Lecellier A, Gaydou V, Mounier J, Hermet A, Castrec L, Barbier G, Ablain W, Manfait M, Toubas D, Sockalingum G D
MéDIAN-Biophotonique et Technologies pour la Santé, Université de Reims Champagne-Ardenne, FRE CNRS 3481MEDyC, UFR de Pharmacie, 51 rue Cognacq-Jay, 51096 REIMS cedex, France.
Laboratoire Universitaire de Biodiversité et Ecologie Microbienne (EA3882), SFR148 SclnBioS, Université Européenne de Bretagne, Université de Brest, ESIAB, Technopôle de Brest Iroise, 29280 Plouzané, France.
Food Microbiol. 2015 Feb;45(Pt A):126-34. doi: 10.1016/j.fm.2014.01.002. Epub 2014 Jan 14.
Filamentous fungi may cause food and feed spoilage and produce harmful metabolites to human and animal health such as mycotoxins. Identification of fungi using conventional phenotypic methods is time-consuming and molecular methods are still quite expensive and require specific laboratory skills. In the last two decades, it has been shown that Fourier transform infrared (FTIR) spectroscopy was an efficient tool for microorganism identification. The aims of this study were to use a simple protocol for the identification of filamentous fungi using FTIR spectroscopy coupled with a partial least squares discriminant analysis (PLS-DA), to implement a procedure to validate the obtained results, and to assess the transferability of the method and database. FTIR spectra of 486 strains (43 genera and 140 species) were recorded. An IR spectral database built with 288 strains was used to identify 105 different strains. It was found that 99.17% and 92.3% of spectra derived from these strains were correctly assigned at the genus and species levels, respectively. The establishment of a score and a threshold permitted to validate 80.79% of the results obtained. A standardization function (SF) was also implemented and tested on FTIR data from another instrument on a different site and permitted to increase the percentage of well predicted spectra for this set from 72.15% to 89.13%. This study confirms the good performance of high throughput FTIR spectroscopy for fungal identification using a spectral library of molds of industrial relevance.
丝状真菌可能会导致食品和饲料变质，并产生对人类和动物健康有害的代谢产物，如霉菌毒素。使用传统表型方法鉴定真菌耗时，而分子方法仍然相当昂贵且需要特定的实验室技能。在过去二十年中，已表明傅里叶变换红外（FTIR）光谱是一种用于微生物鉴定的有效工具。本研究的目的是使用一种简单的方案，通过FTIR光谱结合偏最小二乘判别分析（PLS-DA）来鉴定丝状真菌，实施一个程序来验证所得结果，并评估该方法和数据库的可转移性。记录了486个菌株（43个属和140个种）的FTIR光谱。使用由288个菌株构建的红外光谱数据库来鉴定105个不同的菌株。结果发现，分别有99.17%和92.3%的这些菌株的光谱在属和种水平上被正确分类。通过建立一个分数和一个阈值，可以验证80.79%的所得结果。还实施了一个标准化函数（SF），并在来自不同地点的另一台仪器上的FTIR数据上进行了测试，使得该组中预测良好的光谱百分比从72.15%提高到了89.13%。本研究证实了使用具有工业相关性的霉菌光谱库，高通量FTIR光谱在真菌鉴定方面具有良好的性能。
