As a famous spectroscopy method for substance detection and classification, laser-induced breakdown spectroscopy (LIBS) is not a nondestructive detection method. Considering the precious samples and the experimental environment, sometimes it is difficult to get enough spectra to build the classification model, which is important for qualitative analysis. In this paper, a spectral generation method for extending the spectral database of LIBS is proposed based on generative adversarial nets (GAN). After enough interactive training, the generated spectra looked very similar to the experimental spectra. Evaluated with unsupervised clustering methods PCA and K-means, the generated spectra could not be distinguished from the real spectra. For each type of sample, most of the simulated spectra and experimental spectra were clustered into the same class, which meant the proposed method was effective to extend the spectral database. Using the spectral database extended by this method as training set data to build the SVM model, the results showed that when there were only a few experimental spectra, the combination of the generated spectra and the experimental spectra for building the classification model could achieve better identification results.

Laser-induced breakdown spectroscopy (LIBS) is a promising element detection technique that has been rapidly developed for use in various applications, including the biomedical field. The technique has strong potential to analyze molecularly complex clinical specimens, for instance, human and animal soft tissues. In this work, we review the applications of LIBS in the classification and identification of soft tissues with emphasis on sample preparation and data processing. These applications can be divided into two categories, namely, pathological diagnosis and physiological detection. LIBS has been successfully used to distinguish ex vivo malignant and normal tissues, and classify the different normal tissues. Thus, LIBS may be an effective tool with potential applications in cancer diagnosis and laser surgery. This review then suggests possible development directions for further research on LIBS testing of soft tissue based on our summary and analysis of key results. Extensive studies should be implemented in a manner consistent with clinical requirements to expand the practical applications of LIBS in the future.

The correct classification of pathogenic bacteria is significant for clinical diagnosis and treatment. Compared with the use of whole spectral data, using feature lines as the inputs of the classification model can improve the correct classification rate (CCR) and reduce the analyzing time. In order to select feature lines, we need to investigate the contribution to the CCR of each spectral line. In this paper, two algorithms, important weights based on principal component analysis (IW-PCA) and random forests (RF), were proposed to evaluate the importance of spectra lines. The laser-induced plasma spectra (LIBS) of six common clinical pathogenic bacteria species were measured and a support vector machine (SVM) classifier was used to classify the LIBS of bacteria species. In the proposed IW-PCA algorithm, the product of the loading of each line and the variance of the corresponding principal component were calculated. The maximum product of each line calculated from the first three PCs was used to represent the line’s importance weight. In the RF algorithm, the Gini index reduction value of each line was considered as the line’s importance weight. The experimental results demonstrated that the lines with high importance were more suitable for classification and can be chosen as feature lines. The optimal number of feature lines used in the SVM classifier can be determined by comparing the CCRs with a different number of feature lines. Importance weights evaluated by RF are more suitable for extracting feature lines using LIBS combined with an SVM classification mechanism than those evaluated by IW-PCA. Furthermore, the two methods mutually verified the importance of selected lines and the lines evaluated important by both IW-PCA and RF contributed more to the CCR.