Oct 2026· Journal of Dairy Science· 0 citations· 83 references
Medicine
Abstract
This study investigated the effect of caffeinated Arabica and decaffeinated coffee lyophilizate (0.6 g/100 g), incorporated either before or after fermentation, on the phenolic compound profile, caffeine and diterpene content, antioxidant activity, and physicochemical properties of yogurt during 20 d of refrigerated storage. Yogurts were produced from commercially available pasteurized and homogenized cow's milk (2.0% fat) using a thermophilic starter culture comprising Streptococcus thermophilus, Lactobacillus acidophilus, and Bifidobacterium animalis ssp. lactis (Lyofast SAB 440B). Total phenolic losses during lyophilization amounted to 14.0% and 19.0% for caffeinated and decaffeinated preparations, respectively, with feruloylquinic acid isomers showing the highest susceptibility to degradation (losses of 55-72%). Coffee enrichment resulted in an approximately 9-10-fold increase in total phenolic content relative to control yogurt, with overall phenolic retention reaching 73-85% of theoretical values. After-fermentation addition yielded higher extractable phenolic content, while before-fermentation addition was associated with lower extractable phenolics but superior hydroxyl radical scavenging capacity (OHSC), potentially reflecting the formation of polyphenol-protein complexes during fermentation. Oxygen radical absorbance capacity (ORAC) increased significantly during storage in before-fermentation variants, suggesting progressive release of bound phenolics through starter culture-mediated proteolysis. Decaffeinated lyophilizate added before fermentation produced the firmest yogurt gel, an effect potentially attributable to polyphenol-casein interactions and possible caffeine-mediated modification of starter culture activity. Addition timing rather than coffee type was the primary determinant of surface optical properties. The majority of bioactive compound profiles and physicochemical parameters showed no systematic changes during the 20-d storage period, although significant storage-related changes were observed for selected antioxidant activity indices, textural parameters, surface gloss, and diterpene content in specific treatment groups. These findings indicate that coffee lyophilizate is a promising functional ingredient for yogurt enrichment, with the timing of addition being a critical process parameter that influences bioactive compound availability, antioxidant functionality, and textural quality.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.