During the last decade the awareness of consumers and society in general towards all aspects that concern food consumption has strongly increased. This has led to the new-concept restaurants, food production techniques and ways to enjoy and pair foodstuff, having high quality standards as drivers. The present work is part of a larger project, which is based on the fundamental idea of using analytical chemistry and advanced data analysis to build new tools to aid consumers when choosing foodstuff. A first benchmark, here explored, concerns the classification of different types of beer for assessing the linkage between the “objective” chemical analytical information and the “subjective” consumer’s taste. Analytical data were acquired on a set of one-hundred beer samples differing by brewery, alcohol content, yeast, brew type, etc. In a fingerprint-approach perspective, 1H-NMR spectra were recorded together with NIR, GC-MS and fluorescence spectra, but also the relative sensory and marketing data were collected. In particular, this work focuses on the analysis of NMR spectra aiming at linking gastronomic preference to feature-based beer patterns. Both fingerprinting and metabolic profiling approaches have been used in unsupervised analysis to reveal similarities and peculiarities among the different types of beer. Peaks assignment, on the basis of literature references [1, 2, 3] and brewing process knowledge, have also been carried out in order to give “chemical” names to the new variables, to ease the fruition for non-insiders too. Different chemometric tools (after proper preprocessing) have been applied in both approaches: PCA and ICA for fingerprinting; while a semi-automated strategy has been used for profiling which integrates depicting spectral intervals and decomposition (PCA) / resolution (MCR) techniques to resolve overlapping peaks and finally obtaining a peaks’ features table. References [1] I. Duarte, A. Barros, P. S. Belton, R. Righelato, M. Spraul, E. Humpfer, and A. M. Gil, J. Agric. Food Chem., 50(9), 2475–2481 (2002) [2] A. M. Gil, I. F. Duarte, M. Godejohann, U. Braumann, M. Maraschin, and M. Spraul, Analytica Chimica Acta, 488(1), 35–51 (2003) [3] L. I. Nord, P. Vaag, and J. Ø. Duus, Anal. Chem., 76(16), 4790–4798 (2004)

Feature extraction from 1H-NMR spectra for food characterization / Cavallini, Nicola; Savorani, Francesco; Da Silva Friis, Helena; BRO JORGENSEN, Rasmus; Cocchi, Marina. - (2016). (Intervento presentato al convegno XLV National Congress on Magnetic Resonance tenutosi a Modena nel 5-7 settembre 2016).

Feature extraction from 1H-NMR spectra for food characterization

Nicola Cavallini;Francesco Savorani;Rasmus Bro;Marina Cocchi
2016

Abstract

During the last decade the awareness of consumers and society in general towards all aspects that concern food consumption has strongly increased. This has led to the new-concept restaurants, food production techniques and ways to enjoy and pair foodstuff, having high quality standards as drivers. The present work is part of a larger project, which is based on the fundamental idea of using analytical chemistry and advanced data analysis to build new tools to aid consumers when choosing foodstuff. A first benchmark, here explored, concerns the classification of different types of beer for assessing the linkage between the “objective” chemical analytical information and the “subjective” consumer’s taste. Analytical data were acquired on a set of one-hundred beer samples differing by brewery, alcohol content, yeast, brew type, etc. In a fingerprint-approach perspective, 1H-NMR spectra were recorded together with NIR, GC-MS and fluorescence spectra, but also the relative sensory and marketing data were collected. In particular, this work focuses on the analysis of NMR spectra aiming at linking gastronomic preference to feature-based beer patterns. Both fingerprinting and metabolic profiling approaches have been used in unsupervised analysis to reveal similarities and peculiarities among the different types of beer. Peaks assignment, on the basis of literature references [1, 2, 3] and brewing process knowledge, have also been carried out in order to give “chemical” names to the new variables, to ease the fruition for non-insiders too. Different chemometric tools (after proper preprocessing) have been applied in both approaches: PCA and ICA for fingerprinting; while a semi-automated strategy has been used for profiling which integrates depicting spectral intervals and decomposition (PCA) / resolution (MCR) techniques to resolve overlapping peaks and finally obtaining a peaks’ features table. References [1] I. Duarte, A. Barros, P. S. Belton, R. Righelato, M. Spraul, E. Humpfer, and A. M. Gil, J. Agric. Food Chem., 50(9), 2475–2481 (2002) [2] A. M. Gil, I. F. Duarte, M. Godejohann, U. Braumann, M. Maraschin, and M. Spraul, Analytica Chimica Acta, 488(1), 35–51 (2003) [3] L. I. Nord, P. Vaag, and J. Ø. Duus, Anal. Chem., 76(16), 4790–4798 (2004)
2016
XLV National Congress on Magnetic Resonance
Modena
5-7 settembre 2016
Cavallini, Nicola; Savorani, Francesco; Da Silva Friis, Helena; BRO JORGENSEN, Rasmus; Cocchi, Marina
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1156087
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