Research Interests
Dr. Kucha’s Precision Food Process Engineering Lab applies engineering principles to support digital revolution technologies in agri-food production. The primary research focus of the lab is to develop and apply sensing technologies, data analytics, and systems modeling to address pre-harvest and post-harvest food quality and safety, as well as food processing challenges. Dr. Kucha’s research group conducts research that aims to enhance food processing efficiency, reduce production costs and time, conserve energy and resources, and minimize food loss and waste amidst resilience, climate change, and sustainability concerns. Key research areas include development of machine vision systems for food analysis, applications of artificial intelligence and chemometrics in food quality and safety, development of process analytical technologies for online food processing, reverse engineering for intelligent food design, “big data” applications for enhanced food processing, and non-destructive real-time assessment of food quality and safety.
Courses Taught
FDST 3000: Introduction to Food Science and Technology
FDST 2001: Introduction to Artificial Intelligence in Food Systems
Select Publications
Kucha, C., Olaniyi, E.O. & Ngadi, M. (2024). Miniaturized hand-held near-infrared spectroscopy and machine learning for precision monitoring of solid fat content. Food Measure. https://doi.org/10.1007/s11694-024-02504-y
Kucha, C., & Olaniyi, E. O. (2024). Applications of hyperspectral imaging in meat tenderness detection: Current research and potential for digital twin technology. Food Bioscience, 103754. https://doi.org/10.1016/j.fbio.2024.103754
Liberty, J. T., Sun, S., Kucha, C., Adedeji, A. A., Agidi, G., & Ngadi, M. O. (2024). Augmented reality for food quality assessment: Bridging the physical and digital worlds. Journal of Food Engineering, 111893. https://doi.org/10.1016/j.jfoodeng.2023.111893
Mayorga-Martínez, A.A., Kucha, C., Kwofie, E., & Ngadi, M. (2023), Designing nutrition-sensitive agriculture (NSA) interventions with multi-criteria decision analysis (MCDA): a review, Critical Reviews in Food Science and Nutrition, DOI: 10.1080/10408398.2023.2248616
Mahal, S. S., Kucha, C., Kwofie, E. M., & Ngadi, M. (2023). Design and Development of ‘Diet DQ Tracker’: A Smartphone Application for Augmenting Dietary Assessment. Nutrients, 15(13), 2901. https://doi.org/10.3390/nu15132901
Kucha, C., Liu, L., Ngadi M., & Gariépy C. (2022), Hyperspectral imaging and chemometrics assessment of intramuscular fat in pork Longissimus thoracic et lumborum primal cut. Journal of Food Control. https://doi.org/10.1016/j.foodcont.2022.109379
Kucha, C., Liu, L., Ngadi, M., & Gariépy C. (2022), Prediction and visualization of fat content in polythene-packed meat using near-infrared hyperspectral imaging and chemometrics. Journal of Food Composition and Analysis. https://doi.org/10.1016/j.jfca.2022.104633
Kucha, C., Liu, L., Ngadi M., & Gariépy C. (2021), Improving intramuscular fat assessment in pork by synergy between spectral and spatial features in hyperspectral image. Journal of Food Analytical Methods. https://doi.org/10.1007/s12161-021-02113-1
Kucha, C., Liu, L., Ngadi M., & Gariépy C. (2021), Hyperspectral imaging and chemometrics as a non-invasive tool to discriminate and analyze iodine value of pork fat. Journal of Food Control. https://doi.org/10.1016/j.foodcont.2021.108145.
Kucha, C. Liu, L., Ngadi M., & Gariépy C. (2021), Anisotropic effect on the predictability of intramuscular fat content in pork by hyperspectral imaging. Journal of Meat Science. https://doi.org/10.1016/j.meatsci.2021.108458
Feifei, T., Liu, L., Kucha C., & Ngadi, M. (2021), Rapid and non-destructive detection of cassava flour adulterants in wheat flour using a handheld MicroNIR spectrometer. Biosystems Engineering. https://doi.org/10.1016/j.biosystemseng.2020.12.010
Kucha, C. & Ngadi M. (2020), Rapid assessment of pork freshness using miniaturized NIR spectroscopy. Food Measurement and Characterization. https://doi.org/10.1007/s11694-019-00360-9
Kucha, C. Liu, L., Ngadi M., & Gariépy C. (2020), Intramuscular fat quality assessment in pork based on hyperspectral imaging. Food Engineering Reviews. https://doi.org/10.1007/s12393-020-09246-9
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