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A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy

Authors :
Mitchell Sueker
Amirreza Daghighi
Alireza Akhbardeh
Nicholas MacKinnon
Gregory Bearman
Insuck Baek
Chansong Hwang
Jianwei Qin
Amanda M. Tabb
Jiahleen B. Roungchun
Rosalee S. Hellberg
Fartash Vasefi
Moon Kim
Kouhyar Tavakolian
Hossein Kashani Zadeh
Source :
Sensors, Vol 23, Iss 22, p 9062 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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 similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes.

Details

Language :
English
ISSN :
14248220
Volume :
23
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.0d508fb6a3af4409872748ee3f6e3fb3
Document Type :
article
Full Text :
https://doi.org/10.3390/s23229062