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.
"The input dataset consisted of a 90 × 6 matrix composed by 90 sausage samples (equally divided in INSP and ART category) in the rows and 6 instrumental analyses in the columns which were comprised by content of moisture, protein, fat, nitrite, calcium and sodium. Data vectors belonging to the same category (inspected, INSP and artisanal, ART) were analyzed using the following chemometric procedures: principal component analysis (PCA), hierarchical cluster analysis (HCA) (Aquino et al., 2014; Souza et al., 2011), k-nearest neighbors (k-NN), soft independent modeling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA) (Cruz et al., 2013; Granato et al., 2011). The classification rules achieved by the supervised chemometric techniques were obtained by randomly dividing the complete data set into two parts: training set (75% of the samples) and a test set (25% of the samples). "
[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 · 2.82 Impact Factor
"The score vectors describe the relationship between the samples and allow checking if they are similar or dissimilar. Objects that are similar to each other will tend to cluster close to each other in the plots, whereas objects that are dissimilar will tend to be far apart (Granato et al., 2011; Souza et al., 2011). PCA was applied to the dataset to separate the samples (n = 6) according to their values of total phenolics, total flavonoids, total flavanols, total proanthocyanidins, carotenoids, total chlorophylls, DPPH and FRAP values. "
[Show abstract][Hide abstract] ABSTRACT: The authenticity of genotypes of white cabbage (Brassica oleracea var. capitata f. alba) cultivar ‘Varaždinski’, which originate from three eco-geographical regions, has been evaluated using molecular (RAPD) and chemometric approach. RAPD analyses confirmed intra-cultivar variability depending on the seed origin. At least three clusters were distinguished and organized in two groups and one subgroup (groups I and HR with subgroup SLO), which is mainly in agreement with eco-geographical location of the seed producers. Phytochemical analysis showed statistically significant variations in pigment and polyphenolic contents as well as in antioxidant capacities of the selected genotypes. The principle component analysis (PCA) visualized relationship among genotypes, phytochemical contents and antioxidant capacity. It revealed that the first two components represented 87% of the total variability in investigated parameters. Accordingly, genotypes were clustered in agreement with eco-geographical regions of seed producers. Analysis of seed storage proteins did not show significant differences among genotypes of interest. The authenticity of selected genotypes of white cabbage cv. ‘Varaždinski’ was discussed based on the molecular and phytochemical diversity.
Food Research International 06/2014; 60:266-272. DOI:10.1016/j.foodres.2013.07.015 · 2.82 Impact Factor
"Our approach is based on combined analytical techniques including GC–FID, Iatroscan TLC–FID and NMR spectroscopy, complemented with Chemometrics (principal component analysis, PCA; Orthogonal Partial Least Squares Discriminant Analysis, OPLS-DA). Chemometric techniques have been recently introduced to food science towards discrimination of several products (Cruz et al., 2013; Granato, Branco, Faria, & Cruz, 2011). NMR spectroscopy can be also used as a screening tool for detection of gamma irradiated food items (Zoumpoulakis et al., 2012). "
[Show abstract][Hide abstract] ABSTRACT: Macadamia (Macadamia integrifolia) is an edible nut species with commercial importance in cosmetic and pharmaceutical industries due to its high concentration in monounsaturated fatty acids and its low cholesterol levels. γ-Irradiation is a food processing procedure that allows the extension of shelf life and is broadly applied to dry nuts. Therefore there is an increasing research interest towards the development of new methods and markers for the detection of irradiated food items. In the present article, 60Co-irradiation was applied to macadamia nuts in increasing doses up to 10 kGy using different packaging and storage conditions in order to monitor changes in their lipid profile. Compositional data showed predominance of triglycerides followed by phytosterols in a much smaller proportion in nuts' lipids. The production of hydrolytic compounds as a result of gamma irradiation was statistically significant but didn't affect the macadamias' fat quality. Classification was achieved in relation to irradiation dose, package and storage conditions, using Chemometrics. More specifically, PCA and OPLS-DA analyses on the GC–FID, TLC–FID and color results managed to differentiate samples according to irradiation doses.
NMR based FoodOmic application is employed for the first time, in order to explore any trends in sample classification according to the irradiation dose and the storage or the packaging effect. Minor lipid components (such as β-sitosterol, C18:2 n − 6, C18:3 and sn1,2 and sn1,3 DGs) have shown high discriminant power over the samples. Results correlated storage and packaging effects with macadamia freshness.
Food Research International 01/2014; DOI:10.1016/j.foodres.2014.01.015 · 2.82 Impact Factor
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