Class-modelling of overlapping classes. A two-step authentication approach.

Honeybush is an indigenous herbal tea highly valued for its aroma, flavour and medicinal properties. It is protected as Geographical Indication (GI) since it is produced from a number of Cyclopia species that are endemic to South Africa. Most commonly used for honeybush tea production are C. intermedia, C. subternata and C. genistoides, differing slightly, but distinctly in flavour. Demand for species-specific honeybush tea instead of mixtures have increased, meriting a strategy for authentication of C. intermedia, C. subternata and C. genistoides. Samples of these three species were analysed, using hyperspectral imaging (HSI) in the near-infrared spectral range. The data were pre-processed and used for class-modelling, a general approach well suited for authentication purposes. Unfortunately, since the HSI data of Cyclopia species studied are very similar, the classification results obtained with individual class-models are unsatisfactory, e.g., class-models constructed for C. genistoides and C. subternata yielded correct classification rate (CCR) values of 76.4 and 83.1%, respectively. On the other hand, discriminant modelling, which is another type of classification technique, led to good classification outcomes (CCR 98.9%). However, the classical discriminant model cannot be applied for authentication purposes since it always assigns a new sample to one of the classes studied, even if in reality, it belongs to none of them. Counterfeits or non-representative samples would be incorrectly assigned by the discriminant model to one of the authentic classes. Therefore, in this study, a two-step authentication of overlapping classes is proposed, which combines the advantages of class-modelling and discriminant methods. When applied to the authentication of Cyclopia species studied, the two-step approach yielded a CCR of 97.4%, which is a significant improvement compared to results obtained with the individual class-models. The proposed approach is general and can be applied when classes studied are very similar, and individual class-models lead to unsatisfactory results.

Different strategies for class model optimization. A comparative study.

The Class Modelling (CM) approaches like Soft Independent Modelling of Class Analogy (SIMCA) aim at developing a mathematical model for determination of belongingness of new samples to the studied classes. The main feature of CM is that for each target class an individual model is constructed. CM is widely exploited, e.g., in the food and drug quality testing and authenticity or origin verification. It is well known that the most critical stage in construction of a class model is optimization of its parameters. There exist two basic strategies for optimization of class model, i.e., the "compliant" strategy where the target and nontarget class samples are required in the model optimization process, and the "rigorous" strategy where only the target class samples are used. Since the nontarget class samples are usually available, the compliant scenario is more often explored. In the present study, four different resampling methods for optimization of the SIMCA model (applied in both, a compliant and a rigorous fashion) are thoroughly compared. Each method is tested in combination with two distinct decision threshold estimation criteria: i) an a priori fixing it based on a desired statistical significance level and ii) optimizing it through appropriate data-driven procedures. For the sake of a comprehensive assessment of the studied strategies, several real-world datasets are exploited and final results are post-processed by means of ANalysis Of VAriance (ANOVA). The study reveals that both, a compliant approach with an optimized decision threshold and a rigorous approach with a fixed decision threshold can yield satisfactory classification outcomes, no matter which resampling technique is used. Finally, it is shown how unrepresentativeness of the nontarget classes can lead to the biased classification models when a compliant optimization is carried out. Therefore, a rigorous optimization can be considered as a safer option for the SIMCA model parameter tuning.

Model development for predicting in vitro bio-capacity of green rooibos extract based on composition for application as screening tool in quality control.

Mounting evidence of the ability of aspalathin to target underlying metabolic dysfunction relevant to the development or progression of obesity and type 2 diabetes created a market for green rooibos extract as a functional food ingredient. Aspalathin is the obvious choice as a chemical marker for extract standardisation and quality control, however, often the concentration of a single constituent of a complex mixture such as a plant extract is not directly related to its bio-capacity, i.e. the level of in vitro bioactivity effected in a cell system at a fixed concentration. Three solvents (hot water and two EtOH-water mixtures), previously shown to produce bioactive green rooibos extracts, were selected for extraction of different batches of rooibos plant material (n = 10). Bio-capacity of the extracts, tested at 10 µg ml-1, was evaluated in terms of glucose uptake by C2C12 and C3A cells and lipid accumulation in 3T3-L1 cells. The different solvents and inter-batch plant variation delivered extracts ranging in aspalathin content from 54.1 to 213.8 g kg-1. The extracts were further characterised in terms of other major flavonoids (n = 10) and an enolic phenylpyruvic acid glucoside, using HPLC-DAD. The 80% EtOH-water extracts, with the highest mean aspalathin content (170.9 g kg-1), had the highest mean bio-capacity in the respective assays. Despite this, no significant (P≥ 0.05) correlation existed between aspalathin content and bio-capacity, while the orientin, isoorientin and vitexin content correlated moderately (r≥ 0.487; P < 0.05) with increased glucose uptake by C2C12 cells. Various multivariate analysis methods were then applied with Evolution Program-Partial Least Squares (EP-PLS) resulting in models with the best predictive power. These EP-PLS models, based on all quantified compounds, predicted the bio-capacity of the extracts for the respective cell types with RMSECV values ≤ 11.5, confirming that a complement of compounds, and not aspalathin content alone, is needed to predict the in vitro bio-capacity of green rooibos extracts. Additionally, the composition of hot water infusions of different production batches of green rooibos (n = 29) at 'cup-of-tea' equivalence was determined to relate dietary supplementation with the extract to intake in the form of herbal tea.

Phenolic composition of rooibos changes during simulated fermentation: Effect of endogenous enzymes and fermentation temperature on reaction kinetics.

Phenolic compounds of Aspalathus linearis (rooibos) are susceptible to oxidation during "fermentation", a process characterized by the formation of a red-brown leaf color. The role of enzymes in this process is not yet understood. An experiment with dried green rooibos plant material pre-treated at 170 °C for 30 min to denature and "inactivate" endogenous enzymes was conducted to confirm the role of oxidative enzymes. The phenolic composition of "enzyme inactivated" plant material was not significantly (p ≥ .05) affected by simulated fermentation, compared to control samples, as determined using piece-wise multivariate analysis of variance for successive time intervals. This proves that rooibos enzymes participate in the oxidation of phenolic compounds during fermentation of the plant material. A kinetic modeling approach was subsequently used to establish reaction kinetic parameters for selected rooibos phenolic compounds. The degradation of aspalathin and nothofagin and formation of eriodictyol glucosides during simulated fermentation at four temperatures from 37 to 50 °C were best described by the fractional conversion model based on first-order kinetics (r2 > 0.98), which allows for non-zero equilibrium concentrations. The extent of degradation for other compounds was too low to enable kinetic modeling. Reaction rates for the degradation/formation of phenolic compounds during fermentation followed the Arrhenius law. Less phenolic degradation (higher equilibrium concentration), but a higher reaction rate constant, was observed at higher temperatures, which could possibly be attributed to inactivation of enzymes.

Working with log-ratios.

Instrumental signals of samples cannot be compared and/or analysed directly if their concentrations are unknown. Differences in overall concentration need to be removed at the data normalization step. The choice of normalization method has a profound effect on the final results of data analysis, and especially on biomarker identification. One of the possible approaches to deal with the 'size effect' is to work with size-irrelevant (log) ratios instead of the original variables. In the presented study, the performance of log-ratio methods, namely pairwise log-ratio (plr) and centered log-ratio (clr), is discussed for real and simulated data sets with different characteristics. It was found that the clr method can lead to distribution of local differences along an entire signal and as such, it should be avoided in all studies aiming to identify biomarkers.

Chemometrics in analytical chemistry-part II: modeling, validation, and applications.

The contribution of chemometrics to important stages throughout the entire analytical process such as experimental design, sampling, and explorative data analysis, including data pretreatment and fusion, was described in the first part of the tutorial "Chemometrics in analytical chemistry." This is the second part of a tutorial article on chemometrics which is devoted to the supervised modeling of multivariate chemical data, i.e., to the building of calibration and discrimination models, their quantitative validation, and their successful applications in different scientific fields. This tutorial provides an overview of the popularity of chemometrics in analytical chemistry.

Untargeted analysis of chromatographic data for green and fermented rooibos: Problem with size effect removal.

While analyzing chromatographic data, it is necessary to preprocess it properly before exploration and/or supervised modeling. To make chromatographic signals comparable, it is crucial to remove the scaling effect, caused by differences in overall sample concentrations. One of the efficient methods of signal scaling is Probabilistic Quotient Normalization (PQN) [1]. However, it can be applied only to data for which the majority of features do not vary systematically among the studied classes of signals. When studying the influence of the traditional "fermentation" (oxidation) process on the concentration of 56 individual peaks detected in rooibos plant material, this assumption is not fulfilled. In this case, the only possible solution is the analysis of pairwise log-ratios, which are not influenced by the scaling constant. To estimate significant features, i.e., peaks differentiating the studied classes of samples (green and fermented rooibos plant material), we propose the application of rPLR (robust pair-wise log-ratios) as proposed by Walach et al. [2]. It allows for fast computation and identification of the significant features in terms of original variables (peaks) which is problematic, while working with the unfolded pair-wise log ratios. As demonstrated, it can be applied to designed data sets and in the case of contaminated data, it allows proper conclusions.

Chemometrics in analytical chemistry-part I: history, experimental design and data analysis tools.

Chemometrics has achieved major recognition and progress in the analytical chemistry field. In the first part of this tutorial, major achievements and contributions of chemometrics to some of the more important stages of the analytical process, like experimental design, sampling, and data analysis (including data pretreatment and fusion), are summarised. The tutorial is intended to give a general updated overview of the chemometrics field to further contribute to its dissemination and promotion in analytical chemistry.

Multivariate analysis of variance of designed chromatographic data. A case study involving fermentation of rooibos tea.

An ultimate goal of investigations of rooibos plant material subjected to different stages of fermentation is to identify the chemical changes taking place in the phenolic composition, using an untargeted approach and chromatographic fingerprints. Realization of this goal requires, among others, identification of the main components of the plant material involved in chemical reactions during the fermentation process. Quantitative chromatographic data for the compounds for extracts of green, semi-fermented and fermented rooibos form the basis of preliminary study following a targeted approach. The aim is to estimate whether treatment has a significant effect based on all quantified compounds and to identify the compounds, which contribute significantly to it. Analysis of variance is performed using modern multivariate methods such as ANOVA-Simultaneous Component Analysis, ANOVA - Target Projection and regularized MANOVA. This study is the first one in which all three approaches are compared and evaluated. For the data studied, all tree methods reveal the same significance of the fermentation effect on the extract compositions, but they lead to its different interpretation.

Maize proteomic responses to separate or overlapping soil drought and two-spotted spider mite stresses.

MAIN CONCLUSION: In maize, leaf proteome responses evoked by soil drought applied separately differ from those evoked by mite feeding or both types of stresses occurring simultaneously. This study focuses on the involvement of proteomic changes in defence responses of a conventional maize cultivar (Bosman) to the two-spotted spider mite infestation, soil drought and both stresses coexisting for 6 days. Under watering cessation or mite feeding applied separately, the protein carbonylation was not directly linked to the antioxidant enzymes' activities. Protein carbonylation increased at higher and lower SOD, APX, GR, POX, PPO activities following soil drought and mite feeding, respectively. Combination of these stresses resulted in protein carbonylation decrease despite the increased activity of all antioxidant enzymes (except the CAT). However, maize protein network modification remains unknown upon biotic/abiotic stresses overlapping. Here, using multivariate chemometric methods, 94 leaf protein spots (out of 358 considered; 2-DE) were identified (LC-MS/MS) as differentiating the studied treatments. Only 43 of them had individual discrimination power. The soil drought increased abundance of leaf proteins related mainly to photosynthesis, carbohydrate metabolism, defence (molecular chaperons) and protection. On the contrary, mite feeding decreased the abundance of photosynthesis related proteins and enhanced the abundance of proteins protecting the mite-infested leaf against photoinhibition. The drought and mites occurring simultaneously increased abundance of proteins that may improve the efficiency of carbon fixation, as well as carbohydrate and amino acid metabolism. Furthermore, increased abundance of the Rubisco large subunit-binding protein (subunit ß), fructose-bisphosphate aldolase and mitochondrial precursor of Mn-SOD and decreased abundance of the glycolysis-related enzymes in the mite-free leaf (in the vicinity of mite-infested leaf) illustrate the involvement of these proteins in systemic maize response to mite feeding.

Analysis of variance of designed chromatographic data sets: The analysis of variance-target projection approach.

Direct application of popular approaches, e.g., Principal Component Analysis (PCA) or Partial Least Squares (PLS) to chromatographic data originating from a well-designed experimental study including more than one factor is not recommended. In the case of a well-designed experiment involving two or more factors (crossed or nested), data are usually decomposed into the contributions associated with the studied factors (and with their interactions), and the individual effect matrices are then analyzed using, e.g., PCA, as in the case of ASCA (analysis of variance combined with simultaneous component analysis). As an alternative to the ASCA method, we propose the application of PLS followed by target projection (TP), which allows a one-factor representation of the model for each column in the design dummy matrix. PLS application follows after proper deflation of the experimental matrix, i.e., to what are called the residuals under the reduced ANOVA model. The proposed approach (ANOVA-TP) is well suited for the study of designed chromatographic data of complex samples. It allows testing of statistical significance of the studied effects, 'biomarker' identification, and enables straightforward visualization and accurate estimation of between- and within-class variance. The proposed approach has been successfully applied to a case study aimed at evaluating the effect of pasteurization on the concentrations of various phenolic constituents of rooibos tea of different quality grades and its outcomes have been compared to those of ASCA.

Determination and speciation of trace and ultratrace selenium ions by energy-dispersive X-ray fluorescence spectrometry using graphene as solid adsorbent in dispersive micro-solid phase extraction.

A dispersive micro-solid phase extraction (DMSPE) with graphene as a solid adsorbent and ammonium pyrrolidinedithiocarbamate (APDC) as a chelating agent was proposed for speciation and detemination of inorganic selenium by the energy-dispersive X-ray fluorescence spectrometry (EDXRF). In developed DMSPE, graphene particles are dispersed throughout the analyzed solution, therefore reaction between Se(IV)-APDC complexes and graphene nanoparticles occurs immediately. The concentration of Se(VI) is calculated as the difference between the concentration of selenite after and before prereduction of selenate. A central composite face-centered design with 3 center points was performed in order to optimize conditions and to study the effect of four variables (pH of the sample, concentration of APDC, concentration of Triton-X-100, and sample volume). The best results were obtained when suspension consisting of 200 µg of graphene nanosheets, 1.2 mg of APDC and 0.06 mg of Triton-X-100 was rapidly injected to the 50 mL of the analyzed solution. Under optimized conditions Se ions can be determined with a very good recovery (97.7±5.0% and 99.2±6.6% for Se(IV) and Se(VI), respectively) and precision (RSD=5.1-6.6%). Proposed DMSPE/EDXRF procedure allowed to obtain low detection limits (0.032 ng mL(-1)) and high enrichment factor (1013±15). The proposed methodology was successfully applied for the determination of Se in mineral, tap, lake and sea water samples as well as in biological materials (Lobster Hepatopancreas and Pig Kidney).

Modeling of the total antioxidant capacity of rooibos (Aspalathus linearis) tea infusions from chromatographic fingerprints and identification of potential antioxidant markers.

Models to predict the total antioxidant capacity (TAC) of rooibos tea infusions from their chromatographic fingerprints and peak table data (content of individual phenolic compounds), obtained using HPLC with diode array detection, were developed in order to identify potential antioxidant markers. Peak table data included the content of 12 compounds, namely phenylpyruvic acid-2-O-glucoside, aspalathin, nothofagin, isoorientin, orientin, ferulic acid, quercetin-3-O-robinobioside, vitexin, hyperoside, rutin, isovitexin and isoquercitrin. The TAC values, measured using the oxygen radical absorbance capacity (ORAC) and DPPH radical scavenging assays, could be predicted from the peak table data or the chromatographic fingerprints (prediction errors 9-12%) using partial least squares (PLS) regression. Prediction models created from samples of only two production years could additionally be used to predict the TAC of samples from another production year (prediction errors<13%) indicating the robustness of the models in a quality control environment. Furthermore, the uninformative variable elimination (UVE)-PLS method was used to identify potential antioxidant markers for rooibos infusions. All individual phenolic compounds that were quantified were selected as informative variables, except vitexin, while UVE-PLS models developed from chromatographic fingerprints indicated additional antioxidant markers, namely (S)-eriodictyol-6-C-glucoside, (R)-eriodictyol-6-C-glucoside, aspalalinin and two unidentified compounds. The potential antioxidant markers should be validated prior to use in quality control of rooibos tea.

A new concept for variance analysis of hyphenated chromatographic data avoiding signal warping.

Analysis of variance of chromatographic data is usually performed on the peak table or on entire chromatograms. These two data forms require signal pretreatment. Peak table requires peak detection, their standards and quantification, and the second form of data organization requires warping of the studied chromatograms to eliminate the observed peak shifts, which occurs due to minor variations in chromatographic conditions. In our study, a new form of data representation well suited for chromatographic data originating from multi-channel detection is proposed. It requires neither warping of chromatograms, nor peak detection. Its principles and performance are demonstrated for a real data set (being a part of a larger research project initiated to characterize the infusion of fermented rooibos herbal tea in terms of phenolic composition and antioxidant activity). As the method of choice for the analysis of data variation, the Multiple Analysis of Variance applied to the pairwise data representation was chosen.

Non-parametric multivariate analysis of variance in the proteomic response of potato to drought stress.

Spot detection is a mandatory step in all available software packages dedicated to the analysis of 2D gel images. As the majority of spots do not represent individual proteins, spot detection can obscure the results of data analysis significantly. This problem can be overcome by a pixel-level analysis of 2D images. Differences between the spot and the pixel-level approaches are demonstrated by variance analysis for real data sets (part of a larger research project initiated to investigate the molecular mechanism of the response of the potato to drought stress). As the method of choice for the analysis of data variation, the non-parametric MANOVA was chosen. NP-MANOVA is recommended as a flexible and very fast tool for the evaluation of the statistical significance of the factor(s) studied.

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.

The proteomic analysis of primary cortical astrocyte cell culture after morphine administration.

Astrocytes are supportive cells, necessary for ensure optimal environment for neural cells functioning. They are involved in extracellular K+ level regulation and neurotransmitters removal. They are also dependent for myelination and synapses formation. They may make a contribution in signal propagation in the central nervous system, for example, through Ca2+ signaling. With the use of neonatal pure astrocyte cell culture, we investigated changes in astrocyte's proteomes under the influence of morphine. We found 10 major proteins, which show different expression between physiological cell culture and morphine treatment. With 2D gel electrophoresis and nanoLC-ESI-MS/MS, we identified proteins and characterized their potential role in morphine dependence. Observed differences were also confirmed by Western blotting. Our data suggests a role for astrocytes in the formation of the morphine dependence at the molecular level. This finding may support interpretation of causes of morphine dependence formation based only on behavioral data.

Proteomic analysis of striatal neuronal cell cultures after morphine administration.

Using primary neuronal cell culture assays, combined with 2-D gel electrophoresis and capillary LC-MS, we identified differences in proteomes between control and morphine-treated cells. Statistically significant differences were observed among 26 proteins. Nineteen of them were up-regulated, while seven were down-regulated in morphine-treated cell populations. The identified proteins belong to classes involved in energy metabolism, associated with oxidative stress, linked with protein biosynthesis, cytoskeletal ones, and chaperones. The detected proteins demand further detailed studies of their biological roles in morphine addiction. It is crucial to confirm observed processes in vivo in order to reveal the nature and importance of the biological effect of proteome changes after morphine administration. Further investigations may lead to the discovery of new proteome-based effects of morphine on living organisms.

Classification of genomic data: some aspects of feature selection.

Feature selection, while working with genomic data sets, is of particular interest, not only for classification (diagnostics) improvement, but also for the data interpretability. Application of the multivariate feature selection approaches allows an efficient reduction of data dimensionality, but as demonstrated in our study, sets of the selected variables depend on the objective function of the classifier. It is possible to select different subset of genes for classification due to the correlation of genes but their interpretation ought to be cautiously made.