Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures

Abstract

Gas-phase trace chemical detection techniques such as ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) can be used in many settings, such as evaluating the health condition of patients or detecting explosives at airports. These devices separate chemical compounds in a mixture and provide information to identify specific chemical species of interest. Further, these types of devices operate well in both controlled lab environments and in-field applications. Frequently, the commercial versions of these devices are highly tailored for niche applications (e.g., explosives detection) because of the difficulty involved in reconfiguring instrumentation hardware and data analysis software algorithms. In order for researchers to quickly adapt these tools for new purposes and broader panels of chemical targets, it is critical to develop new algorithms and methods for generating libraries of these sensor responses. Microelectromechanical system (MEMS) technology has been used to fabricate DMS devices that miniaturize the platforms for easier deployment; however, concurrent advances in advanced data analytics are lagging. DMS generates complex three-dimensional dispersion plots for both positive and negative ions in a mixture. Although simple spectra of single chemicals are straightforward to interpret (both visually and via algorithms), it is exceedingly challenging to interpret dispersion plots from complex mixtures with many chemical constituents. This study uses image processing and computer vision steps to automatically identify features from DMS dispersion plots. We used the bag-ofwords approach adapted from natural language processing and information retrieval to cluster and organize these features. Finally, a support vector machine (SVM) learning algorithm was trained using these features in order to detect and classify specific compounds in these represented conceptualized data outputs. Using this approach, we successfully maintain a high level of correct chemical identification, even when a gas mixture increases in complexity with interfering chemicals present.

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

“…Next, the bag-of-words (BoW) model was used. The BoW approach was adapted from NLP and information retrieval [51] as the classic and most popular form of document representation because of its simplicity and general applicability [52]. According to this technique, a collected set of keywords was explored in all the corpus documents.…”

Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification

“…5b). It should be noted that none of the previously studied algorithms 17,18 have provided this high accuracy of prediction for identifying the chemicals in a mixture. More specically, the previous machine learning studies 17,18 have reported the highest accuracy below 95%, while the CNN model, reported in this work, can reach 100% accuracy while identifying chemicals in a mixture.…”

“…16 Signicant ongoing research focuses on formulating effective DMS data analysis strategies. 1,17,18 There are also reports showing a wide range of applications of hyphenated DMS devices, e.g., gas chromatography-DMS, 19 DMS-mass spectrometry, 5 DMS-DMS 20 for rapid separation and detection. Moreover, DMS has a simple design structure making it possible to be miniaturized.…”

“…Building from that work, Rajapakse et al 17 conducted the partial least squared discriminant analysis to distinguish chemicals in their pure form and mixtures. Later on, Yeap et al 18 adopted machine vision methods, natural language processing, and machine learning algorithms to identify chemicals from complex mixtures. AIMS was most recently updated to include automated peak detection algorithms for GC-DMS data and included random forests classification algorithms.…”

Identification of Specific Substances in the FAIMS Spectra of Complex Mixtures Using Deep Learning

“…Such methods have achieved good results with regard to discriminant analysis of single substances [ 11 , 12 ] but cannot be implemented well in the analysis of mixtures. The research team of Cristina E. Davis used computer vision and natural language processing to address this problem and demonstrated the ability to maintain high levels of substance-specific identification despite the presence of other substances [ 13 ]. However, their method requires manual extraction of features from the data, which introduces a subjective element and is cumbersome.…”