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

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

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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 turbo air m3f72-3-n 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 click here 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.