S. Maspoch

Near-infrared spectroscopy in the pharmaceutical industry

Introduction Background and literature sources Specialized journals Internet addresses Previous reviews Fundamentals of the technique Principles of NIR spectroscopy NIR diffuse reflectance spectroscopy Operational procedures in the NIR Mathematical processing of signals Qualitative analysis Identification and qualification of raw materials and pharmaceutical preparations Determination of homogeneity Polymorphism and optical isomers Quantitative analysis Sample selection Multivariate calibration methods Determination of physical parameters Determination of moisture content Determination of active compounds and excipients Calibration transfer Miscellaneous applications Conclusions References

NIR calibration in non-linear systems: different PLS approaches and artificial neural networks

Abstract The frequent non-linearity of the calibration models used in infrared reflectance spectroscopy (NIRSS) is the main source of large errors in analyte determinations with this technique. Non-linearity in this type of system arises from factors such as the multiplicative effect of differences in particle size among samples or an intrinsically non-linear absorbance–concentration relationship resulting from interactions between components, hydrogen bonding, etc. In this work, calibration methods including partial least-squares (PLS) regression, linear quadratic PLS (LQ-PLS), quadratic PLS (QPLS) and artificial neural networks (ANNs) were used in conjunction with the NIRRS technique to determine the moisture content of acrylic fibres, the wide variability in linear density of which results in differential multiplicative effects among samples. Based on the results, PC-ANN is the best choice for the intended application. However, the joint use of an effective spectral pretreatment and computational methods such as PLS and LQ-PLS, the optimization of which is much less labour-intensive, provides comparable results. Standard normal variate (SNV) was found to be the best of the spectral pretreatments compared with a view to reducing the non-linearity introduced by scattering. The subsequent application of PLS provides accurate results with linear systems (absorption band at 1450 nm). A non-linear calibration model must be applied instead, however, if the system concerned is intrinsically non-linear. Under these conditions, the three methods tested for this purpose (LQ-PLS, QPLS and ANN) provide comparable results.

Diode-array detectors in flow-injection analysis Mixture resolution by multi-wavelength analysis

The application of diode-array spectrophotometers to multi-component analysis by flow-injection analysis is reported. Two different aspects are considered: (a) the obtainment of a reproducible spectrum corresponding to the maximum of the FIA peak, and (b) the mathematical treatment involved in determining the mixture of components. The spectrum is recorded by two different procedures according to the type of injection system used. The mixtures are resolved with the aid of three different treatments: (a) a linear equation system, (b) a multi-component analysis program (Hewlett-Packard) and (c) a graphical method (multi-wavelength linear regression analysis). All three procedures have been applied to the determination of Fe(II) and Fe(III) with a mixture of 1,10-phenanthroline and sulphosalicylic acid.

Simultaneous kinetic-spectrophotometric determination of levodopa and benserazide by bi- and three-way partial least squares calibration

A procedure for the simultaneous kinetic-spectrophotometric determination of levodopa (I) and benserazide (II), from their oxidation reaction with KIO(4) in an acidic medium, is described. Both species instantly oxidize, giving rise to compounds which present maximum values of absorbance close to 400 nm. In the presence of an excess of the oxidizing agent, the levodopa derivative evolves to form the corresponding aminochrome (lambda(m)=480 nm), while the benserazide derivative decomposes to yield colorless compounds. The appearance of new compounds, with absorption bands in the region of 500-700 nm, is additionally seen upon adding the oxidizing agent to a mixture of I and II. These compounds also evolve decomposing and forming colorless products. In spite of the complexity of the system studied, the calibration by bi-linear partial least squares (PLS) as well as by three-way partial least squares (nPLS) permit the quantification of both analytes with a precision on the order of 0.7% for levodopa and of 1.5% for benserazide. nPLS also allows for the qualitative interpretation of the phenomena which occur. The proposed method is applied to the quantification of I and II in the commercial, pharmaceutical preparation Madopar, using high performance liquid chromatography (HPLC) as the analytical reference technique.

Near Infrared Spectrometry and Pattern Recognition as Screening Methods for the Authentication of Virgin Olive Oils of Very Close Geographical Origins

The authentication of foods requires the use of sophisticated and expensive analytical techniques. Thus, there is a need for new, fast and inexpensive analytical methodologies for use as effective screening methods. This paper proposes a NIR spectroscopy-based method for discriminating virgin olive oils of two very similar and geographically close denominations of origin, viz. “Siurana” and “Les Garrigues”, which are made from at least 90% of olives of the Arbequina variety. Two chemometric techniques, artificial neural networks (ANNs) and logistic regression (LR), were tested as classifying tools applied to NIR spectra. The results obtained were quite satisfactory in both cases, in spite of the similarity between the two denominations of origin. The proposed method is intended to fill a gap in the authentication of natural products and allow the discrimination of oil samples, and can be applied to the discrimination of other olive oils which belong to different denominations of origin.

Effect of Data Preprocessing Methods in Near-Infrared Diffuse Reflectance Spectroscopy for the Determination of the Active Compound in a Pharmaceutical Preparation

Near-infrared diffuse reflectance spectroscopy (NIRS) with a fiber-optic probe was used for the determination of the active compound in a commercial pharmaceutical preparation. In order to reduce the strong scatter in the spectra and prevent scatter-induced changes in measurements from prevailing over concentration-induced changes, several data preprocessing methods were tested: normalization, derivatives, multiplicative scatter correction, standard normal variate, and detrending. The effectiveness for reducing the scattering of each data preprocessing was assessed, and the best results were obtained with the use of the second derivative. The effect of the treatments on the quantitation of the active compound by partial least-squares regression (PLSR) was studied, similar results being obtained in all cases, with a relative standard error of prediction lower than 1.55%.

Calibration in non-linear near infrared reflectance spectroscopy: a comparison of several methods

Abstract Principal component regression (PCR) and partial least-squares regression (PLSR) are the two calibration procedures most frequently used in quantitative applications of near infrared diffuse reflectance spectroscopy (NIRRS). Some systems, however, exhibit a non-linear relationship that neither methodology can model. Frequently, the main culprit of such non-linearity is the multiplicative effect arising from non-uniform particle sizes or diameters in the samples. In this work, we tested various approaches to minimizing the non-linearity resulting from the multiplicative effect of differences in particle size or sample thickness, using the determination of linear density in acrylic fibres as physical model. The approaches tested involve the prior linearizing of data by logarithmic conversion and/or the use of non-linear calibration systems; in this context, the results of applying stepwise polynomial PCR (SWP-PCR) and PLSR (SWP-PLSR), and those provided by a neural network based on the scores of the PCR model (PC-ANN), were compared. The PC-ANN approach was found to provide the best results with linear density data. On the other hand, the SWP-PLSR approach performed on par with the previous one when the variable was linearized by conversion of its values into decimal logarithms.

Quantitation of the active compound and major excipients in a pharmaceutical formulation by near infrared diffuse reflectance spectroscopy with fibre optical probe

A new method for analyses of a commercial pharmaceutical formulation based on near infrared diffuse reflectance spectroscopy (NIRRS) with fibre optical probe and using partial least-squares regression (PLS) for multivariate calibration is proposed. Analyses include the determination of the active compound and major excipients. The influence of the spectral mode and wavelength range used are studied. Satisfactory predictive results for production samples require calibration with laboratory-made samples covering the concentration range involved in the manufacturing process, as well as samples from various production batches, which introduce the variation sources inherent in such a process. The number of production samples to be used for calibration in order to ensure correct prediction of new production samples is discussed.

Separation of profen enantiomers by capillary electrophoresis using cyclodextrins as chiral selectors.

A method for resolving the enantiomers of various 2-arylpropionic acids (viz. ketoprofen, ibuprofen and fenoprofen) by capillary zone electrophoresis (CZE) using a background electrolyte (BGE) containing a cyclodextrin as chiral selector is proposed. The effects of the type of cyclodextrin used and its concentration on resolution were studied and heptakis-2,3,6-tri- O-methyl-beta-cyclodextrin was found to be the sole effective choice for the quantitative enantiomeric resolution of all the compounds tested. The influence of pH, BGE concentration, capillary temperature and addition of methanol to the BGE on resolution and other separation-related parameters was also studied. The three compounds studied can be enantiomerically resolved with a high efficiency in a short time (less than 20 min) with no capillary treatment. This makes the proposed method suitable for assessing the enantiomeric purity of commercially available pharmaceuticals.

Principal Component Regression for Mixture Resolution in Control Analysis by UV-Visible Spectrophotometry

The potential of principal component regression (PCR) for mixture resolution by UV-visible spectrophotometry was assessed. For this purpose, a set of binary mixtures with Gaussian bands was simulated, and the influence of spectral overlap on the precision of quantification was studied. Likewise, the results obtained in the resolution of a mixture of components with extensively overlapped spectra were investigated in terms of spectral noise and the criterion used to select the optimal number of principal components. The model was validated by cross-validation, and the number of significant principal components was determined on the basis of four different criteria. Three types of noise were considered: intrinsic instrumental noise, which was modeled from experimental data provided by an HP 8452A diode array spectrophotometer; constant baseline shifts; and baseline drift. Introducing artificial baseline alterations in some samples of the calibration matrix was found to increase the reliability of the proposed method in routine analysis. The method was applied to the analysis of mixtures of Ti, AI, and Fe by resolving the spectra of their 8-hydroxyquinoline complexes previously extracted into chloroform.