Abstract

Near-infrared (NIR) hyperspectral imaging was used to study three strains of each of three Fusarium spp. (Fusarium subglutinans, Fusarium proliferatum and Fusarium verticillioides) inoculated on potato dextrose agar in Petri dishes after either 72 or 96 h of incubation. Multivariate image analysis was used for cleaning the images and for making principal component analysis (PCA) score plots and score images and local partial least squares discriminant analysis (PLS-DA) models. The score images, including all strains, showed how different the strains were from each other. Using classification gradients, it was possible to show the change in mycelium growth over time. Loading line plots for principal component (PC) 1 and PC2 explained variation between the different Fusarium spp. as scattering and chemical differences (protein production), respectively. PLS-DA prediction results (including only the most important strain of each species) showed that it was possible to discriminate between species with F. verticillioides the least correctly predicted (between 16 and 47 % pixels correctly predicted). For F. subglutinans, 78–100 % pixels were correctly predicted depending on the training and test sets used. Similarly, the percentage correctly predicted values of F. proliferatum were 60–80 %. Visualisation of the mycelium radial growth in the PCA score images was made possible due to the use of NIR hyperspectral imaging. This is not possible with bulk spectroscopy in the visible or NIR regions. FigurePrincipal component 1 score image showing differences between colonies. F. subglutinans (MRC 0115) are top left followed by F. proliferatum (MRC 2301) and F. verticillioides (MRC 0826).

Highlights

  • Fungi are ubiquitous in nature and grow on most substrates under optimal conditions [1]

  • The non-homogeneous nature of the colonies makes studying the distinction between Fusarium spp. an ideal application for NIR hyperspectral imaging and multivariate image analysis

  • Global diagnostics for the partial least squares discriminant analysis (PLS-DA) models such as R2 and root mean square error of prediction (RMSEP) serve as a guide of modelling accuracy for images because of the large number of pixels and should be used in conjunction with the prediction image

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Summary

Introduction

Fungi are ubiquitous in nature and grow on most substrates under optimal conditions [1]. They are common in tropical and temperate regions and are found in desert, alpine and arctic areas where harsh climatic conditions prevail. Mycotoxin production by Fusarium spp. is of primary concern to the food industry. They are known to produce fumonisins, trichothecenes and zearalenones, as well as other minor mycotoxins. The fumonisins are of particular importance and concern These toxins are natural contaminants of cereal grains worldwide and are mostly found in maize and products derived from maize. Fusarium verticillioides strain MRC 0826, isolated from mouldy maize, was shown to cause ELEM in horses, porcine pulmonary edema syndrome in pigs and liver cancer in rats [3,4,5,6,7]

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