Multi-Element Analysis and Origin Discrimination of Panax notoginseng Based on Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS)

Abstract

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification.

Inductively coupled plasma-mass spectrometry versus flame atomic absorption spectrophotometry for the analysis of Fe, Cu, Zn, Mn and Cr in medicinal plants: a comparison study

“…The high ion density leads to interaction between ions present in the ion beam causing preferential loss of the light ions in the presence of the heavy ones such as Pb + on Li + . Matrix matching, or careful choice of internal standards across the mass range of the analytes, help to compensate for these effects, although this may prove difficult in practice and therefore isotope dilution will be effective though expensive [2], although it can also be overcome most effectively by diluting the sample [4].…”

“…Transport effects in ICP-MS such as the spray chamber effects and differences in viscosity between sample solutions and calibration standards will change the efficiency of aerosol production from one solution to another [2]. This calls for matrix matching, although internal standardization can be used to overcome this effect and rapid scanning speed of ICP-MS does give superior results when using an internal standard [4,9,24,25].…”

“…ICP-MS has a LDR (linear dynamic range) in excess of 10 5 and this can be extended up to 10 7 by de-sensitizing one of the ion lenses, use of detector analog mode, or use of a separate faraday cup as a second detector [1,4,9] however, high matrix component concentrations may cause problems best solved by dilution. Samples with high dissolved solids, ICP-MS method should be used for trace/ultra-trace analysis and with isotope dilution method, very good results are obtained with high concentrations.…”

“…The precision of a given method is measured by the degree of agreement of the data that has been obtained from a given sample in the same way [2,3]. It provides a measure of the random or indeterminate error of analysis and the numerical terms which express these characteristics are absolute standard deviation, relative standard deviation, standard deviation of the mean, coefficient of variation and variance [4,5]. Mogwasi [6] reported that bias of a given https://ojs.luminescience.cn/ATM method is determined by elucidating the mean of one or more reference materials whose analyte concentration and subsequently obtaining their differences.…”

“…Ji et al using ICP-MS/MS method determined accurately 49 elements with the instrumental detection limits (LODs) of 0.0003 and 7.716mg/kg and quantification limits (LOQs) of 0.0011 and 25.7202mg/kg [4]. The method recovery was in the range of 85.82% to 104.98% while the relative standard deviations (RSDs) were lower than 10% and they revealed that the content of multielement in the discriminant and cultivation models of P. notoginseng were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm.…”