The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy
Open accessAbstract
Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
Correlation between cup quality and chemical attributes of Brazilian coffee
Farah, A., Monteiro, M. C., Calado, V., Franca, A. S., & Trugo, L. C. (2006) · Food Chemistry · doi:10.1016/j.foodchem.2005.07.032 · Cited by 465
Cited by
Barbin, D. F., Felicio, A. L. D. S. M., Sun, D.-W., Nixdorf, S. L., & Hirooka, E. Y. (2014) · Food Research International · doi:10.1016/j.foodres.2014.01.005 · Cited by 272
Marquetti, I., Link, J. V., Lemes, A. L. G., Scholz, M. B. D. S., Valderrama, P., & Bona, E. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.12.018 · Cited by 117
Metadata
- Authors
- Ribeiro, J. S., Ferreira, M. M. C., & Salva, T. J. G.
- Venue
- Talanta
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 83
- Issue
- 5
- Pages
- 1352-1358
- ISSN
- 0039-9140 (ISSN-L)
- OpenAlex
- W2144792319
- Cited by
- 274
- References
- 2
- FWCI
- 6.49
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy. https://app.cochat.ai/papers/10.1016/j.talanta.2010.11.001
- The paper
- Ribeiro, J. S., Ferreira, M. M. C., & Salva, T. J. G. (2011). Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy. Talanta. https://doi.org/10.1016/j.talanta.2010.11.001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
