The consumers’ interest in how food is produced and prepared has increased. Consumers tend nowadays to be more aware about the different aspects regarding food consumption and in line with this trend, new-concept restaurants and food production techiques are created. This phenomenon is driven by high quality standards, and analytical chemistry in synergy with advanced data analysis can be profitably used to build new tools to aid consumers when choosing and pairing foodstuff. The aim of this work is assessing the linkage between the “objective” analytical chemical information and the “subjective” consumers’ taste. The analytical information is provided by means of Nuclear Magnetic Resonance (NMR) and Visible-NIR spectroscopies, while the information about taste is represented by a set of users’ ratings collected from social network regarding beer quality. In addition, 30 of the available beer samples have also been assesed by a trained sensory panel. 1H-NMR spectra and Visible-NIR spectra were acquired on a set of one hundred beer samples differing by brewery, alcohol content, yeast, brew style, etc. In a fingerprint-approach perspective, a number of latent features and signals related to specific chemical compounds were extracted from the NMR spectra by means of Multivariate Curve Resolution (MCR), which was applied to selected intervals. A new dataset was assembled using the identified chemical components’ concentrations. These were used in PLS analysis, with which the correlations between the spectral information and the consumers’ ratings were investigated. The prediction performances of the integrated and the original spectral datasets were also compared. PLS and exploratory analysis performed on the NMR datasets also allowed to gain insight into the composition of beer and into the differences among the various beer styles represented in our dataset. PLS regression was also performed using the Visible-NIR spectra, and the results were compared with those obtained from the NMR datasets.

Resolved NMR spectra of pale beer samples for consumers’ ratings and sensory features prediction / Cavallini, Nicola; Savorani, Francesco; BRO JORGENSEN, Rasmus; Cocchi, Marina. - (2017). (Intervento presentato al convegno 7th International Chemometrics Research Meeting - ICRM 2017 tenutosi a Berg en Dal nel 10-14 settembre 2017).

Resolved NMR spectra of pale beer samples for consumers’ ratings and sensory features prediction

Nicola Cavallini;Francesco Savorani;Rasmus Bro;Marina Cocchi
2017

Abstract

The consumers’ interest in how food is produced and prepared has increased. Consumers tend nowadays to be more aware about the different aspects regarding food consumption and in line with this trend, new-concept restaurants and food production techiques are created. This phenomenon is driven by high quality standards, and analytical chemistry in synergy with advanced data analysis can be profitably used to build new tools to aid consumers when choosing and pairing foodstuff. The aim of this work is assessing the linkage between the “objective” analytical chemical information and the “subjective” consumers’ taste. The analytical information is provided by means of Nuclear Magnetic Resonance (NMR) and Visible-NIR spectroscopies, while the information about taste is represented by a set of users’ ratings collected from social network regarding beer quality. In addition, 30 of the available beer samples have also been assesed by a trained sensory panel. 1H-NMR spectra and Visible-NIR spectra were acquired on a set of one hundred beer samples differing by brewery, alcohol content, yeast, brew style, etc. In a fingerprint-approach perspective, a number of latent features and signals related to specific chemical compounds were extracted from the NMR spectra by means of Multivariate Curve Resolution (MCR), which was applied to selected intervals. A new dataset was assembled using the identified chemical components’ concentrations. These were used in PLS analysis, with which the correlations between the spectral information and the consumers’ ratings were investigated. The prediction performances of the integrated and the original spectral datasets were also compared. PLS and exploratory analysis performed on the NMR datasets also allowed to gain insight into the composition of beer and into the differences among the various beer styles represented in our dataset. PLS regression was also performed using the Visible-NIR spectra, and the results were compared with those obtained from the NMR datasets.
2017
7th International Chemometrics Research Meeting - ICRM 2017
Berg en Dal
10-14 settembre 2017
Cavallini, Nicola; Savorani, Francesco; 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/11380/1156089
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