인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Seafood is highly susceptible to quality deterioration and safety risks during post-harvest handling and storage, creating a growing demand for rapid, non-destructive, and reliable assessment technologies. This review critically examines recent advances in sensing technologies combined with artificial intelligence (AI) for seafood quality and safety, with emphasis on computer vision, spectroscopy and hyperspectral imaging, electronic sensing and biosensing, and multimodal data fusion. Computer vision is particularly useful for quantifying visible quality changes, whereas spectroscopic approaches provide information on physicochemical and biochemical alterations. Electronic sensing and biosensing enable the detection of spoilage-related patterns and target-specific safety hazards. Recent studies have increasingly integrated multiple sensing modalities through data-, feature-, and decision-level fusion to capture complementary aspects of seafood deterioration. However, improved model accuracy does not necessarily translate into robust real-world performance. Major limitations remain with respect to dataset size, biological replication, target definition, external validation, instrument dependency, explainability, and environmental variability. Future research should therefore prioritize generalizable and interpretable models, rational sensor combinations, and validation across species, batches, instruments, and processing environments. Sensing combined with AI has strong potential to support practical seafood quality control and safety screening once its performance has been demonstrated beyond laboratory-scale proof of concept.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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