无患子果皮皂苷含量近红外光谱检测研究

Estimation of Pericarp Saponin Concent in Sapindus mukorossi by Using Near Infrared Reflectance Spectroscopy

为建立无患子(Sapindus mukorossi Gaertn.)果皮皂苷含量的快速检测模型,结合高效液相色谱法(HPLC),对采自浙江省天台县145份无患子果皮的皂苷含量进行检测。并依据近红外吸收光谱与HPLC测定数据的相关性,建立2个果皮皂苷含量估测模型,并对模型的准确性进行预测和评价。结果表明:A组(完整果皮)和B组(粉末状果皮)光谱数据分别基于'SNV+1stderivative'和'MSC+1st derivative'预处理法并利用PLS构建的光谱模型表现最佳,模型可靠性最强,预测精度最高,其中B组光谱数据建立的模型预测效果明显优于A组。基于A组和B组建立的模型与HPLC法的测量结果相关系数分别为0.654和0.993,预测标准偏差分别为0.982和0.294。以B组样品建立的模型基本可以代替HPLC法使用,而以A组样品建立的模型可用于测定精度要求不高、比较珍贵或样品量较少的样品。

In the paper we aimed to provide a rapid,simple and accurate model for estimating of pericarp saponin concent in Sapindus mukorossi,based on the near infrared reflectance spectroscopy（ NIRs） and high performance liquid chromatography（ HPLC）. And 145 samples were collected from Tiantai County of Zhejiang Province. Based on the analysis data of HPLC and NIRs,two models of saponin concent determination were established,and the accuracy of the models were evaluated. The spectroscopic data of group A（ the whole fruit） and group B（ the powder pericarp） were obtained from the pretreatments of ＂ SNV ＋ 1^stderivative＂ and ＂ MSC ＋ 1^stderivative＂. The results showed that the spectral models based on this data and established by PLS,were the best and the most reliable. They also had prediction precision. The model by group B was better than the model by group A. The models with groups A and B could reach 0. 654 and 0. 993 of the correlation coefficient between the prediction and the HPLC measured values. The standard deviations of prediction were 0. 982 and 0. 294,respectively. Thus,the model with the whole pericarp could be used to measure low quantity and precious samples with the relative low accuracy. The model with powder pericarp was very proper and could directly replace HPLC method.