Stepwise Discriminant Analysis of Gas Chromatographic Data as an Aid in Classifying the Flavor Quality of Foods

Powers, John J.; Keith, Elizabeth S.

doi:10.1111/j.1365-2621.1968.tb01351.x

Cited by 92 publications

(39 citation statements)

References 23 publications

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“…SLDA renders a number of orthogonal linear discriminant functions equal to the number of categories minus one. This method minimises the variance within categories and maximises the variance between categories [38]. The variables included in the analysis are determined with a stepwise-LDA using a Wilk's Lambda as a selection criterion and an F-statistic factor to establish the significance of the changes in Lambda when a new variable is tested.…”

Section: Discussionmentioning

confidence: 99%

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

Câmara

Alves

Marques

2006

Talanta

105

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In order to differentiate and characterize Madeira wines according to main grape varieties, the volatile composition (higher alcohols, fatty acids, ethyl esters and carbonyl compounds) was determined for 36 monovarietal Madeira wine samples elaborated from Boal, Malvazia, Sercial and Verdelho white grape varieties. The study was carried out by headspace solid-phase microextraction technique (HS-SPME), in dynamic mode, coupled with gas chromatography-mass spectrometry (GC-MS). Corrected peak area data for 42 analytes from the above mentioned chemical groups was used for statistical purposes. Principal component analysis (PCA) was applied in order to determine the main sources of variability present in the data sets and to establish the relation between samples (objects) and volatile compounds (variables). The data obtained by GC-MS shows that the most important contributions to the differentiation of Boal wines are benzyl alcohol and (E)-hex-3-en-1-ol. Ethyl octadecanoate, (Z)-hex-3-en-1-ol and benzoic acid are the major contributions in Malvazia wines and 2-methylpropan-1-ol is associated to Sercial wines. Verdelho wines are most correlated with 5-(ethoxymethyl)-furfural, nonanone and cis-9-ethyldecenoate. A 96.4% of prediction ability was obtained by the application of stepwise linear discriminant analysis (SLDA) using the 19 variables that maximise the variance of the initial data set.

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Section: Discussionmentioning

confidence: 99%

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

Câmara

Alves

Marques

2006

Talanta

105

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“…Stepwise LDA was used to select the more discriminant variables (Powers & Keith, 1968). Leaveone-out cross validation test was used to validate the results of classification (Ramis-Ramos & García-Alvarez, 2001).…”

Section: Multivariate Analysismentioning

confidence: 99%

Content in metallic ions of wines from the Madeira and Azores archipelagos

et al. 2011

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a b s t r a c tThis study determines for the first time Na, K, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li and Rb contents in wines from the archipelagos of Madeira and Azores (Portugal). The greater part of the mean content for the different parameters fell within the ranges described in the literature, except for sodium whose higher content may be due to the effect of marine spray. ANOVA was used to establish the metals with significant differences in mean content between the wines from both archipelagos, between table and liquor wines of Madeira, and between wines of Pico and Terceira Islands from the Azores archipelago. Principal component analysis shows differences in the wines according to the wine-making process and/or the equipment employed. Stepwise linear discriminant analysis achieves a good classification and validation of wines according to the archipelago of origin, and the island in the case of Azores wines.

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“…SLDA renders a number of orthogonal linear discriminant functions equal to the number of categories minus one. This method minimises the variance within categories and maximises the variance between categories (Powers & Keith, 1968). The variables included in the analyses are determined with a stepwise-LDA using a Wilk's Lambda as a selection criterion and an F statistic factor to establish the significance of the changes in Lambda when a new variable is tested.…”

Section: Discussionmentioning

confidence: 99%

Dynamic headspace solid-phase microextraction combined with one-dimensional gas chromatography–mass spectrometry as a powerful tool to differentiate banana cultivars based on their volatile metabolite profile

Pontes

Câmara

2012

Food Chemistry

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a b s t r a c tIn this study the effect of the cultivar on the volatile profile of five different banana varieties was evaluated and determined by dynamic headspace solid-phase microextraction (dHS-SPME) combined with one-dimensional gas chromatography-mass spectrometry (1D-GC-qMS). This approach allowed the definition of a volatile metabolite profile to each banana variety and can be used as pertinent criteria of differentiation. The investigated banana varieties (Dwarf Cavendish, Prata, Maçã, Ouro and Platano) have certified botanical origin and belong to the Musaceae family, the most common genomic group cultivated in Madeira Island (Portugal). The influence of dHS-SPME experimental factors, namely, fibre coating, extraction time and extraction temperature, on the equilibrium headspace analysis was investigated and optimised using univariate optimisation design. A total of 68 volatile organic metabolites (VOMs) were tentatively identified and used to profile the volatile composition in different banana cultivars, thus emphasising the sensitivity and applicability of SPME for establishment of the volatile metabolomic pattern of plant secondary metabolites. Ethyl esters were found to comprise the largest chemical class accounting 80.9%, 86.5%, 51.2%, 90.1% and 6.1% of total peak area for Dwarf Cavendish, Prata, Ouro, Maçã and Platano volatile fraction, respectively. Gas chromatographic peak areas were submitted to multivariate statistical analysis (principal component and stepwise linear discriminant analysis) in order to visualise clusters within samples and to detect the volatile metabolites able to differentiate banana cultivars. The application of the multivariate analysis on the VOMs data set resulted in predictive abilities of 90% as evaluated by the cross-validation procedure.

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Stepwise Discriminant Analysis of Gas Chromatographic Data as an Aid in Classifying the Flavor Quality of Foods

Cited by 92 publications

References 23 publications

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

Content in metallic ions of wines from the Madeira and Azores archipelagos

Dynamic headspace solid-phase microextraction combined with one-dimensional gas chromatography–mass spectrometry as a powerful tool to differentiate banana cultivars based on their volatile metabolite profile

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