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三元指印结合参考气味增强波动感应

Ternary Fingerprints with Reference Odor for Fluctuation-Enhanced Sensing.

机构信息

Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77843-2117, USA.

School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519000, China.

出版信息

Biosensors (Basel). 2020 Aug 9;10(8):93. doi: 10.3390/bios10080093.

Abstract

An improved method for fluctuation-enhanced sensing (FES) is introduced. We enhanced the old binary fingerprinting method, where the fingerprint bit values were ±1, by introducing ternary fingerprint bits utilizing a reference odor. In the ternary method, the fingerprint bit values are -1, 0, and +1, where the 0 value stands for the situation where the slope of the spectrum is identical to that of the reference odor. The application of the reference odor spectrum makes the fingerprint relative to the reference. The ternary nature and the reference feature increase the information entropy of the fingerprints. The method is briefly illustrated by sensing bacterial odor in cow manure isolates.

摘要

介绍了一种改进的波动增强感应(FES)方法。我们通过引入参考气味利用三元指纹位增强了旧的二进制指纹方法,其中指纹位值为±1。在三元方法中,指纹位值为-1、0 和+1,其中 0 值表示光谱斜率与参考气味相同的情况。参考气味光谱的应用使指纹相对于参考而言。三元特性和参考特征增加了指纹的信息熵。该方法通过感测牛粪分离物中的细菌气味简要说明。

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