Article by Dr Paul Williams Department of Food Science, Stellenbosch University Photos provided F or many years scientists have relied on the power of imaging techniques to make the invisible, visible; in so doing advancing scientific knowledge and understanding. Imaging techniques are the future of rapid analytical analyses. Not only do they offer accuracy and rapidity, but a number of them also afford the ability to analyse entire samples at once, non-destructively. One such technique, near infrared (NIR) hyperspectral imaging, utilises the NIR region (750-2500 nm) of the electromagnetic spectrum. It is a combination of digital imaging and conventional NIR spectroscopy that captures spectral and spatial information. Unlike colour digital imaging that only captures three wavelength channels, red, green and blue (RGB), NIR hyperspectral imaging acquires many contiguous spectral bands of an object. Three instrument configurations are available i.e. whiskbroom (point-by-point imaging), staring (focal plane array) and push broom (line-scanning). The setup best-suited for use in the agricultural industry, where rapid non-destructive analyses are required, is the push broom system. As its secondary name suggests, the line-scanning system acquires an image line-by-line. A typical push broom system is illustrated in Figure 1. The samples (in this case maize kernels) are moved on a conveyor belt into the field of view of the camera, where it is illuminated. From there light is reflected towards the detector where a hyperspectral imaging is constructed line-by-line. Two spatial dimensions (x and y pixel coordinates) and a spectral dimension ( z wavelength) is acquired, forming data cubes commonly known as hypercubes. With mathematical processing, scientists are able to explore the nature of the samples thus determining which chemicals are present, how much of the chemicals are present and, importantly, where in the sample it is located. Thus, hyperspectral images can be considered as chemical maps, allowing us to visualise the distribution of chemical compounds in samples. Figure 2 illustrates how one would interpret such a map of a maize kernel. Figure 2a is the classified chemical image, Fig 2b a digital image and Fig 2c a diagram. Using the diagram and cross section of a maize kernel Fig. 2c, it is possible to categorize the hyperspectral image to obtain Fig. 2a. This classification of the different components would not have been possible if there were no chemical differences between the structures. Figure 1. Push broom NIR hyperspectral imaging system. Samples are moved on a conveyor belt into the field of view of the camera. An image is acquired line-by-line until the entire sample has been captured. This setup is ideal for industry as samples are scanned in approximately 10 s.