摘要： 微生物细胞的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy，FT-NIR)反映了细胞成分的分子振动信息，具有的高度特异性，为寻求一种基于FT-NIR的微生物快速鉴定方法提供了可能。文章通过采集1株酵母和5株细菌标准菌株的近红外漫反射光谱，采用主成分分析法对光谱数据进行了分析，构建了基于FT-NIR的微生物快速鉴定模型。分析结果表明：①光谱鉴别指数Dy1y2值范围为1.61±1.05～10.97±6.65，重现性良好;②建立的基于线性判别分析模型的鉴定准确率为100%，基于人工神经网络模型的预测结果平均相对误差为5.75%，预测准确率高。研究结果证实该方法可以实现基于FT-NIR结合多元数学统计方法的微生物快速鉴定，并具有广阔的产业应用前景。
关键词：傅里叶变换近红外光谱;微生物;快速鉴定;线性判别;人工神经网络
Abstract：Fourier transform-near infrared (FT-NIR) spectra of microorganisms reflect the overall molecular composition of the sample. The spectra were specific and can serve as spectroscopic fingerprints that enable highly accurate identification of microorganisms. Bacterial powders of one yeast and five bacteria strains were prepared to collect FT-NIR spectra. FT-NIR measurements were done using a diffuse reflection-integrating sphere. Reduction of data was performed by principal component analysis (PCA) and two identification models based on linear discriminant analysis (LDA) and artificial neural network (ANN) were established to identify bacterial strains. The reproducibility of the method was proved to be excellent (Dy1y2: 1.61±1.05-10.97±6.65) and high identification accuracy was achieved in both the LDA model (Accuracy rate: 100%) and the ANN model (Average relative error: 5.75%). FT-NIR spectroscopy combined with multivariate statistical analysis (MSA) may provide a novel answer to the fields which need for rapid microbial identification and it will have great prospect in industry.
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