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Research

Research Interests

Hyperspectral imaging (HSI) and remote sensing

Near-infrared (NIR) and Fourier-transform infrared (FTIR) spectroscopy

Deep hyperspectral image reconstruction and explainable artificial intelligence (XAI)

Organic compound-based nanozymes and portable biosensors for toxic molecule detection

Real-time process monitoring in food and agricultural systems

 

Research

Our lab focuses on developing innovative sensing technologies and intelligent systems to address critical challenges in food, and agricultural system monitoring. We integrate spectroscopy, advanced imaging, machine learning, and organic nanozyme to create next-generation solutions that are rapid, non-destructive, portable, and cost-effective.

 

Key research areas include:

 

Hyperspectral Imaging and Remote Sensing

We utilize HSI for detailed spatial and spectral analysis to assess quality, detect contamination, and monitor stress in crops and food products. Applications range from controlled lab setups to field-based systems including UAV and satellite platforms.

 

NIR and FTIR Spectroscopy

We apply NIR and FTIR spectroscopy for real-time, non-invasive characterization of agricultural and biological materials, enabling quality evaluation, process optimization, and authenticity detection.

 

Deep Hyperspectral Image Reconstruction and Explainable AI

We develop deep learning models that reconstruct hyperspectral data from RGB images, aiming to reduce hardware complexity while retaining accuracy. We emphasize the use of XAI to ensure model interpretability, reliability, and adoption in real-world applications.

 

Nanozyme-Enabled Biosensing

We design organic compound-based nanozymes and incorporate them into portable biosensing platforms for the detection of toxic compounds such as pesticides, allergens, and heavy metals.

 

Electronic Nose (E-nose) Systems

We employ e-nose systems for volatile compound analysis to detect spoilage, contamination, and product freshness. These sensor arrays mimic the human olfactory system and are integrated with machine learning for classification and prediction.

 

Real-Time Process Monitoring

We develop sensor-integrated platforms for real-time monitoring and control of food and agricultural processes, with a particular focus on drying operations. Using spectroscopy, imaging, and smart sensing technologies, we monitor critical parameters such as moisture and fat content during drying to ensure product quality, energy efficiency, and process optimization.

Overall, our goal is to drive innovation at the intersection of optical sensing, AI, and organic nanozyme to support sustainable and data-driven solutions across the food and agricultural sectors.

 

Supervised PhD Candidate to Completion and Graduation

Dong Hoon Lee (PhD dissertation defended on December 3, 2024)

Md. Toukir Ahmed (PhD dissertation defended on March 27, 2025)

Md Wadud Ahmed (PhD dissertation defended on April 10, 2025)

Supervised MS Candidate to Completion and Graduation

Runyu Zheng (MS dissertation defended on April 4, 2024)

Nathan Schumer (MS dissertation defended on August 1, 2024)

Dong Hoon Lee

Dong Hoon Lee completed his PhD in Agricultural and Biological Engineering in December 2024 and is now a proud alumnus of our lab. During his doctoral journey from August 2022 to December 2024, he made remarkable contributions organic compound based nanozyme research for food and agricultural sensing. He authored several high-impact publications with a cumulative journal impact factor of 69.8. His work appeared in prestigious journals such as Trends in Chemistry (IF: 14), Chemical Engineering Journal (IF: 13.4), Current Opinion in Food Science (IF: 9.6), and Food Chemistry (IF: 8.5), reflecting both the quality and significance of his research. His dedication, scientific curiosity, and collaborative spirit greatly enriched my group, and we look forward to seeing his continued success in the next chapter of his career.

1. Lee, D. H., & Kamruzzaman, M. (2023). Eco-friendly, degradable, peroxidase-mimicking nanozyme for selective antioxidant detection. Materials Today Chemistry. 34, 101809. Link 

2. Lee, D. H., & Kamruzzaman, M. (2023). Organic compound-based nanozymes for agricultural herbicide detection. Nanoscale, 15, 12954-12960. Link

3. Lee, D. H., & Kamruzzaman, M. (2024). Advancements in organic materials-based nanozymes for broader applications. Trends in Chemistry. Link

4. Lee, D.H., Ahmed, M.W., & Kamruzzaman, M. (2024). Nanoscale substance-integrated optical sensing platform for pesticide detection in perishable foods. Current Opinion in Food Science, 60, 101227. Link

5.      Lee, D. H., & Kamruzzaman, M. (2025). Amino acid-based, sustainable organic nanozyme and integrated sensing platform for histamine detection. Food Chemistry, 142751. Link

6, Lee, D. H., & Kamruzzaman, M. (2025). Consolidated sustainable organic nanozyme integrated with Point-of-Use sensing platform for dual agricultural and biological molecule detection. Chemical Engineering Journal159560. Link

7.      Lee, D. H., & Kamruzzaman, M. (2025). Second generation organic nanozyme for effective detection of agricultural herbicides. Advanced Sustainable Systems. 2401029. Link

8.      Lee, D. H., & Kamruzzaman, M. (2025). Sustainable organic nanozyme with an integrated colorimetric sensing system for mycotoxin detection. ACS Applied Nano MaterialsLink

Md Toukir Ahmed

Toukir joined IOSNEL in Fall 2021 as a direct PhD student after completing his undergraduate degree in Computer Science and Engineering from BUET. While the typical direct PhD program takes at least five years, he completed his journey in just 3 years and 7 months. His research focused on explainable artificial intelligence (XAI) and deep learning-based reconstruction of hyperspectral images for applications in the sweetpotato industry. Toukir’s work in hyperspectral imaging, XAI, and image reconstruction has demonstrated significant real-world impact. During his PhD, he published nine research articles as first author, eight of which appeared in Q1 journals. He also excelled academically, earning a perfect 4.0 CGPA. In parallel with his PhD, he pursued a concentration in Data Science Engineering (DSE), further strengthening his research capabilities. We are excited to see the lasting impact Toukir will continue to make in the fields of XAI and hyperspectral image reconstruction.

  1. Ahmed, M. T., Ahmed, M. W., & Kamruzzaman, M. (2025). A systematic review of explainable artificial intelligence for spectroscopic agricultural quality assessment. Computers and Electronics in Agriculture, 235, 110354, (Elsevier, Q1, IF: 7.7) https://doi.org/10.1016/j.compag.2025.110354
  2. Ahmed, M. T., Monjur, O., Khaliduzzaman, A., & Kamruzzaman, M. (2025). A comprehensive review of deep learning-based hyperspectral image reconstruction for agri-food quality appraisal. Artificial Intelligence Review, 58(4), 96, (Springer Q1, IF: 10.7) https://doi.org/10.1007/s10462-024-11090-w
  3. Ahmed, T., Wijewardane, N. K., Lu, Y., Jones, D. S., Kudenov, M., Williams, C., Villordon, A., & Kamruzzaman, M. (2024). Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence. Computers and Electronics in Agriculture, 220, 108855, (Elsevier, Q1, IF: 7.7) https://doi.org/10.1016/j.compag.2024.108855
  4. Ahmed, M. T., Villordon, A., & Kamruzzaman, M. (2025). Hyperspectral imaging and explainable deep-learning for non-destructive quality prediction of sweetpotato. Postharvest Biology and Technology222, 113379, (Elsevier, Q1, IF: 6.4). https://doi.org/10.1016/j.postharvbio.2024.113379
  5. 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, (Elsevier, Q1, 5.3) https://doi.org/10.1016/j.jfoodeng.2024.112223
  6. 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, (Elsevier, Q1, IF: 6.0) https://doi.org/10.1016/j.rineng.2024.102623
  7. Ahmed, M. T., & Kamruzzaman, M. (2024). Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis. Smart Agricultural Technology, 8, 100458, (Elsevier, Q1, IF: 6.3) https://doi.org/10.1016/j.atech.2024.100458
  8. Ahmed, M. T., Ahmed, M. W., & Kamruzzaman, M. (2024). SpectroChat: A windows executable graphical user interface for chemometrics analysis of spectroscopic data. Software Impacts, 21, 100698, (Elsevier, Q3, IF: 1.3) https://doi.org/10.1016/j.simpa.2024.100698
  9. Ahmed, M. T., Ahmed, M. W., Monjur, O., Emmert, J. L., Chowdhary, G., & Kamruzzaman, M. (2024). Hyperspectral image reconstruction for predicting chick embryo mortality towards advancing egg and hatchery industry. Smart Agricultural Technology, 9, 100533, (Elsevier, Q1, IF: 6.3) https://doi.org/10.1016/j.atech.2024.100533

Md Wadud Ahmed

After completing his BSc and MS in Food Engineering from Bangladesh Agricultural University and an MSc in Food Science, Technology, and Business from KU Leuven (Belgium), Wadud joined IOSNEL in Fall 2022 and completed his PhD in just two years and seven months. His research focused on NIR spectroscopy and hyperspectral imaging for evaluating fertility, mortality, sex, and structural attributes of eggs, including shell thickness, shell strength, and yolk ratio. These advancements support the ongoing transformation of the egg industry toward Industry 4.0, where automation, real-time monitoring, and data-driven decision-making are essential for ensuring quality, efficiency, and sustainability. He also worked on the rapid detection of biomass composition using NIR spectroscopy. During his PhD, he published 10 first-author articles, 8 of which appeared in Q1 journals. Additionally, he co-authored six more articles as a second author, five of which were also published in Q1 journals. We look forward to seeing his lasting impact on hyperspectral imaging research in food and biological applications.

  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. Link (Wiley, Q1, IF:14.8).
  2. Ahmed, M. W., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2025). An overview of recent advancements in hyperspectral imaging in the egg and hatchery industry. Computers and Electronics in Agriculture, 230, 109847. Link (Elsevier, Q1, IF: 7.7).
  3. Ahmed, M. W., Sprigler, A., Emmert, J. L., Dilger, R. N., Chowdhary, G., & Kamruzzaman, M. (2025). Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning. Smart Agricultural Technology, 10, 100857. Link (Elsevier, Q1, IF:6.3).
  4. Ahmed, M. W., Sprigler, A., Emmert, J. L., & Kamruzzaman, M. (2025). Non-destructive pre-incubation sex determination in chicken eggs using hyperspectral imaging and machine learning. Food Control, 173, 111233. Link (Elsevier, Q1, IF:5.8).
  5. Ahmed, M. W., Alam, S., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2025). Nondestructive Prediction of Eggshell Thickness Using NIR Spectroscopy and Machine Learning with Explainable AI. ACS Food Science & Technology. Link (ACS publications, Q2, IF:2.6).
  6. Ahmed, M. W., Esquerre, C. A., Eilts, K., Allen, D. P., McCoy, S. M., Varela, S., Singh, V., Leakey, A. D. B., & Kamruzzaman, M. (2024). Rapid and high-throughput determination of sorghum (Sorghum bicolor) biomass composition using near infrared spectroscopy and chemometrics. Biomass and Bioenergy, 186, 107276. Link (Elsevier, Q1, IF:5.8).
  7. Ahmed, M. W., Esquerre, C. A., Eilts, K., Allen, D. P., McCoy, S. M., Varela, S., Singh, V., Leakey, A. D. B., & Kamruzzaman, M. (2025). Influence of particle size on NIR spectroscopic characterization of sorghum biomass for the biofuel industry. Results in Chemistry, 13, 102016. Link (Elsevier, Q3, IF: 2.5).
  8. Ahmed, M. W., Alam, S., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2025). Non-destructive measurement of eggshell strength using NIR spectroscopy and explainable artificial intelligence, Journal of the Science of Food and Agriculture. Link (Wiley, Q1, IF:3.3).
  9. Ahmed, M.W., & Kamruzzaman, M. (2025). Advancing food safety in Bangladesh: Challenges, and the promise of smart sensor technology, Food Safety and Health. Link (Wiley)

Runyu Zheng

Runyu was admitted to Zhejiang University through a 3+2 program and joined at IOSNEL in Fall 2023 as a Master’s student. During her studies, she published one review article and one research article in Q1 journals. In Fall 2024, she began her PhD at IOSNEL and her research focuses on evaluating coffee and biochar using spectral techniques. She is a recipient of the Jonathan Baldwin Turner (JBT) Fellowship in recognition of her outstanding academic record.

1. 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

2. 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

3. Zheng, R., & Kamruzzaman, M. (2025). Near-infrared spectroscopy for microalgae studies: A comprehensive review of applications and outlooks. Algal Research, 104074. Link to DOI

Nathaniel Schumer

Nathan completed his BSc in Materials Science and Engineering from UIUC and developed a strong interest in sustainable bioprocessing. He joined IOSNEL as a Master’s student and conducted research on NIR spectroscopy for authentication and adulteration detection in organic spices. His work was published in Food Composition and Analysis (Q1). He is currently working as a Junior Pilot Plant Specialist at the Integrated Bioprocessing Research Laboratory (IBRL).

Khaliduzzaman

Dr. Khaliduzzaman earned his PhD in Bio-Sensing Engineering from Kyoto University, Japan. During his doctoral studies, he focused on non-destructive optical sensing techniques and imaging technologies for  egg and poultry industry. Following his PhD, he served as a JSPS Postdoctoral Fellow at Kyoto University. As a postdoctoral researcher at IOSNEL, his work focused on hyperspectral imaging  to enhance quality control and efficiency in the egg and poultry industries through the integration of advanced sensing and machine learning.​

1. Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M (2025). Detection of early dead embryos using hyperspectral imaging system. 2025 International Poultry Scientific Forum, January 27-28, 2025, Atlanta, GA