The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods

The applications of laser-induced breakdown spectroscopy (LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement. In this work, to study the methods on classification of complex organics, three kinds of fresh leaves were measured by LIBS. 100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3. Two algorithms of chemometric methods including the partial least squares discriminant analysis (PLS-DA) and principal component analysis Mahalanobis distance (PCA-MD) were used to identify these leaves. By using 23 lines from 16 elements or molecules as input data, these two methods can both classify these three kinds of leaves successfully. The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA. The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA. It means that PLS-DA is better than PCA-MD in classifying plant leaves. Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process. We think that this work can provide a reference for plant traceability using LIBS.