Chyngyz Erkinbaev

Research

Research description

Dr. Erkinbaev’s research program focuses on the development of smart modular sensing technologies to advance and increase the efficacy of food processing from “farm to fork”. He believes that advanced technologies integrating engineering, physical, digital and biological domains will revolutionize food processing and have a significant positive impact on the agri-food sector.

Area

Smart sensing technologies, food process engineering, spectral imaging, chemical and microstructural mapping, and advanced non-destructive analytical techniques with an emphasis on application to real-time food quality monitoring.

Expertise

Spectroscopy, hyperspectral imaging, chemometrics, computer vision, adaptive machine learning, food quality control.

Graduate Student Opportunities

Professor Erkinbaev does not have any graduate student opportunities available at this time.

Selected Publications

L. Dhanapal and C. Erkinbaev* (2024). Non-invasive characterization of color dynamics in plant-based meat burgers using portable hyperspectral imaging device and multivariate image analysis, Future Foods, 9, 100293.

A. Thakuria and C. Erkinbaev* (2024). Real-Time Canola Damage Detection: An End-to-End Framework with Semi-Automatic Crusher and ShuffleNetV2_YOLOV5s, Smart Agricultural Technology, 7 100399.

B. Paziuk, M. Jayasinghe, and C. Erkinbaev* (2024). Non-destructive characterization of micronized pulse seeds using X-ray microcomputed tomography, Applied Food Research 4(1), 100395.

A. Thakuria and C. Erkinbaev* (2023). Improving the network architecture of YOLOv7 to achieve real-time grading of canola based on kernel health. Smart Agricultural Technology, 5, 100300.

L. Dhanapal and C. Erkinbaev* (2023). Portable hyperspectral imaging coupled with multivariate analysis for real-time prediction of plant-based meat analogues quality. Journal of Food Composition and Analysis, 126, 105840.

C. Erkinbaev, M. Nadimi, J. Paliwal (2022). A unified heuristic approach to simultaneously detect fusarium and ergot damage in wheat, Measurement: Food, Volume 7, 100043.

C. Erkinbaev, R.P. Ramachandran, S. Cenkowski, J. Paliwal. (2019). A comparative study on the effect of superheated steam and hot air drying on the microstructure of distillers' spent grain pellets using X-ray micro-computed tomography. Journal of Food Engineering. 241: 127-135.

C. Erkinbaev, K. Henderson, J. Paliwal. (2017). Discrimination of gluten-free oats from contaminants using near infrared hyperspectral imaging technique. Food Control. 80: 197-203.

B. Aernouts1/2, C. Erkinbaev1/2, R. Watté, R. Beers, N.N. Do Trong, B. Nicolai, W. Saeys. (2015). Estimation of bulk optical properties of turbid media from hyperspectral scatter imaging measurements: Metamodeling approach. Optics Express. 23: 27880-27898.

C. Erkinbaev, E. Herremans N.N. Do Trong, E. Jakubczyk, P. Verboven, B. Nicolaï, W. Saeys. (2014). Contactless and non-destructive differentiation of microstructures of sugar foams by hyperspectral scatter imaging. Innovative Food Science & Emerging Technologies. 24: 131-137.