College of Engineering, Huazhong Agricultural University, Wuhan, Hubei, China.
Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, Hubei, China.
Int J Biometeorol. 2019 Oct;63(10):1405-1415. doi: 10.1007/s00484-019-01758-2. Epub 2019 Aug 2.
Rectal temperature is an important physiological indicator used to characterize the reproductive and health status of sows. Infrared thermography, a surface temperature measurement technology, was investigated in this study to explore its feasibility in non-invasive detection of rectal temperature in sows. A total of 124 records of rectal temperature and surface temperature in various body regions of 99 Landrace × Yorkshire crossbred sows were collected. These surface temperatures together with ambient temperature, ambient humidity, and wind speed in pig pens were correlated with the real rectal temperature of sows to establish rectal temperature prediction models by introducing chemometrics algorithms. Two types of models, i.e., full feature models and selected feature models, were established by applying the partial least squares regression (PLSR) method. The optimal model was attained when 7 important features were selected by LARS-Lasso, where correlation coefficients and root mean squared errors of calibration were 0.80 and 0.30 °C, respectively. Particularly, the validity and stability of established simplified models were further evaluated by applying the model to an independent prediction set, where correlation coefficients and root mean squared errors for prediction were 0.80 and 0.35 °C, respectively. The validation of established models is scarce in previous similar studies. Above all, this study demonstrated that introduction of chemometrics methodologies would lead to more reliable and accurate model for predicting sow rectal temperature, thus the potential for ensuring animal welfare in a broader view if extended to more applications.
直肠温度是用于描述母猪繁殖和健康状况的重要生理指标。本研究探讨了红外热成像技术，这是一种表面温度测量技术，以探索其在非侵入性检测母猪直肠温度中的可行性。共收集了 99 头长白 × 大约克夏杂交母猪的 124 条直肠温度和各个身体部位表面温度记录，以及猪圈中的环境温度、环境湿度和风速。这些表面温度与母猪的真实直肠温度相关联，通过引入化学计量学算法建立了直肠温度预测模型。应用偏最小二乘回归（PLSR）方法建立了全特征模型和选择特征模型两种类型的模型。通过 LARS-Lasso 选择了 7 个重要特征，获得了最佳模型，其校准的相关系数和均方根误差分别为 0.80 和 0.30°C。特别是，通过将模型应用于独立预测集，进一步评估了简化模型的有效性和稳定性，预测的相关系数和均方根误差分别为 0.80 和 0.35°C。在以前的类似研究中，对所建立模型的验证很少。总之，本研究表明，引入化学计量学方法可以为预测母猪直肠温度提供更可靠和准确的模型，如果扩展到更多应用，有望更广泛地保障动物福利。
