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电子鼻系统通过检测粪便中挥发性有机化合物,非侵入性检测野猪中复杂感染的方法。

Non-Invasive Method to Detect Infection with Complex in Wild Boar by Measurement of Volatile Organic Compounds Obtained from Feces with an Electronic Nose System.

机构信息

Mechatronics Engineering Department, Universidad Autónoma del Caribe, Barranquilla 080020, Colombia.

GISM Group, Faculty of Engineering and Architecture, University of Pamplona, Pamplona 543050, Colombia.

出版信息

Sensors (Basel). 2021 Jan 15;21(2):584. doi: 10.3390/s21020584.

Abstract

More effective methods to detect bovine tuberculosis, caused by , in wildlife, is of paramount importance for preventing disease spread to other wild animals, livestock, and human beings. In this study, we analyzed the volatile organic compounds emitted by fecal samples collected from free-ranging wild boar captured in Doñana National Park, Spain, with an electronic nose system based on organically-functionalized gold nanoparticles. The animals were separated by the age group for performing the analysis. Adult (>24 months) and sub-adult (12-24 months) animals were anesthetized before sample collection, whereas the juvenile (<12 months) animals were manually restrained while collecting the sample. Good accuracy was obtained for the adult and sub-adult classification models: 100% during the training phase and 88.9% during the testing phase for the adult animals, and 100% during both the training and testing phase for the sub-adult animals, respectively. The results obtained could be important for the further development of a non-invasive and less expensive detection method of bovine tuberculosis in wildlife populations.

摘要

更有效地检测由引起的牛结核病对于防止疾病传播到其他野生动物、牲畜和人类至关重要。在这项研究中,我们分析了从西班牙多纳纳国家公园捕获的自由放养野猪的粪便样本中释放的挥发性有机化合物,使用基于有机功能化金纳米粒子的电子鼻系统。动物们按年龄组分开进行分析。成年 (>24 个月) 和亚成年 (12-24 个月) 动物在采集样本前被麻醉,而幼年 (<12 个月) 动物在采集样本时被手动固定。对于成年和亚成年分类模型,我们获得了良好的准确性:成年动物在训练阶段的准确率为 100%,在测试阶段的准确率为 88.9%,亚成年动物在训练和测试阶段的准确率均为 100%。这些结果对于进一步开发一种非侵入性和成本较低的野生动物牛结核病检测方法可能很重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c79/7829825/166ae34381af/sensors-21-00584-g001.jpg

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