Terahertz technology is the only technology that can achieve specific identification of hazardous chemicals, drugs, explosives, and other contraband inside mail packages without opening them. Therefore, overcoming the challenges of terahertz postal security inspection technology has broad market application prospects and extremely important social significance. In recent years, the combination of terahertz time-domain spectroscopy and deep learning has been widely applied in the field of material identification. However, in practical applications, based on the characteristic absorption spectra in the frequency range, the terahertz absorption spectra of amino acids vary with different packaging materials. Substance classification algorithms based on deep learning and machine learning show high accuracy in offline data models but lower accuracy during real-time online detection. Real-time detection of online amino acid samples based on terahertz time-domain spectroscopy technology should fundamentally solve these issues by increasing the training data, i.e., generating more data from the raw data. Generative adversarial networks (GANs) are a type of deep learning model that can learn the complex distribution of raw data. However, in the field of terahertz material identification, GANs have rarely been used to generate data to improve classifier performance. Therefore, this paper proposes a data augmentation method based on GANs. Then, a terahertz spectrum classification technique combining decision tree (DT), support vector machine (SVM), and convolutional neural network (CNN) is used to identify terahertz spectra within packages.
THz-TDS systems are widely used in non-destructive inspection and chemical inspection and are receiving increasing recognition due to its wide spectrum and the ability to distinguish isomers that can not be easily identified by other types of spectrum. Convolutional neural network is the state-of-the-art technique for feature extraction and classification of single-dimensional and multidimensional matrices and is applicable to identify the THz absorption spectrum of chemicals. We present a method based on a CNN to identify the THz absorption spectrum of several types of amino acids measured by transmission spectroscopy of a THz-TDS system and receive a rate of accuracy close to 100% even for isomers such as L-Tyrosine and DL-Tyrosine. We also present the initial results of quantitatively identifying the amino acids in a mixture, which is among the first outcomes of such research. This technology will support the future application of THz-TDS in drug detection under complex states and environments.
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