Detecting varroosis using a gas sensor system as a way to face the environmental threat

Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry

Volatile compounds not only contribute to the distinct flavors and aromas found in foods and beverages, but can also serve as indicators for spoilage, contamination, or the presence of potentially harmful substances. As the odor of food raw materials and products carries valuable information about their state, gas sensors play a pivotal role in ensuring food safety and quality at various stages of its production and distribution. Among gas detection devices that are widely used in the food industry, metal oxide semiconductor (MOS) gas sensors are of the greatest importance. Ongoing research and development efforts have led to significant improvements in their performance, rendering them immensely useful tools for monitoring and ensuring food product quality; however, aspects related to their limited selectivity still remain a challenge. This review explores various strategies and technologies that have been employed to enhance the selectivity of MOS gas sensors, encompassing the innovative sensor designs, integration of advanced materials, and improvement of measurement methodology and pattern recognize algorithms. The discussed advances in MOS gas sensors, such as reducing cross-sensitivity to interfering gases, improving detection limits, and providing more accurate assessment of volatile organic compounds (VOCs) could lead to further expansion of their applications in a variety of areas, including food processing and storage, ultimately benefiting both industry and consumers.

Automated, non-invasive Varroa mite detection by vibrational measurements of gait combined with machine learning

Little is known about mite gait, but it has been suggested that there could be greater variation in locomotory styles for arachnids than insects. The Varroa destructor mite is a devastating ectoparasite of the honeybee. We aim to automatically detect Varroa-specific signals in long-term vibrational recordings of honeybee hives and additionally provide the first quantification and characterisation of Varroa gait through the analysis of its unique vibrational trace. These vibrations are used as part of a novel approach to achieve remote, non-invasive Varroa monitoring in honeybee colonies, requiring discrimination between mite and honeybee signals. We measure the vibrations occurring in samples of freshly collected capped brood-comb, and through combined critical listening and video recordings we build a training database for discrimination and classification purposes. In searching for a suitable vibrational feature, we demonstrate the outstanding value of two-dimensional-Fourier-transforms in invertebrate vibration analysis. Discrimination was less reliable when testing datasets comprising of Varroa within capped brood-cells, where Varroa induced signals are weaker than those produced on the cell surface. We here advance knowledge of Varroa vibration and locomotion, whilst expanding upon the remote detection strategies available for its control.

Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor

Metal oxide semiconductor (MOS) gas sensors have many advantages, but the main obstacle to their widespread use is the cross-sensitivity observed when using this type of detector to analyze gas mixtures. Thermal modulation of the heater integrated with a MOS gas sensor reduced this problem and is a promising solution for applications requiring the selective detection of volatile compounds. Nevertheless, the interpretation of the sensor output signals, which take the form of complex, unique patterns, is difficult and requires advanced signal processing techniques. The study focuses on the development of a methodology to measure and process the output signal of a thermally modulated MOS gas sensor based on a B-spline curve and artificial neural networks (ANNs), which enable the quantitative analysis of volatile components (ethanol and acetone) coexisting in mixtures. B-spline approximation applied in the first stage allowed for the extraction of relevant information from the gas sensor output voltage and reduced the size of the measurement dataset while maintaining the most vital features contained in it. Then, the determined parameters of the curve were used as the input vector for the ANN model based on the multilayer perceptron structure. The results show great usefulness of the combination of B-spline and ANN modeling techniques to improve response selectivity of a thermally modulated MOS gas sensor.

Volatile biomarkers for non-invasive detection of American foulbrood, a threat to honey bee pollination services

Honey bees provide essential environmental services, pollinating both agricultural and natural ecosystems that are crucial for human health. However, these pollination services are under threat by outbreaks of the bacterial honey bee disease American foulbrood (AFB). Caused by the bacterium, Paenibacillus larvae, AFB kills honey bee larvae, converting the biomass to a foul smelling, spore-laden mass. Due to the bacterium's tough endospores, which are easily spread and extremely persistent, AFB management requires the destruction of infected colonies in many countries. AFB detection remains a significant problem for beekeepers: diagnosis is often slow, relying on beekeepers visually identifying symptoms in the colony and molecular confirmation. Delayed detection can result in large outbreaks during high-density beekeeping pollination events, jeopardising livelihoods and food security. In an effort to improve diagnostics, we investigated volatile compounds associated with AFB-diseased brood in vitro and in beehive air. Using Solid Phase Microextraction and Gas Chromatography Mass-Spectrometry, we identified 40 compounds as volatile biomarkers for AFB infections, including 16 compounds previously unreported in honey bee studies. In the field, we detected half of the biomarkers in situ (in beehive air) and demonstrated their sensitivity and accuracy for diagnosing AFB. The most sensitive volatile biomarker, 2,5-dimethylpyrazine, was exclusively detected in AFB-disease larvae and hives, and was detectable in beehives with <10 AFB-symptomatic larvae. These, to our knowledge, previously undescribed biomarkers are prime candidates to be targeted by a portable sensor device for rapid and non-invasive diagnosis of AFB in beehives.

Varroa destructor from the Laboratory to the Field: Control, Biocontrol and IPM Perspectives-A Review

Varroa destructor is a real challenger for beekeepers and scientists: fragile out of the hive, tenacious inside a bee colony. From all the research done on the topic, we have learned that a better understanding of this organism in its relationship with the bee but also for itself is necessary. Its biology relies mostly on semiochemicals for reproduction, nutrition, or orientation. Many treatments have been developed over the years based on hard or soft acaricides or even on biocontrol techniques. To date, no real sustainable solution exists to reduce the pressure of the mite without creating resistances or harming honeybees. Consequently, the development of alternative disruptive tools against the parasitic life cycle remains open. It requires the combination of both laboratory and field results through a holistic approach based on health biomarkers. Here, we advocate for a more integrative vision of V. destructor research, where in vitro and field studies are more systematically compared and compiled. Therefore, after a brief state-of-the-art about the mite's life cycle, we discuss what has been done and what can be done from the laboratory to the field against V. destructor through an integrative approach.

Beehive Air Sampling and Sensing Device Operation in Apicultural Applications-Methodological and Technical Aspects

The basis of effective beekeeping is the information about the state of the bee colony. A rich source of respective information is beehive air. This source may be explored by applying gas sensing. It allows for classifying bee colony states based on beehive air measurements. In this work, we discussed the essential aspects of beehive air sampling and sensing device operation in apicultural applications. They are the sampling method (diffusive vs. dynamic, temporal aspects), sampling system (sample probe, sampling point selection, sample conditioning unit and sample delivery system) and device operation mode ('exposure-cleaning' operation). It was demonstrated how factors associated with the beehive, bee colony and ambient environment define prerequisites for these elements of the measuring instrument. These requirements have to be respected in order to assure high accuracy of measurement and high-quality information. The presented results are primarily based on the field measurement study performed in summer 2020, in three apiaries, in various meteorological conditions. Two exemplars of a prototype gas sensing device were used. These sensor devices were constructed according to our original concept.

Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors

Varroosis is a dangerous and difficult to diagnose disease decimating bee colonies. The studies conducted sought answers on whether the electronic nose could become an effective tool for the efficient detection of this disease by examining sealed brood samples. The prototype of a multi-sensor recorder of gaseous sensor signals with a matrix of six semiconductor gas sensors TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from FIGARO was tested in this area. There were 42 objects belonging to 3 classes tested: 1st class—empty chamber (13 objects), 2nd class—fragments of combs containing brood sick with varroosis (19 objects), and 3rd class—fragments of combs containing healthy sealed brood (10 objects). The examination of a single object lasted 20 min, consisting of the exposure phase (10 min) and the sensor regeneration phase (10 min). The k-th nearest neighbors algorithm (kNN)—with default settings in RSES tool—was successfully used as the basic classifier. The basis of the analysis was the sensor reading value in 270 s with baseline correction. The multi-sensor MCA-8 gas sensor signal recorder has proved to be an effective tool in distinguishing between brood suffering from varroosis and healthy brood. The five-time cross-validation 2 test (5 × CV2 test) showed a global accuracy of 0.832 and a balanced accuracy of 0.834. Positive rate of the sick brood class was 0.92. In order to check the overall effectiveness of baseline correction in the examined context, we have carried out additional series of experiments—in multiple Monte Carlo Cross Validation model—using a set of classifiers with different metrics. We have tested a few variants of the kNN method, the Naïve Bayes classifier, and the weighted voting classifier. We have verified with statistical tests the thesis that the baseline correction significantly improves the level of classification. We also confirmed that it is enough to use the TGS2603 sensor in the examined context.