化学计量学-荧光分析法同时测定萘、1-萘酚和2-萘酚

根据正交信号校正(OSC)、小波包变换(WPT)及偏最小二乘法(PLS)的算法原理,编制了名为POSC-WPTPLS的程序,结合荧光分析法快速、灵敏、选择性较好的优点,将该程序用于同时测定荧光光谱严重重叠的萘、1-萘酚和2-萘酚多组分体系,并将3种化学计量学方法(OSC-WPT-PLS、WPT-PLS和PLS)进行比较...     

荧光光度法同时测定邻苯二酚、间苯二酚与对苯二酚

将一种直接信号校正(DOSC)-小波包变换(WPT)-偏最小二乘法(PLS)(DOSC-WPT-PLS)新方法用于解析荧光光谱严重重叠的邻苯二酚?间苯二酚和对苯二酚混合物,并对其进行测定。该法将DOSC、WPT及PLS 3种方法结合从而提高了获取特征信息的能力和回归质量。DOSC方法用于除去与浓度无关的结构噪音。利用WPT的时域和频域局部化的特点改进了除噪质量和数据压缩及信息提取能力。PLS方法用于多变量校准和噪音消除。处理该3种组分的荧光光谱数据,并实现了3种化合物的同时测定。设计了PDOSCWPTPLS程序执行相关计算,并对以上3种化学计量学方法进行了比较,其总体相对预测标准偏差分别为4.3%、7.7%、11.5%,结果表明DOSC-WPT-PLS法优于WPT-PLS法和PLS法。将该法用于测定自来水中邻苯二酚?间苯二酚和对苯二酚的含量,其回收率分别为99%~110%?95%~108%和98%~104%,结果满意。     

基于OSC–WT–PLS近红外光谱法测定烟草中芸香苷

采用正交信号校正(OSC)结合小波变换(WT)对烟草光谱进行光谱预处理,将预处理后的烟草光谱结合偏最小二乘法(PLS)建立了烟草光谱对芸香苷的预测模型。利用OSC滤除光谱中与芸香苷含量无关的光谱信息,确定OSC提取的最佳主成分数为7,再选择WT中的最佳小波基函数bior1.1对OSC预处理后的光谱进行压缩及进一步滤噪,然后进行PLS建模,OSC–WT–PLS所建模型决定系数r~2=0.874,校正标准偏差RMSEC=0.85,预测均方根误差RMSEP=0.743,交互验证系数Q_(ext)~2=0.887。结果表明,用OSC–WT–PLS可滤除光谱信息中与待测样品含量无关的信息、减少光谱数据量,降低建立模型的复杂度、提高建模速度及模型的预测能力、准确度。     

Resolution of overlapping signals in spectrometry using a wavelet packet transform and an Elman recurrent neural network

A novel method named a wavelet packet transform based Elman recurrent neural network (WPTERNN) was proposed for the simultaneous UV–visible spectrometric determination of Cu(II), Cd(II) and Zn(II). This method combined wavelet packet denoising with an Elman recurrent neural network. A wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity. An Elman recurrent network was applied for nonlinear multivariate calibration. In this case, using trials, the kind of wavelet function, the decomposition level, and the number of hidden nodes for the WPTERNN method were selected as Daubechies 14, 3, and 8, respectively. A program (PWPTERNN) was designed that could perform the simultaneous determination of Cu(II), Cd(II) and Zn(II). The relative standard errors of prediction (RSEP) obtained for all components using WPTERNN, a Elman recurrent neural network (ERNN), partial least squares (PLS), principal component regression (PCR), Fourier transform based PCR (FTPCR), and multivariate linear regression (MLR) were compared. Experimental results demonstrated that the WPTERRN method was successful even where there was severe overlap of spectra. The results obtained from an additional test case also demonstrated that the WPTERNN method performed very well. Figure The part of WP coefficients obtained by wavelet packet transforms     

Spectrophotometric simultaneous determination of manganese(II) and iron(II) in pharmaceutical by orthogonal signal correction-partial least squares

The simultaneous determination of manganese(II) and iron(II) mixtures by using spectrophotometric methods is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used to remove the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 450-600 nm range for 21 different mixtures of manganese(II) and iron(II). Calibration matrices were containing 0.05-1.2 and 0.1-2.3 microg mL(-1) Mn(II) and Fe(II), respectively. The RMSEP for manganese(II) and iron(II) with OSC and without OSC were 0.0316, 0.0291, and 0.0907, 0.115, respectively. This procedure allows the simultaneous determination of manganese(II) and iron(II) in synthetic and real matrix samples with good reliability of the determination.     

Determination of benzo[a]pyrene in cigarette mainstream smoke by using mid-infrared spectroscopy associated with a novel chemometric algorithm

Determination of benzo[a]pyrene (BaP) in cigarette smoke can be very important for the tobacco quality control and the assessment of its harm to human health. In this study, mid-infrared spectroscopy (MIR) coupled to chemometric algorithm (DPSO-WPT-PLS), which was based on the wavelet packet transform (WPT), discrete particle swarm optimization algorithm (DPSO) and partial least squares regression (PLS), was used to quantify harmful ingredient benzo[a]pyrene in the cigarette mainstream smoke with promising result. Furthermore, the proposed method provided better performance compared to several other chemometric models, i.e., PLS, radial basis function-based PLS (RBF-PLS), PLS with stepwise regression variable selection (Stepwise-PLS) as well as WPT-PLS with informative wavelet coefficients selected by correlation coefficient test (rtest-WPT-PLS). It can be expected that the proposed strategy could become a new effective, rapid quantitative analysis technique in analyzing the harmful ingredient BaP in cigarette mainstream smoke.     

Simultaneous kinetic spectrophotometric analysis of five synthetic food colorants with the aid of chemometrics

This paper describes a simple and sensitive kinetic spectrophotometric method for the simultaneous determination of Amaranth, Ponceau 4R, Sunset Yellow, Tartrazine and Brilliant Blue in mixtures with the aid of chemometrics. The method involved two coupled reactions, viz. the reduction of iron(III) by the analytes to iron(II) in sodium acetate/hydrochloric acid solution (pH 1.71) and the chromogenic reaction between iron(II) and hexacyanoferrate(III) ions to yield a Prussian blue peak at 760 nm. The spectral data were recorded over the 500-1000 nm wavelength range every 2 s for 600 s. The kinetic data were collected at 760 nm and 600 s, and linear calibration models were satisfactorily constructed for each of the dyes with detection limits in the range of 0.04-0.50 mg L⁻¹. Multivariate calibration models for kinetic data were established and verified for methods such as the Iterative target transform factor analysis (ITTFA), principal component regression (PCR), partial least squares (PLS), and principal component-radial basis function-artificial neural network (PC-RBF-ANN) with and without wavelet packet transform (WPT) pre-treatment. The PC-RBF-ANN with WPT calibration performed somewhat better than others on the basis of the %RPE_T (∼9) and %Recovery parameters (∼108), although the effect of the WPT pre-treatment was marginal (∼0.5% RPE_T). The proposed method was applied for the simultaneous determination of the five colorants in foodstuff samples, and the results were comparable with those from a reference HPLC method.     

Improvement of prediction ability of PLS models employing the wavelet packet transform: A case study concerning FT-IR determination of gasoline parameters

The wavelet packet transform (WPT) is a variant of the standard wavelet transform that offers greater flexibility in the decomposition of instrumental signals. Although encouraging results have been published concerning the use of WPT for signal compression and denoising, its application in multivariate calibration problems has received comparatively little attention, with very few contributions reported in the literature. This paper presents an investigation concerning the use of WPT as a feature extraction tool to improve the prediction ability of PLS models. The optimization of the wavelet packet tree is accomplished by using the classic dynamic programming algorithm and an entropy cost function modified to take into account the variance explained by the WPT coefficients. The selection of WPT coefficients for inclusion in the PLS model is carried out on the basis of correlation with the dependent variable, in order to exploit the joint statistics of the instrumental response and the parameter of interest. This WPT-PLS strategy is applied in a case study involving FT-IR spectrometric determination of four gasoline parameters, namely specific mass (SM) and the distillation temperatures at which 10%, 50%, 90% of the sample has evaporated. The dataset comprises 103 gasoline samples collected from gas stations and 6144 wavelengths in the range 2500-15000 nm. By applying WPT to the FT-IR spectra, considerable compression with respect to the original wavelength domain is achieved. The effect of varying the wavelet and the threshold level on the prediction ability of the resulting models is investigated. The results show that WPT-PLS outperforms standard PLS in most wavelet-threshold combinations for all determined parameters.     

Simultaneous quantitative analysis of overlapping spectrophotometric signals using wavelet multiresolution analysis and partial least squares

The mathematical bases and program algorithms of discrete wavelet transform (DWT), multiresolution and Mallat’s pyramid algorithm were described. The multiresolution analysis (MRA) based on Daubechies orthogonal wavelet basis was studied as a tool for removing noise and irrelevant information from spectrophotometric spectra. After wavelet MRA pre-treatment, eight error functions were calculated for deducing the number of factors. A partial least squares based on wavelet MRA (WPLS) method was developed to perform simultaneous spectrophotometric determination of Fe(II) and Fe(III) with overlapping peaks. Data reduction was performed using wavelet MRA and principal component analysis (PCA) algorithm. Two programs, SPWMRA and SPWPLS, were designed to perform wavelet MRA and simultaneous multicomponent determination. Experimental results showed the WPLS method to be successful even where there was severe overlap of spectra.     

Orthogonal signal correction‐based prediction of total antioxidant activity using partial least squares regression from chromatograms

The multivariate calibration methods—partial least squares (PLS), orthogonal signal correction and partial least squares (OSC‐PLS)—were employed for the prediction of total antioxidant activities of four Prunella L. species. High‐performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total antioxidant activity of the Prunella L. samples. Several preprocessing techniques such as smoothing and normalization were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping. The importance of the preprocessing was investigated by calculating the root mean square error for the calibration set for the total antioxidant activity of Prunella L. samples. The models developed on the basis of the preprocessed data were able to predict the total antioxidant activity with a precision comparable to that of the reference 2,2‐azino‐di‐(3‐ethylbenzothialozine‐sulfonic acid) and 2,2‐diphenyl‐1‐picrylhydrazyl methods. The OSC‐PLS model seems preferable because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total antioxidant activity. The contribution of individual phenolic compounds to the total antioxidant activity was identified by HPLC. Copyright © 2012 John Wiley & Sons, Ltd.     

Simultaneous spectrophotometric determination of cobalt,copper and nickel using 1-（2-thiazolylazo）-2-naphthol by chemometrics methods

The simultaneous determination of cobalt, copper and nickel using 1-（2-thiazolylazo）-2-naphthol （first figure of this article） by spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares （PLS） regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction （OSC） is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 550-750-nm range for 21 different mixtures of cobalt, copper and nickel. Calibration matrices were formed from samples containing 0.05-1.05, 0.05-1.30 and 0.05-0.80 μg·mL^-1 for cobalt, copper and nickel, respectively. The root mean square error of prediction （RMSEP） for cobalt, copper and nickel with OSC and without OSC were 0.007, 0.008, 0.011 and 0.031,0.037, 0.032 μg· mL^-1, respectively. This procedure allows the simultaneous determination of cobalt, copper and nickel in synthetic and real samples and good reliability of the determination was proved.     

Kinetic spectrophotometric determination of trace amounts of palladium by whole kinetic curve and a fixed time method using resazurine sulfide reaction

The univariate and multivariate calibration methods were applied for the determination of trace amounts of palladium based on the catalytic effect on the reaction between resazurine and sulfide. The decrease in absorbance of resazurine at 602 nm over a fixed time is proportional to the concentration of palladium over the range of 10.0-160.0 ng mL(-1). The calibration matrix for partial least squares (PLS) regression was designed with 14 samples. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration without loss of prediction ability using spectrophotometric method. The root mean square error of prediction (RMSEP) for palladium determination with fixed-time, PLS and OSC-PLS were 3.71, 2.84 and 0.68, respectively. This procedure allows the determination of palladium in synthetic and real samples with good reliability of the determination.     

Removal of major interference sources in aqueous near-infrared spectroscopy techniques

This work describes a hybrid procedure for eliminating major interference sources in aqueous near-infrared (NIR) spectra, that include aqueous influence, noise, and systemic variations irrelevant to concentration. The scheme consists of two parts: extension of wavelet prism (WPe) and orthogonal signal correction (OSC). First, WPe is employed to remove variations due to aqueous absorbance and noise; then OSC is applied to remove systemic spectral variations irrelevant to concentration. Although water possesses strong absorption bands that overshadow and overlap the absorption bands of analytes, along with noise and systematic interference, successful calibration models can be generated by employing the method proposed here. We show that the elimination of major interference sources from the aqueous NIR spectra results in a substantial improvement in the precision of prediction, and reduces the required number of PLS components in the model. In addition, the strategy proposed here can be applied to various analytical data for quantitative purposes as well.     

Orthogonal signal correction-partial least squares method for simultaneous spectrophotometric determination of cypermethrin and tetramethrin

The simultaneous determination of cypermethrin and tetramethrin mixtures by using spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS) regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 200-350 nm range for 25 different mixtures of cypermethrin and tetramethrin. Calibration matrices were containing 0.1-12.9 and 0.1-13.8 microg mL(-1) for cypermethrin and tetramethrin, respectively. The RMSEP for cypermethrin and tetramethrin with OSC and without OSC were 0.0884, 0.0614 and 0.2915, 0.2309, respectively. This procedure allows the simultaneous determination of cypermethrin and tetramethrin in synthetic and real samples good reliability of the determination was proved.     

Simultaneous spectrophotometric determination of iron, nickel and cobalt in micellar media by using direct orthogonal signal correction-partial least squares method

A very simple and selective spectrophotometric method for simultaneous determination of iron(II), nickel(II) and cobalt(II) based on formation of their complexes with 1-(2-pyridylazo)-2-naphtol (PAN) in micellar media is described. Although the complexes of Fe(II), Ni(II) and Co(II) with reagent show a spectral overlap, they have been simultaneously determined by partial least squares (PLS) with and without preprocessing step using direct orthogonal signal correction (DOSC). The linear range was 0.30-4.50 μg ml⁻¹ for Co(II), 0.20-3.00 μg ml⁻¹ for Ni(II) and 0.30-5.00 μg ml⁻¹ for Fe(II). The results obtained by the PLS and DOSC-PLS were statistically compared. Interference effects of common anions and cations were studied and the proposed method was also applied satisfactorily to the determination of Fe(II), Ni(II) and Co(II) in synthetic samples.     

近红外光谱测定人参与西洋参的主要皂甙总量

采用近红外光谱测定人参与西洋参的主要皂甙总量.采集人参与西洋参的漫反射光谱,分别对光谱进行正交信号校正(OSC)与常规预处理,建立了对应的偏最小二乘(PLS)回归模型.与常规最优预处理方法相比,OSC能很好地消除人参与西洋参的品种差异,显著提高了光谱与皂甙含量的相关系数,同时降低了PLS建模因子数,提高了模型的稳健性与...     

Simultaneous spectrophotometric determination of mercury and palladium with Thio-Michler's Ketone using partial least squares regression and orthogonal signal correction

A simple, novel and sensitive spectrophotometric method was described for simultaneous determination of mercury and palladium. The method is based on the complex formation of mercury and palladium with Thio-Michler's Ketone (TMK) at pH 3.5. All factors affecting on the sensitivity were optimized and the linear dynamic range for determination of mercury and palladium found. The simultaneous determination of mercury and palladium mixtures by using spectrophotometric method is a difficult problem, due to spectral interferences. By multivariate calibration methods such as partial least squares (PLS), it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for PLS calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 360-660 nm range for 25 different mixtures of mercury and palladium. Calibration matrices were containing 0.025-1.60 and 0.05-0.50 microg mL(-1) of mercury and palladium, respectively. The RMSEP for mercury and palladium with OSC and without OSC were 0.013, 0.006 and 0.048, 0.030, respectively. This procedure allows the simultaneous determination of mercury and palladium in synthetic and real matrix samples good reliability of the determination.     

Cloud Point Extraction Simultaneous Spectrophotometric Determination of Zn(II), Co(II) and Ni(II) in Water and Urine Samples by 1-(2-Pyridylazo)2-Naphthol Using Partial Least Squares Regression

A cloud-point extraction (CPE) process using the nonionic surfactant Triton X-114 to simultaneous extraction and spectrophotometric determination of Zn(II), Co(II) and Ni(II) from aqueous solution using partial least squares (PLS) regression is investigated. The method is based on the color reaction of these cations with 1-(2-pyridylazo)2-naphthol and subsequent micelle-mediated extraction of products. The optimum extraction and reaction conditions such as pH, reagents concentration and effect of time have been studied. Linearity was obeyed in the range 2–150, 5–250 and 2–150 ng mL⁻¹ of Zn(II), Co(II) and Ni(II) respectively. The relative standard error (RSE) for the simultaneous determination of 15 test samples of different concentrations of Zn(II), Co(II) and Ni(II) was 4.38%;, 1.18% and 2.42%, respectively. The total relative standard error (RSE_t) for applying the PLS method to 15 synthetic samples in the linear ranges of these metals was 2.36%. The interference effect of some anions and cations was also tested. The method was applied to the simultaneous determination of Zn(II), Co(II) and Ni(II) in water and human urine samples.     

Simultaneous Spectrophotometric Determination of Iron and Cobalt in Micellar Medium by Using a Principal Component Artificial Neural Network and Multivariate Calibration

A simple and reliable method for simultaneous spectrophotometric determination of iron(II) and cobalt(II) has been established. The method is based on complex formation with 1‐(2‐pyridylazo)‐2‐naphtol (PAN) in a micellar medium. Despite a spectral overlap, Fe²⁺ and Co²⁺ have been simultaneously determined with chemometric approaches involving principal component artificial neural network (PC‐ANN), principal component regression (PCR) and partial least squares (PLS). Various synthetic mixtures of iron and cobalt were assessed and the results obtained by the applications of these chemometric approaches were evaluated and compared. It was found that the PC‐ANN method afforded relatively better precision than that of PCR or PLS. The proposed method permits detection limits of 0.05 and 0.07 ng mL^?1 for Co and Fe, respectively. The influences of pH, ligand amount, solvent percentage and time on the absorbance were also investigated. The proposed method was also applied satisfactorily for the determination of Fe(II) and Co(II) in real and synthetic samples.