一种测定中药复方制剂中活性成分的计算分析方法

提出了一种测定中药复方制剂中活性成分含量的计算分析新方法。该方法将主 成分分析和广义秩消因子分析集成用于处理HPLC／DAD数据，并改进了传统的IND因 子指示函数，适用于定量计算分析复杂混和物体系中的待测成分。将其用于测定复 方丹参制剂中三七皂苷R1的含量，计算分析结果表明，本方法明显优于广义秩消因 子法，同时证明IND函数的改进提高了计算分析准确度。     

主成分回归一紫外光度法同时测定盐酸西替利嗪和苯甲酸钠

提出了一种用紫外光度法同时测定盐酸西替利嗪和苯甲酸钠的方法.在pH 4.56的B-R缓冲溶液中对盐酸西替利嗪和苯甲酸钠两组分混合溶液进行吸光度测定,所得的重叠光谱数据用主成分回归法(PCR)和偏最小二乘法(PLS)等化学计量学方法进行处理,结果表明主成分回归(PCR)的预报误差最小.对实际样品进行测定,回收率为88.8...     

主成分分析-支持向量回归建模方法及应用研究

将主成分分析(PCA)用于近红外光谱的特征提取,并与支持向量回归(SVR)相结合,实现了主成分分析-支持向量回归(PCA-SVR)用于近红外光谱定量分析的建模方法。与单纯的SVR方法相比,不仅提高了运算速度,而且提高了模型的预测准确度。将PCA-SVR方法用于烟草样品中总糖和总挥发碱含量的测定,所得结果的预测均方根误差分别为1.323和0.0477;回收率分别为91.8%~112.6%和88.9%~120.2%。     

主成分分析在近红外定量分析校正集样本优选中的应用

将主成分分析方法用于近红外定量分析校正集样本的优选,提出一种根据样本光谱第一主成分得分优选校正集样本的方法,并使用本方法从418个烟草样本中优选得到约105个样本的校正集。通过对烟碱成分实际建模与外部验证,与随机法、含量梯度法两种校正集样本挑选方法的性能进行了对比。结果表明,本方法既克服了随机法挑选样本代表性不足的风险,又可避免含量梯度法必须测定所有样本成分含量而造成的人力物力消耗,具有无需编程、操作简单、易于推广的特点。     

一类用于多元光度分析的小波基主成分回归法

通过将变尺度小波分解滤噪与特征信息提取相结合,提出一类新的多元光度分析算法－小波基主成分回归（ＰＣＲＷ）方法。该法可有效地减小主成分向量残留噪声所引 误差显著提高多元校正准确性。将其用于分析氯霉素、醋酸对塞米松、尼泊金乙脂体系、得到满意的回收率,与ＰＣＲ法相比,分析结果的总平均相对误差从３．３８％降低到０．８３％。     

主成分回归-紫外光度法同时测定食用香料麦芽酚和乙基麦芽酚

报道了一种快速、简便的同时测定食用香料麦芽酚、乙基麦芽酚光度法,方法基于在pH2.87的B R缓冲溶液中对麦芽酚和乙基麦芽酚两组分混合溶液进行光度测定,所得的重叠波谱数据用主成分回归法(PCR)、经典最小二乘法(CLS)和偏最小二乘法(PLS)等化学计量学方法进行处理,结果表明主成分回归法(PCR)的预报误差最小。对样品进行测定,获得了较好的定量分析结果。麦芽酚和乙基麦芽酚的线性范围均为1.0～20.0mg·L-1;检出限分别为0.4347和0 5589mg·L-1。     

小波变换-主成分回归分光光度法同时测定邻苯二酚、间苯二酚和对苯二酚三组分

根据小波变换具有将信号分频的特点,本文提出了将小波变换与主成分回归(PCR)相结合的一种多元校正算法。该法能更有效地去除噪声,提取有用信息,并将其用于分析邻苯二酚、间苯二酚、对苯二酚三组分体系。实验结果表明,本法比直接用主成分回归处理效果好,得到的平均相对误差从2．24％降低到1．19％。     

主成分分光光度法中主成分的选择

主成分分析是全光谱分析度分析中常用的校正方法。本文提出第一主成分并不是与因最线性相关的主成分。为此，我们利用扫描算法众多主成分中选择与因变量（浓度）最相关的主成分，从而使计算结果更准确可信。本文还对单因变量和多因变量两种情况下主成分选择的统计量进行了讨论。     

偏最小二乘-一阶导数分光光度法测定苯甲酸复方制剂中苯甲酸及水杨酸的含量

复方苯甲酸涂剂主要成分是苯甲酸和水杨酸,其在临床上常用于治疗皮肤疾患,由于紫外吸收光谱重叠较严重,用光度法测定时,彼此相互干扰。计量学方法的兴起为多组分的同时测定提供了一种不需分离的新技术。偏最小二乘法可以最大程度地从校正试样中提取信息,具有较强的数据处理能力,因而优于主成分回归等其它计量学方法。本工作将导数光谱的高灵敏度与偏最小二乘法的优良解析性能相结合,提出了同时测定两组分的偏最小二乘导数分光光度法,方法用于复方苯甲酸制剂中两组分的测定,对加和性不太好的体系,也能得到较为满意的结果。     

化学计量学-分光光度法同时测定饲料中的诺氟沙星、氧氟沙星和洛美沙星

在pH1.81的Britton-Robinson(B-R)缓冲溶液中对诺氟沙星、氧氟沙星和洛美沙星三组分混合溶液进行光度测定,所得的重叠光谱数据用经典最小二乘(CLS),主成分回归(PCR),偏最小二乘(PLS)和径向基人工神经网络(RBF-ANN)方法处理和分析,结果表明RBF-ANN对合成样中三种药物浓度的预报结果...     

小波变换结合多维偏最小二乘方法用于近红外光谱定量分析

将小波变换和多维偏最小二乘法相结合用于近红外光谱定量校正模型的建立。首先将原始光谱进行小波变换分解，得到系列小波细节系数，通过选取一组受外界因素少、信息强的小波系数组成三维光谱阵，然后再采用多维偏最小二乘法建立校正模型。实验结果表明，该方法所建近红外校正模捌的预测能力更强，并更具稳健性。     

集成因子分析与卡尔曼滤波的计算光度分析方法

通过将因子分析与卡尔曼滤波法集成,提出一种多元光度分析新算法。该法运用因子分析法提取多组分化学体系量测数据矩阵中的因子构建卡尔曼滤波校正模型,以克服卡尔曼滤波法依赖于纯物质光谱建模的内在局限性。数值仿真及分析实验结果均表明,该法能获得准确可靠的计算分析结果,是一种有效的计算光度分析新算法。     

Strategies for constructing the calibration set for a near infrared spectroscopic quantitation method

Three strategies for the construction of calibration sets have been tried, with the objective to develop and to validate a NIR quantitation method.The first two approaches consist of the use of two types of samples, named: samples of laboratory obtained by mixing the ingredients that compose the drug, and doped samples obtained by under- and over-dosed production samples. In order to improve the prediction results, production samples have been added to each calibration model. The ensuing models were validated with a view to determine their fitness for purpose. However, spectral differences between the laboratory samples and doped samples resulted in spurious predictions in quantifying samples of one type using the model developed from samples of the other.Such differences were studied in depth and a third procedure has been proposed, based on a calibration model constructed with an unique type of sample (laboratory sample) for later to correct it with a few doped samples. This corrected model has a good predictive ability on production samples.     

Optimized sample-weighted partial least squares

In ordinary multivariate calibration methods, when the calibration set is determined to build the model describing the relationship between the dependent variables and the predictor variables, each sample in the calibration set makes the same contribution to the model, where the difference of representativeness between the samples is ignored. In this paper, by introducing the concept of weighted sampling into partial least squares (PLS), a new multivariate regression method, optimized sample-weighted PLS (OSWPLS) is proposed. OSWPLS differs from PLS in that it builds a new calibration set, where each sample in the original calibration set is weighted differently to account for its representativeness to improve the prediction ability of the algorithm. A recently suggested global optimization algorithm, particle swarm optimization (PSO) algorithm is used to search for the best sample weights to optimize the calibration of the original training set and the prediction of an independent validation set. The proposed method is applied to two real data sets and compared with the results of PLS, the most significant improvement is obtained for the meat data, where the root mean squared error of prediction (RMSEP) is reduced from 3.03 to 2.35. For the fuel data, OSWPLS can also perform slightly better or no worse than PLS for the prediction of the four analytes. The stability and efficiency of OSWPLS is also studied, the results demonstrate that the proposed method can obtain desirable results within moderate PSO cycles.     

OWAVEC: a combination of wavelet analysis and an orthogonalization algorithm as a pre-processing step in multivariate calibration

In an spectroscopic context, when a calibration model based on partial least squares is developed to predict a response, it is often the case that a high percentage of variation in the data explained by the first latent variable is not accompanied by an equally high percentage of variation in the studied response. The addition of more components can slowly improve the calibration model, but with negative effects on the robustness and interpretability of the final model. To solve this problem, several pre-processing methods have been proposed to remove only a portion unrelated to the studied response from the spectral matrix.Moreover, the need for efficient compression methods is increasingly important due to the large size of the data currently collected. In this sense, discrete wavelet transform has proven that it can achieve good compression without losing relevant information when used on individual signals.This paper introduces a new pre-processing method, orthogonal wavelet correction (OWAVEC) that tries to lump together two important needs in multivariate calibration: signal correction and compression. The new method has been tested on a set of diesel fuels using viscosity as variable response, and its results have been compared not only with those obtained from original data but also with those provided by other correction methods. The first practical results are encouraging, as the method generates considerably better calibration models compared to the model developed from raw data and provides results as least so good as other orthogonal correction methods.     

Three-way Resolution of the Overlapping Ultrahigh-performance Liquid Spectrochromatograms for the Analysis of a Quaternary Mixture Using Parallel Factor Analysis

This paper presents a new application of three-way parallel factor analysis (3W-PARAFAC) model to the coeluting spectrochromatograms for the quantitative resolution of a quaternary mixture system consisting of paracetamol, propyphenazone, and caffeine with aspirin as an internal standard. Spectrochromatograms of calibration standards, validation sets, and unknown samples were recorded as a function of retention time and wavelength in the range of 0.0–2.5?min and 200–400?nm, respectively, using ultra-performance liquid chromatography with photodiode array detection (UPLC-PDA). Three-way UPLC-PDA data array X (retention time?×?wavelength?×?sample) was obtained from the data matrices of the spectrochromatograms. 3W-PARAFAC decomposition of three-way UPLC-PDA data array provided three loading matrices corresponding to chromatographic mode, spectral mode, and relative concentration mode. Quantitative estimation of paracetamol, propyphenazone, and caffeine in analyzed samples was accomplished using the relative concentration mode obtained by the deconvolution of the UPLC-PDA data set. The validity and ability of 3W-PARAFAC model were checked by analyzing independent test samples. It was observed from analyses that 3W-PARAFAC method has potential to uniquely resolve strongly overlapping peaks of analyzed compounds in a spectrochromatogram, which was obtained under experimental conditions consisting of the lower flow rate, short run time, and simple mobile phase composition. The proposed three-way chemometric approach was successfully applied to the simultaneous quantification of paracetamol, propyphenazone, and caffeine in tablets. Experiments showed that the determination results were in good agreement with label amount in commercial pharmaceutical preparation.     

基于局部最小二乘支持向量机的光谱定量分析

提出了一种基于局部最小二乘支持向量机(LSSVM)的回归方法,以克服待测参数和光谱数据间的非线性。本方法首先通过欧式距离选取局部训练样本子集,然后利用该子集建立LSSVM校正模型。由于每个测试样本建模时要选取不同的训练样本,因此提出相对距离的概念用来改进高斯核函数,使LSSVM的参数对于不同的训练样本具有自调整功能。针对一批汽油样本的实验结果表明,本方法的预测精度优于常见的局部线性建模方法和全局建模方法。     

Improving the performance of hollow waveguide-based infrared gas sensors via tailored chemometrics

The use of chemometrics in order to improve the molecular selectivity of infrared (IR) spectra has been evaluated using classic least squares (CLS), partial least squares (PLS), science-based calibration (SBC), and multivariate curve resolution-alternate least squares (MCR-ALS) techniques for improving the discriminatory and quantitative performance of infrared hollow waveguide gas sensors. Spectra of mixtures of isobutylene, methane, carbon dioxide, butane, and cyclopropane were recorded, analyzed, and validated for optimizing the prediction of associated concentrations. PLS, CLS, and SBC provided equivalent results in the absence of interferences. After addition of the spectral characteristics of water by humidifying the sample mixtures, CLS and SBC results were similar to those obtained by PLS only if the water spectrum was included in the calibration model. In the presence of an unknown interferant, CLS revealed errors up to six times higher than those obtained by PLS. However, SBC provided similar results compared to PLS by adding a measured noise matrix to the model. Using MCR-ALS provided an excellent estimation of the spectra of the unknown interference. Furthermore, this method also provided a qualitative and quantitative estimation of the components of an unknown set of samples. In summary, using the most suitable chemometrics approach could improve the selectivity and quality of the calibration model derived for a sensor system, and may avoid the need to analyze expensive calibration data sets. The results obtained in the present study demonstrated that (1) if all sample components of the system are known, CLS provides a sufficiently accurate solution; (2) the selection between PLS and SBC methods depends on whether it is easier to measure a calibration data set or a noise matrix; and (3) MCR-ALS appears to be the most suitable method for detecting interferences within a sample. However, the latter approach requires the most extensive calculations and may thus result in limited temporal resolution, if the concentration of a component should be continuously monitored.     

Kinetics independent spectrometric analysis using non-linear calibration modelling and exploitation of concentration gradients generated by a flow-batch system for albumin and total protein determination in blood serum

An automatic method for kinetics independent spectrometric analysis is proposed in this study. It uses a non-linear calibration model that explores concentration gradients generated by a flow-batch analyser (FBA) for the samples, dye, and the single standard solution. The procedure for obtaining the gradients of the dye and standard solution is performed once at the beginning of analysis. The same procedure is applied thereafter for each sample. For illustration, the proposed automatic methodology was applied to determine total protein and albumin in blood serum by using the Biuret and Bromocresol Green (BCG) methods. The measurements were made by using a laboratory-made photometer based on a red and green bicolour LED (Light-Emitting Diode) and a phototransistor, coupled to a “Z” form flow cell. The sample throughput was about 50 h⁻¹ for albumin and 60 h⁻¹ for total protein, consuming about 7 μL of sample, 2.6 mL of BCG and 1.2 mL of biuret reagents for each determination. Applying the paired t-test for results from the proposed analyser and the reference method, no statistic differences at 95% confidence level were found. The absolute standard deviation was usually smaller than 0.2 g dL⁻¹. The proposed method is valuable for the determination of total protein and albumin; and can also be used in other determinations where kinetic effects may or may not exist.     

Sucrose as chiral selector for determining enantiomeric composition of phenylalanine by UV-vis spectroscopy and chemometrics

The determination of enantiomeric composition by partial least squares(PLS) modeling of UV-vis spectral data was investigated for samples of phenylalanine(phe) using sucrose as a chiral auxiliary.And a new data preprocess method,reference band normalization,was introduced to eliminate the spectral variations due to the changes of total concentration of phe.The determination coefficient(R~2) and the standard error of calibration set(SEC) of 13 standard samples are 0.9987 and 0.0128 respectively.The standard error of validation set(SECV) of 7 validation samples is 0.0049.The standard error of predict(SEP) of 6 blind samples for evaluating the robustness of the model is 0.0366.The regression model is robust to determine enantiomeric composition when total concentration varied.It is demonstrated that the reference band normalization is a convenient method of compensating for variations in total concentrations without knowing that in advance.