Abstract

A medical electronic nose (e-nose) with 31 gas sensors is used for wound infection detection by analyzing the bacterial metabolites. In practical applications, the prediction accuracy drops dramatically when the prediction model established by laboratory data is directly used in human clinical samples. This is a key issue for medical e-nose which should be more worthy of attention. The host (carrier) of bacteria can be the culture solution, the animal wound, or the human wound. As well, the bacterial culture solution or animals (such as: mice, rabbits, etc.) obtained easily are usually used as experimental subjects to collect sufficient sensor array data to establish the robust predictive model, but it brings another serious interference problem at the same time. Different carriers have different background interferences, therefore the distribution of data collected under different carriers is different, which will make a certain impact on the recognition accuracy in the detection of human wound infection. This type of interference problem is called “transfer caused by different sample carriers”. In this paper, a novel subspace alignment-based interference suppression (SAIS) method with domain correction capability is proposed to solve this interference problem. The subspace is the part of space whose dimension is smaller than the whole space, and it has some specific properties. In this method, first the subspaces of different data domains are gotten, and then one subspace is aligned to another subspace, thereby the problem of different distributions between two domains is solved. From experimental results, it can be found that the recognition accuracy of the infected rat samples increases from 29.18% (there is no interference suppression) to 82.55% (interference suppress by SAIS).

Highlights

  • As an artificial nose, electronic nose (e-nose) can analyze the gas characteristics quickly by simulating the biological olfactory system

  • Motivated by the fact that subspace alignment can be used to transfer the knowledge between two domains and the distribution of sensor data will be changed with different carrier, a novel subspace alignment-based interference suppression (SAIS) method with domain correction capability is proposed to solve the transfer caused by different sample carriers

  • Motivated by the different distribution of data collected under different bacterial carriers and the idea of subspace alignment, a novel subspace alignment-based interference suppression (SAIS) method for suppressing the transfer caused by different sample carriers in e-nose is proposed

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Summary

Introduction

Electronic nose (e-nose) can analyze the gas characteristics quickly by simulating the biological olfactory system. It consists of a gas sensor array and an appropriate pattern recognition system. [6,7,8], biomarkers tea quality assessment [9,10], food quality detection [11,12], etc. The design tea quality assessment [9,10], food quality detection [11,12], etc.

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