Abstract: 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.