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.

Unsupervised learning refers to data science approaches that involve learning without a prior knowledge about the classification of sample data. In Wikipedia, unsupervised learning has been described as "the task of inferring a function to describe hidden structure from'unlabeled' data (a classification of categorization is not included in the observations)". The overarching objectives of this post were to evaluate and understand the co-occurrence and/or co-expression of emotion words in individual letters, and if there were any differential expression profiles /patterns of emotions words among the 40 annual shareholder letters? Differential expression of emotion words was being used to refer to quantitative differences in emotion word frequency counts among letters, as well as qualitative differences in certain emotion words occurring uniquely in some letters but not present in others. This is the second part to a companion post I have on "parsing textual data for emotion terms". As with the first post, the raw text data set for this analysis was using Mr. Warren Buffett's annual shareholder letters in the past 40-years (1977 – 2016).