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.

Atomic spectrometry update: review of advances in the analysis of clinical and biological materials, foods and beverages

“…The complex statistical tools were required to achieve the discrimination level necessary where the difference between adulterants is very small. Another group of researchers 136 analysed four different spices (clove, common wormwood, sweet wormwood and lemongrass), implementing PCA for dimension reduction followed by error back propagation artificial neural network modelling. After cross verifications, the recognition accuracy was 85.25%.…”

Online in situ detection and identification of smoke generated by traditional Chinese medicine fumigation based on laser‐induced breakdown spectroscopy and machine learning

“…The spectral window range is approximately from 200 to 890 nm. Having considering past experience and current devices, the detection delay was set to 1.5 µs 31 and the integration time was 1.05 ms. 32 Then the signal was conveyed via a four-channel spectrometer to the computer for further analyses. In addition, each sample is detected by LIBS to obtain 1000 spectra.…”