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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

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 s...

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Autores principales: Sueker, Mitchell, Daghighi, Amirreza, Akhbardeh, Alireza, MacKinnon, Nicholas, Bearman, Gregory, Baek, Insuck, Hwang, Chansong, Qin, Jianwei, Tabb, Amanda M., Roungchun, Jiahleen B., Hellberg, Rosalee S., Vasefi, Fartash, Kim, Moon, Tavakolian, Kouhyar, Kashani Zadeh, Hossein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674920/
https://www.ncbi.nlm.nih.gov/pubmed/38005450
http://dx.doi.org/10.3390/s23229062
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author Sueker, Mitchell
Daghighi, Amirreza
Akhbardeh, Alireza
MacKinnon, Nicholas
Bearman, Gregory
Baek, Insuck
Hwang, Chansong
Qin, Jianwei
Tabb, Amanda M.
Roungchun, Jiahleen B.
Hellberg, Rosalee S.
Vasefi, Fartash
Kim, Moon
Tavakolian, Kouhyar
Kashani Zadeh, Hossein
author_facet Sueker, Mitchell
Daghighi, Amirreza
Akhbardeh, Alireza
MacKinnon, Nicholas
Bearman, Gregory
Baek, Insuck
Hwang, Chansong
Qin, Jianwei
Tabb, Amanda M.
Roungchun, Jiahleen B.
Hellberg, Rosalee S.
Vasefi, Fartash
Kim, Moon
Tavakolian, Kouhyar
Kashani Zadeh, Hossein
author_sort Sueker, Mitchell
collection PubMed
description 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.
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spelling pubmed-106749202023-11-09 A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy Sueker, Mitchell Daghighi, Amirreza Akhbardeh, Alireza MacKinnon, Nicholas Bearman, Gregory Baek, Insuck Hwang, Chansong Qin, Jianwei Tabb, Amanda M. Roungchun, Jiahleen B. Hellberg, Rosalee S. Vasefi, Fartash Kim, Moon Tavakolian, Kouhyar Kashani Zadeh, Hossein Sensors (Basel) Article 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. MDPI 2023-11-09 /pmc/articles/PMC10674920/ /pubmed/38005450 http://dx.doi.org/10.3390/s23229062 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sueker, Mitchell
Daghighi, Amirreza
Akhbardeh, Alireza
MacKinnon, Nicholas
Bearman, Gregory
Baek, Insuck
Hwang, Chansong
Qin, Jianwei
Tabb, Amanda M.
Roungchun, Jiahleen B.
Hellberg, Rosalee S.
Vasefi, Fartash
Kim, Moon
Tavakolian, Kouhyar
Kashani Zadeh, Hossein
A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy
title A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy
title_full A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy
title_fullStr A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy
title_full_unstemmed A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy
title_short A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy
title_sort novel machine-learning framework based on a hierarchy of dispute models for the identification of fish species using multi-mode spectroscopy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10674920/
https://www.ncbi.nlm.nih.gov/pubmed/38005450
http://dx.doi.org/10.3390/s23229062
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