2025
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Ahmed, M. W., & Kamruzzaman, M. (2025). Advancing food safety in Bangladesh: Challenges and the promise of smart sensor technology.
Food Safety and Health.
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Lee, D. H., & Kamruzzaman, M. (2025). Amino acid-based, sustainable organic nanozyme and integrated sensing platform for histamine detection.
Food Chemistry, 142751.
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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.
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Lee, D. H., & Kamruzzaman, M. (2025). Second generation organic nanozyme for effective detection of agricultural herbicides.
Advanced Sustainable Systems, 2401029.
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Lee, D. H., & Kamruzzaman, M. (2025). Sustainable organic nanozyme with an integrated colorimetric sensing system for mycotoxin detection.
ACS Applied Nano Materials.
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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.
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Zheng, R. & Kamruzzaman, M. (2025). Near-infrared spectroscopy for microalgae studies: A comprehensive review of applications and outlooks.
Algal Research, 89, 104074.
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2024
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Wu, Q., Kamruzzaman, M. (2024). Advancements in nanozyme-enhanced lateral flow assay platforms for precision in food authentication.
Trends in Food Science and Technology.
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Lee, D. H., Kamruzzaman, M. (2024). Advancements in organic materials-based nanozymes for broader applications.
Trends in Chemistry.
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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.
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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.
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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.
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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.
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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.
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Ahemd, M. T., Kamruzzaman, M. (2024). Enhancing Corn Quality Prediction: Variable Selection and Explainable AI in Spectroscopic Analysis.
Smart Agric. Technol. 8, 100458.
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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.
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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.
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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.
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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..
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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.
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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.
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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.
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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.
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2023
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Kamruzzaman, M. (2023). Optical sensing as analytical tools for meat tenderness measurements.
Meat Science, 195, 109007.
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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.
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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.
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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.
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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.
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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.
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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.
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Lee, D. H., Kamruzzaman, M. (2023). Eco-friendly, degradable, peroxidase-mimicking nanozyme for selective antioxidant detection.
Materials Today Chemistry, 34, 101809.
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Lee, D. H., Kamruzzaman, M. (2023). Organic compound-based nanozymes for agricultural herbicide detection.
Nanoscale, 15, 12954-12960.
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Zheng, R., Kamruzzaman, M. (2023). Applications of hyperspectral imaging in the coffee industry: Current research and future outlook.
Applied Spectroscopy Reviews.
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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.
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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.
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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.
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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.
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2022
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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.
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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.
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Wang, Z., Wu, Q., Kamruzzaman, M. (2021). Portable NIR spectroscopy and PLS based variable selection for adulteration detection in quinoa flour.
Food Control, 108970.
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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.
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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.
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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.
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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.
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2021
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Kamruzzaman, M. (2021). Fraud detection in meat using hyperspectral imaging.
Meat and Muscle Biology, 5(3).
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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.
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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.
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Pre-Illinois Publications
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Kamruzzaman, M., Makino, Y., & Oshita, S. (2016). Online monitoring of red meat color using hyperspectral imaging.
Meat Science, 116, 110–117.
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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.
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