共查询到20条相似文献,搜索用时 15 毫秒
1.
Héctor C. Goicoechea 《Analytica chimica acta》2002,453(2):289-300
The use of multivariate spectrophotometric calibration for the simultaneous determination of three active components and one excipient in nasal solutions is presented. The resolution of four-component mixtures of phenylephrine, diphenhydramine, naphazoline and methylparaben in a matrix of excipients has been accomplished by using partial least-squares (PLS-1) and a variant of the so-called hybrid linear analysis (HLA) named net analyte preprocessing (NAP). Notwithstanding the presence of a large number of components and their high degree of spectral overlap, they have been rapidly and simultaneously determined with high accuracy and precision, with no interference, and without resorting to extraction procedures using non-aqueous solvents. A simple and fast method for wavelength selection in the calibration step is used, based on the minimisation of the predicted error sum of squares (PRESS) calculated as a function of a moving spectral window. The use of calibration designs of reduced size has been attempted. Satisfactory results were obtained when the number of calibration samples was reduced from 25 (full central composite) to 17 (fractional central composite) using the net analyte-based NAP method. 相似文献
2.
A method for quantitative determination of metal element in aqueous solution was developed by using adsorption and diffuse reflectance near‐infrared spectroscopy (DRNIRS). In this method, the analyte is firstly adsorbed onto the resin from the dilute solution, and then the adsorbed analyte is directly determined in the sorbent by using DRNIRS. Enrichment of the analyte is achieved by the adsorption from the dilute solution, and quantitative determination is accomplished by using multivariate calibration technique. Taking chromium(VI) in river water as the analytical target, adsorption conditions and the partial least squares (PLS) model was optimized. The results show that chromium(VI) can be immobilized onto the adsorbent and quantitatively measured by DRNIRS and multivariate calibration. With cross validation and external validation, the correlation coefficient between the reference and predicted concentration was found to be above 0.98 in the range of 0.75–29.90 mg·L−1 for the PLS model, and the interference of the coexisting matrix was eliminated with the aid of multivariate calibration. 相似文献
3.
Partial Least Squares (PLS) is by far the most popular regression method for building multivariate calibration models for spectroscopic data. However, the success of the conventional PLS approach depends on the availability of a ‘representative data set’ as the model needs to be trained for all expected variation at the prediction stage. When the concentration of the known interferents and their correlation with the analyte of interest change in a fashion which is not covered in the calibration set, the predictive performance of inverse calibration approaches such as conventional PLS can deteriorate. This underscores the need for calibration methods that are capable of building multivariate calibration models which can be robustified against the unexpected variation in the concentrations and the correlations of the known interferents in the test set. Several methods incorporating ‘a priori’ information such as pure component spectra of the analyte of interest and/or the known interferents have been proposed to build more robust calibration models. In the present study, four such calibration techniques have been benchmarked on two data sets with respect to their predictive ability and robustness: Net Analyte Preprocessing (NAP), Improved Direct Calibration (IDC), Science Based Calibration (SBC) and Augmented Classical Least Squares (ACLS) Calibration. For both data sets, the alternative calibration techniques were found to give good prediction performance even when the interferent structure in the test set was different from the one in the calibration set. The best results were obtained by the ACLS model incorporating both the pure component spectra of the analyte of interest and the interferents, resulting in a reduction of the RMSEP by a factor 3 compared to conventional PLS for the situation when the test set had a different interferent structure than the one in the calibration set. 相似文献
4.
Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample. 相似文献
5.
Near-infrared spectroscopy offers the potential for direct in situ analysis in complex biological systems. Chemical selectivity
is a critical issue for such measurements given the extent of spectral overlap of overtone and combination spectra. In this
work, the chemical basis of selectivity is investigated for a set of multivariate calibration models designed to quantify
glucose, glucose-6-phosphate, and pyruvate independently in ternary mixtures. Near-infrared spectra are collected over the
combination region (4,000–5,000 cm−1) for a set of 60 standard solutions maintained at 37 °C. These standard solutions are composed of randomized concentrations
(0.5–30 mM) of glucose, glucose-6-phosphate, and pyruvate. Individual calibration models are constructed for each solute by
using the partial least-squares (PLS) algorithm with optimized spectral range and number of latent variables. The resulting
standard errors are 0.90, 0.72, and 0.32 mM for glucose, glucose-6-phosphate, and pyruvate, respectively. A pure component
selectivity analysis (PCSA) demonstrates selectivity for each solute in these ternary samples. The concentration of each solute
is also predicted for each sample by using a set of net analyte signal (NAS) calibration models. A comparison of the PLS and
NAS calibration vectors demonstrates the chemical basis of selectivity for these multivariate methods. Selectivity of each
PLS and NAS calibration model originates from the unique spectral features associated with the targeted analyte. Overall,
selectivity is demonstrated for each solute with an order of sensitivity of pyruvate > glucose-6-phosphate > glucose.
Figure Combination near-infrared spectroscopy allows selective analytical measurements for glucose, glucose-6-phosphate, and pyruvate
in ternary mixtures owing to the uniqueness of the individual absorption spectra for each solute 相似文献
6.
A. Muñoz de la Peña A. Espinosa-MansillaM.I. Acedo Valenzuela H.C. GoicoecheaA.C. Olivieri 《Analytica chimica acta》2002,463(1):75-88
A comparative study about advantages and limitations of net analyte signal (NAS)-based methods (NBMs) and partial least squares (PLS) calibration in kinetic analysis has been performed. The different multivariate calibration methods were applied to the determination of binary mixtures of amoxycillin and clavulanic acid, by stopped-flow kinetic analysis. The reactions of oxidation of these compounds with cerium(IV), in sulphuric acid medium, were monitored by following the changes on the fluorescence of the oxidation products, in stopped-flow mode. The differences on the kinetic profiles obtained at λex=256 nm and λem=351 nm, were used to determine mixtures of both compounds by multivariate calibration of the kinetic data, using PLS-1, a modification of hybrid linear analysis (HLA) and net analyte pre-processing combined with classical least squares (NAP/CLS) methods. The NBMs allowed the selection of optimal time data regions by calculating the minimum error indicator function (EIF), improving the results and making NBMs very convenient for the analysis. In addition, the use of the net analyte signal concept allows the calculation of the analytical figures of merit, limit of detection (LOD), sensitivity and selectivity, for each component. 相似文献
7.
Mohammad-Hossein Sorouraddin Kaveh Amini Abdolhossein Naseri Mohammad-Reza Rashidi 《Central European Journal of Chemistry》2010,8(1):207-213
The multivariate calibration methods, partial least squares (PLS) and principle component regression (PCR) have been used
to determine phenanthridine, phenanthridinone and phenanthridine N-oxide in spiked human plasma samples. Resolution of binary
and ternary mixtures of analytes with minimum sample pre-treatment and without analyte separation has been successfully achieved
analyzing the UV spectral data. The net analyte signal (NAS) concept was also used to calculate multivariate analytical figures
of merit such as limit of detection, selectivity and sensitivity. The simultaneous determination of three analytes was possible
by PLS and PCR processing of sample absorbance in the 210–355 nm region. Good recoveries were obtained for both synthetic
mixtures and spiked human plasma samples. 相似文献
8.
Sigrid Pieters Wouter Saeys Tom Van den Kerkhof Mohammad Goodarzi Mario Hellings Thomas De Beer Yvan Vander Heyden 《Analytica chimica acta》2013
Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34–40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest. 相似文献
9.
A method for calibration and validation subset partitioning 总被引:13,自引:0,他引:13
This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies. 相似文献
10.
应用近红外光谱技术建立了白酒基酒中2,3-丁二酮和3-羟基-2-丁酮的快速检测模型。从洛阳杜康酒厂选取182个白酒基酒样品为材料,运用气相色谱法测得两种物质的化学值,同时采集其在12 000~4 000 cm-1范围内的光谱数据,采用偏最小二乘法(PLS)结合内部交叉验证建立校正模型。通过对比不同光谱预处理下PLS模型效果对其进行优化,确定2,3-丁二酮和3-羟基-2-丁酮的最佳预处理方法分别为一阶导数+多元散射校正和二阶导数,最佳光谱区间分别为9 403.2~7 497.9 cm-1和9 403.2~7 497.9 cm-1+6 101.7~5 449.8 cm-1。优化后2,3-丁二酮和3-羟基-2-丁酮校正集样品的化学值和近红外预测值的决定系数(R2)分别为0.960 2和0.963 2,交叉验证均方根误差(RMSECV)分别为0.39、0.22mg/100 mL;通过外部检验,验证集样品的R2分别为0.957 6和0.957 8,预测均方根误差(R... 相似文献
11.
Near infrared spectroscopy (NIRS) was used in combination with partial least squares (PLS) calibration to determine low concentrated analytes. The effect of the orthogonal signal correction (OSC) and net analyte signal (NAS) pretreatments on the models obtained at concentrations of analyte near its detection limit was studied. Both pretreatments were found to accurately resolve the analyte signal and allow the construction of PLS models from a reduced number of factors; however, they provided no substantial advantage in terms of %RSE for the prediction samples. Multiple methodologies for the estimation of detection limits could be found in the bibliography. Nevertheless, detection limits were determined by a multivariate method based on the sample-specific standard error for PLS regression, and compared with the univariate method endorsed by ISO 11483. The two methods gave similar results, both being effective for the intended purpose of estimating detection limits for PLS models. Although OSC and NAS allow isolating the analyte signal from the matrix signal, they provide no substantial improvement in terms of detection limits. The proposed method was used to the determine 2-ethylhexanol at concentrations from 20 to 1600 ppm in an industrial ester. The detection limit obtained, round 100 ppm, testifies to the ability of NIR spectroscopy to detect low concentrated analytes. 相似文献
12.
Cámara MS Ferroni FM De Zan M Goicoechea HC 《Analytical and bioanalytical chemistry》2003,376(6):838-843
An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods. 相似文献
13.
Optimized sample-weighted partial least squares 总被引:2,自引:0,他引:2
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. 相似文献
14.
Maintaining the predictive abilities of multivariate calibration models by spectral space transformation 总被引:1,自引:0,他引:1
Du W Chen ZP Zhong LJ Wang SX Yu RQ Nordon A Littlejohn D Holden M 《Analytica chimica acta》2011,690(1):64-70
In quantitative on-line/in-line monitoring of chemical and bio-chemical processes using spectroscopic instruments, multivariate calibration models are indispensable for the extraction of chemical information from complex spectroscopic measurements. The development of reliable multivariate calibration models is generally time-consuming and costly. Therefore, once a reliable multivariate calibration model is established, it is expected to be used for an extended period. However, any change in the instrumental response or variations in the measurement conditions can render a multivariate calibration model invalid. In this contribution, a new method, spectral space transformation (SST), has been developed to maintain the predictive abilities of multivariate calibration models when the spectrometer or measurement conditions are altered. SST tries to eliminate the spectral differences induced by the changes in instruments or measurement conditions through the transformation between two spectral spaces spanned by the corresponding spectra of a subset of standardization samples measured on two instruments or under two sets of experimental conditions. The performance of the method has been tested on two data sets comprising NIR and MIR spectra. The experimental results show that SST can achieve satisfactory analyte predictions from spectroscopic measurements subject to spectrometer/probe alteration, when only a few standardization samples are used. Compared with the existing popular methods designed for the same purpose, i.e. global PLS, univariate slope and bias correction (SBC) and piecewise direct standardization (PDS), SST has the advantages of implementation simplicity, wider applicability and better performance in terms of predictive accuracy. 相似文献
15.
A. Mu?oz de la Pe?a I. Durán-Merás M. D. Moreno F. Salinas M. Martínez Galera 《Fresenius' Journal of Analytical Chemistry》1995,351(6):571-576
The resolution of ternary mixtures of salicylic, salicyluric and gentisic acids has been accomplished by partial least squares (PLS) and principal component regression (PCR) multivariate calibration. The total luminescence information of the compounds has been used to optimize the spectral data set to perform the calibration. A comparison between the predictive ability of the three multivariate calibration methods, PLS-1, PLS-2 and PCR, on three spectral data sets, excitation, emission and synchronous spectra, has been performed. The excitation spectrum has been the best scanning path for salicylic and salicyluric acid determinations, while the emission spectrum has been the best for the gentisic acid determination. The convenience of analysing the total luminescence spectrum information when using multivariate calibration methods on fluorescence data is demonstrated. 相似文献
16.
Asadpour-Zeynali K Ghavami R Esfandiari R Soheili-Azad P 《Journal of AOAC International》2010,93(6):1995-2001
A novel net analyte signal standard addition method (NASSAM) was used for simultaneous determination of the drugs anthazoline and naphazoline. The NASSAM can be applied for determination of analytes in the presence of known interferents. The proposed method is used to eliminate the calibration and prediction steps of multivariate calibration methods; the determination is carried out in a single step for each analyte. The accuracy of the predictions against the H-point standard addition method is independent of the shape of the analyte and interferent spectra. The net analyte signal concept was also used to calculate multivariate analytical figures of merit, such as LOD, selectivity, and sensitivity. The method was successfully applied to the simultaneous determination of anthazoline and naphazoline in a commercial eye drop sample. 相似文献
17.
Rapid determination of pH in solid-state fermentation of wheat straw by FT-NIR spectroscopy and efficient wavelengths selection 总被引:1,自引:0,他引:1
In the work discussed in this paper we investigated the feasibility of determination of the pH of a fermented substrate in solid-state fermentation (SSF) of wheat straw. Fourier-transform near-infrared (FT-NIR) spectroscopy was combined with an appropriate multivariate method of analysis. A genetic algorithm and synergy interval partial least-squares (GA-siPLS) were used to select the efficient spectral subintervals and wavelengths by k-fold cross-validation during development of the model. The performance of the final model was evaluated by use of the root mean square error of cross-validation (RMSECV) and correlation coefficient (R (c)) for the calibration set, and verified by use of the root mean square error of prediction (RMSEP) and correlation coefficient (R (p)) for the validation set. The experimental results showed that the optimum GA-siPLS model was achieved by use of seven PLS factors, when four spectral subintervals were selected by siPLS and then 45 wavelength variables were chosen by use of the GA. The predicted precision of the best model obtained was: RMSECV = 0.0583, R (c) = 0.9878, RMSEP = 0.0779, and R (p) = 0.9779. Finally, the superior performance of the GA-siPLS model was demonstrated by comparison with four other PLS models. The overall results indicated that FT-NIR spectroscopy can be successfully used for measurement of pH in solid-state fermentation, and use of the GA-siPLS algorithm is the best means of calibration of the model. 相似文献
18.
How to avoid over-fitting in multivariate calibration--the conventional validation approach and an alternative 总被引:1,自引:0,他引:1
This paper critically reviews the problem of over-fitting in multivariate calibration and the conventional validation-based approach to avoid it. It proposes a randomization test that enables one to assess the statistical significance of each component that enters the model. This alternative is compared with cross-validation and independent test set validation for the calibration of a near-infrared spectral data set using partial least squares (PLS) regression. The results indicate that the alternative approach is more objective, since, unlike the validation-based approach, it does not require the use of 'soft' decision rules. The alternative approach therefore appears to be a useful addition to the chemometrician's toolbox. 相似文献
19.
A new variable selection algorithm is described, based on ant colony optimization (ACO). The algorithm aim is to choose, from a large number of available spectral wavelengths, those relevant to the estimation of analyte concentrations or sample properties when spectroscopic analysis is combined with multivariate calibration techniques such as partial least-squares (PLS) regression. The new algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors. The performance has been tested on a simulated system, where it shows a significant superiority over other commonly employed selection methods, such as genetic algorithms. Several near infrared spectroscopic experimental data sets have been subjected to the present ACO algorithm, with PLS leading to improved analytical figures of merit upon wavelength selection. The method could be helpful in other chemometric activities such as classification or quantitative structure-activity relationship (QSAR) problems. 相似文献
20.
Patricia C DamianiMariano D Borraccetti Alejandro C Olivieri 《Analytica chimica acta》2002,471(1):87-96
A direct method for the simultaneous determination of naproxen and salicylate in human serum is reported, based on a combination of spectrofluorometric measurements with two multivariate calibration techniques: partial least-squares (PLS-1) and the novel net analyte preprocessing (NAP). The method is rapid, selective and sensitive, and is based on the measurement of the fluorescence spectra of NH3 alkalinized whole human sera at the excitation wavelength of 315 nm. It can be applied within the ranges of concentrations 50-200 ng ml−1 for naproxen and 100-300 ng ml−1 for salicylate. The employed chemometric techniques have been compared on the basis of the statistical indicators for calibration and validation. Reproducibility and interference studies in abnormal sera have also been carried out. 相似文献