中药材三七中皂苷类成分的近红外光谱快速无损分析新方法

提出了用近红外漫反射光谱快速无损测定三七中皂苷类成分的新方法采用 HPLC分析了中药材三七固皂昔R_1,人参皂苷Hg_1,Rb_1和Rd的含量,用吸附树脂 比色法测定了三七总皂苷（PNS）的含量,共获得R_1,Bg_1,Rb_1,Rd,PNS的含 量范围分别为1,58-5．08,21,68-46.13,11.46-40.41粉．在3500-1100cm~(-1) 扫描样品,以交叉验证误差均方根（RMsECV）为指标,通过筛选,近红外波段和光 谱预处理方法．采用偏最小二乘算法建立了近红外光谱与5个组分PHLC分析值之间 的校正模型,预测了8个未知样本．R_1,Rg_1,Rb_1,Rd及PNS校正模型的RMSECV 分别为0．40,1.47,1.94,0RMSEP分别为0．53,3．15,2．14,0．70,9．03． 该方法快速无损,结果可靠,为中药材复杂体系中化学组分的测定提供了新的绿色 分析手段．     

反相高效液相色谱法同时测定三七药材中4种皂苷的含量

建立了以0．02％磷酸-乙腈为流动相，梯度洗脱反相高效液相色谱同时测定中药材三七中三七皂苷R1、人参皂苷Rg1、Rb1和Rd 4种皂苷的新方法。R1、Rg1、Rb1和Rd 4种皂苷的加样回收率分别为89．54％、90．08％、82．82％与84．46％；线性范围分别为0．244-6．110、0．820-20．510、0．396-9．890与0．260-6．500μg。测定了不同规格、部位和来源的三七药材里的4种皂苷R1、Rg1、Rb1和Rd。方法准确可靠，结果稳定，重现性好，可用于三七及其制剂的质控。     

高效液相色谱法测定竹节参中多种人参皂苷含量

建立了高效液相色谱法(HPLC)测定竹节参中人参皂苷Rg1、Re、Rb1、Rb2、Rg2、Rd含量的方法.运用二极管阵列检测器(DAD)峰纯度和光谱检索功能,结合保留时间定性,外标峰面积法定量.采用C18反相柱,以乙腈-水梯度洗脱测定了同一批竹节参总皂苷中人参皂苷Rg1、Re、Rd的含量分别为0.81%、0.15%、2.99%,回收率为93.46%~94.02%,含量及回收率的RSD均小于5%,该方法简便、灵敏,精密度及准确度在允许范围内,可作为竹节参皂苷提取物中多种人参皂苷的同时测定方法.     

泡沫浮选-固相提取分离富集三七提取液中的人参皂苷

采用泡沫浮选-固相提取联用法,分离富集三七中的R1,Rg1,Re,Rc,Rb2,Rb3,Rd和Rb1,并用液相色谱法测定其含量,检测灵敏度和选择性都有所提高.对泡沫浮选过程的载气流量、浮选时间、样品溶液pH值和固相提取柱的洗脱条件进行了优化.原人参二醉型皂苷R1,Rc,Rb2,Rb3,Rd和Rb1的回收率在85.0%9...     

泡沫浮选法分离富集三七中的4种人参皂苷

采用泡沫浮选法对三七提取液中的人参皂苷Rg1、Re、Rb1和Rd进行了分离富集,并用高效液相色谱法分别测定了含量.考察了浮选液浓度、浮选时间、浮选液pH值、氮气流速和电解质NaCl浓度对浮选效率的影响.结果表明:泡沫浮选法对4种皂苷均有较好的分离富集效果,尤其是对人参二醇型皂苷(Rb1,Rd)效果更为明显.当浮选液浓度为2.0 mg/mL,pH值为2～3,氮气流速为20 mL/min,浮选时间10 min,电解质氯化钠浓度0.20 mol/L,泡沫浮选效果最佳.     

在线二维柱切换高效液相色谱法同时测定牙膏中三七皂苷R1、人参皂苷Rg1、Re和Rb1

使用双梯度液相色谱系统紫外检测器,建立了二维液相色谱法全自动快速同时测定牙膏中三七皂苷R1、人参皂苷Rg1、Re和Rb1的含量。样品经超声提取后,以Syncronis C18为一维分析柱,ODS C18为二维分析柱,利用一维色谱柱完成三七皂苷R1和人参皂苷Rb1分离测定以及人参皂苷Rg1和人参皂苷Re的净化;利用二维色谱柱完成人参皂苷Rg1和人参皂苷Re的分析。一维分析和二维分析均以乙腈-水体系作为流动相,梯度洗脱,检测波长为203 nm,整个分析过程仅需30 min。三七皂苷 R1、人参皂苷 Rg1、Re 和 Rb1在0.5~200 mg/L范围内线性良好,相关系数R2分别为0.9994,0.9996,0.9995和0.9994,平均回收率均在86.4％~95.1％之间。本方法简便快速,测定结果准确可靠,可用于牙膏中三七皂苷R1、人参皂苷Rg1、Re和Rb1含量的测定。     

人参中人参皂苷的直接高压微波辅助降解

采用高效液相色谱-电喷雾质谱联用法测定了人参提取液中的人参皂苷. 考察了天然人参皂苷发生降解的条件, 同时研究了单体人参皂苷Rg1, Re, Rb1, Rc, Rb2和Rd的降解, 并对降解产物进行了分析. 结果表明, 随着提取压力的升高, 提取液中天然人参皂苷的含量逐渐减少, 同时产生多种次级人参皂苷. 当微波提取压力达到600 kPa, 提取时间为10 min时, 提取液中的主要天然人参皂苷达到完全降解, 次级人参皂苷Rg3含量达到最高. 在单体人参皂苷Rb1, Rc, Rb2和Rd的降解产物中均得到人参皂苷Rg3.     

高效液相色谱-飞行时间质谱联用法分析人参及人参皂苷与山楂配伍过程中的水解行为

利用高效液相色谱-飞行时间质谱联用的方法,分别对人参配伍山楂前后人参皂苷的变化进行分析,同时对人参皂苷Re、Rg1、Rb1、Rd与山楂配伍的水解规律进行系统研究,并与单独煎煮液、仿山楂配伍pH值煎煮液的水解产物进行比较,结果发现人参与山楂配伍后人参皂苷Rg1、Rb1含量明显减少,而人参皂苷Re、Rd、Rg2、Rg3、F2、Rh1含量明显增加,其中人参皂苷Re与山楂配伍后水解产物为人参皂苷20(R)-Rg2、20(S)-Rg2,仿山楂配伍pH值水解产物为人参皂苷20(R)-Rg2、20(S)-Rg2、Rg4、Rg6;人参皂苷Rg1与山楂配伍后水解产物为20(S)-Rh1、20(R)-Rh1,仿山楂pH值水解产物为20(S)-Rh1、20(R)-Rh1、Rh4、Rk3;人参皂苷Rb1与山楂配伍后水解产物为Rd、20(S)-Rg3,仿山楂pH值水解产物为F2、20(S)-Rg3;人参皂苷Rd与山楂配伍后水解产物为F2、20(S)-Rg3、20(R)-Rg3,仿山楂pH值水解产物为20(S)-Rg3、20(R)-Rg3。研究表明,不同人参皂苷和山楂配伍后与仿山楂pH值的水解产物并不相同,人参与山楂配伍改变了人参皂苷成分的种类及含量。本研究为临床方剂中人参与山楂配伍后成分的变化提供物质基础数据。     

支持向量机回归法在近红外光谱测定植物纤维原料中甲氧基含量中的应用

以26个植物纤维原料为实验材料,由20个样品作校正样品,采用径向基核函数方法对纤维原料中甲氧基含量与纤维原料样品近红外光谱进行支持向量机(SVM)回归建模.以所建SVM回归模型对6个纤维原料样品中甲氧基含量进行预测,回归模型的预测结果与采用改良的维伯克法确定的甲氧基含量的相关系数为0.977,预测样本集的标准偏差为0.43.将SVM回归模型的预测效果与PLS回归模型的预测结果进行比较,所建近红外光谱测定植物纤维原料中甲氧基含量的SVM回归模型可用于实际植物纤维原料样品的定量分析,且具有较好的分析效果.     

动态泡沫浮选法分离富集人参提取液中的二醇型人参皂苷

采用动态泡沫浮选法分离富集人参提取液中的二醇型人参皂苷, 用高效液相色谱法测定6种人参皂苷Rg1, Re, Rb1, Rc, Rb2和Rd的含量. 考察了浮选液pH值、电解质NaCl浓度、载气流量、料液浓度及料液流速对人参皂苷浮选率的影响, 确定了动态泡沫浮选的最佳条件, 并与溶剂提取法、溶剂浮选法以及静态泡沫浮选法进行了比较. 结果表明, 动态泡沫浮选法对二醇型人参皂苷Rb1, Rc, Rb2和Rd具有高富集效率, 回收率分别为93.3%, 98.6%, 96.9%和98.3%, 而对三醇型人参皂苷Rg1和Re的富集效率却很低, 回收率分别为4.8%和4.2%. 该法是分离纯化二醇型人参皂苷的一种简便有效的方法.     

Determination of Active Components in a Natural Herb with Near Infrared Spectroscopy Based on Artificial Neural Networks

The non-linear relationships between the contents of ginsenoside Rg1, Rb2, Rd and Panax notoginseng saponins(PNS) in Panax notoginseng root herb and the near infrared(NIR) diffuse reflectance spectra of the herb were established by means of artificial neural networks(ANNs). Four three-layered perception feed-for-ward networks were trained with an error back-propagation algorithm. The significant principal components of the NIR spectral data matrix were utilized as the input of the networks. The networks architecture and parameters were selected so as to offer less prediction errors. Relative prediction errors for Rg1, Rb1, Rd and PNS obtained with the optimum ANN models were 8.99%, 6.54%, 8.29%, and 5.17%, respectively, which were superior to those obtained with PLSR methods. It is verified that ANN is a suitable approach to model this complex non-linearity. The developed method is fast, non-destructive and accurate and it provides a new efficient approach for determining the active components in the complex system of natural herbs.     

预测毛细管区带电泳有效淌度的支持向量回归建模方法

提出预测毛细管电泳迁移行为的支持向量回归建模方法。以核苷为实际研究对象,利用正交试验获得的数据,结合二标记物技术,用支持向量回归算法建立毛细管区带电泳的柱温、电压、缓冲液浓度和pH值与3种核苷的有效淌度之间的相关模型。将其与偏最小二乘回归和人工神经网络方法相比较,结果表明所建模型的预测准确性优于后两者,适宜用于毛细管电泳迁移行为的预测。     

Determination of four active saponins of Panax notoginseng in rat feces by high-performance liquid chromatography

A method is developed for the determination of ginsenoside Rg1, Rb1, Rd, and notoginsenoside R1 of Panax notoginseng (PNS) in rat feces after oral and intravenous administration of total saponins of PNS. The fecal samples are treated with organic extraction and solid-phase extraction prior to high-performance liquid chromatography. The calibration curves for the four saponins are linear in the given concentration ranges. The precision of the method is in the range of 1.0-10.0% (relative standard deviation), and the accuracy is between 80.0% and 110%. The recoveries of this method are all over 75%. This method is successfully applied to the analyses of fecal samples of rats treated with PNS.     

Random subspace regression ensemble for near-infrared spectroscopic calibration of tobacco samples.

An ensemble, a model-independent technique based on combining several models for classification/regression tasks, allows us to achieve a high accuracy that is often not achievable with single models. Such combinations have gained increasing attention in many fields. This paper proposes the use of random subspace (RS)-based regression ensemble as an alternative method for near-infrared (NIR) spectroscopic calibration of tobacco samples. Because of the considerable reduction of variables in a random subspace, multiple linear regression (MLR) is used as the base algorithm and the method is therefore also referred to as RS-MLR. The overall performance of the proposed RS-MLR method is compared to those of partial least square regression (PLSR), kernel principal component regression (KPCR) and kernel partial least square regression (KPLSR). The results reveal that the RS-MLR method not only has a simple concept but also can produce a more parsimonious and more accurate calibration model than PLSR, KPCR and KPLSR, at a lower computational cost. Besides, we also found that the RS-MLR method is very appropriate for the so-called small sample problems and that the calibration models built by RS-MLR are less sensitive to overfitting.     

Classification and quantitation of finishing oils by near infrared spectroscopy

One of the steps in the manufacturing of synthetic fibres involves using finishing oils to ensure proper lubricity and adherence between fibres, and also the absence of static electricity. Choosing an appropriate oil and dosage are essential with a view to ensuring effective subsequent processing and use. The aim of this work was to develop a fast method for determining the different finishing oil content in acrylic fibres by use of near infrared spectroscopy (NIRS) in conjunction with partial least-squares regression (PLSR). The high similarity between the NIR spectra of finishing oils led us to assume that a single calibration model might allow determine the oil content. However, the inability to quantify accurately different finishing oils by using a sole calibration model, constrain to the prior classification of the fibres coated with the different finishing oils. Two different pattern recognition methods were used: supervised independent modeling of class analogy (SIMCA) and artificial neural networks (ANNs). However, the low contribution of the finishing oil to the NIR spectrum for the fibre sample, the high similarity between the NIR spectra for the different oils and the substantial contribution of the linear density of the acrylic fibre to the spectrum precluded correct classification by SIMCA; on the other hand, ANNs provided good results. By constructing appropriate PLSR models for the different types of finishing oils, these can be accurately determined in acrylic fibres.     

Determination of water in lubricating oils by mid- and near-infrared spectroscopy

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy were used to determine water in lubricating oils with high additive contents that introduce large errors in determinations by the Karl-Fischer and hydride methods. MIR spectra were obtained in the attenuated total reflectance (ATR) mode and exhibited water specific band absorption in the region 3100–3700cm^–1, which facilitated calibration. Multivariate (partial least-squares regression, PLSR) and univariate calibration (based on peak height and band area as independent variables) were tested. Both led to errors of prediction less than 5%. NIRS determinations rely on absorbance and first-derivative spectra, in addition to two different types of multivariate calibration,viz. inverse multiple linear regression (MLR) and partial least-squares regression (PLSR). Both approaches gave similar results, with errors of prediction less than 2%.For none of the proposed approaches any sample pretreatment for recording spectra is required.     

Quantitative analysis of two adulterants in Cynanchum stauntonii by near-infrared spectroscopy combined with multi-variate calibrations

Authentication of traditional Chinese medicines (TCMs) has become important because they can be adulterated with relatively cheap herbal medicines similar in appearance. Detection of such adulterated samples is needed because their presence is likely to reduce the pharmacological potency of the original TCM and, in the worst cases, the samples may be harmful. The aim of this study was to develop a rapid near-infrared spectroscopy (NIRS) analytical method which was supported by multi-variate calibration, e.g. partial least squares regression (PLSR) and radial basis function artificial neural networks (RBF-ANN), in order to quantify the TCM and the adulterants. In this work, Cynanchum stauntonii (CS), a commonly used TCM, in mixtures with one or two adulterants ?? two morphological types of TCM, Cynanchum atrati (CA) and Cynanchum paniculati (CP), were determined using NIR reflectance spectroscopy. The three sample sets, CS adulterated with CA or CP, and CS with both CA and CP, were measured in the range of 800?C2500 nm. Both PLSR and RBF-ANN calibration models provided satisfactory results, even at an adulteration level of 5 mass %, but the RBF-ANN models with better root mean square error of prediction (RMSEP) values for CS, CA, and CP arguably performed better. Consequently, this work demonstrates that the NIR method of sampling complex mixtures of similar substances such as CS adulterated by CA and/or CP is capable of producing data suitable for the quantitative analysis of mixtures consisting of the original TCM adulterated by one or two similar substances, provided the spectral data are interrogated by multi-variate methods of data analysis such as PLS or RBF-ANN.     

Non-invasive determination of ethanol, propylene glycol and water in a multi-component pharmaceutical oral liquid by direct measurement through amber plastic bottles using Fourier transform near-infrared spectroscopy

Fourier transform near-infrared (FT-NIR) spectroscopy was used to quantify rapidly the ethanol (34-49% v/v), propylene glycol (20-35% v/v) and water (11-20% m/m) contents within a multi-component pharmaceutical oral liquid by measurement directly through the amber plastic bottle packaging. Spectra were collected in the range 7302-12,000 cm-1 and calibration models set-up using partial least-squares regression (PLSR) and multiple linear regression. Reference values for the three components were measured using capillary gas chromatography (ethanol and propylene glycol) and Karl Fischer (water) assay procedures. The calibration and test sets consisted of production as well as laboratory batches that were made to extend the concentration ranges beyond the natural production variation. The PLSR models developed gave standard errors of prediction (SEP) of 1.1% v/v for ethanol, 0.9% v/v for propylene glycol and 0.3% m/m for water. For each component the calibration model was validated in terms of: linearity, repeatability, intermediate precision and robustness. All the methods produced statistically favourable outcomes. Ten production batches independent of the calibration and test sets were also challenged against the PLSR models, giving SEP values of 1.3% v/v (ethanol), 1.0% v/v (propylene glycol) and 0.2% m/m (water). NIR transmission spectroscopy allowed all three liquid constituents to be non-invasively measured in under 1 min.     

Rapid quality assessment of Gentianae Macrophyllae Radix based on near-infrared spectroscopy and capillary electrophoresis

The aim of this study was to establish a rapid quality assessment method for Gentianae Macrophyllae Radix (RGM) using near-infrared (NIR) spectra combined with chemometric analysis. The NIR spectra were acquired using an integrating sphere diffuse reflectance module, using air as the reference. Capillary electrophoresis (CE) analyses were performed on a model P/ACE MDQ Plus system. Partial least squares-discriminant analysis qualitative model was developed to distinguish different species of RGM samples, and the prediction accuracy for all samples was 91%. The CE response values at each retention time were predicted by building a partial least squares regression (PLSR) calibration model with the CE data set as the Y matrix and the NIR spectra data set as the X matrix. The converted CE fingerprints basically match the real ones, and the six main peaks can be accurately predicted. Transforming NIR spectra fingerprints into the form of CE fingerprints increases its interpretability and more intuitively demonstrates the components that cause diversity among samples of different species and origins. Loganic acid, gentiopicroside, and roburic acid were considered quality indicators of RGM and calibration models were built using PLSR algorithm. The developed models gave root mean square error of prediction of 0.2592% for loganic acid, 0.5341% for gentiopicroside, and 0.0846% for roburic acid. The overall results demonstrate that the rapid quality assessment system can be used for quality control of RGM.