We present a quantum-driven rigid-body molecular dynamics (Q-RBMD) framework that computes intermolecular electrostatics from a continuous, periodic charge density on a 3D grid reconstructed from frozen Hartree-Fock density matrices with a nonsingular nuclear contribution. Gaussian basis functions are rigidly transported with molecular translations and rotations, and periodic electrostatics is evaluated via GPU-accelerated FFT convolution to obtain energies, forces, and torques consistent with the underlying grid energy. Dispersion-repulsion is described by a Lennard-Jones term whose parameters are rebalanced against the density-based electrostatics using a simple, reproducible procedure. Numerical self-consistency is validated through electron-number diagnostics, systematic grid refinement, and NVE energy-conservation tests, enabling a posteriori quality control of discretization errors. For long NVT sampling, a geometry-triggered multiple-time-stepping (MTS) strategy reduces electrostatic update frequency while maintaining condensed-phase observables. Using rigid liquid water at 300 K as a controlled testbed, we report a compact set of condensed-phase observables to establish a conservation-validated baseline for continuous-density periodic electrostatics. This baseline employs rigid bodies and frozen densities, and therefore does not include intramolecular flexibility or polarization.

The rapid determination of total plant alkaloids (TPA) content in tobacco products by means of near infrared (NIR) spectroscopy is becoming conventional detection method in tobacco industry. An established NIR model of TPA is anticipated to be shared by other devices as long as possible not needing to update the model. Present study developed a three-step wavelength selection method SISCW (selecting important and stable characteristic wavelengths)to build a robust NIR calibration model of TPA to improve its transferability and to prolong its use period at many devices. For this, 292 flue-cured tobacco leaf samples from more than 10 regions of China, which were harvested in 2011-2013 and tested on the primary NIR device, were used to build the model. 77 samples harvested from 2011 to 2013 were applied to verify the transferability of the model on six secondary NIR devices. 180 samples harvested from 2014 to 2020 that were divided into 7 groups according to their growing years, were used to examine the long-term application capacity of the model on the seven NIR devices. The SISCW method integrates an image processing method of scale invariant feature transformation (SIFT) with analyzing standard deviation of the sample spectra (SDSS) and water absorption coefficients to select important and stable characteristic wavelengths (SISCW). The wavelengths selected by the SISCW were recorded as Uisc. The results indicated that the partial least square (PLS) calibration model of TPA based on Uisc (SISCW-PLS) performed well on both primary and the six secondary devices when it was employed to predict TPA of the 77 samples of 2011-2013. The model has run 7 years on the primary and 3 of the secondary devices from 2014 to 2020. This emphasizes the SISCW method has great potential for improving transferability and service-life of TPA calibration model.

Aiming at guiding agricultural producers to harvest crops at an appropriate time and ensuring the pesticide residue does not exceed the maximum limit, the present work proposed a method of detecting pesticide residue rapidly by analyzing near-infrared microscopic images of the leaves of Shanghaiqing (Brassica rapa), a type of Chinese cabbage with computer vision technology. After image pre-processing and feature extraction, the pattern recognition methods of K nearest neighbors (KNN), naïve Bayes, support vector machine (SVM), and back propagation artificial neural network (BP-ANN) were applied to assess whether Shanghaiqing is sprayed with pesticides. The SVM method with linear or RBF kernel provides the highest recognition accuracy of 96.96% for the samples sprayed with trichlorfon at a concentration of 1 g/L. The SVM method with RBF kernel has the highest recognition accuracy of 79.16~84.37% for the samples sprayed with cypermethrin at a concentration of 0.1 g/L. The investigation on the SVM classification models built on the samples sprayed with cypermethrin at different concentrations shows that the accuracy of the models increases with the pesticide concentrations. In addition, the relationship between the concentration of the cypermethrin sprayed and the image features was established by multiple regression to estimate the initial pesticide concentration on the Shanghaiqing leaves. A pesticide degradation equation was established on the basis of the first-order kinetic equation. The time for pesticides concentration to decrease to an acceptable level can be calculated on the basis of the degradation equation and the initial pesticide concentration. The present work provides a feasible way to rapidly detect pesticide residue on Shanghaiqing by means of NIR microscopic image technique. The methodology laid out in this research can be used as a reference for the pesticide detection of other types of vegetables.

The volatile components of honeysuckle were determined using hydro-distillation (HD), water extraction coupling rectification (WER), and water extraction coupling rectification with solvent extraction (WER-SE). Gas chromatography-mass spectrometry (GC-MS) and headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME/GC-MS) identified 127 compounds. There were 27 common compounds extracted by these methods. A heatmap of the 27 common compounds indicates that WER-SE and WER were complementary approaches. In addition, 59 compounds were detected for the first time in honeysuckle. The results of this study provide a reference for the determination of volatile components in plants.

Two case studies were conducted to verify calibration model transfer methods without standards by multi-step wavelength selection, using 3–7 near infrared spectrometers to predict ingredients in corn and total plant alkaloids (TPA) in tobacco leaves. Based on the characteristic wavelengths of Uc, which are selected using the scale-invariant feature transform (SIFT), this study advances two multistep wavelength selection methods by selecting wavelengths with high independence and a high standard deviation of the sample spectra (SDSS). The first method, SIFT-SDSS-CORX, selects important characteristic wavelengths Uc-i from Uc whose SDSS is greater than a threshold SDSS a crit . Subsequently, rx, the correlation coefficient matrix between spectral signals of Uc-i, is calculated, and only one wavelength is retained from those whose correlation coefficients exceed a threshold, rx a crit. The wavelength set Uc-i-rx, which is finally screened, is important and independent. In the second method, SIFT-CORX-SDSS, Uc-rx is first selected from Uc by retaining only one wavelength from those whose correlation coefficients between spectral signals of Uc exceed a threshold, rx b crit . Subsequently, the wavelengths Uc-rx-i with SDSS exceeding a threshold SDSS b crit are selected from Uc-rx. Near infrared spectroscopy calibration models for predicting protein and oil in corn and TPA in tobacco leaves were built using partial least squares regression (PLS) based on different wavelength sets of Uc, Uc-i, Uc-i-rx, Uc-rx, and Uc-rx-i, respectively. The latent variables used in the PLS models were determined by an accumulative contribution ratio over 99.9%. The results indicate that the PLS models built on Uc-i-rx and Uc-rx-i are effective on both primary and secondary units for corn and tobacco samples. This study utilises a three-step wavelength selection method to select highly independent, important, and characteristic spectral variables, thereby enhancing the robustness, simplicity, and interpretability of NIR) calibration models and facilitating their transfer to secondary units without standards.

该文使用基于光谱图像特征抽提的尺度不变特征变换(SIFT)的多步波长筛选方法建立了烟叶总还原糖(TRS)的近红外光谱(NIRS)稳健模型,实现了其在多台仪器的直接共享和长期应用.首先采用SIFT方法根据代表性主机样品光谱挑选特征光谱点集合Uc,然后从Uc中剔除样本光谱标准方差(SDSS)过低的点,挑选重要特征光谱点集合Uic,此两步波长筛选法简称为SIFT-SDSS.随后进一步从Uic中挑选对水分不敏感(Mois-ture-unsensitive,MUS)的波长点,得到重要且稳定的光谱点集合Uisc,此3步波长筛选法简称为SIFT-SDSS-MUS.从2011～2013年采集的292个主机烟叶样品中按TRS浓度区间选择80%样品作为建模集,建立不同波长集合下烟叶TRS的偏最小二乘回归(PLSR)校正模型.结果表明,基于SIFT-SDSS两步波长筛选的光谱点建立的TRS模型传递到6台从机预测另外77个2011～2013年样品的TRS时,所有从机样品的平均相对误差绝对值(MARE)均小于6%,满足企业内控要求.该模型对5台近红外仪上2014～2020年各年度样品、1台近红外仪上2014～2019年各年度样品的MARE均小于6%.而全波长模型及SIFT波长筛选方法所建模型在7台仪器上对多个不同年份下样品中TRS的MARE大于6%,难以实现长期应用.SIFT-SDSS-MUS方法所建TRS模型变量最少,但其模型传递能力和长期应用能力略逊于SIFT-SDSS模型.SIFT-SDSS所建TRS模型稳健性好、可解释性强、运算速度快,可在7台同型号近红外仪上直接共享、在6台仪器上连续应用至少6年,大大减少了烟叶TRS近红外光谱模型维护及传递的工作量.

为准确、快速测定人造革中甲酰胺残留量,本文分别采用甲醇、丙酮、乙醇、乙酸乙酯和乙腈作为提取溶剂超声萃取人造革,以水-乙腈为流动相,用高效液相色谱(HPLC)方法进行甲酰胺的定性及定量分析.结果表明乙腈为溶剂时,人造革产品中甲酰胺的提取率最高.本文建立的HPLC分析方法操作简便、灵敏度高.该方法在0.1～1.6mg/L范围内线性关系良好,线性相关系数为0.9999.甲酰胺平均回收率在83.4％～85.7％,相对标准偏差在0.28％～0.93％,方法检出限和定量限分别为0.11 mg/kg和0.36 mg/kg,比常规的气相色谱法(GC)、气相色谱-质谱法(GC-MS)方法的检出限要低.该方法简便易行且适用范围广,不仅适用于人造革中甲酰胺残留量的检测,还适用于橡塑保温材料、EVC地垫和瑜伽垫等发泡产品中甲酰胺残留量的检测.

目的:立足整体探讨代谢综合征(MS)中医证候与微观理化指标的相关性.方法:采用标准化四诊信息采集量表收集MS患者中医证候信息,选取出现频率＞10％的条目47项.同时,收集患者理化指标共21项.采用典型相关分析探讨MS中医证候信息与微观理化指标间的整体相关性.结果:共收集符合纳入标准对象450例,提取出4对MS中医证候与微观指标典型相关组合,相关系数分别为0.690、0.577、0.518、0.505(P＜0.01).其中,脉弦与身高、心烦与体质量、脉沉与BMI、嗜烟与体质量等呈较明显的正相关关系,并且部分微观理化指标的组合与中医证候具有一定相关性.结论:微观理化指标与中医证候之间具有较为明显的典型相关关系,立足整体,将微观指标与中医证候变量结合,可为全面客观、中西医结合辨证提供一定参考.

In order to realize calibration model transfer of near infrared (NIR) spectra without standards, scale invariant feature transform (SIFT) algorithm was applied to extract characteristic spectral points of NIR spectra in this study. Three sets of spectral points were selected by SIFT from the spectra of precision detection (SPD) of a radix scutellariae sample by continuously testing the sample three times. Aiming at obtaining high consistency of the three sets, the orthogonal table L9 (34) was used to optimize the parameters of SIFT. Basing on the NIR spectra of several representative radix scutellariae samples, a series of spectral point sets were screened by SIFT with the optimized parameters. Three methods of further treating the spectral points sets to optimize the combination of the spectral points and provided three spectral point sets, which were recorded as Ui, Uu and Uur, respectively. The partial least square (PLS) calibration models for predicting baicalin content of radix scutellariae were built on whole wavelengths, Ui, Uu and Uur at different number of latent variables (nLVs), respectively. Compared with other PLS models, the models of SIFTur-PLS built on Uur, which was obtained by taking union of the firstly selected spectral point sets, then eliminating the points with high deviance of SPD and those with high correlativity from the union, are most robust and always give lower or lowest prediction errors for both master and slave samples at many nLVs. It is a good way to filter stable, highly independent and characteristic spectral points to build robust PLS calibration models by combining SIFT algorithm with standard deviance analysis of SPD and correlative analysis. The models can be directly shared by the slave instrument, without needing transfer sets, and without requiring to correct the spectra of slave instruments or spectral calibration models.

为提升近红外光谱校准模型的稳健性和可共享性,通过比较仪器间差谱的标准偏差(SDDSI)和精密度测试光谱的标准偏差(SDPDS),筛选出仪器间具有稳定一致光谱信号的波长(SWCSS),考察了SWCSS-PLS模型在4台仪器间的传递效果,并与全光谱模型(WW-PLS)、分段直接标准化算法(PDS)校正后全光谱模型的传递结果进行了对比.结果表明,SWCSS-PLS模型传递到4台从机上后,78个外部验证样本的平均相对误差(MRE)为5.22％～5.60％,满足低于6％的企业内控要求,而WW-PLS模型和PDS校正后的WW-PLS模型的MRE分别为9.01％～11.68％和5.43％～8.69％.SWCSS-PLS模型较全光谱模型及PDS校正模型具有更好的稳健性和适应性.

以玉米中水分、蛋白质、脂肪和淀粉4种主要成分含量以及烟叶总植物碱的偏最小二乘近红外光谱(PLS-NIRs)模型传递为例,考察了模型中潜变量个数(nLVs)对模型传递误差的影响.研究发现,根据累积贡献率大于99.9％确定的玉米、烟叶样晶PLS-NIRs模型的nLVs分别为1和13,nLVs=l时建立的玉米模型对两台从机样品4个成分的预测值和主机预测值的重现性指标均满足国标要求;nLVs =13时建立的烟叶总植物碱模型经分段直接校正(PDS)后,可使4台从机样品的平均相对预测误差(MRE)小于6％.采用留一交叉验证或四折交叉验证确定的玉米、烟叶PLS-NIRs模型的nLVs分别为5～10,16与19,在这些nLVs下建立的玉米PLS-NIRs模型对从机样品的预测误差显著增大,超过许可的误差范围,且模型即使经PDS校正后,从机样品预测值与主机样品预测值的重现性指标大多不满足国标要求;nLVs ＞13时所建烟叶总植物碱PLS-NIRs模型的转移误差随nLVs增大而增大,且PDS校正后不能保证模型对所有从机样品的MRE小于6％.根据累积贡献率大干99.9％或接近99.9％为准则选取nLVs,可有效避免过拟合,提高NIRs模型的传递性能.

该研究利用一维尺度不变特征变换(SIFT)算法寻找烟叶近红外光谱(Near infrared spectroscopy,NIRS)的稳定特征波长,根据样品精密度测试光谱筛选的波长计算重现率和重现度,采用L9(33)正交表优化SIET算法中的相关参数,使重现率和重现度尽可能高.基于优化的参数和主机上10个代表性样品的光谱,筛选出10个稳定特征波长集合,以这些波长集合并集的光谱响应为自变量,采用偏最小二乘(PLS)方法构建烟叶总植物碱NIRS模型(简称SIFT-PLS).该模型直接传递到3台从机后,对3台从机样品总植物碱的平均相对预测误差(MRE)均满足小于6％的企业内控要求,而全光谱模型(WW-PLS)直接转移后仅1台从机的MRE满足要求,经分段直接校正(PDS)方法校正从机光谱后,WW-PLS模型也仅对1台从机的MRE小于6％.采用SIFT算法筛选稳定特征波长建立的NIRS模型可在3台从机直接共享,无需转移集,不需对从机光谱或光谱模型进行校正,实现了真正意义的无标样NIRS模型的直接转移.

Fingerprints of lipophilic components in the roots of Salvia miltiorrhiza and S.yunnanensis were analyzed by UPLC-DADand UPLC coupled with mass spectroscopy to evaluate the differences and similarities of the lipophilic components in the two kinds of herbs.The UPLC analysis of 18 batches of S.miltiorrhiza and 16 batches of S.yunnanensis was performed on a 25℃Thermo Accucore C_(18)column(2.1 mm×100 mm,2.6μm)by Shimadzu LC-20AD;mobile phase was 0.026%phosphoric acid(A)-acetonitrile(B)with gradient elution;flow rate was 0.4 m L·min~(-1);detection wavelength was set at 270 nm;injection volume was 2μL.The molecular structures of the lipophilic components were analyzed on a 25℃Thermo Accucore C_(18)column(2.1 mm×100 mm,2.6μm)by Thermo U3000 UPLC Q Exactive Orbitrap LC-MS/MS with a mobile phaseconsisting of 0.1%formic acid water(A)and 0.1%formic acidacetonitrile(B).The mass spectrometry was acquired in positive modes using ESI.There are 10 common peaks in the lipophilic components of S.miltiorrhiza.The similarity between the 16 batches of S.miltiorrhiza and their own reference spectra was greater than 0.942,and the average similarity was 0.973.There are 12 common peaks in the lipophilic components of S.yunnanensis.The similarity between the 18 batches of S.yunnanensis and their own reference spectra was greater than 0.937,and the average similarity was 0.976.The similarity between the reference chromatograms of S.miltiorrhiza and S.yunnanensis was only 0.900.There are three lipophilic components in S.yunnanensis,which are not found in S.miltiorrhiza,and one of which isα-lapachone.There is a lipophilic component in S.miltiorrhiza not found in S.yunnanensis,which may be miltirone.The two herbs contain 8 common lipophilic components including dihydrotanshinoneⅠ,cryptotanshinone,tanshinoneⅠ,tanshinoneⅡ_A,nortanshinone in which the content of tanshinoneⅡ_A,dihydrotanshinoneⅠand cryptotanshinone of S.yunnanensisis significantly lower than that of S.miltiorrhiza(P<0.01),and the contents of tanshinoneⅠand nortanshinone are significantly lower than that of S.miltiorrhiza too(P<0.05).There are significant differences in the types and contents of lipophilic components between the roots of S.miltiorrhiza and S.yunnanensis,and the similarity between the fingerprints of interspecies is much lower than that between the same species.Therefore,the roots of S.miltiorrhiza and S.yunnanensis are two kinds of herbs which are quite different in chemical compounds and compositions.

Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; information, rendering the constructed global and robust models applicable to both primary and secondary instruments.

建立了同时测定洋常春藤中绿原酸、隐绿原酸、芦丁、烟花苷、常春藤皂苷C、常春藤皂苷D、常春藤皂苷B和α-常春藤皂苷8种成分的超高效液相色谱法(UHPLC).以80％甲醇为溶剂,将药材粉末于85℃、料液比1∶100条件下水浴回流1h,制备供试品溶液;采用Agilent ZORBAX Eclipse Plus-C18(2.1mm×100 mm,1.8 μm)色谱柱,以乙腈-0.05％磷酸溶液为流动相进行梯度洗脱.结果表明,上述8种成分均获得良好的分离度;仪器精密度、方法重复性的相对标准偏差(RSD)均小于3.0％;样品溶液在室温条件下24 h内稳定;8种成分在对应质量浓度范围内线性关系良好(r≥0.999 5),检出限为0.40～10.58 μg·mL-1,定量下限为1.31～34.61 μg· mL-1,平均回收率为97.3％～ 108％,RSD(n =6)为0.51％～3.2％.该方法适用于洋常春藤中上述8种化学成分的定量分析.

Near infrared spectroscopy (NIR) was applied to discriminate the roots of salvia miltiorhiza Bunge (Danshen for short) and Salvia yunnanensis C. H. Wright (Zidanshen for short) by means of principal component analysis (PCA), improved and simplified K nearest neighbors (IS-KNN). Furthermore, an ultra-high performance liquid chromatographic (UHPLC) coupled with photodiode-array detector was developed for building fingerprints of lipophilic components of Danshen and Zidanshen, respectively. Basing on NIR information, both PCA and IS-KNN method classified the two kinds of Chinese medical herbs with 100% accuracy. The chromatographic fingerprints of the lipophilic components of Danshen and Zidanshen have 10 and 12 common peaks, respectively. Liquid chromatography coupled with mass spectroscopy (LC-MS-MS) was applied to identify these peaks. Among these, three small peaks in the fingerprints of Zidanshen are not found in Danshen, one of which was identified as alpha-lapachone, and the other two compounds were not yet identified; a small peak after tanshinone IIA in the fingerprints of Danshen was not found in Zidanshen, which was identified as miltirone. The two herbs have 10 common lipophilic components. The similarity between the two reference chromatograms of Zidanshen and Danshen is 0.902, but the mean similaritie between Zidanshen (or Danshen) fingerprints and its own reference chromatogram is 0.973 (or 0.976). The contents of main lipophilic components are significantly lower in Zidanshen than in Danshen (P < 0.01 or P < 0.05). The results indicate that the two Chinese medical materials are not only different in NIR spectra, but also different in species and quantities of lipophilic components. NIR spectra analysis can identify Danshen and Zidanshen rapidly and accurately. UHPLC coupled with MS analysis demonstrates the detail differences between the two herbs both in species and contents of their lipophilic components. (C) 2019 Elsevier B.V. All rights reserved.

Basing on the wavelengths with consistent and stable spectral signals between spectrometers, wavelength combinations were screened by different methods to obtain robust and simple near infrared spectra (NIR) calibration models that can be shared by slave spectrometers directly. Firstly, the wavelength set of Usc, at which the spectral signals between spectrometers are consistent and stable, was obtained by the method of screening the wavelengths with consistent and stable signals between spectrometers (SWCSS for short). Then, the wavelength set of Uscr whose spectral responses are correlated with dependent variables strongly was selected from Usc. Basing on Uscr, the methods of uninformative variable elimination (UVE), variable importance in projection (VIP) and selectivity ratio (SR) were applied to further screen optimal wavelength sets to obtain better NIR calibration models. These sets were recorded as UscrUVE, UscrVIP and UscrSR, respectively. The NIR partial least squares (PLS) models for predicting total alkaloids content of tobacco leaves were built on the three optimal wavelength sets, and named as UscrUVE-PLS, UscrVIP-PLS, UscrSR-PLS, respectively. Both UscrUVE-PLS and UscrVIP-PLS give satisfactory prediction errors for master and slave samples, and work better than the PLS model built on the whole wavelengths (WW-PLS) after piecewise direct standardization (PDS) calibration. The results show that further optimizing wavelength combinations based on consistent and stable spectral information cannot only simplify PLS models and improve the models’ efficiency, but also ensure the models’ accuracy when they are transferred to slave spectrometers. Wavelength selection based on the whole wavelengths without considering spectra consistency between spectrometers can improve the performance of the calibration models on the master spectrometer but cannot ensure the prediction accuracy of the slave samples.

AIM To investigate the effects of drying temperature,growing area and plucking time on hederacoside C,α-hederin from leaves of Hedera helix L..METHODS Three batches of H.helix leaves plucked in different time from two growing areas were dried in a vacuum oven to the constant weight at 60 ℃,70 ℃,80 ℃,90 ℃ and 105 ℃,respectively.Two saponins in the processed leaves were determined by HPLC.The powders of the processed H.helix leaves of different batches were mixed with proper ratios,which were determined by least squares optimization method with constraints.RESULTS The content of hederacoside C in the processed H.helix leaves of the three batches increased while that of α-hederin decreased with increasing temperature.The relative error between measured value and desired contents of hederacoside C and α-hederin in the mixed H.helix leaves was less than 5.5％.CONCLUSION The effects of three factors on the content of two saponins in the H.helix leaves are in the order of drying temperature,growing area and plucking time.Mixing processed H.helix leaves of different quality statues reasonably can control the contents of two saponins in a certain range.

The change of measurement environment and spectral signals of spectroscopy instruments can lead to big error in the process of transferring the model of near infrared (NIR) spectra to slave instruments. Present work advanced a novel method of transferring NIR calibration models without standard samples basing on Screening stable and consistent wavelengths (SSCW). The method firstly eliminates the wavelengths at which the standard variation of difference spectra between master and slave instruments (SDDSI) is bigger than the standard variance of precision detection spectra (SDPDS) to screen the wavelengths at which the spectral signals of different instruments are consistent well. And then deletes the wavelengths at which the values of SDPDS are too high. Thus the spectral signals at the selected wavelengths are stable and consistent between instruments to build the NIR calibration models. Two datasets are applied to validate the method of SSCW : one is consisted of 72 radix scutellaria samples, whose NIR spectra are measured on two different types of NIR spectroscopy instruments, another one is consisted of 80 corn samples, whose NIR spectra measured on three NIR instruments are available online. The results show that the overall prediction effect of SSCW for salves' samples is much better than that of the full-wavelength PLS model. In most cases, the root mean square of error prediction (RMSEP) of salve samples of SSCW is lower than those given by the piecewise direct standardization (PDS) correction, and that of the other model transfer methods without standard samples. Therefore, the method of SSCW is of fewer parameters, robust and can maintain good prediction performance after transferring to salve instruments. It can be used to realize the sharing of PLS calibration model among spectroscopy instruments.

目的:建立洋常春藤及其提取物中常春藤萜苷C、α-常春藤素、常春藤萜苷D、常春藤皂苷B等4个主要皂苷成分一测多评定量检测法.方法:以常春藤萜苷C为参照成分,建立该成分与α-常春藤素、常春藤萜苷D、常春藤皂苷B的相对校正因子,采用校正因子计算这3个常春藤皂苷的含量.考察不同品牌色谱仪、色谱柱、检测波长、柱温、流速下各相对校正因子的变化,评价各校正因子的耐用性.分别采用外标法和所建立的一测多评法测定23批洋常春藤及30批提取物中4个皂苷类成分含量并比较两者的差异,以评价“一测多评”法的准确性.结果:23批洋常春藤中α-常春藤素、常春藤皂苷B、常春藤萜苷D的“一测多评”法测得含量与外标法结果之比分别为(100.56±0.29)％、(99.37±0.32)％、(99.52±0.18)％,30批提取物中相应成分2种方法的比值分别为(100.86±0.87)％、(99.84±0.02)％、(99.84±0.03)％;药材及提取物中2种方法所对应上述皂苷成分的相对偏差RE分别在0.10％～3.28％、0.12％～1.53％、0.11％～0.83％之间;3个常春藤皂苷的相对校正因子在不同仪器、不同色谱柱、不同检测波长、不同柱温及不同流速下的RSD分别为1.1％～3.6％、1.6％～2.0％、0.47％～3.1％、1.5％～2.9％与0.55％～2.5％,耐用性良好.结论:本文所建常春藤皂苷成分同时定量的一测多评方法准确度高、重复性及耐用性好,可用于洋常春藤及其提取物质量的常规检验与快速分析.