A new issue of this journal has just been published. To see abstracts of the papers it contains (with links through to the full papers) click here:
Selected papers from the latest issue:
Testing the performance of pure spectrum resolution from Raman hyperspectral images of differently manufactured pharmaceutical tablets
16 November 2011, 00:13:38
Publication year: 2011
Source: Analytica Chimica Acta, Available online 15 November 2011
Balázs Vajna, Attila Farkas, Hajnalka Pataki, Zsolt Zsigmond, Tamás Igricz, ...
Chemical imaging is a rapidly emerging analytical method in pharmaceutical technology. Due to the numerous chemometric solutions available, characterization of pharmaceutical samples with unknown components present has also become possible. This study compares the performance of current state-of-the-art curve resolution methods (multivariate curve resolution–alternating least squares, positive matrix factorization, simplex identification via split augmented Lagrangian and self-modelling mixture analysis) in the estimation of pure component spectra from Raman maps of differently manufactured pharmaceutical tablets. The batches of different technologies differ in the homogeneity level of the active ingredient, thus, the curve resolution methods are tested under different conditions. An empirical approach is shown to determine the number of components present in a sample. The chemometric algorithms are compared regarding the number of detected components, the quality of the resolved spectra and the accuracy of scores (spectral concentrations) compared to those calculated with classical least squares, using the true pure component (reference) spectra. It is demonstrated that using appropriate multivariate methods, Raman chemical imaging can be a useful tool in the non-invasive characterization of unknown (e.g. illegal or counterfeit) pharmaceutical products.
Source: Analytica Chimica Acta, Available online 15 November 2011
Balázs Vajna, Attila Farkas, Hajnalka Pataki, Zsolt Zsigmond, Tamás Igricz, ...
Chemical imaging is a rapidly emerging analytical method in pharmaceutical technology. Due to the numerous chemometric solutions available, characterization of pharmaceutical samples with unknown components present has also become possible. This study compares the performance of current state-of-the-art curve resolution methods (multivariate curve resolution–alternating least squares, positive matrix factorization, simplex identification via split augmented Lagrangian and self-modelling mixture analysis) in the estimation of pure component spectra from Raman maps of differently manufactured pharmaceutical tablets. The batches of different technologies differ in the homogeneity level of the active ingredient, thus, the curve resolution methods are tested under different conditions. An empirical approach is shown to determine the number of components present in a sample. The chemometric algorithms are compared regarding the number of detected components, the quality of the resolved spectra and the accuracy of scores (spectral concentrations) compared to those calculated with classical least squares, using the true pure component (reference) spectra. It is demonstrated that using appropriate multivariate methods, Raman chemical imaging can be a useful tool in the non-invasive characterization of unknown (e.g. illegal or counterfeit) pharmaceutical products.
Highlights
► MCR-ALS and PMF provide better spectra and concentration maps than SMMA and ► SISAL. ► Homogeneous distribution of a component makes curve resolution much less accurate. ► MCR-ALS can be also well used if a component is homogeneously distributed. ► An unknown product can be characterized regardless how it was manufactured. ► Our results show perspectives in the analysis of unknown (illegal) drugsChemistry of ascorbic acid and sulfur dioxide as an antioxidant system relevant to white wine
16 November 2011, 00:13:38
Publication year: 2011
Source: Analytica Chimica Acta, Available online 15 November 2011
Célia Barril, Andrew C Clark, Geoffrey R Scollary
The impact of the combined ascorbic acid and sulfur dioxide antioxidants on white wine oxidation processes was investigated using a range of analytical techniques, including flow injection analysis for free and total sulfur dioxide and two chromatographic methods for ascorbic acid, its oxidative degradation products and phenolic compounds. The combination of different analytical techniques provided a fast and simultaneous means for the monitoring of oxidation processes in a model wine system. In addition, the initial mole ratio of sulfur dioxide to ascorbic acid was varied and the model wine complexity was increased by the inclusion of metal ions (copper(II) and iron(II)). Sulfur dioxide was found not to be a significant binder of ascorbic acid oxidative degradation products and could not prevent the formation of certain phenolic pigment precursors. The results provide a detailed insight into the ascorbic acid/sulfur dioxide antioxidant system in wine conditions.
Source: Analytica Chimica Acta, Available online 15 November 2011
Célia Barril, Andrew C Clark, Geoffrey R Scollary
The impact of the combined ascorbic acid and sulfur dioxide antioxidants on white wine oxidation processes was investigated using a range of analytical techniques, including flow injection analysis for free and total sulfur dioxide and two chromatographic methods for ascorbic acid, its oxidative degradation products and phenolic compounds. The combination of different analytical techniques provided a fast and simultaneous means for the monitoring of oxidation processes in a model wine system. In addition, the initial mole ratio of sulfur dioxide to ascorbic acid was varied and the model wine complexity was increased by the inclusion of metal ions (copper(II) and iron(II)). Sulfur dioxide was found not to be a significant binder of ascorbic acid oxidative degradation products and could not prevent the formation of certain phenolic pigment precursors. The results provide a detailed insight into the ascorbic acid/sulfur dioxide antioxidant system in wine conditions.
Highlights
► Use of flow injection analysis and liquid chromatography for the monitoring of oxidative processes relevant to white wine. ► A range of sulfur dioxide to ascorbic acid initial mole ratios was used to evaluate the combined effects of the antioxidant system. ► The ascorbic acid degradation products were found to be weak binders of sulfur dioxide under wine conditions.Kappa-Casein Based Electrochemical and Surface Plasmon Resonance Biosensors for the Assessment of the Clotting Activity of Rennet
16 November 2011, 00:13:38
Publication year: 2011
Source: Analytica Chimica Acta, Available online 15 November 2011
Maria A. Panagopoulou, Dimitrios V. Stergiou, Ioannis G. Roussis, George Panayotou, Mamas I. Prodromidis
We report for the first time the development ofkappa-casein (κ-CN)-based electrochemical and surface plasmon resonance (SPR) biosensors for the assessment of the clotting activity of rennet. Electrochemical biosensors were developed over gold electrodes modified with a self-assembled monolayer of dithiobis − N − succinimidyl propionate, while SPR measurements were performed on regenerated carboxymethylated dextran gold surfaces. In both types of biosensor,κ-CN molecules were immobilized onto modified gold surfaces through covalent bonding. In electrochemical biosensors, interactions between the immobilizedκ-CN molecules and chymosin (the active component of rennet) were studied by performing cyclic voltammetry, differential pulsed voltammetry, and electrochemical impedance spectroscopy (EIS) measurements, using hexacyanoferrate(II)/(III) couple as a redox probe.κ-CN is cleaved by rennet at the Phe105 − Met106 bond, producing a soluble glycomacropeptide, which is released to the electrolyte, and the positively charged insolublepara-κ-casein molecule, which remains attached to the surface of the electrode. This induced reduction of the net negative charge of the sensing surface, along with the partial degradation of the sensing layer, results in an increase of the flux of the redox probe, which exists in the solution, and consequently, to signal variations, which are associated with the increased electrocatalysis of the hexacyanoferrate(II)/(III) couple on the gold surface. SPR experiments were performed in the absence of the redox probe and the observed SPR angle alterations were solely attributed to the cleavage of the immobilizedκ-CN molecules. Various experimental variables were investigated and under the selected conditions the proposed biosensors were successfully tried to real samples. The ratios of the clotting power units in various commercial solid or liquid samples, as they are calculated by the EIS-based data, were almost identical to those obtained with a reference method. In addition, EIS measurements showed an excellent reproducibility, lower than 5%.
Source: Analytica Chimica Acta, Available online 15 November 2011
Maria A. Panagopoulou, Dimitrios V. Stergiou, Ioannis G. Roussis, George Panayotou, Mamas I. Prodromidis
We report for the first time the development ofkappa-casein (κ-CN)-based electrochemical and surface plasmon resonance (SPR) biosensors for the assessment of the clotting activity of rennet. Electrochemical biosensors were developed over gold electrodes modified with a self-assembled monolayer of dithiobis − N − succinimidyl propionate, while SPR measurements were performed on regenerated carboxymethylated dextran gold surfaces. In both types of biosensor,κ-CN molecules were immobilized onto modified gold surfaces through covalent bonding. In electrochemical biosensors, interactions between the immobilizedκ-CN molecules and chymosin (the active component of rennet) were studied by performing cyclic voltammetry, differential pulsed voltammetry, and electrochemical impedance spectroscopy (EIS) measurements, using hexacyanoferrate(II)/(III) couple as a redox probe.κ-CN is cleaved by rennet at the Phe105 − Met106 bond, producing a soluble glycomacropeptide, which is released to the electrolyte, and the positively charged insolublepara-κ-casein molecule, which remains attached to the surface of the electrode. This induced reduction of the net negative charge of the sensing surface, along with the partial degradation of the sensing layer, results in an increase of the flux of the redox probe, which exists in the solution, and consequently, to signal variations, which are associated with the increased electrocatalysis of the hexacyanoferrate(II)/(III) couple on the gold surface. SPR experiments were performed in the absence of the redox probe and the observed SPR angle alterations were solely attributed to the cleavage of the immobilizedκ-CN molecules. Various experimental variables were investigated and under the selected conditions the proposed biosensors were successfully tried to real samples. The ratios of the clotting power units in various commercial solid or liquid samples, as they are calculated by the EIS-based data, were almost identical to those obtained with a reference method. In addition, EIS measurements showed an excellent reproducibility, lower than 5%.
Highlights
►k-CN-based electrochemical and SPR biosensors for the clotting power of rennet are described. ► The proposed biosensors were successfully tested at various commercial rennet samples. ► EIS is more reliable and reproducible than DPVA risk-based statistical investigation of the quantification of polymorphic purity of a pharmaceutical candidate by solid-stateF NMR
16 November 2011, 00:13:38
Publication year: 2011
Source: Analytica Chimica Acta, Available online 15 November 2011
Samantha J Barry, Tran N Pham, Phil J Borman, Andrew J Edwards, Simon A Watson
The DMAIC (Define, Measure, Analyse, Improve and Control) framework and associated statistical tools have been applied to both identify and reduce variability observed in a quantitativeF solid-state NMR (SSNMR) analytical method. The method had been developed to quantify levels of an additional polymorph (Form 3) in batches of an active pharmaceutical ingredient (API), where Form 1 is the predominant polymorph. In order to validate analyses of the polymorphic form, a single batch of API was used as a standard each time the method was used. The level of Form 3 in this standard was observed to gradually increase over time, the effect not being immediately apparent due to method variability. In order to determine the cause of this unexpected increase and to reduce method variability, a risk-based statistical investigation was performed to identify potential factors which could be responsible for these effects. Factors identified by the risk assessment were investigated using a series of designed experiments to gain a greater understanding of the method. The increase of the level of Form 3 in the standard was primarily found to correlate with the number of repeat analyses, an effect not previously reported in SSNMR literature. Differences in data processing (phasing and linewidth) were found to be responsible for the variability in the method. After implementing corrective actions the variability was reduced such that the level of Form 3 was within an acceptable range of ±1% w/w in fresh samples of API.
Source: Analytica Chimica Acta, Available online 15 November 2011
Samantha J Barry, Tran N Pham, Phil J Borman, Andrew J Edwards, Simon A Watson
The DMAIC (Define, Measure, Analyse, Improve and Control) framework and associated statistical tools have been applied to both identify and reduce variability observed in a quantitativeF solid-state NMR (SSNMR) analytical method. The method had been developed to quantify levels of an additional polymorph (Form 3) in batches of an active pharmaceutical ingredient (API), where Form 1 is the predominant polymorph. In order to validate analyses of the polymorphic form, a single batch of API was used as a standard each time the method was used. The level of Form 3 in this standard was observed to gradually increase over time, the effect not being immediately apparent due to method variability. In order to determine the cause of this unexpected increase and to reduce method variability, a risk-based statistical investigation was performed to identify potential factors which could be responsible for these effects. Factors identified by the risk assessment were investigated using a series of designed experiments to gain a greater understanding of the method. The increase of the level of Form 3 in the standard was primarily found to correlate with the number of repeat analyses, an effect not previously reported in SSNMR literature. Differences in data processing (phasing and linewidth) were found to be responsible for the variability in the method. After implementing corrective actions the variability was reduced such that the level of Form 3 was within an acceptable range of ±1% w/w in fresh samples of API.
Highlights
► Investigate a quantitativeF solid-state NMR method for polymorphic purity. ► DMAIC framework used to investigate risk factors and reduce variability. ► Analytical method found to directly affect polymorphism. ► Differences in data processing are responsible for variability in the method. ► Specific processing steps implemented to reduce variability.One- and two-dimensional GC-MS and HPLC–DAD fingerprints of complex substances: A comparison of classification performance of similar, complexRhizoma Curcumaesamples with the aid of chemometrics
16 November 2011, 00:13:38
Publication year: 2011
Source: Analytica Chimica Acta, Available online 15 November 2011
Yongnian Ni, Minghua Mei, Serge Kokot
Many complex natural or synthetic products are analysed either by the GC-MS or HPLC-DAD technique, each of which produces a one-dimensional fingerprint for a given sample. This may be used for classification of different batches of a product. GC-MS and HPLC-DAD analyses of complex, similar substances represented by the three common types of the TCM,Rhizoma Curcumaewere analysed in the form of one- and two-dimensional matrices firstly with the use of PCA, which showed a reasonable separation of the samples for each technique. However, the separation patterns were rather different for each analytical method, and PCA of the combined data matrix showed improved discrimination of the three types of object; close associations between the GC-MS and HPLC-DAD variables were observed. LDA, BP-ANN and LS-SVM chemometrics methods were then applied to classify the training and prediction sets. For one-dimensional matrices, all models indicated that several samples would be misclassified by each training model; the same was observed for each prediction set. However, by comparison, in the analysis of the combined matrix, all models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, with the BP-ANN calibration closely behind. This has important implications for comparing complex substances such as the TCMs because clearly the one-dimensional data matrices alone produce inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information.
Source: Analytica Chimica Acta, Available online 15 November 2011
Yongnian Ni, Minghua Mei, Serge Kokot
Many complex natural or synthetic products are analysed either by the GC-MS or HPLC-DAD technique, each of which produces a one-dimensional fingerprint for a given sample. This may be used for classification of different batches of a product. GC-MS and HPLC-DAD analyses of complex, similar substances represented by the three common types of the TCM,Rhizoma Curcumaewere analysed in the form of one- and two-dimensional matrices firstly with the use of PCA, which showed a reasonable separation of the samples for each technique. However, the separation patterns were rather different for each analytical method, and PCA of the combined data matrix showed improved discrimination of the three types of object; close associations between the GC-MS and HPLC-DAD variables were observed. LDA, BP-ANN and LS-SVM chemometrics methods were then applied to classify the training and prediction sets. For one-dimensional matrices, all models indicated that several samples would be misclassified by each training model; the same was observed for each prediction set. However, by comparison, in the analysis of the combined matrix, all models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, with the BP-ANN calibration closely behind. This has important implications for comparing complex substances such as the TCMs because clearly the one-dimensional data matrices alone produce inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information.
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