AIMS: 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. METHODS AND RESULTS: 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. CONCLUSIONS: 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. SIGNIFICANCE AND IMPACT OF THE STUDY: 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.
AIMS: 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. METHODS AND RESULTS: 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. CONCLUSIONS: 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. SIGNIFICANCE AND IMPACT OF THE STUDY: 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.