Terahertz time-domain spectrum article classification method based on neural network

Abstract

The invention discloses a terahertz time-domain spectrum article classification method based on a neural network. The method is realized through the following steps: (1) measuring the terahertz spectrum data of articles to be classified; (2) calculating the optical constant of the articles to be classified; (3) extracting a Pauli decomposition eigenvalue; (4) constructing a convolutional neural network; (5) constructing the characteristic matrix of a training sample and the characteristic matrix of a test sample; (6) training the convolutional neural network; (7) obtaining the class label of each data point in the test sample; and (8) outputting a classification result according to the difference of class labels. According to the terahertz time-domain spectrum article classification methodprovided by the invention, a terahertz time-domain spectrum of the articles is measured and the convolutional neural network is applied to classify the articles, so that the method has the advantagesof broad application scenes, non-contact, non-damage and high classification accuracy.

Description

technical field [0001] The invention belongs to the technical field of physics, and further relates to an object classification method based on neural network terahertz time-domain spectrum in the technical field of object classification. The invention can be used to classify items containing different terahertz spectra. Background technique [0002] Terahertz time-domain spectroscopy (THz-TDS) technology is a new spectral measurement technology developed in recent years, which has many advantages that traditional spectroscopy technology does not have. Terahertz waves are very sensitive to small changes in material structure and environment. Terahertz time-domain spectroscopy technology has great potential in studying the internal structure of materials and intermolecular interactions. It can give unique fingerprint spectra of objects, and the structures are very similar The terahertz spectrum of the items is also very different, so it can be used for item classification. ...

AI Technical Summary

Problems solved by technology

This method uses the Raman spectrum of the item as a feature combined with the method of the support vector machine to classify different items. It can realize the simultaneous matching and inspection of multiple information through one detection, and speed up the on-site inspection efficiency of the item. However, this method still has shortcomings. The advantage is that when the laser is irradiated on the item to obtain the Raman spectrum, the excitation energy is high, which is likely to destroy the tissue structure of the original item, and the non-destructive testing and classification cannot be realized; and the Raman spectrum information is directly used for classification, and the classification accuracy relatively low

The method can detect these six active ingredients in drug samples simply and quickly, and these six active ingredients can be effectively separated. Although the detection method is simple to operate, it is suitable for detecting most cold medicines and detecting It has a wide range of applications, but the method still has the disadvantages that it needs to prepare a mixed solution, the process of preparing the test sample is relatively complicated, there is no way to perform non-contact detection, and the state of the sample will be changed, and the demand for samples is large

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