Afonso João J, Almeida Mariana, Batista Ana Catharina, Guedes Cristina, Teixeira Alfredo, Silva Severiano, Santos Virgínia
Centre for Interdisciplinary Research in Animal Health (CIISA), Faculty of Veterinary Medicine, University of Lisbon, Avenida da Universidade Técnica, 1300-477 Lisboa, Portugal.
Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal.
Animals (Basel). 2024 May 28;14(11):1593. doi: 10.3390/ani14111593.
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-R 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-R 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.
多年来，人们探索了许多技术来评估绵羊胴体的组成和质量。本研究重点关注利用视频图像分析（VIA）来评估轻型羔羊胴体（平均冷胴体重±标准差为4.52±1.34千克）的组成。对55只轻型羔羊胴体的侧面和背面拍摄的照片进行了分析。记录了一套全面的测量数据，包括长度、宽度、角度、面积和周长等尺寸，侧面视图图像共有21项测量数据，背面视图图像有29项。采用K折逐步多元回归分析来构建胴体组织重量（包括肌肉、皮下脂肪、肌间脂肪和骨骼）及其各自百分比的预测模型。利用冷胴体重（CCW）的数据以及背面和侧面视图的测量数据建立了最有效的预测方程。这些模型解释了所有胴体组织重量中很大一部分观察到的变异（K折相关系数范围为0.83至0.98）。就胴体组织百分比而言，虽然解释的变异程度略低（K折相关系数范围为0.41至0.78），但VIA测量值仍是预测模型的重要组成部分。这些发现强调了VIA作为评估轻型羔羊胴体（约4至8千克重的胴体）组成的客观工具的有效性。
