Characterization of aroma volatiles in tomatoes by sensory analyses

Krumbein, Angelika; Auerswald, H.

doi:10.1002/(sici)1521-3803(199812)42:06<395::aid-food395>3.3.co;2-z

Cited by 97 publications

(86 citation statements)

References 13 publications

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“…Different studies have been conducted to decipher the relationship between sensory traits and metabolites, ranging from studies based on principal component analysis (Krumbein and Auerswald 1998;Krumbein et al 2004) to some recent ones, in which networks were constructed to illustrate the correlations between sensory traits and metabolites (Ursem et al 2008;Carli et al 2009). Multiple linear regression seems to be one of the most appropriate platforms to provide quantitative models which link taste attributes to sensory traits.…”

Section: Introductionsupporting

confidence: 65%

Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes

et al. 2011

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The search for models which link tomato taste attributes to their metabolic profiling, is a main challenge within the breeding programs that aim to enhance tomato flavor. In this paper, we compared such models calculated by the traditional statistical approach, stepwise regression, with models obtained by the new generation of regression techniques, known as penalized regression or regularization methods. In addition, for penalized regression, different scenarios and various model selection criteria were discussed to conclude that classical crossvalidation, selects models with many superfluous variables whereas model selection criteria such as Bayesian information criterion, seem to be more suitable, when the goal is to find parsimonious models, to explain tomato taste attributes based on metabolic information. An exhaustive comparison of the discussed methodology was done for six sensory traits, showing that the most important covariates were identified by the stepwise regression as well as by some of the penalized regression methods, despite the general disagreement on the size of the regression coefficients between them. In particular, for stepwise regression the coefficients are inflated due to their high variance which is not the case with penalized regression, showing that this new methodology, can be an alternative to obtain more accurate models.

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Section: Introductionsupporting

confidence: 65%

Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes

et al. 2011

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“…MeSA, the methyl ester of salicylic acid, is a potent odor component of wintergreen (Gaultheria procumbens). MeSA content has been shown to be negatively correlated with typical tomato flavor (Krumbein and Auerswald, 1998). Guaiacol is also a well-known flavoring compound and has been associated with a so-called "pharmaceutical" aroma in tomato fruits (Causse et al, 2002).…”

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confidence: 99%

A Role for Differential Glycoconjugation in the Emission of Phenylpropanoid Volatiles from Tomato Fruit Discovered Using a Metabolic Data Fusion Approach

Tikunov

Vos²,

González-Paramás³

et al. 2009

Plant Physiology

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A role for differential glycoconjugation in the emission of phenylpropanoid volatiles from ripening tomato fruit (Solanum lycopersicum) upon fruit tissue disruption has been discovered in this study. Application of a multiinstrumental analytical platform for metabolic profiling of fruits from a diverse collection of tomato cultivars revealed that emission of three discriminatory phenylpropanoid volatiles, namely methyl salicylate, guaiacol, and eugenol, took place upon disruption of fruit tissue through cleavage of the corresponding glycoconjugates, identified putatively as hexose-pentosides. However, in certain genotypes, phenylpropanoid volatile emission was arrested due to the corresponding hexose-pentoside precursors having been converted into glycoconjugate species of a higher complexity: dihexose-pentosides and malonyl-dihexose-pentosides. This glycoside conversion was established to occur in tomato fruit during the later phases of fruit ripening and has consequently led to the inability of red fruits of these genotypes to emit key phenylpropanoid volatiles upon fruit tissue disruption. This principle of volatile emission regulation can pave the way to new strategies for controlling tomato fruit flavor and taste.

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“…The volatile aroma compounds (VACs) present in fresh and processed tomato are included in various chemical classes such as ketones, aldehydes, alcohols, phenols, esters, ethers, hydrocarbons, sulfur compounds, nitrogen-containing compounds, furan, free acids and lactones [4][5][6][7][8][9][10][11] . The most important volatile compounds are thought to be cis-3-hexenal, â-ionone, hexanal, â-damascenone, l-penten-3-one, 2+3-methylbutanol, trans-2-hexenal, cis-3-hexenol, 2-isobutylthiazole, l-nitro-2-phenylethane, trans-2-heptenal, phenylacetaldehyde, methylsalicylate, 3-methylbutanal, 2-phenylethanol, 6-methyl-5-hepten-2-one, and geranylacetone [12][13][14] . In addition, furaneol 15 , hexanol, methional and l-octen-3-one 16 may also contribute to the tomato aroma taste.…”

Section: Introductionmentioning

confidence: 99%

Headspace-single drop microextraction followed by gas chromatographic determination of key aroma compounds in tomato fruits and their sample products

sawaddipanich

Chanthai²

2016

Orient. J. Chem

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A headspace-single drop microextraction (HS-SDME) is a good choice to analyze volatile and semivolatile compounds in different matrices without any interference of the sample matrix. HS-SDME spends a very little solvent consumption for the determination of volatile aroma compounds (VACs). In this study, some VACs including 2-methylbutyraldehyde, 3-methyl-1-butanol, sec-butyl acetate, 1-octen-3-one and trans,trans-2,4-decadienal were performed with hexadecane as extraction solvent. The parameters affecting the HS-SDME method were investigated in details including extraction temperature, enrichment time, extraction time and sample volume. The optimum conditions were consisted of 45 o C extraction temperature, 15min enrichment, 20min extraction time and 3mL sample volume. Comparison between liquid-liquid extraction (LLE) and HS-SDME for quantitative analysis was carried out using tert-butanol as an internal standard. From results revealed that the recoveries were obtained between 81.06-90.55% for LLE and 80.26-90.09% for HS-SDME. The results demonstrated that the proposed method could be successfully applied for the determination of VACs.

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Characterization of aroma volatiles in tomatoes by sensory analyses

Cited by 97 publications

References 13 publications

Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes

Penalized regression techniques for modeling relationships between metabolites and tomato taste attributes

A Role for Differential Glycoconjugation in the Emission of Phenylpropanoid Volatiles from Tomato Fruit Discovered Using a Metabolic Data Fusion Approach

Headspace-single drop microextraction followed by gas chromatographic determination of key aroma compounds in tomato fruits and their sample products

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