Effects of grape maturity, maceration length during winemaking and interaction of both of them on physicochemical parameters, phenolic and volatile composition were investigated in tropical Syrah wines with the aim of finding the best conditions to produce wines that are rich in phenols and positive aroma active compounds. A headspace solid phase micro extraction and gas chromatography with mass spectrometry (HS-SPME-GC-MS) method was improved and validated for the analysis of 41 volatiles. Principal component analysis (PCA) and analysis of variance (ANOVA)-PCA coupled with inspection of statistically significant loadings were important to access the effects of maturation and maceration on wine composition. Wines made from riper grapes (21-23 ºBrix) stood out in relation to color intensity, total phenolic compounds, and presented high levels of anthocyanins, flavonols, procyanidin A2, and linalool (floral). Thirty days of maceration were linked to higher levels of some flavan-3-ols, gallic acid, a few esters (fruity) and alcohols, (E)-nerolidol (floral), while nonanoic acid (unpleasant aroma) reduced its concentration with prolonged maceration.

Benzo[a]pyrene (BaP) is often used as a marker of polycyclic aromatic hydrocarbons (PAHs) in beverages. This marker is often quantified by chromatographic methods, which require sample preparations involving the use of reagents, solvents, extraction, pre-concentration, and/or cleanup steps. In this study, a new method for quantification of BaP in cachaças (liquors) that does not use any sample preparation was developed. Interferents in cachaças were overcome using excitation-emission matrices data modeling with parallel factor analysis (PARAFAC). The recoveries ranged from 93.20 to 101.13%, and the relative error of prediction and limit of detection were, respectively, estimated at 2.66% and 2.88 ng mL-1. The proposed method is inexpensive and less time consuming than other approaches described in the literature, uses no reagents, solvents or extraction, has no pre-concentration or cleanup steps, contributing to green analytical chemistry.

<p>This work reports the development of a fast chromatographic methodology for quantitation of two phytoestrogens: daidzein (DAI), and genistein (GEN), in river water samples. The proposed method is based on high performance liquid chromatography-diode array detection (HPLC-DAD) data, and multivariate curve resolution-alternative least square (MCR-ALS) second-order calibration. Initially, the method was evaluated analyzing a synthetic validation set; prepared based on a Taguchi design. Subsequently, the method was applied to predict the concentration of the phytoestrogens in spiked river water samples, previously pre-processed by solid phase extraction (SPE). By implementation of the present chromatographic methodology, a 50% reduction in operation time was achieved (from 7.00 to 3.25 min) when compared with previous work in the literature. Precision was achieved even in the presence of non-modeled constituents and strong background. Thus, the proposed method is a rapid and robust alternative for the quantitation of studied phytoestrogens.</p>

Este artigo propõe um método de espectrometria no infravermelho próximo (NIR) de reflectância difusa para a discriminação não-destrutiva de sementes de mamona das cultivares mais comumente empregadas nas plantações brasileiras (BRS Nordestina e BRS Paraguaçu). Para esta finalidade, duas técnicas de classificação são comparadas, o SIMCA (modelagem independente flexível por analogias de classe) e PLS-DA (análise discriminante por mínimos quadrados parciais). Ao aplicar a classificação SIMCA a um conjunto de teste contendo 150 sementes, os modelos para as classes BRS Nordestina e BRS Paraguaçu apresentaram valores de sensibilidade/especificidade de 0,91/0,99 e 0,71/1,00, respectivamente. Melhores resultados foram obtidos usando PLS-DA, que classificou corretamente todas as amostras de teste, proporcionando assim valores de sensibilidade e seletividade de 1,00. Estes resultados sugerem que o método proposto é promissor na identificação de genótipos de sementes de mamona, em lotes de sementes ou para fins de reprodução, antes de serem plantadas.

This article proposes a near-infrared (NIR) diffuse reflectance spectrometric method for non-destructive discrimination of castor seeds from the two cultivars most commonly employed in Brazilian plantations (BRS Nordestina and BRS Paraguaçu). For this purpose, two classification techniques are compared, namely SIMCA (soft independent modelling of class analogies) and PLS-DA (partial least squares discriminant analysis). By applying the SIMCA classifier to a test set comprising 150 seeds, the BRS Nordestina and BRS Paraguaçu class models yielded sensitivity/specificity values of 0.91/0.99 and 0.71/1.00, respectively. Better results were obtained by using PLS-DA, which correctly classified all test samples, i.e., yielded sensitivity and specificity values of 1.00. These findings suggest that the proposed method is a promising approach to identify castor seed genotypes, either in seed lots or for breeding purposes, prior to being planted.

Um método para detecção de adulterações de misturas de biodiesel/diesel (B5) com óleo de soja empregando espectrometria UV-Vis é proposto. O estudo envolve 90 amostras compreendendo misturas B5 com e sem a adição de óleo de soja (0,5 a 2,5% v/v). Uma discriminação apropriada foi obtida utilizando classificadores SIMCA (modelagem independente e flexível por analogia de classe), KNN (K-vizinhos mais próximos), PLS-DA (análise discriminante por mínimos quadrados parciais) e SPA-LDA (análise discriminante linear com algoritmo de projeções sucessivas).

A method for detecting adulterations of biodiesel/diesel blends (B5) with soybean oil using UV-Vis spectrometry is proposed. The study involves 90 samples comprising B5 blends with and without the addition of soybean oil (0.5 to 2.5% v/v). Suitable discrimination was achieved by using SIMCA (soft independent modeling of class analogy), KNN (K-nearest neighbors), PLS-DA (partial least squares discriminant analysis) and SPA-LDA (linear discriminant analysis with spectral variables selected by the successive projections algorithm) classifiers.

O objetivo deste trabalho foi estimar a evapotranspiração de referência (ETo) mensal para o Estado do Piauí, pelo método empírico de Thornthwaite (1948), com base nas equações de estimativa da temperatura média do ar, propostas por Lima & Ribeiro (1998). Estimou-se a ETo para os 222 municípios do Estado, cujos valores foram geoespacializados e agrupados em classes de ETo, com intervalo de 25 mm, gerando-se mapas de ETo mensais. Os valores de ETo mensais variaram de 55,4 a 212,0 mm, evidenciando a variação na demanda evapotranspirativa das diversas regiões do Estado. O período de fevereiro a julho apresentou os menores valores mensais de ETo, enquanto de setembro a dezembro, os maiores valores. Esta tendência é um reflexo da variação espacial da temperatura média do ar mensal nas diferentes regiões do Estado.

The objective of this work was to estimate the mean monthly reference evapotranspiration (ETo) for Piaui State, Brazil. The ETo estimates were performed by the Thornthwaite's (1948) empirical method, based on estimated mean air temperature equations developed by Lima & Ribeiro (1998). The ETo was estimated for 222 municipalites of Piaui State. The values were arranged in a spatial distribution and grouped in classes with 25 mm interval. Maps of monthly ETo were prepared. The monthly ETo values ranged from 55.4 to 212.0 mm, showing large evapotranspiration variation among the several regions of Piaui State. Lower monthly ETo values were obtained for the period of February to July, while the higher values were observed for September to December. The spatial distribution of the monthly ETo values occurred as a function of the spatial variation of the monthly mean air temperature in the different regions of Piaui State.