Background: Classification of volatile organic compounds (VOCs) is of
interest in many fields. Examples include but are not limited to medicine,
detection of explosives, and food quality control. Measurements collected with
electronic noses can be used for classification and analysis of VOCs. One type
of electronic noses that has seen considerable development in recent years is
Differential Mobility Spectrometry (DMS). DMS yields measurements that are
visualized as dispersion plots that contain traces, also known as alpha curves.
Current methods used for analyzing DMS dispersion plots do not usually utilize
the information stored in the continuity of these traces, which suggests that
alternative approaches should be investigated.
Results: In this work, for the first time, dispersion plots were interpreted
as a series of measurements evolving sequentially. Thus, it was hypothesized
that time-series classification algorithms can be effective for classification
and analysis of dispersion plots. An extensive dataset of 900 dispersion plots
for five chemicals measured at five flow rates and two concentrations was
collected. The data was used to analyze the classification performance of six
algorithms. According to our hypothesis, the highest classification accuracy of
88\% was achieved by a Long-Short Term Memory neural network, which supports
our hypothesis.
Significance: A new concept for approaching classification tasks of
dispersion plots is presented and compared with other well-known classification
algorithms. This creates a new angle of view for analysis and classification of
the dispersion plots. In addition, a new dataset of dispersion plots is openly
shared to public.

通过使用时间序列分类算法，利用电子鼻测量的离散图进行分类和分析，本研究首次将离散图解释为一系列逐渐发展的测量，并通过实验证明，长短期记忆神经网络可以在分类上达到最高的准确度为 88%。这一新的分类方法为离散图的分析和分类提供了一种新的视角，并公开分享了一组新的离散图数据集。