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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier B.V.)