Maintenance of a calibration model for near infrared spectrometry by a combined principal component analysis-partial least squares approach

A novel strategy for building and maintaining calibration models has been developed for use when the future boundaries of the sample set are unknown or likely to change. Such a strategy could have an impact on the economics and time required to obtain and maintain a calibration model for routine analysis. The strategy is based on both principal component analysis (PCA) and partial least squares (PLS) multivariate techniques. The principal action of the strategy is to define how “similar” a new sample is to the samples currently defining the calibration dataset. This step is performed by residuals analysis, following PCA. If the new sample is considered to have a spectrum “similar” to previously available spectra, then the model is assumed able to predict the analyte concentration. Conversely, if the new sample is considered “dissimilar”, then there is new information in this sample, which is unknown to the calibration model and the new sample is added automatically to the calibration set in order to improve the model. The strategy has been applied to a real industrial dataset provided by BP Amoco Chemicals. The data consists of spectra of 102 sequential samples of a raw material. The strategy produced an accurate calibration model for both target components starting with only the first four samples, and required a further 17 reference measurements to maintain the model for the whole sampling sequence, which was over a 1-year period.     

Improvement of multivariate calibration techniques applied to 1-to-N component mixtures through an optimized experimental design

This work describes a novel experimental design aimed at building a calibration set constituted by samples containing a different number of components. The algorithm performs a reiteration process to maintain the number of samples at the lower value as possible and to ensure an homogeneous presence of all the concentration levels. The mixture design was applied to a drug system composed by one-to-four components in different combination. The resolution of the system was performed by three multivariate UV spectrophotometric methods utilizing principal component regression (PCR) and partial last squares (PLS1 and PLS2) algorithms. The calibration set was composed by 61 references on four concentration levels, including 15 samples for each quaternary, ternary and binary composition and 16 one-component samples. The calibration models were optimized through a careful selection of number of factors and wavelength zones, in such a way as to remove interferences from instrumental noise and excipients present in the pharmaceutical formulations. The prediction power of the regression models were verified and compared by analysis of an external prediction set. The models were finally used to assay pharmaceutical specialities containing the studied drugs in one-to-four formulations.     

偏最小二乘线性拟合滴定甘蔗糖蜜中多组分有机酸

将滴定体系调节至pH 2.0,用碱标准溶液滴定至特定pH所消耗滴定荆为测量指标,构建了多组分有机酸滴定数据阵,分别以主成分回归法、偏最小二乘法以及人工神经元网络法进行多组分拟合.结果表明,偏最小二乘法的拟合结果最佳,对混合体系中乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸总量的相对预测均方根误差分别为5.80%、8.88%...     

Multivariate analysis of remote laser-induced breakdown spectroscopy spectra using partial least squares,principal component analysis,and related techniques

Quantitative analysis with laser-induced breakdown spectroscopy traditionally employs calibration curves that are complicated by chemical matrix effects. These chemical matrix effects influence the laser-induced breakdown spectroscopy plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, laser-induced breakdown spectroscopy calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis techniques are employed to analyze the laser-induced breakdown spectroscopy spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis and Soft Independent Modeling of Class Analogy are employed to generate a model and predict the rock type of the samples. These Multivariate Analysis techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.     

偏最小二乘-分光光度法同时测定福美锌和代森锰农药残留量

利用紫外可见分光光度法研究了农药福美锌和代森锰与苯基荧光酮的相互作用,发现反应后的产物分别在波长为551和554 nm处最大有吸收,但光谱严重重叠.实验采集了450～700 nm波长范围吸光度数据,并对该数据进行一阶求导后用偏最小二乘法处理,据此建立了偏最小二乘-分光光度法同时测定福美锌和代森锰两种农药的新方法.福美锌和代森锰的线性范围分别为0.3～6.0和0.2～3.5 μg/mL;检出限分别为0.22和0.13 μg/mL.混合样品分析无需分离,方法简单、快速.用于水果、大米和自来水等实际样品测定.     

主成分回归用于分光光度法同时测定6种食品添加剂

山梨酸、苯甲酸钠、香兰素、NaNO2、NaNO3和糖精钠的紫外吸收光谱严重重叠,不经预先分离很难进行单一组分的直接测定.报道了一种同时测定上述6种食品添加剂的分光光度法,这种方法是基于在pH 2.85的Britton-Robinson(B-R)缓冲溶液中对该6种食品添加剂混合溶液进行光度测定,所得的重叠光谱数据用主成分回归(PCR)进行建模,并用该模型对未知样品浓度进行预报.该方法可以不经分离同时测定食品样品中的多种添加剂.     

Simultaneous kinetic spectrophotometric determination of cyanide and thiocyanate using the partial least squares (PLS) regression

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of CN⁻ and SCN⁻ ions is described. The method is based on the difference in the rate of the reaction between CN⁻ and SCN⁻ ions with chloramine-T in a pH 4.0 buffer solution and at 30 °C. The produced cyanogen chloride (CNCl) reacts with pyridine and the product condenses with barbituric acid and forms a final colored product. The absorption kinetic profiles of the solutions were monitored by measuring absorbance at 578 nm in the time range 20-180 s after initiation of the reaction with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 31 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 10.0-900.0 and 50.0-1200.0 ng mL⁻¹ for CN⁻ and SCN⁻ ions, respectively. The proposed method was successfully applied to the simultaneous determination of cyanide and thiocyanate in water samples.     

Mutlivariate calibration with Raman data using fast principal component regression and partial least squares methods

Linear and non-linear calibration methods (principal component regression (PCR), partial least squares regression (PLS), and neural networks (NN)) were applied to a slightly non-linear Raman data set. Because of the large size of this data set, recently introduced linear calibration methods, specifically optimised for speed, were also used. These fast methods achieve speed improvement by using the Lanczos decomposition for the singular value decomposition steps of the calibration procedures, and for some of their variants, by optimising the models without cross-validation (CV). Linear methods could deal with the slight non-linearity present in the data by including extra components, therefore, performing comparably to NNs. The fast methods performed as well as their classical equivalents in terms of precision in prediction, but the results were obtained considerably faster. It, however, appeared that CV remains the most appropriate method for model complexity estimation.     

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.     

Simultaneous determination of halide and thiocyanate ions by potentiometric precipitation titration and multivariate calibration

Halide and thiocyanate ions can be determined by a precipitation titration with silver nitrate as the titrant, and the end-point can be evaluated by a potentiometric method, in which generally a silver indicator electrode is used as the indicator electrode and a double-junction Ag–AgCl electrode as the reference electrode. However, when mixtures of halide and thiocyanate are titrated, it is difficult to determine these components individually for there are overlapping steps in the potentiometric titration curves, especially in the case that there are obvious differences between concentrations of the components. In this paper, the linear equation for the potentiometric precipitation titration of a mixture of halide and thiocyanate ions was developed and it was then used for determining the components in the mixtures simultaneously with the aid of multivariate calibration methods. By application of this model, 27 synthetic mixtures with three- and four-component combinations of chloride, bromide, iodide and thiocyanate with low concentration levels from 1.8×10⁻⁴ to 6.2×10⁻⁴ mol l⁻¹ were analyzed and acceptable results were obtained.     

Simultaneous spectrophotometric determination of iron and vanadium by H-point standard addition method and partial least squares regression in micellar medium

Simultaneous determination of total iron and vanadium by H-point standard addition method (HPSAM) and partial least squares (PLS) is described. Gallic acid (GA) in a cationic micellar solution of CTAB was used for determination of iron and vanadium in different oxidation states at pH 5. The presence of a micellar system enables total iron and vanadium to be determined with improved sensitivities. The total relative standard error for applying the PLS method to 15 synthetic samples in the ranges 0.20–15.00 μg ml⁻¹ iron and 0.20–8.00 μg ml⁻¹ vanadium was 2.2%. The results of applying the H-point standard addition method showed that iron and vanadium can be determined simultaneously with the concentration ratios of iron to vanadium from 10:1 to 1:20 in the mixed sample. Both HPSAM and PLS methods showed suitable abilities to resolve accurately overlapped absorption spectra of the compounds. Both proposed methods were successfully applied to the determination of Fe and V in several synthetic alloy solutions.     

Near-infrared spectroscopy and multivariate calibration for the quantitative determination of certain properties in the petrochemical industry

Near-infrared (NIR) spectroscopy in conjunction with chemometric techniques allows on-line monitoring in real time, which can be of considerable use in industry. If it is to be correctly used in industrial applications, generally some basic considerations need to be taken into account, although this does not always apply. This study discusses some of the considerations that would help evaluate the possibility of applying multivariate calibration in combination with NIR to properties of industrial interest. Examples of these considerations are whether there is a relation between the NIR spectrum and the property of interest, what the calibration constraints are and how a sample-specific error of prediction can be quantified. Various strategies for maintaining a multivariate model after it has been installed are also presented and discussed.     

Averaged and weighted average partial least squares

Two alternative partial least squares (PLS) methods, averaged PLS and weighted average PLS, are proposed and compared with the classical PLS in terms of root mean square error of prediction (RMSEP) for three real data sets. These methods compute the (weighted) average of PLS models with different complexity. The prediction abilities of the alternative methods are comparable to that of the classical PLS but they do not require to determine how many components should be included in the model. They are also more robust in the sense that the quality of prediction depends less on a good choice of the number of components to be included. In addition, weighted average PLS is also compared with the weighted average part of LOCAL, a published method that also applies weighted average PLS, with however an entirely different weighting scheme.     

Simultaneous determination of Sb(III) and Sb(V) by partial least squares regression

A method for simultaneous spectrophotometric determination of Sb(III) and Sb(V) using multivariate calibration method is proposed. This method is based on the development of the reaction between the analytes and pyrogallol red at pH 2.00. The selection of variables was studied. A series of synthetic solutions containing different concentrations of Sb(III) and Sb(V) were used to check the prediction ability of the partial least squares model. The calibration curves were linear over the range of 0.3-3.4 and 0.3-3.0 μg ml⁻¹ for Sb(III) and Sb(V), respectively. The detection limits were 0.177 and 0.200 μg ml⁻¹ for Sb(III) and Sb(V), respectively.     

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.     

偏最小二乘分光光度法同时测定痕量铁、锰、铜、锌、钴和镍

痕量Fe^3 ,Mn^2 ,Cu^2 ,Zn^2 ,Co^2 ,Ni^2 与2－（5－溴－2－吡啶偶氮）－5－二乙氨基苯酚（5－Br-PADAP)和溴化十六烷基三甲基铵（CTMAB）在PH8．3时发生高灵敏显色反应，所形成的三元胶束络合的吸收光谱严重重叠，采用偏最小二乘法（PLS）辅助分光光度法测定了合成试样及饲料中上述6种痕量组分，结果表明，PLS法是化学计量学中一种可适用于基体较复杂的实际试样中痕量组分分光度同时测定的多元计算方法。     

Multivariate calibration techniques applied to the spectrophotometric analysis of one-to-four component systems

The UV spectrophotometric analysis of a multicomponent mixture containing paracetamol, caffeine, tripelenamine and salicylamide by using multivariate calibration methods, such as principal component regression (PCR) and partial least-squares regression (PLS), was described. The calibration set was based on 47 reference samples, consisting of quaternary, ternary, binary and single-component mixtures, with the aim to develop models able to predict the concentrations of unknown samples containing as many as one-to-four components. The calibration models were optimized by an appropriate selection of the number of factors as well as wavelength ranges to be used for building up the data matrix and excluding any information about the interfering excipients included in pharmaceutics. The PCR and PLS models were compared and their predictive performance was inferred by a successful application to the assays of synthetic mixtures and pharmaceutical formulations.     

Solid-phase extraction and simultaneous spectrophotometric determination of trace amounts of Co, Ni and Cu using partial least squares regression

A novel sensitive and simple method for rapid extraction, preconcentration and determination of cobalt, nickel and copper as their 1-(2-pyridilazo)-2-naphthol (PAN) complexes using polytetrafluoroethylene filter as solid phase and multivariate calibration of spectrophotometric data is presented. The analytical wavelengths of 400-700 nm were chosen and the experimental calibration matrix for partial least squares (PLS) was designed with 21 samples of 5.90-41.25, 0.30-29.35 and 0.64-41.30 ng ml⁻¹ for cobalt, nickel and copper, respectively. The cross-validation method was used for selecting the number of components. The root-mean-square errors of predictions (RMSEPs) were between 0.48 and 1.37 ng ml⁻¹. In this work we could reach preconcentration factors of 100 or even higher by using polytetrafluoroethylene as solid phase which is cheap and can be used in a wide range of pH, flow rates and for many times. The proposed method was successfully applied to the simultaneous determination of Co, Ni and Cu in tap and pit water samples.