State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, National Engineering Research Center for Applied Technology of Agricultural Biodiversity, College of Plant Protection, Yunnan Agricultural University, Kunming 650201, China.
Laboratory for Quality Control and Traceability of Food and Agricultural Products, Tianjin Normal University, Tianjin 300387, China.
Molecules. 2022 May 6;27(9):2982. doi: 10.3390/molecules27092982.
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 (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 and thus avoid the fraud of geographic identification.
是一种重要的功能性保健品,由于其广泛的药理活性,已在全球范围内使用,其主要可食用或药用部分是块根。然而,其产地鉴别技术仍需要进一步研究。本研究建立了一种 ICP-MS/MS 法准确测定 49 种元素的方法,仪器检测限(LOD)在 0.0003 至 7.716 mg/kg 之间,定量限(LOQ)在 0.0011 至 25.7202 mg/kg 之间,方法回收率在 85.82%至 104.98%之间,相对标准偏差(RSD)低于 10%。基于 (共 89 个混合样本)中多元素的含量,通过神经网络(预测准确率为 0.9259,ROC 曲线下面积为 0.9750)和支持向量机算法(均为 1.0000)准确确定了产地和种植模式的判别模型。本研究建立的判别模型可用于支持 和供应链的透明度和可追溯性,从而避免地理识别的欺诈。