There is an unmet need for a low-cost instrumented technology for detecting sanitation-related malodor as an alert for maintenance around shared toilets and emerging technologies for onsite waste treatment. In this article, our approach to an electronic nose for sanitation-related malodor is based on the use of electrochemical gas sensors, and machine-learning techniques for sensor selection and odor classification. We screened 10 sensors from different vendors with specific target gases and recorded their response to malodor from fecal specimens and urine specimens, and confounding good odors such as popcorn. The analysis of 180 odor exposures data by two feature-selection methods based on mutual information indicates that, for malodor detection, the decision tree (DT) classifier with seven features from four sensors provides 88.0% balanced accuracy and 85.1% macro F1 score. However, the k-nearest-neighbors (KNN) classifier with only three features (from two sensors) obtains 83.3% balanced accuracy and 81.3% macro F1 score. For classification of urine against feces malodor, a balanced accuracy of 94.0% and a macro F1 score of 92.9% are achieved using only four features from three sensors and a logistic regression (LR) classifier.

中文翻译：

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基于时间序列数据分析的传感器阵列优化，用于卫生相关的恶臭检测。

对于用于检测与卫生有关的恶臭以作为共用厕所周围维护的警报和用于现场废物处理的新兴技术的廉价仪器技术存在未满足的需求。在本文中，我们针对与卫生有关的恶臭的电子鼻的处理方法基于电化学气体传感器的使用以及用于传感器选择和气味分类的机器学习技术。我们用特定的目标气体筛选了来自不同供应商的10个传感器，并记录了它们对粪便标本和尿液标本中恶臭的反应，并混淆了诸如爆米花之类的好气味。通过基于互信息的两种特征选择方法对180种气味暴露数据进行的分析表明，对于恶臭检测，具有来自四个传感器的七个特征的决策树（DT）分类器提供了88种。0％的平衡准确度和85.1％的宏观F1得分。但是，仅具有三个特征的k最近邻（KNN）分类器（来自两个传感器）获得83.3％的平衡准确度和81.3％的宏F1得分。对于尿液与粪便恶臭的分类，仅使用来自三个传感器的四个特征和逻辑回归（LR）分类器即可达到94.0％的平衡准确度和92.9％的宏观F1评分。