Control production of polyester resins by NIR spectroscopy

The control of the esterification reaction for production of polyester saturated resins is followed usually by determination of the acid value (AV) and hydroxyl value (OHV).These parameters are determined by titrimetry, but these methods are slow, intensity working and produce waste. In this paper an alternative methodology is proposed, based in the construction of multivariate models on NIR spectroscopic data and different models are constructed in order to apply to different steps of the production process. The ensuing methodology provides models of good predictive ability and constitute an advantageous alternative to existing titrimetric reference methods as regards expeditiousness and environmentally compatible. The multivariate calibration models established were also used with a different instrument; to this end, the spectra recorded with the original equipment were subjected to Piecewise Direct Standardization (PDS) in order to make them equivalent to those provided by the new equipment. Also, PLS calibration was reproduced by using the same samples, spectral treatment, wavenumber range and number of factors as in the original model, and the AV and OHV results thus obtained were similarly good.     

Multivariate versus univariate calibration for nonlinear chemiluminescence data: Application to chromium determination by luminol-hydrogen peroxide reaction

Multivariate calibration is tested as an alternative to model chromium(III) concentration versus chemiluminescence registers obtained from luminol-hydrogen peroxide reaction. The multivariate calibration approaches included have been: conventional linear methods (principal component regression (PCR) and partial least squares (PLS)), nonlinear methods (nonlinear variants and variants of locally weighted regression) and linear methods combined with variable selection performed in the original or in the transformed data (stepwise multiple linear regression procedure). Both the direct and inverse univariate approaches have been also tested.

The use of a double logarithmic transformation previous to the linear regression has been also evaluated. A new double logarithmic transformation previous to the linear regression is proposed in order to avoid the effect of the noise in the calibration model. Pre-processing, optimization and prediction ability of the multivariate calibration models has been studied at nine different experimental conditions including batch and FIA measurements. Box-plots, PCA and cluster analysis have been employed to test the prediction ability of the different models tested. Nonlinear PCR and nonlinear PLS provide the best results. Real samples have been analyzed and compared with the reference method. The results confirm the successful use of the proposed methodology.     

Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra

Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on its physical presentation.     

NIR analysis of pharmaceutical samples without reference data: improving the calibration

Using an appropriate set of samples to construct the calibration set is crucial with a view to ensuring accurate multivariate calibration of NIR spectroscopic data. In this work, we developed and optimized a new methodology for incorporating physical variability in pharmaceutical production based on the NIR spectrum for the process. Such a spectrum contains the spectral changes caused by each treatment applied to the component mixture during the production process. The proposed methodology involves adding a set of process spectra (viz. difference spectra between those for production tablets and a laboratory mixture of identical nominal composition) to the set of laboratory samples, which span the wanted concentration range, in order to construct a calibration set incorporating all physical changes undergone by the samples in each step of the production process. The best calibration model among those tested was selected by establishing the influence of spectral pretreatments used to obtain the process spectrum and construct the calibration models, and also by determining the multiplying factor m to be applied to the process spectra in order to ensure incorporation of all variability sources into the calibration model. The specific samples to be included in the calibration set were selected by principal component analysis (PCA). To this end, the new methodology for constructing calibration sets for determining the Active Principle Ingredients (API) and excipients was applied to Irbesartan tablets and validated by application to the API and excipients of paracetamol tablets. The proposed methodology provides simple, robust calibration models for determining the different components of a pharmaceutical formulation.     

Efficient use of pure component and interferent spectra in multivariate calibration

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.     

Non-destructive method for determination of hydroxyl value of soybean polyol by LS-SVM using HATR/FT-IR

This paper presents the use of least-squares support vector machine (LS-SVM) for quantitative determination of hydroxyl value (OHV) of hydroxylated soybean oils by horizontal attenuated total reflection Fourier transform infrared (HATR/FT-IR) spectroscopy. A least-squares support vector machine (LS-SVM) calibration model for the prediction of hydroxyl value (OHV) was developed using the range 1805.1-649.9 cm(-1). Validation of the method was carried out by comparing the OHV of a series of hydroxylated soybean oil predicted by the LS-SVM model to the values obtained by the AOCS standard method. A correlation coefficient equal to 0.989 and RMSEP = 4.96 mg of KOH/g was obtained. This study demonstrates a better prediction ability of the LS-SVM technique to determine OHV in hydroxylated soybean oil samples by HATR/FT-IR spectra.     

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.     

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

Near-infrared spectra combined with partial least squares for pH determination of toothpaste of different brands

Near-infrared spectroscopy(NIR),which is generally used for online monitoring of the food analysis and production process, was applied to determine the internal quality of toothpaste samples.It is acknowledged that the spectra can be significantly influenced by non-linearities introduced by light scatter,therefore,four data preprocessing methods,including off-set correction, 1st-derivative,standard normal variate(SNV) and multiplicative scatter correction(MSC),were employed before the date analysis. The multivariate calibration model of partial least squares(PLS) was established and then was used to predict the pH values of the toothpaste samples of different brand.The results showed that the spectral date processed by MSC was the best one for predicting the pH value of the toothpaste samples.     

An improved outdoor calibration procedure for broadband ultraviolet radiometers

This article aims at improving the broadband ultraviolet radiometer's calibration methodology. For this goal, three broadband radiometers are calibrated against a spectrophotometer of reference. Three different one-step calibration models are tested: ratio, first order and second order. The latter is proposed in order to adequately reproduce the high dependence on the solar zenith angle shown by the other two models and, therefore, to improve the calibration performance at high solar elevations. The proposed new second-order model requires no additional information and, thus, keeps the operational character of the one-step methodology. The models are compared in terms of their root mean square error and the most qualified is subsequently validated by comparing its predictions with the spectrophotometer measurements within an independent validation data subset. Results show that the best calibration is achieved by the second-order model, with a mean bias error and mean absolute bias error lower than 2.2 and 6.7%, respectively.     

Determination of organic additives in mortars by near-IR spectroscopy. A novel approach to designing a sample set with high-variability components

Industrial mortars consist primarily of a mixture of cement and an aggregate plus a small amount of additives that are used to modify specific properties. Using too high or too low additive rates usually results in the loss of desirable properties in the end product. This entails carefully controlling the amounts of additives added to mortar in order to ensure correct dosing and/or adequate homogeneity in the final mixture. Near-IR (NIR) spectroscopy has proved effective for this purpose as it requires no sample pretreatment and affords expeditious analyses. The purpose of this work was to determine two organic additives (viz. Ad1 and Ad2) in mortars by using partial least squares regression multivariate calibration models constructed from NIR spectroscopic data. The additives are used to expedite setting and increase cohesion between particles in the mortar. In order to ensure that the sample set contained natural variability in the samples, we used a methodology based on experimental design to construct a representative set of samples. This novel design is based on a hexagonal antiprism that encompasses the concentration ranges spanned by the analytes and the variability inherent in each additive. The D-optimality criterion was used to obtain various combinations between Ad1 and Ad2 additive classes. The partial least squares calibration models thus constructed for each additive provided accurate predictions: the intercept and the slope of the plots of predicted values versus reference values for each additive were close to 0 and 1, respectively, and their confidence ranges included the respective value. The ensuing analytical methods were validated by using an external sample set.     

Simultaneous determination of penicillin and ampicillin by spectral fibre-optical enzyme optodes and multivariate data analysis based on transient signals obtained by flow injection analysis

A multicomponent detection system using optical biosensors and flow injection analysis is described. The analysis of mixtures containing penicillin and ampicillin was realised by evaluating dynamic measurements of Phenol Red spectra in penicillinase optodes in combination with a diode array spectrometer. A variety of optodes has been produced by changing the composition of the receptor gel and the working pH. A set of characteristic quantities (describing dynamic and static features) could be obtained for each optode. These were used to compare the predictivity of classical multivariate calibration methods as well as of an artificial neural network. In addition, different algorithms were applied for the evaluation of the spectral data in order to select the most appropriate method for feature extraction. In consequence, the information obtained from the multivariate calibration models was used to set up an optimal sensor array consisting of four optodes with different types of penicillinase at different working pH.     

Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration

Preprocessing of raw near-infrared (NIR) spectral data is indispensable in multivariate calibration when the measured spectra are subject to significant noises, baselines and other undesirable factors. However, due to the lack of sufficient prior information and an incomplete knowledge of the raw data, NIR spectra preprocessing in multivariate calibration is still trial and error. How to select a proper method depends largely on both the nature of the data and the expertise and experience of the practitioners. This might limit the applications of multivariate calibration in many fields, where researchers are not very familiar with the characteristics of many preprocessing methods unique in chemometrics and have difficulties to select the most suitable methods. Another problem is many preprocessing methods, when used alone, might degrade the data in certain aspects or lose some useful information while improving certain qualities of the data. In order to tackle these problems, this paper proposes a new concept of data preprocessing, ensemble preprocessing method, where partial least squares (PLSs) models built on differently preprocessed data are combined by Monte Carlo cross validation (MCCV) stacked regression. Little or no prior information of the data and expertise are required. Moreover, fusion of complementary information obtained by different preprocessing methods often leads to a more stable and accurate calibration model. The investigation of two real data sets has demonstrated the advantages of the proposed method.     

Classification and determination of total protein in milk powder using near infrared reflectance spectrometry and the successive projections algorithm for variable selection

This paper proposes a methodology for the classification and determination of total protein in milk powder using near infrared reflectance spectrometry (NIRS) and variable selection. Two brands of milk powder were acquired from three Brazilian cities (Natal-RN, Salvador-BA and Rio de Janeiro-RJ). The protein content of 38 samples was determined by the Kjeldahl method and NIRS analysis. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations were used to predict the total protein. Soft independent modeling of class analogy (SIMCA) was also used for full-spectrum classification, resulting in almost 100% classification accuracy, regardless of the significance level adopted for the F-test. Using this strategy, it was feasible to classify powder milk rapidly and nondestructively without the need for various analytical determinations. Concerning the multivariate calibration models, the results show that PCR, PLS and MLR-SPA models are good for predicting total protein in powder milk; the respective root mean square errors of prediction (RMSEP) were 0.28 (PCR), 0.25 (PLS), 0.11 wt% (MLR-SPA) with an average sample protein content of 8.1 wt%. The results obtained in this investigation suggest that the proposed methodology is a promising alternative for the determination of total protein in milk powder.     

Multicomponent analysis of electrochemical signals in the wavelet domain

Successful applications of multivariate calibration in the field of electrochemistry have been recently reported, using various approaches such as multilinear regression (MLR), continuum regression, partial least squares regression (PLS) and artificial neural networks (ANN). Despite the good performance of these methods, it is nowadays accepted that they can benefit from data transformations aiming at removing baseline effects, reducing noise and compressing the data. In this context the wavelet transform seems a very promising tool. Here, we propose a methodology, based on the fast wavelet transform, for feature selection prior to calibration. As a benchmark, a data set consisting of lead and thallium mixtures measured by differential pulse anodic stripping voltammetry and giving seriously overlapped responses has been used. Three regression techniques are compared: MLR, PLS and ANN. Good predictive and effective models are obtained. Through inspection of the reconstructed signals, identification and interpretation of significant regions in the voltammograms are possible.     

OWAVEC: a combination of wavelet analysis and an orthogonalization algorithm as a pre-processing step in multivariate calibration

In an spectroscopic context, when a calibration model based on partial least squares is developed to predict a response, it is often the case that a high percentage of variation in the data explained by the first latent variable is not accompanied by an equally high percentage of variation in the studied response. The addition of more components can slowly improve the calibration model, but with negative effects on the robustness and interpretability of the final model. To solve this problem, several pre-processing methods have been proposed to remove only a portion unrelated to the studied response from the spectral matrix.Moreover, the need for efficient compression methods is increasingly important due to the large size of the data currently collected. In this sense, discrete wavelet transform has proven that it can achieve good compression without losing relevant information when used on individual signals.This paper introduces a new pre-processing method, orthogonal wavelet correction (OWAVEC) that tries to lump together two important needs in multivariate calibration: signal correction and compression. The new method has been tested on a set of diesel fuels using viscosity as variable response, and its results have been compared not only with those obtained from original data but also with those provided by other correction methods. The first practical results are encouraging, as the method generates considerably better calibration models compared to the model developed from raw data and provides results as least so good as other orthogonal correction methods.     

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.     

A study of robustness with multivariate calibration. Application to the polarographic determination of benzaldehyde

A procedure to evaluate the robustness of an analytical method when there are changes in some experimental variables, when using multivariate calibration, is proposed. The procedure consists of analysing the root mean square error of prediction (RMSEP) as a response to a Plackett–Burman experimental design, through which the influence of several experimental factors on the prediction capability of the multivariate partial least squares (PLS) models built is studied. Two different ways of analysing the experimental design response are considered: establishing the residual variance with replicates and using Lenth's method. The proposed methodology has been applied to estimate the robustness of the polarographic determination of benzaldehyde when PLS calibration is used.     

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new “leave one out” method, so that the number of original variables resulted further reduced.     

Improvement of multivariate calibration techniques applied to 1-to-N component mixtures through an optimized experimental design

This work describes a novel experimental design aimed at building a calibration set constituted by samples containing a different number of components. The algorithm performs a reiteration process to maintain the number of samples at the lower value as possible and to ensure an homogeneous presence of all the concentration levels. The mixture design was applied to a drug system composed by one-to-four components in different combination. The resolution of the system was performed by three multivariate UV spectrophotometric methods utilizing principal component regression (PCR) and partial last squares (PLS1 and PLS2) algorithms. The calibration set was composed by 61 references on four concentration levels, including 15 samples for each quaternary, ternary and binary composition and 16 one-component samples. The calibration models were optimized through a careful selection of number of factors and wavelength zones, in such a way as to remove interferences from instrumental noise and excipients present in the pharmaceutical formulations. The prediction power of the regression models were verified and compared by analysis of an external prediction set. The models were finally used to assay pharmaceutical specialities containing the studied drugs in one-to-four formulations.