Monitoring volatile compounds production throughout fermentation by Saccharomyces and non-Saccharomyces strains using headspace sorptive extraction

Morales, M.L.; Fierro-Risco, Jesús; Callejón, Raquel M.; Paneque, Patricia

doi:10.1007/s13197-017-2499-6

Cited by 20 publications

(21 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of the authors' knowledge, 3-methyl-1-hexanol, acetone, 2-heptanone, ethyl butyrate, 3-methylbutyl acetate and 2-methylpropyl acetate were identified for the first time to be produced by A. pullulans isolates. However, these compounds have been previously reported in the culture headspace of other microbial organisms: 3-methyl-1-hexanol from Lysobacter species 36 , 2-heptanone from Escherichia coli 37 , 3-methylbutyl acetate from Sporobolomyces roseus 12 , acetone from Muscodor albus 24 and ethyl butyrate and 2-methylpropyl acetate from Saccharomyces cerevisiae 38 .…”

Section: Discussionmentioning

confidence: 94%

Aureobasidium pullulans volatilome identified by a novel, quantitative approach employing SPME-GC-MS, suppressed Botrytis cinerea and Alternaria alternata in vitro

Don

Schmidtke

Gambetta

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Volatile organic compounds (VOCs) produced by Aureobasidium pullulans were investigated for antagonistic actions against Alternaria alternata and Botrytis cinerea. Conidia germination and colony growth of these two phytopathogens were suppressed by A. pullulans Vocs. A novel experimental setup was devised to directly extract VOCs using solid-phase microextraction-gas chromatographymass spectrometry (SPME-GC-MS) from antagonist-pathogen culture headspace. The proposed system is a robust method to quantify microbial VOCs using an internal standard. Multivariate curve resolutionalternating least squares deconvolution of SPME-GC-MS spectra identified fourteen A. pullulans Vocs. 3-Methyl-1-hexanol, acetone, 2-heptanone, ethyl butyrate, 3-methylbutyl acetate and 2-methylpropyl acetate were newly identified in A. pullulans headspace. Partial least squares discriminant analysis models with variable importance in projection and selectivity ratio identified four VOCs (ethanol, 2-methyl-1-propanol, 3-methyl-1-butanol and 2-phenylethanol), with high explanatory power for discrimination between A. pullulans and pathogen. The antifungal activity and synergistic interactions of the four VOCs were evaluated using a Box-Behnken design with response surface modelling. Ethanol and 2-phenylethanol are the key inhibitory A. pullulans VOCs against both B. cinerea and A. alternata. Our findings introduce a novel, robust, quantitative approach for microbial VOCs analyses and give insights into the potential use of A. pullulans Vocs to control B. cinerea and A. alternata.Microbial antagonists have been widely explored as more environmentally friendly disease management alternatives to reduce the excessive use of synthetic fungicides 1,2 . Among a wide array of mechanisms exploited by microbial antagonists, the production of volatile organic compounds (VOCs) with antimicrobial properties 3 has become popular in the recent research due to their low risk of toxic residues, biodegradability 4 and enormous spectrum of activity regardless of a physical contact of the commodity 5 .Aureobasidium pullulans is a yeast-like saprophytic fungus, naturally inhabiting plant and fruit surfaces 6 . This yeast is a well-known biocontrol agent against a range of pathogenic fungi 7,8 and strains of the organism have been formulated as commercial preparations for the practical management of fungal phytopathogens of a range of horticultural crops 9 . Production of antifungal VOCs has been recently identified as a potential mode of action for its biocontrol 10,11 . One of these studies has confirmed the identity of its four VOCs, 2-methyl-1-butanol, 3-methyl-1-butanol, 2-methyl-1-propanol and 2-phenylethanol 11 . These compounds were shown to suppress conidia germination and mycelium growth of Botrytis cinerea, Colletotrichum acutatum, Penicillium expansum, Penicillium digitatum and Penicillium italicum under both in vitro and in vivo conditions 11 . A similar study identified over 20 VOCs from A. pullulans isolated from grapevine 12 . However, none of these c...

show abstract

Section: Discussionmentioning

confidence: 94%

Aureobasidium pullulans volatilome identified by a novel, quantitative approach employing SPME-GC-MS, suppressed Botrytis cinerea and Alternaria alternata in vitro

Don

Schmidtke

Gambetta

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The ripening period has an important role in the volatile composition, and many volatiles are produced during different developmental stages of plant tissues such as flowering, ripening, or maturation [1]. These volatiles are known as primary aromas, and they are responsible for varietal aromas [25]. These compounds are accumulated in plant storage sites and are released from the surface of the leaf, making this part of the F. carica the largest holder of compounds (except aldehydes and monoterpenes, in highest amounts in fruits [9]).…”

Section: Phytochemical Composition Of Fig Fruit Leaf and The Alcohomentioning

confidence: 99%

“…On the other hand, in products such as wine, spirits, and liqueurs elaborated from fruits of F. carica, other types of aromas come from the different processing steps. Secondary aromas (the greatest pool of volatiles) are mainly produced by yeast as metabolism by-products, while tertiary aromas of finished alcoholic beverages are compounds that illustrate the changes made in the sample matrix during the storage and maturity stages [25]. Fruits [1,2,26,27] and leaves [2,8] of F. carica as well as derived products such as the alcoholic beverages, fig liqueurs [28], and spirits [14,15,17] consist of various volatile compounds which are identified and distributed by distinct chemical classes, such as terpenes (monoterpenes and sesquiterpenes), alcohols, aldehydes, ketones, esters, and miscellaneous compounds.…”

Section: Phytochemical Composition Of Fig Fruit Leaf and The Alcohomentioning

confidence: 99%

“…The monoterpenes citronellol acetate (7), (E)-geranyl acetone (12), (+)-sylvestrene (16), pmentha-1,3,8-triene (17), cumene (26), o-and p-cymene (27,28), and nerol oxide (33) and the sesquiterpenes (E)-nerolidol (35), farnesyl acetate (36), α-curcumene (37), β-bisabolene (38), τ-elemene (40), (À)-δ-cadinol (45), cadina-1 (10),4-diene (48), cadalene (49), α-calacorene (50), valencene (51), acoradiene (53), δ-guaiene (α-bulnesene) (55), α-guaiene (56), isocaryophyllene (57), γ-patchoulene (65), and α-cedrene (68) are compounds only identified in Portuguese monovarietal fig spirits [14]. Linalool acetate (2), geraniol (4), (Z)-8-hydroxylinalool (5), neral (β-citral) (6), geranyl vinyl ether (8), ethyl linalool (9), nerol (10), dihydrocitronellol (11), geranial (α-citral) (13), ocimene (14), p-menth-3-ene (19), α-terpinolene (21), α-terpineol (23), pulegone (24), isodihydrocarveol (25), borneol (30), and (Z)-or (E)-linalool oxide (34), as monoterpenes, and cadina-1,4-diene (47) and dihydroactinidiolide (52), as sesquiterpenes, were identified in synthetic liqueurs elaborated from different Greek F. carica varieties [28].…”

Section: Terpenesmentioning

confidence: 99%

See 1 more Smart Citation

Chemical and Biological Characteristics of Ficus carica L. Fruits, Leaves, and Derivatives (Wine, Spirit, and Liqueur)

Rodríguez‐Solana¹,

Romano²

2020

Modern Fruit Industry

View full text Add to dashboard Cite

Ficus carica L. is a native plant to Southwest Asia and widely spread from ancient times in the Mediterranean region. Its fruits (figs) and leaves present important nutritional components (vitamins, minerals, sugars, amino acids, etc.) and health-related effects due to their phytochemical composition. Numerous bioactive compounds, such as phenolic compounds (phenolic acids), flavonoids (flavonols, flavones, and anthocyanins), coumarins, sterols, and volatiles (monoterpenes, sesquiterpenes, norisoprenoids, ketones, alcohols, esters, etc.), among others, have been isolated from fruits and leaves of F. carica that are the main ingredients used in the production of different alcoholic beverages such as wine, liqueur, and spirit. This chapter aims to review the different chemical and biological characteristics found both in raw materials (fruits and leaves) and in the final product (wine, liqueur, and spirit) that have been consumed and known throughout human history.Phytochemicals or plant secondary metabolites are non-nutritive plant metabolites which are essential for plant survival and proper growth and reproduction [23]. Many of these components have bioactivities toward animal biochemistry and metabolism with the ability to provide health benefits. F. carica plant owns the highest diversity of compounds with the higher quantities of all classes of compounds (except aldehydes and monoterpenes) mainly in leaves, followed by fruits pulps and peels [2,9,24].Phytochemical studies on raw materials (fruits and leaves) and derived products (wine, liqueur, and spirit) of F. carica revealed the presence of numerous bioactive compounds including volatiles, organic acids, phytosterols, triterpenoids, fatty acids, phenolic acids, flavonoids, coumarins, and few other classes of secondary metabolites shown in Figure 2. Volatile compoundsAroma is an important attribute of the sensory appreciation of a product and is usually used as a criterion for its quality assessment. It is a defining element of the 3 Chemical and Biological Characteristics of Ficus carica L. Fruits, Leaves... DOI: http://dx.doi.org/10.5772/intechopen.86660

show abstract

“…Aroma is one of the most important aspects of wine quality [4], but the compounds being responsible for wine aroma are volatile [5]. The volatiles are closely related to the sensory quality of wine, which is extremely important for consumer [6].…”

Section: Introductionmentioning

confidence: 99%

A Model Transfer Learning Framework With Back-Propagation Neural Network for Wine and Chinese Liquor Detection by Electronic Nose

Yang

Liu

2020

IEEE Access

View full text Add to dashboard Cite

Electronic nose, as a non-destructive instrument, is widely used in the field of gas analysis. In this work, E-nose was employed to distinguish wines and Chinese liquors by means of a machine learning technique. First, a multi-hidden layers Back-Propagation Neural Network (BPNN) was designed to build an identification model for the classification of different wines. Then, a BPNN-based transfer-learning framework was developed with minimal changes to the architecture of the BPNN-based model which was trained on the wine sample dataset. Experimental results revealed that the BPNN-based model performed with a 98.27% accuracy in identifying different wines, and the BPNN-based transfer-learning framework performed with a 93.4% accuracy in identifying Chinese liquors by only retraining the output layer. This reduced the model training costs compared with the complete retraining of a new classification model. Results demonstrated the effectiveness of the proposed BPNN-based transfer-learning model, which was capable of identifying different kinds of wines based on their own properties and could be easily applied to the classification of Chinese liquors. The model-based transfer learning framework offered promising potential for different classification tasks of various beverage.

show abstract

Monitoring volatile compounds production throughout fermentation by Saccharomyces and non-Saccharomyces strains using headspace sorptive extraction

Cited by 20 publications

References 35 publications

Aureobasidium pullulans volatilome identified by a novel, quantitative approach employing SPME-GC-MS, suppressed Botrytis cinerea and Alternaria alternata in vitro

Aureobasidium pullulans volatilome identified by a novel, quantitative approach employing SPME-GC-MS, suppressed Botrytis cinerea and Alternaria alternata in vitro

Chemical and Biological Characteristics of Ficus carica L. Fruits, Leaves, and Derivatives (Wine, Spirit, and Liqueur)

A Model Transfer Learning Framework With Back-Propagation Neural Network for Wine and Chinese Liquor Detection by Electronic Nose

Contact Info

Product

Resources

About