2025
2. Md Wadud Ahmed, Sreezan Alam, Alin Khaliduzzaman, Jason Lee Emmert, and Mohammed Kamruzzaman ACS Food Science & Technology Link to DOI
3. 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.” Comput. Electron. Agric. 230: 109847. Link to DOI
4. Lee, D.H., Kamruzzaman, M. Consolidated sustainable organic nanozyme integrated with Point-Of-Use sensing platform for dual agricultural and biological molecule detection. Chem. Eng. J. 506, 159560 (2025). Link to DOI
2024
1. 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
2. Lee, D. H., & Kamruzzaman, M. (2024). Amino acid-based, sustainable organic nanozyme for allergic biomolecule detection. ChemRxiv. Link to DOI
3. Zhao, Y., Jian, Z., Pu, G., Wang, C., Qiu, H., & Zhang, M. (2024). An asymmetric propargylation of cyclopropanols. Trends in Chemistry. Link to DOI
4. 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
5. 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
6. 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
7. 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
8. 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
9. Ahmed, T., Wijewardane, N. K., Lu, Y., Jones, D. S., Kudenov, M., Williams, C.,
&; others. (2024). Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence. Computers and Electronics in Agriculture, 220, 108855. Link to DOI
10. 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
11. 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
12. 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
13. Hossain, A., Ahmed, M. W., Rabin, M. H., Kaium, A., Razzaque, M. A., & Zamil, S. S. (2024). Heavy metal quantification in chicken meat and egg: An emerging food safety concern. Journal of Food Composition and Analysis, 126, 105876. Link to DOI
14. 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
15. Da Silva Ferreira, M. V., 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 to DOI
2023
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. 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.
3. 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
4. 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
5. 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.
6. 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
7. 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
8. Lee, D. H., Kamruzzaman, M., & Kalita, D. (2023). Nanozymes for agricultural herbicide detection. Nanoscale, 15(31), 12954–12960. Link to DOI
9. Nguyen, Q. H., Lee, D. H., Nguyen, P. T., Le, P. G., & Kim, M. I. (2023). Foldable paper microfluidic device based on single iron site-containing hydrogel nanozyme for efficient glucose biosensing. Chemical Engineering Journal, 454(Part 4), 140541. Link to DOI
10. Lee, D. H., & Kamruzzaman, M. (2023). Eco-friendly, degradable, peroxidase- mimicking nanozyme for selective antioxidant detection. Materials Today Chemistry, 34, 101809. Link to DOI
11. 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
12. 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
13. 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
2022
1. Mousa, M. A. A., Wang, Y., Antora, S. A., Al-Qurashi, A. D., Ibrahim, O. H. M., & He, H. J. (2022). 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, 62(29), 8009-8027. Link to DOI
2. Sun, X., Chen, J., Fan, W., Liu, S., & Kamruzzaman, M. (2022). Production of reactive oxygen species via nanobubble water improves radish seed water absorption and the expression of aquaporin genes. Langmuir, 38(38), 11724-11731. Link to DOI
3. 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
4. Fatemi, A., Singh, V., & Kamruzzaman, M. (2022). Identification of informative spectral ranges for predicting major chemical constituents in corn using NIR spectroscopy. Food Chemistry, 383, 132442. Link to DOI
5. 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 Physics & Technology, 122, 104077. Link to DOI
6. 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. Analytica Chimica Acta, 1202, 339390. Link to DOI
7. 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. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 269, 120733.Link to DOI
8. 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, 154, 112602. Link to DOI
9. Pastorelli Latanze, M., Gates, R., Cadwallader, K., & Kamruzzaman, M. (2022). Dry matter loss and lipid oxidation evaluation of soybeans during storage at elevated temperatures and moisture content. American Society of Agricultural and Biological Engineers. Link to DOI
10. 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
2021
1. Kamruzzaman, M. (2021). Fraud detection in meat using hyperspectral imaging. Meat and Muscle Biology, 5(3), 10000-10008. Link to DOI
2. Liu, S., Li, J., Oshita, S., Kamruzzaman, M., Cui, M., & Fan, W. (2021). Formation of a hydrogen radical in hydrogen nanobubble water and its effect on copper toxicity in Chlorella. ACS Sustainable Chemistry & Engineering, 9(33), 11100-11109. Link to DOI
3. Kamruzzaman, M. (2021). Chemical imaging in food authentication. In Food Authentication and Traceability (pp. 131-161). Link to DOI
Pre-Illinois Publications
1. Antora, S. A., Hossain, M. N., Rahman, M. M., Alim, M. A., & Kamruzzaman, M. (2019). Detection of adulteration in edible oil using FT-IR spectroscopy and machine learning. International Journal of Biochemistry Research and Review, 26, 1-14. Link to DOI
2. Kamruzzaman, M. (2018). Applications of hyperspectral imaging for meat quality and authenticity. In Hyperspectral Imaging Analysis and Applications for Food Quality (pp. 175-193). CRC Press. Link to DOI
3. Basantia, N. C., Nollet, L. M. L., & Kamruzzaman, M. (2018). Hyperspectral Imaging Analysis and Applications for Food Quality. CRC Press. Link to DOI
4. Kamruzzaman, M. (2018). Multivariate analysis and techniques. In Hyperspectral Imaging Analysis and Applications for Food Quality (pp. 61-83). CRC Press. Link to DOI
5. 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 to DOI
6. Takahama, S., Reggente, M., Dillner, A., Boris, A., & Kamruzzaman, M. (2017). Organic aerosol composition in monitoring networks–2: Top-down Approach. International Aerosol Modeling Algorithms Conference. Link to DOI
7. Dillner, A., Boris, A., Kamruzzaman, M., & Takahama, S. (2017). Organic aerosol composition in monitoring networks–1: Bottom-up Approach. International Aerosol Modeling Algorithms Conference. Link to DOI
8. Kamruzzaman, M., Makino, Y., & Oshita, S. (2016). Online monitoring of red meat color using hyperspectral imaging. Meat Science, 116, 110-117. Link to DOI
9. 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 to DOI
10. 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 to DOI
11. 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. https://doi.org/10.1016/j.lwt.2015.10.030 Link to DOI
12. Dillner, A. M., Weakley, A. T., Kamruzzaman, M., & Takahama, S. (2016). Organic functional group & OM/OC measurements at select IMPROVE sites using Infrared Spectra: Organosulfates & Amines. International Conference on Atmospheric Optics: Aerosols, Visibility. Link to DOI
13. Kamruzzaman, M. (2016). Food adulteration and authenticity. In Food Safety: Basic Concepts, Recent Issues, and Future Challenges (pp. 127-148). Link to DOI
14. Pu, H., 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 & Technology, 45(1), 86-104. Link to DOI
15. 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 beef. Food and Bioprocess Technology, 8(6), 1054-1062. Link to DOI
16. Pu, H., Xie, A. G., Sun, D. W., Kamruzzaman, M., & Ma, J. (2015). Application of wavelet analysis to spectral data for categorization of lamb muscles. Food and Bioprocess Technology, 8(6), 1-16. Link to DOI
17. Kamruzzaman, M., Nakauchi, S., & ElMasry, G. (2015). Online screening of meat and poultry product quality and safety using hyperspectral imaging. In High throughput screening for food safety assessment (pp. 425-466). Elsevier.
18. 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(18), 7496-7502. Link to DOI
19. 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 to DOI
20. Pu, H., 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 to DOI
21. Kamruzzaman, M., Makino, Y., & Oshita, S. (2014). An appraisal of hyperspectral imaging for non-invasive authentication of geographical origin of beef and pork. International Conference on Agricultural Engineering, AgEng-2014, 6-10.
22. Kamruzzaman, M., Haque, M. E., & Ali, M. R. (2014). Hyperspectral imaging technique for offal quantification in minced meat. Journal of the Bangladesh Agricultural University, 12(1), 189-194.
23. Kamruzzaman, M., ElMasry, G., Sun, D. W., & Allen, P. (2013). Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chemistry, 141(1), 389-396. Link to DOI
24. 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 to DOI
25. 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(11), 999-1023. Link to DOI
26. Kamruzzaman, M., Barbin, D., 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 & Emerging Technologies, 16, 316-325. Link to DOI
27. 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 & Emerging Technologies, 16, 218-226. Link to DOI
28.ElMasry, G., Sun, D. W., Kamruzzaman, M., Barbin, D., & Allen, P. (2012). Hyperspectral imaging—A new era of applications in non-destructive sensing of meat quality. NIR News, 23(6), 9-14. Link to DOI
29. 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 to DOI
30.Kamruzzaman, M., ElMasry, G., Sun, D. W., & Allen, P. (2012). Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering, 104(3), 332-340. Link to DOI
31. Rahman, M., Kamruzzaman, M., & Islam, M. N. (2007). Osmotic dehydration of hog-plum and product development. Journal of the Bangladesh Agricultural University, 5(2), 399-406.
32. Kamruzzaman, M., & Islam, M. N. (2006). Kinetics of dehydration of aroids and developed dehydrated aroids products. Journal of Chemical Engineering, 19-24.