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中国精品科技期刊2020
龚方,刘小菁,康秋燕,等. 傅立叶变换红外光谱技术对常见食源性致病菌和真菌快速分类鉴别[J]. 食品工业科技,2022,43(13):235−241. doi: 10.13386/j.issn1002-0306.2021100062.
引用本文: 龚方,刘小菁,康秋燕,等. 傅立叶变换红外光谱技术对常见食源性致病菌和真菌快速分类鉴别[J]. 食品工业科技,2022,43(13):235−241. doi: 10.13386/j.issn1002-0306.2021100062.
GONG Fang, LIU Xiaojing, KANG Qiuyan, et al. Rapid Differentiation and Identification of Foodborne Pathogenic Bacteria and Fungi by FT-IR Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(13): 235−241. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021100062.
Citation: GONG Fang, LIU Xiaojing, KANG Qiuyan, et al. Rapid Differentiation and Identification of Foodborne Pathogenic Bacteria and Fungi by FT-IR Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(13): 235−241. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021100062.

傅立叶变换红外光谱技术对常见食源性致病菌和真菌快速分类鉴别

Rapid Differentiation and Identification of Foodborne Pathogenic Bacteria and Fungi by FT-IR Spectroscopy

  • 摘要: 目的:为满足食品安全检测中常见食源性致病微生物的检测需求,探索利用傅立叶红外光谱技术(Fourier transform infrared spectroscopy,FT-IR)建立常见食源性致病菌和真菌的种间分类鉴定方法。方法:应用FT-IR技术采集13种常见食源性致病菌977.9~1805.3 cm−1波数范围以及12种真菌900~1800 cm−1和2800~3700 cm−1波数范围的红外光谱,使用主成分分析(principal component analysis,PCA)和分级聚类分析(hierarchical cluster analysis,HCA)两种化学计量分析方法对数据进行分析,通过加标实验检验方法的准确性。结果:建成菌种光谱导数谱数据库,并构建2种聚类分析模型,确定HCA方法能够分别将13种致病菌和12种真菌在种间水平准确聚类,加标样品中的可疑待测菌均能够准确聚类到相应菌种。结论:该研究结果说明本文建立的FT-IR技术结合HCA聚类分析方法,可以实现常见食品中致病菌和真菌菌种的快速鉴定。

     

    Abstract: Objective: In order to satisfy the detection requirements of common foodborne pathogenic microorganisms in food safety detection, Fourier tranform infrared spectroscopy (FT-IR) was used to establish a method for rapid differentiation and identification method for common foodborne pathogenic bacteria and fungi. Methods: FT-IR fingerprints absorption spectra of 977.9~1805.3 cm−1 for 13 species of foodborne pathogenic bacteria, 900~1800 cm−1 and 2800~3700 cm−1 for 12 species of fungi were collected. Two chemometric methods (PCA and HCA) were used for the data analysis. The veracity of the method to classify the suspected species was proved by labeling verification experiment. Results: The standard spectral derivatives library of all species were created. Two discriminant models were created, and the results showed that the HCA cluster analysis model was proved to be suitable to accurately cluster 13 pathogenic bacteria and 12 fungi at the interspecific level. The suspected strains in the labeling verification experiment were accurately clustered to the corresponding species. Conclusion: The FT-IR technique combined with HCA clustering analysis method were proved to be a feasible method to provide rapid identification of common foodborne pathogenic bacteria and fungi species.

     

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