3D-GhostNet: A novel spatial-spectral algorithm to improve foodborne bacteria classification coupled with hyperspectral microscopic imaging technology

文献类型： 外文期刊

第一作者： Kang, Rui

作者： Kang, Rui;Huang, Jiaxing;Kang, Rui;Ouyang, Qin;Park, Bosoon;Kang, Rui;Sun, Shangpeng;Ouyang, Qin

作者机构：

关键词： Hyperspectral microscope imaging; Foodborne pathogen; Rapid detection; 3D hypercube classification

期刊名称：SENSORS AND ACTUATORS B-CHEMICAL （ 影响因子：8.4； 五年影响因子：7.2 ）

ISSN：

年卷期： 2024 年 411 卷

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收录情况： SCI

摘要： A novel 3D-GhostNet has been developed for hyperspectral microscopic imaging (HMI) data analysis to improve classification of foodborne pathogens. The HMI, leveraging microscopic technology, surpasses traditional spectral imaging in terms of sensitivity and resolution by utilizing visible/near-infrared spectroscopy integrated with high -resolution single -cell images. The newly constructed 3D -Ghost network directly processes and recognizes single -cell hypercube, enables HMI technology to become near -real-time detection by combining with automated data processing pipeline. 3D-GhostNet incorporates Ghost modules as its backbone and adds convolutional block attention module (CBAM) for adaptively extracting high -dimensional depth spatial -spectral information. In the task of identifying four different foodborne bacteria cells, our 3D-GhostNet achieved 100% accuracy, surpassing spectral -only -based (SOB) classifiers such as linear discriminant analysis (LDA, 89.7%), support vector machine (SVM, 93.9%), 1D convolutional neural network (1D -CNN, 98%), and long short-term memory (LSTM, 98.5%). Furthermore, our independent blind test results indicate that 3D-GhostNet remains robust for identifying complex mixtures of pathogens. As a label -free detection tool, 3D-GhostNet-assisted HMI technology is fully competent in classification of different foodborne pathogens, suggesting broad applications in food safety and quality.

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