Microwave digestion and inductively coupled plasma mass spectrometry (ICP-MS) were used to determine 44 trace elements in 180 Lycium barbarum L. samples from Ningxia and Qinghai provinces. Nine elements (Sb, La, Tb, Lu, Al, Sc, V, Cr and Se) with significant differences were selected for PCA by element content screening. The results showed that the first two main components could explain 64.2% of the variable, meanwhile the L. barbarum L. samples could be basically distinguished from Ningxia and Qinghai. Based on nine elements with significant differences, the discriminant models of L. barbarum L. from Ningxia and Qinghai were established by partial least squares discriminant analysis (PLS-DA) and back propagation artificial neural network (BP-ANN). In the PLS-DA model, when 100% L. barbarum L. samples were used, the sensitivity and specificity of the model were 100% and 97.5%, respectively. When 75% L. barbarum L. samples were used, the sensitivity and specificity of the model were 98.6% and 98.4%, respectively, and the accuracy of the model was 100% for predicting the remaining 25% L. barbarum L. samples. In the BP-ANN model, when 100% and 75% L. barbarum L. samples were used, the specificity and sensitivity of the model were both 100%. The accuracy of the model was 100% for predicting the remaining 25% L. barbarum L. samples. The sensitivity and specificity of BP-ANN model were better than PLS-DA model. The results showed that the determination of multiple elements in L. barbarum L. by ICP-MS combined with chemometrics could quickly identify L. barbarum L. from Ningxia and Qinghai.
LIAN Siyu
,
XIE Yujie
,
ZHANG Zijuan
,
FAN Chunlin
,
WANG Minglin
,
CHEN Hui
. Rapid discrimination of Lycium barbarum L. from Ningxia and Qinghaibased on multi-element analysis combined with chemometrics[J]. Food and Fermentation Industries, 2020
, 46(13)
: 250
-254
.
DOI: 10.13995/j.cnki.11-1802/ts.023771
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