The Classification of Plant Leaves by Applying Chemometrics Methods on Laser-Induced Breakdown Spectroscopy

Laser induced breakdown spectroscopy (LIBS) is a highly efficient and rapid elemental analysis method. It can be applied to the elemental analysis of various materials. Linear discriminant analysis (LDA) and support vector machine (SVM) are two commonly used supervised algorithms in chemometrics. These two methods both need to build the models with known sample data, and then to classify unknown sample data. In order to achieve high accuracy of recognition for organics by LIBS technology, these two algorithms were used to analyze LIBS spectra. In this experiment, a nanosecond laser with 1 064 nm wavelength was used to ablate three kinds of plant leaves (Ligustrum lucidum, Viburnum odoratissinum, bamboo) to produce plasma. The plasma spectra were acquired by an optical fiber spectrometer in the range of 220 to 432 nm. 100 spectra from each kind of plant leaves were collected. Firstly, the principal component extraction for the original spectral data of 300 samples was carried out. Then the first two principal components (PC1, PC2) were used to make the score plot. The spectra of these three kinds of plant leaves are very similarities so that they could not be identified directly. Then, 70 spectra of each kind of plant sample were set as a train set, and the other 30 spectra were used as the test set to test the classification model. The first 20 principal components extracted by the PCA were used as attribute values for modeling of LDA and SVM. For the LDA, the spectra were processed to obtain the first two discriminant function values. The larger scatter distribution intervals for different types of leaves can be acquired by plotting the discriminant function values. Then combined with the Mahalanobis distance, the average classification accuracy of the test set was up to 96. 67%. Similarly, the SVM method was used to learn the characters of the train set to obtain the classification hyperplane. The average classification accuracy rate of SVM for the test set was up to 98. 89% , which is better than LDA. This work can be helpful to food traceability, in situ identification of biological tissues and remote analysis of organic explosives by LIBS technology.