Nofima AS, Ås, Norway.
J Appl Microbiol. 2013 Mar;114(3):788-96. doi: 10.1111/jam.12092. Epub 2013 Jan 7.
The objective of the study was to evaluate a high-throughput liquid microcultivation protocol and FTIR spectroscopy for the differentiation of food spoilage filamentous fungi.
For this study, fifty-nine food-related fungal strains were analysed. The cultivation of fungi was performed in liquid medium in the Bioscreen C microtitre plate system with a throughput of 200 samples per cultivation run. Mycelium was prepared for FTIR analysis by a simple procedure, including a washing and a homogenization step. Hierarchical cluster analysis was used to study affinity among the different species. Based on the hierarchical cluster analysis, a classification and validation scheme was developed by artificial neural network analysis. The classification network was tested by an independent test set. The results show that 93.9 and 94.0% of the spectra were correctly identified at the species and genus level, respectively.
The use of high-throughput liquid microcultivation protocol combined with FTIR spectroscopy and artificial neural network analysis allows differentiation of food spoilage fungi on the phylum, genus and species level.
The high-throughput liquid microcultivation protocol combined with FTIR spectroscopy can be used for the detection, classification and even identification of food-related filamentous fungi. Advantages of the method are high-throughput characteristics, high sensitivity, low costs and relatively short time of analysis.
本研究旨在评估高通量液体微培养方案和傅里叶变换红外(FTIR)光谱技术,以区分食品腐败丝状真菌。
在这项研究中,分析了 59 株与食品相关的真菌菌株。采用 Bioscreen C 微量滴定板系统中的液体培养基进行真菌培养,每个培养运行的通量为 200 个样本。通过简单的步骤,包括洗涤和均化步骤,为 FTIR 分析制备菌丝体。采用层次聚类分析研究不同物种之间的亲和性。基于层次聚类分析,通过人工神经网络分析开发了分类和验证方案。分类网络通过独立测试集进行测试。结果表明,在种和属水平上,分别有 93.9%和 94.0%的光谱正确识别。
高通量液体微培养方案与 FTIR 光谱和人工神经网络分析的结合可用于区分食源性腐败真菌的门、属和种水平。
高通量液体微培养方案结合 FTIR 光谱可用于检测、分类甚至鉴定与食品相关的丝状真菌。该方法的优点是高通量特性、高灵敏度、低成本和相对较短的分析时间。