贝类是一种易受重金属污染的水产品。贝类重金属污染的快速检测对于保障贝类食品安全具有重要意义。现有的光谱检测技术由于无法对实际过程中所有情况的样本信息以及检测过程中的随机干扰因素进行训练建模，导致难以对实际过程中未知或部分未知的样本信息做出正确的检测。本项目拟在前期基金的研究基础上，采用红外、拉曼以及激光诱导击穿光谱等三种主要的食品重金属光谱检测技术，研究和设计针对贝类重金属光谱检测的开集算法，构建数据描述边界，拒绝识别模型未知的样本。同时，研究待测样本和训练样本的联合后验概率分布，对模型部分未知的待测样本给出不确定度评估。此外，研究多模态层次化数据结构的统一搭建算法，实现异源光谱信息的有效融合，进一步提高开集检测和不确定度分析的精准度。此项目的开展旨在提高光谱技术进行贝类重金属污染快速检测的能力，扩大其应用范围，使其更加满足于实际贝类产业的需要，能够更有效地保障贝类食品安全。

Shellfish is a kind of seafood that is easily polluted by heavy metals. It is very important to guarantee the safety of shellfish products by detecting the heave metal pollution of shellfish in a rapid way. Current spectroscopic techniques cannot make the correct detection on unknown or partial unknown samples in the practice, because the calibration of spectroscopic models cannot includes samples with all pollution situations and random interference factors in the detection process. On the basis of the previous funding, this project will apply three main spectroscopic techniques for heavy metal detection in foods, namely infrared, Raman and laser-induced breakdown spectroscopies to study and develop open set detection algorithms for spectroscopic technique based heavy metal detection of shellfish, establish the data description borders, deny the unknown samples in models. Besides, this project will focus on obtaining the joint posterior probability distribution of prediction samples and training samples to give the evaluation of degree of uncertainty for partial unknown samples under test. In addition, this project will study unified construction algorithms for multi-mode hierarchical data structure to effectively fuse the multiple kinds of spectroscopic information, resulting in further improving the accuracy of the open set detection and the evaluation of degree of uncertainty. This project aims at improving the detection capability of spectroscopic techniques for rapid detection of heavy metal pollution in shellfish, expanding their applying areas, making them more to meet the demand of shellfish industry and guarantee the safety of shellfish products more efficiently.