深度学习
计算机科学
人工智能
对偶(语法数字)
鉴定(生物学)
人工神经网络
表面增强拉曼光谱
模式识别(心理学)
纳米技术
生物系统
拉曼光谱
材料科学
生物
拉曼散射
物理
植物
光学
文学类
艺术
作者
Eojin Rho,Minjoon Kim,Chong-Min Kyung,Bongjae Choi,Hyungjoon Park,Hanhwi Jang,Yeon Sik Jung,Sungho Jo
标识
DOI:10.1016/j.bios.2022.113991
摘要
Universal and fast bacterial detection technology is imperative for food safety analyses and diagnosis of infectious diseases. Although surface-enhanced Raman spectroscopy (SERS) has recently emerged as a powerful solution for detecting diverse microorganisms, its widespread application has been hampered by strong signals from surrounding media that overwhelm target signals and require time-consuming and tedious bacterial separation steps. By using SERS analysis boosted with a newly proposed deep learning model named dual-branch wide-kernel network (DualWKNet), a markedly simpler, faster, and effective route to classify signals of two common bacteria E. coli and S. epidermidis and their resident media without any separation procedures is demonstrated. With outstanding classification accuracies up to 98%, the synergistic combination of SERS and deep learning serves as an effective platform for "separation-free" detection of bacteria in arbitrary media with short data acquisition times and small amounts of training data.
科研通智能强力驱动
Strongly Powered by AbleSci AI