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Raw material influence in mead production

Publication . Santos, Regina; Pereira, Ana Paula; Estevinho, Leticia M.; Caldeira, Ilda; Anjos, Ofélia

Honey can be fermented to produce different types of mead given their sugar concentration [1]. Mead is a beverage produced by the alcoholic fermentation of diluted honey. This beverage present an alcoholic strength ranging between 8 – 18 % volumes [2]. The aim of this work is characterize the quality of honey mead with different kinds of raw-material: Lavandula honey; Castanea honey and a mixture of waxes and Castanea honey resulting from the beekeeper uncaps process. To produce the honey must the different raw materials was diluted with natural spring-water (37 g of honey/100 mL of water) and mixed to homogeneity. The honey must was inoculated with a commercial wine yeast Saccharomyces cerevisiae QA 23 at a concentration of 0,3 g/hL and incubated at 25 0C. Throughout the fermentation process biomass concentrations and reducing sugar of the must were regularly measured. At the end of fermentations the meads were treated with bentonite (100 g/hL). In the meads produced, °Brix, pH, total acidity and assailable nitrogen concentration were evaluated. It was observed that the yeast grow is more efficient in the Castanea honey than in the other samples. Given the results showed in Table 1 it is possible conclude that the meads produced with different raw material are similar and the differences could be attribute to the differences of honey, with different chemical composition. According to the obtained results it can be concluded that it is possible produce mead also with a mixture of waxes and honey resulting from the bee-keeper uncaps process with an important economical benefice.

2016Conference object

Open access

Impact of fining agents on the volatile composition of sparkling mead

Publication . Pascoal, Ananias; Anjos, Ofélia; Feás, Xesús; Oliveira, José M.; Estevinho, Leticia M.

Sparkling mead is obtained by secondary fermentation of the mead involving the addition of starter yeast culture, sucrose, nutrients and fining agents. The aim of this study was to evaluate the effect of different fining agents (tannins vs combined fining agents) on the volatile composition of sparkling mead. Sparkling mead was produced from a base mead using a commercial yeast strain (Saccharomyces bayanus) and the volatile compounds were determined by gas chromatography–flame ionisation detection and gas chromatography–mass spectrometry. Thirty six volatile compounds were quantified and the major groups were alcohols (73.2%), acetates (19.1%), carbonyl compounds (5.5%) and ethyl esters (1.2%), represented by 3-methyl-1-butanol, ethyl acetate, acetaldehyde and monoethyl succinate, respectively. The remaining compounds were present at <1%. Eleven volatile compounds exhibited odour activity values >1, with ethyl octanoate and ethyl hexanoate contributing to the aroma of sparkling mead, with fruity, strawberry and sweet notes. The combined fining agents caused a marked decrease in the concentration of volatile compounds compared with tannins. In general, 3-ethoxy-1-propanol, ethyl lactate, ethyl octanoate, diethyl succinate, diethyl malate, monoethyl succinate, 2-methylpropanoic acid, hexanoic acid, octanoic acid, acetaldehyde, acetoin, furfural, benzaldehyde, 5-hydroxymethylfurfural, trans-furan linalool oxide, cis-furan linalool oxide and 4-oxo-isophorone decreased in concentration. Conversely, 1-propanol and 2-methylpropanoic acid (tannins) and ethyl butyrate (combined fining agents) increased in concentration. The remaining volatile compounds were not affected. Significant differences (p < 0.05) were found for 19 volatile compounds independently of the type of fining agents used.

2019Journal article

Open access

Computational intelligence applied to discriminate bee pollen quality and botanical origin

Publication . Gonçalves, Paulo J.S.; Estevinho, Leticia M.; Pereira, Ana Paula; Sousa, João M.C.; Anjos, Ofélia

The aim of this work was to develop computational intelligence models based on neural networks (NN), fuzzy models (FM), and support vector machines (SVM) to predict physicochemical composition of bee pollen mixture given their botanical origin. To obtain the predominant plant genus of pollen (was the output variable), based on physicochemical composition (were the input variables of the predictive model), prediction models were learned from data. For the inverse case study, input/output variables were swapped. The probabilistic NN prediction model obtained 98.4% of correct classification of the predominant plant genus of pollen. To obtain the secondary and tertiary plant genus of pollen, the results present a lower accuracy. To predict the physicochemical characteristic of a mixture of bee pollen, given their botanical origin, fuzzy models proven the best results with small prediction errors, and variability lower than 10%.

2018Journal article

Open access

Comparison between FTIR-ATR and NIR spectroscopy for Lavandula honey characterization

Publication . Anjos, Ofélia; Pereira, Ana Paula; Santos, António J.A.; Estevinho, Leticia M.

Spectroscopic methods have been widely used in the quality assessment of different matrices, among which food products [1,2,3]. The advantage of these analytical techniques is related to the minimal or no sample preparation, quickness of the procedure and potential to run multiple tests with only one sample. However, different methodologies may be applied, emphasizing the importance of identifying the most accurate method and equipment. The aim of this work was to compare the performance of Near-infrared spectroscopy (NIR) and Fourier Transform infrared spectroscopic method with Attenuated Total Reflectance (FTIR-ATR) for the multivariate characterization of honey’s chemical composition. In the first approach, the analysed parameters were: protein content (%), ascertainedby the Kjeldahl method; and apparent sucrose content (%), evaluated according to the regulatory standards of International Honey Commission. Partial least squares (PLS) regression was used to build the calibration model for these parameters, in comparison to the data obtained with the reference methods. For the 150 Lavandula honey samples two different methodologies were tested: i) a calibration with cross validation followed by ii) a cross validation (70% of the samples) with a test set (remaining 30% of the samples). The obtained models of suitable accuracy for protein content presented an r2 ranging from 96.7% to 81.8% (RPD = 5.5 to 2.4) for FTIR-ATR and between 89.4% and 77.6% (RPD = 3.1 to 2.1) for NIR. Concerning sucrose content, r2 was between 96.9% and 70.0% (RPD = 5.7 to 1.9) for FTIR-ATR, and between 88.5% and 64.4% (RPD = 3.0 to 1.9) for NIR. The root means square error of cross validation and prediction was lower using FTIR-ATR. The precision achieved using these two parameters suggests that the infrared spectroscopy is a suitable technique for the characterization of honey samples. However, in general, FTIR-ATR appeared to be the most accurate method. Different models are in development for other chemical parameters comparing these two techniques.

2016Conference object

Open access