ArticleSensors (Basel, Switzerland)2020
Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 39 citations in OpenAlex.
- Portable electrochemical sensing coupled with machine learning for rapid Arabica and Robusta coffee authentication and blend ratio prediction.Food chemistry: X · 2026Article
- Smart detection of juice adulteration: An approach based on ion mobility spectrometry and machine learning.Journal of the science of food and agriculture · 2026Article
- Rapid Classification of Coffee Varieties Using Single-Bean Hot Gas Extraction Ion-Mobility Spectrometry with Machine Learning.ACS measurement science au · 2026Article
- Phenolic Composition, Antioxidant Activity and Caffeine as Chemical Markers for Differentiating PanamanianMolecules (Basel, Switzerland) · 2026Article
- Unlocking the Quality Potential of Liberoid Coffee: Advances in Composition, Processing, and Microbial Fermentation.Comprehensive reviews in food science and food safety · 2026Review
- Comprehensive Characterization of Aroma Profile of "Glutinous Rice" Flavor inFoods (Basel, Switzerland) · 2025Article
- Utilization of Spent Coffee Grounds as a Food By-Product to Produce Edible Films Based on κ-Carrageenan with Biodegradable and Active Properties.Foods (Basel, Switzerland) · 2024Article
- Making Every Single Puff Count-Simple and Sensitive E-Cigarette Aerosol Sampling for GCxIMS and GC-MS Analysis.Molecules (Basel, Switzerland) · 2023Article
- Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices.Foods (Basel, Switzerland) · 2023Article
- Assessment of Volatile Compound Transference through Firefighter Turnout Gear.International journal of environmental research and public health · 2022Article
- Impact of Coffee Bean Roasting on the Content of Pyridines Determined by Analysis of Volatile Organic Compounds.Molecules (Basel, Switzerland) · 2022Article
- A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater.Sensors (Basel, Switzerland) · 2021Article
- Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.