Differentiation of cocoa nibs from distinct origins using comprehensive two-dimensional gas chromatography and multivariate analysis

Oliveira, Luciana Fontes de; Braga, Soraia Cristina Gonzaga Neves; Augusto, Fábio; Hashimoto, Juliana Campos; Efraim, Priscilla; Poppi, Ronei J.

doi:10.1016/j.foodres.2016.10.047

Cited by 31 publications

(13 citation statements)

References 36 publications

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“…On the other hand, Uraba and Magdalena Medio were described by 11 variables: citric acid, lactic, oxalic and succinic, pH, sucrose, anthocyanins, ashes, moisture content, total polyphenols content, and antioxidant capacity by ORAC. Most of the characteristics could be exploited or controlled to obtain optimal results in this matrix before going to fermentation: by the knowledge a priori of the bromatological, physical and chemical profile with only these variables selected as significant, which may be susceptible to change through the management of agronomic conditions or good agricultural practices, which are considered to be some of the causes of continuous improvement (Bedoya, 2016; Hashim et al., 1998; Oliveira et al., 2016).…”

Section: Resultsmentioning

confidence: 99%

Chemometric approaches for postharvest quality tracing of cocoa: An efficient method to distinguish plant material origin

Garzón

Jaramillo

Bedoya

et al. 2019

Heliyon

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The aim of this study was to compare the quality of a mixture of cocoa harvested and fermented in three subregions of Antioquia (Colombia), from the chemometric profile based on multivariate statistical analysis. A mixture of clones CCN-52, ICS-1, FLE-2, and FEC-2 harvested in Bajo Cauca, Uraba and Magdalena Medio were subjected to a spontaneous fermentation. The characterization of raw and well-fermented cocoa was performed through 38 parameters, and results were compared by a Principal Component Analysis (PCA) and a Cluster Analysis (CA), followed by a Principal Factors Analysis (PFA- CA). The CA showed that there are differences among subregions only in raw cocoa from Bajo Cauca. PCA allowed identifying the variability between raw and fermented cocoa in a representative way and these results were consistent with the chemical profile. Besides, the number of parameters to differentiate raw cocoa from different subregions was reduced (11–13 parameters) and it was possible to characterize well fermented cocoa with only 10 parameters of 38. PFA-CA consolidated in three factors a grouping to identify the cocoa quality according to the process or interest of the sensory or functional properties. Factor 1 (cocoa quality indicators with functional properties), Factor 2 (indicators of quality of the beginning of fermentation) and Factor 3 (indicators of quality of well-fermented cocoa) each one with a weight of 39, 35 and 26 respectively.

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Section: Resultsmentioning

confidence: 99%

Chemometric approaches for postharvest quality tracing of cocoa: An efficient method to distinguish plant material origin

Garzón

Jaramillo

Bedoya

et al. 2019

Heliyon

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“…The solid-phase microextraction (SPME) coupled with gas chromatography (GC) mass spectrometry (MS) methodology has been widely used to identify and quantify VOCs and, more recently, in combination with multivariate analysis to consent the classification and discrimination of cocoa and cocoa-related products for the traceability of such products (Caprioli et al, 2016;Magagna et al, 2017;Oliveira et al, 2016;Tran et al, 2015). However, a limited number of studies have explored the potential applicability of electronic nose technologies to assess cocoa quality and origin (Gu et al, 2013;Olunloyo, Ibidapo, & Dinrifo, 2012).…”

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confidence: 99%

Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics

Barbosa-Pereira

Rojo-Poveda

Ferrocino

et al. 2019

Food Research International

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Cet article publié par ELSEVIER provient du Dépôt institutionnel de l'Université Libre de Bruxelles, DIfusion http://difusion.ulb.ac.be. Il n'est accessible qu'aux membres de la communauté universitaire de l'ULB sur le réseau sécurisé de l'ULB. Tout utilisateur autorisé peut lire, télécharger ou reproduire cet article à des fins d'usage privé ou à des fins non commerciales d'enseignement ou de recherche scientifique. Il ne peut être atteint à l'intégrité de l'article, et les noms des auteurs et de l'éditeur doivent être conservés. Tout téléchargement systématique des articles publiés par ELSEVIER mis à disposition dans DI-fusion est interdit. This article published by ELSEVIER comes from the Institutional repository of Université Libre de Bruxelles, DI-fusion http://difusion.ulb.ac.be. It is accessible only to the members of the university community of ULB on the ULB secure network. Any authorized user may read, download or reproduce this article for private usage, or for non commercial research or educational purposes. The integrity of the article and identification of the author and copyright owner must be preserved. Systematic downloading of articles published by ELSEVIER that are available in DI-fusion is not permitted.

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“…The assessment of quality represents often a central aim in the research field of edibles, and the combination of robust sample preparation and high‐resolution separation techniques contribute to provide dense information about sample composition. Among the most common food products derived from plants, cocoa and hazelnut composition have been studied deeply using HS techniques [90–94]. For example, the volatile metabolome of cocoa from different regions was analyzed using HS‐SPME and GC×GC‐MS [90].…”

Section: Non‐targeted Analysis Applicationsmentioning

confidence: 99%

The role of sample preparation in multidimensional gas chromatographic separations for non‐targeted analysis with the focus on recent biomedical, food, and plant applications

Franchina

Zanella

Dubois

et al. 2020

J of Separation Science

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In this review, we consider and discuss the affinity and complementarity between a generic sample preparation technique and the comprehensive two‐dimensional gas chromatography process. From the initial technical development focus (e.g., on the GC×GC and solid‐phase microextraction techniques), the trend is inevitably shifting toward more applied challenges, and therefore, the preparation of the sample should be carefully considered in any GC×GC separation for an overreaching research. We highlight recent biomedical, food, and plant applications (2016–July 2020), and specifically those in which the combination of tailored sample preparation methods and GC×GC–MS has proven to be beneficial in the challenging aspects of non‐targeted analysis. Specifically on the sample preparation, we report on gas‐phase, solid‐phase, and liquid‐phase extractions, and derivatization procedures that have been used to extract and prepare volatile and semi‐volatile metabolites for the successive GC×GC analysis. Moreover, we also present a milestone section reporting the early works that pioneered the combination of sample preparation techniques with GC×GC for non‐targeted analysis.

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Differentiation of cocoa nibs from distinct origins using comprehensive two-dimensional gas chromatography and multivariate analysis

Cited by 31 publications

References 36 publications

Chemometric approaches for postharvest quality tracing of cocoa: An efficient method to distinguish plant material origin

Chemometric approaches for postharvest quality tracing of cocoa: An efficient method to distinguish plant material origin

Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics

The role of sample preparation in multidimensional gas chromatographic separations for non‐targeted analysis with the focus on recent biomedical, food, and plant applications

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