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.     

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.     

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.     

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.     

Spectrophotometric simultaneous determination of nitroaniline isomers by orthogonal signal correction-partial least squares

The simultaneous determination of nitroaniline isomer 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 for removes 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 200–500 nm range for 21 different mixtures of nitroaniline isomers. Calibration matrices were containing 1–21, 1–15 and 1–18 μg ml⁻¹ of m-nitroaniline, o-nitroaniline and p-nitroaniline, respectively. The RMSEP for m-nitroaniline, o-nitroaniline and p-nitroaniline with OSC and without OSC were 0.6567, 0.2692, and 0.3134, and 1.3818, 1.2181, and 0.3953, respectively. This procedure allows the simultaneous determination of nitroaniline isomers in real matrix samples and good reliability of the determination was proved.     

Simultaneous determination of cobalt, nickel and palladium in micellar media using partial least square regression and direct orthogonal signal correction

A simple, novel and sensitive spectrophotometric method was described for the simultaneous determination of cobalt, nickel and palladium. The method is based on the complex formation of Co, Ni and Pd with 1-(2-pyridylazo)-2-naphtol (PAN) in Tween-80 micellar media. All factors affecting on the sensitivity were optimized and the linear dynamic range for determination of Co, Ni and Pd was found. The experimental calibration matrix was designed by measuring the absorbance over the range of 520-700 nm for 21 samples of 0.10-1.0, 0.050-0.50 and 0.050-4.0 microg ml(-1) of Co, Ni and Pd, respectively. The partial least square (PLS) modeling based on singular value decomposition (SVD) was used for the multivariate calibration of the spectrophotometric data. The direct orthogonal signal correction was used for pre-processing of data matrices and the prediction results of model, with and without using direct orthogonal signal correction, were statistically compared. The effects of various anions and cations on selectivity of the method were investigated. The proposed method was successfully applied to the determination of Co, Ni and Pd in water and alloy samples.     

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.     

Combining orthogonal signal correction and wavelet packet transform with partial least squares to analyze overlapping spectra of three kinds of metal ions

A novel method named OSC-WPT-PLS approach based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as pre-processed tools was proposed for the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). This method combines the ideas of OSC and WPT with PLS regression for enhancing the ability of extracting characteristic information and the quality of regression. OSC is used to remove information in the response matrix D by subtracting the structured noise that is orthogonal to the concentration matrix C. Wavelet packet transform was applied to perform data compression, to extract relevant information, and to eliminate noise and collinearity. PLS was applied for multivariate calibration and noise reduction by eliminating the less important latent variables. In this case, using trials, the kind of wavelet function, the decomposition level, the number of OSC components and the number of PLS factors for the OSC-WPT-PLS method were selected as Daubechies 4, 3, 2 and 3, respectively. A program (POSCWPTPLS) was designed to perform the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). The relative standard errors of prediction (RSEP) obtained for total elements using OSC-WPT-PLS, WPT-PLS and PLS were compared. Experimental results demonstrated that the OSC-WPT-PLS method had the best performance among the three methods and was successful even when there was severe overlap of spectra.     

短波近红外光谱法对蛇床子SFE萃取产物的定量分析

利用中药蛇床子CO2超临界萃取(SFE)的萃取物的短波近红外漫反射光谱(800～1100 nm),以HPLC分析值作参比值,采用化学计量学中的偏最小二乘法(PLS)建立短波近红外漫反射光谱与蛇床子SFE萃取物中主要成分蛇床子素和欧前胡素间定量分析数学模型.实现了快速、无损的测定双组分中药的有效成分.讨论了光谱的预处理方法和主成分数对PLS定量预测蛇床子萃取物中蛇床子素和欧前胡素含量能力的影响,并对预测集样品进行预测.     

Determination of benzene in gasoline using direct injection-mass spectrometry

A high-speed determination of benzene in gasoline samples using a non-separative method based on direct injection into the mass spectrometer is proposed. The results obtained are very similar to those provided with fast GC-MS.The calibration set was made up of gasoline samples in which the benzene was determined chromatographically and samples of gasoline subjected to a process of evaporation - until the complete disappearance of the original benzene - to which known concentrations of this compound had been added. A PLS1 multivariate calibration model was constructed. Cross-validation was used to select the optimum number of PLS components. The prediction capacity of the model was checked with an additional group of gasoline samples that had not been used either in the construction or in the validation of the model.With the direct injection method proposed here it was possible to analyse 24 samples over a period of 1 h. The direct injection method is rapid, simple and - in view of the results - highly suitable for the determination of benzene in gasoline samples.     

Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing.

By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrared (NIR) spectrum is proposed, and it is used in the determination of the chlorogenic acid in plant samples as a preprocessing tool for partial least square (PLS) modeling. It is shown that the benefit of the proposed method lies not only in its performance to improve the quality of PLS model and the prediction precision, but also in its simplicity and practicability. It may become a convenient and efficient tool for preprocessing NIR spectral data sets in multivariate calibration.     

Simultaneous determination of cobalt and palladium in micellar media using H-point standard addition method and partial least square regression

A simple, sensitive and selective spectrophotometric method for the simultaneous determination of Co(II) and Pd(II) using partial least square (PLS) calibration and H-point standard addition method is described. The method is based on the complex formation of Co(II) and Pd(II) with 4-(2-pyridylazo) resorcinol (PAR) in acidic media and in the presence of sodium dodecyl sulfate (SDS) as a micellizing agent. Acidic media and the presence of a micellar system improve selectivity and sensitivity, respectively. By applying PLS calibration, Co(II) and Pd(II) can be determined in the range of 0.20-2.0 and 0.40-4.0 microg ml(-1), respectively. The relative errors of prediction for the determination of Co(II) and Pd(II) in the 10 prediction samples were 1.69 and 1.72%, respectively. The results of applying H-point standard addition method show that Co(II) and Pd(II) can be determined simultaneously with concentration ratio of Co(II) to Pd(II) varying between 7:1 and 1:8 in the mixed samples. Both proposed methods (PLS and HPSAM) were applied to the determination of Co(II) and Pd(II) in several alloy solutions with satisfactory results.     

Determination of the content and identity of lidocaine solutions with UV-visible spectroscopy and multivariate calibration

A method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of ultraviolet-visible (UV-Vis) spectroscopy, orthogonal signal correction (OSC) and multivariate calibration with soft independent modelling of class analogy (SIMCA) classification and partial least squares (PLS) regression. The content was determined with PLS regression and the identity with PLS regression and SIMCA classification. The method was tested on the local anaesthetic compound lidocaine. For the validation, external test sets of both manufactured sample solutions and samples from a stability study were used. For comparison with this new method, liquid chromatography was used as a reference method. The results show that in respect of accuracy, precision and repeatability, the new method is comparable to the reference method. The main advantage over liquid chromatography is the much shorter time of analysis and the simpler analytical procedure. An estimate of the analysis time saved with the proposed method compared with using liquid chromatography, together with practical considerations, is given.     

Determination of amylose in Iranian rice by multivariate calibration of the surface plasmon resonance spectra of silver nanoparticles

A simple and non-separative analytical method for selective determination of amylose in Iranian rice has been developed. It was based on the reduction of silver ions by amylose and production of Ag nanoparticles, which exhibit surface plasmon resonance (SPR) spectra in the ultraviolet/visible region. The formation of Ag nanoparticles in the presence of amylose was monitored by transmission electron microscopy (TEM) and dynamic light scattering (DLS). The experimental conditions were optimized to obtain the highest yield for nanoparticle formation. Partial least square (PLS) regression as an efficient multivariate spectral calibration method was employed to make a connection between the SPR spectra of the generated Ag nanoparticles and the amylose content (AC) of the rice starch. The number of PLS latent variables was optimized by leave-one-out cross-validation utilizing prediction residual error sum of square (PRESS). The proposed model exhibited a high ability for prediction of amylose concentration in both standard starch samples and real rice samples prepared from different regions of Iran. The relative errors of prediction were almost lower than ±5% for different real samples and the detection limit was 3.23 weight percent of amylose in rice. In comparison to the reference method (Juliano method), the proposed method is simpler and does not need tedious sample preprocessing steps.     

Prediction of the acid value,peroxide value and the percentage of some fatty acids in edible oils during long heating time by chemometrics analysis of FTIR-ATR spectra

Edible oils are used in the preparation of foods as a part of their recipe or for frying. So to ensure of food safety, checking the quality of the oils before and after usage is an important subject in food control laboratories. In this study, edible oils from four different sources (canola, corn, sunflower and frying) were heated for 36 h at 170 °C and sampling was done every 6 h. The free fatty acid, peroxide value and the content of some fatty acids (C16:0, C18:0, C18:1, C18:2, C18:3) of the oil samples were determined by standard methods. Then, the ATR-FTIR spectra of the samples were collected. The partial least squares (PLS) regression combined with genetic algorithm was performed on the spectroscopic data to obtain the appropriate predictive models for the simultaneous estimation of acid value, peroxide value and the percentage of five kinds of fatty acids. The effect of some preprocessing methods on these models was also investigated. Preprocessing of data by orthogonal signal correction (OSC) resulted in the best predictive models for all oil properties. The correlation coefficients of calibration set (>0.99) and validation set (>0.86 and in most case >0.94) of the OSC–PLS model suggested suitable predictive modeling for all studied parameters in the oil samples. This method could be suggested as a rapid, economical and environmental friendly technique for simultaneous determination of seven noted parameters in the edible oils.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Determination of low analyte concentrations by near-infrared spectroscopy: effect of spectral pretreatments and estimation of multivariate detection limits

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.     

Spectrophotometric determination of ternary mixtures of thiamin, riboflavin and pyridoxal in pharmaceutical and human plasma by least-squares support vector machines.

Ternary mixtures of thiamin, riboflavin and pyridoxal have been simultaneously determined in synthetic and real samples by applications of spectrophotometric and least-squares support vector machines. The calibration graphs were linear in the ranges of 1.0 - 20.0, 1.0 - 10.0 and 1.0 - 20.0 microg ml(-1) with detection limits of 0.6, 0.5 and 0.7 microg ml(-1) for thiamin, riboflavin and pyridoxal, respectively. The experimental calibration matrix was designed with 21 mixtures of these chemicals. The concentrations were varied between calibration graph concentrations of vitamins. The simultaneous determination of these vitamin mixtures by using spectrophotometric methods is a difficult problem, due to spectral interferences. The partial least squares (PLS) modeling and least-squares support vector machines were used for the multivariate calibration of the spectrophotometric data. An excellent model was built using LS-SVM, with low prediction errors and superior performance in relation to PLS. The root mean square errors of prediction (RMSEP) for thiamin, riboflavin and pyridoxal with PLS and LS-SVM were 0.6926, 0.3755, 0.4322 and 0.0421, 0.0318, 0.0457, respectively. The proposed method was satisfactorily applied to the rapid simultaneous determination of thiamin, riboflavin and pyridoxal in commercial pharmaceutical preparations and human plasma samples.     

Estimation of the composition of heparin mixtures from various origins using proton nuclear magnetic resonance and multivariate calibration methods

A multivariate calibration method for the characterization of heparin samples based on the analysis of (1)H nuclear magnetic resonance (NMR) spectral data is proposed. Heparin samples under study consisted of two-component or four-component mixtures of heparins from porcine, ovine and bovine mucosae and bovine lung. Although the (1)H NMR spectra of all heparin types were highly overlapping, each origin showed some particular features that could be advantageously used for the quantification of the components. These features mainly concerned the anomeric H, which appeared in the range 5.0-5.7 ppm and the peaks of acetamidomethyl protons at 2.0-2.1 ppm. The determination of the percentage of each heparin class depended on these differences and was carried out using partial least squares regression (PLS) as a calibration method. Prior to the PLS analysis, the spectral data were standardized using the internal standard peak (sodium 4,4-dimethyl-4-silapentanoate- 2,2,3,3- d (4), TSP) as the reference. The quantification of each heparin type in the samples using PLS models built with 4 or 5 components was satisfactory, with an overall prediction error ranging from 3% to 10%.     

Kinetic spectrophotometric determination of sulfide using whole kinetic curve and a fixed time method

The univariate and multivariate calibration method was applied for the determination of sulfide based on the addition reaction of sulfide with new fuchsin at pH 8 and 25°C. The decrease in absorbance of new fuchsin at 540 nm over a fixed time is proportional to the concentration of sulfide over the range of 0.05-2.5 μg ml⁻¹. The experimental calibration matrix for partial least squares calibration (PLS) method was designed with 20 samples. The cross-validation method was used for selecting the number of factors. The root mean square difference (RMSD) was 0.0843. The effects of various cations and anions on sulfide determination have been investigated.