An Efficient Approach for Preprocessing Data from a Large-Scale Chemical Sensor Array

Abstract

In this paper, an artificial olfactory system (Electronic Nose) that mimics the biological olfactory system is introduced. The device consists of a Large-Scale Chemical Sensor Array (16, 384 sensors, made of 24 different kinds of conducting polymer materials) that supplies data to software modules, which perform advanced data processing. In particular, the paper concentrates on the software components consisting, at first, of a crucial step that normalizes the heterogeneous sensor data and reduces their inherent noise. Cleaned data are then supplied as input to a data reduction procedure that extracts the most informative and discriminant directions in order to get an efficient representation in a lower dimensional space where it is possible to more easily find a robust mapping between the observed outputs and the characteristics of the odors in input to the device. Experimental qualitative proofs of the validity of the procedure are given by analyzing data acquired for two different pure analytes and their binary mixtures. Moreover, a classification task is performed in order to explore the possibility of automatically recognizing pure compounds and to predict binary mixture concentrations.

Synthetic benchmarks for machine olfaction: Classification, segmentation and sensor damage

“…This feature will particularly suit for examination of the role of diversity and redundancy among the sensors at large scale. Recent examples of the data analysis based on real large sensor arrays include an array of 96 metal-oxide sensors combined with 10 different sensor families modulated in temperature [5] , and an array of 16,384 conducting polymer sensors based on 24 different kinds of polymer materials [6] (both arrays are products of the Neurochem project).…”

Algorithms for Automatic Data Validation and Performance Assessment of MOX Gas Sensor Data Using Time Series Analysis

“…Many research groups like Leo et al [8] mention their data processing as using several individual pieces of heterogeneous, commercial software tools like LabView or Matlab, or scripts. Often, the used sensor-features and statistical methods are presented informally as incomplete textual expressions or as black boxes entirely [9].…”

Detection of Optimum Fermentation Time of Black CTC Tea Using a Voltammetric Electronic Tongue

“…In this experiment, LAPV was used. The choice of LAPV was motivated by the fact that it produced better discrimination among tea samples [15]. LAPV voltage ranging from −0.9 to +0.9 V was applied at an increment of 0.1 V. This voltage range was decided depending upon the results of cyclic voltammetry on tea samples.…”

“…Feature extraction of the acquired signal then becomes a crucial step as it defines the compromise between information content and complexity of data analysis model. Some recent works in this direction has been that of [15] where a large scale chemical sensor array consisting of 16 384 odor sensors was developed for the quantification of gas mixtures by SVM. Such a large chemical sensor array should facilitate software selectivity.…”