Researchers (Prof Marena Manley & Dr Paul J Williams) at the Department of Food Science, Stellenbosch University, have been using this technology for some time now to investigate its efficacy for use as a tool for food quality and safety. Dr Paul James Williams recently completed his PhD (2013) using this method to detect fungal contaminated maize kernels, differentiate between fungi commonly associated with maize infection and study fungal growth. In his work, detecting the cereal killer (Fusarium verticillioides) was of paramount importance. This culprit is responsible for major losses in the grain industry, more importantly it is associated with the production of mycotoxins that are harmful to humans and animals. With current grading practices, infected grain often enters the food chain undetected due to the subjectivity and laboriousness of grading techniques, making NIR hyperspectral imaging the ideal candidate for objective, rapid sorting. It was shown that 20 h after inoculation with F. verticillioides, infected maize kernels could be distinguished from uninfected kernels (P. J. Williams, P. Geladi, T. J. Britz, & M. Manley, 2012). Furthermore, it was possible to differentiate between closely related Fusarium species (P. Williams, P. Geladi, T. Britz, & M. Manley, 2012b), and study their growth on agar (a) (b) media (P. Williams, P. Geladi, T. Britz, & M. Manley, 2012a). Not only was it possible to distinguish between the “growth rings” of the fungal colonies, but it was also possible to construct profiles resembling growth curves. Currently an MSc in Food Science student is using the technique to develop classification models that will be capable of grading maize kernels according to regulations stipulated in South African legislation (Act No, 119 of 1990, as amended by Government Notice No. R. 473 of 8 May 2009). Multispectral imaging (similar to hyperspectral imaging just with fewer wavelengths) and NIR hyperspectral imaging are being evaluated. The kernels studied were divided into 13 groups based on quality by qualified maize graders, and were imaged with both a SisuChema NIR hyperspectral push broom imaging system (Specim, Spectral Imaging Ltd, Oulu, Finland), and a Video meter multispectral imaging system (Videometer A/S, Hørsholm, Denmark). The calibrationcoefficients for the multispectral data ranged 0.46 to 0.95, with correct classifications ranging 83 to 100%. The calibration coefficients for the NIR hyperspectral data ranged 0.65 to 0.88, with correct classifications ranging 69 to 100%. (c) Figure 2. Collection of images illustrating the difference between a chemical image (a), a digital image (b) and a diagram (c). The information in (c) was used to categorize a hyperspectral image to obtain (a), a classified image where each colour represents a different class. These are but a few of the applications currently under investigation, others include detection and differentiation of foodborne pathogens on growth media, distinction of Fusarium species on growth media and shedding light on food fraud. In this manner, researchers at SU are making the invisible, visible. References Williams, P., Geladi, P., Britz, T., & Manley, M. (2012a). Growth characteristics of three Fusarium species evaluated by near-infrared hyperspectral imaging and multivariate image analysis. Applied Microbiology and Biotechnology, 96(3), 803-813. Williams, P., Geladi, P., Britz, T., & Manley, M. (2012b). Near-infrared (NIR) hyperspectral imaging and multivariate image analysis to study growth characteristics and differences between species and strains of members of the genus Fusarium. Analytical and Bioanalytical Chemistry, 404(6), 1759-1769. Williams, P. J., Geladi, P., Britz, T. J., & Manley, M. (2012). Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. Journal of Cereal Science, 55(3), 272278.