Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review

Comparison of ultra-wide band microwave system and ultrasound in live cattle to predict beef carcase subcutaneous fatness

Ultrasound and ultrawide band microwave system (MiS) were directly compared in their ability to scan live cattle to predict carcase traits. Commercial beef cattle (n = 315) were scanned on farm 0-14 days prior to slaughter. Traits measured were subcutaneous fatness at the P8 site (over the gluteus muscle on the rump, at the intersection of a line through the pin bone parallel to the chine and perpendicular through the 3rd sacral crest) and subcutaneous fatness at the rib fat site (between 12th & 13th rib, ¾ of the length ventrally over the longissimus muscle). The precision of prediction of carcase traits was slightly better using MiS. MiS prediction of P8 fat depth had an average RMSEP of 2.48 mm and R2 of 0.65. The MiS could predict carcase rib fat with an average RMSEP of 2.28 mm and R2 of 0.56. The accuracy of prediction was very similar between the two technologies. When predicting P8, the average bias was smallest using MiS at 0.157 mm, but the average slope was smallest using ultrasound at 0.03 mm. When predicting rib fat, MiS had the smallest average bias at 0.204 mm, and smallest average slope deviation at 0.06 mm. The MiS predicted P8 and rib fat carcase traits with similar precision and accuracy as ultrasound.

Non-invasive metabolic biomarkers in initial cognitive impairment in patients with diabetes: A systematic review and meta-analysis

AIM

Diabetic cognitive impairment (DCI), considered one of the most severe and commonly overlooked complications of diabetes, has shown inconsistent findings regarding the metabolic profiles in DCI patients. This systematic review and meta-analysis aimed to identify dysregulated metabolites as potential biomarkers for early DCI, providing valuable insights into the underlying pathophysiological mechanisms.

MATERIALS AND METHODS

A systematic search of four databases, namely PubMed, Embase, Web of Science and Cochrane, was conducted up to March 2024. Subsequently, a qualitative review of clinical studies was performed followed by a meta-analysis of metabolite markers. Finally, the sources of heterogeneity were explored through subgroup and sensitivity analyses.

RESULTS

A total of 774 unique publications involving 4357 participants and the identification of multiple metabolites were retrieved. Of these, 13 clinical studies reported metabolite differences between the DCI and control groups. Meta-analysis was conducted for six brain metabolites and two metabolite ratios. The results revealed a significant increase in myo-inositol (MI) concentration and decreases in glutamate (Glu), Glx (glutamate and glutamine) and N-acetylaspartate/creatine (NAA/Cr) ratios in DCI, which have been identified as the most sensitive metabolic biomarkers for evaluating DCI progression. Notably, brain metabolic changes associated with cognitive impairment are more pronounced in type 2 diabetes mellitus than in type 1 diabetes mellitus, and the hippocampus emerged as the most sensitive brain region regarding metabolic changes associated with DCI.

CONCLUSIONS

Our results suggest that MI, Glu, and Glx concentrations and NAA/Cr ratios within the hippocampus may serve as metabolic biomarkers for patients with early-stage DCI.

Linear and nonlinear models for assessing carcass composition using dual X-ray absorptiometry in egg- and meat-type chickens

The objective of this study was to develop appropriate correction equations for dual energy X-ray absorptiometry (DXA) for total carcass composition of live meat- and egg-type chickens. Linear (bivariate linear and multivariate linear) and nonlinear (polynomial, multivariate polynomial, broken-line and Gompertz) equations were used to estimate carcass composition of DXA-scanned birds based on chemical proximate analysis. A total of 288 laying females (10-30 wk of age) and 305 broiler breeder females (4-32 wk of age) were used. The same birds scanned by DXA were dissected and utilized for whole-body proximate chemical analysis for body lean, fat, and mineral content (ash). As indicators of carcass fat and lean, abdominal fat pad and breast muscle weights were also recorded. Models were evaluated using root mean square error (RMSE), Bayesian Information Criterion (BIC), coefficient of determination (R2), Durbin Watson test for autocorrelation (DW), and residuals observation (RES). Model estimations were done separately by strain or combined. Estimations of composition responses fit at least 1 of each linear and nonlinear models for the egg- and meat-type chickens on all parameters estimated (P < 0.05). In the egg-type chickens, multivariate linear regression was the best fit for body lean with the lowest RMSE and BIC, and highest R2 whereas body fat, body ash, and breast muscle were best predicted by the multivariate polynomial model. In the meat-type chickens, body lean was best predicted by the multivariate linear model with the lowest RMSE and BIC, and the highest R2 whereas the multivariate polynomial was the most parsimonious model for body fat, body ash, and abdominal fat. Positive autocorrelations were observed in several models tested for body fat, body ash, breast muscle, and abdominal fat pad when both strains were analyzed combined (P < 0.05). In summary, a strain-based correction is recommended to all the parameters, with exception of the BW estimation. Correction equations developed in this study demonstrated that the DXA technique is a reliable alternative to proximate chemical analysis in egg- and meat-type chickens.

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 use of computed tomography for in vivo estimation of reticulo-rumen and omasum contents in Alpine goats

Precise in vivo measurement of reticulo-rumen content (volume and mass) is required for the study of digestive processes. Rumen-cannulated animals have been classically used for this purpose, and less invasive alternatives are currently investigated to meet the replacement, reduction, and refinement (3Rs) ethical considerations in animal science. The objective was to compare in vivo reticulo-rumen and omasum volumes assessed by computed tomography (CT) scan with postmortem measurement of their respective digesta masses in dairy goats. Twenty Alpine dairy goats were scanned by CT, and the volumes of the reticulo-rumen and omasum were measured by CT image postprocessing. Goats were slaughtered immediately after CT scan and the masses of reticulo-rumen and omasum digesta were measured. Simple linear regressions were performed between volumes measured in vivo by CT and the corresponding digesta wet masses measured postmortem. Reticulo-rumen and omasum volumes determined by CT were significantly and linearly regressed against the corresponding digesta masses measured postmortem (R2 = 0.72 and 0.87, residual standard deviation = 1.18 and 0.06 kg, and residual coefficient of variation = 11% and 12%, n = 20 and 19, respectively). The use of CT is a promising noninvasive method to measure volume and estimate digesta masses of reticulo-rumen and omasum in small ruminants.

Effect of β-casein A1 or A2 milk on body composition, milk intake, and growth in Holstein, Simmental, and crossbred dairy calves of both sexes

The aim of this study was to compare the effects of feeding homozygous β-CN A1 or A2 milk on the body composition, milk intake, and growth of German Holstein (GH), German Simmental (GS), and crossbred (CR) dairy calves of both sexes during the first 2 wk of life. A total of 104 calves (n = 54 female, f; and n = 50 male, m) from the breed groups GH (n = 23), GS (n = 61), and crossbred GH × GS (n = 20) were evaluated. Calves were weighed after birth and received colostrum ad libitum. On the second day, calves were alternately housed in pairs in double-igloo systems according to their random birth order and received either A1 milk (n = 52; 27 female and 25 male) or A2 milk (n = 52; 27 female and 25 male). They were offered 7.5 L/d, and the individual actual total milk intake was recorded. Daily energy-corrected milk intake was also calculated based on the milk composition (fat and protein). Fecal scores were recorded daily. On d 15, visceral adipose tissue (VAT) volume was assessed by open magnetic resonance imaging and dual-energy X-ray absorptiometry (DXA). In addition, fat and lean mass (g), as well as bone mineral content (g) and bone mineral density (g/cm2), were determined by DXA. The body composition, milk intake, and growth were similar between the 2 types of milk in the first 2 wk of life. Female calves had more VAT and fat mass, but less lean mass than male calves. GH and CR calves had more VAT and less lean mass than GS calves. Male calves were heavier than female calves after birth and on d 15. The average days with diarrhea and diarrhea occurrence were similar between calves fed A1 and A2 milk and between both sex groups. GS calves presented slightly more days with diarrhea and increased odds of having diarrhea compared with GH calves, not differing from CR.

Three-dimensional imaging to estimate in vivo body and carcass chemical composition of growing beef-on-dairy crossbred bulls

The dynamics of cattle body chemical composition during growth and fattening periods determine animal performance and beef carcass quality. The aim of this study was to estimate the empty body (EB) and carcass chemical composition of growing beef-on-dairy crossbred bulls (Brown Swiss breed as dam with Angus, Limousin or Simmental as sire) using three-dimensional (3D) imaging. The 3D images of the cattle's external body shape were recorded in vivo on 48 bulls along growth trajectory (75-520 kg BW and 34-306 kg hot carcass weight [HCW]; set 1) and on 70 bulls at target market slaughter weight, including 18 animals from set 1 (average 517 ± 10 kg BW and 289 ± 10 kg HCW; set 2). The linear, circumference, curve, surface and volume measurements on the 3D body shape were determined. Those predictive variables were used in partial least square regressions, together with the effect of the sire breed whenever significant (P < 0.05), with leave-one-out cross-validation to estimate water, lipid, protein, mineral and energy mass or proportions in the EB and carcass. Mass and proportions were determined directly from postmortem grinding and chemical analyses (set 1) or indirectly using the 11th rib dissection method (set 2). In set 1, bulls' BW and HCW were estimated via 3D imaging, with root mean square error of prediction (RMSEP) of 12 kg and 6 kg, respectively. The EB and carcass chemical component proportions were estimated with RMSEP from 0.2% for EB minerals (observed mean 3.7 ± 0.2%) to 1.8% for EB lipid (11.6 ± 4.2%), close to the RMSEP found for the carcass. In set 2, the RMSEP for estimation via 3D imaging was 9 kg for BW and 6 kg for HCW. The EB energy and protein proportions were estimated, with RMSEP of 0.5 MJ/kg fresh matter (10.1 ± 0.8 MJ/DM) and 0.2% (18.7 ± 0.7%), respectively. Overall, the estimations of chemical component proportions from 3D imaging were slightly less precise for both sets than the mass estimations. The morphological traits from the 3D images appeared to be precise estimators of BW, HCW as well as EB and carcass chemical component masses and proportions.

Fat deposition and partitioning for meat production in cattle and sheep

In markets for beef and sheep meat, an appropriate level of intramuscular fat (IMF) is highly desirable for meat-eating quality, but strategies to improve it usually lead to an undesirable excess in carcase fat, presenting a major challenge to livestock producers. To solve this problem, we need to understand the partitioning of fat among the major fat depots: IMF, subcutaneous fat (SCF) and visceral fat (VF). In most genotypes of cattle and sheep, the rate of accretion is lower for IMF than for SCF and VF, so genetic selection for a high level of IMF, or the use of an increased dietary energy supply to promote IMF deposition, will increase overall fatness and feed costs. On the other hand, feeding postnatal calves with excessive concentrates promotes IMF deposition, so a nutritional strategy is feasible. With genetic strategies, several problems arise: 1) positive genetic correlations between IMF, SCF and VF differ among genotypes in both cattle and sheep; 2) genotypes appear to have specific, characteristic rates of accretion of IMF during periods of growth and fattening; 3) most breeds of cattle and sheep naturally produce meat with relatively low levels of IMF, but IMF does vary substantially among individuals and breeds so progress is possible through accurate measurement of IMF. Therefore, an essential prerequisite for selection will be knowledge of the genetic correlations and fat accretion rates for each genotype. Currently, selection for IMF is based on existing technology that directly measures IMF in the progeny or siblings, or estimates IMF in live animals. New technology is needed to permit the simultaneous measurement of SCF and IMF in the field, thus opening up the possibility of accurate selection, particularly for fat partitioning in live animals. Specifically, there would be great value in detecting individuals with an IMF advantage at an early age so the generation interval could be shortened and genetic gain accelerated. Genetic gain would also be greatly aided if we could select for genes that control adipogenesis and lipogenesis and are also differentially expressed in the various depots.

Using Image Analysis Technique for Predicting Light Lamb Carcass Composition

Over the years, numerous techniques have been explored to assess the composition and quality of sheep carcasses. This study focuses on the utilization of video image analysis (VIA) to evaluate the composition of light lamb carcasses (4.52 ± 1.34 kg, mean cold carcass weight ± SD). Photographic images capturing the lateral and dorsal sides of fifty-five light lamb carcasses were subjected to analysis. A comprehensive set of measurements was recorded, encompassing dimensions such as lengths, widths, angles, areas, and perimeters, totaling 21 measurements for the lateral view images and 29 for the dorsal view images. K-Folds stepwise multiple regression analyses were employed to construct prediction models for carcass tissue weights (including muscle, subcutaneous fat, intermuscular fat, and bone) and their respective percentages. The most effective prediction equations were established using data from cold carcass weight (CCW) and measurements from both dorsal and lateral views. These models accounted for a substantial portion of the observed variation in the weights of all carcass tissues (with K-fold-R2 ranging from 0.83 to 0.98). In terms of carcass tissue percentages, although the degree of variation explained was slightly lower (with K-fold-R2 ranging from 0.41 to 0.78), the VIA measurements remained integral to the predictive models. These findings underscore the efficacy of VIA as an objective tool for assessing the composition of light lamb carcasses, which are carcasses weighing ≈ 4-8 kg.

Predicting fat cover in beef cattle to make on-farm management decisions: a review of assessing fat and of modeling fat deposition

Demands of domestic and foreign market specifications of carcass weight and fat cover, of beef cattle, have led to the development of cattle growth models that predict fat cover to assist on-farm managers make management decisions. The objectives of this paper are 4-fold: 1) conduct a brief review of the biological basis of adipose tissue accretion, 2) briefly review live and carcass assessments of beef cattle, and carcass grading systems used to develop quantitative compositional and quality indices, 3) review fat deposition models: Davis growth model (DGM), French National Institute for Agricultural Research growth model (IGM), Cornell Value Discovery System (CVDS), and BeefSpecs drafting tool (BeefSpecsDT), and 4) appraise the process of translating science and practical skills into research/decision support tools that assist the Beef industry improve profitability. The r2 for live and carcass animal assessments, using several techniques across a range of species and traits, ranged from 0.61 to 0.99 and from 0.52 to 0.99, respectively. Model evaluations of DGM and IGM were conducted using Salers heifers (n = 24) and Angus-Hereford steers (n = 15) from an existing publication and model evaluations of CVDS and BeefSpecsDT were conducted using Angus steers (n = 33) from a research trial where steers were grain finished for 101 d in a commercial feedlot. Evaluating the observed and predicted fat mass (FM) is the focus of this review. The FM mean bias for Salers heifers were 7.5 and 1.3 kg and the root mean square error of prediction (RMSEP) were 31.2 and 27.8 kg and for Angus-Hereford steers the mean bias were -4.0 and -10.5 kg and the RMSEP were 9.14 and 21.5 kg for DGM and IGM, respectively. The FM mean bias for Angus steers were -5.61 and -2.93 kg and the RMSEP were 12.3 and 13.4 kg for CVDS and BeefSpecsDT, respectively. The decomposition for bias, slope, and deviance were 21%, 12%, and 68% and 5%, 4%, and 91% for CVDS and BeefSpecsDT, respectively. The modeling efficiencies were 0.38 and 0.27 and the models were within a 20 kg level of tolerance 91% and 88% for CVDS and BeefSpecsDT, respectively. Fat deposition models reported in this review have the potential to assist the beef industry make on-farm management decisions on live cattle before slaughter and improve profitability. Modelers need to continually assess and improve their models but with a caveat of 1) striving to minimize inputs, and 2) choosing on-farm inputs that are readily available.

Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases

It is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.

Dual-energy X-ray absorptiometry: an effective approach for predicting broiler chicken body composition

Two trials were carried out to develop and validate linear regression equations for body composition prediction using Dual-energy X-ray absorptiometry (DEXA). In Trial 1, 300 Cobb500 male chickens raised from 1 to 42 d of age were scanned in DEXA to estimate total weight, fat mass, soft lean tissue (SLT) mass, bone mineral content (BMC), and fat percentage. DEXA estimates were compared to body ash, crude fat, SLT (sum of protein and water) and scale body weight. The dataset was split, with 70% used for prediction equations development and 30% for testing, and the 5k-fold cross-validation analysis was used to optimize the equations. The R2, mean absolute error (MAE), and root-mean-squared error (RMSE) were used as precision and accuracy indicators. A negative correlation (ρ = -0.27) was observed for ash content, while no correlation was observed for protein content (P > 0.05). Predictive linear equations were developed to assess broiler weight (R2 = 0.999, MAE = 25.12, RMSE = 38.99), fat mass (R2 = 0.981, MAE = 13.87, RMSE = 21.28), ash mass (R2 = 0.956, MAE = 3.98, RMSE = 5.61), SLT mass (R2 = 0.997, MAE = 35.73, RMSE = 52.45), water mass (R2 = 0.997, MAE = 29.56, RMSE = 43.94), protein mass (R2 = 0.989, MAE = 12.94, RMSE = 19.05), fat content (R2 = 0.855, MAE = 0.81, RMSE = 1.05), SLT content (R2 = 0.658, MAE = 1.01, RMSE = 1.28), and water content (R2 = 0.678, MAE = 0.99, RMSE = 1.27). All equations passed the test. In Trial 2, 395 Cobb500 male chickens were raised from 1 to 42 d of age and used for validation of prediction equations. The equations developed for weight, fat mass, ash mass, SLT mass, water mass, and protein mass were validated. In conclusion, DEXA was found to be an effective approach for measuring the body composition of broilers when using predictive equations validated in this study for estimate calibration.

Prediction of carcass traits in fattening Chios and Serres lambs using real-time ultrasonography and live body weight measurements pre-slaughter

The objective of this study was to assess the capability of predicting carcass traits and meat cuts weights, in fattening lambs of indigenous Greek dairy sheep breeds, using ultrasound measurements and live body weight measurements pre-slaughter. A total of 187 lambs of Chios and Serres breeds were involved in the study. Body condition score, live body weight (LBW), and ultrasound measurements of Longissimus lumborum muscle depth (LMD) and subcutaneous fat thickness (SFT) at the lumbar region were recorded pre-slaughter. After slaughter, the carcasses were classified using five-degree grading systems for muscle development and fat deposition, while hot (HCW) and cold carcass (CCW) and meat cuts weights were measured. The statistical analyses included descriptive statistics and linear regression models to estimate the fixed effects of sex and the covariances of LBW, BCS, and ultrasound measurements on the studied traits. High R2 values (0.60 ≤ R2 ≤ 0.92) were observed in the models predicting HCW, CCW, forequarter, leg chump on shank off, the short loin, the eye of the short loin, and foreshank weights. Among the models estimated LMD, SFT, and LBW as significant predictors, the ones predicting hot and cold carcass weights, the short loin, the eye of the short loin, and the eye of the rack weights were successfully validated. Other models including BCS, LBW, sex, and either one or none of the ultrasonography measurements as predictors were also validated and presented.

Prediction models of carcass characteristics from non‑castrated Nellore cattle finished in the feedlot system under tropical conditions

Our objective was to use measures of intake and productive performance to adjust prediction models for the carcass traits of non-castrated Nellore cattle finished in a feedlot. Individual data from 168 non-castrated male Nellore steers finished in feedlot between the years 2016-2021 were used. Descriptive statistical analyzes and Pearson correlation coefficients 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). Models for carcass yield, subcutaneous fat thickness, ribeye area, and shear force were adjusted. Means of 53.5% carcass yield, 4.8 mm subcutaneous fat thickness, 73 cm2 loin eye area, and 8.1 kg shear force were observed. The observed average intakes were 9.9 kg/day of dry matter, 3.3 kg/day of neutral detergent fiber content, 1.5 kg/day of crude protein, and 7.1 kg/day of total digestible nutrients. The average confinement time was 113 days, the average total weight gain was 152.2 kg and the average daily gain was 1.35 kg/day. Intake measures significantly correlated with shear force and subcutaneous fat thickness and ribeye area. Carcass yield was significantly correlated with total weight gain, feedlot time, and hot carcass weight. Measures of nutrient intake, performance, and confinement time can be used as predictors of carcass yield, ribeye area, fat thickness, and shear force of non-castrated Nellore cattle finished in a feedlot. The prediction equations for ribeye area, carcass yield, subcutaneous fat thickness, and shear force showed sufficient precision and accuracy for non-castrated Nellore cattle finished in confinement systems under tropical conditions. All equations can be used with caution to estimate carcass traits of cattle finished in a feedlot using measures of intake and productive performance.

Effects of Dam and Sire Breeds on Lamb Carcass Quality and Composition in Pasture-Based Systems

This study explored the impacts of sire and dam breed on carcass quality and composition in a pasture-based system and the use of DXA to rapidly rank carcasses for leanness. Southdown (SD) and Suffolk (SF) ewes were mated to Texel (TX) or SD rams to produce seventy-nine lambs. Lambs were raised on pasture-based systems with limited grain supplementation. Lamb birth weight was greater (p < 0.01) for TX, regardless of dam breed. Lambing rate was lower (p < 0.01) for SD than SF ewes. Circulating myostatin concentrations were greater (p < 0.05) on d 42 than d 75 or d 110 but did not differ by sire breed. Texel-sired lambs had greater (p < 0.01) carcass weight, ribeye area and quality grade compared to SD-sired. Total and primal fat mass as predicted from DXA was higher (p < 0.05) in carcasses from SD than TX sires. Muscles from TX lambs had greater (p < 0.05) polyunsaturated fatty acid (PUFA) composition than SD-sired. Shear force values were influenced (p < 0.01) by dam breed, muscle cut and postmortem age but not by sire breed. The use of TX sires in pasture-based systems improved carcass leanness and muscle PUFA concentrations without altering tenderness.

Expression of enzymes involved in the urea cycle and muscle and mammary gland development of Holstein × Gyr heifers in a rotational grazing system supplemented with increasing protein levels

Studies evaluating the crude protein (CP) supplementation strategies across the year for grazing cattle and its association with the enzymes involved in the urea cycle and muscle and mammary gland developments are scarce. Thus, we aimed to evaluate the effect of supplementation with different levels of CP on the expression of genes involved in the urea cycle and muscle and mammary gland development of Holstein × Gyr crossbreed heifers grazing intensively managed Brachiaria decumbens throughout the year. Thirty-eight heifers with average initial BW of 172.5 ± 11.15 kg (mean ± SE) and 8.2 ± 0.54 mo of age were randomly assigned to 1 of 4 treatments: 3 protein supplements (SUP) fed at 5g/kg of body weight, plus a control group (CON, non-supplemented animals). The supplement CP levels evaluated were: 12, 24, and 36%. The study was divided into 4 seasons: rainy, dry, rainy-dry transition (RDT), and dry-rainy transition (DRT). On the penultimate day of each season, ultrasound images of the carcass and mammary gland were taken. Five animals from each treatment were randomly chosen on the last day of each season, and liver and muscle tissue biopsies were performed. The target genes were the mammalian target of rapamycin (mTOR) and adenosine monophosphate-activated protein kinase (AMPK) in the muscle samples. Carbamoyl phosphate synthetase (CPS), ornithine transcarbamylase (OTC), argininosuccinate synthetase (ASS), arginosuccinate lyase (ASL), and arginase (ARG) were evaluated in the liver samples. Data were analyzed using PROC GLIMMIX of the SAS with repeated measures. We observed a greater rib eye area (cm2) and fat thickness (mm) in SUP animals than in non-supplemented animals. However, we did not observe differences among SUP levels for both variables. No effects of supplementation were detected on mammary gland development. Nevertheless, seasonal effects were observed, where the RDT and dry season had the most and least accumulated fat in the mammary gland. In muscle, we observed greater expression of AMPK in non-supplemented animals than SUP animals. On the other hand, no differences were observed in gene expression between SUP and non-supplemented animals and among SUP animals for mTOR. Season affected both AMPK and mTOR; heifers had a greater AMPK gene expression on rainy than RDT. For mTOR, we observed greater gene expression in RDT and DRT than in rainy. No differences were observed among RDT, dry, and DRT, and between dry and rainy seasons for mTOR. We observed greater CPS, ASL, and ARG gene expression in SUP animals than in non-supplemented animals. Among SUP animals, supplement CP linearly affected CPS. In conclusion, the supplementation strategy did not affect mammary gland development and mTOR expression in muscle tissue. However, we observed a seasonal effect on mammary gland development and AMPK and mTOR expression. The CP supplementation increased the rib eye area and fat thickness, directly affecting AMPK expression in the muscle. Moreover, the CP supplementation increased urea cycle enzyme expression, indicating greater urea production in the liver.

Genetic analysis of protein efficiency and its association with performance and meat quality traits under a protein-restricted diet

BACKGROUND

An essential component in the development of sustainable pig production is the reduction of nitrogen excretion in fattening pigs. Pig feeds typically contain high levels of dietary crude protein, and due to incomplete conversion to muscle tissue, excess nitrogen is excreted, resulting in environmental problems such as nitrate pollution and greenhouse gas emissions. Therefore, improving protein efficiency (PE), i.e., the proportion of dietary protein that remains in the carcass, is desirable. The aim of this study was to estimate the heritability (h²) of PE and its genetic correlations with phosphorus efficiency, three performance, seven meat quality and two carcass quality traits when pigs were fed a 20% protein-restricted diet, using 1071 Swiss Large White pigs. To determine PE, the intake of feed with known nutrient content was accurately recorded for each pig and the nitrogen and phosphorus content of the carcass was determined using dual-energy X-ray absorptiometry.

RESULTS

We found an average PE of 0.39 ± 0.04 and a heritability of 0.54 ± 0.10. PE showed a high genetic correlation with phosphorus efficiency (0.61 ± 0.16), moderate genetic correlations with feed conversion ratio (- 0.55 ± 0.14) and average daily feed intake (- 0.53 ± 0.14), and a low genetic correlation with average daily gain (- 0.19 ± 0.19). While PE has favourable genetic correlations with the performance traits and some meat quality traits, there is a potentially unfavourable correlation of PE with meat colour (redness [r_g = - 0.27 ± 0.17]; yellowness [r_g = - 0.31 ± 0.18]) and intra-muscular fat (IMF; r_g = - 0.39 ± 0.15). Feed conversion ratio (FCR) also showed unfavourable genetic correlations with meat lightness, redness yellowness, IMF and cooking loss.

CONCLUSIONS

PE is a heritable trait that can be considered in breeding programs to reduce the environmental impact of pig production. We found no strong negative correlation of PE with meat quality traits, and that there is potential to indirectly select for improved phosphorus efficiency. Selecting nutrient efficiencies might be a more suitable strategy to reduce nitrogen pollution from manure than focusing on FCR because the latter also shows genetic antagonism with some meat quality traits in our population.

MRI Anatomical Investigation of Rabbit Bulbourethral Glands

Anatomical MRI is appropriate for the interpretation of soft tissue findings in the retroperitoneal part of the pelvic cavity. The aim of the current study was to use rabbits as an imaging model to optimize MRI protocols for the investigation of bulbourethral glands. The research was conducted on twelve clinically healthy, sexually mature male rabbits, eight months of age (New Zealand White), weighing 2.8 kg to 3.2 kg. Tunnel MRI equipment was used. The transverse MRI in the T2-weighted sequence obtained detailed images that were of higher anatomical contrast than those in T1-weighted sequences. The hyperintensity of the glandular findings at T2, compared to the adjacent soft tissues, was due to the content of secretory fluids. The quality of the anatomical tissue contrast has not shown much dependence on the choice of the sequence in dorsal MRI. The sagittal visualization of the rabbit bulbourethral glands corresponded to the localization of the research plane toward a median plane. The imaging results could be used as a morphological base for clinical practice and reproduction.

Assessing dual-energy X-ray absorptiometry prediction of intramuscular fat content in beef longissimus steaks

This study assessed the capability of dual-energy X-ray absorptiometry (DEXA) to predict intramuscular fat (IMF) content of beef longissimus steaks against chemical IMF as the gold standard. DEXA performance of fat% prediction was assessed using a leave-one-out cross validation method among Angus and Nellore steaks, which generated a chemical fat% range of 14.05-36.82% and 2.46-7.84%, respectively, and using pooled data. There was a significant positive association between DEXA predicted fat and chemical fat content. However, higher precision was found for pooled data (R² = 0.95, RMSECV = 1.95) and Angus (R² = 0.75, RMSECV = 2.39) than Nellore (R² = 0.15, RMSECV = 1.22) group. Accuracy also had the same response with average slope values close to 1 for pooled data and Angus and a lower value (0.42) for Nellore group. DEXA precisely predicts IMF content across a wide range of fat content. However, its precision and accuracy of prediction within low-fat content samples are lower than in high-fat content.

Invited review: Assessment of body condition score and body fat reserves in relation to insulin sensitivity and metabolic phenotyping in dairy cows

The purpose of this article is to review body condition scoring and the role of body fat reserves in relation to insulin sensitivity and metabolic phenotyping. This article summarizes body condition scoring assessment methods and the differences between subcutaneous and visceral fat depots in dairy cows. The mass of subcutaneous and visceral adipose tissue (AT) changes significantly during the transition period; however, metabolism and intensity of lipolysis differ between subcutaneous and visceral AT depots of dairy cows. The majority of studies on AT have focused on subcutaneous AT, and few have explored visceral AT using noninvasive methods. In this systematic review, we summarize the relationship between body fat reserves and insulin sensitivity and integrate omics research (e.g., metabolomics, proteomics, lipidomics) for metabolic phenotyping of cows, particularly overconditioned cows. Several studies have shown that AT insulin resistance develops during the prepartum period, especially in overconditioned cows. We discuss the role of AT lipolysis, fatty acid oxidation, mitochondrial function, acylcarnitines, and lipid insulin antagonists, including ceramide and glycerophospholipids, in cows with different body condition scoring. Nonoptimal body conditions (under- or overconditioned cows) exhibit marked abnormalities in metabolic and endocrine function. Overall, reducing the number of cows with nonoptimal body conditions in herds seems to be the most practical solution to improve profitability, and dairy farmers should adjust their management practices accordingly.

Estimation of carcass chemical composition in beef-on-dairy cattle using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut

The aim of the present study was to estimate the chemical composition (water, lipid, protein, mineral, and energy contents) of carcasses measured postmortem using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut. One hundred and twenty beef-on-dairy (dam: Swiss Brown, sire: Angus, Limousin, or Simmental) bulls (n = 66), heifers (n = 42), and steers (n = 12) were included in the study. The reference carcass composition measured after grinding, homogenization, and chemical analyses was estimated from DXA variables using simple or multiple linear regressions with model training on 70% (n = 84) and validation on 30% (n = 36) of the observations. In the validation step, the estimates of water and protein masses from the half-carcass (R2 = 0.998 and 0.997; root mean square error of prediction [RMSEP], 1.0 and 0.5 kg, respectively) and 11th rib DXA scans (R2 = 0.997 and 0.996; RMSEP, 1.5 and 0.5 kg, respectively) were precise. Lipid mass was estimated precisely from the half-carcass DXA scan (R2 = 0.990; RMSEP = 1.0 kg) with a slightly lower precision from the 11th rib DXA scan (R2 = 0.968; RMSEP = 1.7 kg). Mineral mass was estimated from half-carcass (R² = 0.975 and RMSEP = 0.3 kg) and 11th rib DXA scans (R2 = 0.947 and RMSEP = 0.4 kg). For the energy content, the R2 values ranged from 0.989 (11th rib DXA scan) to 0.996 (half-carcass DXA scan), and the RMSEP ranged from 36 (half-carcass) to 55 MJ (11th rib). The proportions of water, lipids, and energy in the carcasses were also precisely estimated (R2 ≥ 0.882) using either the half-carcass (RMSEP ≤ 1.0%) or 11th rib-cut DXA scans (RMSEP ≤ 1.3%). Precision was lower for the protein and mineral proportions (R2 ≤ 0.794, RMSEP ≤ 0.5%). The cattle category (sex and breed of sire) effect was observed only in some estimative models for proportions from the 11th rib cut. In conclusion, DXA imaging of either a cold half-carcass or 11th rib cut is a precise method for estimating the chemical composition of carcasses from beef-on-dairy cattle.

Growth Performance, Carcass and Meat Traits of Autochthonous Arouquesa Weaners Raised on Traditional and Improved Feeding Systems

Arouquesa is an autochthonous bovine breed known for its Arouquesa PDO beef labeling. There are several production systems under the definition of PDO labeling. This study aimed to compare the effect of different production systems on carcass and meat traits for the Arouquesa breed. Two trials differing in diet and weaning age were conducted. The first trial included a TF group fed the traditional way and weaned at 9 months; a TF + S1 group, equal to TF, but with a starter supplement; and finally, a S1 + S2 group that was fed with a starter and a growth supplement and weaned at 5 months. The second trial was composed of a TF + S3 group fed like the TF + S1 group but reared until 12 months with a finishing supplement, and finally, the S3 group fed like the S1 + S2 group but reared until 12 months. In the first trial, the TF + S1 and S1 + S2 groups showed higher final live weight and average daily gain. In the second trial, we observed differences in the subcutaneous fat that was higher in the S3 group. Regarding meat traits, we observed differences in exudative and cooking losses in the first trial. In general, supplementation improved meat production without affecting meat quality parameters.

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.

Magnetic Resonance Imaging Used to Define the Optimum Needle Length in Pigs of Different Ages

Intramuscular injections result in tissue destruction and alteration. Therefore, it is necessary to evaluate the optimum injection point for intramuscular injections. As animals—especially pigs—vary in size and explicit information about injection depth is not available. To determine the predicted optimum injection depth, magnetic resonance imaging was used in pigs of different ages and weight groups. In total, 730 magnetic resonance images of 136 pigs were used to calculate the optimum injection depth for intramuscular injections. Four age groups were evaluated: <29 days of age, 29−70 days of age, 71−117 days of age and >170 days of age. For fattening pigs (71−117 days of age), the present study recommends a needle length of 20 mm (range: 40−58 mm). For younger pigs (<70 days of age), a needle length of 12 to 14 mm (range: 10−18 mm), and for older pigs (>170 days of age), a needle length of 30 mm (range: 25−37 mm) is recommended. However, more data are needed. Therefore, further studies are necessary, especially in the youngest (suckling pigs) and oldest (sows) age groups, as these are the groups mainly injected/vaccinated. Additionally, age and weight should be examined in more detail compared to fat distribution in the neck, genetics and the sex of the animal.

The application of computer vision systems in meat science and industry - A review

Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisition devices make it technically feasible to obtain information about both the external features and internal structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used in research related to assessing the composition of animal carcasses, which might help determine the impact of cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are under constant development an improvement. The present review covers computer vision system techniques, stages of measurements, and possibilities for using these to assess carcass and meat quality.

Estimation of Empty Body and Carcass Chemical Composition of Lactating and Growing Cattle: Comparison of Imaging, Adipose Cellularity, and Rib Dissection Methods

The aim of present study was to compare in vivo and post mortem methods for estimating the empty body (EB) and carcass chemical compositions of Simmental lactating and growing cattle. Indirect methods were calibrated against the direct post mortem reference determination of chemical compositions of EB and carcass, determined after grinding and analyzing the water, lipid, protein, mineral masses, and energy content. The indirect methods applied to 12 lactating cows and 10 of their offspring were ultrasound (US), half-carcass and 11th rib dual-energy X-ray absorptiometry (DXA) scans, subcutaneous and perirenal adipose cell size (ACS), and dissection of the 11th rib. Additionally, three-dimensional (3D) images were captured for 8 cows. Multiple linear regressions with leave-one-out-cross-validations were tested between predictive variables derived from the methods tested, and the EB and carcass chemical compositions. Partial least square regressions were used to estimate body composition with morphological traits measured on 3D images. Body weight (BW) alone estimated the EB and carcass composition masses with a root mean squared error of prediction (RMSEP) for the EB from 1 kg for minerals to 12.4 kg for lipids, and for carcass from 0.9 kg for minerals to 7.8 kg for water. Subcutaneous adipose tissue thickness measured by US was the most accurate in vivo predictor when associated with BW to estimate chemical composition, with the EB lipid mass RMSEP = 11 kg and R² = 0.75; carcass water mass RMSEP = 6 kg and R² = 0.98; and carcass energy content RMSEP = 236 MJ and R² = 0.91. Post mortem, carcass lipid mass was best estimated by half-carcass DXA scan (RMSEP = 2 kg, R² = 0.98), 11th rib DXA scan (RMSEP = 3 kg, R² = 0.96), 11th rib dissection (RMSEP = 4 kg, R² = 0.92), and perirenal ACS (RMSEP = 6 kg, R² = 0.79) in this respective order. The results obtained by 11th rib DXA scan were accurate and close to the half-carcass DXA scan with a reduction in scan time. Morphological traits from 3D images delivered promising estimations of the cow EB and carcass chemical component masses with an error less than 13 kg for the EB lipid mass and than 740 MJ for the EB energy. Future research is required to test the 3D imaging method on a larger number of animals to confirm and quantify its interest in estimating body composition in living animals.

Comparative X-ray Shielding Properties of Single-Layered and Multi-Layered Bi2O3/NR Composites: Simulation and Numerical Studies

This work theoretically compared the X-ray attenuation capabilities in natural rubber (NR) composites containing bismuth oxide (Bi₂O₃) by determining the effects of multi-layered structures on the shielding properties of the composites using two different software packages (XCOM and PHITS). The shielding properties of the single-layered and multi-layered Bi₂O₃/NR composites investigated consisted of the transmission factor (I/I₀), effective linear attenuation coefficient (µ_eff), effective mass attenuation coefficient (µ_m,eff), and effective half-value layer (HVL_eff). The results, with good agreement between those obtained from XCOM and PHITS (with less than 5% differences), indicated that the three-layered NR composites (sample#4), with the layer arrangement of pristine NR (layer#1)-Bi₂O₃/NR (layer#2)-pristine NR (layer#3), had relatively higher X-ray shielding properties than either a single-layer or the other multi-layered structures for all X-ray energies investigated (50, 100, 150, and 200 keV) due to its relatively larger effective percentage by weight of Bi₂O₃ in the composites. Furthermore, by varying the Bi₂O₃ contents in the middle layer (layer#2) of sample#4 from 10 to 90 wt.%, the results revealed that the overall X-ray shielding properties of the NR composites were further enhanced with additional filler, as evidenced by the highest values of µ_eff and µ_m,eff and the lowest values of I/I₀ and HVL_eff observed in the 90 wt.% Bi₂O₃/NR composites. In addition, the recommended Bi₂O₃ contents for the actual production of three-layered Bi₂O₃/NR composites (the same layer structure as sample#4) were determined by finding the least Bi₂O₃ content that enabled the sample to attenuate incident X-rays with equal efficiency to that of a 0.5-mm lead sheet (with an effective lead equivalence of 0.5 mmPb). The results suggested that the recommended Bi₂O₃ contents in layer#2 were 82, 72, and 64 wt.% for the combined 6 mm, 9 mm, and 12 mm samples, respectively.

Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors

Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400-900 nm) and short-wave infrared (900-1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was higher for PLS-DA compared to RF. The accuracy of the classification models was not significantly different between data pre-processing methods or between visible and short-wave infrared sensors. Hyperspectral sensors, particularly those in the visible spectrum, seem promising to identify organs from slaughtered animals which could be useful for the automation of quality and process control in the food supply chain, such as in abattoirs.

Early Detection of Medical Image Analysis by Using Machine Learning Method

We develop effective medical image classification techniques, with an emphasis on histopathology and magnetic resonance imaging (MRI). The trainer utilized the curriculum as a starting point for a set of data and a restricted number of samples, and we used it as a starting point for a set of data. As calibrating a machine learning model is difficult, we used alternative methods as unsupervised feature extracts or weight-conditioning factors for identifying pathological histology pictures. As a result, the pretrained models will be trained on 3-channel RGB pictures, while the MRI sample has more slices. To alter the working model using the MRI data, the convolutional neural network (CNN) must be fine-tuned. Pretrained models are placed and then used as feature snippets. However, there is a scarcity of well-done medical photos, making training machine learning models a difficult endeavor to begin with. In any case, data augmentation aids in the generation of sufficient training samples; however, it is unclear if data augmentation aids in the prediction of unknown data samples. As a result, we fine-tuned machine learning models without using any additional data. Furthermore, rather than utilizing a standard machine learning classifier for the MRI data, we created a unique CNN that uses both 3D shear descriptors and deep features as input. This custom network identifies the MRI sample after processing our representation of the characteristics from beginning to end. On the hidden MRI dataset, our bespoke CNN outperforms traditional machine learning. Our CNN model is less prone to overfitting as a result of this. Furthermore, we have given cutting-edge outcomes employing machine learning.

Correlation between the Korean pork grade system and the amount of pork primal cut estimated with AutoFom III

It is impossible to know the amount of pork primal cut by pig carcass grade which is determined only by carcass weight and backfat thickness in the Korean Pig Carcass System. The aim of this study was to investigate the correlation between the pig carcass grade and the amount of pork primal cut estimated with AutoFom III. A total of 419,321 Landrace, Yorkshire, and Duroc (LYD) pigs were graded with the Korean Pig Carcass Grade System. Amounts of belly, neck, loin, tenderloin, spare ribs, shoulder, and ham were estimated with AutoFom III. Regression equations for seven primal cuts according to each grade were derived. There were significant differences among the three carcass grades due to heteroscedasticity variance (p < 0.0001). Three regression equations were derived from AutoFom III estimation of primal cuts according to carcass grades. The coefficient of determination of the regression equation was 0.941 for grade 1⁺, 0.982 for grade 1, and 0.993 for grade 2. Regression equations obtained from this study are suitable for AutoFom III software, a useful tool for the analysis of each pig carcass grade in the Korean Pig Carcass Grade System. The high reliability of predicting the amount of primal cut with AutoFom III is advantageous for the management of slaughterhouses to optimize their product sorting in Korea.

Symposium review: The role of adipose tissue in transition dairy cows: Current knowledge and future opportunities

Adipose tissue (AT) is a central reservoir of energy stored in the form of lipids. In addition, AT has been recognized as an immunologically and endocrinologically active tissue of dairy cattle. The recent literature on AT biology of transition dairy cows has often focused on the possible negative effects that originate from excessive body fat. However, the highly efficient energy-storage capability of this tissue is also vital to the adaptability of dairy cattle to the change in nutrient availability, and to support lactation and reproduction. An excessive degree of mobilization of this tissue, however, is associated with high circulating fatty acid concentrations, and this may have direct and indirect negative effects on reproductive health, productivity, and disease risk. Furthermore, rapid lipolysis may be associated with postpartum inflammation. Research on the role of AT is complicated by the greater difficulty of accessing and measuring visceral AT compared with subcutaneous AT. The objective of this review is to provide a transition cow-centric summary of AT biology with a focus on reviewing methods of measuring AT mass as well as to describe the importance for production, health, and reproductive success.

Economic Analysis of an Image-Based Beef Carcass Yield Estimation System in Korea

Simple Summary

Carcass grading is a vital process in the slaughterhouse and is used for the quantification of the overall value of carcasses. Since carcass grading is often performed manually by a team of grading experts, it is subject to human limitations which result in inconsistency and limited operation speed. Considering this, an automatic beef carcass yield estimation system capable of predicting 23 key yield parameters was developed. However, just like any freshly introduced system, analysis of the economic impact of the grading system is vital before deployment in any slaughterhouse business. In this study, a thorough economic analysis to justify deploying the developed beef carcass grading system in a standard slaughterhouse in South Korea was performed through a cost-benefit analysis. The analysis found that the benefits derived from using the developed system outweigh the costs of purchasing and operating the system making the endeavor economically viable.

Abstract

To minimize production costs, reduce mistakes, and improve consistency, modern-day slaughterhouses have turned to automated technologies for operations such as cutting, deboning, etc. One of the most vital operations in the slaughterhouse is carcass grading, usually performed manually by grading staff, which creates a bottleneck in terms of production speed and consistency. To speed up the carcass grading process, we developed an online system that uses image analysis and statistical tools to estimate up to 23 key yield parameters. A thorough economic analysis is required to aid slaughterhouses in making informed decisions about the risks and benefits of investing in the system. We therefore conducted an economic analysis of the system using a cost-benefit analysis (the methods considered were net present value (NPV), internal rate of return (IRR), and benefit/cost ratio (BCR)) and sensitivity analysis. The benefits considered for analysis include labor cost reduction and gross margin improvement arising from optimizing breeding practices with the use of the data obtained from the system. The cost-benefit analysis of the system resulted in an NPV of approximately 310.9 million Korean Won (KRW), a BCR of 1.72, and an IRR of 22.28%, which means the benefits outweigh the costs in the long term.

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

This study aimed to evaluate the accuracy of the leg volume obtained by the Microsoft Kinect sensor to predict the composition of light lamb carcasses. The trial was performed on carcasses of twenty-two male lambs (17.6 ± 1.8 kg, body weight). The carcasses were split into eight cuts, divided into three groups according to their commercial value: high-value, medium value, and low-value group. Linear, area, and volume of leg measurements were obtained to predict carcass and cuts composition. The leg volume was acquired by two different methodologies: 3D image reconstruction using a Microsoft Kinect sensor and Archimedes principle. The correlation between these two leg measurements was significant (r = 0.815, p < 0.01). The models to predict cuts and carcass traits that include leg Kinect 3D sensor volume are very good in predicting the weight of the medium value and leg cuts (R² of 0.763 and 0.829, respectively). Furthermore, the model, which includes the Kinect leg volume, explained 85% of its variation for the carcass muscle. The results of this study confirm the good ability to estimate cuts and carcass traits of light lamb carcasses with leg volume obtained with the Kinect 3D sensor.

APPLICATION OF MAGNETIC RESONANCE IMAGING IN EVALUATING ANKLE MOTION INJURY

ABSTRACT Introduction Discuss the application of magnetic resonance imaging in evaluating ankle motion injury. Objective Verify the influencing factors of magnetic resource imaging (MRI) diagnosis based on the linear regression algorithm model. Methods The experimental group was diagnosed by MRI, while the control group was diagnosed by plain X-ray. After that, the mathematical model of the linear regression algorithm was constructed. Results It could be concluded that the MRI detection rate was 85.71%, and the X-ray plain film detection rate was 77.14%. The linear regression model analysis showed that the P-value of cartilage injury, tendon fracture, bone contusion, and soft tissue swelling was greater than 0.05. Conclusions MRI has more advantages in the application of ankle joint diagnosis. And ligament injury and joint effusion are the influencing factors of MRI diagnosis, which can highly indicate the authenticity of the injury in the ankle joint. Level of evidence II; Therapeutic studies - investigation of treatment results.

Prediction of Carcass Traits of Santa Inês Lambs Finished in Tropical Pastures through Biometric Measurements

The aim of this study was to predict carcass traits of Santa Inês lambs finished in tropical pastures by using biometric measurements. Data originated from two experiments involving 56 lambs (32 in experiment I and 24 in experiment II). In both experiments, the sheep were finished in that were finished in pastures of Panicum maximum and Brachiaria brizantha, experiment I being conducted in the rainy season and experiment II in the dry season. The following biometric measurements were recorded before slaughter: body length (BL), withers height (WH), rump height (RH), thorax width (TW), rump width (RW), chest width (CW), heart girth (HG), thigh circumference (TC), rump circumference (RC) and leg length (LL), in addition to live weight at slaughter (SW). After slaughter, hot carcass weight (HCW), cold carcass weight (CCW) and the weights of primal cuts (shoulder, neck, loin, leg and rib) were recorded. In the equations generated to predict SW, HCW and CCW, R² ranged from 0.58 to 0.91 and the measurements of WH, TC, CW, HG and RW were the most relevant. In the equations developed to predict the weight of primal cuts, in turn, R² ranged from 0.26 to 0.99. In these models, SW, BL, CW, TC, LL and HG explained most of the variation in the weight of primal cuts. Biometric measurements can be used to accurately and precisely predict HCW, CCW and the weight of primal cuts from the carcass of Santa Inês sheep finished in tropical pastures, since the equations presented R² and correlation coefficient and agreement above 0.8.

A Novel Estimation of Unobserved Pig Growth Traits for the Purposes of Precision Feeding Methods

Recent technological advances make it possible to deliver feeding strategies that can be tailored to the needs of individual pigs in order to optimise the allocation of nutrient resources and contribute toward reducing excess nutrient excretion. However, these efforts are currently hampered by the challenges associated with: (1) estimation of unobserved traits from the available data on bodyweight and feed consumption; and (2) characterisation of the distributions and correlations of these unobserved traits to generate accurate estimates of individual level variation among pigs. Here, alternative quantitative approaches to these challenges, based on the principles of inverse modelling and separately inferring individual level distributions within a Bayesian context were developed and incorporated in a proposed precision feeding modelling framework. The objectives were to: (i) determine the average and distribution of individual traits characterising growth potential and body composition in an empirical population of growing-finishing barrows and gilts; (ii) simulate the growth and excretion of nitrogen and phosphorus of the average pig offered either a commercial two-phase feeding plan, or a precision feeding plan with daily adjustments; and (iii) simulate the growth and excretion of nitrogen and phosphorus across the pig population under two scenarios: a two-phase feeding plan formulated to meet the nutrient requirements of the average pig or a precision feeding plan with daily adjustments for each and every animal in the population. The distributions of mature bodyweight and ratio of lipid to protein weights at maturity had median (IQR) values of 203 (47.8) kg and 2.23 (0.814) kg/kg, respectively; these estimates were obtained without any prior assumptions concerning correlations between the traits. Overall, it was found that a proposed precision feeding strategy could result in considerable reductions in excretion of nitrogen and phosphorus (average pig: 8.07 and 9.17% reduction, respectively; heterogenous pig population: 22.5 and 22.9% reduction, respectively) during the growing-finishing period from 35 to 120 kg bodyweight. This precision feeding modelling framework is anticipated to be a starting point toward more accurate estimation of individual level nutrient requirements, with the general aim of improving the economic and environmental sustainability of future pig production systems.

Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements benefit from gathering exact measurements of body composition or body nutrient contents. Those are acquired by standardized dissection or by grinding the body followed by wet chemical analysis, respectively. The two methods do not result in the same type of information, but both are destructive. Harnessing human medical imaging techniques for animal science can enable repeated measurements of individuals over time and reduce the number of individuals required for research. Among imaging techniques, dual-energy X-ray absorptiometry (DXA) is particularly promising. However, the measurements obtained with DXA do not perfectly match dissections or chemical analyses, requiring the adjustment of the DXA via calibration equations. Several calibration regressions have been published, but comparative studies of those regression equations and whether they are applicable to different data sets are pending. Thus, it is currently not clear whether existing regression equations can be directly used to convert DXA measurements into chemical values or whether each individual DXA device will require its own calibration. Our study builds prediction equations that relate body composition to the content of single nutrients in growing entire male pigs (BW range 20-100 kg) as determined by both DXA and chemical analyses, with R² ranging between 0.89 for ash and 0.99 for water and CP. Moreover, we show that the chemical composition of the empty body can be satisfactorily determined by DXA scans of carcasses, with the prediction error ranging between 4.3% for CP and 12.6% for ash. Finally, we compare existing prediction equations for pigs of a similar range of BWs with the equations derived from our DXA measurements and evaluate their fit with our chemical analysis data. We found that existing equations for absolute contents that were built using the same DXA beam technology predicted our data more precisely than equations based on different technologies and percentages of fat and lean mass. This indicates that the creation of generic regression equations that yield reliable estimates of body composition in pigs of different growth stages, sexes and genetic breeds could be achievable in the near future. DXA may be a promising tool for high-throughput phenotyping for genetic studies, because it efficiently measures body composition in a large number and wide array of animals.

Energy Requirements of Beef Cattle: Current Energy Systems and Factors Influencing Energy Requirements for Maintenance

Simple Summary

The accurate estimation of energy requirements for present-day genotypes under current feeding conditions is crucial for improving profitability and reducing the environmental impact of the beef industry. Equations for predicting energy requirements of beef cattle according to the Agricultural and Food Research Council (AFRC) are outdated and require an urgent update. The results from literature review confirmed previous reports on the under prediction of energy requirements for maintenance by the AFRC, especially for growing animals. This may have consequences on the efficiency of use of the dietary energy on productive functions. Although much less research has been conducted over the last decade on energy metabolism for suckler cows, the existing data appears to be relevant as a valid reference for updating AFRC recommendations. The present review also revealed the lack of data on the contribution of both animal and diet-related factors influencing on energy requirements for beef cattle and thus conclusions on this regard are difficult to draw.

Abstract

The present review compared features of the UK system for predicting energy requirements in beef cattle with a number of feeding systems developed from research institutes consortiums around the world. In addition, energy requirements for maintenance calculated from studies conducted at the Agri-Food and Biosciences Institute (AFBI) in Northern Ireland since the 1990s were compared with compiled data from recent peer-review papers published over the last decade (2009–2020). The mean metabolisable energy requirement for the maintenance (ME_m) of growing cattle was 0.672 MJ/kg^0.75 according to values obtained from calorimetry studies conducted at AFBI. This value is respectively 8.2 and 19.5% greater than the ME_m values obtained by the Agricultural and Food Research Council (AFRC), and the National Academies of Sciences, Engineering and Medicine (NASEM) equations, but it is in close agreement with the Institut National de la Recherche Agronomique (INRA) approach, when assuming a Bos taurus bull (300 kg LW) and an efficiency for converting energy for maintenance (k_m) of 0.65. Most of the literature data on energy requirements for the maintenance for this animal category were obtained from studies conducted with Bos indicus animals and their crossbreds in Brazilian conditions with this confirming lower requirements of these animals when compared to pure Bos taurus cattle. A simulation of the total ME requirements calculated for an Angus × Friesian steer (LW = 416 kg) offered good quality grass silage, indicated that both AFRC and NASEM systems overestimate (38.5 and 20.5%, respectively) the observed efficiency of converting ME for growth (k_g). When the total ME requirements (maintenance + growth) were assessed, both systems underpredicted total ME requirement in 15.8 and 22.1 MJ/d. The mean ME_m requirements for suckler cows obtained from the literature (0.596 MJ/kg^0.75) is on average 19.1% greater than predictions given by both AFRC and INRA (lactation) equations when considering a 550 kg cow and a k_m value of 0.72. Although no differences in net energy requirements for maintenance (NE_m) were detected between dry and lactating suckler cows, as expected the later displayed greater variation as a result of differences in milk production. On this regard, the INRA model recognise increased NE_m requirements for lactating animals compared to dry cows. The re-evaluation of the concept of diet metabolisability and the analysis of existing data on compensatory growth responses are recommended for future updates of the British system (AFRC) having in to account the particularities of grass-based systems in the UK.

Pre- and Post-Slaughter Methodologies to Estimate Body Fat Reserves in Lactating Saanen Goats

Simple Summary

In this study, we present the results of a trial on which we compared pre- and post-slaughter methodologies to estimate body fat reserves in dairy goats. Our results evidenced that fat thickness measured with ultrasound in the perirenal region was the best pre-slaughter measurement for estimating fat reserves in lactating Saanen goats, whereas empty body weight and hot carcass weight were the best post-slaughter predictors for estimating fat reserves. Body condition score could be a useful tool, but it seems that it needs to be re-evaluated to predict adequately fat depots in lactating Saanen goats.

Abstract

This work aimed to compare pre- and post-slaughter methodologies to estimate body fat reserves in dairy goats. Twenty-six lactating Saanen goats ranging from 43.6 to 69.4 kg of body weight (BW) and from 1.84 to 2.96 of body condition score (BCS; 0–5 range) were used. Fifteen pre-slaughter and four post-slaughter measurement values were used to estimate the weight of fat in the omental (OM), mesenteric (MES), perirenal (PR), organ (ORG), carcass (CARC), and non-carcass components (NC) and total (TOT, calculated as the sum of CARC and NC) depots in goats. The pre-slaughter measurements were withers height; rump height; rump length; pelvis width; chest depth; shoulder width; heart girth; body length; sternum height; BW; BCS assessed in the lumbar (BCSl) and sternal (BCSs) regions; and fat thickness measured by ultrasound in the lumbar (FTUSl), sternal (FTUSs), and perirenal (FTUSpr) regions. The post-slaughter measurements were hot carcass weight (HCW), empty body weight (EBW), and fat thickness measured by digital caliper in the lumbar (FTDCl) and sternal (FTDCs) regions. Linear and multiple regressions were fit to data collected. BW, BCS (from lumbar and sternal regions), all somatic measurements, and fat thickness measured by ultrasound in the lumbar and sternal regions were not adequate to estimate the weight of total fat in lactating Saanen goats (R² ≤ 0.55). The best pre-slaughter and post-slaughter estimators of OM, MES, PR, ORG, NC, and TOT fat were FTUSpr and EBW, respectively. Among pre- and post-slaughter measurements, BCSl (R² = 0.63) and HCW (R² = 0.82) provided the most accurate predictions of CARC fat, respectively. Multiple regression using the pre-slaughter variables FTUSpr, BW, and BCSl yielded estimates of TOT fat with an R² = 0.92 (RSD = 1.14 kg). On the other hand, TOT fat predicted using the post-slaughter variables HCW and FTDCs had an R² = 0.83 (RSD = 1.41 kg). These results confirm that fat reserves can be predicted in lactating Saanen goats with high precision using multiple regression equations combining in vivo measurements.

Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows

This study determined the potential of computer vision systems, namely the whole-side carcass camera (HCC) compared to the rib-eye camera (CCC) and dual energy X-ray absorptiometry (DXA) technology to predict primal and carcass composition of cull cows. The predictability (R²) of the HCC was similar to the CCC for total fat, but higher for lean (24.0%) and bone (61.6%). Subcutaneous fat (SQ), body cavity fat, and retail cut yield (RCY) estimations showed a difference of 6.2% between both CVS. The total lean meat yield (LMY) estimate was 22.4% better for CCC than for HCC. The combination of HCC and CCC resulted in a similar prediction of total fat, SQ, and intermuscular fat, and improved predictions of total lean and bone compared to HCC/CCC. Furthermore, a 25.3% improvement was observed for LMY and RCY estimations. DXA predictions showed improvements in R² values of 26.0% and 25.6% compared to the HCC alone or the HCC + CCC combined, respectively. These results suggest the feasibility of using HCC for predicting primal and carcass composition. This is an important finding for slaughter systems, such as those used for mature cattle in North America that do not routinely knife rib carcasses, which prevents the use of CCC.

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

Simple Summary

The traditional way of estimating the carcass composition by complete dissection of muscle, fat and bone is an expensive, time-consuming and inconsistent process. The purpose of this study was to evaluate the accuracy of a simple video image analysis (VIA) system to predict the composition and primal cuts using light lamb carcasses. The six cuts of the carcasses were grouped according to their commercial value: high-value cuts (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). Results showed the ability of the VIA system to estimate the weight and yield of the groups of carcass joints.

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.

Non-destructive evaluation of carcass and ham traits and meat quality assessment applied to early and late immunocastrated Iberian pigs

Castration is a common practice in Iberian pigs due to their advanced age and high weight at slaughter. Immunocastration (IC) is an alternative to surgical castration that influences carcass and cut fatness. These traits need to be evaluated in vivo and postmortem. The aims of the present work were (a) to determine the relationship between ham composition measured with computed tomography (CT) and in vivo ultrasound (US) and carcass fat thickness measurements, (b) to apply these technologies to early (EIP) and late (LIP) immunocastrated Iberian pigs in order to evaluate carcass fatness and ham tissue composition and (c) to assess meat quality on these animals and to find the relationships between meat quality traits (namely, intramuscular fat (IMF)) and fat depot thicknesses. For this purpose, 20 purebred Iberian pigs were immunocastrated with three doses of Improvac ®, at either 4.5, 5.5 and 9 or 11, 12 and 14 months of age (EIP or LIP; respectively; n = 10 each) and slaughtered at 17 months of age. Fat depots were evaluated in vivo by US, in carcass with a ruler and in hams by CT. Carcass and cut yields, loin meat quality and loin acceptability by consumers were determined. Also, IMF was determined in the loin and three muscles of the ham. Carcass weight was 14.9 kg heavier in EIP vs LIP, and loin backfat thickness (US- and ruler-measured) was also greater in EIP. Similarly, CT-evaluated ham bone and fat contents were greater and smaller for EIP vs LIP, respectively. Loin and ham IMF were also greater in EIP, but the other meat quality parameters were similar. The acceptability of meat by consumers was high and it did not differ between IC protocols. Correlations between several fat depots measured with the different technologies were high. In conclusion, all these technologies allowed fat depot measurements, which were highly correlated despite being obtained at different anatomical locations.

Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss

One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and quality-related traits in rainbow trout (Oncorhynchus mykiss) using three different phenotyping technologies [invasive and non-invasive: microwave-based, digital image analysis, and magnetic resonance imaging (MRI)], (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits (body weight, condition coefficient, head yield, carcass yield, headless gutted carcass yield), and quality-related traits (total fat, percentage of fat in subcutaneous adipose tissue, percentage of fat in flesh, flesh colour); genotypic data were then obtained for all fish using the 57K SNP Axiom® Trout Genotyping array. Heritability estimates for most of the 14 traits examined were moderate to strong, varying from 0.12 to 0.67. Most traits were clearly polygenic, but our genome-wide association studies (GWASs) identified two genomic regions on chromosome 8 that explained up to 10% of the genetic variance (cumulative effects of two QTLs) for several traits (weight, condition coefficient, subcutaneous and total fat content, carcass and headless gutted carcass yields). For flesh colour traits, six QTLs explained 1-4% of the genetic variance. Within these regions, we identified several genes (htr1, gnpat, ephx1, bcmo1, and cyp2x) that have been implicated in adipogenesis or carotenoid metabolism, and thus represent good candidates for further functional validation. Finally, of the three techniques used for phenotyping, MRI demonstrated particular promise for measurements of fat content and distribution, while the digital image analysis-based approach was very useful in quantifying colour-related traits. This work provides new insights that may aid the development of commercial breeding programmes in rainbow trout, specifically with regard to the genetic improvement of yield and flesh-quality traits as well as the use of invasive and/or non-invasive technologies to predict such traits.

Ultrawide band microwave system as a non-invasive technology to predict beef carcase fat depth

A portable ultra-wide band microwave system (MiS) coupled with an open-ended coaxial probe (OCP) or Antipodal Vivaldi Antenna (VPA) was tested as a non-invasive objective measurement to predict beef carcase single site fat depth at commercial abattoirs. Experiment one tested the effectiveness of MiS coupled with a VPA. The VPA was used to predict hot carcase P8 (fat depth on the rump) across 4 slaughter groups (n = 241). The VPA was also used to predict cold carcase rib fat (at the quartering site, 75% along the rib eye muscle) across 5 slaughter groups (n = 598). Experiment two tested the ability of MiS coupled with OCP to measure hot carcase P8 across two slaughter groups (n = 435). A machine learning stacking ensemble method was used to create the prediction equations. Datasets were grouped by prediction trait (P8 or ribfat) and probe/antenna then randomly divided into 5 groups based on tissue depth. Precision was greatest using OCP to predict P8 fat depth with a RMSEP of 2.47 mm and R2 of 0.70. The VPA precision was similar for the two tissue depths assessed, hot carcase P8 had an average RMSEP of 2.86 mm and R2 of 0.58 compared to cold carcase rib fat RMSEP of 2.60 mm and R2 of 0.55.

Intramuscular Fat Prediction Using Color and Image Analysis of Bísaro Pork Breed

This work presents an analytical methodology to predict meat juiciness (discriminant semi-quantitative analysis using groups of intervals of intramuscular fat) and intramuscular fat (regression analysis) in Longissimus thoracis et lumborum (LTL) muscle of Bísaro pigs using as independent variables the animal carcass weight and parameters from color and image analysis. These are non-invasive and non-destructive techniques which allow development of rapid, easy and inexpensive methodologies to evaluate pork meat quality in a slaughterhouse. The proposed predictive supervised multivariate models were non-linear. Discriminant mixture analysis to evaluate meat juiciness by classified samples into three groups-0.6 to 1.1%; 1.25 to 1.5%; and, greater than 1.5%. The obtained model allowed 100% of correct classifications (92% in cross-validation with seven-folds with five repetitions). Polynomial support vector machine regression to determine the intramuscular fat presented R² and RMSE values of 0.88 and 0.12, respectively in cross-validation with seven-folds with five repetitions. This quantitative model (model's polynomial kernel optimized to degree of three with a scale factor of 0.1 and a cost value of one) presented R² and RSE values of 0.999 and 0.04, respectively. The overall predictive results demonstrated the relevance of photographic image and color measurements of the muscle to evaluate the intramuscular fat, rarther than the usual time-consuming and expensive chemical analysis.

Comparison of smartphone and lab-grade NIR spectrometers to measure chemical composition of lamb and beef

Context Near-infrared reflectance spectroscopy (NIRS) has been extensively investigated for non-destructive and rapid determination of pH and chemical composition of meat including water, crude protein, intramuscular fat (IMF) and stable isotopes. Smaller, cheaper NIRS sensors that connect to a smartphone could enhance the accessibility and uptake of this technology by consumers. However, the limited wavelength range of these sensors could restrict the accuracy of predictions compared with benchtop laboratory NIRS models. Aims To compare the precision and accuracy metrics of predicting pH, water, crude protein and IMF of three sample presentations and two sensors. Methods Fresh intact (FI) store-bought beef and lamb steak samples (n = 43) were ground and freeze-dried (FD), and then oven-dried to create freeze-dried oven-dried (FDOD) samples. All three forms of sample presentation (FI, FD, FDOD) were scanned using the smartphone and benchtop NIRS sensors. Key results The IMF was the best predicted trait in FD and FDOD forms by the smartphone NIRS (R2 &gt;0.75; RPD &gt;1.40) with limited differences between the two sensors. However, predictions on FI meat were poorer for all traits regardless of the NIRS scanner used (R2 ≤ 0.67; RPD ≤ 1.58) and not suitable for use in research or industry. Conclusion The smartphone NIRS sensor showed accuracy and precision comparable to benchtop NIRS to predict meat composition. However, these preliminary results found that neither of the two sensors reliably predicted quality attributes for industry or consumer applications. Implications Miniaturised NIRS sensors connected to smartphones could provide a practical solution to measure some meat quality attributes such as IMF, but the accuracy depends on sample presentation.

Prediction of lamb carcase C-site fat depth and GR tissue depth using a non-invasive portable microwave system

The experiment evaluated the ability of portable ultra-wide band microwave coupled with a Vivaldi patch antenna to predict carcase C-site fat and GR tissue depth. For C-site, 1070 lambs, across 8 slaughter groups were scanned and for GR, 286 lambs across 2 slaughter groups. Prediction equations for reflected microwave signals were constructed with a partial least squares regression two-components model and a machine learning Ensemble Stacking technique. Models were trained and validated using cross validation methods in actual datasets and then in datasets balanced for tissue depth. The precision and accuracy indicators of microwave predicted C-site fat depth across pooled and balanced datasets were RMSEP 1.53 mm, R2 0.54, and bias of 0.03 mm. The precision and accuracy for GR tissue depth across pooled and balanced datasets were RMSEP 2.57 mm, R2 0.79 and bias of 0.33 mm. Using the AUS-MEAT fat score accreditation framework this device was able to accurately predict GR 92.7% of the time.