Methods and Graphical Tools for Exploratory Data Analysis of Artificial Olfaction Experiments

Abstract

Visualization and initial examination of the Electronic Nose data is one of the most important parts of the data analysis cycle. This aspect of data investigation should ideally be performed iteratively together with data collection in order to optimize experimental protocols and final results. Once exploration has been completed, a complete supervised data analysis on a full dataset can be run, leading to prediction and thereby to e-nose performance evaluation. Exploratory Data Analysis (EDA) comprises three tasks: checking the quality of the data, calculating summary statistics, and producing plots of the data to get a feel of their structure. Graphical visualization of data allows checking for instrumental malfunctioning, discovering human errors, removing outliers, understanding the influence of experimental parameters, verifying the ability of the machine in discriminating the examined samples, and eventually formulating new hypotheses. A number of different techniques have been developed for data visualization, including multivariate statistical analysis, non-linear mapping, and clustering techniques. This chapter will present an overview of methods, tools, and software for EDA of artificial olfaction experiments. These will cover visualization and data mining tools for both raw and preprocessed data, such as: histograms, scatter plots, feature and box plots, Principal Component Analysis (PCA), Cluster Analysis (CA), and Cluster Validity (CV). Some case studies that demonstrate the application of the methods to specific chemical sensing problems will be illustrated.