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.
In geological exploration, it is necessary to analyze the geological history according to the rock types. But in places for people to reach, sample collection, and transportation is a costly task. At present, the remote intelligent detection of rock types needs to be further developed. In this study, laserinduced breakdown spectroscopy (LIBS), a sensitive optical technique that can rapidly analyze various elements, is applied to real-time detection and analysis of rock types. Representative rock samples and minerals are selected for spectral analysis and machine learning. The characteristic spectral lines of Ca, Al, Mg, Ti, Si, Na, Fe, K, and Li were observed in the spectra. By comparing the spectra of different samples, the differences among them were discussed.First, principal component analysis (PCA) is used for dimensionality reduction. With the help of PCA, the data are distributed in twodimensional and three-dimensional space and different kinds of rocks and minerals are classified successfully. Then, combined with error back propagation training artificial neural network, the rock and mineral identification model was established, and the recognition rate can reach 100%. The results show that LIBS is a powerful tool for remote intelligent realtime rock detection and classification, and has great application prospects in the exploration of extraterrestrial objects including planets, satellites and asteroids in the future.
Traditional Chinese Medicine fumigation is a traditional treatment and the composition of the air is changed to achieve therapeutic effect. In this study, a new experimental system based on laser‐induced breakdown spectroscopy (LIBS) is developed to online in situ detect the smoke and monitor the influence on air composition by smoke. Atractylodis rhizome, wormwood, and perilla are chosen as samples to test the feasibility and accuracy of this system. Some characteristic lines can be seen in the spectra, and detailed elemental information is obtained. The spectra of three types of smoke were analyzed via an identification system based on principal component analysis, random forest (RF), and support vector machine (SVM). The contribution rate of the first two major components is 81.6% in total. The accuracy of classification by RF reaches 87.5% and SVM realizes a classification accuracy of 91.7%. The innovative and developed system based on LIBS and machine learning is demonstrated having a promising application in online in situ detection of air components and classification of smoke generated by Chinese medicine fumigation.
The damage of kitchen oil fume to the human body and environment cannot be ignored. Based on laser-induced breakdown spectroscopy (LIBS), five kitchen environments are online in situ detected, including the air scene, fry scene, grill scene, steam scene, and stew scene. In the spectra, characteristic elements such as C, H, O, and N are detected in the fry scene containing oil fume, and metal elements such as Mg, Ca, K, and Na are observed in the grill scene containing charcoal smoke. The spectra of five kitchen environments are tested and compared. In the measurement, except for the air scene, obvious carbon–nitrogen molecular spectral lines are detected. LIBS is combined with principal component analysis and backpropagation artificial neural network system to detect and analyze kitchen fumes. Finally, five kitchen scenes are analyzed and identified based on this system, and the final recognition accuracy is 98.60%.
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