Application of fuzzy algorithms in conjunction with 1 H-NMR spectroscopy to differentiate alcoholic beverages.

BACKGROUND: Recent statistics from the European Commission indicate that wine is one of the commodities most commonly subject to food fraud. In this context, the development of reliable classification models to differentiate alcoholic beverages requires, besides sensitive analytical tools, the use of the most suitable data-processing methods like those based on advanced statistical tools or artificial intelligence. RESULTS: The present study aims to establish a new, innovative approach for the differentiation of alcoholic beverages (wines and fruit distillates), which is able to increase the discrimination rate of the models that have been developed. A data dimensionality reduction step was applied to proton nuclear magnetic resonance (1 H-NMR) profiles. This stage consisted of the application of fuzzy principal component analysis (FPCA) prior to the development of classification models through discriminant analysis. The enhancement of the model's classification potential by the application of FPCA in comparison with principal component analysis (PCA) was discussed. CONCLUSION: The association of 1 H-NMR spectroscopy and an appropriate statistical approach provided a very effective tool for the differentiation of alcoholic beverages. To develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, 1 H-NMR spectroscopic data were exploited in conjunction with fuzzy algorithms to reduce data dimensionality. The study proved the greater efficiency of using FPCA scores in comparison with those obtained through the widely applied PCA. The proposed approach enabled wines to be distinguished perfectly according to their geographical origins, cultivar, and vintage, and this could be used for wine classification. Moreover, 100% correctly classified samples were also achieved for the botanical and geographical differentiation of fruit distillates. © 2022 Society of Chemical Industry.

Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles.

Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied in this study, for the classification of several varieties of Romanian white wines, using the elemental profile (concentrations of 30 elements analyzed by ICP-MS). The investigated wines were produced in four different geographical areas of Romania (Transylvania, Moldova, Muntenia and Oltenia). The FDHAC algorithm provided not only a fuzzy partition of the investigated white wines, but also a fuzzy partition of considered characteristics. Furthermore, this method is unique because it allows a 3D bi-plot representation of membership degrees corresponding to wine samples and elements. In this way, it was possible to identify the most specific elements (in terms of highest, smallest or intermediate concentration values) to each fuzzy partition (group) of wine samples. The chemical elements that appeared to be more powerful for the differentiation of the wines produced in different Romanian areas were: K, Rb, P, Ca, B, Na.

Characterization and classification of wines according to geographical origin, vintage and specific variety based on elemental content: a new chemometric approach.

A highly informative chemometric approach using elemental data to distinguish and classify wine samples according to different criteria was successfully developed. The robust chemometric methods, such fuzzy principal component analysis (FPCA), FPCA combined with linear discriminant analysis (LDA), namely FPCA-LDA and mainly fuzzy divisive hierarchical associative-clustering (FDHAC), including also classical methods (HCA, PCA and PCA-LDA) were efficaciously applied for characterization and classification of white wines according to the geographical origin, vintage or specific variety. The correct rate of classification applying LDA was 100% in all cases, but more compact groups have been obtained for FPCA scores. A similar separation of samples resulted also when the FDHAC was employed. In addition, FDHAC offers an excellent possibility to associate each fuzzy partition of wine samples to a fuzzy set of specific characteristics, finding in this way very specific elemental contents and fuzzy markers according to the degrees of membership (DOMs).

Characterization of Clinically Relevant Fungi via SERS Fingerprinting Assisted by Novel Chemometric Models.

Nonculture-based tests are gaining popularity and upsurge in the diagnosis of invasive fungal infections (IFI) fostered by their main asset, the reduced analysis time, which enables a more rapid diagnosis. In this project, three different clinical isolates of relevant filamentous fungal species were discriminated by using a rapid (less than 5 min) and sensitive surface-enhanced Raman scattering (SERS)-based detection method, assisted by chemometrics. The holistic evaluation of the SERS spectra was performed by employing appropriate chemometric tools-classical and fuzzy principal component analysis (FPCA) in combination with linear discriminant analysis (LDA) applied to the first relevant principal components. The efficiency of the proposed robust algorithm is illustrated on the data set including three fungal isolates (Aspergillus fumigatus sensu stricto, cryptic A. fumigatus complex species, and Rhizomucor pusillus) that were isolated from patient materials. The accurate and reliable discrimination between species of common fungal pathogen strains suggest that the developed method has the potential as an alternative, spectroscopic-based routine analysis tool in IFI diagnosis.

Assessment of Lipophilicity Indices Derived from Retention Behavior of Antioxidant Compounds in RP-HPLC.

Reverse phase high pressure liquid chromatography was employed in order to evaluate the lipophilicity of antioxidant compounds from different classes, such as phenolic acids, flavanones, flavanols, flavones, anthocyanins, stilbenes, xantonoids, and proanthocyanidins. The retention time of each compound was measured using five different HPLC columns: RP18 (LiChroCART, Purosphere RP-18e), C8 (Zorbax, Eclipse XDBC8), C16-Amide (Discovery RP-Amide C16), CN100 (Saulentechnik, Lichrosphere), and pentafluorophenyl (Phenomenex, Kinetex PFP), and the mobile phase consisted of methanol and water (0.1% formic acid) in different proportions. The measurements were conducted at two different column temperatures, room temperature (22 °C) and, in order to mimic the environment from the human body, 37 °C. Furthermore, principal component analysis (PCA) was used to obtain new lipophilicity indices and holistic lipophilicity charts. Additionally, highly representative depictions of the chromatographic behavior of the investigated compounds and stationary phases at different temperatures were obtained using two new chemometric approaches, namely two-way joining cluster analysis and sum of ranking differences.

Thin-layer chromatography--an image-processing method for the determination of acidic catecholamine metabolites.

A sensitive and convenient method for acidic catecholamine metabolites (including homovanillic acid, vanillylmandelic acid, 3,4-dihydroxymandelic acid, and 3,4-dihydroxyphenylacetic acid) determination was developed based on thin-layer chromatography and image-processing analysis. The metabolites were separated without a prederivatization step using reversed phase RP-18W high-performance plates. The mobile phase composition, detection, and quantification conditions were systematically investigated through several trials. The reaction with 2,2-diphenyl-1-picrylhydrazyl radical allowed specific detection of acidic catecholamine metabolites with a high sensitivity and a wide linear range. The limit of detection and the limit of quantification were in the range of 13-103 and 18-120 ng/spot, respectively, in all cases. Mean recoveries determined were in the range 95-106% for all of the investigated compounds. The proposed method allowed rapid simultaneous determination of acidic catecholamine metabolites from spiked human urine sample.

Lipophilicity of oils and fats estimated by TLC.

A representative series of natural toxins belonging to alkaloids and mycotoxins classes was investigated by TLC on classical chemically bonded plates and also on oils- and fats-impregnated plates. Their lipophilicity indices are employed in the characterization and comparison of oils and fats. The retention results allowed an accurate indirect estimation of oils and fats lipophilicity. The investigated fats and oils near classical chemically bonded phases are classified and compared by means of multivariate exploratory techniques, such as cluster analysis, principal component analysis, or fuzzy-principal component analysis. Additionally, a concrete hierarchy of oils and fats derived from the observed lipophilic character is suggested. Human fat seems to be very similar to animal fats, but also possess RP-18, RP-18W, and RP-8.

Comprehensive evaluation of lipophilicity of biogenic amines and related compounds using different chemically bonded phases and various descriptors.

The retention behavior for a series of biogenic amines and related sympathomimetic drugs has been investigated in reversed-phase thin-layer chromatography using RP-2, RP-8, RP-18W, and Diol stationary phase and mixtures of phosphate buffer (pH = 7.10) and methanol in different proportions as mobile phases. Several methodologies like arithmetic mean of experimental retention values, extrapolation to zero methanol concentration procedure and principal component analysis were applied to retention data values (R(M)) in order to determine relevant parameters (mean of R(M) - mR(M), R(M0), and scores corresponding to the first principal component - PC1/R(M) respectively) encoding information on the lipophilic behavior of compounds. High similarities in lipophilicity behavior of investigated amines were highlighted by mR(M) and PC1/R(M) lipophilicity indices for all of the studied stationary phases. The experimental results were compared with some computed lipophilicity parameters expressed as distribution coefficients at working pH (logD), partition coefficients (logP(N), logP(I), and diff(logP(N-I))) concerning both neutral and fully protonated species and difference between both species, and also with various lipophilicity values (logP) generated by different commonly used software. Significant correlations were observed between the experimental lipophilicity indices mR(M) respectively PC1/R(M) and diff(logP(N-I) ) values in all cases.

A comparative study concerning the chromatographic behaviour and lipophilicity of certain natural toxins.

The characterization and classification of some natural toxins (mycotoxins and alkaloids) is established by high-performance thin-layer chromatography (HPTLC) using chemically bonded stationary phases of RP-18, RP-18W, RP-8, RP-2, RP-NH(2) , RP-CN and RP-Diol. The used mobile phases are mixtures of methanol-water in different proportions of volume, chosen to create a suitable migration during the chromatographic process. The lipophilicity is described through different chromatographic descriptors, such as R(M0), mean of R(M) (mR(M)), and scores of R(M) values corresponding to the first principal component (PC1/R(M)). The investigated compounds are characterized also by a series of computed lipophilicity descriptors (logP and logD values). The chromatographic behaviour of toxins is evidenced by graphical profiles of the lipophilicity descriptors. In addition, scores and loadings resulting from covariance matrix of retention data enable new information about similarity and differences observed between the investigated compounds. The experimental lipophilicity indices estimated from retention data are directly correlated with the computed values, via computer software and Internet module, at a high significant statistical level.

Prediction of pesticides chromatographic lipophilicity from the computational molecular descriptors.

Quantitative structure-property relationship models were developed for the prediction of pesticides and some PAH compounds lipophilicity based on a wide set of computational molecular descriptors and a set of experimental chromatographic data. The chromatographic lipophilicity of pesticides has been evaluated by high-performance liquid chromatography (HPLC) using different chemically bonded (C18, C8, CN and Phenyl HPLC columns) stationary phases and two different organic modifiers (methanol and acetonitrile, respectively) in the mobile phase composition. Through a systematic study, by using the classic multivariate analysis, several quantitative structure-property/lipophilicity multi-dimensional models were established. Multiple linear regression and genetic algorithm for the variable subset selection were used. The internal and external statistical evaluation procedures revealed some appropriate models for the chromatographic lipophilicity prediction of pesticides. Moreover, the statistical parameters of regression and those obtained by applying t-test for the intercept (a(0)) and for the slope (a(1)) in order to evaluate relationship between experimental and predicted octanol-water partition coefficients in case of the test set compounds, revealed two statistically valid models that can be successfully used in lipophilicity prediction of pesticides.

A comparative study of the performance of passive samplers.

Atmospheric concentrations of benzene, toluene, ethylbenzene, and xylenes (o-xylene and m,p-xylene) were assessed in the Tricity area (Gdansk-Sopot-Gdynia, Poland) with the use of two types of passive samplers: permeation (homemade passive samplers) and diffusive (Radiello and Orsa 5). Samples were collected during 2008 at selected sites in the Tricity area at monitoring stations belonging to the agency of Regional Air Quality Monitoring Foundation. The field study was conducted to compare the performance of these two different types of passive samplers. A statistical approach was formulated, and the experimental data were evaluated using the paired t test, Wilcoxon signed rank-sum test, and Friedman analysis of variance. All the statistical results confirm the hypothesis that the differences between the performances of the three sampling devices are highly significant. Despite the fact that data obtained with the homemade passive sampler indicated that the results were higher compared with those for the Radiello and Orsa 5 diffusive samplers, the authors note that all differences between the homemade permeation sampler and the Radiello and Orsa 5 diffusive samplers are positive.

Multivariate color scale image analysis - Thin layer chromatography for comprehensive evaluation of complex samples fingerprint.

A chemometric evaluation of the information provided by different color scale fingerprints in thin layer chromatographic analysis of complex samples is proposed for the correct classification of a set of medicinal plant extracts. The fingerprints of the samples were acquired on HPTLC Silica gel 60 F254 and HPTLC Silica gel 60 plates using multiple levels of visualization under UV light. Images processing on red (R), green (G), blue (B) and respectively grey (K) color scale selection was used in order to evaluate the complete chromatographic profile of the extracts. Combination of Principal Component Analysis (PCA) and Factor Analysis (FA) method was applied in order to reveal the individual contribution of each color scales in the analysis of chromatographic fingerprints. The suggested technique provides an applicable strategy to screen for efficacy-associated color scale for grouping/classification of the extracts exploiting the information provided by HPTLC fingerprints. The principal component analysis and linear discriminant analysis (PCA-LDA) method was applied for the evaluation of numerical data provided by color scale fingerprints digitization and for samples classification. A correct classification of the analyzed extracts according to the plants phylum was revealed by color scale fingerprints analysis. The proposed methodology could be considered as a promising tool with future applications in plant material investigations even from the taxonomic perspective classification.

Fuzzy characterization and classification of bacteria species detected at single-cell level by surface-enhanced Raman scattering.

Advanced chemometric methods, such as fuzzy c-means, a semi-supervised clustering method, and fuzzy linear discriminant analysis (FLDA), a new robust supervised classification method in combination with principal component analysis (PCA), namely PCA-FLDA, have been successfully applied for characterization and classification of bacterial species detected at single-cell level by surface-enhanced Raman scattering (SERS) spectroscopy. SERS spectra of three species (S. aureus, E. faecalis and P. aeruginosa) were recorded in an original fashion, using in situ laser induced silver spot as metallic substrate. The detection process of bacteria was isolated inside a hermetically sealed in-house built microfluidic device, connected to a syringe pump for injecting the analytes and a portable Raman spectrometer as detection tool. The obtained results (fuzzy partitions) and spectra of the prototypes (robust fuzzy spectra mean corresponding to each fuzzy partition) clearly demonstrated the efficiency and information power of the advanced fuzzy methods in bacteria characterization and classification based on SERS spectra, and allowed a rationale assigning to a specific group. Also, this powerful detection and classification methodology generates the premises for future investigations of Raman and other spectroscopic data obtained for various samples.

Estimation of chromatographic lipophilicity of bile acids and their derivatives by reversed-phase thin layer chromatography.

The main goal of this study was to estimate the lipophilicity and investigate the molecular mechanism of retention of bile acids and their derivatives in order to find an objective manner of quantitative comparison of different chemically bonded stationary phases for high performance TLC in terms of their (dis)similarities. Highly significant correlations were obtained between different experimental indices of lipophilicity (R(M0), S and the scores corresponding to the first principal component) estimated on CN(F254s) and RP-18(F254s) and some computed log P values that combine electronic and topological aspects. The most statistically significant quantitative structure-property relationship models, using descriptors from Dragon software, multiple linear regression and genetic algorithm, were also obtained in the case of CN(F254s) and RP-18(F254s) stationary phases. Cross-validation suggests a good reliability of the results. The contribution of 2D and 3D descriptors, which are related to atomic mass, together with reactivity parameters such as polarizability and electronegativity seem to control the chromatographic mechanism (lipophilicity) on all stationary phases.

Modeling of chromatographic lipophilicity of food synthetic dyes estimated on different columns.

The retention behavior of some food synthetic dyes has been studied by RP-HPLC on chemically bonded C18, C8, C16 and CN stationary phases. Using methanol-ammonium acetate (0.08 mol/L, pH=6.76) as mobile phase, a linear behavior of retention parameters throughout the methanol fraction variance was obtained in all cases (r>0.99). The patterns of chromatographic behavior of the compounds illustrate high similarities between the C18, C8 and C16 columns, respectively. Highly significant correlations were obtained between experimental lipophilicity indices log k(w) and phi(0) estimated on C18 and C8 stationary phases and some computed log P-values. An extensive investigation made for quantitative structure-property (lipophilicity) relationships of studied dyes, using descriptors from Dragon software, multiple linear regression and genetic algorithm, revealed that the molecular descriptors appearing in the best models combine 2-D and 3-D aspects of the molecular structure. The most significant descriptors can be classified as radial distribution function, GETAWAY (autocorrelation), 3D-MoRSE signal, Burden eigenvalues and edge adjacency descriptors.

High-performance thin-layer chromatography and three-dimensional image analysis for the determination of rutin in pharmaceutical preparations.

A novel HPTLC method was developed for fast and simple quantitative determination of rutin in pharmaceutical preparations. The proposed method combines the advantages of HPTLC with the comfort and the convenience of digital processing of images. For the separation of rutin, silica gel with attached amino groups was used as the stationary phase and ethyl acetate-formic acid-methanol-water (10 + 0.9 + 1.1 + 1.7, v/v/v/v) as the mobile phase. The chromatographic plate was sprayed with 2-aminoethyldiphenyl borate solution for visual detection of the spots. For the construction of a three-dimensional chromatogram, Melanie 7.0 software was used together with an HP flatbed scanner that allowed capture of the images on chromatographic plates. The calibration curve was linear within the range of 0.95-4.78 microg/spot with an r value of 0.9984. The RSD for six replicates at three concentration levels was less than 3%, while the recovery was between 97.28 and 103.27%. The proposed method was found to be simple, precise, sensitive, and accurate and has been applied for the determination of rutin in two commercial drugs. The results were compared with the results of other techniques that generate bidimensional chromatograms and validated by UV-Vis spectrophotometry.

Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths.

Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), which offer the excellent possibility to associate each fuzzy partition of samples with a fuzzy set of characteristics (features), have been successfully applied in this study. FDHAC, a method that utilizes specific regions of chromatographic fingerprints or specific peaks as a fuzzy set of characteristics, was effectively applied to the characterization and classification of medicinal plant extracts according to their antioxidant capacities, using their chromatographic profiles monitored at 242, 260, 280, 320, 340, and 380 nm via HPLC with a multistep isocratic and gradient elution system and diode array detection (HPLC-DAD). What is quite new is the partitioning of the chromatographic retention time ranges and peaks (markers) and their association with different plant extract samples with high, moderate or low antioxidant capacity. Furthermore, the degrees of membership of fingerprints (fuzzy markers) are highly relevant with respect to the (dis)similarity of samples because they indicate both the positions and degrees of association of chromatographic peaks from different classes or individual samples. The obtained results clearly demonstrate the efficiency and information power of these advanced fuzzy methods for medicinal plant characterization and authentication, and this study generates the premise for a new chemometrics approach with high-impact for use in analytical chemistry and other fields.

Comprehensive evaluation of radical scavenging, reducing power and chelating capacity of free proteinogenic amino acids using spectroscopic assays and multivariate exploratory techniques.

Among others, amino acids can inhibit oxidative processes through multiple pathways including inactivation of reactive oxygen species, scavenging free radicals, chelation of pro-oxidative transition metals, as well as reduction of hydroperoxides. Moreover, they can be a major target for oxidants as a result of their abundance in biological systems. The purpose of the present study is to examine the radical scavenging, reducing power and chelating capacity of the free proteinogenic amino acids, using several analytical assays based on different mechanisms and monitored via molecular UV-vis absorption spectrophotometry. In most of the assays, the highest antioxidant activity is rendered by Cys and Trp, known for their involvement in physiological radical reactions, followed by Tyr, Asn and Arg. The least reactive amino acids with respect to both radical scavenging and reducing power principle were Ala, Met, Thr and Val. The hierarchy of the amino acids based on their antioxidant activity is shifting, depending on the analytical assay employed. Results from the multivariate exploratory techniques-hierarchical cluster analysis and principal component analysis-could point out the methods that are most similar and best connected to each other, thus classifying the investigated amino acids according to their activity. Considering the results of chemometric techniques, the antioxidant methods that best discriminate between amino acids are DPPH bleaching and SORS assays. Not only the comprehensive study of antioxidant activity of amino acids is novel in this study but also the application of the sum of ranking differences to compare and rank the various antioxidant capacity assays. The obtained results will support the development of amino acids based multifunctional formulations with important physiologically relevant implications for both humans and animals, with a possible future use in the food industry.

The lipophilicity of parabens estimated on reverse phases chemically bonded and oil-impregnated plates and calculated using different computation methods.

The chromatographic behaviour of the parabens has been investigated on RP-18F(254S), RP-18WF(254S), CNF(254S), Diol F(254S) and silica gel 60F(254) plates impregnated with different oils (paraffin, olive, sunflower and corn) using methanol-water mixtures in different volume proportions as mobile phases, the regression determination coefficients being excellent (higher than 0.98 for the majority of compounds). Moreover, highly significant correlations were obtained between different experimental indices of lipophilicity (R(M0), b and scores corresponding to the first principal component (PC1)) and computed log P values. All types of stationary phases investigated appear to be highly suited for estimating the lipophilicity of the parabens.

The lipophilicity indices of flavonoids estimated by reversed-phase liquid chromatography using different computation methods.

The chromatographic behavior of some flavonoids was established by RP-HPLC on RP-18 (LiChroCART, LiChrosphere RP-18e), RP-8 (Zorbax, Eclipse XDB-C8), CN (Säulentechnik, LiChrosphere CN100) columns. The mobile phases were mixtures of methanol-water in different volume proportions from 70 to 80% v/v for RP-18 and RP-8, while for the CN column the proportions were between 66 and 70% v/v. The lipophilicity was expressed through different lipophilicity descriptors such as mean of k (mk), mean of log k (mlog k), log k(W), S, f() and scores of k and log k corresponding to the first principal component. The experimental lipophilicity indices are directly correlated with the computed values, via computer software and internet module, at a high analytical level. Furthermore, the results obtained applying principal component analysis to k and/or log k values allow the prediction and explanation of the interaction involved in the retention mechanism which takes place between the compounds and the employed stationary phases during the development.