U.S. Department of Agriculture, Agricultural Research Service, U.S. National Poultry Research Center, 950 College Station Road, Athens, GA 30605, USA.
U.S. Department of Agriculture, Agricultural Research Service, U.S. National Poultry Research Center, 950 College Station Road, Athens, GA 30605, USA.
J Microbiol Methods. 2023 Jun;209:106739. doi: 10.1016/j.mimet.2023.106739. Epub 2023 May 13.
Identifying live foodborne bacteria is essential for ensuring food safety and preventing foodborne illnesses. This study investigated the use of hyperspectral microscope imaging and deep learning methods to accurately distinguish between live and dead foodborne bacteria based on their spectral and morphological features. Three deep learning models, Fusion-Net I, II, and III, were developed and evaluated for their ability to classify live and dead bacterial cells of six pathogenic strains, including Escherichia coli (EC), Listeria innocua (LI), Staphylococcus aureus (SA), Salmonella Enteritidis (SE), Salmonella Heidelberg (SH), and Salmonella Typhimurium (ST). The models utilized both morphological and spectral characteristics of the bacterial cells, with inputs of average spectra and 546 nm band images. Fusion-Net I achieved high accuracy in identifying live bacterial cells, with a classification accuracy of 100% for LI, SE, ST strains and over 92.9% for EC, SA, SH. Fusion-Net II and III models were even more robust, achieving 100% accuracy consistently in classifying dead cells in all six strains. Fusion-Net III also showed the ability to identify bacterial strains with 96.9% accuracy, making it a dual-task model with potential applications in identifying live foodborne bacteria prior to foodborne outbreaks. These findings suggest that the use of hyperspectral microscope imaging and deep learning methods could provide a new tool for quickly and accurately identifying bacterial viability, thereby improving the efficiency and reliability of food safety inspection.
鉴定活菌对于确保食品安全和预防食源性疾病至关重要。本研究探讨了利用高光谱显微镜成像和深度学习方法，根据食源性致病菌的光谱和形态特征，准确区分活菌和死菌。开发并评估了三种深度学习模型，即融合网络 I、II 和 III，用于对六种致病菌（包括大肠杆菌（EC）、无害李斯特菌（LI）、金黄色葡萄球菌（SA）、肠炎沙门氏菌（SE）、海德尔堡沙门氏菌（SH）和鼠伤寒沙门氏菌（ST））的活菌和死菌细胞进行分类。这些模型利用了细菌细胞的形态和光谱特征，输入的是平均光谱和 546nm 波段图像。融合网络 I 在识别活菌方面具有很高的准确性，对 LI、SE、ST 菌株的分类准确率达到 100%，对 EC、SA、SH 的分类准确率超过 92.9%。融合网络 II 和 III 模型更加稳健，在对所有六种菌株的死细胞进行分类时始终达到 100%的准确率。融合网络 III 还表现出识别细菌菌株的能力，准确率达到 96.9%，这使其成为一种具有潜在应用价值的双任务模型，可用于在食源性疾病爆发之前快速准确地识别活菌。这些发现表明，利用高光谱显微镜成像和深度学习方法可以为快速准确地鉴定细菌的生存能力提供一种新工具，从而提高食品安全检测的效率和可靠性。
