Principal component analysis of Kováts indices for carbonyl compounds in capillary gas chromatography

Volatile Olfactory Profiles of Umbrian Extra Virgin Olive Oils and Their Discrimination through MOX Chemical Sensors

Extra virgin olive oil (EVOO) is the best vegetable oil worldwide but, at the same time, is one of the product victims of fraud in the agri-food sector, and the differences about quality within the extra-virgin olive oil category are often missed. Several scientific techniques were applied in order to guarantee the authenticity and quality of this EVOO. In the present study, the volatile compounds (VOCs) by gas chromatography-mass spectrometry with solid-phase micro-extraction detection (GC-MS SPME), organoleptic analysis by the official Slow Food panel and the detection by a Small Sensor System (S3) were applied. Ten EVOOs from Umbria, a central Italian region, were selected from the 2021 Slow Food Italian extra virgin olive oil official guide, which includes hundreds of high-quality olive oils. The results demonstrated the possibility to discriminate the ten EVOOs, even if they belong to the same Italian region, by all three techniques. The result of GC-MS SPME detection was comparable at the discrimination level to the organoleptic test with few exceptions, while the S3 was able to better separate some EVOOs, which were not discriminated perfectly by the other two methods. The correlation analysis performed among and between the three methodologies allowed us to identify 388 strong associations with a p value less than 0.05. This study has highlighted how much the mix of VOCs was different even among few and localized EVOOs. The correlation with the sensor detection, which is faster and chipper compared to the other two techniques, elucidated the similarities and discrepancies between the applied methods.

[Ensemble hologram quantitative structure activity relationship model of the chromatographic retention index of aldehydes and ketones]

Chromatographic retention index (RI) is an important parameter for describing the retention behavior of substances in chromatographic analysis. Experimentally determining the RI values of different aldehyde and ketone compounds in all kinds of polar stationary phases is expensive and time consuming. Quantitative structure activity relationship (QSAR) is an important chemometric technique that has been widely used to correlate the properties of chemicals to their molecular structures. Irrespective of whether the properties of a molecule have been experimentally determined, they can be calculated using QSAR models. It is therefore necessary and advisable to establish the QSAR model for predicting the RI value of aldehydes and ketones. Hologram QSAR (HQSAR) is a highly efficient QSAR approach that can easily generate QSAR models with good statistics and high prediction accuracy. A specific fragment of fingerprint, known as a molecular hologram, is proposed in the HQSAR approach and used as a structural descriptor to build the proposed QSAR model. In general, individual HQSAR models are built in QSAR researches. However, individual QSAR models are usually affected by underfitting and overfitting. The ensemble modeling method, which integrate several individual models through certain consensus strategies, can overcome the shortcomings of individual models. It is worth studying whether ensemble modeling can improve the prediction ability of the HQSAR method in order to build more accurate and reliable QSAR models. Therefore, this study investigates the QSAR model for chromatographic RI of aldehydes and ketones using ensemble modeling and the HQSAR method. Two individual HQSAR models comprising 34 compounds in two stationary phases, DB-210 and HP-Innowax, were established. The prediction ability of the two established models was assessed by external test set validation and leave-one-out cross validation (LOO-CV). The investigated 34 compounds were randomly assigned into two groups. Group Ⅰ comprised 26 compounds, and Group Ⅱ comprised 8 compounds. In the validation of the external test set, Group Ⅰ was employed to manually optimize the two fragment parameters (fragment distinction (FD) and fragment size (FS)) and build the HQSAR models. Group Ⅱ was used as the test set to assess the predictive performance of the developed models. For the DB-210 stationary phase, the optimal individual HQSAR model was obtained while setting the FD and FS to "donor/acceptor atoms (DA)" and 1-9, respectively. For the HP-Innowax stationary phase, the optimal individual HQSAR model was obtained by setting the FD and FS to "DA" and 4-7 respectively. The squared correlation coefficient of cross validation ( [Formula: see text] for predicting the RI values of the DB-210 and HP-Innowax stationary phases were 0.927 and 0.919, 0.956 and 0.979, 0.929 and 0.963, 0.927 and 0.958, and 0.935 and 0.963, respectively. Compared to the individual HQSAR models, the established ensemble HQSAR models show better robustness and accuracy, thus establishing that ensemble modeling is an effective approach. The combination of HQSAR and the ensemble modeling method is a practicable and promising method for studying and predicting the RI values of aldehydes and ketones.

Volatile Compounds Produced by Lactobacillus paracasei During Oat Fermentation

This study investigated the profiles of volatile compounds produced by Lactobacillus paracasei during oat fermentation using gas chromatography-mass spectrometry coupled with headspace solid-phase microextraction method. A total of 60 compounds, including acids, alcohols, aldehydes, esters, furan derivatives, hydrocarbons, ketones, sulfur-containing compounds, terpenes, and other compounds, were identified in fermented oat. Lipid oxidation products such as 2-pentylfuran, 1-octen-3-ol, hexanal, and nonanal were found to be the main contributors to oat samples fermented by L. paracasei with the level of 2-pentylfuran being the highest. In addition, the contents of ketones, alcohols, acids, and furan derivatives in the oat samples consistently increased with the fermentation time. On the other hand, the contents of degradation products of amino acids, such as 3-methylbutanal, benzaldehyde, acetophenone, dimethyl sulfide, and dimethyl disulfide, decreased in oat samples during fermentation. Principal component analysis (PCA) was applied to discriminate the fermented oat samples according to different fermentation times. The fermented oats were clearly differentiated on PCA plots. The initial fermentation stage was mainly affected by aldehydes, whereas the later samples of fermented oats were strongly associated with acids, alcohols, furan derivatives, and ketones. The application of PCA to data of the volatile profiles revealed that the oat samples fermented by L. paracasei could be distinguished according to fermentation time.

Persistence, variance and toxic levels of organochlorine pesticides in fluvial sediments and the role of black carbon in their retention

The present study assesses the persistence and variation of organochlorine pesticides (OCPs) and their regulation by total organic carbon (TOC) and black carbon (BC) in freshwater sediment. Sediment samples from the Yamuna River, a major tributary of the Ganges (one of the most populated and intensively used rivers in Asia), had high levels of Σ20OCPs (21.41 to 139.95 ng g(-1)). β-Hexachlorocyclohexane (β-HCH) was the most predominant component. ΣHCH and Σdichloro-diphenyl-trichloroethane (DDT) constituted ~86% of Σ20OCPs. Isomer ratios indicated fresh usage of lindane, DDT and technical-grade HCH. Toxicological comparison with freshwater sediment quality guidelines showed γ-HCH and DDT at high levels of concern. β-HCH, α-HCH, endrin, heptachlor epoxide, dichloro-diphenyl-dichloroethane (DDD), dichloro-diphenyl-dichloroethylene and chlordane were above some of the guideline levels. TOC and BC had mean concentrations of 1.37 ± 0.51% and 0.46 ± 0.23 mg g(-1), respectively. BC constituted 1.25 to 10.56% of TOC. We observed low to moderate correlations of BC with isomers of HCH, p,p'-DDT and methoxychlor while of TOC with Σ20OCPs, γ-HCH, endosulfan sulfate and methoxychlor. Principal component analysis enabled correlating and clustering of various OCPs, BC and TOC. OCP distribution was related with pH, electrical conductivity, soil moisture and finer fractions of sediment. OCPs with similarity in properties that determine their interactions with carbonaceous components of sediment clustered together. A number of factors may, thus, be involved in the regulation of interactive forces between BC and OCPs. BC in this study may be more important than TOC in the retention of some OCPs into fluvial sediments, thereby reducing their bioavailability. The finding is probably the first of its kind to report and emphasises the role of BC in the persistence of OCPs in fluvial sediments.

QSRR Models for Kováts' Retention Indices of a Variety of Volatile Organic Compounds on Polar and Apolar GC Stationary Phases Using Molecular Connectivity Indexes

Quantitative structure-retention relationship (QSRR) approaches, based on molecular connectivity indices are useful to predict the gas chromatography of Kováts relative retention indices (GC-RRIs) of 132 volatile organic compounds (VOCs) on different 12 (4 apolar and 8 polar) stationary phases (C₆₇, C₁₀₃, C₇₈, C_∞, POH, TTF, MTF, PCL, PBR, TMO, PSH and PCN) at 130 °C. Full geometry optimization based on Austin model 1 semi-empirical molecular orbital method was carried out. The sets of 30 molecular descriptors were derived directly from the topological structures of the compounds from DRAGON program. By means of the final variable selection method, which is elimination selection stepwise regression algorithms, three optimal descriptors were selected to develop a QSRR model to predict the RRI of organic compounds on each stationary phase with a correlation coefficient between 0.9378 and 0.9673 and a leave-one-out cross-validation correlation coefficient between 0.9325 and 0.9653. The root mean squares errors over different 12 phases were within the range of 0.0333–0.0458. Furthermore, the accuracy of all developed models was confirmed using procedures of Y-randomization, external validation through an odd–even number and division of the entire dataset into training and test sets. A successful interpretation of the complex relationship between GC RRIs of VOCs and the chemical structures was achieved by QSRR. The three connectivity indexes in the models are also rationally interpreted, which indicated that all organic compounds’ RRI was precisely represented by molecular connectivity indexes.

Use of principal component analysis and a spectral mapping technique for the evaluation of the antifungal activity of anthracene-based synthetic dyes

The antifungal activity of 14 anthracene-based synthetic dyes and 6 reference compounds was measured on 36 fungal strains and the data matrix was evaluated separately by principal component analysis (PCA) and using a spectral mapping technique (SPM). The dimensionality of the maps of principal component loadings and variables and the selectivity maps was reduced to two by non-linear mapping. Except for two compounds, the dyes showed marked antifungal activity. Calculations proved that both the strength and selectivity of the biological effect of anthracene-based dyes were highly dependent on the chemical structure of the dye and on the type of fungi. PCA and SPM revealed different aspects of the antifungal activity, therefore, their simultaneous application in future quantitative structure-activity relationship studies is highly recommended.

Headspace solid-phase microextraction-gas chromatographic-time-of-flight mass spectrometric methodology for geographical origin verification of coffee

Increasing consumer awareness of food safety issues requires the development of highly sophisticated techniques for the authentication of food commodities. The food products targeted for falsification are either products of high commercial value or those produced in large quantities. For this reason, the present investigation is directed towards the characterization of coffee samples according to the geographical origin. The conducted research involves the development of a rapid headspace solid-phase microextraction (HS-SPME)-gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) method that is utilized for the verification of geographical origin traceability of coffee samples. As opposed to the utilization of traditional univariate optimization methods, the current study employs the application of multivariate experimental designs to the optimization of extraction-influencing parameters. Hence, the two-level full factorial first-order design aided in the identification of two influential variables: extraction time and sample temperature. The optimum set of conditions for the two variables was 12 min and 55 degrees C, respectively, as directed by utilization of Doehlert matrix and response surface methodology. The high-throughput automated SPME procedure was completed by implementing a single divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 microm metal fiber with excellent durability properties ensuring the completion of overall sequence of coffee samples. The utilization of high-speed TOFMS instrument ensured the completion of one GC-MS run of a complex coffee sample in 7.9 min and the complete list of benefits provided by ChromaTOF software including fully automated background subtraction, baseline correction, peak find and mass spectral deconvolution algorithms was exploited during the data evaluation procedure. The combination of the retention index (RI) system using C(8)-C(40) alkanes and the mass spectral library search was utilized for the confirmation of analyte identity in the reference authentic Brazilian coffee sample. The semi-quantitative results were then submitted to statistical evaluation, namely principal component analysis (PCA) for the establishment of geographical origin discriminations.

A novel semi-empirical topological descriptorNtand the application to study on QSPR/QSAR

A novel semi-empirical topological descriptor Nt was proposed by revising the traditional distance matrix based on the equilibrium electronegativity and the relative bond length. Nt can not only efficiently distinguish structures of organic compounds containing multiple bonds and/or heteroatoms, but also possess good applications of QSPR/QSAR (quantitative structure-property/activity relationships) to a large diverse set of compounds, which are alkanes, alkenes, alkynes, aldehydes, ketones, thiols, and alkoxy silicon chlorides with all the correlation coefficients of the models over 0.99. The LOO CV (leave-one-out cross-validation) method was used to testify the stability and predictive ability of the models. The validation results verify the good stability and predictive ability of the models employing the cross-validation parameters: RCV, SEPCV and SCV, which demonstrate the wide potential of the Nt descriptor for applications to QSPR/QSAR.

Quantitative structure-(chromatographic) retention relationships

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Chemometric studies of retention in capillary gas chromatographic separation of hydrocarbons in coupled columns

This paper describes how different multivariate analysis and classification methods can be used, to characterize the gas chromatographic separation of complex hydrocarbon mixtures in three columns coupled in series. Principal component analysis (PCA), correspondence factor analysis (CFA), and hierarchical ascending classification (HAC) were used as potential tools for evaluating the experiments on single columns and on column series. It has been demonstrated that: (1) multivariate analysis with PCA and CFA offers a powerful strategy to search for the main factors influencing the separation of hydrocarbons without a priori knowledge of the key factors of the separation. (2) With CFA the contribution of retention due to vapour pressure can be minimized. The use of retention indices, which use the n-alkanes as reference compounds, also helps to decrease the dominant focus on vapour pressure in favor of the more selectivity-based interaction forces. (3) CFA helps to analyze the degree of relevance of the chosen experimental design to the most important factors, controlling chromatographic selectivity.

Temperature dependence of the Kovats retention indices for alkyl 1, 3-diketones on a DB-5 capillary column

A series of alkyl 1,3-diketones were used to study the temperature dependence of the Kovats retention indices in the temperature range 130?190 ?C (403?463 K). The temperature dependence is described by the empirical equation I = B B/T + ClnT. On the basis of this equation, the activation enthalpy, ?H?, and the chemical potential of the partitioning of one methylene group between the two phases of the chromatographic system, ??p(CH2), were calculated. Also the Kovats retention indices ? boiling point correlations (linear and reciprocal) for alkyl 1,3-diketones were studied and ??p(CH2) was calculated.

Data mining for seeking accurate quantitative relationship between molecular structure and GC retention indices of alkanes by projection pursuit

Primary data mining on alkanes for seeking accurate quantitative relationship between molecular structure and retention indices of gas chromatography is developed in this paper. Based on the results obtained from projection pursuit (PP), a new variable named class distance variable, which essentially describes the branching structure of the alkanes, is proposed. With the help of the new variable, both fitting and prediction accuracy of the regression model can be dramatically improved. The results obtained in this work show that the technique of PP developed in statistics is a quite promising tool for seeking accurate quantitative structure-activity relationship (QSAR) and/or quantitative structure-property relationship (QSPR) researches.

Binding of some environmental pollutants to corn protein zein studied by high-performance liquid chromatography

The strength and the character of the binding of 12 organic solvents to the corn protein zein was determined by preparing zein-coated carbon stationary phase and by measuring the retention of solvents on a high-performance liquid chromatographic column filled with this stationary phase. The relationship between the physicochemical parameters and binding characteristics of solvents was elucidated by principal component analysis. Various interactive forces are involved in the binding of solvent to the protein. The strength of the binding is dominantly influenced by the molecular hydrophobicity while the Gaussian or non-Gaussian distribution of the adsorption strength is markedly dependent on the polarity parameters of the solvent. Sterical correspondences exerted considerable influence on each binding parameter.

Temperature dependence of Kováts indices in gas chromatography revisited

Temperature dependence of the Kováts retention index (I) was measured for some aliphatic ketones and aldehydes on a poly(dimethyl siloxane) (HP-1) stationary phase. An interesting minimum (non-linearity) was observed for the I versus isothermal column temperature (T) relationships. A novel empirical model is proposed: I=A+B/T+C ln T, where A, B and Care equation constants and B/C = T(min). A detailed statistical analysis clearly shows superiority of the extended model (i.e., of this containing the logarithm of the temperature (ln T) term) over the earlier established Antoine-type reciprocal equation. The minimum temperature (and the energy like quantity=RT(min), where R is the gas constant) changes in a systematic manner. The factors effecting the (RT(min)) term are as follows: (i) this term decreases with the increase of the molecular mass of the respective oxo compounds; (ii) ketones have higher absolute values of (RT(min) than aldehydes; (iii) branching of the carbon chain lowers the mentioned (RT(min)). This enthalpy term is unambiguously bound to the polarity of solutes.

Using spectrophotometry and spectral mapping technique for the study of the production of manganese-dependent and manganese-independent peroxidases by Pleurotus ostreatus

The activity of manganese-dependent and manganese-independent peroxidases produced by Pleurotus ostreatus in culture media composed of agro-residues was measured by visible spectrophotometry. The overall enzyme activity and its selectivity were separated by using spectral mapping technique followed by nonlinear mapping. The relationships between the parameters of enzyme production and the composition of culture media and fermentation time was assessed by stepwise regression analysis. Calculations proved that the addition of extract of straw to the culture media significantly decreased the overall production of both enzymes, whereas the selectivity of enzyme production was influenced by amount of potato extract and the concentration of total sugar in the culture media. Enzyme activity depended quadratically on the fermentation time.

Evaluation of peptide electropherograms by multivariate mathematical-statistical methods. I. Principal component analysis

Depository effects in slowly metabolised proteins, typically glycation or the estimation of products arising from the reaction of unsaturated long-chain-fatty acid metabolites (possessing aldehydic groups) are very difficult to assess owing to their extremely low concentration in the protein matrix. In order to reveal such alterations we applied deep enzymatic fragmentation resulting in a set of small peptides, which, if modified, are likely to change their electrophoretic properties and can be visualised on the resulting profile. Peptide maps of collagen (a mixture of collagen types I and III digested by bacterial collagenase) were applied as the model protein structure for detecting the nonenzymatic posttranslational changes originating during various physiological conditions like high fructose diet and hypertriglyceridemic state. Capillary electrophoresis in acidic media (sodium phosphate buffer, pH 2.5) was used as the separation method capable of (partial) separation of over 60 peptide peaks. Two to 13 changes were revealed in the profiles obtained reflecting the physiological conditions of the animals tested. Combination of peptide profiling with subsequent t-test evaluation of individual peak areas and principal component analysis based on cumulative peak areas of individual sections of the electropherograms allowed to determine in which section (part) of the electropherogram the physiological state indicating changes occurred. Simultaneously it was possible to reveal the qualitative differences between the four physiological regimes investigated (i.e., which regime affects the collagen molecules most and which affects them least). The approach can be used as guidance for targeted preseparation of the very complex peptide mixture.