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首页> 外文期刊>Journal of dairy research >A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: highlighting the predictive power of lactose and electrical conductivity
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A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: highlighting the predictive power of lactose and electrical conductivity

机译：通过对牛奶成分特征进行属性加权分析对奶牛亚临床乳腺炎指标进行大规模研究：突出乳糖的预测能力和电导率

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摘要

Sub-clinical mastitis (SCM) affects milk composition. In this study, we hypothesise that large-scale mining of milk composition features by pattern recognition models can identify the best predictors of SCM within the milk composition features. To this end, using data mining algorithms, we conducted a large-scale and longitudinal study to evaluate the ability of various milk production parameters as indicators of SCM. SCM is the most prevalent disease of dairy cattle, causing substantial economic loss for the dairy industry. Developing new techniques to diagnose SCM in its early stages improves herd health and is of great importance. Test-day Somatic Cell Count (SCC) is the most common indicator of SCM and the primary mastitis surveillance approach worldwide. However, test-day SCC fluctuates widely between days, causing major concerns for its reliability. Consequently, there would be great benefit to identifying additional efficient indicators from large-scale and longitudinal studies. With this intent, data was collected at every milking (twice per day) for a period of 2 months from a single farm using in-line electronic equipment (346 248 records in total). The following data were analysed: milk volume, protein concentration, lactose concentration, electrical conductivity (EC), milking time and peak flow. Three SCC cut-offs were used to estimate the prevalence of SCM: Australian≥250000 cells/ml, European ≥200000 cells/ml and New Zealand ≥ 150 000 cells/ml. At first, 10 different Attribute Weighting Algorithms (AWM) were applied to the data. In the absence of SCC, lactose concentration featured as the most important variable, followed by EC. For the first time, using attribute weighted modelling, we showed that the concentration of lactose in milk can be used as a strong indicator of SCM. The development of machine-learning expert systems using two or more milk variables (such as lactose concentration and EC) may produce a predictive pattern for early SCM detection.
机译：亚临床乳腺炎（SCM）影响牛奶成分。在这项研究中，我们假设通过模式识别模型大规模挖掘牛奶成分特征可以确定牛奶成分特征中SCM的最佳预测因子。为此，我们使用数据挖掘算法进行了大规模的纵向研究，以评估各种牛奶生产参数作为SCM指标的能力。 SCM是奶牛最流行的疾病，给奶业造成巨大的经济损失。开发早期诊断SCM的新技术可改善畜群健康，这一点非常重要。测试日的体细胞计数（SCC）是SCM的最常见指标，也是全世界主要的乳腺炎监测方法。但是，测试日SCC在几天之间波动很大，引起对其可靠性的重大关注。因此，从大规模和纵向研究中确定其他有效指标将大有裨益。为此，在一个挤奶场使用联机电子设备在每个挤奶（每天两次）进行为期2个月的收集数据（总共346248条记录）。分析了以下数据：牛奶体积，蛋白质浓度，乳糖浓度，电导率（EC），挤奶时间和峰值流量。使用三个SCC临界值来估计SCM的患病率：澳大利亚≥250000细胞/ ml，欧洲≥200000细胞/ ml和新西兰≥150000细胞/ ml。首先，将10种不同的属性加权算法（AWM）应用于数据。在没有SCC的情况下，乳糖浓度是最重要的变量，其次是EC。首次使用属性加权建模，我们表明牛奶中乳糖的浓度可以用作SCM的有力指标。使用两个或多个牛奶变量（例如乳糖浓度和EC）的机器学习专家系统的开发可能会为早期SCM检测提供预测模式。

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  • 来源
    《Journal of dairy research》 |2018年第2期|193-200|共8页
  • 作者

    Esmaeil Ebrahimie; Faezeh Ebrahimi; Mansour Ebrahimi; Sarah Tomlinson; Kiro R Petrovski;

  • 作者单位

    School of Medicine, The University of Adelaide, Adelaide 5005, Australia,Division of Information Technology, Engineering & Environment, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia,Faculty of Science and Engineering, School of Biological Sciences, Flinders University, Adelaide, Australia,Institute of Biotechnology, Shiraz University, Shiraz, Iran;

    Department of Biology, University of Qom, Qom, Iran;

    Department of Biology, University of Qom, Qom, Iran;

    School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide 5371, Australia;

    School of Animal and Veterinary Sciences, The University of Adelaide, Adelaide 5371, Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Attribute Weighting; Expert system; Machine Learning; Mastitis; Milk Composition;

    机译：属性权重;专业系统;机器学习;乳腺炎;牛奶成分;

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