Yuan Quan, Gu Bin, Liu Wei, Wen Xin-Ru, Wang Ji-Liang, Tang Jia-Wei, Usman Muhammad, Liu Su-Ling, Tang Yu-Rong, Wang Liang
School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China.
Department of Laboratory Medicine, Shengli Oilfield Central Hospital, Dongying, China.
J Cell Mol Med. 2024 Apr;28(8):e18292. doi: 10.1111/jcmm.18292.
Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.
食源性疾病，尤其是由具有超过2600种血清型的肠炎沙门氏菌引起的疾病，对公共卫生构成了重大挑战。因此，快速准确地鉴定肠炎沙门氏菌血清型对于临床意义至关重要，这有助于了解肠炎沙门氏菌的传播途径并确定疫情源头。目前，通过分子亚型和基因组标记的经典血清分型方法存在各种局限性，如劳动强度大、耗时等。因此，迫切需要开发新的诊断技术。表面增强拉曼光谱（SERS）是一种非侵入性诊断技术，可产生拉曼光谱，基于此可实现对细菌病原体的快速准确鉴别。为了生成SERS光谱，需要一台拉曼光谱仪来检测和收集信号，这些信号分为两种类型：昂贵的台式光谱仪和廉价的手持式光谱仪。在本研究中，我们比较了两种拉曼光谱仪对四种密切相关的肠炎沙门氏菌血清型的鉴别性能，即肠炎沙门氏菌亚种肠炎血清型都柏林、肠炎、伤寒和鼠伤寒。应用六种机器学习算法对这些SERS光谱进行分析。支持向量机（SVM）模型对手持式（99.97%）和台式（99.38%）拉曼光谱仪均显示出最高的准确率。本研究表明，当与机器学习模型结合时，手持式拉曼光谱仪可实现与台式光谱仪相似的预测准确率，为快速、准确且经济高效地鉴定密切相关的肠炎沙门氏菌血清型提供了一种有效的解决方案。
