马氏距离
激光诱导击穿光谱
主成分分析
污染
数学
人工智能
模式识别(心理学)
分类器(UML)
计算机科学
光谱学
物理
生态学
量子力学
生物
作者
Zongwu Xie,Xiaoyi Feng,Xiao Chen,Guangzao Huang,Xiaojing Chen,Limin Li,Wen Shi,Chengxi Jiang,Shuwen Yu
标识
DOI:10.25165/j.ijabe.20231604.7666
摘要
Laser-induced breakdown spectroscopy (LIBS) can be used for the rapid detection of heavy metal contamination of Tegillarca granosa (T. granosa), but an appropriate classification model needs to be constructed. In the one-class classification method, only target samples are needed in training process to achieve the recognition of abnormal samples, which is suitable for rapid identification of healthy T. granosa from those contaminated with uncertain heavy metals. The construction of a one-class classification model for heavy metal detection in T. granosa by LIBS has faced the problem of high-dimension and small samples. To solve this problem, a novel one-class classification method was proposed in this study. Here, the principal component scores and the intensity of the residual spectrum were combined as extracted features. Then, a one-class classifier based on Mahalanobis distance using the extracted features was constructed and its threshold was set by leave-one-out cross-validation. The sensitivity, specificity and accuracy of the proposed method were reached to 1, 0.9333 and 0.9667 respectively, which are superior to the previously reported methods. Keywords: laser-induced breakdown spectroscopy, Heavy metal contamination, Tegillarca granosa, one-class classification DOI: 10.25165/j.ijabe.20231604.7666 Citation: Xie Z H, Feng X A, Chen X, Huang G Z, Chen X J, Li L M, et al. Development of one-class classification method for identifying healthy T. granosa from those contaminated with uncertain heavy metals by LIBS. Int J Agric & Biol Eng, 2023; 16(4): 201-206.
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