Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network

An intelligent sensing platform for detecting and identifying biochemical substances based on terahertz spectra

This paper presents the development of an intelligent sensing platform dedicated to accurately identifying terahertz (THz) spectra obtained from various biochemical substances. The platform currently has two distinct identification modes, which focus on identifying five amino acids, namely phenylalanine, methionine, lysine, leucine, and threonine, and five carbohydrates, namely aspartame, fructose, glucose, lactose monohydrate, and sucrose based on their THz spectra. The first mode, called One-dimensional THz Spectrum Identification (OTSI), combines THz time-domain spectroscopy (THz-TDS) with the proposed mini convolutional neural network (MCNN) model. THz-TDS detects biochemical substances, while the MCNN model identifies the THz spectra. The MCNN model has a simple structure and only needs to deal with the THz absorption coefficients of biochemical substances, which are less computationally intensive and easily converged. The model can achieve 99.07 % accuracy in identifying one-dimensional THz spectra of the ten biochemical substances. The second mode, THz Spectrum Image-based Identification (TSII), applies the YOLO-v5 target detection model to THz spectral image recognition. The YOLO-v5 model uses THz absorption peaks as identification features and can identify biochemical substances based on only one or several THz absorption peaks. The overall identifying accuracy of the YOLO-v5 model for ten biochemical substances is 96.20 %. We also compared the MCNN and YOLO-v5 models with other deep learning and machine learning models, which demonstrate that they have better performance. This feature broadens the platform's utility in biomolecular analysis and paves the way for further research and development in detecting and analyzing diverse biological compounds.

Accurate Characterization of the Adhesive Layer Thickness of Ceramic Bonding Structures Using Terahertz Time-Domain Spectroscopy

Ceramic adhesive structures have been increasingly used in aerospace applications. However, the peaks of the signal on the upper and lower surface of the adhesive layer are difficult to measure directly due to the thin thickness of the adhesive layer and the effect of the attenuation dispersion of the ceramic layer. Thus, the existing non-destructive testing techniques have been ineffective in detecting adhesive quality. In this paper, the thickness of the adhesive layer is measured using terahertz time-domain spectroscopy. A sparse deconvolution method is proposed for the terahertz time-domain spectral signal of ceramic adhesive structures with different adhesive layer thicknesses. The results show that the methods proposed in this paper can realize the separation of reflection signals for glue layers with a thickness of 0.20 mm. By comparing with a wavelet denoising method and a modified covariance method (AR/MCM), the effectiveness of the sparse deconvolution method in estimating the thickness of the glue layer is demonstrated. This work will provide the theoretical and experimental basis for using terahertz time-domain spectroscopy to detect the homogeneity of ceramic adhesive structures.

A Novel Method for Carbendazim High-Sensitivity Detection Based on the Combination of Metamaterial Sensor and Machine Learning

Benzimidazole fungicide residue in food products poses a risk to consumer health. Due to its localized electric-field enhancement and high-quality factor value, the metamaterial sensor is appropriate for applications regarding food safety detection. However, the previous detection method based on the metamaterial sensor only considered the resonance dip shift. It neglected other information contained in the spectrum. In this study, we proposed a method for highly sensitive detection of benzimidazole fungicide using a combination of a metamaterial sensor and mean shift machine learning method. The unit cell of the metamaterial sensor contained a cut wire and two split-ring resonances. Mean shift, an unsupervised machine learning method, was employed to analyze the THz spectrum. The experiment results show that our proposed method could detect carbendazim concentrations as low as 0.5 mg/L. The detection sensitivity was enhanced 200 times compared to that achieved using the metamaterial sensor only. Our present work demonstrates a potential application of combining a metamaterial sensor and mean shift in benzimidazole fungicide residue detection.