Determination of Honey Geographic Origin According to Its Elemental Composition by the Method of X-ray Fluorescence

The aim of the research was to determine features of the elemental composition of polyfloral honey from the Odessa region (Ukraine) by the method of x-ray fluorescence for using these features in the geographic marking of the region of honey origin. A choice of honey from the Odessa region is explained by its relatively good ecology, optimal maritime climate and enough motley grass resources for gathering nectar by bees. At the same time the Odessa region occupies the fourth place among regions – honey producers in Ukraine with a right to export honey beyond the country with specific weight 10 % from the total export of this product.11 samples of fresh honey and 14 samples of honey, stored during one year were studied as to the content of 22 mineral elements. The elemental analysis of honey was realized on the energy-dispersive x-ray fluorescent spectrometer ElvaX Light SDD. Each sample was studied for 5 times. The obtained results were statistically processed by the standard methodology. The determination distinctness of mass shares of metals is no worse than 0,1 %. Limits of finding admixtures of heavy metals in the light matrix are no worse than 1 ppm. The studies were realized during 2016-2017.It has been established, that lyophilization of honey doesn\u27t essentially influence determination parameters of the elemental composition of honey by the method of x-ray fluorescence. The analysis of energy-dispersive spectrums of mineral elements determined that marker elements for honey from the Odessa region are Cl, K, Ca, that prevail among other studied mineral elements. There has been established the typical intensity of a signal of energy-dispersive spectrums for Cl, K, Ca of honey from the Odessa region that correspond to the following values: for fresh honey Cl from 27075 to 29429; K from 47 296 to 41 546; Ca from 7572 to 6928; for honey, stored during one year: Cl from 40383 to 37044; K from 43 589 to 42 591;Ca from 15495 to 10006. These parameters may serve as markers for honey from the Odessa region. At the same time the obtained results may be used for controlling the quality of natural honey by its element composition for identifying a geographic place of origin

Determination of honey geographic origin according to its elemental composition by the method of x-ray fluorescence

The aim of the research was to determine features of the elemental composition of polyfloral honey from the Odessa region (Ukraine) by the method of x-ray fluorescence for using these features in the geographic marking of the region of honey origin. A choice of honey from the Odessa region is explained by its relatively good ecology, optimal maritime climate and enough motley grass resources for gathering nectar by bees. At the same time the Odessa region occupies the fourth place among regions – honey producers in Ukraine with a right to export honey beyond the country with specific weight 10 % from the total export of this product. 11 samples of fresh honey and 14 samples of honey, stored during one year were studied as to the content of 22 mineral elements. The elemental analysis of honey was realized on the energy-dispersive x-ray fluorescent spectrometer ElvaX Light SDD. Each sample was studied for 5 times. The obtained results were statistically processed by the standard methodology. The determination distinctness of mass shares of metals is no worse than 0,1 %. Limits of finding admixtures of heavy metals in the light matrix are no worse than 1 ppm. The studies were realized during 2016–2017. It has been established, that lyophilization of honey doesn’t essentially influence determination parameters of the elemental composition of honey by the method of x-ray fluorescence. The analysis of energy-dispersive spectrums of mineral elements determined that marker elements for honey from the Odessa region are Cl, K, Ca, that prevail among other studied mineral elements. There has been established the typical intensity of a signal of energy-dispersive spectrums for Cl, K, Ca of honey from the Odessa region that correspond to the following values: for fresh honey Cl from 27075 to 29429; K from 47 296 to 41 546; Ca from 7572 to 6928; for honey, stored during one year: Cl from 40383 to 37044; K from 43 589 to 42 591; Ca from 15495 to 10006. These parametersmay serve as markers for honey from the Odessa region. At the same time the obtained results may be used for controlling the quality of natural honey by its element composition for identifying a geographic place of origin

A Three Species Model to Simulate Application of Hyperbaric Oxygen Therapy to Chronic Wounds

Chronic wounds are a significant socioeconomic problem for governments worldwide. Approximately 15% of people who suffer from diabetes will experience a lower-limb ulcer at some stage of their lives, and 24% of these wounds will ultimately result in amputation of the lower limb. Hyperbaric Oxygen Therapy (HBOT) has been shown to aid the healing of chronic wounds; however, the causal reasons for the improved healing remain unclear and hence current HBOT protocols remain empirical. Here we develop a three-species mathematical model of wound healing that is used to simulate the application of hyperbaric oxygen therapy in the treatment of wounds. Based on our modelling, we predict that intermittent HBOT will assist chronic wound healing while normobaric oxygen is ineffective in treating such wounds. Furthermore, treatment should continue until healing is complete, and HBOT will not stimulate healing under all circumstances, leading us to conclude that finding the right protocol for an individual patient is crucial if HBOT is to be effective. We provide constraints that depend on the model parameters for the range of HBOT protocols that will stimulate healing. More specifically, we predict that patients with a poor arterial supply of oxygen, high consumption of oxygen by the wound tissue, chronically hypoxic wounds, and/or a dysfunctional endothelial cell response to oxygen are at risk of nonresponsiveness to HBOT. The work of this paper can, in some way, highlight which patients are most likely to respond well to HBOT (for example, those with a good arterial supply), and thus has the potential to assist in improving both the success rate and hence the cost-effectiveness of this therapy

Construction of a Method for Predicting the Number of Enterobacteria in Milk Using Artifical Neural Networks

It is now established that artificial neural networks (ANNs) provide better simulation and prediction of the number of microorganisms in raw materials and foodstuffs. In this case, ANNs could be used as informative, Fast, and cost-effective means. According to the European requirements to food products, basic microbiological indicators are the total number of microorganisms and bacteria from the Enterobacteriaceae family, since they are most commonly associated with food-borne diseases and poisonings. The aim of this work was to devise a method for predicting the number of bacteria from the Enterobacteriaceae family in raw milk at its chilled storage and to estimate the predictive capability of ANN. Construction of the method included 4 stages. At the first stage, we examined the number of enterobacteria depending on the physical-chemical composition of raw milk, temperature and duration of storage in a refrigerator. At the second stage, we compiled a base of experimental data obtained from research models. At the next stage, we introduced the received database to ANN. And at the last stage we assessed effectiveness of the predicting technique. The constructed ANN consists of three layers: an input layer (5 parameters: milk storage temperature (4, 6, 8, and 10 °C), duration of milk storage (from 1 to 48 hours); the acidity of milk (17‒20 %), the fat content (3.2; 3.6; 4.0; 4.5 %) and protein content (2.9; 3.0; 3.3 %) in milk; hidden layers (with 30 neurons) and the output layer (the projected number of bacteria). In order to train and optimize the ANN, we used 1,200 experimental data, which revealed that the prediction had the highest rate of deviation of 2.497 % (or 370 bacterial cells per 1 ml). Thus, the devised predicting method could be used to predict the number of bacteria taking into consideration the complex of environmental variables in different food products. In addition, a given approach could be employed as artificial intelligence when assessing microbiological risks and for quick monitoring of food safety