The seafood industry faces challenges in identifying fish species and assessing freshness. Approximately 20% of fish are mislabeled due to their similar appearance, and there's no quick and cost-effective method to determine freshness. Current fish identification involves DNA analysis and polymerase chain reaction, which are time-consuming, costly, and require specialized equipment and personnel. Traditional freshness assessments involve sensory evaluation, but this method is invasive and requires skilled labor. Our team introduced a hand-held spectroscopy system that combines various spectroscopic modes for identification and freshness grading. Using this device, we studied fifteen fish samples from three species over ten days, validating species through DNA barcoding and freshness via ATP and K-value. We used the data, which employed spectroscopic fusion at the feature and decision levels, to train machine learning models leading to a system capable of accurately determining both the species and its freshness.
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