Multivariate analysis of the volatile components in tobacco based on infrared‐assisted extraction coupled to headspace solid‐phase microextraction and gas chromatography‐mass spectrometry

A novel infrared‐assisted extraction coupled to headspace solid‐phase microextraction followed by gas chromatography with mass spectrometry method has been developed for the rapid determination of the volatile components in tobacco. The optimal extraction conditions for maximizing the extraction efficiency were as follows: 65 μm polydimethylsiloxane‐divinylbenzene fiber, extraction time of 20 min, infrared power of 175 W, and distance between the infrared lamp and the headspace vial of 2 cm. Under the optimum conditions, 50 components were found to exist in all ten tobacco samples from different geographical origins. Compared with conventional water‐bath heating and nonheating extraction methods, the extraction efficiency of infrared‐assisted extraction was greatly improved. Furthermore, multivariate analysis including principal component analysis, hierarchical cluster analysis, and similarity analysis were performed to evaluate the chemical information of these samples and divided them into three classifications, including rich, moderate, and fresh flavors. The above‐mentioned classification results were consistent with the sensory evaluation, which was pivotal and meaningful for tobacco discrimination. As a simple, fast, cost‐effective, and highly efficient method, the infrared‐assisted extraction coupled to headspace solid‐phase microextraction technique is powerful and promising for distinguishing the geographical origins of the tobacco samples coupled to suitable chemometrics.     

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

Direct‐injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e‐noses) in classification tasks are briefly reviewed, with an emphasis on food‐related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR‐MS), and many results obtained using the powerful PTR‐time of flight‐MS (PTR‐ToF‐MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR‐ToF‐MS. A supervised multivariate data analysis based on partial least squares regression‐discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR‐MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.     

Accurate discrimination of Gastrodia elata from different geographical origins using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis

Gastrodia elata from different geographical origins varies in quality and pharmacological activity. This study focused on the classification and identification of Gastrodia elata from six producing areas using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis. Before recognition analysis, a principal component analysis was applied to ascertain the discrimination possibility with high‐performance liquid chromatography fingerprints. And then, boosting partial least‐squares discriminant analysis and conventional partial least‐squares discriminant analysis were applied in this study. Experimental results indicated that the adaptive iteratively reweighted penalized least‐squares algorithm could eliminate the baseline drift of high‐performance liquid chromatography chromatograms effectively. And compared with partial least‐squares discriminant analysis, the total recognition rates using high‐performance liquid chromatography fingerprint combined with boosting partial least‐squares discriminant analysis for the calibration sets and prediction sets were improved from 94 to 100% and 86 to 97%, respectively. In conclusion, high‐performance liquid chromatography combined with boosting partial least‐squares discriminant analysis, which has such advantages as effective, specific, accurate, non‐polluting, has an edge for discrimination of traditional Chinese medicine from different geographical origins. And the proposed methodology is a useful tool to classify and identify Gastrodia elata from different geographical origins.     

Differentiating Westlake Longjing tea from the first‐ and second‐grade producing regions using ultra high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry‐based untargeted metabolomics in combination with chemometrics

There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first‐ and second‐grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry‐based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high‐resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two‐way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.     

Characterization and classification of Western Greek olive oils according to cultivar and geographical origin based on volatile compounds

The aim of the present study was to characterize and classify olive oils from Western Greece according to cultivar and geographical origin, based on volatile compound composition, by means of Linear Discriminant Analysis. A total of 51 olive oil samples were collected during the harvesting period 2007-2008 from six regions of Western Greece and from six local cultivars. Forty-five of the samples were characterized as extra virgin olive oils. The analysis of volatile compounds was performed by Headspace Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS). Fifty-three (53) different volatile compounds were tentatively identified and semi-quantified. Using selected volatile compound composition data (selection was based on the application of ANOVA to total volatiles to determine those variables showing substantial differences among samples of different geographical origin/cultivar), the olive oil samples were satisfactorily classified according to geographical origin (87.2%) and cultivar (74%).     

Study of metabolite differences of flue‐cured tobacco from different regions using a pseudotargeted gas chromatography with mass spectrometry selected‐ion monitoring method

A pseudotargeted method based on gas chromatography and mass spectrometry with selected‐ion monitoring was established to investigate the metabolite differences of flue‐cured tobacco from three different growing regions. The mixed solvent of acetonitrile/isopropanol/water (3:3:2, v/v/v) was chosen as the optimal extraction system based on the good repeatability and extraction efficiency. A self‐developed software coupled with commercial software was used to establish the pseudotargeted method including 289 peaks and 47 groups. Multivariable statistical analysis indicated that tobacco samples can be obviously separated based on the geographical origins. On the basis of a Mann–Whitney U test, organic acids, phenols, and alkaloids had higher levels in Hunan province. In contrast, a large proportion of amino acids (including l ‐tyrosine, l ‐proline, and serine), sucrose, and linoleic acid were the highest in Yunnan province. Meanwhile, multiple metabolic pathways (including carbohydrate metabolism, tricarboxylic acid cycle, and nitrogen metabolism) were influenced by growing regions. Twenty‐eight differential metabolites, which had great contributions to the classification of tobacco samples of three growing regions, were further defined. The results demonstrated that the developed pseudotargeted method was a powerful tool to investigate the metabolic profiling of tobacco leaves and discriminate tobacco leaves of different growing regions.     

Non‐targeted volatile profiles for the classification of the botanical origin of Chinese honey by solid‐phase microextraction and gas chromatography–mass spectrometry combined with chemometrics

A potential method for the discrimination and prediction of honey samples of various botanical origins was developed based on the non‐targeted volatile profiles obtained by solid‐phase microextraction with gas chromatography and mass spectrometry combined with chemometrics. The blind analysis of non‐targeted volatile profiles was carried out using solid‐phase microextraction with gas chromatography and mass spectrometry for 87 authentic honey samples from four botanical origins (acacia, linden, vitex, and rape). The number of variables was reduced from 2734 to 70 by using a series of filters. Based on the optimized 70 variables, 79.12% of the variance was explained by the first four principal components. Partial least squares discriminant analysis, naïve Bayes analysis, and back‐propagation artificial neural network were used to develop the classification and prediction models. The 100% accuracy revealed a perfect classification of the botanical origins. In addition, the reliability and practicability of the models were validated by an independent set of additional 20 authentic honey samples. All 20 samples were accurately classified. The confidence measures indicated that the performance of the naïve Bayes model was better than the other two models. Finally, the characteristic volatile compounds of linden honey were tentatively identified. The proposed method is reliable and accurate for the classification of honey of various botanical origins.     

Chemical differentiation of volatile compounds in crude and processed Atractylodis Macrocephalae Rhizoma by using comprehensive two‐dimensional gas chromatography with time‐of‐flight mass spectrometry combined with multivariate data analysis

In the present study, comprehensive 2D GC—TOF‐MS combined with multivariate data analysis was applied to analyze the differences of the volatile components in crude and processed Atractylodis Macrocephalae Rhizoma (AMR) samples. As a result, 26 compounds that were found in crude AMR samples disappeared in processed AMR samples, and 19 compounds were newly generated and identified in AMR after processing with wheat bran. Meanwhile, principal component analysis demonstrated that there were significant chemical differences between crude and processed AMR samples, and processing procedure caused obvious quantitative and qualitative changes of volatile components in AMR. The established method could be used to explain the chemical differentiation between crude and processed AMR, and to further understand the processing mechanism of herbal medicines.     

Characterization of volatile substances in apples from Rosaceae family by headspace solid‐phase microextraction followed by GC‐qMS

The volatile composition of different apple varieties of Malus domestica Borkh. species from different geographic regions at Madeira Islands, namely Ponta do Pargo (PP), Porto Santo (PS), and Santo da Serra (SS) was established by headspace solid‐phase microextraction (HS‐SPME) procedure followed by GC‐MS (GC‐qMS) analysis. Significant parameters affecting sorption process such as fiber coating, extraction temperature, extraction time, sample amount, dilution factor, ionic strength, and desorption time, were optimized and discussed. The SPME fiber coated with 50/30 μm divinylbenzene/carboxen/PDMS (DVB/CAR/PDMS) afforded highest extraction efficiency of volatile compounds, providing the best sensitivity for the target volatiles, particularly when the samples were extracted at 50°C for 30 min with constant magnetic stirring. A qualitative and semi‐quantitative analysis between the investigated apple species has been established. It was possible to identify about 100 of volatile compounds among pulp (46, 45, and 39), peel (64, 60, and 64), and entire fruit (65, 43, and 50) in PP, PS, and SS apples, respectively. Ethyl esters, terpenes, and higher alcohols were found to be the most representative volatiles. α‐Farnesene, hexan‐1‐ol and hexyl 2‐methylbutyrate were the compounds found in the volatile profile of studied apples with the largest GC area, representing, on average, 24.71, 14.06, and 10.80% of the total volatile fraction from PP, PS, and SS apples. In PP entire apple, the most abundant compounds identified were α‐farnesene (30.49%), the unknown compound m/z (69, 101, 157) (21.82%) and hexyl acetate (6.57%). Regarding PS entire apple the major compounds were α‐farnesene (16.87%), estragole (15.43%), hexan‐1‐ol (10.94), and E‐2‐hexenal (10.67). α‐Farnesene (30.3%), hexan‐1‐ol (18.90%), 2‐methylbutanoic acid (4.7%), and pentan‐1‐ol (4.6%) were also found as SS entire apple volatiles present in a higher relative content. Principal component analysis (PCA) of the results clustered the apples into three groups according to geographic origin. Linear discriminant analysis (LDA) was performed in order to detect the volatile compounds able to differentiate the three kinds of apples investigated. The most important contributions to the differentiation of the PP, PS, and SS apples were ethyl hexanoate, hexyl 2‐methylbutyrate, E,E‐2,4‐heptadienal, p‐ethyl styrene, and E‐2‐hexenal.     

A purge and trap technique to capture volatile compounds combined with comprehensive two‐dimensional gas chromatography/time‐of‐flight mass spectrometry to investigate the effect of sulfur‐fumigation on Radix Angelicae Dahuricae

Sulfur‐fumigation is known to reduce volatile compounds that are the main active components in herbs used in herbal medicine. We investigated changes in chemical composition between sun‐dried and sulfur‐fumigated Radix Angelicae Dahuricae using a purge and trap technique to capture volatile compounds, and two‐dimensional gas chromatography/time‐of‐flight mass spectrometry for identification. Using sun‐dried Radix Angelicae Dahuricae samples as a reference, the results showed that 73 volatile compounds, including 12 sulfide compounds, were found to be present only in sulfur‐fumigated samples. Furthermore, 32 volatile compounds that were found in sun‐dried Radix Angelicae Dahuricae samples disappeared after sulfur‐fumigation. The proposed method can be applied to accurately discriminate sulfur‐fumigated Radix Angelicae Dahuricae from different commercial sources. Copyright © 2014 John Wiley & Sons, Ltd.     

Static and dynamic headspace analysis of instant coffee blends by proton‐transfer‐reaction mass spectrometry

Instant coffee is a widespread product, generally related to a high consumer acceptability, also because of its ease of preparation. The present work addresses the characterization of the headspace of freshly brewed instant coffees resulting from different blends, during and immediately after preparation. The sample set consisted of 10 coffees, obtained by mixing three different blends in different proportions. The employment of Proton Transfer Reaction‐Mass Spectrometry (PTR‐MS) allowed for direct and real‐time sampling from the headspace, under conditions that mimic those that are encountered above the cup during and right after brewing. Different coffee brews were separated on the basis of the respective volatile profiles, and data showed good consistency with the respective blend compositions. When the headspace evolution was monitored during preparation, similar results were obtained in terms of blend separation; moreover, different blends displayed different and reproducible ‘signatures’ in terms of time evolution. A straightforward method for the prediction of headspace composition is proposed, allowing to predict the volatile profiles of two‐component and three‐component blends on the basis of the respective parent components. Overall, the results constitute a successful example of the applicability of PTR‐MS as a tool for product development in food science. Copyright © 2015 John Wiley & Sons, Ltd.     

Discrimination between 2AP producing and non‐producing rice rhizobacterial isolates using volatile profiling: a chemometric approach

2‐Acetyl‐1‐pyrroline (2AP) is known as a principal basmati aroma compound. The present study aims at discriminating rhizobacteria isolated from soils cultivated with basmati and non‐basmati rice for long duration. Volatile profiling was used as marker to discriminate the rhizobacterial isolates. Quantification of 2AP and other volatile compounds (VCs) produced by rhizobacteria was undertaken using HS‐SPME coupled with GC‐MS. Chemometrics tools such as hierarchical cluster analysis (HCA), principle component analysis (PCA) and multi dimensional scaling (MDS) were applied for volatile profiling of different isolates. Results showed significant discrimination of all 2AP producing (AP‐P) and non‐producing rhizobacterial isolates (AP‐NP) on the basis of their VC profile. This was validated by bacterial identification data as well. The frequency distribution for 2AP levels indicates that basmati isolates had higher frequency for 2AP production as compared to non‐basmati control. AP‐P and AP‐NP isolates have different VC profiling pattern irrespective of their origin. These isolates were found belonging to different groups when identified using 16S rDNA sequencing data. Chemometric analysis (PCA, HCA and MDS) helped to identify volatiles, which could be used as biomarker in discriminating the AP‐P and AP‐NP isolates. VC pattern of rhizobacteria could be used as volatile markers to distinguish between AP‐P and AP‐NP rhizobacterial isolates. Copyright © 2015 John Wiley & Sons, Ltd.     

Denoising and multivariate analysis of time‐of‐flight SIMS images

Time‐of‐flight SIMS (ToF‐SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF‐SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF‐SIMS image data. Three established denoising algorithms—down‐binning, boxcar and wavelet filtering—were applied to ToF‐SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down‐binning the images to increase pixel size and signal was the most effective technique to improve PCA performance. Copyright © 2003 John Wiley & Sons, Ltd.     

Geographical classification of Epimedium based on HPLC fingerprint analysis combined with multi‐ingredients quantitative analysis

A reliable and comprehensive method for identifying the origin and assessing the quality of Epimedium has been developed. The method is based on analysis of HPLC fingerprints, combined with similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and multi‐ingredient quantitative analysis. Nineteen batches of Epimedium , collected from different areas in the western regions of China, were used to establish the fingerprints and 18 peaks were selected for the analysis. Similarity analysis, HCA and PCA all classified the 19 areas into three groups. Simultaneous quantification of the five major bioactive ingredients in the Epimedium samples was also carried out to confirm the consistency of the quality tests. These methods were successfully used to identify the geographical origin of the Epimedium samples and to evaluate their quality.     

Coupling needle‐trap devices with comprehensive two‐dimensional gas chromatography with high‐resolution time‐of‐flight mass spectrometry to rapidly reveal the chemical transformation of volatile components from sulfur‐fumigated ginseng

Sulfur‐fumigation could alter the quality of white ginseng by damaging the bioactive compounds and generating sulfur‐containing materials. In the present study, coupling needle‐trap devices with comprehensive two‐dimensional gas chromatography and high‐resolution time‐of‐flight mass spectrometry was applied to rapidly reveal chemical transformation of volatile components from sulfur‐fumigated ginseng. Thirty‐two volatile compounds were not in white ginseng samples after sulfur‐fumigation. Furthermore, 20 sulfur‐containing compounds were identified for the first time in volatile oil of sulfur‐fumigated white ginseng. The established approach could be applied to discriminate sulfur‐fumigated white ginseng among commercial samples and to control the quality of white ginseng.     

Recognition of volatile compounds as markers in geographical discrimination of Spanish extra virgin olive oils by chemometric analysis of non-specific chromatography volatile profiles

Chromatographic profiles obtained by headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography (GC) were processed as continuous and non-specific signals through multivariate analysis techniques in order to select and identify the most discriminate volatile marker compounds related to the geographical origin of extra virgin olive oils. The blind analysis of the chromatographic profiles was carried out on several steps including preliminary mathematical treatments, explorative analysis, feature selection and classification. The results obtained through the application of stepwise linear discriminant analysis (SLDA) method revealed a perfect discrimination between the different Spanish geographical regions considered (La Rioja, Andalusia and Catalonia). The assignment success rate was 100% in both classification and prediction by using cross validation procedure. In addition, it must be noted that the proposed strategy was able to verify the geographical origin of the samples involving only a reduced number of discriminate retention times selected by the stepwise procedure. This fact emphasizes the quality of the accurate results obtained and encourages the feasibility of similar procedures in olive oil quality and traceability studies. Finally, volatile compounds corresponding to the predictors retained were identified by gas chromatography-mass spectrometry (GC-MS) for a chemical interpretation of their importance in quality virgin olive oils.     

Determination of volatile organic acids in tobacco by single‐drop microextraction with in‐syringe derivatization followed by GC‐MS

In this work, the novel technique based on headspace single‐drop microextraction with in‐syringe derivatization followed by GC‐MS was established to determine the volatile organic acids in tobacco. The parameters for headspace single‐drop microextraction and in‐syringe derivatization were optimized, including extraction time, and volume of derivatization reagent and in‐syringe derivatization time. The method validations including linearity, precision, recovery and LOD were also studied. The obtained results illustrated that the optimized technique was easy, highly efficient and sensitive. Finally, the proposed method was successfully applied to the analyses of volatile organic acids in tobacco samples with seven different brands. It was further demonstrated that the present technique developed in this study does offer a simple and fast approach to determine volatile organic acids in tobacco.     

Influence of image‐analysis software on quantitation of two‐dimensional gel electrophoresis data

Image analysis of two‐dimensional gels is a crucial step in a proteomic workflow and has a direct impact on obtained qualitative and quantitative data. Since the analysis is a complex process and creates large data amounts, the use of a respective software is inevitable. There are only a few papers published addressing the issue of analysis‐based variance; therefore, our aim was to highlight the discrepancy of received results when different commercially available image‐tools are used for gel analysis especially in terms of comparability of the obtained outcome when the same digital image set is used. A set of six gels (three replicates per group) of real‐life samples was created and examined with two different versions of PD‐Quest (Bio‐Rad) (version 6.1 and its update version 8.0) and with an external image‐tool Delta 2D (Decodon) (version 3.6). Replicate groups were analyzed and compared with each other with regard to volume ratios of a group of significantly changed spots. The study points out significant variations among results depending on the software package used, underlining the importance of a careful investigation of post‐experimental processes to receive comparable and reliable results.     

Determination and comparison of agarwood from different origins by comprehensive two‐dimensional gas chromatography–quadrupole time‐of‐flight mass spectrometry

Agarwood, a species of resinous heartwood, is a precious medicinal plant and a type of rare natural spice, which is widely used in medicine, cosmetics, religious activities, and other fields. In this study, agarwood samples from eight different regions across four countries were analyzed by comprehensive two‐dimensional gas chromatography?quadrupole time‐of‐flight mass spectrometry. A total of 232 species were identified (the match factors of these compounds were above 750). The main compounds of agarwood are oxygenated sesquiterpenes and chromones. The compositions of India1 and Malaysia2 were significantly different from those of other samples, which might be attributed to the different production processes of agarwood. For further investigation, factor analysis was conducted for six agarwood samples. The results showed that the data classification possessed a regional characteristic; according to the retention time and relative content, characteristic compositions were determined by factor scores. Finally, the differences of characteristic compositions were simply analyzed, and the reasons were speculated.     

Comparison of the volatile constituents of different parts of Cortex magnolia officinalis by GC‐MS combined with chemometric resolution method

Volatile compositions of different parts (stem, branch and root barks) of Cortex magnolia officinalis, cultivated in China, were investigated for the first time by GC‐MS with the help of heuristic evolving latent projection (HELP). Identification of components was conducted by similarity matching to NIST mass library but also assisted by comparison of temperature‐programmed retention indices (PTRIs) with the data web available. A total of 90, 82 and 76 volatile compounds in the essential oils of the three samples taken from the same batch aforementioned were qualitatively and quantitatively determined, representing 84.03, 83.68 and 83.10% of the total content, respectively. Among the constituents determined, there were 50 components coexisting. Eudesmol and its isomers were shown to be the principal compounds in the studied samples, accounting for 47.66, 36.74 and 36.31%, respectively. The three kinds of isomers (α‐, β‐ and γ‐eudesmol) in houpo volatile oils have been tentatively qualified and quantified simultaneously for the first time. By comparative analysis, significant qualitative and semi‐quantitative differences and similarities were observed among the three samples. The results achieved provide a scientific evidence for further exploitation of Magnolia bark and clinical medication.