Martinez-Lozano Sinues, Pablo and Alonso-Salces, Rosa M. and Zingaro, Lorenzo and Finiguerra, Alessandro and Holland, Margaret V. and Guillou, Claude and Cristoni, Simone. (2012) Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition. Analytica Chimica Acta, 755. pp. 28-36.
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Official URL: https://edoc.unibas.ch/75588/
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Abstract
A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.
Faculties and Departments: | 03 Faculty of Medicine > Departement Biomedical Engineering > Imaging and Computational Modelling > Translational Medicine Breath Research (Sinues) |
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UniBasel Contributors: | Sinues, Pablo |
Item Type: | Article, refereed |
Article Subtype: | Research Article |
Publisher: | Elsevier |
ISSN: | 0003-2670 |
e-ISSN: | 1873-4324 |
Note: | Publication type according to Uni Basel Research Database: Journal article |
Identification Number: |
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Last Modified: | 06 Nov 2020 10:19 |
Deposited On: | 06 Nov 2020 10:19 |
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