化学
拉曼光谱
压舱物
细菌
环境化学
光谱学
分析化学(期刊)
海洋学
光学
遗传学
量子力学
生物
物理
地质学
作者
Anqi Yang,Zhipeng Hu,Xiaer Zou,Yuan Zhang,Jiao Qian,Shuo Li,Junbo Liang,Sailing He
出处
期刊:Talanta
[Elsevier BV]
日期:2024-11-22
卷期号:284: 127266-127266
被引量:4
标识
DOI:10.1016/j.talanta.2024.127266
摘要
The increasing global trade has facilitated the transfer of ship ballast water, which has emerged as a primary pathway for alien species invasion into marine ecosystems, posing significant threats to marine biodiversity. Addressing the technical challenges in rapid microorganism detection and treatment efficiency assessment, this study developed a confocal Raman microscopic imaging (CRMI) system integrated with a metal-insulator-metal (MIM) broadband surface-enhanced Raman scattering (SERS) chip, enabling efficient acquisition of single-cell Raman spectroscopy (SCRS). By incorporating machine learning algorithms, the system achieved precise identification of up to 10 bacterial types in ballast water, exhibiting remarkable performance metrics with average accuracy, sensitivity, specificity, and precision above 95.5 %, 95.5 %, 99.5 %, and 95.5 %, respectively. To evaluate the efficacy of ultraviolet (UV) treatment, a Raman spectroscopy-based approach combined with heavy water labeling was introduced to characterize the changes in bacterial single-cell metabolic activity under UV 254 irradiation. Experimental results demonstrated that a 10-min UV 254 exposure at an effective intensity of 2 mW/cm 2 was sufficient to achieve complete bacterial sterilization for the specific ballast water used in our experiment. This study not only established an efficient and accurate method for rapid detection of mixed bacteria but also provided a novel perspective for assessing UV treatment effects. It holds significance and practical value for optimizing ship ballast water management strategies and safeguarding the safety of marine ecosystems. • CRMI system combined with MIM broadband SERS chip for rapid and label-free individual bacterium detection. • Machine learning integration achieves over 95 % accuracy in identifying 10 bacterial types in ballast water. • Evaluating the efficacy of UV 254 disinfection via Raman spectroscopy combined with heavy water labeling.
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