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 B Roungchun; Rosalee S Hellberg; Fartash Vasefi; Moon Kim; Kouhyar Tavakolian; Hossein Kashani Zadeh

doi:10.3390/s23229062

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

Sensors (Basel). 2023 Nov 9;23(22):9062. doi: 10.3390/s23229062.

Authors

Mitchell Sueker¹, Amirreza Daghighi², Alireza Akhbardeh², Nicholas MacKinnon², Gregory Bearman², Insuck Baek³, Chansong Hwang³, Jianwei Qin³, Amanda M Tabb⁴, Jiahleen B Roungchun⁴, Rosalee S Hellberg⁴, Fartash Vasefi², Moon Kim³, Kouhyar Tavakolian¹, Hossein Kashani Zadeh^{1

2

5}

Affiliations

¹ Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA.
² SafetySpect Inc., Grand Forks, ND 58202, USA.
³ USDA ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD 20705, USA.
⁴ Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA.
⁵ Department of Mechanical Engineering, University of North Dakota, Grand Forks, ND 58202, USA.

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.

Keywords: classification; data fusion; deep learning; dispute models; fish fraudulence; fish species identification; machine learning; multi-mode spectroscopy.

MeSH terms

Animals
Artificial Intelligence*
Dissent and Disputes*
Fishes
Machine Learning
Spectrum Analysis

Grants and funding

NA21OAR0210305/National Oceanic and Atmospheric Administration