Method for detecting bed bugs

Abstract

The present invention relates to a method for detecting bed bugs in a premises, which method is characterised by the steps of: - selecting a group of target volatile organic compounds (VOCs); - using the group of target volatile organic compounds to construct a decision tree according to the correlation between the volatile organic compound and the presence of bed bugs; and - collecting the air in at least one determined location of a premises; - extracting the volatile organic compounds from the group of volatile organic compounds that are present in the collected air; and - traversing the decision tree according to the concentrations of the volatile organic compounds that are present in the collected air in order to infer therefrom the absence or presence of bed bugs in the premises.

Description

Bedbug Detection Method

[0001] The present invention relates to the technical field of the detection of harmful insects and relates to a method for detecting bedbugs, in particular in premises (housing or indoor environments).

[0002] Bedbugs are blood-sucking insects that are ectoparasites of humans and have been on the rise since the late 1990s in industrialized countries. The resurgence of this harmful insect is observed on all continents, including in France. The increase in national and international travel of people and the resistance of bedbugs to the main authorized insecticides partly explain the increase in the number of new outbreaks of infestation.

[0003] Such biting / sucking insects are often vectors of disease, although no vector-borne disease specifically incriminating bedbugs has been described to date. However, the consequences, whether allergic or psychological, may require medical attention.

[0004] Most indoor environments occupied by humans can be affected by bedbugs: homes, hotels, transport, cinemas, healthcare establishments, etc.

[0005] Bed bug treatment can be expensive and the budget to spend is difficult to predict.

[0006] The usual techniques for detecting and monitoring the presence of these harmful insects, complex and expensive, rely on a thorough visual inspection based on the search for insects and their droppings by an expert and, to a lesser extent, canine detection. This last approach gives very good results due to the very good olfactory detection capacity of dogs. However, the availability of animals trained daily with a single reference handler and their ability to concentrate for significant periods of time do not allow for a mass scale of the practice.

[0007] In this context, early detection and / or post-remediation monitoring of bedbugs constitutes a major issue.

[0008] For many years, the Applicant has been developing indices based on the presence / absence of volatile organic compounds in the air specific to biological agents harmful to buildings, property and / or people. These allow for the early diagnosis of infestations of fungal or entomological origin that are not detected / detectable visually, such as the presence of mold, dry rot or insects. These tools are complementary to the usual detection techniques based essentially on visual inspection by an expert. These previous indices are based on a calculation that takes into account the presence / absence of volatile chemical targets with or without weighting. Such techniques are for example described in French patent applications FR3075964, FR2913501 and FR3028043.

[0009] Methods for detecting bedbugs according to the prior art are described in European patent applications EP2756755A2, EP3629723B1, and the document "Hygiene, disinfection and 3D. Social landlords innovate against bedbugs", 20 / 11 / 2019, https: / / www.batiment-entretien.fr / actualite / hygiene-desinfection-et-3d-les-bailleurs-sociaux-innovent-contre-les-punaises (extracted on 12 / 09 / 2023).

[0010] The paper "Simultaneous sampling and analysis of indoor air infested with Cimex lectularius L. (Hemiptera: Cimicidae) by solid phase microextraction, thin film microextraction and needle trap device" (Simultaneous sampling and analysis of indoor air infested with Cimex lectularius L. (Hemiptera: Cimicidae) by solid phase microextraction, thin film microextraction and needle trap device), Analytica Chimica Acta, vol. 715, 06 / 07 / 2011, pages 2-10, ISSN 0003-2670 describes a method for characterizing indoor air in a room using three different methods: SPME fiber coatings, thin film microextraction devices, and trapping devices. The sampled air is analyzed by gas chromatography / mass spectrometry (GC-MS).

[0011] Thus, although the bedbug is a major biological agent, no evidence, particularly chemical, has been developed to date, or only with inconclusive results.

[0012] The diversity of volatile organic compounds emitted by bedbugs makes an exhaustive, expensive and complex analysis.

[0013] The present invention therefore relates to a method for detecting bedbugs in a room, characterized in that it comprises the steps consisting of:

[0014] - select a group of target volatile organic compounds (VOCs);

[0015] - construct with the group of target volatile organic compounds a decision tree based on the correlation between each volatile organic compound of the group of target volatile organic compounds and the presence of bedbugs, the inputs of the constructed decision tree being discriminating organic compounds representing a subgroup of the target organic compounds; and, for each detection of bedbugs in a room:

[0016] - take air from at least one specific location in the room;

[0017] - extract volatile organic compounds from the group of volatile organic compounds present in the sampled air; and

[0018] - go through the decision tree based on the concentrations of discriminating volatile organic compounds present in the sampled air to deduce the absence or presence of bedbugs in the room.

[0019] The leaves of the decision tree each correspond to either the presence or the absence of bedbugs, such that the journey through the decision tree with the concentrations of discriminating volatile organic compounds found allows us to conclude on the presence or absence of bedbugs at the end of the journey through the decision tree.

[0020] The method of the present invention, the decision tree being constructed once and for all, is therefore parsimonious in that it makes it possible to analyze in the sampled air only the volatile organic compounds discriminating from the group of target volatile organic compounds, which reduces the complexity and cost of analysis, to obtain a reliable detection result. The decision tree is established only once and will be used to detect the absence or presence of bedbugs in any room. In other words, the decision tree is independent of the room for which it is desired to detect the absence or presence of bedbugs.

[0021] Furthermore, unlike previous techniques (visual examination, canine detection), the method of the invention is minimally invasive, since a simple air sample in the room is sufficient, with a very good probability of detecting the presence of bedbugs and not subject to the variability of the operators and their level of expertise.

[0022] The premises may be any premises regardless of their use (residential use (private, collective housing, hotel), educational use, office use, professional use, commercial use, leisure use (cinema, performance halls), etc.) and may preferably be a sleeping room or a room adjacent to the sleeping room.

[0023] According to one embodiment, the selection of the group of target volatile organic compounds comprises the steps of: sampling and identifying the volatile organic compounds present in at least one breeding environment, the presence of bedbugs and their stage of development in said at least one breeding environment being known; sampling and identifying the volatile organic compounds (VOCs) present in at least one calibration room, the presence or absence of bedbugs in said at least one calibration room being known.

[0024] The selection of the group of target volatile organic compounds is therefore made robust, initially, by an analysis of the volatile organic compounds present in a breeding environment, in the laboratory, then in a calibration room, in particular in premises, which makes it possible to establish the correlation between the presence or absence of bedbugs and the presence of a volatile organic compound in the group of target organic compounds. This second step is generally carried out on a panel of premises with good statistical representativeness, for example on a panel of around a hundred premises.

[0025] According to one embodiment, in step A, the different stages of development are: eggs, larvae and adults.

[0026] According to one embodiment, the discriminating organic compounds are chosen from 6-methyl-5-hepten-2-one (6-MHO), 1-hexanol, (2E)-octenal, dimethyl disulfide (DMDS), E-geranylacetone, 2-pentanone, 3-methyl-butanal, acetophenone, hexanal, nonanal, decanal, optionally from benzaldehyde, benzyl acetate, hexane, 2-n-butylfuran, (2E, 4Z)-octadienal, 3-(N-methyl-2-pyrrolidinyl)pyridine, (2E)-butenal, acetone, phenylmethanol, 3-methyl-2-butenal, (2E, 4E)-heptadienal.

[0027] According to one embodiment, the decision tree is constructed by a CART algorithm (Classification And Regression Tree) using as variables the target volatile organic compounds (VOCs) and as segmentation criterion the Gini diversity index (defined for example here: https: / / www.insee.fr / fr / metadonnees / definition / c1551), by successive selection of the variables taken one by one and best separating the sample with respect to the presence or absence of bedbugs, by testing the possible cut-off points for each variable and by selecting the cut-off threshold which maximizes the segmentation criterion, with at each level of the tree an average Gini index G which accounts for the loss of information associated with a volatile organic compound j (VOCs j ) and a cut-off threshold c:

[0028]

[0029] with :

[0030]

[0031]

[0032] : number of premises for which the values ​​of X j <c du COV j

[0033] : number of premises for which the values ​​of X j ≥c of the COV j

[0034] : frequency of the i-th modality of the variable to be predicted (modality 0 for absence and modality 1 for presence of bedbugs) for the premises verifying X j <c du COV j

[0035] : frequency of the i-th modality of the variable to be predicted (modality 0 for absence and modality 1 for presence of bedbugs) for the premises verifying X j ≥c of the COV j ,

[0036] M: the number of modalities to predict which is 2 (modality 0 – absence and modality 1 – presence of bedbugs)

[0037] n: the total number of premises,

[0038] then we measure the improvement index associated with each division by calculating J:

[0039] with , f i denoting the frequency of the i-th modality of the binary variable associated with the presence / absence of bedbugs, determined at the parent node (before division) and M the number of modalities to be predicted, the volatile organic compound having the highest J value being retained for the corresponding division of the tree with its associated threshold value c.

[0040] According to one embodiment, the minimum segmentation number of the node is set to 5, the maximum number of levels of the tree is 10, the specialization threshold or stopping criterion is 1, the admissibility number is 1 calibration location, and the tree is without pruning.

[0041] According to one embodiment, the air sampling is carried out on a thermodesorbable adsorbent, preferably Tenax ®TA, with a sampling volume of between 9 and 15 liters of air, which corresponds to a duration of 1 hour at a flow rate of 150 to 250 mL / min, preferably 10 liters of air.

[0042] According to one embodiment, the volatile organic compounds are trapped on a tube containing the adsorbent during sampling and are analyzed by a thermodesorption – gas chromatography – mass spectrometer (TD-GC-MS) technique. Adsorbents suitable for a solvent desorption technique could be used, however, adapting the sampling volume so as to obtain the same performance in terms of detection limit of the target VOCs as thermodesorption.

[0043] According to one embodiment, the air is taken from the calibration or diagnostic room, typically in a sleeping room or an immediately adjacent room suspected of harboring bedbugs.

[0044] The present invention therefore differs from the prior art in that it is less costly, more repeatable, and less intrusive. It was not obvious to those skilled in the art, knowing the techniques for detecting volatile organic compounds applied to the detection of other insects, that the detection of volatile organic compounds could enable the detection of bedbugs. Furthermore, the methods of the prior art have been transposed to bedbugs, but have led to greater computational complexity than the decision tree according to the present invention, which makes the method of the present invention more immediately applicable and less expensive.

[0045] The detection method according to the present invention can be used for the purpose of diagnosing the presence or absence of bedbugs and / or for the purpose of monitoring the presence or absence of bedbugs after treatment.

[0046] The Applicant has therefore invented an innovative detection method using volatile chemical tracers specific to bedbugs. According to a particular embodiment, the air sample taken in a room is analyzed, preferably by a TD-GC-MS chemical analysis technique, in order to determine the concentration of target volatile organic compounds (VOCs). Then, a statistical analysis, preferably by binary decision tree using the CART algorithm, makes it possible to determine whether a bedbug infestation is in progress or not.

[0047] To better illustrate the subject matter of the present invention, a preferred embodiment of the invention will now be described, with reference to the accompanying drawing. In this drawing:

[0048] is a binary decision tree obtained according to the method of the present invention.

[0049] The method of the invention will now be described in more detail in its different phases.

[0050] For example, in a first phase of the method, the chemical fingerprint of bedbugs is characterized in vitro in the laboratory, more precisely in a laboratory breeding environment. The volatile organic compounds present in at least one breeding environment are sampled and identified, the presence of bedbugs and their stage of development in said at least one breeding environment being known.

[0051] A known population of bedbugs (Cimex lectularius) at different stages of their development (eggs, larvae and adults) is placed in an insectary suitable for collecting VOC emissions in the air, also called a static emission chamber. Air samples on adsorbent tubes followed by analysis by thermodesorption – gas chromatography – mass spectrometry (TD-GC-MS) then allow these emissions to be characterized.

[0052] The species Cimex lectularius used in the experiments is sensitive to insecticides. The bugs are reared in polypropylene bottles containing Whatman filter paper. ®folded like an accordion. This support allows the bedbugs to aggregate and / or hide. The cap is fitted with a fine-mesh synthetic fabric, which allows air to pass through and allows the insects to feed. The bedbugs are incubated at 24°C, at a relative humidity of 60% and a 12 h / 12 ​​h photoperiod is imposed on them. The bedbugs are artificially "gorged" with human blood, previously heated (37°C), through a polymer membrane (Parafilm ® ) twice a week. Under these conditions, the complete development cycle (from egg to adult stage) is approximately 1 month. The bedbugs are sorted (eggs, larvae and female and male adults), extemporaneously, before being placed in the static emission chamber.

[0053] During the duration of the experiments (3, 7 and / or 14 days) in a static emission chamber, the thermohydric conditions are as follows: temperature of 22 ± 2 °C, relative humidity of approximately 50% and natural photoperiod.

[0054] VOCs produced during life in the bedbug emission collection chamber are collected on adsorbent tubes.

[0055] Air sampling is active, carried out using a mass flow pump. The sampling flow rate is set at 100 ml / min and the sampling time is sufficient to renew the 300 ml chamber volume at least thirty times. The tubes are then analyzed by TD-GC-MS.

[0056] Air samples are taken from two different types of tubes, shown in Table 1:

[0057] Tube referenceNature of adsorbentsRange of identifiable VOCs (number of carbon atoms)TTATenax ® TAC6~C 26MIXEDCarbosieve ® SIIIC2~C4Tenax ® TAC6~C 26

[0058] TTA tubes contain only Tenax ® TA, which is the adsorbent presenting a wide range of identifiable compounds (C6~ C 26 ). The combination of adsorbents in MIXED tubes, which contain the same amount of Tenax ® TA than TTA tubes, allows to expand this range (C2~ C 26 ) by integrating highly volatile organic compounds thanks to the presence of Carbosieve ® SIII.

[0059] The analysis of the VOCs collected on the different adsorbents is carried out by chromatography following three phases:

[0060] 1) Extraction by thermodesorption of compounds adsorbed on the collection tubes (TD)

[0061] 2) Separation of compounds by Gas Chromatography (GC)

[0062] 3) Identification of compounds by Mass Spectrometry (MS)

[0063] The analytical chain used is a Perkin Elmer chain ® including an automatic thermodesorber (Turbomatrix ® 650), a gas chromatograph (Clarus ® 580) equipped with a capillary column, as well as a mass spectrometer (Clarus ® SQ8S).

[0064] Compounds are identified by comparing their mass spectra with those of standards available in the NIST International Mass Spectral Library (2012).

[0065] Quantification of each compound is performed relative to their own response factor (i.e., specific quantification) or relative to the response factor of toluene (i.e., relative quantification in toluene equivalent). Specific quantification involves the performance of dedicated calibration ranges for each compound considered.

[0066] The analytical conditions applied comply with the standards:

[0067] - NF EN ISO 16000-6 (2012): Indoor air - Part 6: Determination of volatile organic compounds in the indoor air of premises and test chambers by active sampling on the Tenax TA sorbent ® , thermal desorption, and gas chromatography using MS or MS / FID (AFNOR 2012)

[0068] - NF EN 16516 (2017): Construction products: assessment of the emission of dangerous substances - Determination of emissions into indoor air (AFNOR 2017).

[0069] The parameters of the generic TD-GC-MS analysis method used for VOC analysis are as follows:Thermal desorption (TD) extraction:

[0070] Tube desorption: 280°C for 20 minutes at a flow rate of 50 mL / min of nitrogen

[0071] Inlet split = 0 mL / min

[0072] Trap temperature (Tenax TA ® ) during desorption: -30°C

[0073] Trap temperature during injection: from -30 to 280°C with a ramp of 40°Cs -1 then keep at 280°C for 10 min

[0074] Transfer line and injection valve temperature: 210°C

[0075] Column injection, Outlet split: 7 mL / minGas chromatography (GC):

[0076] Carrier gas: Helium

[0077] Capillary column: Elite 5 ms 60 mx 0.25 mm x 1 μm (5% diphenyl dimethylpolysiloxane) (Perkin Elmer)

[0078] Temperature program: from 0 min to 5 min, temperature of 40°C; then from 5 min, temperature ramp of 2.5°C / min up to 170°C; then from 73 min, temperature ramp of 7.5°C / min up to 300°C; maintained for 26.34 min.

[0079] Analysis time: 100 minutesMass spectrometry (MS):

[0080] Ionization mode: electron impact (El+), full scan from 33 to 550 m / z.

[0081] A preliminary bibliographic work allows to establish a list of potential target tracer compounds of Cimex lectularius: acetone, propanal, 2-butanone, dimethyl disulfide, (2E)-hexenal, hexanal, benzaldehyde, benzyl alcohol, heptanal, (2E,4Z)-octadienal, (2E,4E)-octadienal, 6-methyl-5-hepten-2-one, (2E)-octenal, octanal, D-limonene, nonanal, benzyl acetate, decanal, undecanal, (E)-geranylacetone, (Z)-geranylacetone.

[0082] From the standard standards of these compounds (when available), a specific calibration is carried out in order to (i) have the retention time and mass spectrum of the compound under the analytical conditions used in the laboratory conditions of this first phase and thus be able to confirm its identification and (ii) determine its own analytical response factor allowing it to be quantified specifically. This calibration also makes it possible to define the limits of detection and quantification of each compound.

[0083] Four experiments (PS, S1, S2 and S3) are then carried out in the laboratory, to exhaustively characterize the VOC emissions with regard to the development stage of the insects (eggs, larvae and adults), after different incubation times and by trapping the emissions generated on two different types of adsorbent tubes, the first experiment (PS) making it possible to highlight the emissions of volatile compounds by a reduced mixed population consisting of larvae and adults. The experiments are indicated in Table 2 below, representing the results of the experiments carried out specifying the numbers of insects, the nature of the adsorbents used to collect the VOC emissions and the duration of the incubations in chambers before collection.

[0084] ChamberChamber ContentsDifferent ExperimentsPSS1S2S31Filter Paper (PF)XXXX2PF + eggs-Count: 110--3PF + engorged larvae from stage 1 to 5---Count: 1004PF + Adult females and males-Count: 25 females + 25 malesCount: 25 females + 25 malesCount: 25 females + 25 males5PF + Feces from chamber 4-XXX6PF + Adults (A) and larvae (L)Count: 13A + 5L---Tube TypeTTAXXXMIXEDXXXSample taken after3 and 14 days7 days7 days7 days

[0085] The identification of VOC emissions by bedbugs from samples taken in the collection rooms is organized in two phases: a first phase of systematic research of VOCs from the bibliography indicated above and quantified specifically, and a second phase of research of other potential VOC tracers of bedbugs, by individual identification of each peak and quantification in toluene equivalent.

[0086] Peaks corresponding to compounds detected only in the presence of bedbugs are selected and identified on the chromatograms obtained by comparison of their retention time and analysis of their mass spectrum with those available in particular in the international library (NIST).

[0087] A summary of the results is presented below.

[0088] Samples taken from static emission chambers containing the same number of adult insects, in the different series, do not systematically lead to the same VOCs identified and / or quantified in equivalent proportions. These differences can be explained by the heterogeneity of the biological material used in the different experiments which is never entirely in the same physiological state, despite the rigor implemented in the reproducibility of the method.

[0089] The S1 series allows the study of VOC emissions from adults, eggs and droppings. Overall, when VOCs are identified in adults in significant quantities (greater than 1 ng / tube under the test conditions), they are systematically identified in eggs and / or for droppings but in lower relative quantities. However, this trend is not observed for two molecules (2-butanone and hexane) which are found in significantly higher quantities in the chamber containing eggs. Regarding droppings, it is worth noting the persistence of two specific bedbug tracers, (2E)-hexenal and (2E)-octenal, whose quantities remain significant (~500 ng / tube) even in the absence of insects.

[0090] The results obtained on the TTA and MIXED tubes are qualitatively identical, the same VOCs are identified (S2 series after 15 days of life in the bedbug room). Even if the quantitative information differs depending on the nature of the adsorbent, it always remains above the quantification limits.

[0091] As in the case of droppings, VOCs present in adults are frequently found in larvae in lower quantities. The amount of (2E)-hexenal in larvae is drastically lowered while the amount of (2E)-octenal is almost identical, for a population of 50 adults or 100 larvae in static emission chambers. (2E)-octenal is described as an alarm, defense and aggregation pheromone, hence its production by the larval and adult stages.

[0092] The experiments carried out in this laboratory approach allow us to characterize the volatile chemical footprint of Cimex lectularius with a sampling methodology using different adsorbents, coupled with a TD / GC / MS analysis method. In total, 45 VOCs are identified, including 21 already described in the literature.This approach allows the identification of 26 new molecules, thus contributing to a more complete characterization of the volatile chemical fingerprint of Cimex lectularius: acetone, propanal, acetic acid, 2-butanone, butanal, 2-pentanone, pentanal, 3-methyl-butanal, hexane, 3-methyl-2-butenal, phenol, dimethyl disulfide, (2E,4E)-hexadienal, (2E)-hexenal, 3-hexenal, hexanal, (2E)-hexen-1-ol, benzaldehyde, benzyl alcohol, (2E,4E)-heptadienal, heptanal, 2-heptanone, 1-hexanol, acetophenone, (2E,4Z)-octadienal, (2E,4E)-octadienal, 2-n-butyl furan, 6-methyl-5-hepten-2-one, (2E)-octenal, 1-nonene, dimethyl trisulfide, octanal, 2-ethyl-1-hexanol, d-limonene, nonanal, 2-nonen-1-ol, 1-nonanol, benzyl acetate, decanal, dihydromyrcenol, 2-decen-1-ol, undecanal, E-geranylacetone, Z-geranylacetone, and 2,4,4-trimethyl-3-(3-methylbutyl)cyclohex-2-enone.

[0093] The quantification of these bedbug tracer VOCs reveals a wide range of values ​​from 1 ng (or even below, i.e. at the detection limit) to nearly 2000 ng detected in the sampling tubes.

[0094] The VOCs identified as insect-specific in this first laboratory phase may also potentially come from other sources in indoor environments: emissions from other insects, microorganisms, construction and decoration products, cleaning products, etc. These other potential sources, which may or may not have biological origins, are called confounding factors.

[0095] Thus, in insects, (E)-2-hexenal has been identified in two species of cockroaches, Blatta orientalis and Periplanata americana (Brossut, R, 1983, “Allomonal Secretions in Cockroaches,” Journal of Chemical Ecology 9 (1): 143-58, https: / / doi.org / 10.1007 / BF00987778; Krivosheina, GG, and KS Shatov, 1995, “Functions of the cockroach (Blattidae) sternal gland,” which are insect pests that can be found in indoor environments. In addition, (E)-2-octenal is a VOC that can be found in certain fungi (Lacaze, Isabelle, 2016, “Study of the mechanisms of colonization of construction products by micromycetes”, Université Paris Diderot - Paris 7) which can also be contaminants found in enclosed spaces.

[0096] To go further, a search for the two most emblematic substances emitted by bedbugs, (2E)-hexenal and (2E)-octenal, was carried out in the PANDORE database (comPilAtioN of inDOor aiR pollutAnt emissions, Abadie MO., Blondeau P (2011): PANDORA database: A compilation of indoor air pollutant emissions, HVAC&R Research, 17:4, 602-613). The database includes the specific emission parameters of VOCs and particles of 599 household products and materials, from a literature review covering the period 1982-2014.

[0097] At the end of this first laboratory phase, and after studying the potential confounding factors indicated above, the VOCs are categorized in order to account for their relevance. The categories are defined as follows:

[0098] • Category 1: VOCs specific to bedbugs, according to the literature and / or experiments carried out in this first phase, possibly having an origin other than that of bedbugs but rarely observed in homes and therefore relevant: acetone, 2-butanone, 2-pentanone, dimethyl disulfide, (2E)-hexenal, 3-hexenal, hexanal, (2E)-hexen-1-ol, benzaldehyde, heptanal, 1-hexanol, (2E,4Z)-octadienal, (2E,4E)-octadienal, 2-n-butyl furan, (2E)-octenal, dimethyl trisulfide, octanal, nonanal, 2-nonen-1-ol, decanal;

[0099] • Category 2: VOCs specific to bedbugs according to the experiments carried out in this first phase, but to date not described in the literature and possibly having an origin other than that of bedbugs. These VOCs are generally observed in homes and therefore have relative relevance: 3-methyl-2-butenal, (2E,4E)-hexadienal, (2E,4E)-heptadienal, 2-heptanone, 1-nonanol, and 2,4,4-trimethyl-3-(3-methylbutyl)cyclohex-2-enone;

[0100] • Categories 3: Ubiquitous VOCs found very frequently in French homes and almost systematically coming from an origin other than that of bedbugs: butanal, pentanal, 3-methyl-butanal, hexane, acetophenone, 2-ethyl-1-hexanol, dihydromyrcenol.

[0101] The more frequently a VOC is found in homes, the more its relevance to bedbugs can be questioned. Thus, sixteen substances are systematically detected, five substances are very often detected (more than 40% of the time) and six substances are less often detected (between 1% and 9% of the time).

[0102] Acetone is strongly associated with human metabolism and therefore with the presence of occupants. It is therefore not considered a specific target for bedbugs because of numerous confounding factors. Similarly, propanal is not systematically tested for because it is measured using the same analytical method as acetone, but it is generally observed at high concentrations both indoors and outdoors. It is therefore also not considered specific to bedbugs.

[0103] Only 18 VOCs are never detected in new housing. Among these VOCs, 8 are substances identified in the bibliography and 10 are considered relevant at the end of the experimental phase. The VOCs that are both experimentally relevant and previously observed in other studies are four in number, these are the following compounds: (2E)-hexenal, (2E)-octenal and the 2 isomers of octadienal (2E,4Z) and (2E,4E).

[0104] Other non-bed bug-specific VOCs may nevertheless be useful for managing confounding factors.

[0105] In a second phase of the process, the volatile organic compounds (VOCs) present in at least one calibration room (a statistically representative panel of inhabited premises) are sampled and identified, the presence or absence of bedbugs in these calibration rooms being known. The Applicant company carried out this phase on a panel of 102 premises.

[0106] In this second phase, the active sampling methodology employed is based on the use of two types of sampling tubes (TTA and MIXED) associated with the analysis methodology presented and deployed in the first phase of the process. This second phase allows the validation of the list of VOCs retained during the first phase.

[0107] Under realistic environmental conditions, the targets (2E)-hexenal and (2E)-octenal are found in infested or formerly infested premises. However, (2E)-hexenal is never detected in premises not infested by bedbugs and is quantified specifically at masses between 1 and 24 ng in infested or formerly infested premises. This is not the case for (2E)-octenal which is detected or not whether the infestation is non-existent, ongoing or past, at very variable quantities between 0.02 ng and 60 ng.

[0108] The following molecules: (2E,2Z)-octadienal, (2E,2E)-octadienal, 3-hexenal, (2E)-hexen-1-ol, 1-nonanol, dimethyl trisulfide and 2,4,4-triethyl-1-hexene were not identified in the premises, whether they were uninfested, previously infested or infested. These targets, although detected in the first phase of the process, were not found during the second phase of the process and were therefore not retained.

[0109] All other molecules are identified at varying concentrations.

[0110] In this second phase of the process, the relevant targets identified at the end of the first phase are again classified into three categories in order to develop them according to their relevance for a bedbug infestation:

[0111] • A: relevant target: 3-methyl-2-butenal, dimethyl disulfide, (2E,4E)-hexadienal, (2E)-hexenal, benzyl alcohol, (2E,4E)-heptadienal, 2-n-butyl furan, (2E)-octenal, undecanal, Z-geranylacetone, E-geranylacetone;

[0112] • B: potentially relevant target: 2-pentanone, 2-heptanone;

[0113] • C: optional target, not to be excluded, potentially relevant: 3-hexenal, (2E)-hexen-1-ol, (2E,4Z)-octadienal, (2E,4E)-octadienal, dimethyl trisulfide, and 2,4,4-trimethyl-3-(3-methylbutyl)cyclohex-2-enone.

[0114] This classification takes into account the results obtained on TTA tubes, which provide significantly more qualitative information than MIXED tubes.

[0115] A statistical analysis of the data is then carried out.

[0116] At the end of the first two phases, we arrive at a list of 40 target VOCs, with VOCs never encountered in real situations being eliminated.

[0117] A predictive model is then built using a CART classification tree fed with input data from the concentration data of the 40 target VOCs (continuous variables) measured in 102 premises, some of which are infested. This is a parsimonious model that will select the most relevant variables.

[0118] The model building process is done by successively selecting the variables taken one by one and best separating the sample with regard to the variable to be predicted (binary variable of presence / absence of bedbugs). This binarization process makes it possible to establish a decision tree, with at each level a variable selected allowing the best separation of the observations, until only the purest terminal nodes are obtained (composed solely of observations of absence cases or of observations of presence of bedbugs).

[0119] The criterion for selecting variables at each level is based on the Gini index by testing all possible cut-off points for each variable and deducing an optimal cut-off threshold that maximizes the segmentation criterion.

[0120] At each level of the tree, there is an average Gini index G which accounts for the loss of information associated with a COV j and a cut-off threshold c:

[0121] With :

[0122]

[0123]

[0124] : number of premises for which the values ​​of Xj <c du COVj

[0125] : number of premises for which the values ​​of Xj≥c of the COVj

[0126] : frequency of the i-th modality of the variable to be predicted (modality 0 for absence and modality 1 for presence of bedbugs) for the premises verifying Xj <c du COVj

[0127] : frequency of the i-th modality of the variable to be predicted (modality 0 for absence and modality 1 for presence of bedbugs) for the premises verifying Xj≥c of the COVj,

[0128] M: the number of modalities to predict which is 2 (modality 0 – absence and modality 1 – presence of bedbugs)

[0129] n: the total number of premises,

[0130] The objective of the model is to minimize the loss of information at each division of the tree and therefore that the value G(j,c) is as low as possible. To this optimal value G(j*,c*), will therefore correspond the optimal variable j* (therefore the most relevant COV) and an optimal cut-off threshold c* (optimal concentration value).

[0131] We then measure the improvement index (gain) associated with each division by calculating J:

[0132] with ,

[0133] where f idenotes the frequency of the i-th modality of the binary variable associated with the presence / absence of bedbugs, determined at the level of the parent node (before division) and M the number of modalities to be predicted.

[0134] This improvement index J typically varies between 0.5 (maximum gain for an initial distribution in two modalities at 50 / 50) and 0 (no gain).

[0135] The impact of each COV and model parameter is given at each division by this improvement index J. The COV with the highest impact value at each division is retained in the model. It is said to be active.

[0136] At the tree level, each VOC is assigned an overall impact value that represents the weighted average of the impact on each segmentation, giving less importance to the lower parts of the tree. A VOC can have a high overall impact while remaining inactive (the VOC has never been retained for a given division, it very often came in second or third position for example).

[0137] The other model parameters were set as follows to establish the segmentation stopping criteria:

[0138] Minimum node segmentation count: 5 (no segmentation for a node with 4 or fewer locations)

[0139] Maximum number of tree levels set to 10

[0140] Specialization threshold or stopping criterion: 1

[0141] Eligibility: 1 room

[0142] No tree pruning

[0143] The final tree retained illustrated in.

[0144] The overall impact of each VOC is given in Table 3 below. The VOCs listed in bold are those that directly intervene in the decision tree, namely the so-called discriminating VOCs. Other VOCs, such as 3-methyl-2-butenal, are said to be inactive. They can sometimes be well positioned but are outranked by other VOCs.

[0145] The VOC that has the greatest impact overall is an inactive VOC, the decanal. Although well positioned in the first segmentations, it was outclassed by the 6-MHO for the first node, it is in 6 ème position in the second node, third in the third node and in equal competition with hexanal for node 11.

[0146] [Table 3]: Overall impact of each VOC and parameter in the CART model (active VOC in bold)VariableOverall impactAssociated node(s)Decanal0.01643, 86-MHO (6-methyl-5-hepten-2-one)0.01591, 12Nonanal0.014323E-geranylacetone0.013073-methyl-2-butenal0.0125Octanal0.0122Heptanal0.0118Hexanal0.011811(2E)-Octenal0.01172, 6D-limonene0.0101Dihydromyrcenol0.0096(2E,4E)-hexadienal0.0086Acetyl Benzyl0.0072(2E)-Butenal0.0070DMDS (Dimethyl Disulfide)0.00695(2E)-Hexenal0.00662-Pentanone0.006310Nicotine0.00622-Heptanone0.0060(2E,4E)-Heptadienal0.0057Acetophenone0.005719Pyridine0.00561-Hexanol*0.0055Benzaldehyde0.00552-n-Butylfuran0.0054Propanal0.0051Undecanal0.00512-Butanone0.0049Alcohol Benzyl 0.0049 Acetone 0.0047 2-Ethyl-1-hexanol 0.0046 (2E,4E)-octadienal 0.0040 (2E,4Z)-octadienal 0.0037 Pentanal 0.0033 3-Methyl-butanal 0.0030 Hexane 0.0029 Z-Geranylacetone 0.0027 2-Nonen-1-ol 0.0025 Butanal 0.0025 1-Nonanol 0.0024

[0147] Table 4 below lists the active VOCs per node as well as the direct competitor(s) with a lower impact value. In bold are the retained active VOCs (discriminating VOCs) or the competing VOCs that have an identical impact value. A competing VOC (with its own cutoff threshold) can therefore perform as well as the retained active VOC. A substitute VOC, on the other hand, has a lower local impact than the active VOC, which would result in a degradation of the model's performance.

[0148] [Table 4]: Impact of active VOCs at each segmentation node and first competitorsNodeActive VOCLocal impactCompetitors / substitutesLocal impact16-MHO0.1531Decanal0.1292(2E)-octenal0.0309Nicotine0.02943Decanal0.0436E-2-butenal0.04025DMDS0.0423E-geranyl acetone0.03776(2E)-octenal0.02456 VOC0.02247E-geranyl acetone0.0487Acetophenone0.03168Decanal0.0182 VOC0.018102-Pentanone0.018Benzaldehyde0.00911Hexanal0.03392 COV0.0339126-MHO0.02945 COV0.016319Acetophenone0.0191(2E,4E)-heptadienal0.009323Nonanal0.00923-methyl-butanal0.0092

[0149] For example, at node 8, decanal, the active VOC, has the same impact value as two other competing VOCs, 1-hexanol and acetophenone. Similarly, at node 11, the local impact of hexanal is the same as two other VOCs, nonanal and decanal. 3-Methylbutanal is a competing VOC of nonanal at node 23.

[0150] For node 6, (2E)-octenal was selected as the active VOC with an impact value of 0.0245. But six other VOCs are substitutes with a lower impact value (benzaldehyde, benzyl acetate, E-geranylacetone, 2-n-butylfuran, (2E,4Z)-octadienal, hexane). This means that one of these VOCs could possibly substitute for (2E)-octenal (if it is not available) by degrading the model a little.

[0151] For node 12, the five alternate VOCs are acetone, benzyl alcohol, E-geranylacetone, 3-methyl-2-butenal, and DMDS.

[0152] The VOCs selected for the application of the model are the nine VOCs and their cut-off thresholds selected with the highest local impact at each division.

[0153] Equally competitive inactive COVs can supplant an active COV for corresponding nodes as needed. The use of substitute inactive COVs is not recommended.

[0154] An equivalent tree can therefore be constructed by replacing, for example, nonanal (threshold > 13.65 µg / m3) with 3-methyl-butanal (threshold ≤ 0.02 µg / m3) at node 23. The two trees result in exactly the same classification of observations.

[0155] For comparison, by taking the indices defined below respectively 2H2O, 2HZGA and 2H2P as indices of infestation on a panel of 41 infested premises, similar to what exists for other harmful biological agents, the Applicant company obtained a predictive value of the presence of bedbugs, respectively 46%, 31% and 36%, which is clearly insufficient:

[0156] - 2H2O index (value 0, 1 or 2): an analytical detection limit (LD) of 2-hexenal (2H) of 0.005 μg / m 3 for a sampling volume of 12 L is defined: if 2H < LD (0.005 μg / m 3) then 2H2O=0, and if 2H ≥ LD, then if the ratio (2E)-octenal / (2E)-hexenal < 2, then 2H2O=1, if the ratio (2E)-octenal / (2E)-hexenal ≥ 2, then 2H2O=2.

[0157] - 2H2P index (value 0, 1 or 2): an analytical detection limit (LD) of 2-hexenal (2H) of 0.005 μg / m 3 for a sampling volume of 12 L is defined: if 2H < LD (0.005 μg / m 3 ) then 2H2P=0, and if 2H ≥ LD, then if the 2-octenal / 2-pentanone ratio < 4, then 2H2P=1, if the 2-octenal / 2-pentanone ratio ≥ 4, then 2H2P=2.

[0158] - 2HZGA index (value 0, 1 or 2): an analytical detection limit (LD) of 2-hexenal (2H) of 0.005 μg / m 3 for a sampling volume of 12 L is defined: if 2H < LD (0.005 μg / m 3 ) then 2HZGA=0, and if 2H ≥ LD, then if the concentration of Z-geranyl-acetone < 0.05 μg / m 3 , 2HZGA=1 and if the concentration of Z-geranyl-acetone ≥ 0.05 μg / m 3 , 2HZGA=2.

[0159] The decision tree according to the present invention, constructed as detailed above and applied to an independent panel of 41 premises, allows for 93% detection of whether a premises is infested. Similarly, a negative diagnosis by the decision tree proves to be true at 95%. With minimal intrusive impact, a simple implementation cost, since the decision tree is constructed only once and can subsequently be used on all samples taken in premises, we therefore surprisingly obtain an excellent prediction, by the prior operation of choosing the VOCs used to construct the decision tree.

Claims

Method for detecting bedbugs in a room, characterized in that it comprises the steps of:- selecting a group of target volatile organic compounds (VOCs) by:A - sampling and identifying the volatile organic compounds present in at least one breeding environment, the presence of bedbugs and their stage of development in said at least one breeding environment being known;B - sampling and identifying the volatile organic compounds (VOCs) present in at least one calibration room, the presence or absence of bedbugs in said at least one calibration room being known;- constructing with the group of target volatile organic compounds a decision tree based on the correlation between each volatile organic compound of the group of target volatile organic compounds and the presence of bedbugs, the inputs of the constructed decision tree being discriminating organic compounds representing a subgroup of the target organic compounds; and, for each detection of bedbugs in a room: - sampling the air in at least one determined location of the room; - extracting the volatile organic compounds from the group of volatile organic compounds present in the sampled air; and - traversing the decision tree based on the concentrations of the discriminating volatile organic compounds present in the sampled air to deduce the absence or presence of bedbugs in the room.; Method according to claim 1, characterized in that, in step A, the development stages are eggs, larvae and adults. Process according to one of claims 1 and 2, characterized in that the discriminating organic compounds are chosen from 6-methyl-5-hepten-2-one (6-MHO), 1-hexanol, (2E)-octenal, dimethyl disulfide (DMDS), E-geranylacetone, 2-pentanone, 3-methyl-butanal, acetophenone, hexanal, nonanal, decanal, optionally from benzaldehyde, benzylacetate, hexane, 2-n-butylfuran, (2E, 4Z) octadienal, 3-(N-methyl-2-pyrrolidinyl)pyridine, (2E)-butenal, acetone, phenylmethanol, 3-methyl-2-butenal, (2E, 4E)-heptadienal. Method according to one of claims 1 to 3, characterized in that the decision tree is constructed by a CART algorithm using the target volatile organic compounds (VOCs) as variables and the Gini diversity index as segmentation criterion, by successive selection of the variables taken one by one and best separating the sample with respect to the presence or absence of bedbugs, by testing the possible cut-off points for each variable and by selecting the cut-off threshold which maximizes the segmentation criterion, with at each level of the tree an average Gini index G which takes into account the loss of information associated with a volatile organic compound j (VOCj) and a cut-off threshold c: with : : number of premises for which the values ​​of Xj <c du COVj : number of premises for which the values ​​of Xj≥c of the COVj : frequency of the i-th modality of the variable to be predicted (modality 0 for absence and modality 1 for presence of bedbugs) for the premises verifying Xj <c du COVj : frequency of the i-th modality of the variable to be predicted (modality 0 for absence and modality 1 for presence of bedbugs) for the premises verifying Xj≥c of the COVj,M: the number of modalities to be predicted which is 2 (modality 0 – absence and modality 1 – presence of bedbugs)n: the total number of premises,then we measure the improvement index associated with each division by calculating J: with , f idenoting the frequency of the i-th modality of the binary variable associated with the presence / absence of bedbugs, determined at the parent node (before division) and M the number of modalities to be predicted, the volatile organic compound having the highest J value for a given division of the tree being retained for the corresponding division of the tree with its associated c value. Method according to claim 4, characterized in that the minimum segmentation number of the node is set to 5, the maximum number of levels of the tree is 10, the specialization threshold or stopping criterion is 1, the admissibility number is 1 calibration location, and the tree is without pruning. Method according to one of claims 1 to 5, characterized in that the air sampling is carried out on a thermodesorbable adsorbent, preferably Tenax ® TA with a sampling volume of between 9 and 15 liters of air, preferably 10 liters of air. Method according to claim 6, characterized in that the volatile organic compounds are trapped on a tube containing the adsorbent during sampling and are analyzed by a thermodesorption technique – gas chromatography – mass spectrometer (TD-GC-MS). Method according to one of claims 1 to 7, characterized in that the air is taken from the room suspected of harboring bedbugs.

Images