Interpreting sensory data by combining principal component analysis and analysis of variance

Luciano, Giorgio; Næs, Tormod

doi:10.1016/j.foodqual.2008.08.003

Cited by 48 publications

(37 citation statements)

References 18 publications

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“…Principal component analysis has been applied to interpret sensory data in food [16], in milk [17] and also in fermented food products [18]. It was used for evaluating the dependence of the antioxidant potential on the conventional or torrefacto process of roasting coffee beans [19,20].…”

Section: Introductionmentioning

confidence: 99%

Contribution of phenolic acids isolated from green and roasted boiled-type coffee brews to total coffee antioxidant capacity

Górnaś

Dwiecki

Siger

et al. 2015

Eur Food Res Technol

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50 % (on average over 36 and 45 % in green and roasted coffee extracts, respectively). Significant differences between the AC values determined for CB and PA samples were noticed only for the MCA and DPPH methods which reflect the different molecular mechanisms underlying each of the assays. Additionally, the statistical methods, including principal component analysis, applied to results of antioxidant capacity obtained with different analytical techniques confirmed their feasibility to distinguish between coffee brews with different degrees of roasting, regardless of coffee origin. Abstract The antioxidant capacity (AC) of boiled-type coffee brews (CB) and phenolic acids (PA) isolated from them, obtained from the caffeinated and decaffeinated beans of different geographical origins and species and with different roasting degrees, was examined. The AC of PA and CB samples was tested in five antioxidant assays: a total antioxidants reducing capacity assay using a Folin-Ciocalteu reagent (FCR), a ferric ion reducing antioxidant power (FRAP) assay, a DPPH · radical-scavenging activity (DPPH) assay, a metal chelating activity (MCA) assay and a total radical trapping antioxidant parameter (TRAP) assay. In most samples, the total amount of phenolic acids, determined by HPLC, decreased with the increasing degree of roasting the coffee beans, leading to reduced AC. All used methods showed that CB exhibits higher AC compared with the PA samples. Phenolic acids isolated from CB samples have the main contribution (on average over 95 and 84 % in green and roasted coffee extracts, respectively) in AC of the CB samples in FCR, FRAP and TRAP assays, whereas in DPPH and MCA tests, the phenolic acid contribution in AC of CB samples was below Keywords

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Section: Introductionmentioning

confidence: 99%

Contribution of phenolic acids isolated from green and roasted boiled-type coffee brews to total coffee antioxidant capacity

Górnaś

Dwiecki

Siger

et al. 2015

Eur Food Res Technol

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“…The use of flaxseed to enrich foods without causing radical changes in eating habits is promising (Pagamunici et al, 2014a(Pagamunici et al, , 2014bSouza et al, 2014). Multivariate analysis enables extracting more information when compared to univariate analysis (Luciano & Naes, 2009), which is a chemometric tool allows for pattern recognition, the gathering of information, reduction of data dimensionality and the organization of data in different groups according to similarities in an easier to understand structure (Matos et al, 2003). Principal component analysis (PCA) is based on performing linear comparisons of the original variables.…”

Section: Introductionmentioning

confidence: 99%

Chemometrics applied to the incorporation of omega-3 in tilapia fillet feed flaxseed flour

et al. 2014

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“…Granitto, Biasioli, Endrizzi, and Gasperi (2008) were among the first authors to propose using PLS-DA for the analysis of sensory profiling data with a fixed or free choice vocabulary but their focus was on the discrimination and the classification of the products rather than their description. Rossini et al (in press) backed the use of this statistical method to analyse conventional sensory profiling data on the ground that it stands at the crossroads of different popular methods used in sensory analysis, namely, principal component analysis (PCA) on the averaged data, PCA on the dataset formed by stacking the assessors' datasets one on top of the other (Luciano & Naes, 2009), canonical variate analysis (CVA, Monrozier & Danzart, 2001). PLS-DA aims at finding latent variables (or components) that maximise the discrimination among the products.…”

Section: Taking the Weights Into Account In Pls-damentioning

confidence: 99%

Weighted PLS-discriminant analysis with application to conventional sensory profiling

Verdun

Cariou

Qannari

2012

Food Quality and Preference

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Interpreting sensory data by combining principal component analysis and analysis of variance

Cited by 48 publications

References 18 publications

Contribution of phenolic acids isolated from green and roasted boiled-type coffee brews to total coffee antioxidant capacity

Contribution of phenolic acids isolated from green and roasted boiled-type coffee brews to total coffee antioxidant capacity

Chemometrics applied to the incorporation of omega-3 in tilapia fillet feed flaxseed flour

Weighted PLS-discriminant analysis with application to conventional sensory profiling

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