Please note the time zone for the webinar is CT (Chicago, USA).
Due to the great demand of edible oils worldwide, adulteration is a major issue. In this webinar, Hefei Zhao, a PhD candidate in the Department of Food Science and Technology at the University of Nebraska-Lincoln, will tell you about his research that tries to enhance the workflow efficiency and performance of Raman spectra through augmented detection of features. While Raman spectroscopy has been used to detect food quality and adulteration, manual spectral analysis still limits this technique to a time-consuming and low-throughput tool. In Zhao’s research, spectra from ten common types of edible oils with four independent biological replicates were collected. You’ll learn more about the implications of his results and how machine learning classification of Raman spectra is promising for practical rapid Raman detection in this webinar.