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.

[1]  M. Siadat,et al.  Reduction of drift impact in gas sensor response to improve quantitative odor analysis , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[2]  R. Einspanier,et al.  P3.10 Proof-of-concept trial of the portable electronic nose PEN3 for detection of formic acid concentration in the beehive , 2019, Tagungsband.

[3]  Robert Rusinek,et al.  Identification of Volatile Organic Compounds and Their Concentrations Using a Novel Method Analysis of MOS Sensors Signal. , 2019, Journal of food science.

[4]  Arjen Boersma,et al.  A Versatile Capacitive Sensing Platform for the Assessment of the Composition in Gas Mixtures , 2020, Micromachines.

[5]  S. S. Kim,et al.  A Novel X-Ray Radiation Sensor Based on Networked SnO2 Nanowires , 2019, Applied Sciences.

[6]  A. D. Wilson,et al.  Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases , 2018, Chemosensors.

[7]  Marek Molenda,et al.  Influence of Changes in the Level of Volatile Compounds Emitted during Rapeseed Quality Degradation on the Reaction of MOS Type Sensor-Array , 2020, Sensors.

[8]  Evor L. Hines,et al.  Prediction of health of dairy cattle from breath samples using neural network with parametric model of dynamic response of array of semiconducting gas sensors , 1999 .

[9]  Changsheng Xie,et al.  Metal-oxide-semiconductor based gas sensors: screening, preparation, and integration. , 2017, Physical chemistry chemical physics : PCCP.

[10]  E. Domashevskaya,et al.  APPLICATION OF SEMICONDUCTOR GAS SENSORS FOR MEDICAL DIAGNOSTICS , 1999 .

[11]  M. Katz Methods of Air Sampling and Analysis , 1977 .

[12]  Matteo Tonezzer,et al.  Selective gas sensor based on one single SnO2 nanowire , 2019, Sensors and Actuators B: Chemical.

[13]  Caspar Schöning,et al.  Evidence for damage-dependent hygienic behaviour towards Varroa destructor-parasitised brood in the western honey bee, Apis mellifera , 2012, Journal of Experimental Biology.

[14]  Yizhong Huang,et al.  Single-Nanowire Fuse for Ionization Gas Detection , 2019, Sensors.

[15]  Julian W. Gardner,et al.  A brief history of electronic noses , 1994 .

[16]  P. John Clarkson,et al.  Early detection of diseases in tomato crops: An Electronic Nose and intelligent systems approach , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[17]  Maryam Siadat,et al.  Orthogonal Signal Correction to Improve Stability Regression Model in Gas Sensor Systems , 2017, J. Sensors.

[18]  D. Shearer,et al.  Volatile Acids from Honeybee Larvae Infected with Bacillus Larvae and from a Culture of the Organism , 1981 .

[19]  Y. Le Conte,et al.  Potential mechanism for detection by Apis mellifera of the parasitic mite Varroa destructor inside sealed brood cells , 2002 .

[20]  A. D. Wilson,et al.  Development of conductive polymer analysis for the rapid detection and identification of phytopathogenic microbes. , 2004, Phytopathology.

[21]  Monika Maciejewska,et al.  Gas Sensor Array and Classifiers as a Means of Varroosis Detection , 2019, Sensors.

[22]  Monika Maciejewska,et al.  Semiconductor gas sensor as a detector of Varroa destructor infestation of honey bee colonies - Statistical evaluation , 2019, Comput. Electron. Agric..

[23]  Sam P. Brown,et al.  Synergistic Parasite-Pathogen Interactions Mediated by Host Immunity Can Drive the Collapse of Honeybee Colonies , 2012, PLoS pathogens.

[24]  Z. Huang,et al.  Varroa destructor changes its cuticular hydrocarbons to mimic new hosts , 2015, Biology Letters.

[25]  A. Szczurek,et al.  Detecting varroosis using a gas sensor system as a way to face the environmental threat. , 2020, The Science of the total environment.

[26]  Gunn,et al.  The transmission of deformed wing virus between honeybees (Apis mellifera L.) by the ectoparasitic mite varroa jacobsoni Oud , 1999, Journal of invertebrate pathology.

[27]  James D. Ellis,et al.  Standard methods for varroa research , 2013 .

[28]  M. Carroll,et al.  Collection of volatiles from honeybee larvae and adults enclosed on brood frames , 2012, Apidologie.

[29]  Anne-Claude Romain,et al.  Long term stability of metal oxide-based gas sensors for e-nose environmental applications: An overview , 2009 .

[30]  Ida A. Casalinuovo,et al.  Application of Electronic Noses for Disease Diagnosis and Food Spoilage Detection , 2006, Sensors (Basel, Switzerland).

[31]  Bryan A. Chin,et al.  Sensors for Agriculture and the Food Industry , 2010 .

[32]  P. Rosenkranz,et al.  Biology and control of Varroa destructor. , 2010, Journal of invertebrate pathology.