Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

NMR Spectroscopy of Aromatic Compounds01:14

NMR Spectroscopy of Aromatic Compounds

Aromatic compounds can be identified or analyzed using proton NMR and carbon‐13 NMR. Typically, aromatic hydrogens or hydrogens directly bonded to the aromatic rings are strongly deshielded by the aromatic ring current. Therefore, they absorb in the range of 6.5–8.0 ppm in proton NMR spectra. For instance, aromatic hydrogens directly bonded to the benzene ring absorb at 7.3 ppm. However, aromatic hydrogens of larger rings absorb farther upfield or downfield than the ideal range. Consider...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Aromatic Compounds: Overview01:25

Aromatic Compounds: Overview

In general, the term ‘aromatic’ indicates a pleasant smell or fragrance from fresh flowers, freshly prepared coffee, etc. In the early history of organic chemistry, many benzene derivatives were isolated from the pleasant odor oils of the plants. For example, vanillin was isolated from the oil of vanilla, methyl salicylate from the oil of wintergreen, and cinnamaldehyde from the oil of cinnamon. They all had a pleasant odor; hence the name aromatic was given.
In 1825, Faraday isolated benzene...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
What is an ANOVA?01:16

What is an ANOVA?

The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ketamine for negative and depressive symptoms in schizophrenia: the evidence so far.

Frontiers in psychiatry·2026
Same author

Electric-Field-Tunable Spin-Orbit Gap in a Bilayer Graphene/WSe<sub>2</sub> Quantum Dot.

Nano letters·2025
Same author

Opioid prescribing patterns in trauma patients: assessing the impact of injury and treatment factors.

ANZ journal of surgery·2025
Same author

Multiscale Imaging to Monitor Functional SHED-Supported Engineered Vessels.

Journal of dental research·2024
Same author

Dermatology-related quality-of-life outcomes in patients with RAS wild-type metastatic colorectal cancer treated with fluorouracil and folinic acid with or without panitumumab (Pmab) maintenance after FOLFOX + Pmab induction: a prespecified secondary analysis of the phase II randomized PanaMa (AIO KRK 0212) trial.

ESMO open·2024
Same author

Determinants of beta-lactam PK/PD target attainment in critically ill patients: A single center retrospective study.

Journal of critical care·2024
Same journal

Single-cell mass spectrometry reveals metabolic divergence mediated by contact-dependent cell-cell interactions.

Talanta·2026
Same journal

Highly sensitive hydrogel surface-enhanced Raman scattering chips with multi-analyte detection ability.

Talanta·2026
Same journal

Quality by design-based RP-HPLC methods for pharmaceutical analytical estimation: A review.

Talanta·2026
Same journal

Development of Teduglutide purity certified reference material (GBW09342) through amino acid-based isotope dilution mass spectrometry and sulfur-based isotope dilution inductively coupled plasma mass spectrometry.

Talanta·2026
Same journal

Maximizing molecular coverage in ultrahigh-resolution orbitrap analysis of marine dissolved organic matter using two-stage design-of-experiments strategy.

Talanta·2026
Same journal

A new HPLC-ICP-MS method for gadolinium speciation in freshwaters using ion exchange chromatography and aqueous mobile phases.

Talanta·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2026

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

Coffee aroma--statistical analysis of compositional data.

M Korhonová1, K Hron, D Klimcíková

  • 1Department of Analytical Chemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic. korhonovam@seznam.cz

Talanta
|October 20, 2009
PubMed
Summary
This summary is machine-generated.

This study analyzed volatile compounds in coffee using gas chromatography-mass spectrometry to differentiate Arabica and Robusta. Six key compounds effectively distinguished between the two coffee species.

More Related Videos

Tea Aroma Analysis Based on Solvent-Assisted Flavor Evaporation Enrichment
04:36

Tea Aroma Analysis Based on Solvent-Assisted Flavor Evaporation Enrichment

Published on: May 26, 2023

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds
10:13

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds

Published on: November 12, 2016

Related Experiment Videos

Last Updated: Jun 19, 2026

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

Tea Aroma Analysis Based on Solvent-Assisted Flavor Evaporation Enrichment
04:36

Tea Aroma Analysis Based on Solvent-Assisted Flavor Evaporation Enrichment

Published on: May 26, 2023

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds
10:13

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds

Published on: November 12, 2016

Area of Science:

  • Analytical Chemistry
  • Food Chemistry
  • Chemometrics

Background:

  • Volatile compounds significantly influence coffee aroma and flavor profiles.
  • Differentiating between Arabica and Robusta coffee is crucial for quality control and consumer preference.
  • Advanced analytical techniques are needed to identify and quantify these key aroma compounds.

Purpose of the Study:

  • To determine volatile compounds in commercial coffee samples using headspace solid-phase microextraction and gas chromatography-mass spectrometry.
  • To identify specific volatile compounds that can serve as markers for distinguishing between Arabica and Robusta coffee varieties.
  • To apply chemometric methods for data analysis and classification of coffee samples.

Main Methods:

  • Solid-phase microextraction (SPME) in headspace mode for sample preparation.
  • Gas chromatography-mass spectrometry (GC-MS) for separation and identification of volatile compounds.
  • Cluster analysis and principal component analysis (PCA) for data processing and pattern recognition.

Main Results:

  • Quantification of six major volatile compounds (acetic acid, 2-methylpyrazine, furfural, 2-furfuryl alcohol, 2,6-dimethylpyrazine, 5-methylfurfural) across 30 coffee samples.
  • Successful differentiation of Arabica and Robusta coffee based on the profiles of these selected volatile markers.
  • Chemometric analysis confirmed the distinct chemical profiles between the two coffee species.

Conclusions:

  • The selected volatile compounds are reliable markers for differentiating Arabica and Robusta coffee.
  • Headspace SPME-GC-MS coupled with chemometrics provides an effective method for coffee authentication.
  • This approach aids in understanding coffee varietal differences and ensuring product quality.