Near infrared (NIR) hyperspectral imaging and X-ray computed tomography combined with statistical and multivariate data analysis to study Fusarium infection in maize

[摘要] ENGLISH ABSTRACT: Maize (Zea mays L.) is used for human and animal consumption in diverse forms, from specialisedfoods in developed countries, to staple food in developing countries. Unfortunately, maize is proneto infection by different Fusarium species that can produce harmful mycotoxins. Fusariumverticillioides is capable of asymptomatic infection, where infected kernels show no sign of fungalgrowth, but are contaminated with mycotoxins. If fungal contamination is not detected early on,mycotoxins can enter the food chain. Rapid and accurate methods are required to detect, identifyand distinguish between pathogens to enable swift decisions regarding the fate of a batch orconsignment of cereal.Near infrared (NIR) hyperspectral imaging and multivariate image analysis (MIA) wereevaluated to investigate the fungal development in maize kernels over time. When plotting principalcomponent (PC) 4 against PC5, with percentages sum of squares (%SS) 0.49% and 0.34%, threedistinct clusters were apparent in the score plot and this was associated with degree of infection.Prominent peaks at 1900 nm and 2136 nm confirmed that the source of variation was due tochanges in starch and protein. Variable importance plots (VIP) confirmed the peaks observed inthe PCA loading line plots. Early detection of fungal contamination and activity (20 h afterinoculation) was possible before visual symptoms of infection appeared.Using NIR hyperspectral imaging and MIA it was possible to differentiate between species ofFusarium associated with maize. It was additionally applied to examine the fungal growth kineticson culture media. Partial least squares discriminant analysis (PLS-DA) prediction results showedthat it was possible to discriminate between species, with F. verticillioides the least correctlypredicted (between 16-47% pixels correctly predicted). For F. subglutinans 78-100% and for F.proliferatum 60-80% pixels were correctly predicted. Three prominent bands at 1166, 1380 and1918 nm were considered to be responsible for the differences between the growth zones.Variations in the bands at 1166 and 1380 nm were correlated with the depletion of carbohydratesas the fungus grew while the band at 1918 nm was a possible indication of spore and new mycelialformation. By plotting the pixels from the individual growth zones as a function of time, it waspossible to visualise the emergence and interaction of the growth zones as separate growthprofiles.The microstructure of fungal infected maize kernels was studied over time using highresolution X-ray micro-computed tomography (μCT). The presence of voids and airspaces couldbe seen in two dimensional (2D) X-ray transmission images and in the three dimensional (3D)tomograms. Clear differences were detected between kernels imaged after 20 and 596 h ofinoculation. This difference in voids as the fungus progressed showed the effect of fungal damageon the microstructure of the maize kernels.Imaging techniques are important for rapid, accurate and objective evaluation of products forquality and safety. NIR hyperspectral imaging offers rapid chemical evaluation of samples in 2D images while μCT offers 3D microstructural information. By combining these image techniquesmore value was added and this led to a comprehensive evaluation of Fusarium infection in maize.

[发布机构] Stellenbosch University