Synthetic phantoms enable calibration between abattoir based dual energy X-ray absorptiometers used for prediction of lamb carcass composition.

Dual energy x-ray absorptiometry (DXA) devices were installed at two Australian abattoirs to predict computed tomography (CT) determined fat % and lean % of lamb carcasses. This study tested three algorithms developed for these devices, termed ß1, ß2 and ß3, and assessed their accuracy and precision in predicting CT composition. Algorithm ß3 included the use of a plastic phantom calibration block scanned by both DXA devices to adjust prediction equations, resulting in superior accuracy to the algorithms without phantom calibration (ß1 and ß2). When compared to the gold-standard CT composition, the bias of the DXA predictions was lowest when using algorithm ß3 at the two sites (-1.17%, -0.49% for fat %, 0.11%, -0.37% for lean %). The difference of DXA composition predictions between sites was lowest when using algorithm ß3, which demonstrated between site differences of 0.59 CT fat %, and 0.46 CT lean%. In contrast, algorithm ß1 and ß2 produced differences of 23.7% and 30.8% for CT fat, and 17.3% and 21.9% for CT lean between the two DXA devices. There was a small difference of 0.78% between the fat predictions of the first DXA image compared to the second DXA image for each carcass. The precision of predictions improved slightly using algorithm ß3. This work demonstrates that the in-line DXA systems can produce comparable results across sites.

Quantification of hardware effects of an on-line Dual Energy X-ray Absorptiometry scanner when predicting lamb carcass composition.

An on-line Dual Energy X-ray Absorptiometry (DXA) scanner's tissue composition prediction precision and accuracy was tested across the entire height of the unit's detector, and the hardware was assessed for robustness by measuring X-ray photon intensity throughout production days. There was good precision when predicting the tissue composition of 5 different lamb fat and lean muscle mixtures across 3 different thicknesses (R2 = 0.93 to 0.98, RMSE = 3.18% to 5.83%), however was less precise at the greatest thickness of 200 mm (R2 = 0.59, RMSE = 11.4%). There was no significant difference in the prediction of tissue composition at 8 of the 9 detector positions, however the position at the perpendicular of the X-ray photon beam was significantly different, with a fat prediction error of -4%, although no lamb carcass is detected in this position during normal production. A significant upwards drift in X-ray photon intensity was found over the course of production, especially immediately after restarting the DXA scanner following a period of inactivity. This upwards drift may affect tissue composition predictions over the span of a production day if uncorrected.

Dual energy X-ray absorptiometry predicts lamb carcass composition at abattoir chain speed with high repeatability across varying processing factors.

An on-line Dual Energy X-ray Absorptiometry (DXA) scanner was used in an Australian abattoir to predict computed tomography (CT) determined composition % of fat, lean muscle and bone in lamb carcasses at chain speed. This study assessed the effect of spray-chilling on these estimates, as well as their repeatability over a 10-min period, and over a 72 h period. There was no prediction bias between the 15 spray-chilled and 15 non-spray-chilled carcasses. When repeat DEXA scans were undertaken across a 10-min period, there was a high level of repeatability for the prediction of CT Fat %. When repeat scans were conducted at 6 time points across a 72 h period the precision of the DXA prediction of CT Fat % of 30 carcasses remained high (R2 = 0.94, RMSEP = 1.20%), although small biases existed between time points (P < 0.01). These biases were minimised when the DXA scanner had been operational prior to experimentation, suggesting a 'warm-up' effect.