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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Novel Subspace Alignment-Based Interference Suppression Method for the Transfer Caused by Different Sample Carriers in Electronic Nose
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A Novel Subspace Alignment-Based Interference Suppression Method for the Transfer Caused by Different Sample Carriers in Electronic Nose

机译：一种基于子空间比对的干扰抑制方法用于电子鼻中不同样本载体的转移

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摘要

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).
机译：通过分析细菌代谢产物，将具有31个气体传感器的医用电子鼻（电子鼻）用于伤口感染检测。在实际应用中，当通过实验室数据建立的预测模型直接用于人类临床样本时，预测精度会急剧下降。这是医学电子鼻的关键问题，应引起更多关注。细菌的宿主（载体）可以是培养液，动物伤口或人类伤口。同样，容易获得的细菌培养液或动物（例如：小鼠，兔子等）通常被用作实验对象，以收集足够的传感器阵列数据来建立鲁棒的预测模型，但是这给实验室带来了另一个严重的干扰问题。同时。不同的载体具有不同的背景干扰，因此在不同的载体下收集的数据分布也不同，这将对人类伤口感染的检测识别率产生一定的影响。这种类型的干扰问题称为“由不同样本载体引起的转移”。本文提出了一种具有域校正能力的基于子空间比对的干扰抑制（SAIS）新方法来解决该干扰问题。子空间是空间的一部分，其尺寸小于整个空间，并且具有一些特定的属性。该方法首先得到不同数据域的子空间，然后将一个子空间与另一个子空间对齐，从而解决了两个域之间分布不同的问题。从实验结果可以发现，被​​感染大鼠样品的识别准确度从29.18％（无干扰抑制）提高到82.55％（由SAIS抑制干扰）。

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