I. Stanimirova

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.

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3-(CH2)n-) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol.

A comparison of positive matrix factorization and the weighted multivariate curve resolution method. Application to environmental data.

In recent years, positive matrix factorization, PMF, has gained popularity in environmental sciences and it has been recommended by the U.S. Environmental Protection Agency as a general modeling tool in air quality control. Among the attractive features contributing to its popularity is that measurement uncertainty information can be incorporated into the PMF model, which allows the handling of missing measurements and data below the reporting limits. In addition, the solutions obtained from PMF obey constraints such as the non-negativity of the source compositions and source contributions of samples that make their interpretation physically meaningful. A less popular multivariate curve resolution method based on a weighted alternating least-squares algorithm, MCR-WALS, also incorporates the measurement error information and non-negativity constraints, which makes this method a potential tool when obtaining composition and contribution profiles of environmental data. Both methods use the same loss function, but they differ in the way the profiles are obtained. The goal of this study was to compare the performance of PMF with the performance of MCR-WALS for data sets simulated with different correlation and error structures. The results showed that the profiles extracted by both methods are virtually the same for data with different error structures.

Discrimination of biofilm samples using pattern recognition techniques

Biofilms are complex aggregates formed by microorganisms such as bacteria, fungi and algae, which grow at the interfaces between water and natural or artificial materials. They are actively involved in processes of sorption and desorption of metal ions in water and reflect the environmental conditions in the recent past. Therefore, biofilms can be used as bioindicators of water quality. The goal of this study was to determine whether the biofilms, developed in different aquatic systems, could be successfully discriminated using data on their elemental compositions. Biofilms were grown on natural or polycarbonate materials in flowing water, standing water and seawater bodies. Using an unsupervised technique such as principal component analysis (PCA) and several supervised methods like classification and regression trees (CART), discriminant partial least squares regression (DPLS) and uninformative variable elimination–DPLS (UVE-DPLS), we could confirm the uniqueness of sea biofilms and make a distinction between flowing water and standing water biofilms. The CART, DPLS and UVE-DPLS discriminant models were validated with an independent test set selected either by the Kennard and Stone method or the duplex algorithm. The best model was obtained from CART with 100% correct classification rate for the test set designed by the Kennard and Stone algorithm. With CART, one variable describing the Mg content in the biofilm water phase was found to be important for the discrimination of flowing water and standing water biofilms.

Improving QSAR models for the biological activity of HIV Reverse Transcriptase inhibitors: Aspects of outlier detection and uninformative variable elimination

The goal of this study is to derive a methodology for modeling the biological activity of non-nucleoside HIV Reverse Transcriptase (RT) inhibitors. The difficulties that were encountered during the modeling attempts are discussed, together with their origin and solutions. With the selected multivariate techniques: robust principal component analysis, partial least squares, robust partial least squares and uninformative variable elimination partial least squares, it is possible to explore and to model the contaminated data satisfactory. It is shown that these techniques are versatile and valuable tools in modeling and exploring biochemical data.

Relating gas chromatographic profiles to sensory measurements describing the end products of the Maillard reaction.

Often in analytical practice, a set of samples is described by different types of measurements in the hope that a comprehensive characterisation of samples will provide a more complete picture and will help in determining the similarities among samples. The main focus is then on how to combine the information described by different measurement variables and how to analyse it simultaneously. In other words, the main goal is to find a common representation of samples that emphasises the individual and common properties of the different blocks of variables. Several methods can be adopted for the simultaneous analysis of multiblock data with a common object mode. These are: consensus principal component analysis (CPCA), SUM-PCA, multiple factor analysis (MFA) and structuration des tableaux à trois indices de la statistique (STATIS).In this article we present a comparison of the performances of these methods for data describing the chemistry and sensory profiles of the Maillard reaction products. The aroma compounds formed during the reaction of thermal heating between one or two selected amino acids and one or two reducing sugars have been analysed by head space gas chromatography and the intensity and nature of the odour of the resulting products has been evaluated according to selected descriptors by a panel of sensory experts.The results showed that using the information of the chromatographic and sensory data in conjunction enhanced the interpretability of the data. SUM-PCA and more specifically multiple factor analysis, MFA, allowed for a detailed study of the similarities of mixtures in terms of reaction products and sensory profiles.

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.

Wine authenticity verification as a forensic problem: An application of likelihood ratio test to label verification

The aim of the study was to investigate the applicability of the likelihood ratio (LR) approach for verifying the authenticity of 178 samples of 3 Italian wine brands: Barolo, Barbera, and Grignolino described by 27 parameters describing their chemical compositions. Since the problem of products authenticity may be of forensic interest, the likelihood ratio approach, expressing the role of the forensic expert, was proposed for determining the true origin of wines. It allows us to analyse the evidence in the context of two hypotheses, that the object belongs to one or another wine brand. Various LR models were the subject of the research and their accuracy was evaluated by the Empirical cross entropy (ECE) approach. The rates of correct classifications for the proposed models were higher than 90% and their performance evaluated by ECE was satisfactory.

A study of the antioxidant properties of beers using electron paramagnetic resonance

The antioxidant properties of various kinds of beers were investigated using electron paramagnetic resonance (EPR) spectroscopy. This was possible by measuring the changes in the intensity of the EPR spectrum that resulted from the interaction of the stable radical DPPH (1,1-diphenyl-2-picrylhydrazyl) with the antioxidants found in a beer sample. The antioxidant capacity was then presented in Trolox Equivalents, e.g. μM trolox in a beer sample of 100ml. The influence of the type, colour, the content of the extract and alcohol on the antioxidant activities of commercial beer samples was investigated using two-way hierarchical clustering and analysis of variance. The results showed that all of the beers investigated exhibit antioxidant properties. By performing an analysis of variance, it was found that the value of the antioxidant capacity significantly (0.05 level of significance) depends on the content of the extract and the colour of the beer. It seems that additives also influence the antioxidant properties to some extent, but neither the alcohol content nor the kind of fermentation affects the antioxidant properties of beer.

Interpretation of analysis of variance models using principal component analysis to assess the effect of a maternal anticancer treatment on the mineralization of rat bones.

The goal of the present study is to assess the effects of anticancer treatment with cyclophosphamide and cytarabine during pregnancy on the mineralization of mandible bones in 7-, 14- and 28-day-old rats. Each bone sample was described by its X-ray fluorescence spectrum characterizing the mineral composition. The data collected are multivariate in nature and their structure is difficult to visualize and interpret directly. Therefore, methods like analysis of variance-principal component analysis (ANOVA-PCA) and ANOVA-simultaneous component analysis (ASCA), which are suitable for the analysis of highly correlated spectral data and are able to incorporate information about the underlined experimental design, are greatly valued. In this study, the ASCA methodology adapted for unbalanced data was used to investigate the impact of the anticancer drug treatment during pregnancy on the mineralization of the mandible bones of newborn rats and to examine any changes in the mineralization of the bones over time. The results showed that treatment with cyclophosphamide and cytarabine during pregnancy induces a decrease in the K and Zn levels in the mandible bones of newborns. This suppresses the development of mandible bones in rats in the early stages (up to 14 days) of formation. An interesting observation was that the levels of essential minerals like K, Mg, Na and Ca vary considerably in the different regions of the mandible bones.