Hennessy Grading Probe Research Articles

Intramuscular fat % in the loin of Australian pork demonstrates a stronger relationship with computed tomography lean and fat % than abattoir measures of P2 and lean.

Intramuscular fat (IMF) % is an important measure of pork eating quality, with reduced IMF % linked to the selection of pigs with low backfat P2 thickness and more muscular genotypes over several decades. This experiment tested the association of IMF % from the M. longissimus thoracis et lumborum of 345 pigs at three abattoirs (Sites) with computed tomography (CT) determined carcass composition (% lean or fat), accredited abattoir measures of P2 backfat (mm) (Hennessy Grading Probe, AutoFom III and PorkScan Lite) and the lean % output from abattoir devices (Hennessy Grading Probe, AutoFom III and PorkScan Plus). There was a negative relationship between carcass CT lean % with IMF % at all Sites (P < 0.05), with the strength of this relationship varying between Sites. The strongest relationship was demonstrated at Site 1 (R2 0.30, RMSE 0.59), and across a 4 standard deviation range in the carcass lean % at each Site, resulted in a decrease in loin IMF % of 1.56, 0.44 and 0.80. There was a positive relationship of carcass CT fat % with IMF %, with similar, albeit slightly stronger relationship to IMF than with CT lean %. In contrast, only one of the three Sites with an accredited P2 measurement device demonstrated a significant association (P < 0.05) with IMF %. This is the first time the associations of IMF % with carcass CT composition has been assessed alongside that of P2 backfat measures from commercially deployed abattoir devices. Given P2 backfat is the key industry measure on which carcass value is determined in Australia, this experiment demonstrates the limitations that P2 has in measuring and monitoring the associations of carcass lean and fat % with IMF. Ideally, an independent measure of IMF % would allow for independent selection for lean % and IMF %, however there is currently no commercial device that can measure pork IMF %.

Verification of lean meat percentage estimation formulae for pig carcass classification in Croatia

Submitted 2020-07-23 | Accepted 2020-08-16 | Available 2020-12-01 https://doi.org/10.15414/afz.2020.23.mi-fpap.265-268 The study was performed on 136 pig carcasses representing the Croatian pig population with regards to the breed structure. The carcasses were dissected according to the EU reference method and lean meat percentage was estimated using the Hennessy Grading Probe (HGP7) device and the “Two points” (ZP) method. Comparison of lean meat percentage obtained by dissection and two prediction methods showed significant differences in estimating the lean meat percentage of pig carcasses (P <0.05). The distribution of carcasses according to SEUROP system showed a difference in the classification depending on the applied method, indicating a need for adjustment of current formulae for lean meat percentage estimation in Croatia. Keywords: pig, carcass, lean meat percentage, dissection, EU reference method References Causeur, D., Daumas, G., Dhorne, T., Engel, B., Fonti-Furnols, M., Hojsgaard, S. (2003). Statistical handbook for assessing pig classification methods: recommendations from the ‘EUPIGCLASS’ project group. EC working document. EEC Commission. (1994). Commission Regulation (EC) No 3127/94 of 20 December 1994 amending Regulation (EC) No 2967/85 laying Dowd detailed rules for the application of the Community scale for grading pig carcases. Official Journal of the European Communities, 43-44. EEC Commission. (2006). Commission Regulation (EC) No 1197/2006 of 7 August 2006 amending regulation (EEC) No 2967/85 laying down detailed rules for the application of the Community scale for grading pig carcasses. Official Journal of the European Union, 49, L 217, 8/8/2006, 6-7. EEC Commission. (2008). Commission Regulation (EC) No. 1249/2008 (2008, 12.16). of 10 December 2008 laying down detailed rules on the implementation of the community scales for the classification of beef, pig and sheep carcases and the reporting of prices thereof. Official Journal of the European Union, L337. EEC Commission. (2013). Regulation (EU) No. 1308/2013 (2013, 12.20). of the European Parliament and of the Council of 17 December 2013 establishing a common organisation of the markets in agricultural products and repealing Council Regulations (EEC) No 922/72, (EEC) No 234/79, (EC) No 1037/2001 and (EC) No 1234/2007. Official Journal of the European Union, L347. EEC Commission. (2017). Commission Implementng Regulaton (EU) 2017/1184 of 20 April 2017 laying down rules for the applicaton of Regulaton (EU) No 1308/2013 of the European Parliament and of the Council as regards the Union scales for the classifcaton of beef, pig and sheep carcasses and as regards the reportng of market prices of certain categories of carcasses and live animals. Official Journal of the European Union. htps://eur-lex.europa.eu/legal-content/EN/TXT/?uri= CELEX%3A32017R1184. Font-i-Furnols, M., Candek-Potokar, M., Daumas, G., Gispert, M., Judas, M. & Seynaeve, M. (2016). Comparison of national ZP equations for lean meat percentage assessment in SEUROP pig classification. Meat science, 113, 1-8. Gangsei, L. E., Kongsro, J., Olsen, E. V., Roe, M., Alvseike, O. & Saebo, S. (2016). Prediction precision for lean meat percentage in Norwegian pig carcasses using ‘Hennessy grading probe 7’: Evaluation of methods emphasized at exploiting additional information from computed tomography. Acta Agriculturae Scandinavica, Section A — Animal Science, 66(1), 17-24. Kusec, G., Kralik, G., Djurkin, I., Margeta, V., Maltar, Z. & Petricevic, A. (2006). Comparison of different methods for lean percentage evaluation in pig carcasses. Acta Agraria Kaposvariensis, 10(2), 57-62. Kusec, G., Đurkin, I., Petricevic, A., Kralik, G., Maltar, Z., Margeta, V. & Hanžek, D. (2007). Equations for lean share estimation in swine carcasses in Croatia. Poljoprivreda, 13 (1), 70-73. Kusec, G., Đurkin, I., Petricevic, A., Kralik, G., Maltar, Z. & Margeta, V. (2009). Carcass leanness of pigs in Croatia estimated by EU referent method. Italian Journal of Animal Science, 8(3), 249-251. Kusec, G., Đurkin, I., Lukic, B., Radisic, Ž., Petricevic, A. & Maltar, Z. (2011). The Equation for Prediction of Lean Meat Percentage by Hennessy Grading Probe in Croatia. Agriculturae Conspectus Scientificus, 76 (4), 329-331. NN 71/2018 Pravilnik o razvrstavanju i oznacivanju goveđih, svinjskih i ovcjih trupova te oznacivanju mesa koje potjece od goveda starih manje od 12 mjeseci. Sack, E. (1983). Using instruments to grade pork sides. Fleischwirtschaft, 63(3), 372–379. Vester-Christensen, M., Erbou, S.G.H., Hansen, M.F., Olsen, E.V., Christensen, L.B., Hviid, M., Ersboll, B.K. & Larsen, R. (2009). Virtual dissection of pig carcasses. Meat Science, 81, 699–704 Walstra, P. & Merkus, G. S. M. (1996). Procedure for assessment of the lean meat percentage as a consequence of the new EU reference dissection method in pig carcass classification: based on discussion in the EU Management Committee on Pig Meat and based on discussions with dissection experts during a meeting on May 18-19, 1994 at Zeist, NL (No. 96.014). ID-DLO.

Monitoring lean meat percentage predictions from optical grading probes by a commercial cutting pattern

The lean meat percentage (LMP) classification in Norwegian slaughterhouses is obtained by Hennessy Grading Probe 7 (HGP7), an optical tool. Even though the HGP7 method is validated frequently, there is industrial and legislative demand to reconsider the applied LMP equation, typically due to the introduction of new breeds.A deboning pilot plant generates precise yield data using cutting and deboning stratified pork carcasses by a specific commercial cutting pattern (CCP) at an annual rate of approximately 250 slaughter pigs. This paper shows how results obtained by CCP can be used to measure LMP in pork and how these results can be used for monitoring the quality of LMP predicted by HGP7. The effect of gender, maternal- and paternal lines on validity of HGP7 predictions was evaluated. The effect of introducing a new maternal line (TN70) seems to be substantial, whereas the effects of the tested paternal lines are small to negligible.

Prediction precision for lean meat percentage in Norwegian pig carcasses using ‘Hennessy grading probe 7’: Evaluation of methods emphasized at exploiting additional information from computed tomography

ABSTRACTThe present study aims at improving the prediction of lean meat percentage (LMP) for pig carcasses based on on-line measurements from the slaughterhouses using the ‘Hennessy Grading Probe 7’ (HGP7) and auxiliary information such as gender and breed. The prediction performance is evaluated using an empirical Bayes method capable of utilizing information from a surrogate variable, that is, LMP from computed tomography. HGP7 measures thicknesses of fat and meat layers. The HGP7 measurements of subcutaneous fat, sirloin height and interior fat layer should be included as predictor variables together with gender. For efficiency at the slaughter-line gender might be omitted. The empirical Bayes method improved prediction precision only marginally compared with the standard ordinary least-squares method when applied to the full set of data. However, simulations show that the empirical Bayes method enables a considerable reduction of the data sample size without appreciable loss of prediction precision.

Evaluation of the Hennessy Grading Probe for use in lamb carcases

An evaluation of the Hennessy Grading Probe (HGP) was undertaken using 557 lamb carcases for the measurement of subcutaneous fat depth (Fat C) and muscle depth (EMD; m. longissimus thoracis et lumborum (LL)) between the 12th/13th ribs. All carcases were probed on the moving chain at up to 10 carcases per min and data analysis was constrained to 320 carcases which were probed at the target site (over the greatest depth of the LL at the midpoint between the 12th and 13th ribs). Analysis of the data revealed wide variability between HGP and equivalent carcase measures, but probe type (versions GP4 and GP7) had minimal impact on the usefulness of the HGP. The HGP as used in the study does not appear to be a viable option for the Australian sheep processing industry.

The South African pig carcass classification system.

The current regression equations for the prediction of pig carcass lean content in South Africa were calculated in 1992 and had not been updated since, whilst pig carcasses became much leaner and often heavier than the 90 kg originally used. It therefore became necessary to critically evaluate the classification regressions to establish whether the genetic improvement had a substantial effect on lean prediction and whether the same regression may be extrapolated to heavier carcasses. Ultimately, it must be established whether the producer is fairly remunerated. Eighty-one pigs, consisting of five representative genotypes and three sexes (gilts, boars and barrows) at a live mass between 20 kg and 30 kg, were allocated to eight slaughter groups, fed a standard diet ad libitum and slaughtered at live masses ranging from 74 kg to 160 kg. Fat measurements were taken on the carcasses using a Hennessy Grading Probe® and an Intrascope®, whereafter the left sides were divided into wholesale cuts and dissected into lean, fat, skin and bone. Fat measurements were also taken on the live animals prior to slaughter using a Renco Lean Meater®. Stepwise linear regression analysis was used to calculate regression equations between lean percentage and fat measurements as well as eye muscle diameter. Although the original equations still hold, even for heavier carcasses, alarming deviations from frequencies of observed carcass classifications were observed. This implies that either the producers are paid less than what they deserve, or the meat trade and consumer pay more than what they should. However, the price differences between both the two leanest classes and the two fattest classes are too small to have a significant effect on the price of pork.

The prediction of meat yield in lamb carcasses using primal cut weights, carcass measures and the Hennessy Grading Probe

A wide selection of crossbred lambs (n = 268) of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir. Tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured in the chiller using a GR knife (GR) and fatscore (1–5) was assessed on each carcass by abattoir personnel. Each carcass was subsequently broken down to a range of trimmed cuts (subprimals) and the meat yield in kilograms determined as the sum of the weights of these cuts. The best model for the prediction of meat yield was based on the weight of the 4-rib, untrimmed forequarter, fatscore and GR, which had a mean-squared prediction error of 0.96, but a simpler model based on weight of the forequarter and GR only had a marginally higher mean-squared prediction error at 0.97. In both models as either forequarter weight, GR or fatscore increased the meat yield increased. The predominant industry model for predicting meat yield in Australia uses carcass weight and tissue depth at the GR site, but these predictors were less useful than models based on forequarter weight. There was no significant improvement for the prediction of meat yield from the use of muscle or fat depths measured with a Hennessy Grading Probe or directly on the carcass with a ruler when a subset of 97 carcasses was examined. In this case the final model was based on the weight of the forequarter and the weight of the hind leg (R2 = 95%). It is feasible to collect the weight of the forequarter before subprimal cut preparation and if this can be achieved under commercial conditions, a method for predicting meat yield automatically during this procedure could be applied.

Lean meat prediction with HGP, CGM and CSB-Image-Meater, with prediction accuracy evaluated for different proportions of gilts, boars and castrated boars in the pig population

Prediction equations for the percentage lean meat in pig carcasses in The Netherlands were derived for the Hennessy Grading Probe 7, Capteur Gras/Maigre — Sydel and CSB-Image-Meater. Because castrated males are expected to vanish from the Dutch pig population in the near future, accuracy of prediction was evaluated for different scenarios representing a wide range of different proportions for entire males, castrated males and females in the Dutch pig population. The prediction equations for the instruments are in compliance with the EC regulations for prediction accuracy for the different scenarios. So, these equations will remain valid when castrated males are (gradually) removed from the Dutch slaughter population. Results of this study are of interest for researchers from countries or areas contemplating the use of one of the aforementioned instruments. The statistical approach for evaluation of prediction accuracy is of particular interest when changes in proportions of important subpopulations in the target population are foreseen.

Prediction of lean meat content in pork carcasses using the Hennessy Grading Probe and the Fat-O-Meater in Argentina

Rapid evolution of pork production in Argentina requires new calibrations for predicting carcass lean meat percentage with the Fat-O-Meater (FOM) and Hennessy Grading Probe (HGP), first adopted in 1995. The second objective was to unify the lean percentage units with those applied by the European Union. Carcasses of 59 gilts and 56 barrows from different environments and breeds were tested. Carcass weights were from 65 to 117kg, and lean content was from 38% to 62%. Predicting lean content by multiple regression equations, the coefficients of determination R2 were 0.801 and 0.794 for the FOM and HGP equations, and the residual standard deviations (RSD) were 2.40% and 2.45%, respectively. Both instruments had the same precision and were accurate enough to be adopted in national carcass grading classification. Hot carcass weight was not selected as a significant variable. The same prediction equations could be used for gilts and barrows. Quadratic terms did not improve predictions.

Prediction of the percentage lean of pig carcasses with a small or a large number of instrumental carcass measurements – an illustration with HGP and Vision

AbstractIn this paper we report on the results of a recent dissection experiment in The Netherlands where prediction formulae for the percentage lean meat in pig carcasses with the Hennessy Grading Probe (HGP) and a vision system (from now on referred to as Vision) were determined. Predictions with the HGP were based on one fat and one muscle depth measurement only, while predictions with Vision were based on as many as 115 direct and derived measurements. The data from this dissection experiment were used to illustrate the statistical calculations involved in relation to the number of carcass measurements. Prediction with instruments that gather a large number of measurements per carcass is not covered by the present European Community (EC) regulations. Therefore the calculations were conducted according to new regulations for statistical methodology in pig carcass grading that are expected to be adopted by the EC in the near future. The calculations included consideration of 3 subpopulations (females, entire males and castrated males). The Vision data were also used to show that ordinary regression after selection of a subset of carcass measurements severely under estimates the accuracy of prediction: instruments and associated prediction formulae are seemingly much more accurate than they truly are. When standard regression methods are used for instruments that gather a large number of measurements, there is a considerable risk that measurement instruments will be selected for the wrong reasons. Accuracy of approved instruments may not even comply with the EC-regulations, with poor consequences for harmonization within the EC.

The IGF2-intron3-G3072A substitution explains a major imprinted QTL effect on backfat thickness in a Meishan×European white pig intercross

A paternally expressed QTL for muscle growth and backfat thickness (BFT) has previously been identified near the IGF2 locus on the distal tip of pig chromosome 2 (SSC2p) in three experimental F2 populations. Recently, a mutation in a regulatory element of the IGF2 gene was identified as the quantitative trait nucleotide (QTN) underlying the major QTL effect on muscle growth and BFT in crosses between Large White and Wild Boar or Pietrain. This study demonstrates that the IGF2 mutation also controls the paternally expressed QTL for backfat thickness in a cross between Meishan and European Whites. In addition, a comparison of QTL of backfat thickness measured by Hennessy grading probe (HGP) and by ultrasound measurement (USM) was made. In the USM analyses, the IFG2 mutation explains the entire QTL effect on SSC2p, whereas in the HGP analysis the presence of a second minor QTL can not be excluded. Finally, this study shows that this particular IGF2 mutation does not cause the paternally expressed QTL for teat number mapping to the same region of SSC2p as the BFT QTL.

Efeito do genótipo halotano e de diferentes sistemas de produção no desempenho produtivo e na qualidade da carcaça suína

O objetivo deste trabalho foi verificar o efeito dos genótipos halotano homozigoto dominante e heterozigoto e de três sistemas de criação (confinado sobre piso de cimento, confinado sobre cama de maravalha e ao ar livre) no desempenho produtivo e na qualidade da carcaça e da carne suína. Foram utilizados 96 suínos machos castrados selecionados pelo exame de DNA genômico no sangue utilizando-se a técnica de reação em cadeia da polimerase (PCR) para amplificar a região do receptor rianodina. A região amplificada foi clivada pela técnica do polimorfismo do comprimento dos fragmentos de restrição (RFLP). Nas fases de crescimento e terminação, avaliaram-se ganho de peso, consumo de ração e conversão alimentar. Após o abate, as carcaças foram pesadas e tipificadas eletronicamente. Vinte e quatro horas após o abate, foram separados e pesados o pernil e o carrê e medida a área de olho de lombo. A presença do gene halotano não afetou o desempenho dos suínos, mas aumentou a porcentagem de carne magra e diminuiu a quantidade de gordura na carcaça. Suínos criados no sistema intensivo ao ar livre apresentaram menor ganho de peso e pior conversão alimentar. No sistema intensivo de criação confinado sobre cama, os suínos produziram carcaças com menor quantidade de carne magra e maior quantidade de gordura. Não houve efeito significativo da interação genótipo halotano e sistema de criação para as características avaliadas.

Predicting loin-eye area from ultrasound and grading probe measurements of fat and muscle depths in pork carcasses

The mathematical relationships between loin-eye area (m. longissimus thoracis) and linear measurements of fat and muscle depth were studied on digitalized images from 250 hog loins cut between the 3rd- and 4th-last ribs. Depth measurements were collected using (1) an Ultrascan 50 ultrasound system on immobilized, live animals, (2) a Hennessy grading probe on hanging carcasses under normal slaughtering conditions and (3) image analysis on digitalized images of chops separated between the 3rd- and 4th-last ribs. Loin-eye area was accurately predicted by its depth when the measurement was performed on digitalized images (R2 &gt; 0.86; RSD &lt; 1.87 cm2). The accuracy of the relationship between loin-eye area and muscle depth was reduced using ultrasound (R2 = 0.58, RSD = 3.29 cm2) or the probe (R2 = 0.29, RSD = 4.28 cm2) due to measurement errors on muscle depth. Muscle flatness, the perimeter irregularity or its angle in relation to the midline did not improve prediction accuracy. Consequently, muscle depth as measured by the Ultrascan 50 ultrasound system should be used with caution to predict loin-eye area since the measurement is only moderately accurate. Measurements obtained with the Hennessy probe under normal slaughtering conditions are not recommended for predicting loin-eye area in pork carcasses. Key words: Pork, backfat, muscle depth, loin-area, ultrasound, prediction

Prediction of lean meat proportion based on ultrasonic backfat thickness measurements of live pigs

AbstractIn The Netherlands a system has been developed for classification of live pigs based on lean meat proportion. Ultrasonic backfat thickness measurements were taken on 377 live pigs to assess the ability of the developed system for estimation of the lean meat proportion. The measurements were made at the sites mid point (half the distance from the occipital bone to the base of the tail), mid point –2·5 cm and mid point +2·5 cm, 5 cm off the dorsal mid line.On the same day, these pigs were slaughtered and the lean meat proportions of the carcasses were estimated using the Hennessy Grading Probe (HGP). HGP measurements were taken between the third and fourth from last rib, 6 cm off the dorsal mid line on carcasses (3/4 LR). The day after slaughter, 88 left carcass sides were randomly chosen to be dissected according to a simplified European Union (EU) reference method.From the fat thicknesses measured, the one at the site mid point on live pigs was the most accurate predictor for the EU lean meat proportion. The use of multiple site measurements, compared with a single site measurement, significantly reduced the residual standard deviation for the estimation of lean meat proportion.The site mid point on live pigs differed in longitudinal as well as in dorsal-ventral direction from 3/4 LR on carcasses.The results of this study suggest that multiple backfat thickness measurements on live pigs can be used for prediction of lean meat proportion with sufficient precision for practical use. Therefore, the developed system can serve as a classification system for live pigs.

Objectively Predicting Ultimate Quality of Post-Rigor Pork Musculature:II. Practical Classification Method on the Cutting-Line

To investigate the practical assessing method of pork quality, 302 carcasses were selected randomly to represent commercial conditions and were probed at 24 hr postmortem (PM) by Danish Meat Quality Marbling (MQM), Hennessy Grading Probe (HGP), Sensoptic Resistance Probe (SRP) and NWK pH-K21 meter (NpH). Also, filter paper wetness (FPW), lightness (L*), ultimate pH (pHu), subjective color (SC), firmness/wetness (SF) and marbling scores (SM) were recorded. Each carcass was categorized as either PSE (pale, soft and exudative), RSE (Reddish-pink, soft and exudative), RFN (reddish-pink, firm and non-exudative) or DFD (dark, firm and dry). When discriminant analysis was used to sort carcasses into four quality groups the highest proportion of correct classes was 65% by HOP 60% by MQM 52% by NpH and 32% by SRP. When independent variables were combined to sort carcasses into groups the success was only 67%. When RSE and RFN groups were merged so that there were only three groups (PSE, RSE+RFN, DFD) differentiating by color MQM was able to sort the same set of data into the new set of three groups with 80% accuracy. The proportions of correct classifications for HGP, NpH and SRP were 75%, 61% and 35% respectively. There was a decline in predication accuracy when only two groups, exudative (PSE and RES) and non exudative (RFN and DFD) were sorted. However when two groups designated PSE and non-PSE (RSE, RFN, and DFD) were sorted then the proportion of correct classification by MQM, HGP, SRP and NpH were 87%, 81%, 71% and 66% respectively. Combinations of variables only increased the prediction accuracy by 1 or 2% over prediction by MQM alone. When the data was sorted into three marbling groups based on SM this was not well predicted by any of the probe measurements. The best prediction accuracy was 72% by a combination of MQM and NpH.

Objectively Predicting Ultimate Quality of Post-Rigor Pork Musculature: I. Initial Comparison of Techniques

A total of 290 pork loins were selected to include a wide variation of quality to investigate the quality categories into which most pork falls, selection criteria for these categories and methods to objectively assess ultimate pork quality. They were probed at 24 h postmortem (PM) for the following: A) light reflectance by Danish Meat Quality Marbling (MOM), Hennessy Grading Probe (HGP) and Sensoptic Invasive Probe (SIP); B) electrical properties by NWK LT-K21 conductivity (NLT) and Sensoptic Resistance Probe (SRP): and C) pH by NWK pH-K21 (NpH). Also, measurements of % drip loss (PDL) and filter paper wetness (FPW), color brightness (L*), ultimate pH (pHu), lipid content, subjective color (SC), firmness/wetness (SF) and marbling scores (SM) were assessed. Each loin was categorized as either pale, soft and exudative (PSE), reddish-pink, soft and exudative (RSE), reddish-pink, firm and non-exudative (RFN) or dark, firm and dry (DFD). Statistically comparing coefficients of determination (CD), the results indicated that overall, the HGP predicted quality groups slightly better than MQM (CD=71 and 62% respectively), NpH and SRP were less effective (CD= 56 and 44% respectively), and SIP and NLT had the lowest values (CD=36 and 5% respectively). Combining various independent variable did not greatly improve the variation accounted for. When the data was sorted into marbling groups based on lipid content, this was not accurately predicted by any of the probe measurements. The MQM probe remained the best predictor for marbling class and accounted for about 25% of the lipid content variation. This was slightly improved to 33% when pHu was combined with MQM.

Choice of probing site for classification of live pigs using ultrasonic measurements

AbstractBody length and ultrasonic fat thickness measurements were taken on 86 live pigs in order to find an optimal probing site for estimation of lean meat proportion. The next day pigs were slaughtered and measurements with the Hennessy Grading Probe (HGP) were made in order to estimate the lean meat proportion.Fat thickness, 6 cm off the dorsal mid line, increased from a value of 9·5 mm at a site 4 cm cranial to the last rib, progressively through intermediate sites to a value of 12·4 mm, 22 cm cranial to the last rib. Fat thickness measurements at different sites (live pigs) were highly correlated with HGP fat thickness at the site between 3rd and 4th from last rib (3/4 LR) and estimated lean meat proportion (carcasses); correlations ranged from 0.80 to 0.89 and -071 to -0.85 respectively. The most accurate predictor of estimated lean meat proportion from the live pig measurements was the measurement at 18 cm cranial to the last rib. Measurement at the site half the distance between the occipital bone and the base of the tail (midpoint) was the second-best for estimated lean meat proportion.Generally, this midpoint on live pigs was situated around the 3/4 LR on carcasses. However, the range was considerable. Half of the number of animals had a midpoint in the range of -2.5 to 2.5 cm from 3/4 LR. The site midpoint is easily located on the animal and the results of this study suggest that it can be used as an accurate predictor of estimated lean meat proportion. Therefore it can serve as the probing site for classification of live pigs.

A comparison of the optical probe HGP and the ultrasonic devices Renco and Pie Medical for estimation of the lean meat proportion in pig carcasses

AbstractFat (and muscle) thickness measurements were carried out in five slaughterhouses on 174 selected carcasses to compare the Hennessy Grading Probe (HGP) with the Renco Lean-Meater (Renco) and Pie Medical ultrasonic devices for their ability to predict the lean meat proportion of pig carcasses. The day after selection, all left carcass sides were dissected according to the simplified European Union (EU) reference method. In addition 44 of these sides were further dissected according to the full EU reference method.In a further set of slaughterhouses it was found impossible to measure fat thickness with the Renco and Pie Medical ultrasonic device due to extreme singeing of the carcasses. From the five slaughterhouses, between third and fourth from last rib, 6 cm off the dorsal mid line, observations on fat thickness with HGP and Pie Medical were similar but observations with Renco were significantly lower (F &lt; 0·001). Correlations between fat thickness measurements taken with HGP, Renco and Pie Medical all exceeded 0·95.In terms of residual standard deviations of prediction formulae, the HGP performed highly better than Renco or Pie Medical (23·7, 25·1 and 24·9 g/kg respectively). These results, together with practical problems, make the use of ultrasonic measurements under practical conditions at present less feasible than using the HGP.

Real-time ultrasound image analysis for the estimation of carcass yield and pork quality

Average backfat thickness measurements (liveweight of 92.5 kg) were made on 276 pigs using the Krautkramer USK7 ultrasonic machine. Immediately preceding and 1 h after slaughter real-time ultrasonic images were made between the 3rd and 4th last ribs with the Tokyo Keiki LS-1000 (n = 149) and/or CS-3000 (n = 240) machines. Image analysis software was used to measure fat thickness (FT), muscle depth (MD) and area (MA) as well as scoring the number of objects, object area and percentage object area of the loin to be used for predicting meat quality. Carcasses were also graded by the Hennessy Grading Probe (HGP). Prediction equations for lean in the primal cuts based on USK7 and LS-1000 animal fat measurements had R2-values (residual standard deviations, RSD) of 0.62 (27.0) and 0.66 (25.7). Adding MD or MA to LS-1000 FT measurements increased the R2-values to 0.68 and 0.66. Prediction equations using animal fat measurements made by the USK7 and CS-3000 had R2-values (RSD) of 0.66 (26.5) and 0.76 (22.4). Adding MD or MA to CS-3000 FT measurements made no further improvement in the R2-values. Estimation of commercial lean yield from carcass FT and MD measurements made by the HGP and LS-1000 had R2-values (RSD) of 0.58 (1.72) and 0.65 (1.56). Adding MA to LS-1000 measurements made no further improvement in the R2-values. Prediction equations based on carcass FT and MD measurements made by the HGPandCS-3000 had R2-values (RSD) of 0.68 (1.65) and 0.72 (1.54). Adding MA to CS-3000 measurements made no further improvement in the prediction equation. It was concluded that RTU has most value for predicting carcass lean content and further improvements in precision will come from more accurate FT measurements from RTU images made by image analysis software. Correlation of the number of objects, object area and of percent object area of image from RTU images with intramuscular fat or marbling score made on the live pig or carcass were low, and presently do not appear suitable for predicting intramuscular fat. Key words: Carcass composition, meat quality, marbling, intramuscular fat, sex, pigs

Use of electronic probes for classifying lamb carcasses

Three commercially available electronic lamb carcass grading probes (Hennessy Grading Probe, AUS-Meat Sheep Probe, Swedish FTC Lamb Probe) and one developmental probe (Ruakura GR lamb probe) were assessed for accuracy and suitability for use on-line on the lamb dressing chain for objectively classifying lamb carcasses. GR is the total tissue thickness over the 12th rib taken at a distance 11 cm from the mid-line, used to define fatness in lamb carcass grading in New Zealand. Probe measurements were compared with manually measured GR (sometimes used at present) and Toland probe measurements (total tissue depth between the ribs) for accuracy of predicting both GR on the right side of the carcass and also carcass water (indicating muscle) and fat content. Probes were tested on six occasions, each having approximately 50 lambs. All probes combined with carcass weight accounted for over 70% of the variation ( R 2) of GR right, with GR left accounting for 92% of the variation of GR right. Inclusion of a quadratic probe term was not quite as useful as inclusion of carcass weight for predicting GR right. The three commercial electronic probes individually when combined with carcass weight could account for 36–49% of the variation in the percentage of water and fat in the carcass, with the manual GR probe (right) on the chilled carcass accounting for 55% of the variation in water and 58% of the variation in fat percentages. Chilled carcass measurements are expected to give better results than readings on hot carcasses. The use of a quadratic probe coefficient instead of carcass weight with the probe readings gave similar accuracy of prediction of carcass composition. There was little difference between the two sides in the accuracy of prediction. Small differences were found between regressions relating probe readings to GR and composition for the different probing occasions. The electronic probe results from this trial are consistent with overseas results where probes are in use for objectively grading the carcasses of meat animals.