Monitoring the concentrations of Cd, Cu, Pb, Ni, Cr, Zn, Mn and Fe in cultivated Haplic Luvisol soils using near-infrared reflectance spectroscopy and chemometrics.

This study illustrates the successful application of near-infrared reflectance spectroscopy extended with chemometric modeling to profile Cd, Cu, Pb, Ni, Cr, Zn, Mn, and Fe in cultivated and fertilized Haplic Luvisol soils. The partial least-squares regression (PLSR) models were built to predict the elements present in the soil samples at very low contents. A total of 234 soil samples were investigated, and their reflectance spectra were recorded in the spectral range of 1100-2500 nm. The optimal spectral preprocessing was selected among 56 different scenarios considering the root mean squared error of prediction (RMSEP). The partial robust M-regression method (PRM) was used to handle the outlying samples. The most promising models were obtained for estimating the amount of Cu (using PRM) and Pb (using the classic PLS), leading to RMSEP expressed as a percentage of the response range, equal to 9.63% and 11.5%, respectively. The respective coefficients of determination for validation samples were equal to 0.86 and 0.58, respectively. Assuming similar variability of model residuals for the model and test set samples, coefficients of determination for validation samples were 0.94 and 0.89, respectively. Moreover, the favorable PLS models were also built for Zn, Mn, and Fe with coefficients of determinations equal to 0.87, 0.87, and 0.79.

Multi-wavelength imaging of HPTLC plates using a constructed illumination chamber with a smartphone camera as the detector.

In this article, we present a new type of inexpensive multi-illumination source chamber. The innovation makes use of a smartphone camera which possesses the ability of capturing multiple images. Its performance was compared to a commercially available densitometer. Similar devices and suitable strategies for data analysis will help to solve diverse classification and/or regression problems, which will be far beyond a TLC characterization of ink samples. The multi-illumination chamber was used in an exemplary forensic application. The differences in the chemical composition of various brands of fountain pen inks were revealed on images of high-performance thin-layer chromatographic plates. Reducing image data simplified the visualization and facilitated a multivariate exploratory of the ink samples. Compared to the samples that were characterized by single wavelength densitograms, the multi-wavelength characterization using the illumination chamber with a smartphone camera or densitometer improved the clustering tendency of studied samples and enhanced their interpretation. The constructed chamber for multi-wavelength imaging is an inexpensive alternative (ca. 20 Euros) to the commercially available densitometers. The discussed approaches for image acquisition and chemometric data processing support a more reliable and objective analysis of TLC multi-wavelength data.

Detecting chemical markers to uncover counterfeit rebated excise duty diesel oil.

In this study, differences in the chemical compositions of rebated excise duty diesel oil samples that were caused by fuel laundering were investigated. Two possible laundering pathways were simulated using either reduction or adsorption agents in model samples that were spiked with Solvent Yellow 124 and Solvent Red 19. The samples were characterized by their chromatographic fingerprints, which were recorded using gas chromatography coupled with a nitrogen chemiluminescence detector. The collections of fingerprints were further analyzed by discriminant partial least squares and the models with the optimal complexities presented the correct discrimination rates in the range of 69.1%-99.6%, respectively. The most informative fingerprint sections that were associated with the investigated differences were identified using the variable importance in projection, selectivity ratio and uninformative variable elimination methods. The reduced multivariate discriminant models presented a relatively high performance with the correct classification rates in the range of 74.9%-99.8%, respectively. O-toluidine and 2,5-diaminotoluene were identified as potential markers of diesel oil counterfeiting by laundering through a reduction agent.

Identification of ground meat species using near-infrared spectroscopy and class modeling techniques - Aspects of optimization and validation using a one-class classification model.

Chemometric methods permit the construction of classifiers that effectively assist in monitoring safety, quality and authenticity of meat based on the near-infrared (NIR) spectral fingerprints. Discriminant techniques are often considered in multivariate quality control. However, when the authenticity of meat products is the primary concern, they often lead to an incorrect recognition of new samples. The performances of two class modeling techniques (CMT) in order to recognize meat sample species based on their NIR spectra was compared - a one-class classifier variant of the partial least squares method (OCPLS) and the soft independent modeling of class analogy (SIMCA). Based on obtained sensitivity and specificity values, OCPLS and SIMCA can be considered as an effective CMT for the classification of complex natural samples such as studied meat samples (with a relatively large variability). Moreover, particular attention was paid to the optimization and validation of a one-class classification model.

Chemometrics and the identification of counterfeit medicines-A review.

This review article provides readers with a number of actual case studies dealing with verifying the authenticity of selected medicines supported by different chemometric approaches. In particular, a general data processing workflow is discussed with the major emphasis on the most frequently selected instrumental techniques to characterize drug samples and the chemometric methods being used to explore and/or model the analytical data. However, further discussion is limited to a situation in which the collected data describes two groups of drug samples - authentic ones and counterfeits.

The Monte Carlo validation framework for the discriminant partial least squares model extended with variable selection methods applied to authenticity studies of Viagra® based on chromatographic impurity profiles.

The aim of this work was to develop a general framework for the validation of discriminant models based on the Monte Carlo approach that is used in the context of authenticity studies based on chromatographic impurity profiles. The performance of the validation approach was applied to evaluate the usefulness of the diagnostic logic rule obtained from the partial least squares discriminant model (PLS-DA) that was built to discriminate authentic Viagra® samples from counterfeits (a two-class problem). The major advantage of the proposed validation framework stems from the possibility of obtaining distributions for different figures of merit that describe the PLS-DA model such as, e.g., sensitivity, specificity, correct classification rate and area under the curve in a function of model complexity. Therefore, one can quickly evaluate their uncertainty estimates. Moreover, the Monte Carlo model validation allows balanced sets of training samples to be designed, which is required at the stage of the construction of PLS-DA and is recommended in order to obtain fair estimates that are based on an independent set of samples. In this study, as an illustrative example, 46 authentic Viagra® samples and 97 counterfeit samples were analyzed and described by their impurity profiles that were determined using high performance liquid chromatography with photodiode array detection and further discriminated using the PLS-DA approach. In addition, we demonstrated how to extend the Monte Carlo validation framework with four different variable selection schemes: the elimination of uninformative variables, the importance of a variable in projections, selectivity ratio and significance multivariate correlation. The best PLS-DA model was based on a subset of variables that were selected using the variable importance in the projection approach. For an independent test set, average estimates with the corresponding standard deviation (based on 1000 Monte Carlo runs) of the correct classification rate, sensitivity, specificity and area under the curve were equal to 96.42% ± 2.04, 98.69% ± 1.38, 94.16% ± 3.52 and 0.982 ± 0.017, respectively.

Chromatographic impurity fingerprinting of genuine and counterfeit Cialis® as a means to compare the discriminating ability of PDA and MS detection.

Public health is threatened worldwide by counterfeit medicines. Their quality, safety and efficacy cannot be guaranteed since no quality control is performed during and/or after the manufacturing process. Characterization of these products is a very important topic. During this study a High Performance Liquid Chromatography-Photodiode Array (HPLC-PDA) and a High Performance Liquid Chromatography - Mass Spectrometry (HPLC-MS) method were developed to analyse both genuine and counterfeit samples of Cialis®. The obtained PDA and MS fingerprints were explored and modelled using unsupervised Principal Component Analysis (PCA) and supervised Partial Least Squares and its discriminant variant (PLS, PLS-DA) as well the classification methods including Soft Independent Modelling of Class Analogy (SIMCA) and the k Nearest Neighbour classifier (kNN). Both MS1 and MS2 data and data measured at 254 nm and 270 nm were used with the aim to test the potential complementarity of PDA and MS detection. First, it was checked if both groups of fingerprints can support differentiation between genuine and counterfeit medicines. Then, it was verified if the obtained multivariate models could be improved by combining information present in MS and PDA fingerprints. Survey of the models obtained for the 254 nm data, 270 nm data and 254_270 nm data combination showed that a tendency of discrimination could be observed with PLS. For the 270 nm data and 254_270 nm data combination a perfect discrimination between genuine and counterfeit medicines is obtained with PLS-DA and SIMCA. This shows that 270 nm alone performs equally well compared to 254_270 nm. For the MS1 and MS1_MS2 data perfect models were obtained using PLS-DA and kNN, indicating that the MS2 data do not provide any extra useful information to acquire the aimed distinction. When combining MS1 and 270 nm perfect models were gained by PLS-DA and SIMCA, which is very similar to the results obtained for PDA alone. These results show that both detectors have a potential to reveal chemical differences between genuine and counterfeit medicines and thus enable the construction of diagnostic models with excellent recognition. However, if a larger sample set, including more possible sources of variation, is analysed more sophisticated techniques such as MS might be necessary.

Simultaneous determination of Solvent Yellow 124 and Solvent Red 19 in diesel oil using fluorescence spectroscopy and chemometrics.

Differences in tax levels for diesel oil stimulate the illegal removal of characteristic diazo compounds purposely added to designate its possible usage. In order to reduce the losses in the national income, there is a strong need to develop a sensitive and cost-effective analytical procedure for the detection of this illegal action. In this study, we describe a novel analytical approach for a qualitative and quantitative determination of two diazo compounds (Solvent Yellow 124 and Solvent Red 19) that are usually added to diesel oil. The methodology proposed combines the use of excitation-emission matrix fluorescence spectroscopy as an analytical technique and partial least squares regression as a multiple modeling tool. With this new methodology, relatively low root mean square errors of prediction (for independent set of test samples) that are equal to 0.223 for Solvent Red 19 and 0.263 for Solvent Yellow 124, were obtained and the results were stable, which were indicated by an analysis performed after 48 and 96 h. The methodology is also nondestructive and allows for (i) simultaneous detection of diesel oil additives, (ii) determination of satisfactory limits of detection (0.048 and 0.042 mg L(-1) for Solvent Red 19 and Solvent Yellow 124, respectively), and (iii) obtaining of considerably low relative standard deviations of 2.33% for Solvent Yellow 124 and of 3.23% for Solvent Red 19 in comparison with the existing norm level.

Methods for the exploratory analysis of two-dimensional chromatographic signals.

In this article several approaches for the exploratory analysis of two-dimensional chromatographic signals (fingerprints) are presented. Their usefulness is illustrated on experimental chromatographic data obtained from high performance liquid chromatography using the photodiode-array detector (HPLC-DAD). Among the methods discussed are principal component analysis (PCA), hierarchical clustering methods and several N-way techniques such as PARAFAC, PARAFAC2 and Tucker3. In addition to the N-way methods, other approaches that allow for comparing samples represented by two-dimensional fingerprints are also presented (the Rv coefficient, the STATIS approach and 'fuzzy' variants of the similarity matrix). Exploratory analysis of the HPLC-DAD data with peak shifts is also discussed.

Automated alignment of one-dimensional chromatographic fingerprints.

A general framework for the automatic alignment of one-dimensional chromatographic signals is presented in this article. The alignment of signals was achieved by explicitly modeling the warping function. Its shape was estimated using a linear combination of several B-spline functions. The coefficients of the spline functions were found in the course of an optimization procedure to maximize the Pearson's correlation coefficient between a target chromatogram and aligned chromatogram(s). The computational requirements of the method are discussed with respect to the correlation optimized warping method, frequently used for the alignment of chromatographic signals. As illustrated with two sets of one-dimensional chromatographic fingerprints, the automatic alignment approach performs well even when non-linear peak shifts need to be corrected. It can be applied in an on-the-fly manner since the alignment of signals is rapid.

Recognizing paracetamol formulations with the same synthesis pathway based on their trace-enriched chromatographic impurity profiles.

The development of a new drug substance is an expensive and time-consuming process. Therefore, the developers want to maximize the profit from the drug by patenting the concerned molecule as well as its synthesis pathway. In a later stage a faster or cheaper manufacturing process can be developed and patented. The aim of this study is to recognize paracetamol-containing drug formulations in relation to their synthesis pathways, in order to demonstrate the possibility to reveal fraudulently synthesized paracetamol. Since different synthesis pathways require different starting materials, solvents, reagents and catalysts and since they can produce different intermediates, it is expected that drug products originating from a different synthesis pathway will exhibit a different impurity profile. Therefore, in this study several paracetamol samples, synthesized in four different ways, are analyzed using trace-enrichment high-performance liquid chromatography (HPLC). The resulting chromatographic data were chemometrically treated in order to reveal clustering tendencies in the hope of distinguishing the different pathways. When performing principal component analysis (PCA) only 3 vaguely outlined clusters appeared. Projection pursuit (PP) was able to reveal 4 clusters and the samples with known synthesis pathway, except one, were classified in the different clusters. When hierarchical clustering and auto-associative multivariate regression trees (AAMRT) were applied, the samples of the four synthesis pathways could also be distinguished. AAMRT has an added value, since it can indicate the variables (peaks and thus also the impurities) that are responsible for the differences between the samples synthesized differently.

Near-infrared reflectance spectroscopy and multivariate calibration techniques applied to modelling the crude protein, fibre and fat content in rapeseed meal.

Near-infrared reflectance spectroscopy (NIRS) is often applied when a rapid quantification of major components in feed is required. This technique is preferred over the other analytical techniques due to the relatively few requirements concerning sample preparations, high efficiency and low costs of the analysis. In this study, NIRS was used to control the content of crude protein, fat and fibre in extracted rapeseed meal which was produced in the local industrial crushing plant. For modelling the NIR data, the partial least squares approach (PLS) was used. The satisfactory prediction errors were equal to 1.12, 0.13 and 0.45 (expressed in percentages referring to dry mass) for crude protein, fat and fibre content, respectively. To point out the key spectral regions which are important for modelling, uninformative variable elimination PLS, PLS with jackknife-based variable elimination, PLS with bootstrap-based variable elimination and the orthogonal partial least squares approach were compared for the data studied. They enabled an easier interpretation of the calibration models in terms of absorption bands and led to similar predictions for test samples compared to the initial models.

Isotopic ratios to detect infringements of patents or proprietary processes of pharmaceuticals: two case studies.

Because of the increasing problem of drug counterfeiting and the potential danger related as well as the economic losses involved, the pharmaceutical industry and the regulatory instances are interested in the development of anti-counterfeiting and patent protection methodologies. In this paper, the evaluation of measured isotopic ratios by means of explorative chemometric techniques was performed to distinguish groups in two data sets containing samples of acetyl salicylic acid and ibuprofen, respectively. The samples in the data sets originated from different countries and manufacturers. For both compounds a clear distinction of groups of samples could be obtained. These groups could be explained based on the origin of the samples, both geographically as well as based on the manufacturer. Hypotheses were formulated concerning the synthetic pathways of the molecules and they were linked to the groups obtained with the chemometric tools.

No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry.

Hyphenated techniques such as capillary electrophoresis-mass spectrometry (CE-MS) or high-performance liquid chromatography with diode array detection (HPLC-DAD), etc., are known to produce a huge amount of data since each sample is characterized by a two-way data table. In this paper different ways of obtaining sample-related information from a set of such tables are discussed. Working with original data requires alignment techniques due to time shifts caused by unavoidable variations in separation conditions. Other pre-processing techniques have been suggested to facilitate comparison among samples without prior peak alignment, for example, 'binning' and/or 'blurring' the data along the time dimension. All these techniques, however, require optimization of some parameters, and in this paper an alternative parameter-free method is proposed. The individual data tables (X) are represented as Gram matrices (XXT), where the summation is taken over the time dimension. Hence the possible variations in time scale are eliminated, while the time information is at least partly preserved by the correlation structure between the detection channels. For comparison among samples, a similarity matrix is constructed and explored by principal component analysis and hierarchical clustering. The Gram matrix approach was tested and compared to some other methods using 'binned' and 'blurred' data for a data set with CE-MS runs on urine samples. In addition to data exploration by principal component analysis and hierarchical clustering, a discriminant partial least squares model was constructed to discriminate between the samples that were taken with and without the prior intake of a drug. The result showed that the proposed method is at least as good as the others with respect to cluster identification and class prediction. A distinct advantage is that there is no need for parameter optimization, while a potential drawback is the large size of the Gram matrices for data with high mass resolution.

Target selection for alignment of chromatographic signals obtained using monochannel detectors.

The alignment of instrumental signals, such as chromatograms, is regarded as an important step before applying multivariate chemometric techniques for data analysis. Nowadays, many alignment techniques are available and they differ in achieving their goal. They can correct peak shifts in a set of chromatograms with differing degrees of success. Almost all alignment techniques, with few exceptions [e.g., W. Yu, B. Wu, N. Lin, K. Stone, K. Williams, H. Zhao, Comput. Biol. Chem. 30 (2006) 27], require a careful choice of the target profile. The selection of a target signal is not an easy task and some difficulties related to this selection are discussed in this paper. An analysis of several simulated sets of chromatographic signals showed that the target selection can be a crucial step if the aligned signals are then used as input to unsupervised approaches, such as, e.g., principal component analysis and to supervised methods like discriminant partial least squares. Different proposals for target selection known-to-date are reviewed. As demonstrated in our study the target profile with the highest correlation coefficient among all the signals studied gave the most satisfactory results.

Robust partial least squares model for prediction of green tea antioxidant capacity from chromatograms.

In this paper a robust version of the partial least squares model (partial robust M-regression, PRM) was built to predict the total antioxidant capacity of green tea extracts. In order to construct a calibration model, chromatograms obtained by a fast high-performance liquid chromatographic method on a monolithic silica column were related with the total antioxidant capacity of green tea extracts as determined by the Trolox antioxidant capacity method. Since natural samples are the subject of the study, some outlying samples are present in the data, as shown in an earlier work. Therefore, to construct reliable calibration models, they were detected and removed prior to modeling. With the applied robust partial least squares approach, where a weighting scheme is embedded to down-weight the negative influence of outliers upon the model it is possible to construct a robust calibration model, without prior identification of outlying objects. It was shown that a robust model, allowing satisfactory prediction for test samples, can be used in controlling green tea antioxidant capacity based on their chromatograms. The constructed robust partial least squares model was shown to have virtually the same fit and predictive power as the classical partial least squares model when outlying samples were removed from the data.

Start-to-end processing of two-dimensional gel electrophoretic images.

Gel electrophoresis serves as a basic analytical tool in the proteomic studies. However, processing of gel electrophoretic images is still the main bottleneck of data analysis, and there is an increasing need for the fully automated approaches. The proposed start-to-end strategy of analyzing the gel images consists of chemometric tools, which allow their effective preprocessing, automatic warping, and data modeling. The image preprocessing techniques: denoising in the wavelet domain and the penalized asymmetric least squares approach for the background estimation are proposed. Matching of images is based on fuzzy warping of features, extracted from the gel images. For the classification or calibration purpose, multivariate approaches such, as partial least squares (PLS) or kernel-PLS methods are used. Performance of the proposed strategy is demonstrated on the real set of the two-dimensional gel images.

Dealing with missing values and outliers in principal component analysis.

An efficient methodology for dealing with missing values and outlying observations simultaneously in principal component analysis (PCA) is proposed. The concept described in the paper consists of using a robust technique to obtain robust principal components combined with the expectation maximization approach to process data with missing elements. It is shown that the proposed strategy works well for highly contaminated data containing different amounts of missing elements. The authors come to this conclusion on the basis of the results obtained from a simulation study and from analysis of a real environmental data set.

Retention prediction of peptides based on uninformative variable elimination by partial least squares.

A quantitative structure-retention relationship analysis was performed on the chromatographic retention data of 90 peptides, measured by gradient elution reversed-phase liquid chromatography, and a large set of molecular descriptors computed for each peptide. Such approach may be useful in proteomics research in order to improve the correct identification of peptides. A principal component analysis on the set of 1726 molecular descriptors reveals a high information overlap in the descriptor space. Since variable selection is advisable, the retention of the peptides is modeled with uninformative variable elimination partial least squares, besides classic partial least squares regression. The Kennard and Stone algorithm was used to select a calibration set (63 peptides) from the available samples. This set was used to build the quantitative structure-retention relationship models. The remaining 27 peptides were used as independent external test set to evaluate the predictive power of the constructed models. The UVE-PLS model consists of 5 components only (compared to 7 components in the best PLS model), and has the best predictive properties, i.e., the average error on the retention time is less than 30 s. When compared also to stepwise regression and an empirical model, the obtained UVE-PLS model leads to better and much better predictions, respectively.