{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T03:26:58Z","timestamp":1782876418910,"version":"3.54.5"},"reference-count":26,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000192","name":"National Oceanic and Atmospheric Administration (NOAA) Small Business Innovation Research (SBIR)","doi-asserted-by":"publisher","award":["NA21OAR0210305"],"award-info":[{"award-number":["NA21OAR0210305"]}],"id":[{"id":"10.13039\/100000192","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23229062","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T10:19:27Z","timestamp":1699525167000},"page":"9062","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy"],"prefix":"10.3390","volume":"23","author":[{"given":"Mitchell","family":"Sueker","sequence":"first","affiliation":[{"name":"Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amirreza","family":"Daghighi","sequence":"additional","affiliation":[{"name":"SafetySpect Inc., Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Akhbardeh","sequence":"additional","affiliation":[{"name":"SafetySpect Inc., Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7124-7420","authenticated-orcid":false,"given":"Nicholas","family":"MacKinnon","sequence":"additional","affiliation":[{"name":"SafetySpect Inc., Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Bearman","sequence":"additional","affiliation":[{"name":"SafetySpect Inc., Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1044-349X","authenticated-orcid":false,"given":"Insuck","family":"Baek","sequence":"additional","affiliation":[{"name":"USDA ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD 20705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chansong","family":"Hwang","sequence":"additional","affiliation":[{"name":"USDA ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD 20705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8061-4373","authenticated-orcid":false,"given":"Jianwei","family":"Qin","sequence":"additional","affiliation":[{"name":"USDA ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD 20705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda M.","family":"Tabb","sequence":"additional","affiliation":[{"name":"Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahleen B.","family":"Roungchun","sequence":"additional","affiliation":[{"name":"Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3953-2007","authenticated-orcid":false,"given":"Rosalee S.","family":"Hellberg","sequence":"additional","affiliation":[{"name":"Food Science Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fartash","family":"Vasefi","sequence":"additional","affiliation":[{"name":"SafetySpect Inc., Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moon","family":"Kim","sequence":"additional","affiliation":[{"name":"USDA ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD 20705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5785-358X","authenticated-orcid":false,"given":"Kouhyar","family":"Tavakolian","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Kashani Zadeh","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Program, University of North Dakota, Grand Forks, ND 58202, USA"},{"name":"SafetySpect Inc., Grand Forks, ND 58202, USA"},{"name":"Department of Mechanical Engineering, University of North Dakota, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"key":"ref_1","unstructured":"Warner, K., Mustain, P., Lowell, B., Geren, S., and Talmage, S. (2016). Deceptive Dishes: Seafood Swaps Found Worldwide Acknowledgements, Oceana."},{"key":"ref_2","unstructured":"Reilly, A. (2023, March 26). Overview of Food Fraud in the Fisheries Sector\u2014ProQuest. Available online: https:\/\/www.proquest.com\/docview\/2060924242?fromopenview=true&pq-origsite=gscholar."},{"key":"ref_3","unstructured":"Stromberg, J. (2023, August 26). The DNA Detectives That Reveal What Seafood You\u2019re Really Eating | Science| Smithsonian Magazine. Available online: https:\/\/www.smithsonianmag.com\/science-nature\/the-dna-detectives-that-reveal-what-seafood-youre-really-eating-180948066\/."},{"key":"ref_4","unstructured":"FDA (2021). Potential Species-Related and Process-Related Hazards, Fish and Fishery Products Hazards and Controls Guidance."},{"key":"ref_5","unstructured":"Miller, D.D., and Sumaila, U.R. (2016). Seafood Authenticity and Traceability: A DNA-Based Perspective, Academic Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1093\/jaoac\/94.1.201","article-title":"A Single-Laboratory Validated Method for the Generation of DNA Barcodes for the Identification of Fish for Regulatory Compliance","volume":"94","author":"Handy","year":"2011","journal-title":"J. AOAC Int."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cermakova, E., Lencova, S., Mukherjee, S., Horka, P., Vobruba, S., Demnerova, K., and Zdenkova, K. (2023). Identification of Fish Species and Targeted Genetic Modifications Based on DNA Analysis: State of the Art. Foods, 12.","DOI":"10.3390\/foods12010228"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9401","DOI":"10.1039\/C5AY02048D","article-title":"Point-and-shoot: Rapid quantitative detection methods for on-site food fraud analysis\u2014Moving out of the laboratory and into the food supply chain","volume":"7","author":"Ellis","year":"2015","journal-title":"Anal. Methods"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.tifs.2018.12.002","article-title":"Novel techniques for evaluating freshness quality attributes of fish: A review of recent developments","volume":"83","author":"Wu","year":"2018","journal-title":"Trends Food Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"134632","DOI":"10.1016\/j.foodchem.2022.134632","article-title":"Multivariate versus machine learning-based classification of rapid evaporative Ionisation mass spectrometry spectra towards industry based large-scale fish speciation","volume":"404","author":"Birse","year":"2023","journal-title":"Food Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107234","DOI":"10.1016\/j.foodcont.2020.107234","article-title":"Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques","volume":"114","author":"Qin","year":"2020","journal-title":"Food Control"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1177\/0967033516678801","article-title":"Classification of freshwater fish species by linear discriminant analysis based on near infrared reflectance spectroscopy","volume":"25","author":"Lv","year":"2017","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"134043","DOI":"10.1016\/j.foodchem.2022.134043","article-title":"Rapid identification of fish species by laser-induced breakdown spectroscopy and Raman spectroscopy coupled with machine learning methods","volume":"400","author":"Ren","year":"2023","journal-title":"Food Chem."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, X., Cheng, G., Liu, S., Meng, S., Jiao, Y., Zhang, W., Liang, J., Zhang, W., Wang, B., and Xu, X. (2022). Probing 1D convolutional neural network adapted to near-infrared spectroscopy for efficient classification of mixed fish. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 279.","DOI":"10.1016\/j.saa.2022.121350"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chauvin, J., Duran, R., Tavakolian, K., Akhbardeh, A., MacKinnon, N., Qin, J., Chan, D.E., Hwang, C., Baek, I., and Kim, M.S. (2021). Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced?. Appl. Sci., 11.","DOI":"10.3390\/app112210628"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zadeh, H.K., Hardy, M., Sueker, M., Li, Y., Tzouchas, A., MacKinnon, N., Bearman, G., Haughey, S.A., Akhbardeh, A., and Baek, I. (2023). Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence. Sensors, 23.","DOI":"10.3390\/s23115149"},{"key":"ref_17","first-page":"1461","article-title":"Training highly multiclass classifiers","volume":"15","author":"Gupta","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","unstructured":"Thrampoulidis, C., Oymak, S., and Soltanolkotabi, M. (2020, January 6\u201312). Theoretical Insights into Multiclass Classification: A High-Dimensional Asymptotic View. Proceedings of the 2020 Conference on Neural Information on Neural Information Processing Systems, Online."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1134\/S1054661819030246","article-title":"On a Classification Method for a Large Number of Classes","volume":"29","author":"Zhuravlev","year":"2019","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"96","DOI":"10.2352\/issn.2168-3204.2010.7.1.art00018","article-title":"Capturing the Color of Black and White","volume":"7","author":"Williams","year":"2010","journal-title":"Arch. Conf."},{"key":"ref_21","first-page":"95","article-title":"A Color Rendition Chart","volume":"2","author":"McCamy","year":"1976","journal-title":"Appl. Photo Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Williams, D., and Burns, P.D. (2012, January 12\u201315). Targeting for Important Color Content: Near Neutrals and Pastels. Proceedings of the IS&T Archiving Conference, Copenhagen, Denmark.","DOI":"10.2352\/issn.2168-3204.2012.9.1.art00042"},{"key":"ref_23","unstructured":"Vasefi, F., Barton, K.E., Bearman, G., Kashani Zadeh, H., and Akhbardeh, A. (2022). System and Method for Assessing Product. (U.S. Patent 20230142722A1)."},{"key":"ref_24","unstructured":"(2023, August 12). Buy Fresh Seafood Online | Fresh Seafood Delivery\u2014Fulton Fish Market. Available online: https:\/\/fultonfishmarket.com\/."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Spencer, E.T., and Bruno, J.F. (2019). Fishy Business: Red Snapper Mislabeling Along the Coastline of the Southeastern United States. Front. Mar. Sci., 6.","DOI":"10.3389\/fmars.2019.00513"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater reliability: The kappa statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochem. Med. 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