ArticleNanomaterials (Basel, Switzerland)2022
Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network.
Article in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Review
- Distinguishing different proteins based on terahertz spectra by visual geometry group 16 neural network.iScience · 2025Article
- The theory, technology, and application of terahertz metamaterial biosensors: A review.Fundamental research · 2025Review
- Accurate Characterization of the Adhesive Layer Thickness of Ceramic Bonding Structures Using Terahertz Time-Domain Spectroscopy.Materials (Basel, Switzerland) · 2022Article
- A Novel Method for Carbendazim High-Sensitivity Detection Based on the Combination of Metamaterial Sensor and Machine Learning.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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Abstract
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.
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