Dickinson T A, White J, Kauer J S, Walt D R
The Max Tishler Laboratory for Organic Chemistry, Department of Chemistry, Tufts University, Medford, Massachusetts 02155, USA.
Nature. 1996 Aug 22;382(6593):697-700. doi: 10.1038/382697a0.
The vertebrate olfactory system has long been recognized for its extraordinary sensitivity and selectivity for odours. Chemical sensors have been developed recently that are based on analogous distributed sensing properties, but although an association between artificial devices and the olfactory system has been made explicit in some previous studies, none has incorporated comparable mechanisms into the mode of detection. Here we describe a multi-analyte fibre-optic sensor modelled directly on the olfactory system, in the sense that complex, time-dependent signals from an array of sensors provide a 'signature' of each analyte. In our system, polymer-immobilized dye molecules on the fibre tips give different fluorescent response patterns (including spectral shifts, intensity changes, spectral shape variations and temporal responses) on exposure to organic vapours, depending on the physical and chemical nature (for example, polarity, shape and size) of both the vapour and the polymer. We use video images of temporal responses of the multi-fibre tip as the input signals to train a neural network for vapour recognition. The system is able to identify individual vapours at different concentrations with great accuracy. 'Artificial noses' such as this should have wide potential application, most notably in environmental and medical monitoring.
脊椎动物的嗅觉系统长期以来因其对气味的非凡敏感性和选择性而闻名。最近开发了基于类似分布式传感特性的化学传感器,尽管在一些先前的研究中明确建立了人工设备与嗅觉系统之间的联系,但没有一个将类似的机制纳入检测模式。在这里,我们描述了一种直接以嗅觉系统为模型的多分析物光纤传感器,从某种意义上说,来自传感器阵列的复杂的、随时间变化的信号提供了每种分析物的“特征”。在我们的系统中,光纤尖端上固定有聚合物的染料分子在暴露于有机蒸汽时会给出不同的荧光响应模式(包括光谱位移、强度变化、光谱形状变化和时间响应),这取决于蒸汽和聚合物的物理和化学性质(例如极性、形状和大小)。我们使用多光纤尖端时间响应的视频图像作为输入信号来训练神经网络进行蒸汽识别。该系统能够非常准确地识别不同浓度的单个蒸汽。这样的“人工鼻子”应该具有广泛的潜在应用,最显著的是在环境和医疗监测方面。