Skip to content

2025

l

2024

l

2023

l

2022

l

2021

l

Pre-Illinois Publications

2025

  1. Ahmed, M.T., Villordon, A., & Kamruzzaman, M. (2025). Hyperspectral imaging and explainable deep-learning for non-destructive quality prediction of sweetpotato. Postharvest Biology and Technology, 222, 113379. Link PlumX
  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, 96. Link PlumX
  3. 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. Link PlumX
  4. 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 PlumX
  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 PlumX
  6. 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 PlumX
  7. Ahmed, M. W., Springler, 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 PlumX
  8. Ahmed, M. W., Springler, A., Emmert, J. L., Dilger, Ryan., Chowdhary, G., & Kamruzzaman, M. (2025). Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning. Smart Agricultural Technology, 100857. Link PlumX
  9. 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 PlumX
  10. Ahmed, M. W., & Kamruzzaman, M. (2025). Advancing food safety in Bangladesh: Challenges and the promise of smart sensor technology. Food Safety and Health. Link PlumX
  11. Lee, D. H., & Kamruzzaman, M. (2025). Amino acid-based, sustainable organic nanozyme and integrated sensing platform for histamine detection. Food Chemistry, 142751. Link PlumX
  12. 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 Journal, 159560. Link PlumX
  13. Lee, D. H., & Kamruzzaman, M. (2025). Second generation organic nanozyme for effective detection of agricultural herbicides. Advanced Sustainable Systems, 2401029. Link PlumX
  14. Lee, D. H., & Kamruzzaman, M. (2025). Sustainable organic nanozyme with an integrated colorimetric sensing system for mycotoxin detection. ACS Applied Nano Materials. Link PlumX
  15. Schumer, N. G., Ahmed, M. W., Rausch, K., Singh, V., & Kamruzzaman, M. (2025). Chemometric-based approach for economically motivated fraud detection in organic spices via NIR spectroscopy. J. Food Compos. Anal., 142, 107538. Link PlumX
  16. Zheng, R. & Kamruzzaman, M. (2025). Near-infrared spectroscopy for microalgae studies: A comprehensive review of applications and outlooks. Algal Research, 89, 104074. Link PlumX

2024

  1. Wu, Q., Kamruzzaman, M. (2024). Advancements in nanozyme-enhanced lateral flow assay platforms for precision in food authentication. Trends in Food Science and Technology. Link PlumX
  2. Lee, D. H., Kamruzzaman, M. (2024). Advancements in organic materials-based nanozymes for broader applications. Trends in Chemistry. Link PlumX
  3. Lee, D.H., Ahmed, M.W., Kamruzzaman, M. (2024). Nanoscale substance-integrated optical sensing platform for pesticide detection in perishable foods. Curr. Opin. Food Sci. 60, 101227. Link PlumX
  4. Ahemd, M. T., Villordon, A., Kamruzzaman, M. (2024). Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications. Results Eng. 23, 102623. Link PlumX
  5. Ahemd, M. T., Monjur, O., Kamruzzaman, M. (2024). Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product. J. Food Eng. 382, 112223. Link PlumX
  6. Ahemd, M. T., Wijewardane, N., Lu, Y., Jones, D., Kudenov, M., Williams, C., Villordon, A., Kamruzzaman, M. (2024). Advancing Sweetpotato Quality Assessment with Hyperspectral Imaging and Explainable Artificial Intelligence. Comput. Electron. Agric. 220, 108855. Link PlumX
  7. Ahemd, M. T., Ahmed, M, W., Kamruzzaman, M. (2024). SpectroChat: A ChatGPT-assisted graphical user interface for chemometrics analysis of spectroscopic data. Software Impacts, 100698. Link PlumX
  8. Ahemd, M. T., Kamruzzaman, M. (2024). Enhancing Corn Quality Prediction: Variable Selection and Explainable AI in Spectroscopic Analysis. Smart Agric. Technol. 8, 100458. Link PlumX
  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 Agric. Technol. 9, 100533. Link PlumX
  10. 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 Bioenergy 186, 107276. Link PlumX
  11. 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 Chem. 140062. Link PlumX
  12. Ferreira, M.V.d.S., Ahmed, M.W., Oliveira, M., Sarang, S., Ramsay, S., Liu, X., Malvandi, A., Lee, Y., Kamruzzaman, M. (2024). AI-enabled optical sensing for smart and precision food drying: techniques, applications, and future directions. Food Eng. Rev.. Link PlumX
  13. He, H-J., Zhang, C., Bian, X., An, J., Wang,Y., Ou, X., Kamruzzaman, M. (2024). Improved prediction of vitamin C and reducing sugar content in sweetpotatoes using hyperspectral imaging and LARS-enhanced LASSO variable selection. J. Food Compos. Anal. 132, 106350. Link PlumX
  14. He, H-J., da Silva Ferreira, M. V., Wu, Q., Karami, H., & Kamruzzaman, M. (2024). Portable and miniature sensors in supply chain for food authentication: a review. Crit. Rev. Food Sci. Nutr. 1–21. Link PlumX
  15. Wang, Y., Ou, X., He, H.-J., & Kamruzzaman, M. (2024). Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chem.: X 21,101235. Link PlumX
  16. Karami,H., Kamruzzaman, M., Covington, A., Hassouna, M., Darvishi, Y., Ueland, M., Fuentes, S., & Gancarz, M. (2024). Advanced Evaluation Techniques: Gas Sensor Networks, Machine Learning, and Chemometrics for Fraud Detection in Plant and Animal Products. Sens. Actuators A: Phys. 115192. Link PlumX

2023

  1. Kamruzzaman, M. (2023). Optical sensing as analytical tools for meat tenderness measurements. Meat Science, 195, 109007. Link PlumX
  2. Wu, Q., & Kamruzzaman, M. (2023). Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics. Current Research in Food Science, 6, 100483. Link PlumX
  3. Wu, Q., Oliveira, M., Achata, E. M., & Kamruzzaman, M. (2023). Reagent-free detection of multiple allergens in gluten free-flour using NIR spectroscopy and multivariate analysis. Food and Compositional Analysis, 120, 105324. Link PlumX
  4. 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, 238, 104844. Link PlumX
  5. Song, D., 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 PlumX
  6. Achata, E. M., & Kamruzzaman, M. (2023). Multivariate optimization of hyperspectral imaging for adulteration detection of ground beef: Towards the development of generic algorithms to predict adulterated ground beef and for digital sorting. Food Control, 153, 109907. Link PlumX
  7. Ahmed, 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 PlumX
  8. Lee, D. H., Kamruzzaman, M. (2023). Eco-friendly, degradable, peroxidase-mimicking nanozyme for selective antioxidant detection. Materials Today Chemistry, 34, 101809. Link PlumX
  9. Lee, D. H., Kamruzzaman, M. (2023). Organic compound-based nanozymes for agricultural herbicide detection. Nanoscale, 15, 12954-12960. Link PlumX
  10. Zheng, R., Kamruzzaman, M. (2023). Applications of hyperspectral imaging in the coffee industry: Current research and future outlook. Applied Spectroscopy Reviews. Link PlumX
  11. Oliveira, 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 PlumX
  12. Ferreira, M. V. S., Barbosa, 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, 15, 6120-6138. Link PlumX
  13. Rasekh, M., Karami, H., Kamruzzaman, M., Azizi, V., Gancarz, M. (2023). Impact of different drying approaches on VOCs and chemical composition of Mentha spicata L. essential oil: A combined analysis of GC/MS and E-nose with chemometrics methods. Industrial Crops and Products, 206, 117595. Link PlumX
  14. Karami, H., Chemeh, S. K., Azizi, V., Sharifnasab, H., Ramos, J., Kamruzzaman, M. (2023). Gas Sensor-based Machine Learning Approaches for Characterizing Tarragon Aroma and Essential Oil under Various Drying Conditions. Sensors and Actuators A: Physical, 365, 114827. Link PlumX

2022

  1. Kamruzzaman, M., Kalita, D., Ahmed, M. T., ElMasry, G., & Makino, Y. (2022). Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data. Anal. Chim. Acta, 339390. Link PlumX
  2. Fatemi, A., Singh, V., Kamruzzaman, M. (2022). Identification of informative spectral ranges for predicting major chemical constituents in corn using NIR spectroscopy. Food Chem., 132442. Link PlumX
  3. Wang, Z., Wu, Q., Kamruzzaman, M. (2021). Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour. Food Control, 108970. Link PlumX
  4. Kapoor, R., Malvandi, A., Feng, H. & Kamruzzaman, M. (2022). Real-time moisture monitoring of edible coated apple chips during hot air drying using miniature NIR spectroscopy and chemometrics. LWT Food Sci. Technol., 154, 112602. Link PlumX
  5. Malvandi, A., Feng, H. & Kamruzzaman, M. (2022). Application of NIR spectroscopy and multivariate analysis for non-destructive evaluation of apple moisture content during ultrasonic drying. Spectrochim. Acta A Mol. Biomol. Spectrosc., 269, 120733. Link PlumX
  6. Malvandi, A., Kapoor, R., Feng, H. & Kamruzzaman, M. (2022). Non-destructive measurement and real-time monitoring of apple hardness during ultrasonic contact drying via portable NIR spectroscopy and machine learning. Infrared Phys. Technol., 104077. Link PlumX
  7. Latanze, M., P., Gates, R. S., Cadwallader, K., Kamruzzaman, M., Tumbleson, M., Rausch, K. (2022). Dry Matter Loss and Lipid Oxidation Evaluation of Soybeans During Storage at Elevated Temperatures and Moisture Content. J. ASABE, 65, 1039–1048. Link PlumX

2021

  1. Kamruzzaman, M. (2021). Fraud detection in meat using hyperspectral imaging. Meat and Muscle Biology, 5(3). Link PlumX
  2. Mousa, M. A. A., Wang, Y., Antora, S. A., Al-qurashi, A. D., Ibrahim, O. H. M., He, H.-J., & Kamruzzaman, M. (2021). An overview of recent advances and applications of FT-IR spectroscopy for quality, authenticity, and adulteration detection in edible oils. Critical Reviews in Food Science and Nutrition, 12, 1–19. Link PlumX
  3. Khaliduzzaman, A., Omwange, K. A., Riza, D. F. A., Konagaya, K., Kamruzzaman, M., Alom, M. S., Gao, T., Saito, Y., & Kondo, N. (2021). Antioxidant assessment of agricultural produce using fluorescence techniques: a review. Critical Reviews in Food Science and Nutrition. Link PlumX

Pre-Illinois Publications

  1. Kamruzzaman, M., ElMasry, G., Sun, D-W., & Allen, P. (2011). Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering, 104, 332–340. Link PlumX
  2. ElMasry, G., Kamruzzaman, M., Sun, D.-W., & Allen, P. (2012). Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Critical Reviews in Food Science and Nutrition, 52, 999–1023. Link PlumX
  3. Kamruzzaman, M., ElMasry, G., Sun, D-W., & Allen, P. (2012). Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat. Innovative Food Science and Emerging Technologies, 16, 316–235. Link PlumX
  4. Kamruzzaman, M., ElMasry, G., Sun, D-W., & Allen, P. (2012). Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innovative Food Science and Emerging Technologies, 16, 218–226. Link PlumX
  5. Kamruzzaman, M., ElMasry, G., Sun, D-W., & Allen, P. (2012). Prediction of some quality attributes of lamb meat using near infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta, 714, 57–67. Link PlumX
  6. Kamruzzaman, M., Sun, D-W., ElMasry, G., & Allen, P. (2013). Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis. Talanta, 103, 130–136. Link PlumX
  7. Kamruzzaman, M., ElMasry, G., Sun, D-W., & Allen, P. (2013). Non-destructive assessment of instrumental and sensory tenderness of lamb meat by NIR hyperspectral imaging. Food Chemistry, 141, 389–396. Link PlumX
  8. Pu, H-B., Xie, A., Sun, D.-W., Kamruzzaman, M., Ma, J. (2014). Application of wavelet analysis to spectral data for categorization of lamb muscles. Food and Bioprocess Technology, 8, 1–16. Link PlumX
  9. Pu, H-B., Sun, D-W., Ma, J., Liu, D., Kamruzzaman, M. (2014). Hierarchical variable selection for predicting chemical constituents in lamb meats using hyperspectral imaging. Journal of Food Engineering, 143, 44–52. Link PlumX
  10. Kamruzzaman, M., Makino, Y., & Oshita, S. (2015). Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: a review. Analytica Chimica Acta, 853, 19–29. Link PlumX
  11. Kamruzzaman, M., Makino, Y., Oshita, S. & Liu, S. (2015). Assessment of visible near-infrared hyperspectral imaging as a tool for detection of horsemeat adulteration in minced. Food & Bioprocess Technology, 8, 1054–1062. Link PlumX
  12. Pu, H.-B., Kamruzzaman, M., Sun, D.-W. (2015). Selection of feature wavelengths for developing multispectral imaging systems for quality, safety and authenticity of muscle foods-a review. Trends in Food Science and Technology, 45, 86–104. Link PlumX
  13. Kamruzzaman, M., Makino, Y., & Oshita, S. (2015). Hyperspectral imaging in tandem with multivariate analysis and image processing for non-invasive detection and visualization of pork adulteration in minced beef. Analytical Methods, 7, 7496–7502. Link PlumX
  14. Kamruzzaman, M., Makino, Y., & Oshita, S. (2016). Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. Journal of Food Engineering, 170, 8–15. Link PlumX
  15. Kamruzzaman, M., Makino, Y., & Oshita, S. (2016). Parsimonious model development for real-time monitoring of moisture in red meat using hyperspectral imaging. Food Chemistry, 196, 1084–1091. Link PlumX
  16. Kamruzzaman, M., Makino, Y., & Oshita, S. (2016). Hyperspectral imaging for real-time monitoring of water holding capacity in red meat. LWT-Food Science and Technology, 66, 685–691. Link PlumX
  17. Kamruzzaman, M., Makino, Y., & Oshita, S. (2016). Online monitoring of red meat color using hyperspectral imaging. Meat Science, 116, 110–117. Link PlumX
  18. Kamruzzaman, M., Takahama, S., & Dillner, A. M. (2018). Quantification of amine functional groups and their influence on OM/OC in the IMPROVE network. Atmospheric Environment, 172, 124–132. Link PlumX

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