Untargeted GC-MS Research Articles

Unraveling Cecal Alterations in Clostridioides difficile Colonized Mice through Comprehensive Metabolic Profiling.

The disruption of gut microbiota caused by antibiotics favors the intestinal colonization of Clostridioides difficile - a Gram-positive, spore-forming anaerobic bacterium that causes potentially fatal gastrointestinal infections. In an endeavor to elucidate the complexities of the gut-brain axis in the context of Clostridium difficile infection (CDI), a murine model has been used to investigate the potential effects of antibiotic administration and subsequent colonization by C. difficile, as well as the impact of three different 10-day treatments (metronidazole, probiotics, and fecal microbiota transplantation), on the cecal metabolome for the first time. This follows our previous research which highlighted the metabolic effect of CDI and these treatments in the brain and employs the same four different metabolomics-based methods (targeted GC-MS/MS, targeted HILIC-MS/MS, untargeted RP-LC-HRMS/MS and untargeted GC-MS). A total of 286 unique metabolites have been identified in the mouse cecal profiles and statistical analysis revealed that CDI, as well as the subsequent treatments, significantly alters cecal metabolites and lipids implicated in various biochemical pathways centered around amino acid metabolism, glycerophospholipid metabolism, and central carbon metabolism. To our knowledge, this study represents the first exploration of the effects of C. difficile-induced colitis and potential treatments on the cecal tissue metabolome.

Integrative LC-MS and GC-MS metabolic profiling unveils dynamic changes during barley malting

Malting involves complex biochemical transformations affecting sensory and quality attributes. Despite extensive research on storage carbohydrates and proteins in malting, the lack of a detailed metabolic understanding of this process limits our ability to assess and enhance malt quality. This study employed untargeted GC-MS and LC-MS metabolite profiling across six malting timepoints to identify 4980 known metabolites, 82 % of which exhibited significant changes during the malting process. Here we identified stage-dependent metabolic shifts and dynamic chemical classes and pathways between each studied stage. These results can guide the fine-tuning of malting conditions to improve malt quality for beer production and other malt-based applications. Additionally, metabolites with antimicrobial properties were identified, underscoring the interplay between barley and microbial metabolic processes during malting. Further research into these microbial metabolites and cognate microbes may lead to novel malting assessment traits for high-quality and safe malted barley.

Chemical characterization of different wood fragments and their volatile composition in model spirit solutions

This study characterizes oak (sessile and pedunculate oak) and alternative wood (black locust, Myrobalan plum, wild cherry, and mulberry) species as important sources of volatile compounds of aged spirits. Nowadays, their fragments are used to hasten the brandies? aging process. The ATR-FTIR spectra of analyzed wood samples are similar, only the mulberry FTIR spectrum contains unique peaks primarily due to its highest lignin content (40.93%). Using the untargeted GC-MS approach, a total of forty-one volatile compounds were identified in the wood extracts in a model spirit solution. The volatile profiles of alternative wood extracts in a model spirit solution were significantly different, both quantitatively and qualitatively, compared to those of oak. Coniferyl (23.14 ?g/g - 26.6 ?g/g) and sinapyl (23.56 ?g/g - 25.82 ?g/g) alcohols were the most abundant volatile compounds in investigated oak extracts. Resorcinol and coniferyl alcohol were the most abundant volatile compounds in black locust, sakuranin in wild cherry, while resorcinol and ?-resorcinaldehyde in mulberry wood. To the best of our knowledge, sakuranin has not been detected in wild cherry wood until now. Besides wood chemical characteristics, the technology used during the aging process strongly influences on volatile profiles of aged brandies, thus, these compounds are potential chemical markers for discrimination between wood species as well as aging technologies.

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Untargeted GC-MS metabolomics combined with multivariate statistical analysis as an effective method for discriminating the geographical origin of shrimp paste

Joint QTL Mapping and Transcriptome Sequencing Analysis Reveal Candidate Seed-Shattering-Related Genes in Common Buckwheat.

Common buckwheat (Fagopyrum esculentum M.) is an important traditional miscellaneous grain crop. However, seed-shattering is a significant problem in common buckwheat. To investigate the genetic architecture and genetic regulation of seed-shattering in common buckwheat, we constructed a genetic linkage map using the F2 population of Gr (green-flower mutant and shattering resistance) and UD (white flower and susceptible to shattering), which included eight linkage groups with 174 loci, and detected seven QTLs of pedicel strength. RNA-seq analysis of pedicel in two parents revealed 214 differentially expressed genes DEGs that play roles in phenylpropanoid biosynthesis, vitamin B6 metabolism, and flavonoid biosynthesis. Weighted gene co-expression network analysis (WGCNA) was performed and screened out 19 core hub genes. Untargeted GC-MS analysis detected 138 different metabolites and conjoint analysis screened out 11 DEGs, which were significantly associated with differential metabolites. Furthermore, we identified 43 genes in the QTLs, of which six genes had high expression levels in the pedicel of common buckwheat. Finally, 21 candidate genes were screened out based on the above analysis and gene function. Our results provided additional knowledge for the identification and functions of causal candidate genes responsible for the variation in seed-shattering and would be an invaluable resource for the genetic dissection of common buckwheat resistance-shattering molecular breeding.

Effect of 1-year daily protein supplementation and physical exercise on muscle protein synthesis rate and muscle metabolome in healthy older Danes: a randomized controlled trial

BackgroundThe skeletal muscle mass decreases with age and the responsiveness of aging muscles’ protein synthesis rate (MPS) to protein intake seems to deteriorate.ObjectiveThis study investigated the impact of 12 months of protein supplementation with or without physical exercise training on the basal and postprandial MPS and the skeletal muscle metabolome of healthy older Danes (> 65 years, 29 females/37 males).MethodsSubjects were randomized to follow one of five intervention groups: (1) carbohydrate, (2) collagen protein, (3) whey protein, (4) home-based light resistance training with whey protein, and (5) center-based heavy-load resistance training with whey protein. Before and after the intervention, a tracer infusion trial was conducted to measure basal and postprandial MPS in response to intake of a cocktail consisting of 20 g whey hydrolysate + 10 g glucose. In addition, the skeletal muscle metabolome was measured using gas chromatography–mass spectrometry (GC–MS) at basal state and 4 h after the intake of the cocktail.ResultsOne year of daily protein or carbohydrate supplementation did not alter the basal and protein-stimulated postprandial muscle protein synthesis rate or the muscle metabolome of healthy older Danes. Basal MPS (%/h) at baseline for all subjects were 0.0034 ± 0,011 (mean ± SD). In contrast to previous studies, no difference was observed in basal MPS between males and females (p = 0.75). With the developed untargeted GC–MS methodology, it was possible to detect and tentatively annotate > 70 metabolites from the human skeletal muscle samples.ConclusionOne year of protein supplementation in comparison to an isocaloric-control supplement seems to affect neither the MPS at basal or postprandial state nor the skeletal muscle metabolome.Clinical trial registryNumber: NCT02115698, clinicaltrials.gov/ct2/show/NCT02115698.

Effects of electroacupuncture on urinary metabolome and microbiota in presenilin1/2 conditional double knockout mice.

The treatment of Alzheimer's disease (AD) is still a worldwide problem due to the unclear pathogenesis and lack of effective therapeutic targets. In recent years, metabolomic and gut microbiome changes in patients with AD have received increasing attention, and the microbiome-gut-brain (MGB) axis has been proposed as a new hypothesis for its etiology. Considering that electroacupuncture (EA) efficiently moderates cognitive deficits in AD and its mechanisms remain poorly understood, especially regarding its effects on the gut microbiota, we performed urinary metabolomic and microbial community profiling on EA-treated AD model mice, presenilin 1/2 conditional double knockout (PS cDKO) mice, to observe the effect of EA treatment on the gut microbiota in AD and find the connection between affected gut microbiota and metabolites. After 30 days of EA treatment, the recognition memory ability of PS cDKO mice was evaluated by the Y maze and the novel object recognition task. Urinary metabolomic profiling was conducted with the untargeted GC-MS method, and 16S rRNA sequence analysis was applied to analyze the microbial community. In addition, the association between differential urinary metabolites and gut microbiota was clarified by Spearman's correlation coefficient analysis. In addition to reversed cognitive deficits, the urinary metabolome and gut microbiota of PS cDKO mice were altered as a result of EA treatment. Notably, the increased level of isovalerylglycine and the decreased levels of glycine and threonic acid in the urine of PS cDKO mice were reversed by EA treatment, which is involved in glyoxylate and dicarboxylate metabolism, as well as glycine, serine, and threonine metabolism. In addition to significantly enhancing the diversity and richness of the microbial community, EA treatment significantly increased the abundance of the genus Mucispirillum, while displaying no remarkable effect on the other major altered gut microbiota in PS cDKO mice, norank_f_Muribaculaceae, Lactobacillus, and Lachnospiraceae_NK4A136 group. There was a significant correlation between differential urinary metabolites and differential gut microbiota. Electroacupuncture alleviates cognitive deficits in AD by modulating gut microbiota and metabolites. Mucispirillum might play an important role in the underlying mechanism of EA treatment. Our study provides a reference for future treatment of AD from the MGB axis.

Metabolic Phenotyping Study of Mouse Brain Following Microbiome Disruption by C.difficile Colonization.

Clostridioides difficile infection (CDI) is responsible for an increasing number of cases of post-antibiotic diarrhea worldwide, which has high severity and mortality among hospitalized elderly patients. The disruption of gut microbiota due to antibacterial medication facilitates the intestinal colonization of C. difficile. In the present study, a murine model was used to investigate the potential effects of antibiotic administration and subsequent colonization by C. difficile, as well as the effects of three different 10-day treatments (metronidazole, probiotics, and fecal microbiota transplantation), on the brain metabolome for the first time. Four different metabolomic-based methods (targeted HILIC-MS/MS, untargeted RP-LC-HRMS/MS, targeted GC-MS/MS, and untargeted GC-MS) were applied, resulting in the identification of 217 unique metabolites in the brain extracts, mainly glycerophospholipids, glycerolipids, amino acids, carbohydrates, and fatty acids. Univariate and multivariate statistical analysis revealed that CDI, as well as the subsequent treatments, altered significantly several brain metabolites, probably due to gut dysbiosis, and affected the brain through the gut-brain axis. Notably, none of the therapeutic approaches completely restored the brain metabolic profile to the original, healthy, and non-infected phenotype, even after 10 days of treatment.

Evaluation of ensemble data preprocessing strategy on forensic gasoline classification using untargeted GC–MS data and classification and regression tree (CART) algorithm

The purpose of this work is to evaluate the ensemble data preprocessing (DP) strategy composing the selected variant of normalization, parametric time warping and baseline correction techniques in varying sequences for modelling a gas chromatography-mass spectrometry (GC–MS) data via classification and regression tree (CART) algorithm. Firstly, the relative merits between single-DP and ensemble-DP strategies were carefully compared using the best-performing sub-retention time (RT) windows reported elsewhere. Then, all the preprocessed sub-datasets were assessed based on predictive capability estimated via the CART algorithm. Performances of CART models were estimated from 50 pairs of training and testing samples that were prepared by a stratified random resampling method. Then, the three shortlisted sub-datasets were further evaluated using increased pairs of training and testing samples. Additionally, the most discriminative RT points were also identified using the three sub-datasets. Eventually, the most desired CART model was constructed using the shortlisted RT points after being treated by the most outstanding DP strategy. Results showed that 3-DP strategies tended to outperform the 1-DP and 2-DP strategies. However, the sequence of application must be carefully optimized as not all the 3-DP strategies induced positive impacts. It was found that the data aligned before baseline correction or normalization will likely outperform those being first normalized or baseline corrected. In conclusion, the untargeted GC–MS data of neat gasoline preferably be first aligned, followed by normalization, and ended by baseline correction.

Fully automatic resolution of untargeted GC-MS data with deep learning assistance

DeepResolution (Deep learning-assisted multivariate curve Resolution) has been proposed to solve the co-eluting problem for GC-MS data. However, DeepResolution models must be retrained when encountering unknown components, which is undoubtedly time-consuming and burdensome. In this study, a new pipeline named DeepResoution2 was proposed to overcome these limitations. DeepResolution2 utilizes deep neural networks to divide the profile into segments, estimate the number of components in each segment, and predict the elution region of each component. Subsequently, the information obtained by these deep learning models is used to assist the multivariate curve resolution procedure. Only seven models (1 + 1 + 5) are required to automate the whole analysis procedure of untargeted GC-MS data, which is an important improvement over DeepResolution. These seven models are stable and universal. Once established, they can be used to resolve most GC-MS data. Compared with MS-DIAL, ADAP-GC, and AMDIS, DeepResolution2 can obtain more reasonable mass spectra, chromatograms and peak areas to identify and quantify compounds. DeepResoution2 (0.955) outperformed AMDIS (0.939), MS-DIAL (0.948) and ADAP-GC (0.860) in terms of the linear correlation between concentrations and peak areas on overlapped peaks in fatty acid dataset. In real biological samples of human male infertility plasma, the peak areas and mass spectra of 136 untargeted GC-MS files were automatically extracted by DeepResolution2 without any prior information and manual intervention. DeepResolution2 includes all the functions for analyzing untargeted GC-MS datasets from the feature extraction of raw data files to the establishment of discriminant models.

Untargeted GC-MS metabolomics reveals the metabolic responses in the gills of Chinese mitten crab (Eriocheir sinensis) subjected to air-exposure stress

Desiccation is a common stress experienced by crabs during aquaculture and transportation. In China, the crustacean, Chinese mitten crab (Eriocheir sinensis), is economically important. However, little is known about the molecular pathways underlying physiological stress. Here, by using untargeted gas chromatography-mass spectrometry metabolomics, we investigated the metabolic responses of the gills of E. sinensis subjected to air-exposure stress by six biological replicates of the control group (CG) and the air-exposure stress group (AG). Metabolomic analysis identified 43 differential metabolites in the AG versus the CG that could be potential biomarkers of desiccation stress. In addition, integrated analysis of key metabolic pathways revealed the involvement of histidine metabolism; glycine, serine and threonine metabolism; the pentose phosphate pathway; the citrate cycle (TCA cycle); and nicotinate and nicotinamide metabolism. These findings indicated the special physiological responses to air-exposure stresses in this species.

Identification of volatile compounds correlated with consumer acceptability of strawberry preserves: Untargeted GC–MS analysis

Volatile compounds that impact the acceptability of strawberry preserves were investigated by untargeted GC–MS flavoromics analysis. Chemical profiles for fifteen strawberry preserves were modeled against consumer liking scores by orthogonal partial least squares (OPLS) with good fit (R2Y = 0.998) and predictive ability (Q2 = 0.853). Ten highly predictive compounds were selected, nine of which were identified, and eight of which were quantified using tandem mass spectrometry. Sensory recombination difference testing revealed that the addition of predictive compounds to an average-liked preserve at levels found in the most-liked preserve induced a perceptible difference to frequent consumers of strawberry jams. One of the highly predictive compounds was identified (MS and synthesis) as a novel linalool oxide product 2-methyl-2-vinyl-tetrahydrofuran with a fruity, herbal-minty, piney aroma and a low odor threshold value.

Untargeted GC-MS reveals differential regulation of metabolic pathways in cyanobacterium Anabaena and its biofilms with Trichoderma viride and Providencia sp.

Cyanobacteria and their biofilms are used as biofertilizing options to improve plant growth, soil fertility, and grain quality in various crops, however, the nature of metabolites involved in such interactions is less explored. The present investigation compared the metabolite profiles of cyanobacterial biofilms: Anabaena torulosa- Trichoderma viride (An-Tr) and A. torulosa- Providencia sp. (An-PW5) against the individual culture of A. torulosa (An) using untargeted gas chromatography-mass spectroscopy. Metabolites were identified using the NIST mass spectral library and the relative peak area of cultures analysed, after normalization with an internal standard, ribitol. An-Tr biofilm recorded approximately 66.85% sugars, with increased quantity and numbers of sugars and their conjugates, which included maltose, lactose, and d-mannitol, but decreased amino acids concentrations, attributable to the effect of Tr as partner. Heat map and cluster analysis illustrated that An-Tr biofilm possessed a unique cluster of metabolites. Partial least square-discriminate analysis (PLS-DA) and pathway analyses showed distinct modulation in terms of metabolites and underlying biochemical routes in the biofilms, with both the partners- PW5 and Tr eliciting a marked influence on the metabolite profiles of An, leading to novel cyanobacterial biofilms. In the An-PW5 biofilm, the ratios of sugars, lactose, mannitol, maltose, mannose, and amino acids serine, ornithine, leucine and 5‑hydroxy indole acetic acid were significantly higher than An culture. Such metabolites are known to play an important role as chemoattractants, facilitating robust plant -microbe interactions. This represents a first-time study on the metabolite profiles of cyanobacterial biofilms, which provides valuable information related to their significance as inoculants in agriculture.

Gasoline Classification Using Untargeted Gc-Ms Data and Classification and Regression Tree (Cart) Algorithm for Forensic Investigation: Part I. Comparison of Normalization, Peak Alignment and Baseline Correction Algorithms

Gasoline Classification Using Untargeted Gc-Ms Data and Classification and Regression Tree (Cart) Algorithm for Forensic Investigation: Part I. Comparison of Normalization, Peak Alignment and Baseline Correction Algorithms

Use of metabolomics in refining the effect of an organic food intervention on biomarkers of exposure to pesticides and biomarkers of oxidative damage in primary school children in Cyprus: A cluster-randomized cross-over trial

BackgroundExposure to pesticides has been associated with oxidative stress in animals and humans. Previously, we showed that an organic food intervention reduced pesticide exposure and oxidative damage (OD) biomarkers over time; however associated metabolic changes are not fully understood yet. ObjectivesWe assessed perturbations of the urine metabolome in response to an organic food intervention for children and its association with pesticides biomarkers [3-phenoxybenzoic acid (3-PBA) and 6-chloronicotinic acid (6-CN)]. We also evaluated the molecular signatures of metabolites associated with biomarkers of OD (8-iso-PGF2a and 8-OHdG) and related biological pathways. MethodsWe used data from the ORGANIKO LIFE + trial (NCT02998203), a cluster-randomized cross-over trial conducted among primary school children in Cyprus. Participants (n = 149) were asked to follow an organic food intervention for 40 days and their usual food habits for another 40 days, providing up to six first morning urine samples (>850 samples in total). Untargeted GC–MS metabolomics analysis was performed. Metabolites with RSD ≤ 20% and D-ratio ≤ 50% were retained for analysis. Associations were examined using mixed-effect regression models and corrected for false-discovery rate of 0.05. Pathway analysis followed. ResultsFollowing strict quality checks, 156 features remained out of a total of 610. D-glucose was associated with the organic food intervention (β = −0.23, 95% CI: −0.37,−0.10), aminomalonic acid showed a time-dependent increase during the intervention period (βint = 0.012; 95% CI:0.002, 0.022) and was associated with the two OD biomarkers (β = −0.27, 95% CI:−0.34,−0.20 for 8-iso-PGF2a and β = 0.19, 95% CI:0.11,0.28 for 8-OHdG) and uric acid with 8-OHdG (β = 0.19, 95% CI:0.11,0.26). Metabolites were involved in pathways such as the starch and sucrose metabolism and pentose and glucuronate interconversions. DiscussionThis is the first metabolomics study providing evidence of differential expression of metabolites by an organic food intervention, corroborating the reduction in biomarkers of OD. Further mechanistic evidence is warranted to better understand the biological plausibility of an organic food treatment on children’s health outcomes.

GC-MS/MS Profiling of Plant Metabolites.

Gas chromatography coupled to electron ionization (EI) quadrupole mass spectrometry (GC-MS) is currently one of the most developed and robust metabolomics technologies. This approach allows for simultaneous measurements of large number of chemically diverse compounds including organic acids, amino acids, sugars, sugar alcohols, aromatic amines, and fatty acids. Untargeted GC-MS profiling based on full scan data acquisition requires complicated raw data processing and sometime provides ambiguous metabolite identifications. Targeted analysis using GC-MS/MS can provide better specificity, increase sensitivity, and simplify data processing and compound identification but wider application of targeted GC-MS/MS approach in metabolomics is hampered by the lack of extensive databases of MRM transitions for non-derivatized and derivatized endogenous metabolites. The focus of this chapter is the automation of GC-MS/MS method development which makes it feasible to develop quantitative methods for several hundred metabolites and use this strategy for plant metabolomics applications.

Contribution of fruit microbiome to raspberry volatile organic compounds emission

Raspberry fruit (Rubus idaeus L.) is highly appreciated by consumers for its quality characteristics and aromatic profile, determined by nearly 300 volatile organic compounds (VOCs). Although several microbes produce VOCs, their direct involvement in fruit aroma determination has been largely overlooked. In this study, the contribution of fruit-associated microbiota to fruit volatile emissions was evaluated by performing an untargeted GC–MS analysis of VOCs occurring in control (C), sterile (S) and artificially reinoculated berries (R). C and R bacterial fruit microbiomes were characterised by next generation sequencing (NGS). The treatments significantly affected the fruit volatilomes, thus confirming the role of bacteria in fruit aroma construction. In particular, aldehydes, monoterpenes, norisoprenoids, and other aroma-active compounds were significantly lower in S raspberries, and recolonisation could only partially restore the emission of terpenoid compounds. Significant correlations were found among NGS data and volatile emissions, including a positive correlation between Lactobacillus and Paenibacillus spp. and norisoprenoids, and a negative correlation between Enterobacteriaceae and monoterpenes. Several VOC-emitting bacterial taxa (including Bacillus, Lactobacillus, Methylobacterium, Paenibacillus, Pseudomonas spp.) are recurrently found in the raspberry-associated microbiome, suggesting that future applications aimed at the control of microbial colonisation may enhance fruit aroma.

Metabolomics profiles associated with an organic diet intervention in school children in Limassol, Cyprus: A cluster-randomized cross-over trial

BACKGROUND AND AIM: Exposure to pesticides has been associated with oxidative stress in animals and humans. Previously, we showed that an organic food intervention reduced pesticide exposure and oxidative damage biomarkers over time, however associated metabolic changes are not fully understood yet. We assessed perturbations of the urine metabolome in response to an organic food intervention for children and its association with pesticides biomarkers [3-phenoxybenzoic acid (3-PBA) and 6-chloronicotinic acid (6-CN)]. We also evaluated the molecular signatures of metabolites associated with biomarkers of oxidative damage (OD) (8-iso-PGF2a and 8-OHdG) and related biological pathways. METHODS: We used data from the ORGANIKO LIFE+ trial (NCT02998203), a cluster-randomized cross-over trial conducted among primary school children in Cyprus. Participants (n=149) were asked to follow an organic food intervention for 40 days and their usual food habits for another 40 days, providing up to six first morning urine samples (850 samples in total). Untargeted GC-MS metabolomics analysis was performed. Metabolites with RSD≤20% and D-ratio≤50% were retained for analysis. Associations were examined using mixed-effect regression models and corrected for false-discovery rate of 0.05. Pathway analysis followed. RESULTS:Following strict quality checks, 156 features remained out of a total of 610. D-glucose was associated with the organic food intervention (β=-0.23, 95% CI: -0.37,-0.10), aminomalonic acid with the organic food intervention (β=-0.44, 95% CI:-0.68,-0.19) and the two OD biomarkers (β=-0.27, 95% CI:-0.34,-0.20 for 8-iso-PGF2a and β=0.19, 95% CI:0.11,0.28 for 8-OHdG) and uric acid with 8-OHdG (β=0.19, 95% CI:0.11,0.26). The most affected pathways were the starch and sucrose metabolism and pentose and glucuronate interconversions. CONCLUSIONS:Changes in the metabolomic profile of primary school children following a 40-day long systematic organic food intervention were observed. This is the first study providing evidence of differential expression of metabolites by an organic food intervention, corroborating the reduction in biomarkers of oxidative damage using metabolomics. KEYWORDS: organic food, metabolomics, pesticides, cluster randomized trial, oxidative stress, lipid damage

Non-volatile molecular composition and discrimination of single grape white wines of Chardonnay, Riesling, Sauvignon Blanc and Silvaner using untargeted GC-MS analysis

This study developed and applied a GC–MS method aiming at molecular fingerprinting of 120 commercial single grape white wines (Chardonnay, Riesling, Sauvignon Blanc and Silvaner) for possible authentication according to grape variety. The method allowed detection of 372 peaks and tentative identification of 146 metabolites including alcohols, organic acids, esters, amino acids and sugars. The grape variety effect explained 8.3% of the total metabolite variation. Univariate tests showed two-thirds of the metabolites being different between grape varieties. Partial least squares-discriminant analysis based classification models were developed for each grape variety and a panel of classifiers (42 metabolites) was established. All the classification models for grape variety showed a high certainty (>91%) for an independent test set. Riesling contained the highest relative concentrations of sugars and organic acids, while concentrations of hydroxytyrosol and gallic acid, common antioxidants in wine, decreased in the order of Chardonnay > Riesling > Sauvignon Blanc > Silvaner.

Effect of cultivar, pasteurization and storage on the volatile and taste compounds of strawberry puree

An untargeted GC-MS approach was performed to study the stability of the volatile fraction and taste of five different single-cultivar strawberry purees during pasteurization (95 °C/1 min) and accelerated storage (14 d at 35 °C). The PLS-DA results showed that the volatile fraction varied markedly among the cultivars in fresh purees; with Honeoye and Sengana exhibiting the most distinctive profiles with more than 8 discriminative markers. However, the differences among the five cultivars remained similar upon pasteurization and storage. Pasteurization generally resulted in changes on the volatile fraction, that could be cultivar dependent, and decrease in glucose and fructose. The contents of hexanal, alcohols and some esters decreased whereas acetate esters increased. During storage, losses of esters, alcohol terpenes, hexanal and the formation of off-flavors (e.g. dimethyl sulfide, α-terpineol and hydrocarbons) occurred for all cultivars, whereas sugar contents remained constant. Food manufactures should pay attention to an adequate raw material selection as the strawberry cultivar strongly influences the volatile fraction and taste of the resulting puree after pasteurization and during storage.