Use of ultrasonographic examination for in vivo evaluation of body composition and for prediction of carcass quality of sheep

Modelling and predicting fat deposition rates in various South African sheep crosses using ultrasound technology

Producers require an accurate predictive tool that can determine the optimal point of slaughter based on fat depth. The modelling of fat deposition with a simple mathematical model could supply in this need. Dohne Merino and Merino ewes were crossed with Dorper, Dormer and Ile de France rams or rams of their own breeds to create two purebred (Dohne Merino and Merino) and six crossbred groups (Dohne x Dorper, Dohne x Dormer, Dohne x Ile de France, Merino x Dorper, Merino x Dormer and Merino x Ile de France) of offspring. Fat deposition of four lambs of each sex per genotypic group was monitored from 80 to 360 days using ultrasound, and the data subsequently fitted to various equations and evaluated for goodness of fit. A linear fitting of fat depth to age (R2 > 0.77) and live weight (R2 > 0.56) were deemed to provide the best fit. The slope parameters of the equations indicated that ewes deposited fat faster than rams and that Dorper crosses had the highest fat deposition rate. An attempt was also made to model loin muscle growth, but the model fit was judged to be unsatisfactory. The predictive models developed here are deemed suitable for inclusion in feedlot management systems to aid in the production of optimally classified lamb carcasses.

Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods

This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluated were decision trees, random forests, support vector machines, and multi-layer perceptrons and were used to predict the total carcass bone (TCB), total carcass fat (TCF), and total carcass muscle (TCM). The best model for predicting the three parameters, TCB, TCF, and TCM was random forests, with mean squared error (MSE) of 0.31, 0.33, and 0.53; mean absolute error (MAE) of 0.26, 0.29, and 0.53; and the coefficient of determination (R2) of 0.67, 0.69, and 0.76, respectively. The results showed that machine learning methods from in vivo ultrasound measurements can be used as determinants of carcass tissue composition, resulting in reliable results.

Prediction models of intake and productive performance of non-castrated Nellore cattle finished in the feedlot system under tropical conditions

The objective of this study was to understand and predict the intake and performance of Nellore cattle finished in the feedlot. Individual data from 144 non-castrated male Nellore steers finished in the feedlot between the years 2016 and 2020 were used. Descriptive statistical analyses and Pearson's correlation were performed. The outliers were tested by evaluating the studentized residuals in relation to the values predicted by the equations. Residues that were outside the range of - 2.5 to 2.5 were removed. The goodness of fit of the developed equations was evaluated by the coefficients of determination (r²) and root mean square error (RMSE). The mean dry matter intake (DMI) was 10.2 kg/day, neutral detergent fiber intake (NDFI) was 3.4 kg/day, corresponding to 33.3% of DMI, crude protein intake (CPI) was 1.6 kg/day, and total digestible nutrient intake (TDNI) was 7.1 kg/day. The CPI to ADG ratio was 1.3 kg CPI/kg ADG and the TDNI to CPI ratio was 4.5 kg TDNI/kg CPI. The averages of productive performance were 1.3 kg/day for average daily gain (ADG), 152.6 kg for total weight gain (TWG), and 497.8 kg for final body weight (FBW) in average days in the confinement of 115.7 days. The intake measures correlated significantly with the performance measures, except for carcass yield and days in the feedlot. TWG had a high positive correlation with ADG (r = 0.84), while FBW had a positive correlation (r = 0.86) with hot carcass weight (HCW). Measures of intake, performance, and days in the feedlot can be used as predictors of DMI, FBW, HCW, TWG, and ADG. The prediction equations had satisfactory precision and accuracy for non-castrated Nellore cattle finished in feedlot systems under tropical conditions.

In Vivo Ultrasound Prediction of the Fillet Volume in Senegalese Sole (Solea senegalensis)

Simple Summary

The ability to obtain in vivo information on characteristics related to fish composition is necessary for aquaculture. In addition, there is growing interest in production traits, such as growth, feed efficiency or fillet weight, but it remains difficult to precisely record in vivo individual fish traits that report to these production traits, which can increase edible fish meat production and decrease the environmental impact. In the present study, we performed an ultrasound approach for the in vivo prediction of fillet volume of the Senegalese sole (Solea senegalensis), a species considered a promising flatfish species for marine fish farming. The results show that models based on ultrasound fillet volume measurements explain above 95% of the variation observed in fillet volume. However, for fillet yield estimation, the results were modest. Therefore, further studies are necessary to better understand the potential of the ultrasound approach to this trait. Nevertheless, this work allows us to conclude that the approach with ultrasound is promising for measuring in vivo fish composition traits.

Abstract

Senegalese sole (Solea senegalensis) has been considered a promising new flatfish species for Mediterranean marine fish farming. Accurate prediction of fillet traits in live animals may allow for more efficient control of muscle deposition in fish. In this sense, this study was undertaken to develop a non-invasive method to predict in vivo fish fillet volume and yield using real-time ultrasonography (RTU). The trial was conducted with 44 market weight Senegalese sole (298.54 ± 87.30 g). Fish were scanned with an Aloka SSD 500V with a 7.5 MHz probe. Ten RTU cross-sectional images were taken from the operculum to the caudal fin at regular intervals. These images were analyzed using Fiji software. These data were then used to estimate the partial volumes of the fillet. Actual fillet volume was determined using Archimedes’ principle. Simple and stepwise multiple regression analyses were then used to develop prediction models of fillet volume and yield. The most cranial RTU sections of the fish fillet were the best single predictors of both fillet volume and fillet yield and were the ones included in the best stepwise models. The best RTU slice area explained 82% of the variation observed in fillet volume, but the other RTU slice areas used as predictors of fillet volume showed poor to moderate accuracy (0.035 ≤ R² ≤ 0.615). Single RTU partial volumes showed poor to very high accuracy (0.395 ≤ R² ≤ 0.970) as predictors of fillet volume. The best stepwise model based on the RTU slice areas included three independent variables and explained 88.3% of the observed variation. The best stepwise models based on RTU partial volumes (single volumes and/or combinations of single volumes) explained about 97% of the variation observed in fillet volume. Two RTU volume traits, V_1–5 + V_6–9, and V₁₊₍₎₊₉, showed to be practically direct predictors of the actual fillet volume, explaining, respectively, 97% and 96% of the variation observed in the actual fillet volume. The fillet yields show lower correlations with slice areas (r between 0.044 and 0.601) than with volumes (r between 0.288 and 0.637). While further studies are clearly necessary to better understand the potential of RTU for the estimation of fillet yield in fish in general and Senegalese sole in particular, the present results showed that RTU traits can be very good predictors of Senegalese sole’s fillet volume, either used in regression models or as direct predictors.

Predictability of carcass traits in live Tan sheep by real-time ultrasound technology with least-squares support vector machines

This study aimed to investigate the performance of least-squares support vector machines to predict carcass characteristics in Tan sheep using noninvasive in vivo measurements. A total of 80 six-month-old Tan sheep (37 rams and 43 ewes) were examined. Back fat thickness and eye muscle area between the 12th and 13th ribs were measured using real-time ultrasound in live Tan sheep. All carcasses were dissected to hind leg, longissimus dorsi muscle, lean meat, fat, and bone to determine carcass composition. Multiple linear regression (MLR), partial least squares regression (PLSR), and least-squares support vector machines (LSSVM) were applied to correlate the live Tan sheep characteristics with carcass composition. The results showed that the LSSVM model had a better efficacy for estimating carcass weight, longissimus dorsi muscle weight, lean meat weight, fat weight, lean meat, and fat percentage in live lambs (R = 0.94, RMSE = 0.62; R = 0.73, RMSE = 0.02; R = 0.86, RMSE = 0.47; R = 0.78, RMSE = 0.63; R = 0.73, RMSE = 0.02; R = 0.65, RMSE = 0.03, respectively). LSSVM algorithm was a potential alternative to the conventional MLR method. The results demonstrated that LSSVM model might have great potential to be applied to the evaluation of sheep with superior carcass traits by combining with real-time ultrasound technology.

Prediction of carcass composition through measurements in vivo and measurements of the carcass of growing Santa Inês sheep

In vivo and carcass measurements were evaluated to predict carcass physical and chemical composition and to list the measurements that best fit the prediction of the composition of growing Santa Inês sheep carcasses. Thirty-three animals were used to measure the loin eye area by ultrasound in vivo (LEAu) and in the carcass. We used 39 animals for biometric measurement in vivo and 42 sheep for morphometric measurement in the carcass. For the physical and chemical compositions of carcasses, dissection of the half left carcass was carried out in 42 animals. The data were submitted to Pearson’s correlation analysis and t test. Simple and multiple linear regressions were performed using a stepwise procedure. All correlations between in vivo measurements and the physical and chemical compositions of carcasses (in kg) were significant, except for LEAu. Biometric measurements and hot (HCW) and cold (CCW) carcass weights were considered as predictors of the carcasses’ physical and chemical compositions. Slaughter body weight (SBW) was the variable that most influenced the equations in the assessment of in vivo measurements and HCW and CCW most influenced the equations for measurements on carcasses. Biometric measurements of Santa Inês sheep can be used together with the SBW to estimate the physical and chemical compositions of carcasses, with emphasis on body compactness index, breast width, wither height, and croup height. The morphometric measurements can be used together with carcass weight to estimate the physical and chemical compositions of carcasses, with emphasis on croup width, carcass compactness index, croup perimeter, external and internal carcass lengths, chest width, and leg length and perimeter. The HCW can be used to predict the physical and chemical composition of carcasses without affecting the accuracy of the prediction model.

Evaluator effect on the ultrasound measurement of subcutaneous fat deposition and loin eye area from weaning to slaughter lambs

Background and Aim:

Ultrasound is a non-invasive technique that enables animal evaluation and body condition classification of animals. Although it is not difficult to obtain an image, the analysis of this image can influence results quality. This study aimed to evaluate the repeatability and reproducibility of the technician trained in image interpretation obtained using different ultrasound frequencies.

Materials and Methods:

Ninety-six lambs were used, ranging in weight from 15 to 40 kg. The images were captured using a linear probe of 13 cm, with a 3.5 megahertz (MHz) frequency and an acoustic couplant aid “standoff” or using a multifrequency transducer (6 and 8 MHz), on B mode, with a linear probe and 8.0 MHz frequency. All measurements were performed by the same technician, on the left side, between the 12^th and 13^th rib. Five different evaluators, at two different times, with aid of Image J software measured the loin eye area (LEA; only for images obtained with 3.5 MHz), Longissimus thoracis et lumborum depth (DLM), subcutaneous fat thickness (SFT), subcutaneous fat thickness plus skin (SFST), and skin thickness (ST).

Results:

For LEA, DLM, SFT, SFST, and ST, variation was observed (p<0.01) between evaluators; however, there was no difference (p>0.05) between the 2 times of evaluation. Images measurements obtained with a frequency of 8.0 MHz had better repeatability indices and reproducibility indices. Accordingly, the identity test demonstrated that measurements performed on images obtained using 3.5 or 8.0 MHz were not equivalent.

Conclusion:

Ultrasound image measurements obtained using an 8.0 MHz frequency were more accurate and precise. It is important to use only one evaluator or providing the simultaneous training for all evaluators.

Intake, digestibility, and performance of lambs fed spineless cactus cv. Orelha de Elefante Mexicana

Objective

To evaluate the effects of the carmine cochineal-resistant spineless cactus genotypes cv. Orelha de Elefante Mexicana (Opuntia) and Miúda (Nopalea) on the intake and digestibility of nutrients, ingestive behavior, performance, and ultrasound measurements of growing lambs.

Methods

Thirty-six male (non-castrated) Santa Inês lambs were used, with an average age of 6 months and an initial average weight of 22.0±2.9 kg. They were distributed in a completely randomized design with 3 treatments (Tifton hay, Nopalea and Opuntia) and 12 replications, using initial weight as a covariate. The experimental period was 86 days, with the first 30 days used for the adaptation of the animals to the facilities, diets and management, and the remaining 56 days used for evaluation and data collection.

Results

The intake and apparent digestibility of dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF), total carbohydrates (TC), non-fibrous carbohydrates (NFC), and total digestible nutrients (TDN) showed a significant difference (p<0.05) as a function of the diets, with the Nopalea treatment (p<0.05) increasing DM intake (g/kg and % body weight [BW]), CP, TDN, and TC digestibility, whereas the Tifton hay diet led to a high (p<0.001) neutral detergent fiber corrected for ash and protein (NDFap) g/d intake, NDFap (BW %) and digestibility of said nutrient. There was no effect of treatments (p>0.05) on feeding time, however, rumination time and total chewing time were higher (p<0.05) for animals fed Tifton hay. The performance of the animals was similar (p>0.05). For the ultrasound measurements, Nopalea promoted an increase in the final loin eye area, compared to Tifton hay.

Conclusion

The use of spineless cactus variety Miúda leads to the greater intake and digestibility of nutrients. The evaluated carmine cochineal-resistant spineless cactus genotypes are alternatives for semi-arid regions as they do not negatively affect the performance of growing lambs.

Advances in Sheep and Goat Meat Products Research

The main goal of this chapter was to review the state of the art in the recent advances in sheep and goat meat products research. Research and innovation have been playing an important role in sheep and goat meat production and meat processing as well as food safety. Special emphasis will be placed on the imaging and spectroscopic methods for predicting body composition, carcass and meat quality. The physicochemical and sensory quality as well as food safety will be referenced to the new sheep and goat meat products. Finally, the future trends in sheep and goat meat products research will be pointed out.