首页> 外文期刊>Transactions of the ASABE >DETECTION OF BACTERIA WITH LOW-RESOLUTION RAMAN SPECTROSCOPY
【24h】

DETECTION OF BACTERIA WITH LOW-RESOLUTION RAMAN SPECTROSCOPY

机译:低分辨拉曼光谱法检测细菌

获取原文
获取原文并翻译 | 示例
           

摘要

Bacteria detection methods that are presently used in laboratories and quality control inspections, such as serological testing, biological enrichment, culturing, and gas chromatograph mass spectroscopy (GCMS), are expensive, labor intensive, and time consuming. Therefore, in order to ensure that consumers receive a safe and high-quality product, rapid and reliable methods need to be developed for detection of pathogens. Raman spectroscopy, an optical technique based on light scattering, was investigated as a means of rapid on-site produce safety assessment. In this study, a dispersive system spectrophotometer, with a 785 nm diode laser, was employed. Chemometric methods such as partial least squares (PLS) regression and classification analysis were used to evaluate low-concentration suspensions of Erwinia carotovora pv. carotovora (ECC) and Clavibacter michiganense (CBM). The pathogens chosen represent Gram-positive and Gram-negative bacteria. A clear distinction between samples containing bacteria and clean samples was obtained by this method. The system was able to determine bacterial concentrations within 2% of the level in the basic bacteria suspension, based on PLS regression models. Classification analysis enables researchers to detect the presence of each of the tested bacteria in mixed-bacteria suspensions that contain between 10 and 100 cells/mL of ECC and CBM
机译:当前在实验室和质量控制检查中使用的细菌检测方法(例如血清学检测,生物富集,培养和气相色谱质谱法(GCMS))昂贵,费力且费时。因此,为了确保消费者获得安全优质的产品,需要开发快速可靠的方法来检测病原体。拉曼光谱法是一种基于光散射的光学技术,已作为一种快速的现场产品安全性评估手段进行了研究。在这项研究中,采用了带有785 nm二极管激光器的色散系统分光光度计。化学计量学方法(例如偏最小二乘(PLS)回归和分类分析)用于评估胡萝卜小叶欧文氏菌(Erwinia carotovora pv)的低浓度悬浮液。胡萝卜(ECC)和密歇根杆菌(CBM)。选择的病原体代表革兰氏阳性和革兰氏阴性细菌。通过这种方法可以清楚地区分含有细菌的样品和干净的样品。该系统能够基于PLS回归模型确定基本细菌悬浮液中2%以内的细菌浓度。分类分析使研究人员能够检测混合细菌悬浮液中每种被测细菌的存在,这些悬浮液包含10至100个细胞/ mL的ECC和CBM

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号