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The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

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Honey bees are subject to a number of stressors. In recent years, there has been a worldwide decline in the population of these insects. Losses raise a serious concern, because bees have an indispensable role in the food supply of humankind. This work is focused on the method of assessment of honey bee colony infestation by Varroa destructor. The approach allows to detect several categories of infestation: “Low”, “Medium” and “High”. The method of detection consists of two components: (1) the measurements of beehive air using a gas sensor array and (2) classification, which is based on the measurement data. In this work, we indicate the sensitivity of the bee colony infestation assessment to the timing of measurement data collection. It was observed that the semiconductor gas sensor responses to the atmosphere of a defined beehive, collected during 24 h, displayed temporal variation. We demonstrated that the success rate of the bee colony infestation assessment also altered depending on the time of day when the gas sensor array measurement was done. Moreover, it was found that different times of day were the most favorable to detect the particular infestation category. This result could indicate that the representation of the disease in the beehive air may be confounded during the day, due to some interferences. More studies are needed to explain this fact and determine the best measurement periods. The problem addressed in this work is very important for scheduling the beekeeping practices aimed at Varroa destructor infestation assessment, using the proposed method.

The responses of a PQQ-GDH entrapped in a polymer structure to mixtures of glucose and maltose were evaluated. Each compound was considered in the concentration range of 0-0.2 mM. Imaging was performed at constant height in the enzymatic feedback mode of scanning electrochemical microscopy (SECM). The enzyme-polymer spot was discretized into 15 x 15 mum(2) substructures which were treated as independent individual microsensors. The response surfaces of the individual microsensors were approximated with a linear regression model. The coefficients in the derived equations represent contributions from topography, glucose concentration, maltose concentration, and the competition of glucose and maltose for the same active site of PQQ-GDH to the measured signal. The ratio of glucose and maltose contributions to the current at the SECM tip was constant for all microsensors and it was predominantly determined by the ratio of the turnover rates of both analytes in the PQQ-GDH catalyzed reaction. Using the difference between these coefficients, it was possible to select the microsensors within the overall enzyme-polymer spot that provided the best data for quantifying glucose and maltose by the artificial neural network used. The quantification of glucose and maltose was successful, except when the contributions from the components of the mixture were n (g)=k n units of glucose and simultaneously n (m)= 1.86(1-k)n units of maltose, for each constant n > 0 and k E <0,1>.

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