Browse
Submit Data
Databases
API
Help

Dataset Information

29 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS.

ABSTRACT: We report on the analysis of volatile compounds by SPME-GC-MS for individual roasted coffee beans. The aim was to understand the relative abundance and variability of volatile compounds between individual roasted coffee beans at constant roasting conditions. Twenty-five batches of Arabica and robusta species were sampled from 13 countries, and 10 single coffee beans randomly selected from each batch were individually roasted in a fluidised-bed roaster at 210?°C for 3?min. High variability (CV?=?14.0-53.3%) of 50 volatile compounds in roasted coffee was obtained within batches (10 beans per batch). Phenols and heterocyclic nitrogen compounds generally had higher intra-batch variation, while ketones were the most uniform compounds (CV?

SUBMITTER: Caporaso N

PROVIDER: S-EPMC5960070 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS.

Caporaso Nicola N Whitworth Martin B MB Cui Chenhao C Fisk Ian D ID

Food research international (Ottawa, Ont.) 20180403

We report on the analysis of volatile compounds by SPME-GC-MS for individual roasted coffee beans. The aim was to understand the relative abundance and variability of volatile compounds between individual roasted coffee beans at constant roasting conditions. Twenty-five batches of Arabica and robusta species were sampled from 13 countries, and 10 single coffee beans randomly selected from each batch were individually roasted in a fluidised-bed roaster at 210 °C for 3 min. High variability (CV =  ...[more]

PMID: 29735099

Similar Datasets

Stable radical content and anti-radical activity of roasted Arabica coffee: from in-tact bean to coffee brew.

Project description:The roasting of coffee beans generates stable radicals within melanoidins produced by non-enzymatic browning. Roasting coffee beans has further been suggested to increase the antioxidant (AO) capacity of coffee brews. Herein, we have characterized the radical content and AO capacity of brews prepared from Coffea arabica beans sourced directly from an industrial roasting plant. In-tact beans exhibited electron paramagnetic resonance signals arising from Fe3+, Mn2+ and at least three distinct stable radicals as a function of roasting time, whose intensity changed upon grinding and ageing. In coffee brews, the roasting-induced radicals were harboured within the high molecular weight (> 3 kD) melanoidin-containing fraction at a concentration of 15 nM and was associated with aromatic groups within the melanoidins. The low molecular weight (< 3 kD) fraction exhibited the highest AO capacity using DPPH as an oxidant. The AO activity was not mediated by the stable radicals or by metal complexes within the brew. While other non-AO functions of the roasting-induced radical and metal complexes may be possible in vivo, we confirm that the in vitro antiradical activity of brewed coffee is dominated by low molecular weight phenolic compounds.

| S-EPMC4391752 | biostudies-literature

Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum.

Project description:Aroma is one of the main characteristics of coffee specimens. Different mixtures of Arabica and Robusta coffees are usually found in the market to offer specific aroma or flavor profiles to consumers. However, the mixed samples or their proportions are not always identified in the product labels. Since the price of Arabica is much higher than that of Robusta, this lack of information is not only an economical issue but a possible fraud to consumers, besides the potential allergic reaction that these mixtures may trigger in some individuals. In this paper, two sample preparation techniques were compared before the analysis of the total volatile organic compounds (VOCs) found in Robusta, Arabica, and in the mixture from both coffee types. The comparison of the signals obtained from the analyses showed that the VOCs concentration levels obtained from the headspace (HS) analyses were clearly higher than those obtained from the pre-concentration step where an adsorbent, an active charcoal strip (ACS + HS), was used. In the second part of this study, the possibility of using the headspace gas-chromatography ion mobility spectrometry (HS-GC-IMS) for the discrimination between Arabica, Robusta, and mixed coffee samples (n = 30) was evaluated. The ion mobility sum spectrum (IMSS) obtained from the analysis of the HS was used in combination with pattern recognition techniques, namely linear discrimination analysis (LDA), as an electronic nose. The identification of individual compounds was not carried out since chromatographic information was not used. This novel approach allowed the correct discrimination (100%) of all of the samples. A characteristic fingerprint for each type of coffee for a fast and easy identification was also developed. In addition, the developed method is ecofriendly, so it is a good alternative to traditional approaches.

| S-EPMC7309026 | biostudies-literature

Characterization of Arabica and Robusta volatile coffees composition by reverse carrier gas headspace gas chromatography-mass spectrometry based on a statistical approach.

Project description:Nineteen samples of Arabica and 14 of Robusta coming from various plantation were analysed by dynamic headspace capillary gas chromatography-mass spectrometry to characterize the volatile fraction of green and roasted samples and the relationships of the same species with geographical origin. As concerns green beans, Arabica species appear characterized by high content of n-hexanol, furfural and amylformate, while Robusta species by greater content of ethylpyrazine, dimethylsulfone and 2-heptanone. Four variables, 4-methyl-2,3-dihydrofuran, n-hexanol, limonene and nonanal, appear involved in the characterization of the geographical origin of the analysed samples. The volatile fraction of the roasted Arabica samples, appear characterized by high content of pyridine, diacetyl, propylformate, acetone and 2,3-pentanedione, while Robusta samples by high content of methylbutyrate, 2,3-dimethylpyrazine and 3-hexanone. Considering geographical origin of the analysed samples, four compounds appear involved, in particular 2-butanone, methylbutyrate, methanol and ethylformate. Very accurate (error rate lower than 5%) rules to classify samples as Arabica or Robusta according to their compounds profile were developed.

| S-EPMC7492344 | biostudies-literature

Comparative study of the volatile fingerprints of roasted and unroasted oolong tea by sensory profiling and HS-SPME-GC-MS.

Project description:Roasting plays important roles in shaping the volatile profile of oolong tea. In this study, the sensory attributes and volatile compositions of 153 roasted or unroasted oolong tea samples, belonging to four typical types, namely, High Mountain oolong tea (HMT), Tieguanyin tea (TGYT), Dongding oolong tea (DDT) and Wuyi rock tea (WRT), were studied in detail. Based on the sensory evaluation by tea evaluation experts, their respective sensory profiles were established and compared. Unroasted teas had more pronounced fresh and green flavors, while roasted teas had higher scores in pungent and caramel flavors. In particular, WRT demonstrated a unique fragrance of floral fruity flavors. By using HS-SPME-GC-MS analysis, a total of 128 compounds were identified across all samples. Notably, it was found that roasting largely increased the variety of volatile compounds in oolong tea. Furthermore, the characteristic volatile compounds of each type of tea were identified by PLS-DA modeling. Linalool and geraniol were the characteristic volatiles of HMT. Four volatiles, including (E)-nerolidol, jasmin lactone, benzeneacetaldehyde, and 4-methyl benzaldehyde oxime were identified as the characteristic volatiles of TGYT. Seven volatiles, including N-ethyl pyrrole, 3-(hydroxy methyl) pyridine, 4-pyridylcarbinol, 1-methyl pyrrole-2-carboxaldehyde, 2-ethyl-3,5-dimethyl pyrazine, 4-amino-2,3-xylenol, and 4,6-dimethyl pyrimidine were the characteristic volatiles of DDT. For WRT, 2,2,6-trimethyl cyclohexan-1-one, hexanoic acid, benzaldehyde, benzyl alcohol, β-cyclocitral, (E)-β-ionone, α-ionone, and octanoic acid were the characteristic volatiles. These findings expand our knowledge of the volatile fingerprints of oolong tea.

| S-EPMC9852928 | biostudies-literature

Modifying Robusta coffee aroma by green bean chemical pre-treatment.

Project description:Green Robusta beans were subjected to pre-treatment with the aim of reducing the perceived aroma difference between Arabica and Robusta coffee. Treatment was a short soaking procedure with varying concentrations of acetic acid (up to 5%). Samples were subjected to thermal treatment (roasted) and ground to a standardised particle size distribution. Aroma compounds were evaluated by headspace analysis using solid-phase microextraction and gas chromatography-mass spectrometry. Pre-treatment significantly affected aroma formation during roasting and resulted in a modified level of pyrazines, furanic compounds and sulfur-containing compounds (p?<?0.05). Principal component analysis illustrated that the aroma profile of the pre-treated Robusta coffee was closer to the target Arabica coffee after roasting. Sensory results confirmed that the aroma of the 2% acetic acid pre-treated Robusta brew was similar to Arabica; the maximum inclusion level of Robusta coffee in a blend could be increased from 20% to 80%.

| S-EPMC6191532 | biostudies-literature

The effect of bean origin and temperature on grinding roasted coffee.

Project description:Coffee is prepared by the extraction of a complex array of organic molecules from the roasted bean, which has been ground into fine particulates. The extraction depends on temperature, water chemistry and also the accessible surface area of the coffee. Here we investigate whether variations in the production processes of single origin coffee beans affects the particle size distribution upon grinding. We find that the particle size distribution is independent of the bean origin and processing method. Furthermore, we elucidate the influence of bean temperature on particle size distribution, concluding that grinding cold results in a narrower particle size distribution, and reduced mean particle size. We anticipate these results will influence the production of coffee industrially, as well as contribute to how we store and use coffee daily.

| S-EPMC4834475 | biostudies-literature

Analysis of Volatile Compounds in Pears by HS-SPME-GC×GC-TOFMS.

Project description:Aroma plays an important role in fruit quality and varies among different fruit cultivars. In this study, a sensitive and accurate method based on headspace solid-phase microextraction (HS-SPME) coupled with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) was developed to comprehensively compare aroma components of five pear cultivars. In total, 241 volatile compounds were identified and the predominant volatile compounds were esters (101 compounds), followed by alcohols (20 compounds) and aldehydes (28 compounds). The longyuanyangli has the highest relative concentration (838.12 ng/g), while the Packham has the lowest (208.45 ng/g). This study provides a practical method for pear aroma analysis using SPME and GC×GC-TOFMS.

| S-EPMC6539139 | biostudies-literature

The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans.

Project description:Arabica coffee beans are sold at twice the price, or more, compared to Robusta beans and consequently are susceptible to economically motivated adulteration by substitution. There is a need for rapid, non-destructive, and efficient analytical techniques for monitoring the authenticity of Arabica coffee beans in the supply chain. In this study, multispectral imaging (MSI) was applied to discriminate roasted Arabica and Robusta coffee beans and perform quantitative prediction of Arabica coffee bean adulteration with Robusta. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, built using selected spectral and morphological features from individual coffee beans, achieved 100% correct classification of the two coffee species in the test dataset. The OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. MSI analysis has potential as a rapid screening tool for the detection of fraud issues related to the authenticity of Arabica coffee beans.

| S-EPMC9108882 | biostudies-literature

Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee.

Project description:Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis-PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America).

| S-EPMC8750839 | biostudies-literature

Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GC×GC-TOFMS, Chemometrics, and Machine Learning.

Project description:Coffee, one of the most popular beverages in the world, attracts consumers by its rich aroma and the stimulating effect of caffeine. Increasing consumers prefer decaffeinated coffee to regular coffee due to health concerns. There are some main decaffeination methods commonly used by commercial coffee producers for decades. However, a certain amount of the aroma precursors can be removed together with caffeine, which could cause a thin taste of decaffeinated coffee. To understand the difference between regular and decaffeinated coffee from the volatile composition point of view, headspace solid-phase microextraction two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) was employed to examine the headspace volatiles of eight pairs of regular and decaffeinated coffees in this study. Using the key aroma-related volatiles, decaffeinated coffee was significantly separated from regular coffee by principal component analysis (PCA). Using feature-selection tools (univariate analysis: t-test and multivariate analysis: partial least squares-discriminant analysis (PLS-DA)), a group of pyrazines was observed to be significantly different between regular coffee and decaffeinated coffee. Pyrazines were more enriched in the regular coffee, which was due to the reduction of sucrose during the decaffeination process. The reduction of pyrazines led to a lack of nutty, roasted, chocolate, earthy, and musty aroma in the decaffeinated coffee. For the non-targeted analysis, the random forest (RF) classification algorithm was used to select the most important features that could enable a distinct classification between the two coffee types. In total, 20 discriminatory features were identified. The results suggested that pyrazine-derived compounds were a strong marker for the regular coffee group whereas furan-derived compounds were a strong marker for the decaffeinated coffee samples.

| S-EPMC8948847 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data