Detection of counterfeit Scotch whisky samples using mid-infrared spectrometry with an attenuated total reflectance probe incorporating polycrystalline silver halide fibres

Two methods of analysis were developed to permit detection of counterfeit Scotch whisky samples using a novel attenuated total reflectance (ATR) diamond-tipped immersion probe for mid-infrared (MIR) spectrometry. The first method allowed determination of the ethanol concentration (35–45% (v/v)) in situ without dilution of the samples; the results obtained compared well with the supplied concentrations (average relative error of 1.2% and 0.8% for univariate and multivariate partial least squares (PLS) calibration, respectively). The second method involved analysis of dried residues of the whisky samples and caramel solutions on the diamond ATR crystal; principal component analysis (PCA) of the spectra was used to classify the samples and investigate the colorant added. Seventeen test whisky samples were successfully categorised as either authentic or counterfeit in a blind study when both MIR methods were used.     

Multicomponent Determination of Organophosphorus Pesticides in Grain Samples by Linear Sweep Stripping Voltammetry and Multivariate Calibration

Abstract

Organophosphorus pesticides, such as parathion methyl (PTM), fenitrothion (FT), parathion (PT), and isocarbophos (ICP), have sensitive but overlapped voltammetric peaks with peak potentials ?309, ?364, ?317, and ?480 mV, respectively, in Britton‐Robinson buffer of pH 4.8 by application of linear sweep stripping voltammetry (LSSV). In this work, two multivariate calibration methods, partial least squares (both PLS‐1 and PLS‐2), and principal component regression (PCR), were applied to quantitatively resolve the overlapping voltammogram of the mixtures of these four pesticides. The prediction results obtained from a set of independent test samples showed that PLS‐1 method performed better prediction ability than PLS‐2 and PCR methods. The proposed method was successfully applied to the determination of these four pesticides in grain samples after a pre‐extraction step with a solvent of acetone.     

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.     

Kohonen artificial neural networks as a tool for wavelength selection in multicomponent spectrofluorimetric PLS modelling: application to phenol,o-cresol,m-cresol and p-cresol mixtures

Different strategies for wavelength selection for partial least squared (PLS) calibration models have been proposed. In this article, Kohonen artificial neural networks (K-ANN) are used to select optimal sets of wavelengths for PLS calibration of mixtures with stray overlapping. This kind of variable selection appears simple and very effective due to the well known high correlation of spectroscopic data; a measure of the multivariate correlation of the different wavelength subsets is also given. This strategy has been applied to the resolution of mixtures of phenol, o-cresol, m-cresol and p-cresol by spectrofluorimetry. The number of samples to obtain the calibration matrix is also reduced with respect to the number necessary when the full spectrum is used, and the predictive ability of the PLS method is improved.     

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).     

Spectrophotometric Simultaneous Determination of Amaranth,Ponceau 4R,Allura Red and Red 2G by Partial Least Squares and Principal Component Regression Multivariate Calibration

ABSTRACT

The multivariate calibration methods, partial least square regression type 1 (PLS 1) and principal component regression (PCR), were proposed for the simultaneous spectrophotometry determination of Amaranth (E-123), Ponceau 4R (E-124), Allura red (E-129) and Red 2G (E-128) in their mixtures. The parameters of the chemometric procedures were optimized and the proposed method was validated with synthetic samples and applied to analyze these dyes in spiked samples of beverages with satisfactory results.     

Simultaneous kinetic spectrophotometric determination of periodate and iodate based on their reaction with pyrogallol red in acidic media by chemometrics methods

The univariate and multivariate calibration methods were applied for the simultaneous determination of iodate and periodate in water. The method is based on the reaction of periodate and iodate with pyrogallol red in sulfuric acid media. The reaction was monitored spectrophotometrically by measuring the decrease in absorbance of pyrogallol red at 470 nm. The calibration curve was linear over the concentration ranges of 0.1-12 and 0.1-14 μg ml⁻¹ for iodate and periodate, respectively. The experimental calibration matrix for partial least squares (PLS) and orthogonal signal correction (OSC-PLS) method was designed with 35 mixtures. The results obtained by the PLS and OSC-PLS were statistically compared. The effects of various cations and anions on iodate and periodate determination have been investigated.     

Evaluation of Multivariate Calibration Using a Tikhonov Regularization Approach and the Generalized Pair‐Correlation Method with Nonlinear Data

Abstract

In order to reduce data nonlinearity and overfitting with the multivariate calibration model y=Xb, a modified Tikhonov regularization (TR) algorithm is evaluated for selecting key variables from an X augmented with extra columns that contain the original measured variables (x _ij ) as squared terms (x _ij ²) and other orders. The TR approach simultaneously develops the multivariate calibration model. The new generalized pair‐correlation method (GPCM) is also studied for variable selection followed by partial least squares (PLS) for multivariate calibration. Results from synthetic spectral data are compared when using the modified TR approach, GPCM, and PLS without variable selection. The GPCM usually performs slightly better than the TR approach for tabulated bias and variance measures and in some cases, at a sacrifice to parsimony. The method of PLS without variable selection performs the worst. By using synthetic spectral data sets, how the methods work could be studied. Thus, results from this study will aid investigators of real spectral data sets exhibiting nonlinear behavior.     

Comparison of Multicomponent Determination of Iprodione,Procymidone and Chlorothalonil by Partial Least Squares Modelling Using Spectrophotometric and High-Performance Liquid Chromatography Data

Abstract

A Partial Least Squares (PLS) calibration method was applied to the simultaneous determination of iprodione, procymidone and chlorothalonil in mixtures, by uv-vis absorption spectrophotometry and by high performance liquid chromatography (HPLC). Signals and first-derivative (¹D) signals were used to optimize the calibration matrices by the PLS-1 method. Quantitative results are presented for synthetic mixtures and for extracts from soil and groundwater samples. Significant improvements were achieved by using the PLS-1 method built with first-derivative chromatograms, in the determination of iprodione, procymidone and chlorothalonil in environmental samples.     

Detection limit estimator for multivariate calibration by an extension of the IUPAC recommendations for univariate methods

A methodology is proposed to estimate the limit of detection (LOD) of analytical methods when multivariate calibration is applied. It tries to follow the same premises as the IUPAC methodology for univariate calibration. The mathematical support is given and algorithms such as partial least squares (PLS) regression, PLS2 and principal component regression (PCR) are used. Only multivariate raw data are used; that is, no surrogate univariate signal is deduced. Non-linearities are allowed. Near infrared (NIR) data of 5 component pseudo-gasoline samples together with simulated fluorescence synchronous spectra of binary mixtures (first order data) are used for evaluation. Experimental verification is performed using different kinds of data, namely: binary mixtures of bentazone and fenamiphos (very overlapped spectra, second order data) obtained by sequential injection (SI), and kinetic data of the reaction between the Fenton's reagent (FR) and pesticides such as atrazine, bentazone and alachlor (individual or binary mixtures, second order data). Results are always compared with independent methods previously proposed in the literature, based in the use of surrogate univariate signals. In general, similar results are found and no statistically significant differences seem to be present, except in a few cases when complex chemical systems are involved.     

Application of an optimization procedure in adsorptive stripping voltammetry for the determination of trace contaminant metals in aqueous medium

A procedure for the determination of the contaminant metal In(III) by differential pulse adsorptive stripping voltammetry (DPAdSV) using ammonium pyrrolidine dithiocarbamate (APDC) as a complexing agent, has been optimized. The selection of the experimental conditions was made using experimental design methodology by means of a robust regression method which allows the elimination of anomalous points. The detection limit obtained was 1.3×10⁻⁹ mol dm⁻³. Possible interferences from concomitant metal ions were evaluated. Among all the metals analyzed, only Cd(II) was found to create an interference. This fact made impossible to carry out the determination of In(III) in the presence of Cd(II) using a univariate calibration. This problem was solved using multivariate regression techniques such as partial least squares (PLS). The procedure was successfully applied to the determination of indium in different aqueous samples.     

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.     

A method for calibration and validation subset partitioning

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.     

Multivariate calibration of spectra obtained by Laser Induced Breakdown Spectroscopy of plutonium oxide surrogate residues

Laser Induced Breakdown Spectroscopy (LIBS) was used to determine elemental concentration of plutonium oxide surrogate (cerium oxide) residue for monitoring the fabrication of lanthanide borosilicate glass. Quantitative analysis by LIBS is affected by the severe limitation of variation in the induced plasma due to changes in the matrix. Multivariate calibration was applied to LIBS data to predict the concentrations of Ce, Cr, Fe, Mo, and Ni. A total of 18 different samples were prepared to compare calibration from univariate data analysis and from multivariate data analysis. Multivariate calibration was obtained using Principal Component Regression (PCR) and Partial Least Squares (PLS). Univariate calibration was obtained from background-corrected atomic emission lines. Calibration results show improvement in the coefficient of determination from 0.87 to 0.97 for Ce compared to univariate calibration. The root mean square error also reduced from 7.46 to 2.93%. A similar trend was obtained for Cr, Fe, Mo, and Ni also. These results clearly demonstrate the feasibility of using LIBS for online process monitoring in a hazardous waste management environment.     

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.     

Multivariate calibration applied to the time-resolved chemiluminescence for the simultaneous determination of morphine and its antagonist naloxone

A novel alternative for the simultaneous determination of compounds with similar structure is described, using the whole chemiluminescence-time profiles, acquired by the stopped-flow technique, in combination with mathematical treatments of multivariate calibration. The proposed method is based on the chemiluminescent oxidation of morphine and naloxone by their reaction with potassium permanganate in an acidic medium, using formaldehyde as co-factor. The whole chemiluminescence-time profiles, acquired using the stopped-flow technique in a continuous-flow system, allowed the use of the time-resolved chemiluminescence (CL) data in combination with multivariate calibration techniques, as partial least squares (PLS), for the quantitative determination of both opiate narcotics in binary mixtures.In order to achieve overcoat the additivity of the CL profiles and beside to obtain CL profiles for each drug the most separated as possible in the time, the optimum chemical conditions for the CL emission were investigated. The effect of common emission enhancers on the CL emission obtained in the oxidation reaction of these compounds in different acidic media was studied. The parameters selected were sulphuric acid 1.0 mol L⁻¹, permanganate 0.2 mmol L⁻¹ and formaldehyde 0.8 mol L⁻¹. A calibration set of standard samples was designed by combination of a factorial design, with three levels for each factor and a central composite design. Finally, with the aim of validating the chemometric proposed method, a prediction set of binary samples was prepared. Using the multivariate calibration method proposed, the analytes were determined in synthetic samples, obtaining recoveries of 97-109%.     

Simultaneous spectrophotometric determination of phenanthridine, phenanthridinone and phenanthridine N-oxide using multivariate calibration methods

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.     

Experimental design and multivariate calibration in the development, set-up and validation of a differential pulse polarographic and UV spectrophotometric method for the simultaneous plasmatic determination of the therapeutic metronidazole-pefloxacin combination

Partial least squares regression (PLS1 and PLS2) and GOLPE variable selection procedures were used for the treatment of differential pulse polarographic and UV spectrophotometric data obtained from the analysis of the therapeutic combination of metronidazole and pefloxacin. The analytical method used for the determination was set up using experimental design strategies (Doehlert's design, full factorial design, fractional face center cube design, etc.) and by involving the simultaneous optimization of several responses (desirability function). Method validation was also performed, determining accuracy, precision, linearity and range, detection and quantification limits and robustness. The quantitative prediction abilities in determining metronidazole and pefloxacin plasma levels of the PLS1 and PLS2 models were tested on spiked plasma samples and good results were obtained (metronidazole, 97.5%, RSD = 4.8%, n = 3; pefloxacin, 100.6%, RSD = 3.6%, n = 3). The use of multivariate calibration was particularly useful for spectrophotometric quantification because of the highly overlapping spectra of the binary mixture.     

偏最小二乘回归用于近红外光谱分析的稳健策略

偏最小二乘法（PLS）在近红外光谱（NIR）定量分析中占有重要地位,但预测结果往往容易受到样本分组和奇异样本等因素的影响,稳健性不强。多模型PLS (EPLS）方法在模型稳健性上得到提高,然而它无法识别样本中存在的奇异样本。为了同时提高模型的预测准确性和稳健性,本文提出了一种根据取样概率重新取样的多模型PLS方法,称为稳健共识PLS（RE-PLS）方法。该方法通过迭代赋权偏最小二乘法(IRPLS)计算样本回归残差得到每个校正集样本的取样概率,然后根据样本的取样概率来选择训练子集建立多个PLS模型,最后将所有PLS模型的预测结果平均作为最终预测结果。该方法用于两种不同植物样品的近红外光谱建模,并与传统的PLS及EPLS方法进行比较。结果表明该方法可以有效的避免校正集中奇异样本对模型的影响,同时可以提高预测精确度和稳健性。对于含有较多奇异样本的,复杂近红外光谱烟草实际样本,利用简单PLS或者EPLS方法建模预测效果不是很理想,而RE-PLS凭借其独特优势则有望在这种复杂光谱定量分析中得到广泛的应用。     

Coupling of air-assisted liquid–liquid microextraction method with partial least squares for simultaneous spectrophotometric determination of some preservatives

An air-assisted liquid–liquid microextraction method coupled with a multivariate calibration method, namely partial least squares (PLS), was developed for the extraction and simultaneous determination of benzoic acid (BA) and sorbic acid (SA) via a spectrophotometric approach. In this work, a two-step microextraction method was used. In the first step, analytes were extracted from acidic aqueous solution into octanol, as an organic solvent, and in the second step, the analytes were simultaneously back-extracted into an alkaline aqueous solution. The high absorption signal of octanol was the main reason to perform this back-extraction step. The effects of different parameters on the method efficiency were investigated; the parameters included extraction solvent volume, ionic strength of aqueous solution, pH, number of extraction cycles, and aqueous sample volume. Under optimum conditions, calibration graphs were seen to be linear over the range of 0.1–2.0 µg mL^?1 for the both analytes. Other analytical parameters were obtained as follows: Enrichment factors (EFs) were found to be 14.98 and 13.03, and limits of detection were determined to be 0.03 and 0.04 µg mL^?1 for BA and SA, respectively. As the last step, binary mixtures of the analytes were prepared and simultaneously extracted using the proposed method. Finally, PLS modeling was used for multivariate calibration of spectrophotometric data. It was successfully utilized for the analysis of the target analytes in real samples.