1H NMR spectroscopy combined with multivariate data analysis for differentiation of Brazilian lager beer according to brewery
Autor: | Luis Augusto da Silva, Danilo Luiz Flumignan, Leonardo Pezza, Helena Redigolo Pezza |
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Přispěvatelé: | Universidade Estadual Paulista (Unesp), Science and Technology (IFSP) |
Rok vydání: | 2019 |
Předmět: |
0303 health sciences
Chromatography 030309 nutrition & dietetics Chemistry Lager beer Chemical shift 1H NMR 04 agricultural and veterinary sciences General Chemistry Linear discriminant analysis 040401 food science Biochemistry Industrial and Manufacturing Engineering NMR spectra database Chemometrics 03 medical and health sciences 0404 agricultural biotechnology Principal component analysis Partial least squares regression Proton NMR Spectroscopy Food Science Biotechnology |
Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP |
ISSN: | 1438-2385 1438-2377 |
DOI: | 10.1007/s00217-019-03354-5 |
Popis: | Made available in DSpace on 2019-10-06T17:18:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01 In this study, 1H NMR spectroscopy was used to classify samples of beer, considering three categories (Ambev, Heineken, and Grupo Petrópolis), employing chemometric methods: principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and soft independent modeling of class analogies (SIMCA). The full NMR spectra were evaluated, although only the aliphatic region (0–3 ppm) was used for multivariate analysis, since it provided superior results, compared to the use of other regions or the full spectrum. It was necessary to use an alignment procedure to eliminate small deviations in the chemical shifts caused by variations of pH and intermolecular interactions. Organic acids (lactic, acetic, and succinic acids) were the chemical compounds most susceptible to these variations. In the PCA, the first two components explained 82.1% of the variability of the dataset, while PLS-DA and SIMCA both provided accuracy higher than 92% in the prediction sets. Institute of Chemistry São Paulo State University (UNESP), Rua Prof. Francisco Degni 55 São Paulo Federal Institute of Education Science and Technology (IFSP), Rua Stefano D’avassi 625 Institute of Chemistry São Paulo State University (UNESP), Rua Prof. Francisco Degni 55 |
Databáze: | OpenAIRE |
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