Enhanced quasi-meshing hotspot effect integrated embedded attention residual network for culture-free SERS accurate determination of Fusarium spores

Yehang Wu; Pan Li; Tao Xie; Rui Yang; Rui Zhu; Yulong Liu; Shengyu Zhang; Shizhuang Weng

doi:10.1016/j.bios.2024.117053

Enhanced quasi-meshing hotspot effect integrated embedded attention residual network for culture-free SERS accurate determination of Fusarium spores

Biosens Bioelectron. 2024 Dec 9:271:117053. doi: 10.1016/j.bios.2024.117053. Online ahead of print.

Authors

Yehang Wu¹, Pan Li², Tao Xie³, Rui Yang¹, Rui Zhu¹, Yulong Liu¹, Shengyu Zhang¹, Shizhuang Weng⁴

Affiliations

¹ National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, 230601, Anhui, China; School of Electronics and Information Engineering, Anhui University, Hefei, 230601, Anhui, China.
² Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, Anhui, China.
³ Key Laboratory of Biomimetic Sensor and Detecting Technology of Anhui Province, School of Materials and Chemical Engineering, West Anhui University, Lu'an, 237012, Anhui, China.
⁴ National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, 230601, Anhui, China; School of Electronics and Information Engineering, Anhui University, Hefei, 230601, Anhui, China. Electronic address: weng_1989@126.com.

PMID: 39708494
DOI: 10.1016/j.bios.2024.117053

Abstract

Determination of Fusarium spores is essential for precision control of Fusarium head blight and ensuring agri-food safety. As a highly sensitive real-time detection technique with rich fingerprint information and little influence from water, surface-enhanced Raman spectroscopy (SERS) has been widely applied in the determination of microorganisms. However, fungi determination faces significant challenges including low sensitivity, and poor specificity. The enhanced quasi-meshing hotspot effect (EQMHE) integrated with the embedded attention residual network (EARNet) was proposed to realize a label-free and accurate SERS determination of various Fusarium spores. The EQMHE can promote the binding of spores and nanoparticles to form numerous hotspots, significantly enhancing signal quality and improving the detection limit by at least four orders of magnitude. The key factors inducing EQMHE were validated through various characterization techniques. Moreover, due to its excellent feature extraction and recognition capabilities, EARNet successfully overcomes the limitations of spectral similarity, achieving determination accuracies of 100% in the training set, 98.33% in the validation set, and 100% in the prediction set for three Fusarium species from actual samples. EARNet requires only a small amount of training data and provides rapid and accurate diagnostics. Throughout the process, the spores do not require culturing or lysing, providing an effective determination method for the practical determination of mixed fungal spores. Overall, the proposed strategy effectively addresses challenges such as the need for fungal spore cultivation, difficulties in SERS hotspot formation, and suboptimal signal quality, and it holds significant promise for applications in disease control, food safety, and agricultural production.

Keywords: Convolutional neural network; Enhanced quasi-meshing hotspot effect; Fungal determination; Fusarium spores; Surface-enhanced Raman spectroscopy.