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Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification.
Talanta. 2011 Sep 30; 85(4):2159-65.T

Abstract

This paper proposes an analytical method to detect adulteration of diesel/biodiesel blends based on near infrared (NIR) spectrometry and supervised pattern recognition methods. For this purpose, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) coupled with the successive projections algorithm (SPA) have been employed to build screening models using three different optical paths and the following spectra ranges: 1.0mm (8814-3799 cm(-1)), 10mm (11,329-5944 cm(-1) and 5531-4490 cm(-1)) and 20mm (11,688-5952 cm(-1) and 5381-4679 cm(-1)). The method is validated in a case study involving the classification of 140 diesel/biodiesel blend samples, which were divided into four different classes, namely: diesel free of biodiesel and raw vegetal oil (D), blends containing diesel, biodiesel and raw oils (OBD), blends of diesel and raw oils (OD), and blends containing a fraction of 5% (v/v) of biodiesel in diesel (B5). LDA-SPA models were found to be the best method to classify the spectral data obtained with optical paths of 1.0 and 20mm. Otherwise, PLS-DA shows the best results for classification of 10mm cell data, which achieved a correct prediction rate of 100% in the test set.

Links

Publisher Full Text
linkinghub.elsevier.com
linkinghub.elsevier.com

Authors+Show Affiliations

Pontes MJ
Universidade Federal da Paraíba, Departamento de Química, João Pessoa, PB, Brazil. marciocoelho@quimica.ufpb.br
Pereira CF
No affiliation info available
Pimentel MF
No affiliation info available
Vasconcelos FV
No affiliation info available
Silva AG
No affiliation info available

MeSH

BiofuelsDiscriminant AnalysisGasolineLeast-Squares AnalysisMultivariate AnalysisPattern Recognition, AutomatedPlant OilsPrincipal Component AnalysisSpectrophotometry, Infrared

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

21872073

Citation

Pontes, Márcio José Coelho, et al. "Screening Analysis to Detect Adulteration in Diesel/biodiesel Blends Using Near Infrared Spectrometry and Multivariate Classification." Talanta, vol. 85, no. 4, 2011, pp. 2159-65.
Pontes MJ, Pereira CF, Pimentel MF, et al. Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification. Talanta. 2011;85(4):2159-65.
Pontes, M. J., Pereira, C. F., Pimentel, M. F., Vasconcelos, F. V., & Silva, A. G. (2011). Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification. Talanta, 85(4), 2159-65. https://doi.org/10.1016/j.talanta.2011.07.064
Pontes MJ, et al. Screening Analysis to Detect Adulteration in Diesel/biodiesel Blends Using Near Infrared Spectrometry and Multivariate Classification. Talanta. 2011 Sep 30;85(4):2159-65. PubMed PMID: 21872073.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Screening analysis to detect adulteration in diesel/biodiesel blends using near infrared spectrometry and multivariate classification. AU - Pontes,Márcio José Coelho, AU - Pereira,Claudete Fernandes, AU - Pimentel,Maria Fernanda, AU - Vasconcelos,Fernanda Vera Cruz, AU - Silva,Alinne Girlaine Brito, Y1 - 2011/07/23/ PY - 2011/06/13/received PY - 2011/07/16/revised PY - 2011/07/18/accepted PY - 2011/8/30/entrez PY - 2011/8/30/pubmed PY - 2011/12/28/medline SP - 2159 EP - 65 JF - Talanta JO - Talanta VL - 85 IS - 4 N2 - This paper proposes an analytical method to detect adulteration of diesel/biodiesel blends based on near infrared (NIR) spectrometry and supervised pattern recognition methods. For this purpose, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) coupled with the successive projections algorithm (SPA) have been employed to build screening models using three different optical paths and the following spectra ranges: 1.0mm (8814-3799 cm(-1)), 10mm (11,329-5944 cm(-1) and 5531-4490 cm(-1)) and 20mm (11,688-5952 cm(-1) and 5381-4679 cm(-1)). The method is validated in a case study involving the classification of 140 diesel/biodiesel blend samples, which were divided into four different classes, namely: diesel free of biodiesel and raw vegetal oil (D), blends containing diesel, biodiesel and raw oils (OBD), blends of diesel and raw oils (OD), and blends containing a fraction of 5% (v/v) of biodiesel in diesel (B5). LDA-SPA models were found to be the best method to classify the spectral data obtained with optical paths of 1.0 and 20mm. Otherwise, PLS-DA shows the best results for classification of 10mm cell data, which achieved a correct prediction rate of 100% in the test set. SN - 1873-3573 UR - https://www.unboundmedicine.com/medline/citation/21872073/Screening_analysis_to_detect_adulteration_in_diesel/biodiesel_blends_using_near_infrared_spectrometry_and_multivariate_classification_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0039-9140(11)00639-4 DB - PRIME DP - Unbound Medicine ER -