Browsing by Person "Njoroge, Daniel"

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Effects of Ugali maize flour fortification with chia seeds (Salvia hispanica L.) on its physico-chemical properties and consumer acceptability
(2024) Chemutai, Susan; Mburu, Monica; Njoroge, Daniel; Zettel, Viktoria
The study investigated the effect of incorporating whole chia seeds (WCS) and defatted chia seed flour (DCF) into whole maize meal for ugali preparation. Both were incorporated at substitution levels of 3%, 6%, and 9% separately, and the resulting treatments subjected to laboratory analysis. In addition, ugali samples were prepared from all the resulting flour formulations and subjected to consumer acceptability assessment. Incorporation of both DCF and WCS resulted in increased water absorption capacity (ranging from 0.78 to 0.98 g/mL), swelling index (ranging from 0.15 to 3.25 mL/g), and swelling capacity (ranging from 2.46 to 5.74 g/g). WCS decreased the bulk density and oil absorption capacity. DCF, however, resulted in an increase in bulk density and oil absorption capacity. Both DCF and WCS lowered the lightness (L*) of the products. Proximate composition ranged from 4.78 to 7.46% for crude fat, 7.22% to 9.16% for crude protein, and 1.74 to 4.27% for crude fiber. The obtained results show the potential of chia seeds as a good fortificant of maize flour since it resulted in nutritionally superior products (crude ash, crude protein, crude fat, and energy value) when compared to control. The freshly prepared ugali samples were generally acceptable to the panelists up to 9% WCS and 6% DCF substitution levels.
Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder
(2023) Munyendo, Leah; Njoroge, Daniel; Zhang, Yanyan; Hitzmann, Bernd
Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta‐adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.