Skip to content

Illinois Optical Sensing and
Nanozyme Engineering Lab

About us

Welcome to ISONEL, where innovative thinking combines with cutting-edge technology. We are excited to present a ground-breaking combination of smart sensors: optical sensor (i.e., Near infrared spectroscopy – NIR, VIS-NIR hyper-spectral imaging), olfactive sensors (i.e, e-nose). Together with nanozymes technology that pushes the limits of both scientific research and practical applications.

With its capacity to record and examine a wide variety of spectrum data, hyperspectral imaging offers unmatched insights into the make-up, traits, and behavior of materials. We give academics, scientists, and business professionals the tools they need to access hidden data and make educated decisions with previously unheard-of precision.

With the help of our incorporation of nanozymes, all the research we do is enhanced and performance is elevated to a new level. The exceptional catalytic activity and durability of nanozymes, which are created at the nanoscale, enable a variety of chemical processes. This innovative technology creates opportunities for quick and effective procedures in industries including industrial manufacturing, environmental monitoring, and biomedicine.

We have incorporated smart sensors (i.e., RGB, NIR and olfactive sensors) into our systems to fully use the capabilities of these cutting-edge technology. These sophisticated sensors are capable of instantaneous parameter detection, measurement, and analysis. Smart sensors improve process management, optimize resource use, and enable predictive maintenance by continuously monitoring crucial variables and delivering helpful feedback. This ensures peak performance and efficiency across a variety of applications.

At our core, we are committed to pushing the limits of what is possible and are passionate about innovation. We work with top industry experts, draw on state of the art research, and apply the most recent technological developments to offer solutions that transform companies and facilitate scientific discovery.

We urge you to browse our website and learn about the transformational potential of hyper- spectral imaging, nanozymes, and smart sensors, whether you are an academic researcher, an industrial expert, or an inventor looking to address complicated problems. Let’s work together to influence how science and technology are explored in the future.

Our lab is located in the College of Agricultural, Consumer and Environmental Sciences, within the Agricultural & Biological Engineering department at the University of Illinois Urbana-Champaign.

Discover ISONEL's integration of hyperspectral imaging, nanozymes, and smart sensors, revolutionizing research, industry, and science.

Click Here!

OUR LABS

Postdoc

Dr. Marcus Ferreira

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.

Publications

Dr. Khaliduzzaman

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.

Publications

PhD

Md Toukir Ahmed

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.

Publications

Md Wadud Ahmed

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.

Publications

Di Song

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.

Publications

Dong Hoon Lee

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.

Publications

Master

Qianyi Wu (Lisa)

Lisa is a first-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.

Publications

Ocean Monjur

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.

Publications

Runyu Zheng

As a food engineer and chemist, his pioneering research revolves around the cutting-edge utilization of smart sensors, including optical (RGN, NIR, and NIR-HIS) and olfactive sensors (electronic nose), in conjunction with advanced technologies such as artificial intelligence, machine learning, and chemometrics. His primary focus lies in developing and optimizing innovative application of these sensor technologies on lab-made equipment, specifically tailored for enhancing agricultural practices and processes.

Publications

Undergraduate Researcher

Smital Pravin Lunawat

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.

No publications Yet

Belle Kuang

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.

No publications Yet

Sreezan Alam

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.

No publications Yet

Alumni

Ayesha Syed

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.

No publications Yet

Marciano Oliveira

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.

Publications

JUST PUBLISHED

Advancements in nanozyme-enhanced lateral flow assay platforms for precision in food authentication

Nexus: Next-generation, sustainable organic nanozymes and their application for effective biomolecule detection

Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi-country corn kernels via NIR spectroscopy

Hyperspectral Image Reconstruction for Predicting Chick Embryo Mortality Towards Advancing Egg and Hatchery Industry

All Publications

RESEARCH

Full Profile

Professor Kamruzzaman’s Group

Research Interests

  • Sustainability of bioprocessing technologies
  • Optical sensing technologies (NIR, FT-IR spectroscopy and Hyperpectral imaging)
  • Real-time quality assessment/control of bioproducts/bioprocesses
  • Internet of Foods (IoF) technologies
    Machine learning in agriculture

Mission Statement

Dr. Kamruzzaman applies optical sensing technologies such as spectroscopy and hyperspectral imaging in tandem with chemometrics and machine learning to address the sustainability of bioprocessing technologies to promote the sustainable use of renewable resources. He also works on novel and innovative process routes for rapid and real-time characterization and quality assessment/control of bioproducts/bioprocesses to improve food security, quality, safety, and nutrition, while simultaneously accounting for environmental and socio-economic impacts.

CONTACT US

© 2018 University of Illinois Board of Trustees

ABOUT US

Welcome to ISONEL, where innovative thinking combines with cutting-edge technology. We are excited to present a ground-breaking combination of smart sensors: optical sensor (i.e., Near infrared spectroscopy – NIR, VIS-NIR hyper-spectral imaging), olfactive sensors (i.e, e-nose). Together with nanozymes technology that pushes the limits of both scientific research and practical applications.

With its capacity to record and examine a wide variety of spectrum data, hyperspectral imaging offers unmatched insights into the make-up, traits, and behavior of materials. We give academics, scientists, and business professionals the tools they need to access hidden data and make educated decisions with previously unheard-of precision.

With the help of our incorporation of nanozymes, all the research we do is enhanced and performance is elevated to a new level. The exceptional catalytic activity and durability of nanozymes, which are created at the nanoscale, enable a variety of chemical processes. This innovative technology creates opportunities for quick and effective procedures in industries including industrial manufacturing, environmental monitoring, and biomedicine.

We have incorporated smart sensors (i.e., RGB, NIR and olfactive sensors) into our systems to fully use the capabilities of these cutting-edge technology. These sophisticated sensors are capable of instantaneous parameter detection, measurement, and analysis. Smart sensors improve process management, optimize resource use, and enable predictive maintenance by continuously monitoring crucial variables and delivering helpful feedback. This ensures peak performance and efficiency across a variety of applications.

At our core, we are committed to pushing the limits of what is possible and are passionate about innovation. We work with top industry experts, draw on state of the art research, and apply the most recent technological developments to offer solutions that transform companies and facilitate scientific discovery.

We urge you to browse our website and learn about the transformational potential of hyper- spectral imaging, nanozymes, and smart sensors, whether you are an academic researcher, an industrial expert, or an inventor looking to address complicated problems. Let’s work together to influence how science and technology are explored in the future.

Our lab is located in the College of Agricultural, Consumer and Environmental Sciences, within the Agricultural & Biological Engineering department at the University of Illinois Urbana-Champaign.

ISAL

Located in room 228, the ISAL Lab hosts our advanced analytical center, equipped with cutting-edge technology including smart sensors such as Near-Infrared spectroscopy (NIRs) and microwave technology. These tools enable precise measurements of fat, moisture, and protein content.

NIRs are integral to olfactory sensor devices (e.g., e-noses), while advanced dryers in our lab integrate multiple technologies including NIR-HSI (Near-Infrared Hyperspectral Imaging) and e-nose sensors.

RGB FUSION NOSE

The eNose is an electronic nose system equipped with eight Metal Oxide Semiconductor (MOS) gas sensors. Each sensor is specialized in detecting specific gases, making the eNose capable of identifying and differentiating a wide range of volatile organic compounds (VOCs) and gases. This versatile sensor array is commonly used for environmental monitoring, industrial applications, and research purposes. This equipment combines the power of an RGB sensor with MOS gas sensors to provide comprehensive detection and analysis capabilities.

  • Multi-Gas Detection: Equipped with eight different MOS sensors, each targeting specific gases, enabling the detection and differentiation of a wide range of VOCs and gases.
  • High Sensitivity: Provides high sensitivity to low concentrations of gases, allowing for accurate detection and analysis.
  • Compact and Portable Design: The eNose system is designed to be compact and portable, making it suitable for field use and on-site analysis.
  • Real-Time Analysis: Capable of real-time monitoring and analysis, providing immediate results for quick decision-making.
  • Easy Integration: Can be easily integrated into existing systems and platforms for data collection and analysis.
  • Data Logging: Features data logging capabilities, allowing for the recording and storage of measurement data for further analysis.
  • RGB Camera: Equipped with a Raspberry Pi Sony RGB camera, enhancing its capability to capture and analyze visual data in conjunction with gas detection.
  •  
  1. MQ-2: Detects LPG, i-butane, propane, methane, alcohol, hydrogen, and smoke.
  2. MQ-3: Sensitive to alcohol, benzine, CH4, hexane, LPG, CO.
  3. MQ-4: Primarily detects methane (CH4) and other natural gases.
  4. MQ-5: Sensitive to LPG, natural gas, town gas, and CH4.
  5. MQ-6: Detects LPG, butane, propane, methane, and alcohol.
  6. MQ-8: Sensitive to hydrogen (H2) gas.
  7. MQ-135: Detects a variety of harmful gases including ammonia (NH3), nitrogen oxides (NOx), alcohol, benzene, smoke, and CO2.
  8. MQ-138: Sensitive to toluene, acetone, ethanol, CO, NH3, H2S.
  • Environmental Monitoring: Utilized for detecting and monitoring air quality, pollutants, and hazardous gases in the environment, with the RGB sensor aiding in visual data collection.
  • Industrial Applications: Employed in industries for leak detection, safety monitoring, and quality control, where the RGB sensor can provide visual confirmation and support.
  • Food and Beverage: Used for monitoring freshness, spoilage, and quality control in food products, with the RGB sensor assisting in identifying visual changes indicative of quality.
  • Healthcare: Assists in medical diagnostics by detecting specific biomarkers in breath analysis, where the RGB sensor can be used for visual assessment and confirmation.
  • Research and Development: Facilitates research in various fields by providing accurate gas detection and analysis, with the RGB sensor enhancing data collection through visual monitoring.
  •  

Smart Convective Dryer

The smart convective dryer is an advanced drying system equipped with capabilities for precise temperature control and airflow management. It reaches temperatures up to 70°C and airflow velocities up to 5 m/s, optimizing drying efficiency and uniformity. Additionally, it is integrated with Near-Infrared (NIR), RGB (Red, Green, Blue), and Hyperspectral Imaging (HSI) systems to enhance its functionality for comprehensive analysis during the drying process.

  • Temperature Control: Offers precise temperature control up to 70°C, ensuring optimal drying conditions for various materials and products.
  • Airflow Velocity: Capable of airflow velocities up to 5 m/s, facilitating rapid and uniform drying.
  • Convective Drying: Utilizes convective heat transfer principles to efficiently remove moisture from samples.
  • Integrated Sensors:
    • Near-Infrared (NIR): Provides real-time moisture content analysis during drying, offering insights into drying kinetics and quality assessment.
    • RGB Imaging: Enables visual monitoring and assessment of product color changes and uniformity during the drying process.
    • Hyperspectral Imaging (HSI): Allows detailed analysis of chemical composition and quality attributes of dried products across a wide spectral range.
  •  
  • Food Industry: Used for drying fruits, vegetables, herbs, and spices while monitoring moisture content and color changes with NIR and RGB systems.
  • Pharmaceuticals: Facilitates drying of pharmaceutical ingredients and products with precise control over drying parameters and quality assessment using HSI and NIR technologies.
  • Chemical and Material Sciences: Supports drying of chemicals, polymers, and other materials while analyzing composition changes and uniformity with HSI and NIR imaging.
  • Research and Development: Utilized in R&D labs for studying drying kinetics, optimizing drying protocols, and developing new drying processes based on comprehensive analysis from integrated imaging systems.
  •  

SPRINT RAPID PROTEIN ANALYZER

The Sprint Rapid Protein Analyzer from CEM Corporation is an advanced instrument designed for the quick and accurate determination of protein content in various samples. It utilizes a unique dye-binding method to provide precise results without the need for hazardous chemicals.

  • Dye-Binding Method: Uses a proprietary dye-binding technique to measure protein content accurately and reliably, eliminating the need for traditional methods involving hazardous chemicals.
  • Rapid Analysis: Delivers results in just a few minutes, significantly faster than traditional Kjeldahl or combustion methods.
  • High Precision and Accuracy: Ensures repeatable and reliable protein content measurements, suitable for quality control and compliance with industry standards.
  • User-Friendly Interface: Features an intuitive touchscreen interface with simple operation and minimal training requirements.
  • Environmentally Friendly: Does not require hazardous chemicals or reagents, making it safer for the operator and the environment.
  • Versatility: Capable of analyzing a wide range of sample types, including food products, animal feed, and agricultural samples.
  • Compact Design: Occupies minimal bench space, making it suitable for laboratories with limited space.
  •  
  • Food Industry: Used for determining protein content in various food products, such as meat, dairy, and processed foods, supporting quality control and nutritional labeling.
  • Animal Feed: Assists in analyzing protein content in animal feed, ensuring proper nutritional balance and quality.
  • Agriculture: Supports protein analysis in agricultural products, aiding in quality assessment and crop management.
  • Research and Development: Facilitates protein content measurement in research and development projects, aiding in product formulation and quality assessment.
  •  

SMART 6 Moisture and Solids Analyzer

The SMART 6 Moisture and Solids Analyzer from CEM Corporation is an advanced instrument designed for rapid and precise measurement of moisture and solids content in various samples. Utilizing a combination of microwave and infrared drying technologies, the SMART 6 delivers accurate results quickly and efficiently.

  • Dual Technology: Combines microwave and infrared drying technologies to provide fast and accurate moisture and solids analysis.
  • Rapid Analysis: Delivers results in minutes, significantly reducing the time compared to traditional drying methods.
  • Versatility: Capable of analyzing a wide range of sample types, including food products, chemicals, pharmaceuticals, and more.
  • User-Friendly Interface: Features an intuitive touchscreen interface and easy-to-follow prompts, minimizing the need for extensive operator training.
  • High Precision and Accuracy: Provides reliable and repeatable results, suitable for quality control and regulatory compliance.
  • Non-Destructive Testing: Allows for further analysis of the sample if needed, as the measurement process does not alter the sample significantly.
  • Compact and Efficient Design: Designed to occupy minimal bench space while delivering maximum performance.
  •  
  • Food Industry: Used for moisture and solids content analysis in various food products, supporting quality control and ensuring product consistency.
  • Pharmaceuticals: Assists in determining moisture content in pharmaceutical products, crucial for stability and efficacy.
  • Chemical Industry: Utilized for moisture analysis in chemicals and raw materials, ensuring compliance with industry standards.
  • Agriculture and Feed: Supports moisture and solids analysis in agricultural products and animal feed, aiding in quality assessment and nutritional evaluation.
  • Environmental Testing: Employed in environmental laboratories for analyzing moisture content in soil and other samples.
  •  

ORACLE RAPID NMR FAT ANALYZER

The Oracle Rapid NMR Fat Analyzer is an advanced nuclear magnetic resonance (NMR) instrument designed for quick and precise fat content analysis in various samples. This state-of-the-art analyzer provides a non-destructive, solvent-free method for determining fat content.

  • Precision and Accuracy: Provides highly accurate and repeatable fat content measurements, suitable for quality control and compliance with industry standards.
  • Rapid Analysis: Capable of delivering results in minutes, significantly faster than traditional methods such as Soxhlet extraction.
  • Non-Destructive Testing: The NMR technique does not alter the sample, allowing for further analysis if needed.
  • Solvent-Free: Eliminates the need for hazardous solvents, making the analysis safer and more environmentally friendly.
  • Ease of Use: Features an intuitive user interface and automated operation, minimizing the need for extensive operator training.
  • Versatility: Can analyze a wide range of sample types, including food products, animal feed, and cosmetics.
  • Compliance: Meets regulatory requirements and industry standards for fat content analysis, ensuring reliable and accepted results.
  •  
  • Food Industry: Used for determining fat content in dairy products, meats, oils, and processed foods, supporting quality control and product labeling.
  • Animal Feed: Assists in the analysis of fat content in animal feed, ensuring proper nutritional balance and quality.
  • Cosmetic Industry: Utilized for analyzing fat content in cosmetic products, ensuring consistency and quality.
  • Research and Development: Supports R&D activities by providing precise fat content measurements, aiding in product formulation and quality assessment.
  •  

IHIL

Located in room 210A, the IHIL lab is home to advanced NIR (Near Infrared spectroscopy) and NIR-HSI (Near Infrared Hyperspectral Imaging) devices renowned for their extensive wavelength range and versatile applications. NIR spectroscopy analyzes materials based on their absorption of near-infrared light, while NIR-HSI provides detailed spectral and spatial data.

These instruments enable comprehensive recording and analysis across a wide spectrum, offering unparalleled insights into material composition, characteristics, and behaviors. Our lab provides academics, scientists, and business professionals with essential tools to reveal concealed data, fostering informed decisions with unparalleled precision.

Portable IQ Camera

The Specim IQ is a hyperspectral imaging (HSI) camera that is known for its advanced capabilities and applications in various fields such as agriculture and  food analysis.

Typically covers a spectral range from 400 nm to 1000 nm, which includes the visible to near-infrared spectrum.

Provides high spatial and spectral resolution, allowing for detailed analysis of the spectral characteristics of the sample.

Compatible with various software tools for advanced data analysis and interpretation. It often comes with proprietary software for managing and analyzing hyperspectral data.

Vegetation Research, Food Analysis, Health Sector

Resonon Hyperspectral Imaging Equipment

Resonon provides advanced hyperspectral imaging (HSI) equipment designed for detailed spectral and spatial analysis. Their Pika series cameras are versatile and used across various scientific and industrial fields.

Pika L Camera

  • Spectral Range: 400 nm – 1000 nm (Visible to Near-Infrared)
  • Description: The Pika L camera captures high-resolution hyperspectral data in the visible to near-infrared range. It is ideal for applications requiring detailed analysis of the visible spectrum along with the near-infrared.

Use: Crop health monitoring, food quality control, mineral identification, and environmental monitoring.

Pika NIR Camera

  • Spectral Range: 900 nm – 1700 nm (Near-Infrared)
  • Description: The Pika NIR camera captures hyperspectral data in the near-infrared range, providing detailed information that is beyond the visible spectrum.

Use: Industrial inspection, material identification, biomedical applications, and water quality analysis.

Vegetation Research, Food Analysis, Health Sector

NIR INSTALAB 700

The Instalab® 700 (IL700) is a self-contained optical reflectance instrument designed for the
rapid and precise measurement of constituent concentrations such as moisture, protein,
oil, starch, fiber, and ash in various commodities prevalent in the grain, feed, and food
industries.

1100 nm to 2500 nm (Near-Infrared)

This non-NTEP grain analyzer stands out by its ability to detect protein and moisture in whole
grain wheat, bypassing the conventional process of grinding and preparing samples.

TANGO FT-NIR Spectrometer

The TANGO FT-NIR spectrometer offers a wide spectral range and high-resolution capabilities. It is designed to provide quick and reliable results with minimal sample preparation, making it ideal for routine analysis and quality control.

4000 cm⁻¹ to 12,500 cm⁻¹ (Near-Infrared)/ 800 nm – 2500 nm (Near-Infrared)

Pharmaceutical industry (e.g., raw material identification, quality control), food and beverage industry (e.g., ingredient analysis, quality assessment), chemical industry (e.g., process monitoring, composition analysis), and agricultural applications (e.g., feed and crop analysis).

INEL

In room 308E of the INEL lab, nano enzyme research leverages the capabilities of nanozymes to elevate and enrich our scientific pursuits. These nanoscale-engineered enzymes demonstrate exceptional catalytic activity and durability, enabling a broad spectrum of chemical processes. This groundbreaking technology paves the way for rapid and effective procedures in industries ranging from industrial manufacturing to environmental monitoring and biomedicine.

Corning LSE Mini Microcentrifuge

The Corning LSE Mini Microcentrifuge is a benchtop centrifuge ideal for routine laboratory tasks requiring small volume centrifugation. It offers reliable performance in a compact design, making it suitable for academic, research, and clinical laboratories.

  • Compact Design: Space-saving footprint ideal for crowded laboratory benches or limited workspace.
  • Capacity: Typically accommodates up to 8 standard microcentrifuge tubes (1.5/2.0 mL) per run.
  • Speed: Variable speed settings, commonly ranging from 1000 to 6000 rpm (revolutions per minute), providing flexibility for different applications.
  • Rotor Options: Includes various rotor options compatible with microcentrifuge tubes, allowing for different tube sizes and applications.
  • Quick Spin-downs: Efficiently separates liquid contents from solids or pellets within minutes.
  • Quiet Operation: Low noise level during operation, suitable for quiet laboratory environments.
  • Safety Features: Lid interlock mechanism ensures safe operation by preventing the centrifuge from running with an open lid.
  • User-Friendly Controls: Simple interface with easy-to-use controls for setting speed and runtime.
  • Portable: Lightweight design allows for easy transport between workstations or laboratories.
  •  
  • Routine Laboratory Tasks: Used for a variety of routine applications including DNA/RNA extraction, protein precipitation, cell pelleting, and microfiltration.
  • Sample Preparation: Facilitates quick separation of biological samples, tissues, and cell cultures in molecular biology and biochemical research.
  • Clinical Diagnostics: Supports sample preparation in clinical laboratories for diagnostic testing and research.
  • Teaching and Training: Suitable for educational purposes in teaching laboratories to demonstrate centrifugation principles and techniques.
  •  

AQUASEARCHER AB41PH

The AQUASEARCHER AB41PH is a water quality monitoring device designed for precise and reliable measurement of pH levels in various water samples. It is commonly used in environmental monitoring, water treatment facilities, research laboratories, and industrial applications where accurate pH analysis is crucial.

  • pH Measurement: Provides accurate measurement of pH levels in water samples, helping to assess acidity or alkalinity.
  • Digital Display: Typically equipped with a digital display to show real-time pH readings.
  • Electrode: Includes a pH electrode sensor for immersion into the water sample, designed to withstand continuous use and provide consistent measurements.
  • Calibration: Allows for calibration to ensure accuracy over time, usually with standard pH calibration solutions.
  • Data Logging: Some models may offer data logging capabilities to record pH measurements over time for monitoring and analysis purposes.
  • Portable and Benchtop Models: Available in portable handheld versions for field use or benchtop models for laboratory settings.
  • User-Friendly Interface: Often features intuitive controls and menus for easy operation and adjustment of settings.
  • Waterproof Design: Designed to withstand immersion in water and harsh environmental conditions, ensuring durability and reliability.
  •  
  • Environmental Monitoring: Used in lakes, rivers, and groundwater monitoring to assess water quality and pollution levels based on pH measurements.
  • Water Treatment: Employed in water treatment plants to monitor pH levels and ensure proper treatment processes.
  • Industrial Applications: Used in various industries such as manufacturing, food and beverage, and pharmaceuticals for process control and quality assurance.
  • Research and Education: Utilized in research laboratories and educational institutions for experiments, studies, and teaching purposes related to water chemistry and environmental science.
  •  

Marcus Ferreira

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

Khaliduzzaman

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

Md Toukir Ahmed

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

Md Wadud Ahmed

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

Di Song

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

Dong Hoon Lee

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

Qianyi Wu (Lisa)

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

 

 

Ocean Monjur

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

 

 

Runyu Zheng

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

 

 

Marciano Oliveira

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