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.
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%.
The rapid detection and identification of the electronic waste (e-waste) containing rare earth (RE) elements is of great significance for the recycling of RE elements. However, the analysis of these materials is extremely challenging due to extreme similarities in appearance or chemical composition. In this research, a new system based on laser induced breakdown spectroscopy (LIBS) and machine learning algorithms is developed for identifying and classifying e-waste of rare-earth phosphors (REPs). Three different kinds of phosphors are selected and the spectra is monitored using this new developed system. The analysis of phosphor spectra shows that there are Gd, Yd, and Y RE element spectra in the phosphor. The results also verify that LIBS could be used to detect RE elements. An unsupervised learning method, principal component analysis (PCA), is used to distinguish the three phosphors and training data set is stored for further identification. Additionally, a supervised learning method, backpropagation artificial neural network (BP-ANN) algorithm is used to establish a neural network model to identify phosphors. The result show that the final phosphor recognition rate reaches 99.9%. The innovative system based on LIBS and machine learning (ML) has the potential to improve rapid in situ detection of RE elements for the classification of e-waste.
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