Evaluation of an Image Analysis Approach to Predicting Primal Cuts and Lean in Light Lamb Carcasses

Abstract

Carcass dissection is a more accurate method for determining the composition of a carcass; however, it is expensive and time-consuming. Techniques like VIA are of great interest once they are objective and able to determine carcass contents accurately. This study aims to evaluate the accuracy of a flexible VIA system to determine the weight and yield of the commercial value of carcass cuts of light lamb. Photos from 55 lamb carcasses are taken and a total of 21 VIA measurements are assessed. The half-carcasses are divided into six primal cuts, grouped according to their commercial value: high-value (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). K-folds cross-validation stepwise regression analyses are used to estimate the weights of the cuts in the groups and their lean meat yields. The models used to estimate the weight of AllC, HVC, MVC and LVC show similar results and a k-fold coefficient of determination (k-fold-R2) of 0.99 is achieved for the HVC and AllC predictions. The precision of the weight and yield of the three prediction models varies from low to moderate, with k-fold-R2 results between 0.186 and 0.530, p < 0.001. The prediction models used to estimate the total lean meat weight are similar and low, with k-fold-R2 results between 0.080 and 0.461, p < 0.001. The results confirm the ability of the VIA system to estimate the weights of parts and their yields. However, more research is needed on estimating lean meat yield.

Assessing the Feasibility of Using Kinect 3D Images to Predict Light Lamb Carcasses Composition from Leg Volume

“…Additionally, the correlation values are generally less significant with the fat trait, whereas the correlation values are comparable for the cut and carcass weight and muscle. The value of the measurements obtained in the leg, in general, have been observed by other authors who have used linear and area measurements to predict cuts and lean meat variation of carcasses [ 23 ]. In this work, which studied light carcass, although there is no simple correlation between the measurements obtained in the carcass and the cuts, it is possible to observe that area and perimeter of the leg are included on the HVC, MVC, and LVC cut weight prediction models, whereas for models’ prediction of lean meat weight always include leg area measurements.…”

“…Then, the carcasses were split along the spine, and the left side was used to perform the remaining measurements. For this, the procedure using image analysis proposed by Batista et al [ 23 ] was used. Briefly, the carcass measurements were recorded from photographic images of the left outer side.…”

Application of artificial intelligence techniques in meat processing: A review

“…Video image analysis (VIA) is one of the most researched techniques and solutions in beef, lamb, and pork carcass evaluation and its usefulness has been proven (Pabiou et al, 2011). Applications such as the prediction of lamb carcass composition, weight, and yield using VIA systems have demonstrated the great advantages of VIA technology in carcass yield prediction (Batista et al, 2021). In addition, Segura et al (2021) found that CVS technology can accurately predict tissue composition in mature cows and Segura et al (2023) evaluated the feasibility of two CVS systems in predicting beef carcass yield, and the results showed that CVS could predict post‐processing weights, tissue compositions, and yield percentages.…”

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

“…In beef, Segura et al (2021) demonstrated the feasibility of using the variables from a combination of VBS2000 (whole-side carcass camera: eþv ® Technology GmbH, Oranienburg, Germany) and VBG2000 (rib-eye camera: eþv ® Technology GmbH, Oranienburg, Germany) cameras along with PLSR analysis to accurately predict carcass tissue composition (R 2 p = 0.84 to 0.93) and tissue composition of most individual cuts (R 2 p = 0.71 to 0.88), as well as lean meat and retail cut yield (R 2 p = 0.90 and 0.86, respectively) (Figures 2 and 3). Regarding lamb, the ability of CVS to predict carcass commercial cut weight and yield of light lambs was evaluated by Batista et al (2021). The results of this study confirmed previous reports on the ability of CVS technology to estimate the weights of different cuts (R 2 cv = 0.96 to 0.99) and their lean meat yields (R 2 cv = 0.96 to 0.99), whereas the accuracy was more limited for predicting cut weight (R 2 cv < 0.43) and lean meat in percentage (R 2 cv < 0.44).…”