The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Classification tools in chemistry. Part 1: linear models. PLS-DA
Abstract
The common steps to calibrate and validate classification models based on partial least squares discriminant analysis are discussed in the present tutorial. All issues to be evaluated during model training and validation are introduced and explained using a chemical dataset, composed of toxic and non-toxic sediment samples. The analysis was carried out with MATLAB routines, which are available in the ESI of this tutorial, together with the dataset and a detailed list of all MATLAB instructions used for the analysis.
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
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735
Computer Aided Design of Experiments
Kennard, R. W., & Stone, L. A. (1969) · Technometrics · doi:10.1080/00401706.1969.10490666 · Cited by 3,648
Partial least squares for discrimination
Barker, M., & Rayens, W. (2003) · Journal of Chemometrics · doi:10.1002/cem.785 · Cited by 2,841
Höskuldsson, A. (1988) · Journal of Chemometrics · doi:10.1002/cem.1180020306 · Cited by 1,881
A new algorithm for optimal, distance-based experimental design
Marengo, E. A., & Todeschini, R. (1992) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(92)80076-g · Cited by 109
A chemometric approach to the environmental problem of predicting toxicity in contaminated sediments
Álvarez-Guerra, M., Ballabio, D., Amigo, J. M., Viguri, J. R., & Bro, R. (2009) · Journal of Chemometrics · doi:10.1002/cem.1264 · Cited by 24
Self-citation
Cited by
On-line application of near infrared (NIR) spectroscopy in food production
Porep, J. U., Kammerer, D. R., & Carle, R. (2015) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2015.10.002 · Cited by 611
Chavan, S., Friedman, R., & Nicholls, I. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms160511659 · Cited by 43
Zimmerman, B. J., Abraham, I., Schmidt, S. A., Baryshnikov, Y., & Husain, F. T. (2019) · Network Neuroscience · doi:10.1162/netn_a_00053 · Cited by 35
Villar-Vesga, J., Henao-Restrepo, J., Voshart, D. C., Aguillon, D., Villegas, A., Castaño, D., Arias-Londoño, J. D., Zuhorn, I. S., Ribovski, L., Barazzuol, L., et al. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.587989 · Cited by 29
Villamil-Ortiz, J. G., Barrera-Ocampo, A., Arias-Londoño, J. D., Villegas, A., Lopera, F., & Cardona-Gómez, G. P. (2018) · Journal of Alzheimer’s Disease · doi:10.3233/jad-170554 · Cited by 24
Metadata
- Authors
- Ballabio, D., & Consonni, V.
- Venue
- Analytical Methods
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2013
- Volume
- 5
- Issue
- 16
- Pages
- 3790
- ISSN
- 1759-9660 (ISSN-L)
- 1759-9679
- OpenAlex
- W1970458547
- Cited by
- 1,255
- References
- 6
- FWCI
- 32.84
- Type
- journal article
- Language
- en
- Open access
- closed
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: Classification tools in chemistry. Part 1: linear models. PLS-DA. https://app.cochat.ai/papers/10.1039/c3ay40582f
- The paper
- Ballabio, D., & Consonni, V. (2013). Classification tools in chemistry. Part 1: linear models. PLS-DA. Analytical Methods. https://doi.org/10.1039/c3ay40582f
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.
