ArticleFood science and biotechnology2017
Discrimination and geographical origin prediction of washed specialty Bourbon coffee from different coffee growing areas in Rwanda by using electronic nose and electronic tongue.
Article in Food science and biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 44 citations in OpenAlex.
- HPLC/GC-MS and Electronic Sensing Reveal Tissue-Wide Differences in Bioactive and Flavor Compound Distribution in Coffee Fruits Across Multiple Varieties.Foods (Basel, Switzerland) · 2026Article
- GC-MS Combined with Fast GC E-Nose for the Analysis of Volatile Components of Chamomile (Foods (Basel, Switzerland) · 2024Article
- Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems.BMC plant biology · 2024Article
- Rapid analysis technologies with chemometrics for food authenticity field: A review.Current research in food science · 2024Review
- Juice Quality Evaluation with Multisensor Systems-A Review.Sensors (Basel, Switzerland) · 2023Review
- Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity.Sensors (Basel, Switzerland) · 2021Article
- Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods.Frontiers in nutrition · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study aimed at discriminating washed specialty Bourbon coffee from major coffee growing areas in Rwanda and evaluating the feasibility of using flavor to predict the geographical origin of Arabica coffees from different origins. Discrimination was achieved by performing a principal component analysis, and a discriminant factorial analysis (DFA) model was used to predict the geographical origin of coffee samples based on their intrinsic flavor. Discrimination results from both e-nose and e-tongue indicated clear grouping of coffee samples from areas within the same geographical sub-regions. A DFA model using e-nose was successful in predicting the geographical origin of coffee samples but not with e-tongue. Therefore, the study demonstrated that aroma could reliably be used to predict the geographical origin of coffee samples from different origins than their taste profile.
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Registered trials
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