Sampling data set

In areas where livestock is bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. Existing electronic nose systems, however, struggle with slow response times and limited detection accuracy. This paper introduces a novel solution: an active pumping ammonia detection system using artificial olfaction based on a biomimetic chamber. We analyzed the biomimetic chamber’s structure and the sensor array's surface airflow to determine the system’s sensing units. The system employs an electronic nose to detect ammonia and ethanol gases in a circulating airflow within a closed box. The captured signals underwent processing, followed by the application of classification and regression models for data prediction. Our results suggest that this system, leveraging the biomimetic chamber, offers rapid gas detection response times. Incorporating a backpropagation (BP) neural network algorithm, it realizes high classification prediction accuracy, with a determination coefficient R² value of 0.99 for single-output regression and over 0.98 for multi-output regression predictions. These outcomes demonstrate the system’s effectiveness in accurately detecting ammonia emitted during livestock fermentation, meeting the ammonia detection requirements of breeding farms.

在畜牧养殖区域，对养殖环境氨气浓度的精准、实时、稳定监测存在迫切需求。然而现有电子鼻（electronic nose）系统普遍存在响应速度缓慢、检测精度有限的问题。本文提出一种新型解决方案：基于仿生腔（biomimetic chamber）的人工嗅觉（artificial olfaction）主动抽气式氨气检测系统。我们通过分析仿生腔的结构与传感器阵列表面的气流场，确定了系统的传感单元。该系统利用电子鼻在密闭箱体内的循环气流中完成氨气与乙醇气体的检测。采集到的信号经信号处理后，通过分类与回归模型实现数据预测。实验结果表明，搭载仿生腔的该系统可实现快速的气体检测响应速度。结合反向传播（backpropagation，BP）神经网络算法后，该系统可实现高精度的分类与预测性能：单输出回归的决定系数R²达0.99，多输出回归预测的决定系数R²亦超过0.98。上述结果证明，该系统可精准检测畜牧发酵过程中产生的氨气，能够满足养殖场的氨气检测需求。