Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 1 of 1
Full-Text Articles in Food Processing
A Novel Machine-Learning Framework Based On A Hierarchy Of Dispute Models For The Identification Of Fish Species Using Multi-Mode Spectroscopy, Mitchell Sueker, Amirreza Daghighi, Alireza Akhbardeh, Nicholas Mackinnon, Gregory Bearman, Insuck Baek, Chansong Hwang, Jianwei Qin, Amanda M. Tabb, Jiahleen Roungchun, Rosalee S. Hellberg, Fartash Vasefi, Moon Kim, Kouhyar Tavakolian, Hossein Kashini Zadeh
A Novel Machine-Learning Framework Based On A Hierarchy Of Dispute Models For The Identification Of Fish Species Using Multi-Mode Spectroscopy, Mitchell Sueker, Amirreza Daghighi, Alireza Akhbardeh, Nicholas Mackinnon, Gregory Bearman, Insuck Baek, Chansong Hwang, Jianwei Qin, Amanda M. Tabb, Jiahleen Roungchun, Rosalee S. Hellberg, Fartash Vasefi, Moon Kim, Kouhyar Tavakolian, Hossein Kashini Zadeh
Food Science Faculty Articles and Research
Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of …