Next Article in Journal
Recent Progress on Nanomaterials for NO2 Surface Acoustic Wave Sensors
Next Article in Special Issue
Biocompatible Casein Electrolyte-Based Electric-Double-Layer for Artificial Synaptic Transistors
Previous Article in Journal
Microwave-Assisted Synthesis of Zn2SnO4 Nanostructures for Photodegradation of Rhodamine B under UV and Sunlight
Previous Article in Special Issue
An AI-Assisted and Self-Powered Smart Robotic Gripper Based on Eco-EGaIn Nanocomposite for Pick-and-Place Operation
 
 
Article

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

1
Quenda Terahertz Technologies, Ltd., 600 Jiushui E Rd., Qingdao 266102, China
2
School of Space Science and Physics, Shandong University, 180 Wenhua W Rd., Weihai 264209, China
3
College of Engineering Physics, Shenzhen Technology University, 3002 Lantian Rd., Shenzhen 518060, China
4
Institute of Fluid Physics, 64 Mianshan Rd., Mianyang 621900, China
*
Author to whom correspondence should be addressed.
Academic Editors: Filippo Giannazzo and Mircea Dragoman
Nanomaterials 2022, 12(12), 2114; https://doi.org/10.3390/nano12122114
Received: 10 May 2022 / Revised: 16 June 2022 / Accepted: 17 June 2022 / Published: 20 June 2022
(This article belongs to the Special Issue Intelligent Nanomaterials and Nanosystems)
Terahertz (THz) spectroscopy is the de facto method to study the vibration modes and rotational energy levels of molecules and is a widely used molecular sensor for non-destructive inspection. Here, based on the THz spectra of 20 amino acids, a method that extracts high-dimensional features from a hybrid spectrum combined with absorption rate and refractive index is proposed. A convolutional neural network (CNN) calibrated by efficient channel attention (ECA) is designed to learn from the high-dimensional features and make classifications. The proposed method achieves an accuracy of 99.9% and 99.2% on two testing datasets, which are 12.5% and 23% higher than the method solely classifying the absorption spectrum. The proposed method also realizes a processing speed of 3782.46 frames per second (fps), which is the highest among all the methods in comparison. Due to the compact size, high accuracy, and high speed, the proposed method is viable for future applications in THz chemical sensors. View Full-Text
Keywords: terahertz spectroscopy; CNN; ECA; amino acid; sensor terahertz spectroscopy; CNN; ECA; amino acid; sensor
Show Figures

Figure 1

MDPI and ACS Style

Wang, B.; Qin, X.; Meng, K.; Zhu, L.; Li, Z. Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network. Nanomaterials 2022, 12, 2114. https://doi.org/10.3390/nano12122114

AMA Style

Wang B, Qin X, Meng K, Zhu L, Li Z. Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network. Nanomaterials. 2022; 12(12):2114. https://doi.org/10.3390/nano12122114

Chicago/Turabian Style

Wang, Bo, Xiaoling Qin, Kun Meng, Liguo Zhu, and Zeren Li. 2022. "Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network" Nanomaterials 12, no. 12: 2114. https://doi.org/10.3390/nano12122114

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop