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.     

偏最小二乘近红外光谱法测定瘦肉脂肪酸组成的研究

利用偏最小二乘将瘦肉的近红外光谱数据分别与其棕榈酸、棕榈油酸、硬脂酸、油酸、亚油酸含量建立校正模型,并用交互校验和外部检验来考查模型的可靠性.各脂肪酸模型的校正相关系数分别为0.9998、0.9844、0.9963、0.9754、0.9969,均方估计残差(RMSEC)分别为0.0231、0.0485、0.111、0.373、0.311,交互校验均方残差(RMSECV)分别为0.509、0.115、0.225、0.848、0.649.应用所建立的各脂肪酸近红外模型对瘦肉脂肪酸组成进行预测,并对各脂肪酸的预测值与气相色谱法测定值进行配对t-检验,结果表明两者差异均不显著(p>0.05).     

beta-Correction as a preprocessing method for partial least squares in simultaneous determination of zinc and lead

An error analysis of predicted values using spectral correction matrix and partial least squares (PLS) modeling is applied for the determination of Zn²⁺ and Pb²⁺ with methylthymol blue (MTB) as a metallochromic indicator. The concentration ranges for Pb²⁺ and Zn²⁺ in standard solution sets are 0.5-5.2 and 0.1-2.5 μg ml⁻¹, respectively. The experimental calibration set was composed of 20 sample solutions using a random design for two component mixtures. The absorption spectra were recorded from 400 to 700 nm. The two wavelengths, which exert the minimum error in prediction of two metal ion concentrations, are chosen according to an error analysis of different pairs of wavelengths. The effect of the pH on the sensitivity in determination of Zn²⁺ and Pb²⁺ using MTB was studied in order to choose the optimum pH (pH=6) for determination. The values of root mean square difference (RMSD) for lead and zinc using β-correction partial least squares were 0.0977 and 0.1266, respectively. The effect of diverse ions and several experimental parameters were studied. The method was used for the determination of lead and zinc in alloy samples.     

Rapid Determination of Phenolics,Flavonoids, and Antioxidant Properties of Physalis ixocarpa Brot. ex Hornem. and Physalis angulata L. by Infrared Spectroscopy and Partial Least Squares

Physalis ixocarpa Brot. ex Hornem. and Physalis angulata L. are two edible species of the family Solanaceae, which have an important variety of antioxidant compounds present in their roots, stems, leaves, calyces, and fruits. This work reports the development of multivariate models based on the use of partial least square (PLS) analysis and Fourier transform infrared (FTIR) spectroscopy for the quantitative determination of total phenolics, total flavonoids, free radical scavenging activity, total antioxidant capacity, and reducing power in the extracts of roots, stems, and leaves of both P. ixocarpa and P. angulata. Standard chromatographic and colorimetric techniques were used to determine the quantitative actual values (references) in the extracts, which served as input data to develop the multivariate PLS models. Optimized FTIR-PLS models were realized by cross-validation procedures, obtaining the determination coefficients for prediction between 0.792 and 0.905 for P. ixocarpa, and between 0.756 and 0.893 for P. angulata. In this form, FTIR spectroscopy with multivariate analysis could represent a versatile tool to evaluate quantitatively concentrations of bioactive compounds and antioxidant properties in the extracts of both species, requiring a very short time at low cost.     

Simultaneous kinetic spectrophotometric determination of Cu(II), Co(II) and Ni(II) using partial least squares (PLS) regression

A partial least squares (PLS-1) calibration model based on kinetic—spectrophotometric measurement, for the simultaneous determination of Cu(II), Ni(II) and Co(II) ions is described. The method was based on the difference in the rate of the reaction between Co(II), Ni(II) and Cu(II) ions with 1-(2-pyridylazo)2-naphthol in a pH 5.8 buffer solution and in micellar media at 25°C. The absorption kinetic profiles of the solutions were monitored by measuring the absorbance at 570 nm at 2 s intervals during the time range of 0–10 min after initiation of the reaction. The experimental calibration matrix for the partial least squares (PLS-1) model was designed with 30 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 0.1-2 μg mL⁻¹ for each cation. The proposed method was successfully applied to the simultaneous determination of Cu(II), Ni(II) and Co(II) ions in water and in synthetic alloy samples.      

Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares

Near-infrared (NIR) spectra in the region of 5000-4000 cm⁻¹ with a chemometric method called searching combination moving window partial least squares (SCMWPLS) were employed to determine the concentrations of human serum albumin (HSA), γ-globulin, and glucose contained in the control serum IIB (CS IIB) solutions with various concentrations. SCMWPLS is proposed to search for the optimized combinations of informative regions, which are spectral intervals, considered containing useful information for building partial least squares (PLS) models. The informative regions can easily be found by moving window partial least squares regression (MWPLSR) method. PLS calibration models using the regions obtained by SCMWPLS were developed for HSA, γ-globulin, and glucose. These models showed good prediction with the smallest root mean square error of predictions (RMSEP), the relatively small number of PLS factors, and the highest correlation coefficients among the results achieved by using whole region and MWPLSR methods. The RMSEP values of HSA, γ-globulin, and glucose yielded by SCMWPLS were 0.0303, 0.0327, and 0.0195 g/dl, respectively. These results prove that SCMWPLS can be successfully applied to determine simultaneously the concentrations of HSA, γ-globulin, and glucose in complicated biological fluids such as CS IIB solutions by using NIR spectroscopy.     

Analysis of interferents by means a D-optimal screening design and calibration using partial least squares regression in the spectrophotometric determination of Cr(VI)

Using a central composite design, the signal of the process for the spectrophotometric determination of hexavalent chromium (λ = 543 nm) is maximised and its variability minimised using as complexing agent 1,5-diphenylcarbazide in sufficiently acid medium. To analyse the interference of various analytes (Mo(VI), V(V), Fe(III) and Mn(VII)) on the Cr(VI) as a function of concentration of interferent, a factorial design was prepared at three levels of each (zero, medium and high concentration), which implies performing 81 determinations. However, a D-optimal design with just nine experiments is sufficiently good to estimate the model proposed.The interference of these metals makes it impossible to determine Cr(VI) when they are present in the sample. To avoid prior separation steps, a multivariate regression by partial least squares, PLS, is proposed to calibrate the Cr(VI) in the presence of these analytes varying the concentration of the Cr(VI) between 0.1 and 0.9 μg ml⁻¹ and that of the interferents between 3 and 5 μg ml⁻¹. The average errors obtained were 4.5% and 3.29% fitted and in prediction, respectively, with a standard error in prediction (RMSEP) of 0.016% presenting absence of both constant and proportional bias.The detection limit with the PLS regression in the presence of interferents is 0.1 μg ml⁻¹ with a probability of false positive equal to 5% and less than 5% for false negative. The capability of detection is similar to that obtained with the univariate calibration (absorbance at 543 nm) in absence of interferents.With the PLS regression it is possible to discriminate 0.085 μg ml⁻¹ of Cr(VI) in a sample with 0.5 μg ml⁻¹ of Cr(VI) with probabilities of false compliance and false non-compliance equal to 0.05. For the univariate calibration without interferents, it was established at 0.0971 μg ml⁻¹ of Cr(VI) for the same nominal concentration.In relation to interference of V(V), Fe(III) and Mn(VII), the PLS calibration could be an efficient alternative to the separation step for Cr(VI) spectrophotometric determination using 1,5-diphenylcarbazide.     

Improving the robustness of a partial least squares (PLS) model based on pure component selectivity analysis and range optimization: case study for the analysis of an etching solution containing hydrogen peroxide

Pure component selectivity analysis (PCSA) was successfully utilized to enhance the robustness of a partial least squares (PLS) model by examining the selectivity of a given component to other components. The samples used in this study were composed of NH₄OH, H₂O₂ and H₂O, a popular etchant solution in the electronic industry. Corresponding near-infrared (NIR) spectra (9000-7500 cm⁻¹) were used to build PLS models. The selective determination of H₂O₂ without influences from NH₄OH and H₂O was a key issue since its molecular structure is similar to that of H₂O and NH₄OH also has a hydroxyl functional group. The best spectral ranges for the determination of NH₄OH and H₂O₂ were found with the use of moving window PLS (MW-PLS) and corresponding selectivity was examined by pure component selectivity analysis. The PLS calibration for NH₄OH was free from interferences from the other components due to the presence of its unique NH absorption bands. Since the spectral variation from H₂O₂ was broadly overlapping and much less distinct than that from NH₄OH, the selectivity and prediction performance for the H₂O₂ calibration were sensitively varied depending on the spectral ranges and number of factors used. PCSA, based on the comparison between regression vectors from PLS and the net analyte signal (NAS), was an effective method to prevent over-fitting of the H₂O₂ calibration. A robust H₂O₂ calibration model with minimal interferences from other components was developed. PCSA should be included as a standard method in PLS calibrations where prediction error only is the usual measure of performance.