Problems of calibration in trace, in situ-micro and surface analysis

Summary A systematic survey will be given on different strategies of calibration in dependence on given analytical and statistical conditions, particularly on several procedures of least squares regression (ordinary, orthogonal, unweighted and weighted LSR), of robust regression, addition methods and multicomponent calibration. In this connection calibration by means of latent variables (principal component regression PCR, partial least squares PLS) will be dealt with. The special conditions in the case of microanalysis and surface analysis will be considered under practical analytical as well as chemometrical aspects. Problems of homogeneity, representativness of samples and sample regions will be treated.     

用四种离散变换主组分回归方法分辨重叠吸收光谱

将离散小波变换、小波包变换、傅里叶变换和离散余弦变换与主组分回归方法结合构成4种离散变换主组分回归方法,编制了离散变换主组分回归方法的计算程序。将离散变换主组分回归方法用于处理对硝基甲苯、对硝基酚和对硝基苯胺混合物的重叠紫外吸收光谱数据。结果表明,离散变换主组分回归方法优于主组分回归方法,试样质量浓度的预测值与实际值的相对预测标准误差由3.81%降至约1.11%。     

人工神经网络在纸浆卡伯值光学定量分析中的应用

卡伯值 (硬度 )是纸浆的重要质量指标 ,是制浆过程控制的关键参数 .目前的测量方法包括化学分析法和光学分析法两大类型 ,国内大多数的制浆造纸厂采用离线的传统化学分析法来测定纸浆的卡伯值 ,需要比较长的时间 .而光学分析法因具有实时性好、精度和可靠性高等优点 ,已逐步用于卡伯值的在线测量和控制 .研究 [1] 发现 ,在 460～ 580 nm的可见光谱范围内 ,蒸煮液吸光度的变化可以表征纸浆中木素含量的变化 .本文将可见分光光谱技术应用于蒸煮液中木素含量的在线测量 ,根据蒸煮液在所选波段的吸光度来预测纸浆的卡伯值 ,建立纸浆卡伯值与蒸煮…     

Assessment of maximum likelihood PCA missing data imputation

Maximum likelihood principal component analysis (MLPCA) was originally proposed to incorporate measurement error variance information in principal component analysis (PCA) models. MLPCA can be used to fit PCA models in the presence of missing data, simply by assigning very large variances to the non‐measured values. An assessment of maximum likelihood missing data imputation is performed in this paper, analysing the algorithm of MLPCA and adapting several methods for PCA model building with missing data to its maximum likelihood version. In this way, known data regression (KDR), KDR with principal component regression (PCR), KDR with partial least squares regression (PLS) and trimmed scores regression (TSR) methods are implemented within the MLPCA method to work as different imputation steps. Six data sets are analysed using several percentages of missing data, comparing the performance of the original algorithm, and its adapted regression‐based methods, with other state‐of‐the‐art methods. Copyright © 2016 John Wiley & Sons, Ltd.     

Multivariate concentration determination using principal component regression with residual analysis

Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method.     

HPLC and chemometrics-assisted UV-spectroscopy methods for the simultaneous determination of ambroxol and doxycycline in capsule

High-performance liquid chromatography (HPLC) and multivariate spectrophotometric methods are described for the simultaneous determination of ambroxol hydrochloride (AM) and doxycycline (DX) in combined pharmaceutical capsules. The chromatographic separation was achieved on reversed-phase C(18) analytical column with a mobile phase consisting of a mixture of 20mM potassium dihydrogen phosphate, pH 6-acetonitrile in ratio of (1:1, v/v) and UV detection at 245 nm. Also, the resolution has been accomplished by using numerical spectrophotometric methods as classical least squares (CLS), principal component regression (PCR) and partial least squares (PLS-1) applied to the UV spectra of the mixture and graphical spectrophotometric method as first derivative of the ratio spectra ((1)DD) method. Analytical figures of merit (FOM), such as sensitivity, selectivity, analytical sensitivity, limit of quantitation and limit of detection were determined for CLS, PLS-1 and PCR methods. The proposed methods were validated and successfully applied for the analysis of pharmaceutical formulation and laboratory-prepared mixtures containing the two component combination.     

Multivariate data analysis of quality parameters in drinking water

The quality of water destined for human consumption has been treated as a multivariate property. Since most of the quality parameters are obtained by applying analytical methods, the routine analytical laboratory (responsible for the accuracy of analytical data) has been treated as a process system for water quality estimation. Multivariate tools, based on principal component analysis (PCA) and partial least squares (PLS) regression, are used in the present paper to: (i) study the main factors of the latent data structure and (ii) characterize the water samples and the analytical methods in terms of multivariate quality control (MQC). Such tools could warn of both possible health risks related to anomalous sample composition and failures in the analytical methods.     

A new regression method based on independent component analysis

Based on independent component analysis (ICA), a new regression method, independent component regression (ICR), was developed to build the model of NIR spectra and the routine components of plant samples. It is found that ICR and principal component regression (PCR) are completely equivalent when they are applied in quantitative prediction. However, independent components (ICs) can give more chemical explanation than principal components (PCs) because independence is a high-order statistic that is a much stronger condition than orthogonality. Three ICs are obtained by ICA from the NIR spectra of plant samples; it is found that they are strongly correlated to the NIR spectra of water, hydrocarbons and organonitrogen compounds, respectively. Therefore, ICA may be a promising tool to retrieve both quantitative and qualitative information from complex chemical data sets.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

稳健算法及其在红外光谱分析中的应用

总结了稳健主成分分析、稳健主成分回归、稳健偏最小二乘回归和稳健连续回归等各种稳健算法的新近成果. 研究表明,稳健算法可以检测并规避异常值的影响. 稳健算法应用红外光谱分析中可望优化定性、定量预测模型.     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

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。     

New validated liquid chromatographic and chemometrics-assisted UV spectroscopic methods for the determination of two multicomponent cough mixtures in syrup

Multivariate spectrophotometric calibration and liquid chromatographic (LC) methods were applied to the determination of 2 multicomponent mixtures containing diprophylline, guaiphenesin, methylparaben, and propylparaben (Mixture 1), or clobutinol, orciprenaline, saccharin sodium, and sodium benzoate (Mixture 2). For the multivariate spectrophotometric calibration methods, principal component regression (PCR) and partial least-squares regression (PLS-1), a calibration set of the mixtures consisting of the components of each mixture was prepared in 0.1 M HCl. Analytical figures of merit such as sensitivity, selectivity, limit of quantitation, and limit of detection were determined for both PLS-1 and PCR. The LC separation was achieved on a reversed-phase C18 analytical column by using isocratic elution with 20 mM potassium dihydrogen phosphate, pH 3.3-acetonitrile (55 + 45, v/v) as the mobile phase and UV detection at 260 and 220 nm for Mixture 1 and Mixture 2, respectively. The proposed methods were validated and successfully applied to the analysis of pharmaceutical formulations and laboratory-prepared mixtures containing the 2 multicomponent combinations.     

Three-way partial least-squares regression for the simultaneous kinetic-enzymatic determination of xanthine and hypoxanthine in human urine

The performance of three-way principal component analysis and three-way partial least-squares regression when applied to a complex kinetic-enzymatic system is studied, in order to investigate the analytical potential of the combined use of these chemometric technologies for non-selective enzymatic systems. A enzymatic-kinetic procedure for the simultaneous determination of hypoxanthine and xanthine in spiked samples of human urine is proposed. The chemical system involves two consecutive reactions catalyzed by xanthine oxidase (EC 1.17.3.2). This enzyme catalyzes the oxidation of hypoxanthine, first to xanthine and then to uric acid, a competitive inhibitor of the reactions. The influence of uric acid during quantitative determination was considered in the design of the calibration set. The sample and enzyme solution were mixed in a stopped-flow module and the reaction was monitored using a diode array spectrophotometer. The recorded data have an intrinsical three-component structure (samples, time and wavelength). This data array was studied via three-way principal component analysis and was modeled for quantitative purposes using a three-way partial least-squares calibration procedure. Results are compared with those obtained by applying classical bilinear PLS to the previously unfolded data matrix.     

Multivariate Calibration,an Overview

Abstract

Numerous methods of multivariate calibration methods exist with ridge regression, principal component regression, and partial least squares being the most popular methods in analytical chemistry. This mini‐review overviews multivariate calibration and provides a common theme with respect to the bias/variance tradeoff (harmony) and the harmony/parsimony tradeoff for model selection. Other multivariate calibration considerations are briefly reviewed. A few applications are noted.     

Multivariate monitoring of soybean oil ethanolysis by FTIR

In this work, an analytical procedure was developed to monitor the ethanolysis of degummed soybean oil (DSO) using Fourier-transformed mid-infrared spectroscopy (FTIR) and methods of multivariate analysis such as principal component analysis (PCA) and partial least squares regression (PLS). The triglycerides (reagents) and ethyl esters (products) involved in ethanolysis were shown to have similar FTIR spectra. However, when the FTIR spectra derived from seven standard mixtures of triolein and ethyl oleate were treated by PCA at the region that represents the CO stretching vibration of ester groups (1700-1800 cm⁻¹), only two principal components (PC) were shown to capture 99.95% of the total spectral variance (92.37% for the former and 7.58% for the latter PC). This observation supported the development of a multivariate calibration model that was based on the PLS regression of the FTIR data. The prevision capability of this model was measured against 40 reaction aliquots whose ester content was previously determined by size exclusion chromatography. Only small discrepancies were observed when the two experimental data sets were treated by linear regression (R²=0.9837) and these deviations were attributed to the occurrence of non-modeled transient species in the reaction mixture (reaction intermediates), particularly at short reaction times. Therefore, the FTIR/PLS model was shown to be a fast and accurate method to predict reaction yields and to follow the in situ kinetics of soybean oil ethanolysis.     

一个基于诊断的稳健主成分分析方法

经典的主成分分析方法易受异常点影响。本文根据该方法的特点，提出一新的诊断方法，将多变量数据中异常剔除后再进行主成分分析，构成有效的稳健主成分分析法。用此法处理二组实际数据，结果令人满意。     

The clas program for classification and evaluation

Multivariate classification methods are needed to assist in extracting information from analytical data. The most appropriate method for each problem must be chosen. The applicability of a method mainly depends on the distributional characteristics of the data population (normality, correlations between variables, separation of classes, nature of variables) and on the characteristics of the data sample available (numbers of objects, variables and classes, missing values, measurement errors). The CLAS program is designed to combine classification methods with evaluation of their performance, for batch data processing. It incorporates two-group linear discriminant analysis (SLDA), independent class modelling with principal components (SIMCA), kernel density estimation (ALLOC), and principal component class modelling with kernel density estimation (CLASSY). Most of these methods are implemented so as to give probabilistic classifications. Multiple linear regression is provided for, and other methods are scheduled. CLAS evaluates the classification method using the training set data (resubstitution), independent test data, and pseudo test data (leave-one-out method). This last method is optimized for faster computation. Criteria for classification performance and reliability of the given probabilities, etc. are determined. The package contains flexible possibilities for data manipulation, variable transformation and missing data handling.     

A new approach to near-infrared spectral data analysis using independent component analysis

This paper presents a new approach to near-infrared spectral (NIR) data analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based method is therefore particularly useful in identifying the unknown components in a mixture as well as in estimating their concentrations. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regression (PCR), multiple linear regression (MLR), and partial least squares (PLS) as well as Fourier and wavelet transforms.     

化学计量学在过程分析中的应用及进展

评述了化学计量学方法在生产过程分析中各个方面 ,如过程优化、过程模拟、仪器及仪器校正、过程监测等方面的应用 ,并展望了化学计量学在过程分析中的应用前景