors that will detect
the quality traits that most affect the utilization of pork products.
These detectors must be precise, fast and robust enough to stand
up to industrial conditions.
Summary and Conclusions
The entire pork chain is working together as never before to improve
the quality of the products they place before their consumers.
The biggest roadblock is the availability of accurate information
upon which to base decisions on breeding and selection, as well
as production and management. Technology is sorely needed to
identify critical control points for the origin of meat quality
problems in the industry. Once these are identified, the opportunity
for rapid improvement in the quality and uniformity of pork is
Chizzolini, R., E. Novelli, A. Badiana, P. Rosa and
G. Delbono. 1993. Objective measurements of pork quality: Evaluation
of various techniques. Meat Sci. 34:49.
Bendall, J.R., and J. Wismer-Pedersen. 1962. Some
properties of fibular proteins of normal and watery pork muscle.
J. Fd. Sci. 27:144-159.
Bendall, J.R., and H.J. Swatland. 1988. A review
of the relationships of pH with physical aspects of pork quality.
Meat Sci. 24:85-126.
Brethour, J.R. 1994. Estimating marbling score in
live cattle from images using pattern recognition and neural network
procedures. J. Anim. Sci. 72:1425.
Fortin, A., and D.P. Raymond. 1987. The use of electronic
grading probes for the objective assessment of PSE and DFD in
pork carcasses. Meat Sci. 21:159-173.
Garrido, M.D., J. Pedauye, S. Banon and J. Laencina.
1994. Objective assessment of pork quality. Meat Sci. 37:411-420.
George, M.H., J.D. Tatum, H.G. Dolezal, J.B. Morgan,
J.W. Wise, C.R. Calkins, T. Gordon, J.O. Reagan and G.C. Smith.
1997. Comparison of USDA Quality Grade with Tendertec for the
assessment of beef palatability. J. Anim. Sci. 75:1538-1546.
Hedrick, H.B., E.D. Aberle, J.C. Forrest, M.D. Judge
and R.A. Merkel. 1994. Principles of Meat Science. Third edition,
Kendall/Hunt Publishing Company, Dubuque, Iowa, USA.
Irie, M., and H.J. Swatland. 1993. Prediction of
fluid losses from pork using subjective and objective paleness.
Meat Sci. 33:277-292.
Jones, S.D.M., A. Fortin and M. Atin. 1984. A comparison
of methods to detect pork quality 24 hours post mortem from measurements
made within one hour of slaughter. Can. Inst. Sci. Technol. J.
Kauffman, R.G., G. Eikelenboom, P.G. van der Wal,
B. Engel and M. Zaar. 1986. A comparison of methods to estimate
water-holding capacity in post-rigor porcine muscle. Meat Sci.
Kauffman, R.G., R.G. Cassens, A. Scherer and D.L.
Meeker. 1992. Variations in pork quality. National Pork Producers
Council, Des Moines, IA.
Kauffman, R.G., W. Sybesma, F.J.M. Smulders, G. Eikelenboom,
B. Engel, R.L.J.M. van Laack, A.H. Hoving-Bolink, P. Sterrenberg,
E.V. Nordheim, P. Walstra and P.G. van der Wal. 1993. The effectiveness
of examining early post mortem musculature to predict ultimate
pork quality. Meat Sci. 34:283-300.
Kauffman, R.G. 1996. Development of procedures to
commercially classify variation in pork quality and to explain
the biological phenomena responsible for these variations. University
Kim, B.C., R.G. Kauffman, J.M. Norman and S.T. Joo.
1995. Measuring water-holding capacity in pork musculature with
a tensiometer. Meat Sci. 39:363-374.
Lundstrom, K., and G. Malmfors. 1985. Variation in
light scattering and water-holding capacity along the porcine
longissimus dorsi muscle. Meat Sci. 15:203-214.
MacDougall, D.B., and S.J. Jones. 1980. Use of a
fibre optic probe for segregating pale, soft, exudative and dark,
firm, dry carcasses. J. Sci. Food Agric. 31:1371.
Miller, R.K., M.S. Rubio, A.D. Whittaker, J. Ophir,
G. Emesih, Y. Huang, I. Cespedes and D.S. Hale. 1995. Elastography
to predict pork quality. Proceedings of the 41st Annual International
Congress of Meat Science and Technology. San Antonio, TX.
National Pork Producers Council. 1991. Procedures
to evaluate market hogs, 3rd Edition. National Pork Producers
Council, Des Moines, IA.
Nielsen, T. 1995. Vision image analysis for on-line
colour measurements on pork loins. Proceedings of the 41st Annual
International Congress of Meat Science and Technology. San Antonio,
TX. pp. 185-186.
Offer, G. 1991. Modelling of the formation of pale,
soft and exudative meat: effects of chilling regime and rate and
extent of glycolysis. Meat Sci. 30(2):157-183.
Oliver, M.A., M. Gispert, J. Tibau and A. Diestre.
1991. The measurement of light scattering and electrical conductivity
for the prediction of PSE pig meat at various times post mortem.
Meat Sci. 29:141-151.
Rasmussen, A.J., and M. Andersson. 1996. New method
for determination of drip loss in pork muscles. Poster Proceedings
of the 42nd International Congress of Meat Science and Technology,
Lillehammer, Norway. p 286.
Roseiro, L.C., C. Santos and R.S. Melo. 1994. Muscle
pH60, colour (L,a, b) and water-holding capacity and
the influence of post-mortem meat temperature. Meat Sci. 38:353-359.
Sheiss, E.B. 1998. Evaluation of tetrapolar impedance
measurement for the detection of quality abnormalities in pork.
M.S. Thesis, Purdue University, West Lafayette, Indiana 47907
Swatland, H.J. 1985. Optical and electronic methods
of measuring pH and other predictors of meat quality in pork carcasses.
J. Anim. Sci. 61:887-891.
Swatland, H.J. 1987. Remote monitoring of postmortem
metabolism in pork carcasses. In: P.V. Tarrant, G. Eikelenboom,
and B. Monin. (Eds.) Evaluation and Control of Meat Quality of
Pigs. pp. 143-163. Martinus Nijhoff Publishers, Dordrecht, The
Swatland, H.J. 1997. Observations on rheological,
electrical, and optical changes during rigor development in pork
and beef. J. Anim. Sci. 75:975-985.
Tan, F-J. 1996. Assessment of fresh meat quality with color machine vision. M.S. Thesis, Purdue University, West Lafayette, Indiana 47907, USA.
Trout, G.R. 1988. Techniques for measuring water
binding capacity in muscle foods. Meat Sci. 23:235-252.
van Laack, R.L.J.M., R.G. Kauffman, W. Sybesma, F.J.M.
Smulders, G. Eikelenboom and J.C. Pinheiro. 1994. Is colour brightness
(I-value) a reliable indicator of water-holding capacity in porcine
muscle? Meat Sci. 38:193-201.
Warris, P.D., and S.N. Brown. 1987. The relationship
between initial pH, reflectance and exudation in pig muscle. Meat
Warris, P.D., S.N. Brown, C. Lopez-Bote, E.A. Bevis
and S.J.M. Adams. 1989. Evaluation of lean meat quality in pigs
using two electronic probes. Meat Sci. 25:282-291.
Whitman, T.A., J.C. Forrest, M.T. Morgan and M.R.
Okos. 1996. Electrical measurement for detecting early postmortem
changes in porcine muscle. J. Anim. Sci. 74:80-90.
Wismer-Pedersen, J., and E.J. Briskey. 1961b. Relationship
of post-mortem acidity and temperature. Food Tech. 15:232-236.