Using chemical intramuscular fat percentage to predict visual marbling scores in Australian beef carcasses.

Meat Standards Australia (MSA) marbling score and AUS-MEAT marbling represent key determinants of carcass value in the Australian beef industry and are well recognised traits in national and international markets. However, with the emergence of objective measurement technologies there are opportunities to grade beef carcasses using objective traits such as chemical intramuscular fat (IMF%) but abrupt changes to MSA and AUS-MEAT grading practices would cause significant disruption to the industry. Therefore, the objective of the study was to develop and validate models that transform chemical IMF% into equivalent MSA marbling scores and AUS-MEAT marbling. The second objective was to compare IMF%-derived and grader-derived MSA marbling scores when used as input values in the MSA model to generate predicted eating quality scores (MQ4). Carcasses (n = 5513) from industry experiments across 7 years (2017-2023) were graded for MSA marbling and AUS-MEAT marbling and sampled for chemical IMF%. Data were utilised to develop IMF%-derived models for MSA marbling score (IMF-MSAMB) and AUS-MEAT marbling (IMF-AUMB). Calibration performance was maintained when cross-validated and independently validated. The IMF-MSAMB model described 91% of the variation in MSA marbling score (Residual Standard Error (RSEV) = 57.9), with a slope of 0.90 and very small bias of -0.54. Similarly, IMF-AUMB described 88% of the variation in grader AUS-MEAT marbling (RSEV = 0.68) with a slope very close to 1 (0.94) and negligible bias (0.06). In addition, predicted MQ4 scores were almost equivalent irrespective of which marbling input value was used, across a suite of cut and cook combinations. Therefore, there is an opportunity for models to assist transition to the use of chemical IMF% in place of visual marbling scores. This would enable grading technologies to be calibrated and validated against chemical IMF%, whilst minimising industry disruption.

Ageing of Australian lamb beyond 14 days does not further improve eating quality.

Limited studies are available assessing the impact of extended ageing on lamb eating quality of a wide range of cuts. From lamb (n = 153) and young mutton (n = 40) carcasses, seven cuts (eye of rack, eye of shoulder, knuckle, loin, outside, rump and topside) were collected and aged based on three ageing times (5, 14 or 21 days). Additionally, residual glycogen was determined from the loin at the corresponding ageing time. Untrained consumers assessed samples for tenderness, juiciness, flavour liking and overall liking. Increasing ageing time from 5 to 14 or 21 days significantly improved cut eating quality; however, ageing beyond 14 days showed no additional benefit. The ageing effect reduced when corrected for pH and temperature measurements, confirming ageing can improve eating quality when pH and temperature variation exists. Loin residual glycogen had no impact on eating quality at each ageing time. Our results confirm the importance of establishing optimum ageing times for cuts to ensure the highest consumer acceptability.

Predicting lamb carcase composition from tissue depth measured at a single point with an ultrawide-band microwave scanner.

This study evaluated the ability of portable ultra-wide band microwave system (MiS) to predict lamb carcase computed tomography (CT) determined composition % of fat, lean muscle and bone. Lamb carcases (n = 343) from 6 slaughter groups were MiS scanned at the C-site (45 mm from spine midline at the 12th /13th rib) prior to CT scanning to determine the proportion of fat, muscle and bone. A machine learning ensemble stacking technique was used to construct the MiS prediction equations. Predictions were pooled and divided in 5 groups stratified for each CT composition trait (fat, lean or bone%) and a k-fold cross validation (k = 5) technique was used to test the predictions. MiS predicted CT fat% with an average RMSEP of 2.385, R2 0.78, bias 0.156 and slope 0.095. The prediction of CT lean% had an average RMSEP of 2.146, R2 0.64, bias 0.172 and slope 0.117. CT bone% prediction had an average RMSEP of 0.990, R2 0.75, bias 0.051 and slope 0.090. Predictions for CT bone% met AUS-MEAT device accreditation error tolerances on the whole range of the dataset. Predictions for CT lean% and fat% met AUS-MEAT error tolerances on a constrained dataset.

Dynamic correction of dual-energy x-ray absorptiometry images improves chain speed prediction of lamb composition in abattoirs.

A prototype, on-line Dual Energy X-ray Absorptiometer (DXA) has shown high precision of the prediction of carcass composition for the purpose of improved sheep meat grading in the Australian lamb supply chain, albeit with small inaccuracies over time. These inaccuracies were present across hours, and more significantly across days, which were unacceptable for any accreditation of this device as an objective carcass measurement tool in Australia. This inaccuracy demanded the creation of a novel image-processing algorithm for the prototype DXA. This DXA was tested for repeatability of predictions of lamb carcass composition over minutes, hours, and days, using two developed image processing algorithms. There was high immediate repeatability for both algorithms when predicting lean muscle % in 40 lamb carcasses, with a maximum CV of 0.65% over five repeated scans. There was a decrease in the CV of the prediction of lean muscle % of 30 lambs scanned three times over a 48-h period from 5.93 to 1.19% when the superior algorithm was used. The inaccuracies of lean muscle % predictions were associated with increases in the unattenuated space pixel values in DXA images. Improvements of the current algorithm are required to demonstrate repeatability over time for the purpose of accreditation within the Australian sheep meat industry, and for possible expansion of this technology into international supply chains.

Improved cut weight predictions from DEXA scans of lamb carcasses enables more accurate allocation of cuts to processing plans.

The value of precise dual energy X-ray absorptiometry (DEXA) cut weight predictions to lamb allocation to cut plans is unknown. Lambs (n = 191) varying in carcase weight (HSCW) and GR (tissue depth over the 12th rib) were DEXA scanned and boned out to weigh retail cuts. Cut weights were predicted using HSCW; HSCW + GR; HSCW + DEXA and HSCW + DEXA image components in GLM models. DEXA improved cut weight predictions in most cuts (P < 0.05). A dataset of 10,000 carcases was then simulated using the associations between HSCW, GR and cut weights, before being truncated to 4500 lambs representing onel day's HSCW distribution. A lamb Carcase Optimisation Tool scenario was developed with 2-3 cut options per carcase section and cut weight thresholds applied to several cuts. Processing costs, market values and actual cut weights were input into the Optimiser to determine carcase allocation to cut options for optimised profits. This scenario was repeated using the predicted cut weights to determine the cut misallocations caused. DEXA-predicted cut weights produced 16.7% and 8.0% less misallocations than HSCW and GR. DEXA produced 20.8% and 14.3% less misallocations than HSCW and GR in shortloins, and 25.5% and 12.9% less in hindquarters. While cut misallocations have little direct impact on total profits, as product is over and under-valued when misallocated, reducing cut misallocations will improve processor compliance when sorting carcases into cut plans- reducing their need to retrim, downgrade and repackage product or the erosion of customer confidence caused by supplying product not meeting market specifications.

Ultra-wideband microwave precisely and accurately predicts sheepmeat hot carcase GR tissue depth.

A portable ultra-wideband microwave system (MiS) coupled with an antipodal slot Vivaldi patch antenna (VPA) was used as an objective measurement technology to predict sheep meat carcase GR tissue depth, tested against AUS-MEAT national accreditation standards. Experiment one developed the MiS GR tissue depth prediction equation using lamb carcasses (n = 832) from two slaughter groups. To create the prediction equations, a two layered machine learning stacking ensemble technique was used. The performance of this equation was tested within the dataset using a k-fold cross validation (k = 5), which demonstrated excellent precision and accuracy with an average R2 of 0.91, RMSEP 2.11, bias 0.39 and slope 0.03. Experiment two tested the prediction equation against the AUS-MEAT GR tissue depth accreditation framework which stipulates predictions from a device must assign the correct fat score, with a tolerance of ±2 mm of the score boundary, and 90% accuracy. For a device to be accredited three measurements captured within the same device, as well as measurements across three different devices, must meet the AUS-MEAT error thresholds. Three MiS devices scanned lamb carcases (n = 312) across three slaughter days. All three MiS devices met the AUS-MEAT accreditation thresholds, accurately predicting GR tissue depth 96.1-98.4% of the time. Between the different devices, the measurement accuracy was 99.4-100%, and within the same device, the measurement accuracy was 99.7-100%. Based on these results MiS achieved AUS-MEAT device accreditation as an objective technology to predict GR tissue depth.

The association of P2 and lean % estimates from commercial measurement systems with computed tomography determined composition within Australian pork abattoirs.

Pork carcasses were obtained from three abattoirs in Australia (n = 345) where technologies enabled collection of post slaughter measures of P2 fat depth (mm) (Hennessy Grading Probe (HGP), AutoFom III, PorkScan Lite) and estimates of carcass lean % (HGP, AutoFom III, PorkScan Plus). Computed tomography (CT) was used to scan carcasses and determine lean and fat %, with the strength of associations with abattoir measurement devices determined. The AutoFom III lean % demonstrated the strongest associations with whole carcass CT lean % (R2 0.63, RMSE 1.73) and fat % (R2 0.68, RMSE 1.80) and with section (fore, loin, belly and hind) CT composition. The association of P2 from AutoFom III was lower in comparison, however remained superior to other commercial devices (PorkScan Lite and HGP). Porkscan Plus lean % demonstrated moderate associations with whole carcass and section CT lean and fat %, with R2 values generally less than half those of the AutoFom III. The HGP demonstrated weakest associations with CT lean and fat % using either lean % or P2 outputs, which is likely related to data being collected from only the P2 measurement site. This is the first experiment to compare the strength of associations between multiple pork abattoir measurement devices and CT lean and fat % in Australia. P2 is the current industry standard for the assessment of lean yield in pork, however demonstrates weaker associations with carcass CT composition than devices capable of capturing multiple measures across the carcass like AutoFom III and PorkScan Plus.

Validation of a handheld near-infrared spectrophotometer for measurement of chemical intramuscular fat in Australian lamb.

The objective of the study was to independently validate a calibrated commercial handheld near infrared (NIR) spectroscopic device and test its repeatability over time using phenotypically diverse populations of Australian lamb. Validation testing in eight separate data sub-groups (n = 1591 carcasses overall) demonstrated that the NIR device had moderate precision (R2 = 0.4-0.64, RMSEP = 0.70-1.22%) but fluctuated in accuracy between experimental site demonstrated by variable slopes (0.50-0.94) and biases (-0.86-0.02). The repeatability experiment (n = 10 carcasses) showed that time to scan post quartering affected NIR measurement from 0 to 24 h (P < 0.001). On average, NIR IMF% was 0.97% lower (P < 0.001) at 24 h (4.01% ± 0.166), compared to 0 h. There was no difference (P > 0.05) between Time 0 and 1 h or Time 0 and 4 h or between replicate scans within each time point. This study demonstrated the SOMA NIR device could predict lamb chemical IMF% with moderate precision and accuracy, however additional work is required to understand how loin preparation, blooming and surface hydration affect NIR measurement.