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dc.contributor.advisorMagwaza, Lembe Samukelo.
dc.contributor.advisorTesfay, Samson Zeray.
dc.contributor.advisorMphahlele, Rebogile Ramasele.
dc.creatorNdlovu, Phindile Faith.
dc.date.accessioned2021-11-30T14:08:30Z
dc.date.available2021-11-30T14:08:30Z
dc.date.created2021
dc.date.issued2021
dc.identifier.urihttps://researchspace.ukzn.ac.za/handle/10413/19988
dc.descriptionDoctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.en_US
dc.description.abstractA general lack of strict regulations in South Africa to monitor processed foodstuff increases chances of unfair producers and traders to intentionally mislabel and adulterate high valued food products with inferior lookalikes. Recently, unripe banana flour (UBF) has gained global attention and has been identified as a replacement for cereals flours due to its gluten free traits and resistant starch nutritional qualities, yet has no quality control standards. The objective of this research was to develop rapid prediction models based on a visible to near infrared (Vis- NIR) spectroscopy (Vis-NIRS) combined with multivariate analysis to classify, detect, and quantify different adulteration levels of staple flours (i.e. wheat and maize flours) in unripe banana flour. The other aim was to identify important biomarkers of unripe banana flour that could be used to discriminate unripe banana flour adulteration at different concentration levels. A critical evaluation of the portable Vis-NIR spectroscopy combined with chemometrics analysis indicated that it was possible to discriminate between unripe banana flour with wheat and maize flours and associated different adulteration levels. The partial least square (PLS) regression (PLSR) analysis quantified individually maize and wheat flours, based on different adulteration levels, showed that optimal PLSR detection models performances were obtained using the first derivative Savitsky-Golay (7-point smoothing, 2nd order polynomial) and the second derivative Savitsky-Golay (19-point smoothing, 2nd order polynomial). The study to optimise and test the handheld Vis-NIR instruments’ feasibilty to simulteniously develop a standard model for rapid solution to detect both maize and wheat flours adulteration indicated high classification and prediction accuracies could be achived through principal component analysis (PCA) and partial least squeres regression (PLSR). The study found that gluten could be utilised as a biomarker to test for unwanted adulteration of unripe banana flour with wheat flour, and showed good and reliable rapid spectroscopic PLSR model was achieved with high precision. Near infrared spectroscopy showed great potential to detect the nutritional changes of unripe banana flour during adulteration based on resistant starch content. The results of this investigation indicated that wheat adulteration is a threat to unripe banana flour importnt attribute as signification reduction of this parameter was observed with the increasing levels wheat adultearation. Vis-NIR spectroscopy with multivariate analysis detected the varying resistant starch concentration unripe banana flour samples successfully with high accuracy. The results and stability of the models developed in this study demonstrated clearly that the Vis- NIRS method has a potential of providing unripe banana flour processing industry with a rapid and non-destructive technique to manage unripe banana flour quality as well as adulteration by staple flours, therefore ensuring fair and safe trading of the product in retail markets of South Africa.en_US
dc.language.isoenen_US
dc.subject.otherProduct authenticity.en_US
dc.subject.otherMultivariate analysis.en_US
dc.subject.otherNear infrared spectroscopy.en_US
dc.subject.otherStaple flour adulteration.en_US
dc.subject.otherConsumer protection.en_US
dc.subject.otherAdulterated foods.en_US
dc.subject.otherProcessed foodstuffs.en_US
dc.titleRapid monitoring and quantification of unripe banana flour adulteration using visible-near infrared spectroscopy.en_US
dc.typeThesisen_US


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