Isomers are widely present in volatile organic compounds (VOCs), and it is a tremendous challenge to rapidly distinguish the isomers of VOCs in the atmosphere. In this paper, laser induced breakdown spectroscopy (LIBS) technology was developed to online distinguish VOCs and their isomers in the atmosphere. First, LIBS was used to directly detect halogenated hydrocarbons (typical class of VOCs) in the atmosphere and the characteristic peaks of the related halogens were observed in the LIBS spectra. Then, comparing the LIBS spectra of various samples, it was found that for the VOCs with different molecular formulas, although the spectra are completely the same in element composition, there are still significant differences in the relative intensity of the spectral lines and other information. Finally, in light of the shortcomings of traditional LIBS technology in identifying isomers, machine learning algorithms were introduced to develop the LIBS technology to identify the isomers of VOCs in the atmosphere, and the recognition results were very good. It is proved that LIBS technology combined with machine learning algorithms is promising for online traceability of VOCs in the atmosphere.

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The carbon dioxide, sulfur dioxide, and metal ions produced by straw burning can severely pollute the atmosphere; thus, online detection and traceability for straw burning is very important. However, to our best knowledge, there is no comprehensive system that can satisfy online detection, classification, and traceability due to the challenging online detection and traceability of straw burning. In this paper, a new system based on laser-induced breakdown spectroscopy (LIBS) and machine learning is developed, and this developed system is employed for the first time in online detection and traceability of straw combustion. Four different types of straw are selected and the straw burning smoke is monitored online using this developed system. The analysis of straw smoke spectra shows that there are Fe, Mn, and Ba heavy metal spectra in the smoke spectra. By comparing the smoke spectra of different types of straw, the characteristic spectral lines with large differences are selected and dimensionality reduction is performed by linear discriminant analysis algorithm. Then, combined with random forest to achieve classification, the final smoke recognition accuracy reaches 87.0%. Straw ash is then used as a reference analysis and the same operation is performed on it. Mn, Ba, and Li heavy metal spectral lines are found in the spectra of ash, and the final recognition accuracy is 92.6%. The innovative and developed system based on LIBS and machine learning is fast, online, and in situ and has far-reaching application prospects in the environment.

It is important to detect and distinguish different spices because spices are widely used around the world. In this study, wormwood, artemisia annua, lemongrass and clove are taken as examples. First, laser-induced breakdown spectroscopy (LIBS) is applied to detect and analyze the ash of different spice samples in situ. In the spectra of the ash of different samples, some characteristic lines of metal elements are observed, such as Ca, Na, Mg, K, and so on. By comparing the spectra of the ash, the relative intensities of the characteristic peaks are different, which can be employed to identify and distinguish different spice samples. Then, using LIBS combined with principal component analysis (PCA) and error back propagation artificial neural network (BP-ANN), the model of classification is established to distinguish different spices. In PCA, the dimension of the spectra of the ash is reduced, and the cumulative contribution rate of the first two PCs exceeds 90%. The samples after dimension reduction by PCA are classified by BP-ANN, and the recognition rate can reach 100%. After 10 cross-verifications, the final recognition accuracy can reach 85.25%. All of the results show the model of classification has the potential in the field of identification and distinction of different spices.