Characterization of Brazilian lager and brown ale beers based on color, phenolic compounds, and antioxidant activity using chemometrics.

Department of Food and Experimental Nutrition, Faculty of Pharmaceutical Sciences, University of São Paulo, Av. Prof. Lineu Prestes, 580, B14, 05508-000, São Paulo, São Paulo, Brazil.
Journal of the Science of Food and Agriculture (Impact Factor: 1.88). 02/2011; 91(3):563-71. DOI: 10.1002/jsfa.4222
Source: PubMed

ABSTRACT Epidemiological studies have shown that beer has positive effects on inhibiting atherosclerosis, decreasing the content of serum low-density lipoprotein cholesterol and triglycerides, by acting as in vivo free radical scavenger. In this research, the antioxidant activity of commercial Brazilian beers (n = 29) was determined by the oxygen radical absorbance capacity (ORAC) and 1,1-diphenyl-2-picrylhydrazyl (DPPH(·) ) assays and results were analyzed by chemometrics.
The brown ale samples (n = 11) presented higher (P < 0.05) flavonoids (124.01 mg L(-1) ), total phenolics (362.22 mg L(-1) ), non-flavonoid phenolics (238.21 mg L(-1) ), lightness (69.48), redness (35.75), yellowness (55.71), color intensity (66.86), hue angle (59.14), color saturation (0.9620), DPPH(·) values (30.96% inhibition), and ORAC values (3, 659.36 µmol Trolox equivalents L(-1) ), compared to lager samples (n = 18). Brown ale beers presented higher antioxidant properties (P < 0.05) measured by ORAC (1.93 times higher) and DPPH (1.65 times higher) compared to lager beer. ORAC values correlated well with the content of flavonoids (r = 0.47; P = 0.01), total phenolic compounds (r = 0.44; P < 0.01) and DPPH (r = 0.67; P < 0.01). DPPH values also correlated well to the content of flavonoids (r = 0.69; P < 0.01), total phenolic compounds (r = 0.60; P < 0.01), and non-flavonoid compounds (r = 0.46; P = 0.01).
The results suggest that brown ale beers, and less significantly lager beers, could be sources of bioactive compounds with suitable free radical scavenging properties.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Classification of wine represents a multi-criteria decision-making problem characterized by great complexity, non-linearity and lack of objective information regarding the quality of the desired final product. Volatile compounds of wines elaborated from four Galician (NW Spain) autochthonous white Vitis vinifera from four consecutive vintages were analysed by gas chromatography (FID, FPD and MS detectors), and several aroma compounds were used for correctly classifying autochthonous white grape varieties (Albariño, Treixadura, Loureira and Dona Branca). The objective of the work is twofold: to find a classification model able to precisely differentiate between existing grape varieties (i.e. assuring the authenticity), and to assess the discriminatory power of different family compounds over well-known classifiers (i.e. guaranteeing the typicality). From the experiments carried out, and given the fact that Principal Component Analysis (PCA) was not able to accurately separate all the wine varieties, this work investigates the suitability of applying different machine learning (ML) techniques (i.e.: Support Vector Machines, Random Forests, MultiLayer Perceptron, k-Nearest Neighbour and Naïve Bayes) for classification purposes. Perfect classification accuracy is obtained by the Random Forest algorithm, whilst the other alternatives achieved promising results using only part of the available information.
    Food Research International 06/2014; 60:230. DOI:10.1016/j.foodres.2013.09.032 · 3.05 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The objectives of this study were to characterize organic, biodynamic, and conventional purple grape juices (n = 31) produced in Europe based on instrumental taste profile, antioxidant activity, and some chemical markers and to propose a multivariate statistical model to analyze their quality and try to classify the samples from the 3 different crop systems. Results were subjected to ANOVA, correlation, and regression analysis, principal component analysis (PCA), hierarchical cluster analysis (HCA), soft independent modeling of class analogy (SIMCA), and partial least-squares discriminant analysis (PLSDA). No statistical significant differences (P > 0.05) were observed among juices from the 3 crop systems. Using PCA and HCA, no clear separation among crop systems was observed, corroborating the ANOVA data. However, PCA showed that the producing region highly affects the chemical composition, electronic tongue parameters, and bioactivity of grape juices. In this sense, when organic and biodynamic were grouped as “nonconventional” juices, SIMCA model was able to discriminate 12 out of 13 organic/biodynamic juices and 17 out of 18 conventional juices, presenting an efficiency of 93.5%, while 11 out of 13 non-conventional and 100% conventional grape juices were correctly classified using PLSDA. The use of electronic tongue and the determination of antioxidant properties and major phenolic compounds have shown to be a quick and accurate analytical approach to assess the quality of grape juices.Practical ApplicationIndustry needs quick and accurate methods to assess fruit juice quality traits. Here we used electronic tongue and spectrophotometric measurements of chemical compounds and antioxidant activity coupled with chemometrics aiming the characterization and classification of grape juices from 3 crop systems.
    Journal of Food Science 12/2014; DOI:10.1111/1750-3841.12722 · 1.79 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Edible oils and fats are one of the foods most frequently counterfeited in many countries. Therefore, monitoring the authenticity and overall quality of these products is ultimately required. Chemometric analyses, such as Partial Least Square (PLS), Linear Discriminant Analysis (LDA), Soft Independent Modeling of Class Analogy (SIMCA), and others, applied to vibrational spectroscopic data have enabled the development of methods useful to assess quality aspects (authenticity, adulteration, free fatty acids and trans content, iodine, peroxide and saponification values, and others) of edible fats and oils. The methods are potential analytical tools for industries and inspection agencies for characterization of samples during the development, processing, quality control and inspection of oils and fats. In this original review, applications of near, mid and Raman infrared spectroscopy combined with multivariate analysis to authenticate, detect adulteration and determine intrinsic quality parameters in edible fats and oils are discussed.
    Food Research International 06/2014; 60:255-261. DOI:10.1016/j.foodres.2013.08.041 · 3.05 Impact Factor