Characterization of Brazilian lager and brown ale beers based on color, phenolic compounds, and antioxidant activity using chemometrics.
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.
- SourceAvailable from: sciencedirect.com[Show abstract] [Hide abstract]
ABSTRACT: Chemometric techniques were used to assess the quality of 51 commercial Brazilian sugarcane spirits (cachaca) based on chemical markers. Benzo(a)pyrene, methanol, 2,3-methyl-1-butanol, acetaldehyde, isobutyl alcohol, n-propanol, density, alcoholic strength, and higher alcohols were quantified using chromatographic methods and results were subjected to Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Linear Discriminant Analysis (LDA). No significant differences (p >= 0.071) were observed in the chemical markers of Brazilian aged and non-aged cachaca samples. Besides non-significant (p = 0.922), the content of benzo(a) pyrene in aged sugarcane spirits was 1.83 times higher than in non-aged ones. Differences in alcoholic strength (p = 0.001) and n-propanol (p = 0.015) were observed among cachacas produced by double distilling, alembic and in stainless steel columns. PCA was not suitable to separate the samples according to the provenance, aging and distilling process, while HCA was effective in separating alembic cachacas produced by from two distinct producing regions. LDA seemed to be very suitable to assess not only the provenance but also the distilling and aging processes that cachaga undergoes, yielding about 91% accuracy to discriminate non-aged from aged cachaga, 81.82% and 86.61% accuracy to discriminate samples from Minas Gerais and Sao Paulo, respectively. (c) 2013 Elsevier Ltd. All rights reserved.Food Research International 06/2014; 60:212-217. DOI:10.1016/j.foodres.2013.09.044 · 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; 80(1). DOI:10.1111/1750-3841.12722 · 1.79 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: The performance of different chemometric approaches to discriminate artisanal and industrial pork sausages using traditional physicochemical parameters was investigated. A total of 90 samples of sausages marketed in various supermarkets and open-markets in Rio de Janeiro, Brazil were analyzed for their content of moisture, protein, fat, nitrite, sodium and calcium. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used as exploratory methods, while linear and non-linear classification methods, such as k-nearest neighbors (k-NN), soft independent modeling of class analogy (SIMCA), partial least square discriminant analysis (PLSDA) and artificial neural networks (ANN) were used for assessing the data. Different behaviors for all parameters were analyzed between the classes. Principal component analysis and hierarchical cluster analysis did not show a complete discrimination of the samples. KNN and ANN results showed excellent performance for both categories with 100% correct prediction while SIMCA and PLSDA presented performance of 100% and 85.7% for inspected and artisanal sausages, respectively. According to the SIMCA, PLSDA and ANN, the contents of moisture and fat showed the highest discriminative power. Overall, the findings emphasize the use of multivariate techniques to evaluate the quality of processed foods, as pork sausages.Food Research International 07/2014; 64:380-386. DOI:10.1016/j.foodres.2014.07.003 · 3.05 Impact Factor