As a food engineer and chemist, his pioneering research revolves around the cutting-edge utilization of smart sensors, including optical (RGB, NIR and NIR-HIS) and olfactive sensors (electronic nose), in conjunction with advanced technologies sush as artificial intelligence, machine learning, and chemometrics. His primary focus lies in developing and optimizing innovative applications of these sensor technologies on lab-made equipment, specifically tailored for enhacing agricultural practices and processes.
1. Ferreira, M. V. S., Barbosa Jr, J. L., Kamruzzaman, M., & Barbin, D. F. (2023). Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: Recent advances and future trends. Analytical Methods: Advancing Methods and Applications. Link to DOI
2. Oliveira, M. M., Ferreira, M. V. S., Kamruzzaman, M., & Barbin, D. F. (2023). Prediction of impurities in cocoa shell powder using NIR spectroscopy. Journal of Pharmaceutical and Biomedical Analysis Open, 2, 100015. Link to DOI
3. Sobreira, C. H., Ferreira, M. V. S., & Kamruzzaman, M. (2023). Authentication of premium tea based on geographical origin using NIR spectroscopy and multivariate analysis. 2023 ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers.
4. Ferreira, M. V. S., Kamruzzaman, M., & Ahmed, M. W. (2024). Portable and field- deployable sensor technologies for rapid food analysis: Applications and future directions. 2024 ASABE Annual International Meeting, 1. Link to DOI
5. He, H. J., Ferreira, M. V. S., Wu, Q., Karami, H., & Kamruzzaman, M. (2024). Portable and miniature sensors in supply chain for food authentication: A review. Critical Reviews in Food Science and Nutrition, 1–21. Link to DOI
His groundbreaking work focuses on the transformation of the egg industry through the integration of advanced technologies. By utilizing non-destructive optical sensing, IoT, AI, big data, and cloud computing, he aims to develop smart systems for egg production, quality inspection, and grading. His research highlights the potential of these innovations to enhance automation, biosecurity, and animal welfare. A significant part of his work involves applying various CNN and transfer learning models for nondestructive chicken egg fertility detection, demonstrating their high accuracy and potential for industry-wide implementation
1. Ahmed, M. W., Hossainy, S. J., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2023). Non-destructive optical sensing technologies for advancing the egg industry toward industry 4.0: A review. Comprehensive Reviews in Food Science and Food Safety, 22, 4378–4403. Link to DOI
Dong Hoon is a Ph.D. student in Agricultural and Biological Engineering at UIUC. As an enthusiastic biological engineer/researcher with 7+ years of research experience in nanozymes and their agricultural and biological applications.
1. Lee, D. H., & Kamruzzaman, M. (2024). Amino acid-based, sustainable organic nanozyme for allergic biomolecule detection. ChemRxiv. Link to DOI
2. Lee, D. H., Kamruzzaman, M., & Kalita, D. (2023). Nanozymes for agricultural herbicide detection. Nanoscale, 15(31), 12954–12960. Link to DOI
3. Lee, D. H., & Kamruzzaman, M. (2023). Eco-friendly, degradable, peroxidase- mimicking nanozyme for selective antioxidant detection. Materials Today Chemistry, 34, 101809. Link to DOI
4. Lee, D. H., Kamruzzaman, M., & Kalita, D. (2023). Nanozymes for agricultural herbicide detection. Nanoscale, 15(31), 12954–12960. Link to DOI
Di is a second-year PhD student, the research area is Crop Phenotype. The current contents are hyperspectral image processing, machine learning application, and deep learning studying. He has also interest in hardware development.
1. Song, D., Ngumbi, E., & Kamruzzaman, M. (2023). Rapid and low-cost measurement method of normalized difference vegetation index in different scenes. 2023 ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers.
2. Song, D., De Silva, K., Brooks, M. D., & Kamruzzaman, M. (2023). Biomass prediction based on hyperspectral images of the Arabidopsis canopy. Computers and Electronics in Agriculture, 210, 107939. Link to DOI
3. Song, D., Wu, Q., & Kamruzzaman, M. (2023). Appropriate use of chemometrics for feasibility study for developing low-cost filter-based multi-parameter detection spectroscopic device for meat proximate analysis. Chemometrics and Intelligent Laboratory Systems, 239, 104844. Link to DOI
Md Toukir Ahmed is a doctoral student in the Agricultural and Biological Engineering department at the University of Illinois at Urbana-Champaign. His research interests include machine learning-based spectral data analysis, hyperspectral image analysis, image reconstruction, and spectroscopic software design.
1. Ahmed, M. T., & Kamruzzaman, M. (2024). SpectroChat: A Windows executable graphical user interface for chemometrics analysis of spectroscopic data. Software Impacts, 100698. Link to DOI
2. Ahmed, M. T., Monjur, O., & Kamruzzaman, M. (2024). Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product. Journal of Food Engineering, 382, 112223. Link to DOI
3. Ahmed, M. T., & Kamruzzaman, M. (2024). Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis. Smart Agricultural Technology, 8, 100458. Link to DOI
4. Ahmed, M. T., Villordon, A., & Kamruzzaman, M. (2024). Comparative analysis of hyperspectral image reconstruction using deep learning for agricultural and biological applications. Results in Engineering, 102623. Link to DOI
5. Ahmed, M. T., Ahmed, M. W., Monjur, O., Emmert, J. L., Chowdhary, G., & others. (2024). Hyperspectral image reconstruction for predicting chick embryo mortality towards advancing egg and hatchery industry. arXiv preprint arXiv:2405.13843. Link to DOI
6. Ahmed, M. T., & Kamruzzaman, M. (2024). Hyperspectral imaging and optimized convolutional neural network for quality assessment of sweetpotato. 2024 ASABE Annual International Meeting, 1. Link to DOI
His current research focuses on developing an efficient and accurate system for early detection of egg fertility, embryonic mortality, and prediction of chick embryo sex using hyperspectral imaging with chemometrics and machine learning. He has received a bachelor’s in Food Engineering from Bangladesh Agricultural University and Masters’s in Food Science, Technology and Business from KU Leuven.
1. Ahmed, M. W., Hossainy, S. J., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2023). Non-destructive optical sensing technologies for advancing the egg industry toward industry 4.0: A review. Comprehensive Reviews in Food Science and Food Safety, 22, 4378–4403. Link to DOI
2. Ahmed, M. W., & Kamruzzaman, M. (2024). Real-time analysis of chemical composition in food products using portable hyperspectral imaging and deep learning. 2024 ASABE Annual International Meeting, 1. Link to DOI
3. Ahmed, M. W., & Kamruzzaman, M. (2024). Portable hyperspectral imaging device for assessing agricultural crops: A design and optimization approach. 2024 ASABE Annual International Meeting, 1. Link to DOI
4. Ahmed, M. W., Schulnies, F., & Kleinschmidt, T. (2024). Residence time distribution and kinetics of insolubility of skim milk powder during spray drying. Journal of Food Engineering, 435, 112277. Link to DOI
5. Ahmed, M. W., Esquerre, C. A., Eilts, K., Allen, D. P., McCoy, S. M., Varela, S.,
Singh, V., & others. (2024). Rapid and high-throughput determination of sorghum (Sorghum bicolor) biomass composition using near infrared spectroscopy and chemometrics. Biomass and Bioenergy, 186, 107276. Link to DOI
6. Ahmed, M. W., Hossainy, S. J., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2023). Non-destructive optical sensing technologies for advancing the egg industry toward industry 6.0: A review. Comprehensive Reviews in Food Science and Food Safety, 22, 4378–4403. Link to DOI
Lisa is a second-year Phd student focusing on nanozyme incorporated electrochemical sensor and its applications in agricultural and food industries. She previously earned her bachelor’s degree in ABE and Chemistry from UIUC and was an undergraduate research assistant in Dr.Kamruzzaman’s lab working on NIR/hyper-spectroscopical detection of flour adulterations.
1. Wang, Z., Wu, Q., & Kamruzzaman, M. (2022). Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour. Food Control, 138, 108970. Link to DOI
2. Wu, Q., Oliveira, M. M., Achata, E. M., & Kamruzzaman, M. (2023). Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis. Journal of Food Composition and Analysis, 119, 105274. Link to DOI
3. Wu, Q., Mousa, M. A., Al-qurashi, A. D., Ibrahim, O. H., Abo-Elyousr, K. A.,
Rausch, K., … & Kamruzzaman, M. (2023). Global calibration for non targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics. Current Research in Food Science, 100483. Link to DOI
Runyu is a first year PhD Student, prior to which she got her Master Degree in Agricultural Engineering at UIUC at IOSNEL. Her current research interests are crop growth status monitoring and food quality tests based on hyperspectral imaging technologies
1. Wang, Z., Zheng, R., & Kamruzzaman, M. (2024). Advanced feature selection techniques in NIR spectroscopy for predicting food quality: A review. Journal of Food Engineering, 434, 112339. Link to DOI
2. Zheng, R., Jia, Y., Ullagaddi, C., Allen, C., Rausch, K., Singh, V., Schnable, J. C., & Kamruzzaman, M. (2024). Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi- country corn kernels via NIR spectroscopy. Food Chemistry, 140062. Link to DOI
3. Zheng, R., & Kamruzzaman, M. (2023). Applications of hyperspectral imaging in the coffee industry: Current research and future outlook. Applied Spectroscopy Reviews, 1-25. Link to DOI
Ocean Monjur is a master’s student at the Agricultural and Biological Engineering Department at the University of Illinois Urbana-Champaign. His research focus includes Computer Vision, Hyperspectral Image analysis and reconstruction.
1. Ahmed, M. T., Monjur, O., & Kamruzzaman, M. (2024). Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product. Journal of Food Engineering, 382, 112223. Link to DOI
2. Ahmed, M. T., Ahmed, M. W., Monjur, O., Emmert, J. L., Chowdhary, G., & others. (2024). Hyperspectral image reconstruction for predicting chick embryo mortality towards advancing egg and hatchery industry. arXiv preprint arXiv:2405.13843. Link to DOI
Smital Lunawat is a Master’s student in the Computer Science Department at the University of Illinois at Urbana-Champaign (UIUC). Her research interests lie at the intersection of artificial intelligence (AI) and computer vision, with a particular focus on promoting fairness within AI systems. This focus reflects her commitment to responsible technological development that benefits society as a whole.
I am Sreezan Alam, a rising senior in the Department of Chemical and Biomolecular Engineering at the University of Illinois at Urbana-Champaign. My research focuses on hyperspectral imaging analysis, smart drying monitoring for food moisture content, and machine learning algorithms for moisture content pattern analysis and predictive modeling.
Xiuning (Belle) Kuang is a sophomore in the (iSchool) School of Information Science at the University of Illinois at Urbana-Champaign. She is a laboratory assistant in Dr. Kamruzzaman’s group.
Asher Sprigler is an Undergraduate Computer Engineering Major at the Milwaukee School of Engineering who is visiting UIUC to assist with the Data Science and Model Building aspects of agricultural research. His main research foci have been on sexing and determining the fertility of chicken eggs using hyperspectral imaging and machine learning.
Dr. Shigeru Ichiura, a PhD graduate from the United Graduate School of Agricultural Sciences at Iwate University, Japan, is currently a Project Lecturer at Yamagata University’s Advanced Research Center for Agri-Food Systems. He serves as the CEO of ViAR&E Corporation, leveraging his extensive experience in electrical engineering and technology development, which includes roles at Toshiba, Softbank, Motorola, and NVIDIA. He has focused his research on the application of AI and robotics in agriculture, including developing a robot for safflower harvesting, tracking chicken behavior, and estimating duck weight using AI techniques. At IOSNEL, Dr. Ichiura will work on gender detection of eggs.
During my visit to IOSNEL under the supervision of Dr. Kamruzzaman, as a PhD candidate from UNICAMP, Brazil, I applied Near-Infrared (NIR) spectroscopy for the prediction of impurities in cocoa shell powder. Additionally, I utilized Hyperspectral Imaging (HSI) in beef analysis.
1. Wu, Q., Oliveira, M. M., Achata, E. M., & Kamruzzaman, M. (2023). Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis. Journal of Food Composition and Analysis, 119, 105274. Link to DOI
2. Oliveira, M. M., Ferreira, M. V. S., Kamruzzaman, M., & Barbin, D. F. (2023). Prediction of impurities in cocoa shell powder using NIR spectroscopy. Journal of Pharmaceutical and Biomedical Analysis Open, 2, 100015. Link to DOI
A PhD candidate from the University of Agriculture Faisalabad, Pakistan, and a visiting scholar at IOSNEL under the supervision of Dr. Kamruzzaman, I immersed myself in the application of Near-Infrared (NIR) spectroscopy in analyzing sugarcane juice. This study combined NIR spectroscopy with machine learning techniques for the classification and prediction of Total Soluble Solids (TSS) in the stems.