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用于细菌鉴定的生化反应的光谱分析。

Spectral analysis of biochemical reactions used for identification of bacteria.

作者信息

Hilger A E, Lancaster M V

出版信息

Eur J Clin Microbiol. 1984 Aug;3(4):310-5. doi: 10.1007/BF01977479.

Abstract

To define parameters for optimizing automated discrimination of bacterial biochemical reactions certain theoretical considerations of spectrophotometric analysis were explored. One-hundred and one recent clinical isolates of gram-negative bacilli (21 species) were inoculated into AP1 20 E strips and read manually after 24 hours. With spectrophotometric scanning, the AP1 reactions could be classified into three analytical categories: pH change, production of new products, and darkening of the medium. Whereas single wavelength analysis gave 2.9% disagreement from the visual, multiple wavelength analyses were uniformly more accurate. The best results for pH change reactions were obtained by calculating a ratio of two wavelengths. New color reactions were best interpreted by demonstration of the new peak, whereas darkening reactions required quantitation of the area under the entire curve. With these methods, a 99.3% overall agreement of individual reactions and a 97% agreement of identification were achieved. Multiple-point analysis of spectra coupled with computerized interpretation of the data should help resolve the problem of equivocal reactions in bacterial identification schemes optimized for spectral analysis.

摘要

为了确定优化细菌生化反应自动判别的参数,我们探讨了分光光度分析的一些理论考量。将101株近期临床分离的革兰氏阴性杆菌(21个种)接种到AP1 20 E试条中,24小时后进行人工判读。通过分光光度扫描,AP1反应可分为三类分析范畴:pH变化、新产物生成以及培养基变黑。单波长分析与肉眼判读的不一致率为2.9%,而多波长分析总体上更为准确。对于pH变化反应,通过计算两个波长的比值可获得最佳结果。新颜色反应通过展示新峰能得到最佳解读,而变黑反应则需要对整个曲线下的面积进行定量。采用这些方法,单个反应的总体一致率达到99.3%,鉴定一致率达到97%。光谱的多点分析结合数据的计算机化解读,应有助于解决在为光谱分析优化的细菌鉴定方案中存在的模糊反应问题。

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