小波变换-偏最小二乘算法及其在复方甲硝唑注射液分析中的应用

提出了结合小波变换的偏最小二乘法（ＷＰＬＳ）,即先对光谱信号进行小波变换,去除噪声,再用偏最小二乘法对多组分同时测定。将该法用于模拟体系及复方甲硝唑注射液体系,结果表明,该法优于偏最小二乘法。     

非线性偏最小二乘法在速效伤风胶囊紫外分光光度分析中的应用

本文介绍了非线性偏最小二乘法的基本原理及算法。以速效伤风胶囊的紫外分光光度分析为例,扑热息痛、咖啡因、扑尔敏、人工牛黄四组分的测定结果满意,且明显好于线性偏最小二乘法。本法为多组分混合体系的分光光度分析提供了更为理想的新途径。     

偏最小二乘法用于铽、钍、铒的同时测定

偏最小二乘法用于紫外可见光度分析测定多组分间相互作用较显著的铽、钍、铒稀土体系得到较好的结果.考查了波长和间隔选取对计算精度的影响,与卡尔曼滤波法对比,证明了本法的优越性和广泛适用性.     

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。     

同时测定合成样中铜钴镍钒含量的分光光度法--主成分回归法和偏最小二乘法比较

研究了主成分回归和偏最小二乘方法在多组分分光光度法分析中的应用,以５－Ｂｒ－ＰＡＤＡＰ（２〔５－溴－２－吡啶）－偶氮〕５－二乙氨基苯酚）为显色剂,ＯＰ（聚乙二醇辛基苯基醚）作为增溶增稳剂,在ｐＨ＝３．６的条件下,用主成分回归及偏最小二乘分光光度法同时测定了合成样中的铜、钴、镍、钒４组分含量,测定相对误差在－６．００％～４．００％之间。实验证明,对于加和性不好的体系偏最小二乘分光光度法要优于主成分回     

模糊聚类—催最小二乘回归光度法同时测定地质样品中金铂钯铑的研究

将模糊聚类分析与偏最小二乘法相结合,对地质样品中吸收光谱严重重叠的贵金属多组分体系进行解析,较好地解决了计算光度分析中校准模型的优化问题,使计算结果的精度得到了显著提高,分析结果的相对误差小于１０％,标准偏差小于０．６７,明显优于一般偏最小二乘（ＰＬＳ）法。采用小铳试金法消除样品中贱金属元素的干扰,其回收率为９２％￣１０７％,标准偏差为０．１０￣０．６７;相对标准偏差为４．７％￣１１．０％,并对影     

模式识别技术用于果酸混合物分析

研究了偏最小二乘法和人工神经网络法用于水果的紫外可见多组分光度分析。当混合物中诸组分存在相互作用，光谱加和性受到 扰动时，ＰＬＳ法和ＡＮＮ法用于混和物的分析仍能得到较满意的结果。     

紫外分光光度法-局部PLSR同时测定三种人造甜味剂

本文报道了一种简便、快速、准确的同时测定三种人造甜味剂安赛蜜、阿斯巴甜和糖精钠的方法。方法基于在pH为3.21的盐酸溶液中对安赛蜜、阿斯巴甜和糖精钠三组分混合溶液进行紫外光度测定,所得重叠光谱数据分别用偏最小二乘回归法（partial least squares regression,PLSR）、特征峰结合PLSR法和特征峰结合局部偏最小二乘回归法（local partial least squares regression,LPLSR）进行处理。结果表明,选取特征波段的峰值作为自变量,采用4个局部样本做拟合的预报误差最小,总相对偏差仅为3.05%。对果汁样品进行测定,获得了很好的定量分析结果。安赛蜜、阿斯巴甜和糖精钠的定量线性范围分别为1.0 - 30.0 mg/L、1.0 - 10.0 mg/L和1.0 – 10.0 mg/L。     

最小一乘稳健多元分析校正

本文论述最小一乘求解的多元分析校正算法，探讨了最小一乘较常规最小二乘法及其他隐健算法的优点。用计算机数值模拟及实际多组分光谱体系对方法进行了检验，展示了最小一乘法在分析化学计量学中实际应用的可行性。     

岭回归法解析病态光谱体系数据及其在复方维生素B分析中的应用

当多组分体系的吸收光谱出现严重重叠而构成病态体系时，用以最小二乘估计的各种化学计算学方法算其含量时，往往导致偏差，本文用岭回归法处理复方维生素Ｂ四组分体系的光谱数据，所得分析结果表明优于最二乘法。     

偏最小二乘法—流动注射pH梯度技术用于同时测定铜和钴

以PAR作显色剂,用流动注射pH梯度技术测定多个不同pH下的吸光度,以偏最小二乘法建立校正模型并预测,对Cu~(2+)、Co~(2+)二元素进行了同时测定,其计算结果优于主成分回归及多元线性回归法。     

Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy

Visible and short-wave near infrared (Vis/SWNIR) spectroscopy combined with chemometrics was investigated for the fast determination of soluble solids content (SSC) and pH values of rice vinegars. Two hundred and twenty-five samples (45 for each variety) were selected randomly for the calibration set, whereas, 75 samples (15 for each variety) for the validation set, and the remaining 25 samples for the independent set. After some preprocessing, partial least squares (PLS) analysis was implemented for calibration models with different wavelength bands including visible, SWNIR and Vis/SWNIR regions. The best PLS models were achieved with Vis/SWNIR (550–1000 nm) region. Furthermore, different latent variables (5, 6, 7, 8 LVs) were used as inputs of least squares-support vector machine (LS-SVM) to develop the LV-LS-SVM models with grid search technique and RBF kernel. The optimal models were obtained with 6 LVs and they outperformed PLS models. Moreover, effective wavelengths (EWs) were selected according to regression coefficients. The EW-LS-SVM models were developed and an excellent prediction precision was achieved, and the effectiveness of the EWs was also validated. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for the best prediction by EW-LS-SVM were 0.999, 0.189 and 0.051 for SSC, whereas 0.999, 0.008 and −1.7 × 10⁻³ for pH, respectively. The overall results indicated that Vis/SWNIR spectroscopy combined with LS-SVM could be applied as a high precision and fast way for the determination of SSC and pH values of rice vinegars.     

偏最小二乘回归用于近红外光谱分析的稳健策略

偏最小二乘法（PLS）在近红外光谱（NIR）定量分析中占有重要地位,但预测结果往往容易受到样本分组和奇异样本等因素的影响,稳健性不强。多模型PLS (EPLS）方法在模型稳健性上得到提高,然而它无法识别样本中存在的奇异样本。为了同时提高模型的预测准确性和稳健性,本文提出了一种根据取样概率重新取样的多模型PLS方法,称为稳健共识PLS（RE-PLS）方法。该方法通过迭代赋权偏最小二乘法(IRPLS)计算样本回归残差得到每个校正集样本的取样概率,然后根据样本的取样概率来选择训练子集建立多个PLS模型,最后将所有PLS模型的预测结果平均作为最终预测结果。该方法用于两种不同植物样品的近红外光谱建模,并与传统的PLS及EPLS方法进行比较。结果表明该方法可以有效的避免校正集中奇异样本对模型的影响,同时可以提高预测精确度和稳健性。对于含有较多奇异样本的,复杂近红外光谱烟草实际样本,利用简单PLS或者EPLS方法建模预测效果不是很理想,而RE-PLS凭借其独特优势则有望在这种复杂光谱定量分析中得到广泛的应用。     

Near-infrared spectroscopy quantitative determination of Pefloxacin mesylate concentration in pharmaceuticals by using partial least squares and principal component regression multivariate calibration

Pefloxacin mesylate, a broad-spectrum antibacterial fluoroquinolone, has been widely used in clinical practice. Therefore, it is very important to detect the concentration of Pefloxacin mesylate. In this research, the near-infrared spectroscopy (NIRS) has been applied to quantitatively analyze on 108 injection samples, which was divided into a calibration set containing 89 samples and a prediction set containing 19 samples randomly. In order to get a satisfying result, partial least square (PLS) regression and principal components regression (PCR) have been utilized to establish quantitative models. Also, the process of establishing the models, parameters of the models, and prediction results were discussed in detail. In the PLS regression, the values of the coefficient of determination (R²) and root mean square error of cross-validation (RMSECV) of PLS regression are 0.9263 and 0.00119, respectively. For comparison, though applying PCR method to get the values of R² and RMSECV we obtained are 0.9685 and 0.00108, respectively. And the values of the standard error of prediction set (SEP) of PLS and PCR models are 0.001480 and 0.001140. The result of the prediction set suggests that these two quantitative analysis models have excellent generalization ability and prediction precision. However, for this PFLX injection samples, the PCR quantitative analysis model achieved more accurate results than the PLS model. The experimental results showed that NIRS together with PCR method provide rapid and accurate quantitative analysis of PFLX injection samples. Moreover, this study supplied technical support for the further analysis of other injection samples in pharmaceuticals.     

Multicomponent Determination of Organophosphorus Pesticides in Grain Samples by Linear Sweep Stripping Voltammetry and Multivariate Calibration

Abstract

Organophosphorus pesticides, such as parathion methyl (PTM), fenitrothion (FT), parathion (PT), and isocarbophos (ICP), have sensitive but overlapped voltammetric peaks with peak potentials ?309, ?364, ?317, and ?480 mV, respectively, in Britton‐Robinson buffer of pH 4.8 by application of linear sweep stripping voltammetry (LSSV). In this work, two multivariate calibration methods, partial least squares (both PLS‐1 and PLS‐2), and principal component regression (PCR), were applied to quantitatively resolve the overlapping voltammogram of the mixtures of these four pesticides. The prediction results obtained from a set of independent test samples showed that PLS‐1 method performed better prediction ability than PLS‐2 and PCR methods. The proposed method was successfully applied to the determination of these four pesticides in grain samples after a pre‐extraction step with a solvent of acetone.     

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...     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.     

Simultaneous kinetic-spectrophotometric determination of sulfide and sulfite by partial least squares and genetic algorithm variable selection

Simultaneous multicomponent analysis is usually carried out by multivariate calibration models such as partial least squares (PLS) that utilize the full spectrum. It has been demonstrated by both experimental and theoretical considerations that better results can be obtained by a proper selection of the spectral range to be included in calculations. A genetic algorithm is one of the most popular methods for selecting variables for PLS calibration of mixtures with almost identical spectra without loss of prediction capacity. In this work, a simple and precise method for rapid and accurate simultaneous determination of sulfide and sulfite ions based on the addition reaction of these ions with new fuchsin at pH 8 and 25°C by PLS regression and using a genetic algorithm (GA) for variable selection is proposed. The concentrations of sulfide and sulfite ions varied between 0.05–2.50 and 0.15–2.00 μg/mL, respectively. A series of synthetic solutions containing different concentrations of sulfide and sulfite were used to check the prediction ability of GA-PLS models. The root mean square error of prediction with PLS on the whole data set was 0.19 μg/mL for sulfide and 0.09 μg/mL for sulfite. After the application of GA, these values were reduced to 0.04 and 0.03 μg/mL, respectively. The text was submitted by the authors in English.     

On-Line Multicomponent Determination of the Flux of Mixtures of Phenols Through a Liquid Membrane in Real Time

 The analysis of mixtures of four phenolic compounds in an on-line system using UV-visible measurements with a fibre optic probe is discussed in this work. The aim of this system is to provide accurate real time concentration profiles in order to monitor the transport of phenols across a solid supported liquid membrane in both the feed and stripping phases. Different calibration models are taking into account the pH of the solution, using experimental designs and the first derivative in combination with different multivariate approaches like multiple linear regression (MLR), inverted least squares (ILS) and partial least squares regression (PLS). The comparison of all these combinations is carried out by means of the predictive residual error sum of squares (PRESS) evaluated from an independent set of spectra. From this comparison it is concluded that a PLS model using first derivative spectra offers the most accurate and robust prediction in the permeation experiments. Additionally, the stability of the model and the figures of merit obtained are also discussed. Received July 22, 1999. Revision October 23, 2000.     

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L⁻¹ for Penicillin V and 0.32 g L⁻¹ for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L⁻¹ for Penicillin V and 0.15 g L⁻¹ for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.