DEVELOPMENT OF OLFACTORY SIMULATOR BY USlNG A NEURAL NETWORK

我尝试开发一个人类嗅觉模拟器使用神经网络与大气压质谱仪作为传感器的气味。1.对现有大气压质谱仪的改进I旨在提高大气压质谱仪的灵敏度，对离子导入部分的透镜组和离子检测系统进行改进，采用转换双极。通过这些改进，高质量部件的灵敏度提高了一倍。2.研制了质谱仪与计算机接口的硬件和软件，并尝试研制了数据采集系统，实现了对质谱数据的连续测量和积累，提高了测量精度。由于光谱仪的电源装置等存在不稳定部件，质量数的测量变得不稳定。由于该故障未能在本年度完成该系统，因此通过光谱仪的离线操作继续进行实验。嗅觉模拟器软件开发及运行结果根据传统的研究方法，将气味分为七种基本气味。制备了各种基本气味的化合物，并测定了它们的质谱。将每个质谱模式放入输入层200、中间层50和输出层7的神经网络中，进行学习，直到确定对应气味的分类。之后，将新样品的质谱模式放入模拟器中，并确定是否可以将样品分类为对应气味。结果，证实了能够通过使用神经网络从质谱中对气味进行分类。

I try to develop a human olfactory simulator using a neural network with an atmospheric pressure mass spectrometer as a sensor of the smell.1. Improvement of existing atmospheric pressure mass spectrometerI aimed at rising the sensitivity of the atomospheric pressure mass spectrometer with the improvement of lens group of the ion introduction part and the ion detection system by using the conversion di-node. Sensitivity in a high mass part has doubled by these improvements.2. Developing the hardware and the software to interface between the mass spectrometer and the computer.I try to make the data acquisition system which is measuring the spectrum continuously and accumulating them to improve accuracy of measurements. Because an unstable part existed in the power-supply unit etc. of the spectrometer, the measurement of the number of mass became unstable. As the completion of the system in the current year was not able to be expected by this trouble, the experiment is continued by off-line operation of the spectrometer.3. Development of software for simulator of olfactory and operation resultThe smell is classified into seven kinds of basic smells according to a traditional research. Some compounds which are classified each basic smell were prepared and the mass spectra were measured. Each mass spectrum pattern was put into the neural network in input layer 200, middle layer 50, and output layer 7, which was made to study until settling to the classification of the corresponding smell After that, a mass spectrum pattern of a new sample was put into the simulator, and whether it was possible to classify the sample into the corresponding smell.As a result, it was confirmed to be able to classify the smell from the mass spectrum by using the neural network.