Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves

Abstract

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 so-called 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. 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. The highest classification accuracy of 88% was achieved by a Long-Short Term Memory neural network, which supports the hypothesis that interpreting DMS measurements as sequential data is beneficial and outperformed classification algorithms traditionally used for DMS-based VOC identification.

Keywords

• Classification
• differential mobility spectrometry
• long-short term memory
• machine learning
• neural networks