Masters Degrees (Food Science)

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Browsing Masters Degrees (Food Science) by browse.metadata.advisor "Botes, Willem"

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    Wheat and triticale whole grain near infrared hyperspectral imaging for protein, moisture and kernel hardness quantification
    (Stellenbosch : Stellenbosch University, 2021-03) Orth, Sebastian Helmut; Manley, Marena; Botes, Willem; Williams, Paul James; Stellenbosch University. Faculty of AgriSciences. Dept. of Food Science.
    ENGLISH ABSTRACT: Wheat (Triticum aestivum) is one of the most important cereal crops grown globally. Triticale (× Triticosecale sp. Wittmack ex A. Camus 1927) is an important cereal crop for feed and fodder production and is also emerging as an alternative cereal for human consumption. Both these cereals are grown and produced in a diverse climatic environment and they vary with regards to their physicochemical properties. Quantitative techniques for determining protein and moisture content and kernel hardness is of importance for grading of the grains. The use of non-invasive and rapid techniques such as near-infrared hyperspectral imaging (NIR-HSI) show potential for quantification of these quality parameters. This study aimed to investigate the use of NIR-HSI (HySpex SWIR 384) with partial least squares regression (PLS-R) analysis for wheat and triticale bulk sample and single kernel image approaches. The study considered South African wheat and triticale samples produced in three Western Cape localities, i.e. Napier, Tygerhoek and Vredenburg, comprising 180 wheat and 177 triticale samples. Of these, 39 kernels per sample were used for single kernel protein and moisture content and kernel hardness prediction, resulting in data sets with a total of 7020 wheat, 6903 triticale and 13923 combined single kernel images. This was further split into training (70%) and validation (30%) sets using the Duplex algorithm. NIR (1100-2100 nm) hyperspectral images were acquired and the spectral data obtained for each pixel were averaged for each kernel. PLS-R was used to develop quantitative prediction models. Principal component analysis (PCA) was performed on the average spectral data and the PCA plot (PC1 vs. PC2) indicated separation between locality, with both wheat and triticale separating in the direction of PC1 from left to right. A PCA (PC1 vs. PC2) was performed for the wheat and triticale combined data set – no separation was noted. Bulk sample protein, moisture content and kernel hardness models were first evaluated which showed favourable prediction accuracy, comparable to conventional NIR spectroscopy studies performed on wheat and triticale. The combined wheat and triticale data sets for protein and moisture content and kernel hardness prediction had RMSEP-values of 0.41%, 0.49% and 8.66, respectively. Single kernel analysis involved two main quantitative data analysis methods (PLS-R and Robust-PLS) which were tested with an independent test set. The results being favourable for the conventional PLS-R method when only the validation set RMSEP (protein content: 0.37-0.84%, moisture content: 0.23-0.57% and kernel hardness: 1.74-3.64) was considered. The independent test set for protein content prediction achieved better results with the Robust-PLS (RMSEP protein content: 1.95-2.37%) method, proving that the method did indeed have an effect on making the calibration data sets more robust. Spectral imaging showed that it is capable to accurately quantifying protein and moisture content and kernel hardness of bulk and single kernel samples – good robust models proved to optimally quantify these parameters. The technique shows good potential for further study and to build onto the current data sets in order to increase variance across seasons. Further the technique showcases the functionality of SK NIR-HSI analysis and can be used both as a quality control measure and as an early generation selection method by the grain breeding sector.