In situ detection of spices with medicinal value based on laser‐induced breakdown spectroscopy

Abstract 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.