Born and raised in China, Dr. Li (Laura) Liu obtained her PhD in Computer Science and Engineering from Hong Kong Polytechnic University. Interestingly, though her career has largely been spent researching food science with McGill’s Dr. Michael Ngadi, her academic focus was on biometric recognition. The relevance becomes apparent when one learns what her work entails. As Chief Technical Officer of Montreal-based MatrixSpec Solutions, she leads two CAAIN-supported hyperspectral imaging research projects, one of which is titled Development of Automated Smart Device for Pork Marbling Assessment.
“I’ll be honest,” she laughs. “At first the idea of working in agriculture didn’t really interest me. But I was intrigued by the potential of hyperspectral imaging, and accepted an offer to interview with Dr. Ngadi. I came away convinced he’d be a good mentor, so I took a leap of faith. It was the right decision. I love my work and have learned so much from him and our other colleagues. I can’t imagine doing anything else because our research is exciting and has such tremendous upside.”
Marbling in red meat is so named because the streaks of fat resemble a marble pattern, adding flavour, which makes it one of the main criteria for judging meat quality. Typically, the more marbling, the better the cut. Canada Pork International, the export promotion agency of our nation’s pork industry, tasked an expert panel, which included Dr. Liu, with creating a quality-based classification system that captures the importance of marbling in determining meat grade. Unfortunately, assessments based on those guidelines rely on visual inspection, which requires the sample to be cut, a potentially inconsistent process that can devalue the product. In an effort to standardise pork grading, MatrixSpec has developed, and validated in research-scale testing, an automated loin chop marbling assessment tool.
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