Fiber array-based large spot confocal Raman system for rapid in situ detection of pathogenic bacterial colonies.

IF 5.6 1区化学 Q1 CHEMISTRY, ANALYTICAL

Talanta Pub Date : 2025-04-01 Epub Date: 2024-12-16 DOI:10.1016/j.talanta.2024.127407

Hao Peng, Yu Wang, Lindong Shang, Xusheng Tang, Xiaodong Bao, Peng Liang, Yuntong Wang, Bei Li

引用次数: 0

Abstract

Pathogenic bacteria infections are a major public health problem in current society. Rapid and reliable identification of these pathogens can help avoid the misuse of antibiotics and enable precision therapy. In this study, we present a large-spot confocal Raman system based on fiber array (LSCR-FA) for the in situ detection of microbial colonies on agar plates. This method can alleviate the problem of spatial heterogeneity of colonies to a certain extent and is fast and high-throughput. Additionally, we also applied machine learning algorithms with 5-fold cross-validation to analyze colony Raman spectral data and classify seven different pathogenic bacteria. Among them, the Support Vector Machine (SVM) achieved a high accuracy of 98.74 %. The results of the study demonstrate that the mentioned LSCR-FA system combined with machine learning algorithms provides a new, fast, and effective strategy for the identification of pathogenic bacteria and precise clinical treatment.

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基于光纤阵列的大点共聚焦拉曼系统用于病原菌菌落的快速原位检测。

致病菌感染是当今社会一个重大的公共卫生问题。快速和可靠地鉴定这些病原体有助于避免抗生素的滥用，并使精确治疗成为可能。在这项研究中，我们提出了一种基于光纤阵列的大点共聚焦拉曼系统（LSCR-FA），用于琼脂板上微生物菌落的原位检测。该方法在一定程度上缓解了菌落的空间异质性问题，具有快速、高通量的特点。此外，我们还应用了具有5倍交叉验证的机器学习算法来分析菌落拉曼光谱数据并对7种不同的致病菌进行分类。其中，支持向量机（SVM）的准确率达到了98.74%。研究结果表明，上述LSCR-FA系统与机器学习算法相结合，为病原菌鉴定和精准临床治疗提供了一种新的、快速、有效的策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Talanta 化学-分析化学

CiteScore

12.30

自引率

4.90%

发文量

861

审稿时长

29 days

期刊介绍： Talanta provides a forum for the publication of original research papers, short communications, and critical reviews in all branches of pure and applied analytical chemistry. Papers are evaluated based on established guidelines, including the fundamental nature of the study, scientific novelty, substantial improvement or advantage over existing technology or methods, and demonstrated analytical applicability. Original research papers on fundamental studies, and on novel sensor and instrumentation developments, are encouraged. Novel or improved applications in areas such as clinical and biological chemistry, environmental analysis, geochemistry, materials science and engineering, and analytical platforms for omics development are welcome. Analytical performance of methods should be determined, including interference and matrix effects, and methods should be validated by comparison with a standard method, or analysis of a certified reference material. Simple spiking recoveries may not be sufficient. The developed method should especially comprise information on selectivity, sensitivity, detection limits, accuracy, and reliability. However, applying official validation or robustness studies to a routine method or technique does not necessarily constitute novelty. Proper statistical treatment of the data should be provided. Relevant literature should be cited, including related publications by the authors, and authors should discuss how their proposed methodology compares with previously reported methods.