Browsing by Author "Guelpa, Anina"
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- ItemThe CT scanner facility at Stellenbosch University : an open access X-ray computed tomography laboratory(Elsevier, 2016-10) Du Plessis, Anton; Le Roux, Stephan Gerhard; Guelpa, AninaThe Stellenbosch University CT Scanner Facility is an open access laboratory providing non-destructive Xray computed tomography (CT) and a high performance image analysis services as part of the Central Analytical Facilities (CAF) of the university. Based in Stellenbosch, South Africa, this facility offers open access to the general user community, including local researchers, companies and also remote users (both local and international, via sample shipment and data transfer). The laboratory hosts two CT instruments, i.e. a micro-CT system, as well as a nano-CT system. A workstation-based Image Analysis Centre is equipped with numerous computers with data analysis software packages, which are to the disposal of the facility users, along with expert supervision, if required. All research disciplines are accommodated at the X-ray CT laboratory, provided that non-destructive analysis will be beneficial. During its first four years, the facility has accommodated more than 400 unique users (33 in 2012; 86 in 2013; 154 in 2014; 140 in 2015; 75 in first half of 2016), with diverse industrial and research applications using X-ray CT as means. This paper summarises the existence of the laboratory’s first four years by way of selected examples, both from published and unpublished projects. In the process a detailed description of the capabilities and facilities available to users is presented.
- ItemLaboratory X-ray micro-computed tomography : a user guideline for biological samples(Oxford University Press, 2015) Du Plessis, Anton; Broeckhoven, Chris; Guelpa, Anina; Le Roux, Stephan GerhardLaboratory X-ray micro-computed tomography (micro-CT) is a fast growing method in scientific research applications that allows for non-destructive imaging of morphological structures. This paper provides an easily operated “how-to” guide for new potential users and describes the various steps required for successful planning of research projects that involve micro-CT. Background information on micro-CT is provided, followed by relevant set-up, scanning, reconstructing and visualization methods and considerations. Throughout the guide, a Jackson’s chameleon specimen, which was scanned at different settings, is used as an interactive example. The ultimate aim of this paper is make new users familiar with the concepts and applications of micro-CT, in an attempt to promote its use in future scientific studies.
- ItemMaize endosperm texture characterisation using the rapid visco analyser (RVA), X-ray micro-computed tomography (μCT) and micro-near infrared (microNIR) spectroscopy(Stellenbosch : Stellenbosch University, 2015-04) Guelpa, Anina; Manley, Marena; Geladi, Paul; Du Plessis, Anton; Stellenbosch University. Faculty of Agrisciences. Dept. of Food Science.ENGLISH ABSTRACT: Maize kernels consists of two types of endosperm, a harder vitreous endosperm and a softer floury endosperm, and the ratio of the vitreous and floury endosperm present mainly determines the hardness of the kernel. Maize (Zea mays L.) is a staple food in many countries, including South Africa, and is industrially processed into maize meal using dry-milling. For optimal yield and higher quality products, hard kernels are favoured by the milling industry. Despite many maize hardness methods available, a standardised method is still lacking, furthermore, no dedicated maize milling quality method exists. Using an industrial guideline (chop percentage), a sample set of different maize hybrids was ranked based on milling performance. Unsupervised inspection (using principal component analysis (PCA) and Spearman’s rank correlation coefficients) identified seven conventional methods (hectoliter mass (HLM), hundred kernel mass (HKM), protein content, particle size index (PSI c/f), percentage vitreous endosperm (%VE) as determined using near infrared (NIR) hyperspectral imaging (HSI) and NIR absorbance at 2230 nm (NIR @ 2230 nm)) as being important descriptors of maize milling quality. Additionally, Rapid Visco Analyser (RVA) viscograms were used for building prediction models, using locally weighted partial least squares (LW-PLS). Hardness properties were predicted in the same order or better than the laboratory error of the reference method, irrespective of RVA profile being used. Classification of hard and soft maize hybrids was achieved, based on density measurements as determined using an X-ray micro-computed tomography (µCT) density calibration constructed from polymers with known densities. Receiver operating classification (ROC) curve threshold values of 1.48 g.cm-3 , 1.67 g.cm-3 and 1.30 g.cm-3 were determined for the entire kernel (EKD), vitreous (VED) and floury endosperm densities (FED), respectively at a maximum of 100% sensitivity and specificity. Classification based on milling quality of maize hybrids, using X-ray µCT derived density and volume measurements obtained from low resolution (80 µm) µCT scans, were achieved with good classification accuracies. For EKD and vitreous-to-floury endosperm ratio (V:F) measurements, 93% and 92% accurate classifications were respectively obtained, using ROC curve. Furthermore, it was established that milling quality could not be described without the inclusion of density measurements (using PCA and Spearman’s rank correlation coefficients). X-ray µCT derived density measurements (EKD) were used as reference values to build NIR spectroscopy prediction models. NIR spectra were acquired using a miniature NIR spectrophotometer, i.e. a microNIR with a wavelength range of 908 – 1680 nm. Prediction statistics for EKD for the larger sample set (where each kernel was scanned both germ-up and germ-down) was: R2 V = 0.60, RMSEP = 0.03 g.cm-3 , RPD = 1.67 and for the smaller sample set (where each kernel was scanned only germ-down): R2 V = 0.32, RMSEP = 0.03 g.cm-3 , RPD = 1.67. The results from the larger sample set indicated that reasonable predictions can be made at the fast NIR scan rate that would be suitable for breeders as a rough screening method.