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An open-access computer image analysis (CIA) method to predict meat and fat content from an android smartphone-derived picture of the bovine 5th-6th rib
Methods ( IF 4.8 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.ymeth.2020.06.023 Bruno Meunier 1 , Jérôme Normand 2 , Benjamin Albouy-Kissi 3 , Didier Micol 1 , Mohammed El Jabri 4 , Muriel Bonnet 1
Methods ( IF 4.8 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.ymeth.2020.06.023 Bruno Meunier 1 , Jérôme Normand 2 , Benjamin Albouy-Kissi 3 , Didier Micol 1 , Mohammed El Jabri 4 , Muriel Bonnet 1
Affiliation
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Marbling and rib composition are important attributes related to carcass yields and values, beef quality, consumer satisfaction and purchasing decisions. An open-access computer image analysis method based on a fresh beef rib image captured under nonstandardized and uncontrolled conditions was developed to determine the intramuscular, intermuscular and total fat content. For this purpose, cross-section images of the 5th-6th rib from 130 bovine carcasses were captured with a Galaxy S8 smartphone. The pictures were analyzed with a program developed using ImageJ open source software. The 17 processed image features that were obtained were mined relative to gold standard measures, namely, intermuscular fat, total fat and muscles dissected from a rib and weighed, and intramuscular fat content (IMF - marbling) determined by the Soxhlet method. The best predictions with the lowest prediction errors were obtained by the sparse partial least squares method for both IMF percent and rib composition and from a combination of animal and image analysis features captured from the caudal face of the 6th rib captured on a table. These predictions were more accurate than those based on animal and image analysis features captured from the caudal face of the 5th rib on hanging carcasses. The external-validated prediction precision was 90% for IMF and ranged from 71 to 86% for the total fat, intermuscular and muscle rib weight ratios. Therefore, an easy, low-cost, user-friendly and rapid method based on a smartphone picture from the 6th rib of bovine carcasses provides an accurate method for fat content determination.
中文翻译:
一种开放式计算机图像分析 (CIA) 方法,可从来自安卓智能手机的牛第 5-6 肋骨图片中预测肉和脂肪含量
大理石花纹和肋骨成分是与胴体产量和价值、牛肉质量、消费者满意度和购买决策相关的重要属性。开发了一种基于在非标准化和不受控制的条件下捕获的新鲜牛肋骨图像的开放式计算机图像分析方法,以确定肌肉内、肌肉间和总脂肪含量。为此,使用 Galaxy S8 智能手机拍摄了 130 头牛尸体的第 5-6 肋的横截面图像。这些图片使用 ImageJ 开源软件开发的程序进行分析。获得的 17 个处理过的图像特征是相对于黄金标准测量值挖掘的,即肌肉间脂肪、总脂肪和从肋骨解剖并称重的肌肉,以及通过索氏方法确定的肌肉内脂肪含量(IMF - 大理石花纹)。具有最低预测误差的最佳预测是通过稀疏偏最小二乘法获得的 IMF 百分比和肋骨成分以及从桌子上捕获的第 6 肋骨尾部面部捕获的动物和图像分析特征的组合。这些预测比基于从悬挂尸体第 5 肋尾面部捕获的动物和图像分析特征的预测更准确。IMF 的外部验证预测精度为 90%,总脂肪、肌肉间和肌肉肋骨重量比的范围为 71% 至 86%。因此,一种基于牛尸体第 6 肋骨的智能手机图片的简单、低成本、用户友好且快速的方法提供了一种准确的脂肪含量测定方法。
更新日期:2021-02-01
中文翻译:
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一种开放式计算机图像分析 (CIA) 方法,可从来自安卓智能手机的牛第 5-6 肋骨图片中预测肉和脂肪含量
大理石花纹和肋骨成分是与胴体产量和价值、牛肉质量、消费者满意度和购买决策相关的重要属性。开发了一种基于在非标准化和不受控制的条件下捕获的新鲜牛肋骨图像的开放式计算机图像分析方法,以确定肌肉内、肌肉间和总脂肪含量。为此,使用 Galaxy S8 智能手机拍摄了 130 头牛尸体的第 5-6 肋的横截面图像。这些图片使用 ImageJ 开源软件开发的程序进行分析。获得的 17 个处理过的图像特征是相对于黄金标准测量值挖掘的,即肌肉间脂肪、总脂肪和从肋骨解剖并称重的肌肉,以及通过索氏方法确定的肌肉内脂肪含量(IMF - 大理石花纹)。具有最低预测误差的最佳预测是通过稀疏偏最小二乘法获得的 IMF 百分比和肋骨成分以及从桌子上捕获的第 6 肋骨尾部面部捕获的动物和图像分析特征的组合。这些预测比基于从悬挂尸体第 5 肋尾面部捕获的动物和图像分析特征的预测更准确。IMF 的外部验证预测精度为 90%,总脂肪、肌肉间和肌肉肋骨重量比的范围为 71% 至 86%。因此,一种基于牛尸体第 6 肋骨的智能手机图片的简单、低成本、用户友好且快速的方法提供了一种准确的脂肪含量测定方法。