人工智能在食品安全檢測中的應(yīng)用研究

摘 要：本文梳理了食品安全檢測的內(nèi)涵和人工智能的應(yīng)用基礎(chǔ)，指出當(dāng)前食品安全檢測存在的主要問題，并提出了基于深度學(xué)習(xí)優(yōu)化檢測算法精度、機(jī)器學(xué)習(xí)構(gòu)建多靶標(biāo)識別模型、智能傳感器提升現(xiàn)場檢測效率等應(yīng)對策略。人工智能技術(shù)有望顯著提升食品安全檢測的靈敏度、特異性和實時性，為保障食品安全提供有力支撐。

關(guān)鍵詞：食品安全檢測；人工智能；深度學(xué)習(xí)

Abstract： This article combs the connotation of food safety testing and the application basis of artificial intelligence， points out the main problems existing in current food safety testing， and proposes optimization of detection algorithm accuracy based on deep learning， machine learning to build multi-target recognition models， and smart sensors to improve on-site detection efficiency and other coping strategies. Artificial intelligence technology is expected to significantly improve the sensitivity， specificity and real-time performance of food safety testing， providing strong support for ensuring food safety.

Keywords： food safety testing; artificial intelligence; deep learning

近年來，食品安全問題頻發(fā)，嚴(yán)重危害人們身體健康和生命安全。（剩余4490字）