Mensching André, Zschiesche Marleen, Hummel Jürgen, Schmitt Armin Otto, Grelet Clément, Sharifi Ahmad Reza
Animal Breeding and Genetics Group, Department of Animal Sciences, University of Goettingen, Albrecht-Thaer-Weg 3, 37075 Goettingen, Germany.
Center for Integrated Breeding Research (CiBreed), University of Goettingen, Albrecht-Thaer-Weg 3, 37075 Goettingen, Germany.
Animals (Basel). 2020 Aug 13;10(8):1412. doi: 10.3390/ani10081412.
The aim of this work was to develop an innovative multivariate plausibility assessment (MPA) algorithm in order to differentiate between 'physiologically normal', 'physiologically extreme' and 'implausible' observations in simultaneously recorded data. The underlying concept is based on the fact that different measurable parameters are often physiologically linked. If physiologically extreme observations occur due to disease, incident or hormonal cycles, usually more than one measurable trait is affected. In contrast, extreme values of a single trait are most likely implausible if all other traits show values in a normal range. For demonstration purposes, the MPA was applied on a time series data set which was collected on 100 cows in 10 commercial dairy farms. Continuous measurements comprised climate data, intra-reticular pH and temperature, jaw movement and locomotion behavior. Non-continuous measurements included milk yield, milk components, milk mid-infrared spectra and blood parameters. After the application of the MPA, in particular the pH data showed the most implausible observations with approximately 5% of the measured values. The other traits showed implausible values up to 2.5%. The MPA showed the ability to improve the data quality for downstream analyses by detecting implausible observations and to discover physiologically extreme conditions even within complex data structures. At this stage, the MPA is not a fully developed and validated management tool, but rather corresponds to a basic concept for future works, which can be extended and modified as required.
这项工作的目的是开发一种创新的多变量似真性评估（MPA）算法，以便在同时记录的数据中区分“生理正常”、“生理极端”和“不合理”的观测值。其基本概念基于这样一个事实，即不同的可测量参数通常在生理上是相关联的。如果由于疾病、事件或激素周期出现生理极端观测值，通常不止一个可测量特征会受到影响。相比之下，如果所有其他特征的值都在正常范围内，单个特征的极端值很可能是不合理的。为了演示目的，MPA应用于一个时间序列数据集，该数据集是在10个商业奶牛场的100头奶牛身上收集的。连续测量包括气候数据、瘤胃内pH值和温度、颌部运动和运动行为。非连续测量包括产奶量、牛奶成分、牛奶中红外光谱和血液参数。应用MPA后，特别是pH数据显示出约5%的测量值为最不合理的观测值。其他特征显示的不合理值高达2.5%。MPA显示出能够通过检测不合理观测值来提高下游分析的数据质量，甚至在复杂的数据结构中发现生理极端情况。在这个阶段，MPA不是一个完全开发和验证的管理工具，而是对应于未来工作的一个基本概念，可以根据需要进行扩展和修改。
