Shi Yeping, Shi Yunbo, Niu Haodong, Liu Jinzhou, Sun Pengjiao
The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin, China.
Electronics and Communication Engineering School, Jilin Technology College of Electronic Information, Jilin, China.
PLoS One. 2024 Dec 3;19(12):e0309228. doi: 10.1371/journal.pone.0309228. eCollection 2024.
Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. We design and develop an electronic nose system containing a bionic chamber that imitates the nasal-cavity structure of humans and canines. The sensors are positioned based on fluid simulation results. Response data for ammonia and ethanol gases and the response/ recovery times of an ammonia sensor under three concentrations are collected using the electronic nose system. Response data are classified and regressed using a sparrow search algorithm (SSA)-optimized backpropagation neural network (BPNN). The results show that the sensor has a relative mean deviation of 1.45%. The ammonia sensor's output voltage is 1.3-2.05 V when the ammonia concentration ranges from 15 to 300 ppm. The ethanol gas sensor's output voltage is 1.89-3.15 V when the ethanol gas concentration ranges from 8 to 200 ppm. The average response time of the ammonia sensor in the chamber is 13 s slower than that of the sensor directly exposed to the gas being measured, while the average recovery time is 19 s faster. In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. It also outperforms the other models at regression prediction, with smaller absolute, mean absolute, and root mean square errors. Its coefficient of determination (R2) is greater than 0.99, surpassing those of the SVM and RF models. The theoretical and experimental results both indicate that the proposed electronic nose system containing a bionic chamber, when used with the SSA-BPNN, offers a promising approach for detecting ammonia in livestock- and poultry-breeding facilities.
氨被广泛认为是一种应激源，也是动物饲养环境中最有害的气体之一。在畜禽养殖设施中，一种精确、快速且经济实惠的氨浓度检测方法至关重要。我们设计并开发了一种电子鼻系统，该系统包含一个模仿人类和犬类鼻腔结构的仿生腔室。传感器根据流体模拟结果进行定位。使用该电子鼻系统收集氨和乙醇气体的响应数据以及氨传感器在三种浓度下的响应/恢复时间。响应数据使用麻雀搜索算法（SSA）优化的反向传播神经网络（BPNN）进行分类和回归。结果表明，该传感器的相对平均偏差为1.45%。当氨浓度在15至300 ppm范围内时，氨传感器的输出电压为1.3 - 2.05 V。当乙醇气体浓度在8至200 ppm范围内时，乙醇气体传感器的输出电压为1.89 - 3.15 V。腔室内氨传感器的平均响应时间比直接暴露于被测气体的传感器慢13 s，而平均恢复时间快19 s。在比较SSA - BPNN、支持向量机（SVM）和随机森林（RF）模型性能的测试中，SSA - BPNN的分类准确率达到99.1%，优于SVM和RF模型。在回归预测方面，它也优于其他模型，具有更小的绝对误差、平均绝对误差和均方根误差。其决定系数（R2）大于0.99，超过SVM和RF模型。理论和实验结果均表明，所提出的包含仿生腔室的电子鼻系统与SSA - BPNN配合使用时，为畜禽养殖设施中的氨检测提供了一种有前景的方法。
