Corporate social responsibility and environmental sustainability: classification of the initiatives of the FMCG companies operating in Brazil

Este trabalho tem como objetivo fornecer uma análise detalhada do cenário da sustentabilidade ambiental e iniciativas de responsabilidade social corporativa nas empresas que operam no mercado de bens de consumo brasileiro. Para alcançar este objetivo as dez maiores empresas do mercado-alvo presente no Brasil foram analisadas por meio da classificação das suas iniciativas em três perspectivas amplas. Com esta classificação o cenário do mercado pode ser visto. As perspectivas utilizadas para a elaboração do trabalho são: (1) iniciativa ambiental ou social; (2) o foco interno ou externo e (3) a marca ou o custo como motivador. Depois de classificar todas as iniciativas, foi possível ver que as empresas similares, que operam em mercados semelhantes, têm estratégias que são muito parecidos entre si. Além disso, ficou claro que a estratégia de negócios da empresa influencia as suas políticas ambientais e sociais, em particular os objetivos que estas políticas procuram obter.Embora este trabalho apresente um panorama abrangente do setor de bens de consumo em relação a políticas de comportamento responsável das empresas, ele tem algumas limitações. A limitação mais significativa diz respeito a metodologia. As iniciativas foram avaliadas pela quantidade e a abrangência dos benefícios do impacto positivo não foram avaliados, impossibilitando assim a comparação do tamanho do impacto de cada empresa. Uma vez que pode haver um projeto de uma empresa que tem maior impacto do que vários outros feitos por alguma outra empresa. A metodologia foi baseada em clusters de categorias, no entanto, as iniciativas não são completamente uma coisa ou outra, ou seja, uma iniciativa pode ter diferentes impactos, drivers ou foco, nesses casos, os aspectos mais relevantes foram a escolhidos para classificá-los.

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

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.

Classification of Boal, Malvazia, Sercial and Verdelho wines based on terpenoid patterns

Thirty-six Madeira wine samples from Boal, Malvazia, Sercial and Verdelho white grape varieties were analyzed in order to estimate the free fraction of monoterpenols and C13 norisoprenoids (terpenoid compounds) using dynamic headspace solid phase micro-extraction (HS-SPME) technique coupled with gas chromatography–mass spectrometry (GC–MS). The average values from three vintages (1998–2000) show that these wines have characteristic profiles of terpenoid compounds. Malvazia wines exhibits the highest values of total free monoterpenols, contrary to Verdelho wines which had the lowest levels of terpenoids but produced the highest concentration of farnesol. The use of multivariate analysis techniques allows establishing relations between the compounds and the varieties under investigation. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to the obtained matrix data. A good separation and classification power between the four groups as a function of their varietal origin was observed.

Antimicrobial resistance of heterotrophic marine bacteria isolated from seawater and sands of recreational beaches with different organic pollution levels in southeastern Brazil: evidences of resistance dissemination

Antimicrobial resistance of marine heterotrophic bacteria to different antimicrobials agents were evaluated in seawater, dry and wet sands from three marine recreational beaches with different pollution levels. In all studied beaches, the greatest frequencies of resistance were found in relation to penicillin. on Gonzaguinha, the most polluted beach, 72.3% of all isolated strains showed simple resistance, whilst 8.33% had multiple resistance. The values found on Ilha Porchat beach, were 70.8% and 6.9% for simple and multiple resistances, respectively. on GuaraA(0), the less polluted beach, only 35.3% of isolated strains had simple resistance. Multiple resistance was not observed. While samples from Gonzaguinha and Ilha Porchat beach showed isolated strains resistant to seven and six different antimicrobial agents, respectively, samples from GuaraA(0) beach were resistant only to penicillin and erytromicin. The positive correlations obtained between the degree of seawater contamination and frequency and variability of bacterial resistance indicate that polluted marine recreational waters and sands are sources of resistant bacteria contributing thus, to the dissemination of bacterial resistance.

Antimicrobial resistance and species composition of Enterococcus spp. isolated from waters and sands of marine recreational beaches in Southeastern Brazil

Density, species composition and antimicrobial resistance in bacteria of the Enterococcus genus were evaluated in seawater and sands from 2 marine recreational beaches with different levels of pollution. The 2 beaches showed predominance of Enterococcus faecalis and Enterococcus faecium, in the water and the sand. Dry sand presented higher densities of Enterococcus sp. and higher frequency of resistant strains than wet sand and seawater. The beach with a higher degree of pollution presented higher percentages of resistant strains (66.7% and 61.5%, in sand and in water, respectively) and resistance to a larger number of antimicrobials compared with the less polluted beach, Ilha Porchat (35.7% and 31.25% of resistant strains in sand and water, respectively). in water samples, the highest frequencies of resistance were obtained against streptomycin (38.5%) and erythromycin (25%), whilst in sand, the highest frequencies were observed in relation to erythromycin and tetracycline (38.1% and 14.3%, respectively). These results show that water and sands from beaches with high indexes of faecal contamination of human origin may be potential sources of contamination by pathogens and contribute to the dissemination of bacterial resistance. (C) 2007 Elsevier Ltd. All rights reserved.

Ingredient classification according to the digestible amino acid profile: an exploratory analysis

This study aimed: 1) to classify ingredients according to the digestible amino acid (AA) profile; 2) to determine ingredients with AA profile closer to the ideal for broiler chickens; and 3) to compare digestible AA profiles from simulated diets with the ideal protein profile. The digestible AA levels of 30 ingredients were compiled from the literature and presented as percentages of lysine according to the ideal protein concept. Cluster and principal component analyses (exploratory analyses) were used to compose and describe groups of ingredients according to AA profiles. Four ingredient groups were identified by cluster analysis, and the classification of the ingredients within each of these groups was obtained from a principal component analysis, showing 11 classes of ingredients with similar digestible AA profiles. The ingredients with AA profiles closer to the ideal protein were meat and bone meal 45, fish meal 60 and wheat germ meal, all of them constituting Class 1; the ingredients from the other classes gradually diverged from the ideal protein. Soybean meal, which is the main protein source for poultry, showed good AA balance since it was included in Class 3. on the contrary, corn, which is the main energy source in poultry diets, was classified in Class 8. Dietary AA profiles were improved when corn and/or soybean meal were partially or totally replaced in the simulations by ingredients with better AA balance.

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Environmental Fate of Neonicotinoids and Classification of Their Potential Risks to Hypogean, Epygean, and Surface Water Ecosystems in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A classification of Pepper yellow mosaic virus isolates into pathotypes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Wine classification by taste sensors made from ultra-thin films and using neural networks

This paper reports on a sensor array able to distinguish tastes and used to classify red wines. The array comprises sensing units made from Langmuir-Blodgett (LB) films of conducting polymers and lipids and layer-by-layer (LBL) films from chitosan deposited onto gold interdigitated electrodes. Using impedance spectroscopy as the principle of detection, we show that distinct clusters can be identified in principal component analysis (PCA) plots for six types of red wine. Distinction can be made with regard to vintage, vineyard and brands of the red wine. Furthermore, if the data are treated with artificial neural networks (ANNs), this artificial tongue can identify wine samples stored under different conditions. This is illustrated by considering 900 wine samples, obtained with 30 measurements for each of the five bottles of the six wines, which could be recognised with 100% accuracy using the algorithms Standard Backpropagation and Backpropagation momentum in the ANNs. (C) 2003 Elsevier B.V. All rights reserved.

Efficient supervised optimum-path forest classification for large datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Self-Organizing Maps as a New Tool for Classification of Plants at Lower Hierarchical Levels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mapping the knowledge covered by library classification systems

This study explores, in 3 steps, how the 3 main library classification systems, the Library of Congress Classification, the Dewey Decimal Classification, and the Universal Decimal Classification, cover human knowledge. First, we mapped the knowledge covered by the 3 systems. We used the 10 Pillars of Knowledge: Map of Human Knowledge, which comprises 10 pillars, as an evaluative model. We mapped all the subject-based classes and subclasses that are part of the first 2 levels of the 3 hierarchical structures. Then, we zoomed into each of the 10 pillars and analyzed how the three systems cover the 10 knowledge domains. Finally, we focused on the 3 library systems. Based on the way each one of them covers the 10 knowledge domains, it is evident that they failed to adequately and systematically present contemporary human knowledge. They are unsystematic and biased, and, at the top 2 levels of the hierarchical structures, they are incomplete.