John Ogilvie Research Innovation Award
The CSBE/SCGAB John Ogilvie Research Innovation Award is to acknowledge outstanding contributions to research, in any field of research relevant to CSBE/SCGAB, by an individual or team of researchers (which may include graduate or undergraduate students). The Research Innovation Award is not intended to acknowledge the cumulative impact of a career’s worth of research contributions; rather, it is intended to recognize the innovation or ingenuity of a single research project. The research team (individual or group of researchers) is required to prepare a brief nomination that clearly explains why the research is innovative.
Recipients of the CSBE/SCGAB John Ogilvie Research Innovation Award will be selected by the CSBE/SCGAB Awards Committee. Up to three Research Innovation Awards may be awarded each year. All members of a research team must be members of CSBE/SCGAB in good standing. Individuals may be awarded the Research Innovation Award multiple times throughout their career, however, not in consecutive years.
Le Prix John Ogilvie pour l'innovation en recherche de la CSBE/SCGAB vise à reconnaître les contributions exceptionnelles à la recherche, dans tout domaine de recherche pertinent pour la CSBE/SCGAB, d'une personne ou d'une équipe de chercheurs (qui peut comprendre des étudiants diplômés ou de premier cycle). Le Prix d'innovation en recherche ne vise pas à reconnaître l'impact cumulatif de la valeur d'une carrière de contributions à la recherche, il vise plutôt à reconnaître l'innovation ou l'ingéniosité d'un seul projet de recherche. L'équipe de recherche (individu ou groupe de chercheurs) doit préparer une brève mise en candidature qui explique clairement pourquoi la recherche est innovatrice.
Les récipiendaires du Prix John Ogilvie pour l'innovation en recherche seront choisis par le Comité des prix CSBE/SCGAB. Jusqu'à trois bourses d'innovation en recherche peuvent être attribuées chaque année. Tous les membres d'une équipe de recherche doivent être membres en règle de la CSBE/SCGAB. Les personnes peuvent se voir décerner le Prix de l'innovation en recherche à plusieurs reprises au cours de leur carrière, mais pas au cours d'années consécutives.
Nomination Form
2026 JOHN OGILVIE RESEARCH INNOVATION AWARD
“Sustainable Approaches for Upcycling of Industrial Pea-Starch Waste into Nanomaterials for Potential Agricultural and Food Applications”
Canada is one of the world’s largest producers and exporters of pulses, with pulse exports valued at approximately $3 billion in 2024. While this export strength has traditionally been built on raw pulse commodities, Canada has a major opportunity to expand further into higher-value plant-protein ingredients and food applications. Realizing this opportunity requires rapid, reliable, and evidence-based tools for assessing protein quality, particularly amino acid composition. However, current amino acid and protein-quality testing methods are often slow, costly, destructive, and difficult to apply in high-throughput quality-control settings.
To address this need, a multidisciplinary collaborative research project was carried out from 2022 to 2026, bringing together researchers from the Department of Biosystems Engineering and the Department of Electrical and Computer Engineering at the University of Manitoba, the Department of Computer Science at the University of Winnipeg, and the National Research Council Canada (NRC). The project focused on evaluating protein quality using protein extracted from field peas (Pisum sativum), a crop central to Canada’s pulse-protein isolate industry. The team, consisting of engineers, chemists, plant breeders, and computer scientists, has developed an integrated platform combining microfluidic lab-on-a-chip sample preparation, surface-enhanced Raman spectroscopy (SERS), bicinchoninic acid assay (BCA), UV-visible spectroscopy, and artificial intelligence-based spectral modelling.
This seminal initiative encompassing two large projects received funding from the Saskatchewan Pulse Growers, Mitacs, NSERC, and NRC, securing $1.8 million over a six-year period. The project has so far resulted in six journal manuscripts, including two published papers, one accepted paper, one manuscript submitted as a revised version following peer review, one manuscript with revisions in preparation for resubmission, and one submitted manuscript, as well as three conference presentations/posters and one invention disclosure currently in process. It has also trained 13 highly qualified personnel, including 7 postdoctoral fellows/research associates, 2 graduate students, and 4 undergraduate students, across engineering, spectroscopy, microfluidics, artificial intelligence, and food/protein analysis.
By demonstrating reliable and rapid cysteine prediction in pea protein using a low-cost, non-destructive microfluidic-SERS-AI platform, the project advances a practical analytical approach for next-generation protein-quality assessment. Its outcomes are relevant to pulse breeding, protein ingredient quality control, and value-added growth in Canada’s plant-protein sector. Some of the highly qualified personnel (HQP) trained through this initiative have progressed to careers in industry and academia, while others remain engaged in the research. The success of our HQP demonstrates the project’s role in building interdisciplinary talent and supporting Canada’s value-added plant-protein sector.
2026 JOHN OGILVIE RESEARCH INNOVATION AWARD
“Automated Smart Imaging System for Real-Time Canola Seed Quality Detection”
Started as a small seed grant of $9,984 funded by the University Research Grant Program (URGP) of the University of Manitoba, Dr. Erkinbaev had proposed the development of a smart imaging sensor for real-time determination of canola seed quality. He was driven by the idea of moving away from the visual inspection of canola seed by trained personnel, which was a labour-intensive, subjective and time-consuming process. Later, this research has grown into a larger initiative with several funded research and infrastructure projects, which aimed to help with quality control of canola from farm-to-fork and provide an intelligent tool for better and more efficient decision making in the canola processing industry. With a team of only one MSc student (Mr. Angshuman Thakuria), a hypothesis was tested by developing detection algorithm of damaged seeds based on image and video recordings coupled with an edge device. They published their research findings and results on achieving higher accuracy, less computational models and faster speed in a peer-reviewed journal in the area of smart agricultural technology. Inspired by the preliminary results from this seed grant, they were excited to expand the project with a focus on industrial application, which resulted in securing the highly competitive Proof-of-Concept Grant ($100,000) from the Research Manitoba and the Manitoba Government ($129,000) for the development of a smart imaging system for real-time canola quality prediction (Mr. Thirukumaran Ramesh). Innovative nature of the project led to expanding the objectives from monitoring of incoming raw canola seeds to prediction of intermediate and final canola oil and meal products resulted in securing two capacity building grants ($316,800) from the Manitoba Sustainable Canadian Agricultural Partnership, Manitoba Government, for development of an online quality control system for canola processing: from single kernel characterization to oil and meal products.
2026 JOHN OGILVIE RESEARCH INNOVATION AWARD
“ProteinProcessIO: A GPU-Accelerated, Experimentally Validated Digital Twin for the Complete Dry Fractionation of Canadian Pulse Crops”
ProteinProcessIO is the first integrated, physics-based digital twin that predicts the end-to-end performance of the complete dry fractionation chain in a single computational environment. Developed at McGill University in collaboration with NRC Canada under the federal Sustainable Protein Production Program, the platform covers all three interdependent stages — RF dielectric pretreatment, hammer milling, and air classification — and couples them in an automated pipeline where the outlet conditions of each stage feed directly into the inlet of the next. This end-to-end coupling is scientifically critical: a change in pretreatment temperature modifies seed brittleness, which reshapes the milling particle-size distribution, which in turn shifts the air classifier cut-point and protein recovery yield. Stage-isolated models cannot capture this cross-stage sensitivity.
The RF pretreatment module is a full digital twin of the QMTI GP-15 15 kW RF heating system at NRC Canada. It couples ten physics and control sub-models per timestep — including RF field propagation, volumetric dielectric heating, thermal conduction, moisture diffusion, evaporation kinetics, belt kinematics, oscillator circuit dynamics, and full PLC controller logic. The model was calibrated on actual PLC telemetry from a 61 kg NRC Canada production run, simultaneously matching outfeed temperature, anode current trajectory, and electrode gap. It was then blind-validated on an independent 90 kg run, predicting electrode gap within 1.2% (93.0 mm predicted vs. 94.1 mm measured). The milling module reproduces measured D50 to within 0.1 μm at 6,000 RPM using a calibrated Rosin-Rammler breakage function. The air classification module implements Lagrangian particle tracking through 36 fully parameterized equipment components — venturi eductor, zigzag preclassifier, high-speed wheel classifier (to 5,000g), three-stage cyclone cascade, and bag filter — accelerated via NVIDIA Warp GPU kernels.
The platform is released free of charge as a self-contained desktop application for Windows, macOS, and Linux, placing the simulation capacity of a fully equipped pilot plant in the hands of any Canadian research group or processor with a standard laptop.
2025 JOHN OGILVIE RESEARCH INNOVATION AWARDS
“Development of RZWQM2-P model for reducing phosphorus loading from agricultural field to surface water bodies” by Zhiming Qi
Agricultural soils in the humid temperate regions of Canada—particularly in Ontario and Quebec—are a major source of non-point phosphorus (P) pollution in freshwater rivers and lakes, especially when treated with chemical fertilizers, manures, or biosolids. Over the past several decades, the frequency and severity of cyanobacterial blooms have increased significantly. These blooms, along with broader eutrophication issues in freshwater bodies, have been closely linked to the enrichment of phosphorus (P) and nitrogen (N) via subsurface drainage effluent. Subsurface drainage systems are a primary pathway for P transport from agricultural fields in these regions. In response, various strategies have been developed to manage agricultural practices and mitigate P pollution. Among these strategies is the development of computer models capable of simulating and assessing P loss from agricultural lands. Although some models have been updated or newly developed, most lack integration of field management practices and crop growth dynamics, limiting their effectiveness as decision-support tools. Moreover, none currently simulate both dissolved and particulate P losses through subsurface drains simultaneously. To address these limitations, a new phosphorus management tool—RZWQM2-P—was developed. This tool builds upon the Root Zone Water Quality Model 2 (RZWQM2) by incorporating the latest scientific advances in soil and water phosphorus dynamics, while leveraging the model’s existing hydrologic and agricultural management capabilities. The performance of RZWQM2-P was evaluated using data from tile-drained agricultural fields treated with both organic and inorganic phosphorus sources across the Lake Erie watershed (encompassing Ontario, Ohio, and Michigan). Results showed that the model reliably simulated monthly dissolved reactive P and total P losses via surface runoff and tile drainage, achieving a Nash-Sutcliffe efficiency coefficient greater than 0.35, percent bias within ±25%, and an index of agreement exceeding 0.75 across the study sites. RZWQM2-P represents a promising advancement in phosphorus management, particularly for subsurface-drained agricultural systems.
2025 JOHN OGILVIE RESEARCH INNOVATION AWARDS
“Development of AI-Assisted Imaging and Spectroscopic Techniques for Pulse Quality Assurance Systems” by Manickavasagan Annamalai, Senthilkumar Thiruppathi & Chandra Singh
Canada is the world’s second-largest producer of pulses, exporting approximately 6 million tonnes annually to over 150 countries, with a net export value of $4.2 billion. However, nearly 30% of international pulse trade contracts lead to disputes requiring arbitration, primarily due to inconsistencies in quality standards, methodologies, and nomenclature.
To address these challenges, a five-year collaborative research project (2020–2025) was launched by a consortium comprising the University of Guelph (ON), the University of Prince Edward Island (PEI), and Lethbridge Polytechnic (AB). The project focused on developing artificial intelligence-driven, non-destructive testing technologies for pulse quality assessment, targeting critical factors such as adulteration, pesticide residue, protein content, and varietal purity.
This initiative led to an industry-academic partnership with Hensall Co-op/Hensall Foods and NSERC Alliance, securing $540,000 in funding. The project has resulted in over thirteen peer-reviewed publications in leading journals and 20 conference presentations. Additionally, it provided advanced training in pulse quality assurance to eleven highly qualified personnel (HQPs) and numerous undergraduate students.
The developed technologies are now in the implementation phase, being deployed in small and medium-sized enterprises across the pulse supply chain both in Canada and globally. The HQPs trained through this initiative are securing positions in the pulse industry and the broader food processing sector, underscoring the project’s significant contributions to workforce development and industry innovation.
