Beef carcass grading and classification

This paper addresses the principles related to different grading and classification systems of the world with specific focus on beef quality related outcomes. The paper uses the definitions that classification is a set of descriptive terms describing features of the carcass that are useful as guidelines to those involved in the production, trading and consumption of carcasses, whereas grading is the placing of different values on carcasses for pricing purposes, depending on the market and requirements of traders and consumers. The literature shows that the criteria used in grading systems rank carcasses fairly accurately according to expected eating experience of the loin muscles but not of higher connective tissue cuts of the hind and fore quarter. Criteria used in classification systems give limited descriptions of the quality related characteristics of the carcass. Only the Meat Standards Australia (MSA) cuts based grading system of Australia seeks to define or predict consumer satisfaction with a cooked meal for each cut of the carcass. Its success is based on a palatability assured critical control point (PACCP) approach to satisfy the consumer. However, MSA requires high technical skills, a well organised infrastructure and proper traceability, high level of integrity from different role players and could be very costly, involving high additional personnel cost. The South African classification system should probably focus on distinguishing between young feedlot and somewhat more mature pasture animals with different criteria within each sub category to describe the variation in product quality. Correct pre-slaughter and slaughter management (stress, weight, chilling rate, electrical stimulation, post mortem aging) could improve consistency within age group.Keywords: Classification, grading, beef, dentition, age

Determination of age of lamb carcasses from pelvic ossification

Papillary Urothelial Neoplasm of Low Malignant Potential: Evolving Terminology and Concepts

Beef carcass classification in Europe is predicated on the EUROP grid for both fatness and conformation. Although this system performs well for grouping visually similar carcasses, it cannot be used to accurately predict meat yields from these groups, especially when considered on an individual cut level. Deep Learning (DL) has proven to be a successful tool for many image classification problems but has yet to be fully proven in a regression scenario using carcass images. Here we have trained DL models to predict carcass cut yields and compared predictions to more standard machine learning (ML) methods. Three approaches were undertaken to predict the grouped carcass cut yields of two categories of cuts, namely Grilling cuts and Roasting cuts from a large dataset of 54,598 and 69,246 animals respectively. The approaches taken were (1) animal phenotypic data used as features for a range of machine learning (ML) algorithms, (2) carcass images used to train Convolutional Neural Networks, and (3) carcass dimensions measured directly from the carcass images, combined with the associated phenotypic data and used as feature data for ML algorithms. For Grilling cuts, models developed in Approach 1 had the lowest coefficient of determination (R2) compared to the two other approaches. Deep Learning models had a slightly improved performance for Grilling cuts but approach 3 performed best. Similarly, for Roasting cuts approach 3 performed best, whereas approaches 1 and 2 performed similarly. Our results show that DL models can be trained to predict carcass cuts but an approach that uses carcass dimensions in ML algorithms performs slightly better in absolute terms. However, as our DL models use only image data these models can be deployed more practically at an abattoir level.

In-line image analysis in the slaughter industry, illustrated by Beef Carcass Classification

Pancreatic neuroendocrine tumors (PNETs) are the second most common neoplasm of the pancreas after ductal adenocarcinoma. The clinical behavior of PNETs is very difficult to predict, especially for well-differentiated PNETs. Several classification systems have been developed to differentiate benign from malignant PNETs. The current 5th edition WHO (2019) updated the grading system of PNETs which is now entirely based on proliferative rate (either mitotic count or Ki-67 index). In this article, we systemically reviewed the evolution of the histologic classification and grading systems for well-differentiated PNETs. Meanwhile, we also report a comprehensive summary of immunohistochemical and molecular prognostic factors for PNETs patients, including CK19, KIT, PD-L1/PD-L1, UCH-L1 and CNPY2. We believe that more reliable grading systems and prognostic markers are emerging and will change the landscape of treatment for well-differentiated PNETs.

Background:Pathology involving the acetabular fossa is often identified during hip arthroscopy, with many of the descriptive classification systems focusing on the ligamentum teres (LT). Recently, a novel grading system for ligamentous-fossa-foveolar complex (LFFC) lesions was introduced for open hip preservation surgery that included evaluation of the cotyloid fossa and perifoveal cartilage.Purpose:To validate the LFFC grading system for hip arthroscopy and correlate these results with preoperative and intraoperative findings.Study Design:Cohort study (diagnosis); Level of evidence, 3.Methods:High-resolution arthroscopic images of the central compartment were obtained identifying the LT, cotyloid fossa, and perifoveal cartilage in a prospective series of patients undergoing hip arthroscopy. Each structure was graded according to increasing pathology on a scale of 0 to 4 based on a modified LFFC classification system to include the pulvinar intra-articular adipose tissue. Five surgeons graded the images, which were then randomized and regraded for intraobserver reliability. Agreement was quantified by the intraclass correlation coefficient (ICC) and kappa (κ) statistic to determine inter- and intraobserver reliability. Grading discrepancies were resolved in conference with the senior author, and final LFFC grades were compared with preoperative clinical and radiographic data as well as intraoperative findings.Results:A total of 93 patients were included in the study. LFFC component intraobserver reliability for 2 rounds of grading resulted in an LT ICC of 0.78 to 0.90, cotyloid fossa ICC of 0.85 to 0.93, and perifoveal cartilage ICC of 0.78 to 0.87 with an LFFC total score ICC of 0.87 to 0.95. Interobserver reliability resulted in an LT ICC of 0.73 to 0.91, cotyloid fossa ICC of 0.84 to 0.95, perifoveal cartilage ICC of 0.83 to 0.91, and LFFC total score ICC of 0.89 to 0.96. Severe central compartment pathology (LFFC total score >6) was significantly associated with age (48.2 vs 34.4; P = .0002), preoperative Tönnis grade 1 (43% vs 4%; P < .0001), preoperative Tönnis angle (8.7 vs 5.2; P = .002), and intraoperative femoral head weightbearing chondral lesions (14% vs 0%; P = .02) when compared to patients with an LFFC score <6.Conclusion:The modified LFFC grading system demonstrated satisfactory intraobserver and interobserver reliability for patients undergoing hip arthroscopy that compares favorably with existing arthroscopic classification systems for the acetabular fossa. The addition of a descriptive classification system for the pulvinar intra-articular adipose tissue did not decrease the reliability of the grading system. Increasing LFFC scores were found to be associated with known risk factors for inferior outcomes after hip arthroscopy providing enhanced clinical utility for this grading system.

Meat standards and grading

Although rosacea is a chronic cutaneous inflammatory disorder that's commonly seen in adults, the etiology and pathogenesis of the illness remain unclear. A well established diagnostic classification and grading system may play a critical role in performing research and it would serve as a diagnostic reference in the clinical field. We sought to classify the patients with the new standard classification and grading system and we wanted determine the peculiar features and relationships of each subtype. We also analyzed the relationships between the degree of sun exposure and each subtype. We reviewed the medical records and clinical photos of 168 patients who were diagnosed with rosacea from 2002 to 2007 at our hospital. The standard classification and grading system suggested by the National Rosacea Society (NRS) Expert Committee was adopted to evaluate each patient's subtype and the severity. The male:female ratio was 1:2.29. The mean age at the time of diagnosis was 47.8 years. The mean duration of disease was 3.5 years. Sun exposure and hot baths/exercise were the two most common precipitating factors, while the majority of patients did not have any specific factor that relieved their symptoms. According to the NRS classification and grading system, the patients were classified into four subtypes. One hundred sixty two (96.4%) patients were diagnosed with the erythematotelangiectatic subtype irrespective of severity. Eighty five (50.6%) patients had the papulopustular subtype and 24 (14.3%) patients had ocular rosacea. Eight (4.8%) patients displayed mild phymatous change. The degree of sun exposure had significant correlation with the development and severity of the erythematotelangiectatic subtype (p<0.05), while it had no correlation with the papulopustular, ocular and phymatous subtypes. Although the erythematotelangiectatic subtype was the most common subtype of rosacea, many patients also had other subtypes of rosacea simultaneously. Based on our results, we proved that ocular rosacea is an extension of the clinical spectrum of erythematotelangiectatic rosacea. In addition, the results of our study particularly suggest that sun exposure has a different influence on each subtype of rosacea.

Accuracy of live ultrasound measurements to evaluate the total tissue depth (GR), as well as fat and LM depths at different scanning sites, was studied in 96 purebred Suffolk and Dorset lambs of both sexes slaughtered between 36 and 54 kg of BW. Before slaughter, 7 real-time ultrasound measurements were taken on lambs: fat and LM depths between the 12th and 13th ribs (transverse) and between the 3rd and 4th lumbar vertebrae (transverse and longitudinal), and GR. After slaughter, the measurements equivalent to ultrasound measurements were taken on digitized images of the cuts on the left half carcass of each lamb. Ultrasound GR and fat depth measurements were closely correlated with the corresponding carcass measurements (0.76 < or = r < or = 0.81). Ultrasound GR measurement exhibited a large error of central tendency, but the level of error due to the disturbance (ED) was comparable with fat depth measurements (ED = 8.5%; residual SD = 2.24 mm; CV of residuals = 9.5%). Ultrasound fat depth measurements were more accurate between the 12th and 13th ribs (error due to regression = 1.20; ED = 0.82) than between the 3rd and 4th lumbar vertebrae (error due to regression = 5.58 and 5.4; ED = 1.10 and 0.93, transverse and longitudinal, respectively), mainly due to image interpretation errors in the lumbar region. Measurements of LM depth demonstrated low variability in the population under study (SD = 2.6 mm), and these ultrasound measurements showed low correlation with the corresponding carcass measurements (0.34 < or = r < or = 0.43). The results of this study demonstrated that ultrasound measurements were more accurate for evaluating fat depth and the GR measurements than for estimating LM depths. Ultrasound GR measurement is a promising measurement, especially where carcass grading systems are based on this carcass measurement.

Letter from the editor

The European beef carcass grading scheme prioritizes meat yield over consumer eating experience, in contrast to the Meat Standards Australia (MSA) grading scheme, which is more focused on eating quality. The Italian–French beef production system, mostly characterized by young bulls and heifers imported from France and then fattened within Italian specialized fattening units, has been underexplored using the MSA grading system. This study examines the impact of animal sex on performance, MSA traits, and predicted MSA quality scores (MQ4 and MSA index) using carcasses from an Italian commercial abattoir. It also assesses how animal performance and MSA traits influence predicted beef eating quality. A Random Forest classifier demonstrated high performance with an accuracy of .98, a specificity of .99, and a sensitivity of .97 after 10-fold cross-validations, confirming key traits such as hot carcass weight, European carcass grading scheme fatness score, and general muscular characteristics as being significantly different due to sex. Entire males had greater muscle development, whereas females had notably higher MSA marbling scores, MQ4 scores, and MSA index (P &amp;lt; .05). Furthermore, categorizing the MSA index into 2 classes and applying a similar Random Forest classifier approach revealed that MSA marbling was the primary factor influencing variability of the MSA index. These findings suggest that beef cuts from females may better meet consumer expectations when using the MSA grading scheme in Europe, making them a suitable category for premium beef branding. Grading carcasses from entire male animals for sensory quality is also important, as it demonstrates their potential to produce good-quality beef with a higher lean meat yield; however, their sensory quality tends to be lower than that of heifers and this is in part reflected by their lower marbling score resulting from reduced intramuscular fat deposition.

The carcasses of South African pigs are classified into six groups (PORCUS) according to their calculated lean meat content and measured backfat thickness. Pigs with the highest lean meat content and lowest backfat thickness belong to the P group. Fat quality of backfat from 2107 pig carcasses, sampled within the different pork classification groups at a major South African pig abattoir, was evaluated to determine the relationship between carcass classification and backfat quality. Extracted fat was used to determine the iodine value of these samples. Significant differences in carcass characteristics and iodine value were observed between the classification groups. Improved fat quality, indicated by a lower iodine value, was associated with increased backfat thickness and decreased lean meat content. The P and O classification groups (with backfat thickness &lt;17 mm) could not conform to international standards proposed for good technological quality backfat. The C, U and S groups (with backfat between 23 and &gt;32 mm) possessed good technological quality backfat. Backfat from the R group had borderline fat quality. Correlation analyses were employed to acquire equations to describe relationships between iodine value and carcass classification data. The French classification system predicts fat quality by utilizing backfat thickness and lean meat content. It was proposed, through modification of the French system, that South African pig carcasses with a backfat thickness &gt;17 mm and a lean meat content &lt;67% would potentialy deliver good quality backfat. Results from this study indicate that carcass classification data may be used to improve the probability of selecting pig carcasses with good quality fat.Keywords: Backfat, fatty acid(s), iodine value, meat processing, South Africa, survey, swine

Post-endoscopy Esophageal Neoplasia in Barrett’s Esophagus: Consensus Statements From an International Expert Panel

Classification systems in Gestational trophoblastic neoplasia - Sentiment or evidenced based?