School of Biology, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
J Biophotonics. 2010 Aug;3(8-9):534-41. doi: 10.1002/jbio.201000017.
A method was developed whereby high quality FTIR spectra could be rapidly acquired from soil-borne filamentous cyanobacteria using ATR FTIR spectroscopy. Spectra of all strains displayed bands typical of those previously reported for microalgae and water-borne cyanobacteria, with each strain having a unique spectral profile. Most variation between strains occurred in the C-O stretching and the amide regions. Soft Independent Modelling by Class Analogy (SIMCA) was used to classify the strains with an accuracy of better than 93%, with best classification results using the spectral region from 1800-950 cm(-1). Despite this spectral region undergoing substantial changes, particularly in amide and C-O stretching bands, as cultures progressed through the early-, mid- to late-exponential growth phases, classification accuracy was still good (approximately 80%) with data from all growth phases combined. These results indicate that ATR/FTIR spectroscopy combined with chemometric classification methods constitute a rapid, reproducible, and potentially automated approach to classifying soil-borne filamentous cyanobacteria.
建立了一种方法,使用衰减全反射傅里叶变换红外光谱(ATR FTIR 光谱)可以快速获得土壤丝状蓝藻的高质量 FTIR 光谱。所有菌株的光谱均显示出先前报道的微藻和水栖蓝藻的典型谱带,每个菌株都有独特的光谱特征。菌株间的大多数差异发生在 C-O 伸缩和酰胺区域。使用软独立建模分类分析(SIMCA)对菌株进行分类,准确率超过 93%,使用光谱区域为 1800-950 cm(-1) 时分类效果最佳。尽管该光谱区域发生了很大变化,特别是在酰胺和 C-O 伸缩带中,但当培养物经历早期、中期到晚期指数生长阶段时,结合所有生长阶段的数据,分类准确率仍然很高(约 80%)。这些结果表明,ATR/FTIR 光谱结合化学计量分类方法是一种快速、可重复且具有潜在自动化的分类土壤丝状蓝藻的方法。