Barasa Mercy Mukite
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Barasa Mercy Mukite has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Flora Vitalis
Department of Food Measurements and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary
1 shared publication - Gabriella Kiskó
Department of Food Microbiology, Hygiene and Safety, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary
1 shared publication - Matyas Lukacs
Department of Food Measurement and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary
1 shared publication - Tamás Somogyi
Department of Food Measurement and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), Somlói út 14-16., H-1118 Budapest, Hungary
1 shared publication - Tamás Stefaniga
Department of Food Measurement and Process Control, Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), Somlói út 14-16., H-1118 Budapest, Hungary
1 shared publication - Tamás Zsom
Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences (MATE), H-1118 Budapest, Hungary
1 shared publication
Latest Publications
- Open Access
Investigation of the Ultrasonic Treatment-Assisted Soaking Process of Different Red Kidney Beans and Compositional Analysis of the Soaking Water by NIR Spectroscopy
The processing of beans begins with a particularly time-consuming procedure, the hydration of the seeds. Ultrasonic treatment (US) represents a potential environmentally friendly method for process acceleration, while near-infrared spectroscopy (NIR) is a proposedly suitable non-invasive monitoring tool to assess compositional changes. Our aim was to examine the hydration process of red kidney beans of varying sizes and origins. Despite the varying surface areas, the beans’ soaking times of 13–15, 15–17, and 17–19 mm did not reveal significant differences between any of the groups (control; low power: 180 W, 20 kHz; high power: 300 W, 40 kHz). US treatment was observed to result in the release of greater quantities of water-soluble components from the beans. This was evidenced by the darkening of the soaking water’s color, the increase in the a* color parameter, and the rise in the dry matter value. NIRs, in combination with chemometric tools, are an effective tool for predicting the characteristics of bean-soaking water. The PLSR- and SVR-based modelling for dry matter content and light color parameters demonstrated robust model fits with cross and test set-validated R2 values (>0.95), suggesting that these techniques can effectively capture the chemical information of the samples.
Open AccessInvestigation of the Ultrasonic Treatment-Assisted Soaking Process of Different Red Kidney Beans and Compositional Analysis of the Soaking Water by NIR Spectroscopy
The processing of beans begins with a particularly time-consuming procedure, the hydration of the seeds. Ultrasonic treatment (US) represents a potential environmentally friendly method for process acceleration, while near-infrared spectroscopy (NIR) is a proposedly suitable non-invasive monitoring tool to assess compositional changes. Our aim was to examine the hydration process of red kidney beans of varying sizes and origins. Despite the varying surface areas, the beans’ soaking times of 13–15, 15–17, and 17–19 mm did not reveal significant differences between any of the groups (control; low power: 180 W, 20 kHz; high power: 300 W, 40 kHz). US treatment was observed to result in the release of greater quantities of water-soluble components from the beans. This was evidenced by the darkening of the soaking water’s color, the increase in the a* color parameter, and the rise in the dry matter value. NIRs, in combination with chemometric tools, are an effective tool for predicting the characteristics of bean-soaking water. The PLSR- and SVR-based modelling for dry matter content and light color parameters demonstrated robust model fits with cross and test set-validated R2 values (>0.95), suggesting that these techniques can effectively capture the chemical information of the samples.