Search results

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%.

The Fourier transform infrared (FTIR) spectroscopic method with attenuated total reflectance (ATR) was used for predicting the alcoholic strength, the methanol, acetaldehyde and fusel alcohols content of grape-derived spirits. FTIR-ATR spectrum in the mid-IR region (4000-400 cm(-1)) was used for the quantitative estimation by applying partial least square (PLS) regression models and the results were correlated with those obtained from reference methods. In the developed method, a cross-validation with 50% of the samples was used for PLS analysis along with a validation test set with 50% of the remaining samples. Good correlation models with a great accuracy were obtained for methanol (r(2)=99.4; RPD=12.8), alcoholic strength (r(2)=97.2; RPD=6.0), acetaldehyde (r(2)=98.2; RPD=7.5) and fusel alcohols (r(2) from 97.4 to 94.1; RPD from 6.2 to 4.1). These results corroborate the hypothesis that FTIR-ATR is a useful technique for the quality control of grape-derived spirits, whose practical application may improve the efficiency and quickness of the current laboratory analysis.

Bee pollen is one of nature's healthful food products with promising nutritional and therapeutic properties due to its chemical composition, particularly its protein content, which includes almost all the essential amino acids. Nevertheless the composition in lipids it is not completely known and supposedly will be highly variable depending on the floral origin. As such, this parameter must be ascertained in line with the research for bioactivity [1]. The aim of this work was to evaluate some health-related lipid indexes of bee pollen, namely omega-6/omega-3 fatty acid ratio (n-6/n-3); polyunsaturated fatty acid/saturated fatty acid ratio (PUFA/SAT), atherogenic index (AI) and thrombogenic index (TI) in samples harvested in Portugal. The selected parameters were calculated from the Fatty-Acid Profile, which was determined as previously reported by Bárbara et al. [3]. Bee pollen samples, after harvest, were cleaned and frozen at -20 oC and were codified according the predominant pollen. Figure 1 associates the lipid indexes of the different samples with their botanical origins. n-6/n-3 and PUFA/SAT ratios were in within the limits recommended by World Health Organization (below 4.0 and above 0.45, respectively), suggesting that bee pollen is a good product with the nutritional point of view, with potential beneficial effects for the consumer's health. Also, both AI and TI indexes of this natural product were low, even though this effect depended on bee pollen's botanical origin (Figure 1).

HPLC/DAD fingerprint of standardize extracts from Ligustrum lucidum Aiton berries, for bioactive activity screening.

info:eu-repo/semantics/publishedVersion