WO2002003047A2 - Procede et appareil de detection de produits volatiles dans un echantillon - Google Patents

Procede et appareil de detection de produits volatiles dans un echantillon Download PDF

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Publication number
WO2002003047A2
WO2002003047A2 PCT/US2001/021031 US0121031W WO0203047A2 WO 2002003047 A2 WO2002003047 A2 WO 2002003047A2 US 0121031 W US0121031 W US 0121031W WO 0203047 A2 WO0203047 A2 WO 0203047A2
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Prior art keywords
volatile
products
gas
transducer means
confined space
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PCT/US2001/021031
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English (en)
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WO2002003047A3 (fr
Inventor
Evangelyn C. Alocilja
Steve A. Marquie
Cynthia Meeusen
Spring M. Younts
Daniel L. Grooms
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Michigan State University
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Application filed by Michigan State University filed Critical Michigan State University
Priority to AU2001271760A priority Critical patent/AU2001271760A1/en
Publication of WO2002003047A2 publication Critical patent/WO2002003047A2/fr
Publication of WO2002003047A3 publication Critical patent/WO2002003047A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
    • G01N33/0034—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array comprising neural networks or related mathematical techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
    • G01N33/0047—Organic compounds
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food

Definitions

  • the present invention relates to a method and apparatus for detection of volatile products from a sample using a transducer which changes voltage as a function of contact of the volatile products with the transducer to produce a gas signature of the volatile products and a spectrophotometer to analyze the volatile products to produce a spectral footprint of the volatile products.
  • the apparatus and method are used to detect spoilage of a biological material, such as a food.
  • the apparatus is also used to detect microorganisms and by comparing the gas signature and spectral footprint to a library of gas signatures and spectral footprints, the apparatus enables identification of the microorganisms and in particular identification of pathogenic microorganisms .
  • Escherichia coli (E. coli ) 0157 :H7 has been recognized as a significant bacterial pathogen belonging to a group of enterohemorrhagic E. coli associated with bloody diarrhea. It is important public health concern because of its association with commonly consumed foods, such as ground beef. Infection with this organism can cause hemorrhagic colitis, he olytic uremic syndrome, and thrombotic thrombocytopenic purpura. The association of E. coli 0157 :H7 with ground beef has led to the identification of cattle as a reservoir for the organism. Recent pre-harvest food safety efforts have emphasized identifying factors within cattle production systems for the monitoring and control of E. coli 0157:H7.
  • the sense of smell has long been used as a diagnostic tool by medical professionals, law enforcement, food handlers, and countless others in everyday life.
  • the human nose contains approximately 50 million cells in the olfactory epithelium that act as primary receptors to odorous molecules (Gardner et al . , 1990; Vandendorpe, 1998) .
  • This parallel architecture led to the construction of the electronic nose, which mimics the biological system.
  • the electronic nose is a state-of-the-art technology that can be used to provide rapid and continuous monitoring of a wide array of different volatile compounds.
  • the term "electronic nose” is applied to an array of chemical sensors, where each sensor has only partial specificity to a wide range of odorant molecules (Bartlett et al . , Food Technol.
  • U.S. Patent No. 5,807,701 to Payne et al provides a method for identifying a microorganism that includes abstracting gas or vapor associated with the microorganism from a detection region and flowing the same over an array of sensors of which an electrical property varies according to exposure to gases or vapors and observing the response of the sensors.
  • An apparatus for detecting a microorganism is also disclosed having a detector means for detecting a gas or vapor associated with the microorganism which includes an array of sensors of which an electrical property varies according to exposure to the gases or vapors.
  • U.S. Patent No. 6,017,440 to Lewis et al provides a sensor array for detecting a microorganism comprising first and second sensors electrically connected to an electrical measuring apparatus, wherein the sensors comprise a region of nonconducting organic material and a region of conducting material that is different than the nonconducting organic material and an electrical path through the regions of nonconducting organic material and the conducting material. Further provided is a system for identifying microorganisms using the sensor array, a computer and a pattern recognition algorithm, such as a neural net are also disclosed.
  • U.S. Patent 6,244,096 to Lewis et al provides a device for detecting the presence of an analyte, wherein the analyte is a microorganism marker gas.
  • the device comprises a sample chamber having a fluid inlet port for the influx of the microorganism marker gas; a fluid concentrator in flow communication with the sample chamber, wherein the fluid concentrator has an absorbent material capable of absorbing the microorganism marker gas and thereafter releasing a concentrated microorganism marker gas; and an array of sensors in fluid communication with the concentrated microorganism marker gas.
  • the sensor array detects and identifies the marker gas upon its release from fluid concentrate.
  • U.S. Patent 6,234,006 and 6,085,576 to Sunshine et al provides a handheld vapor sensing device for use in sensing the presence and concentration of a wide variety of specified vapors as resulting from gases released during either decomposition and spoilage of food stuffs, or as released into either the breath or body fluids of a sick patient being medically diagnosed.
  • U.S. Patent 6,212,938 to Staples provides a process whereby the olfactory response of a gas chromatograph, equipped with a focused surface acoustic wave interferometer integrating detector is converted to a visual image for the purpose of performing pattern recognition.
  • a Surface Acoustic Wave Interferometer is used to monitor the condensation and re-evaporation of these analytes by periodically measuring the resonant frequency of the interferometer. A time varying output parameter is then converted to a polar display.
  • This form of electronic nose provides a recognizable visual image of specific vapor mixtures (fragrances) containing possibly hundreds of different chemical species.
  • U.S. Patent 6,190,858 to Persaud et al provides a method for identifying a micro-organism comprising the steps of providing at least one gas sensor; compiling a database of responses to at least one known micro-organism under a variety of culturing conditions; abstracting gas or vapor from a detection region and flowing the same over at least one gas sensor and observing the response of the sensor or sensors; and comparing the response to the database.
  • Spectrophotometers are conventional and well known in the prior art.
  • the present invention uses such equipment in a novel manner to detect pathogenic microorganisms and microorganisms that cause spoilage.
  • the present invention provides an apparatus for detection of volatile products from a sample which comprises (a) a wall or walls defining a confined space comprising therein an open container for containing the sample which produces the volatile products in the confined space, the open container having a bottom with sidewalls extending upwards therefrom to form the open container and wherein the sidewall has two openings positioned to be opposed to each other, each opening having a collimating lens therein; (b) one or more transducer means in a circuit mounted on an inner surface of the container defining the confined space which detects one or more volatile products of the volatile products produced from the sample to produce an analog signal; (c) an analog to digital conversion means in the circuit for converting the analog signal from the transducer means to a digital signal in the circuit; (d) an acquisition means in the circuit which stores the digital signal resulting from the analog signal in memory as a first detectable signal and retrieves the first detectable signal to provide the detection of the one or more volatile products, wherein the one or more volatile products in the confined space are
  • the apparatus detects the multiple of volatile products produced by a microorganism in the sample, in particular, wherein the microorganism is pathogenic such as a pathogenic Escherichia coli or a Salmonella sp.
  • the volatile product detected by the apparatus is selected from the group consisting of ammonia, ammonium compounds, sulfides, amines, ketones, alcohols, methane, butanes, oxides, carbon dioxide, and other gaseous compounds.
  • the acquisition means is a computer with a video screen for visualizing the gas signature and spectral footprint or a computer with a video screen for visualizing the gas signature and spectral footprint and in addition the gas signature and spectral footprint are recorded in a graph.
  • the computer further comprises an artificial neural network to analyze the gas signature and the spectral footprint by comparing the gas signature and the spectral footprint to a library of gas signatures and spectral footprints stored in the computer.
  • the one or more transducer means are mounted in the confined space wherein at least a part of the wall or walls is removable for sealing and unsealing.
  • At least one resistor is provided in the circuit with the one or more transducer means to enable reproducible results from the one or more transducer means.
  • the analog to digital conversion means is a 12-bit multiple channel analog to digital converter.
  • a heating block with an opening for holding the open container to maintain the sample at a particular temperature.
  • the present invention further provides a method for detecting volatile products from a sample comprising (a) providing an apparatus adjacent to the sample which comprises: a circuit comprising one or more transducer means mounted on an inner surface of a wall or walls defining a confined space which contains therein an open container for containing the sample which produces the volatile products in the confined space, the open container having a bottom with sidewalls extending upwards therefrom to form the open container and wherein the sidewall has two openings positioned to be opposed to each other, each opening having a collimating lens therein, and wherein the one or more transducer means in the circuit detects one or more volatile products of the volatile products from the sample to produce an analog signal in the circuit; an analog to digital conversion means in the circuit for converting the analog signal from the transducer means to a digital signal; an acquisition means in the circuit, which stores the digital signal resulting from the analog signal in a memory as a first detectable signal and retrieves the first detectable signal to provide the detection of the one or more volatile products from the sample,
  • the volatile by-product is selected from the group consisting of ammonia, ammonium compounds, sulfides, amines, ketones, alcohols, methane, butanes, oxides, carbon dioxide, and other gaseous compounds, which is detected repeatedly over a period of time.
  • the ammonium is produced by a microorganism such as a pathogenic Escherichia coli .
  • the sample in the open container is placed in the confined space which is sealable which is then sealed, and wherein the one or more transducer means are adjacent to the sample in the sealed container.
  • the acquisition means further includes an artificial neural network to compare the gas signature and the spectral footprint to the library of gas signatures and spectral footprints stored in the computer.
  • the apparatus further- includes a heating block with an opening for holding the open container to maintain the sample at a particular temperature.
  • the present invention further provides an apparatus for determining whether a food material is spoiling by detecting volatile by-products of the spoiling which comprises (a) a circuit comprising one or more transducer means mounted on an inner surface of a wall or walls defining a confined space having therein an open container for containing the food material in the confined space which produces the volatile products, the open container having a bottom with sidewalls extending upwards therefrom to form the open container and wherein the sidewall has two openings positioned to be opposed to each other, each opening having a collimating lens therein, and wherein the one or more transducer means in the circuit detects one or more volatile by-products of the volatile by-products produced in the confined space to produce an analog signal in the circuit; (b) an analog to digital converter means in the circuit for converting the analog signal from the transducer means to a digital signal; (c
  • the food material is selected from' the group consisting of vegetables, fruits, meats, grains, herbs, spices, and legumes .
  • the circuit further comprises a transducer means which detects temperature. In a further embodiment of the apparatus, the circuit further comprises a transducer means which detects humidity.
  • the acquisition means is in a computer with a video screen for visualizing the gas pattern and spectral footprint. In a further embodiment of the above embodiments of the apparatus, the acquisition means is in a computer with a video screen for visualizing the gas pattern and the spectral footprint and wherein the gas pattern and the spectral footprint are recorded in a graph.
  • the transducer means in addition detects temperature, wherein the transducer means in addition detects humidity; and wherein the acquisition means is in a computer which detects each of the humidity, the temperature and the one or more volatile by-products to produce a series of first detectable signals which are reproducible over a series of detections.
  • the one or more transducer means are mounted in the confined space wherein at least a part of the wall or walls is removable for sealing and unsealing.
  • the transducer means is mounted on a cover for the sealable container.
  • At least one resistor is provided in the circuit with the transducer means to enable reproducible results from the transducer means.
  • At least one resistor is provided in the circuit with the transducer means to enable reproducible results from the transducer means and wherein the resistor is mounted outside of the container.
  • the converter means is a 12-bit multiple channel analog to digital converter.
  • the apparatus further includes a heating block with an opening .for holding the open container to maintain the sample at a particular temperature.
  • the present invention also provides a method for determining whether a biological material is spoiling by detecting volatile by-products which comprises (a) providing an apparatus for detecting the volatile by-products produced by the spoiling which comprises: a circuit comprising one or more transducer means mounted on an inner surface of a wall or walls defining a confined space having therein an open container for containing the biological material which produces the volatile products in the confined space, the open container having a bottom with sidewalls extending upwards therefrom to form the open container and wherein the sidewall has two openings positioned to be opposed to each other, each opening having a collimating lens therein, and wherein the one or more transducer means in the circuit detects one or more of the volatile by-products of the volatile by-products produced in the confined space by the biological material to produce an analog signal; an analog to digital converter means in the circuit for converting the analog signal from the transducer means to a digital signal; an acquisition means in the circuit which stores the digital signal resulting from the analog signal in memory as
  • the volatile by-product is an alcohol.
  • the biological material is selected from the group consisting of vegetables, fruits, meats, grains, herbs, spices, and legumes and the volatile by-product is an alcohol.
  • the biological material in the open container is placed in the confined space which is sealable which is then sealed, and wherein the one or more transducer means are adjacent to the sample in the sealed container.
  • the acquisition means is a computer with an artificial neural network to compare the gas signature and the spectral footprint to the library of gas signatures and spectral footprints.
  • the apparatus further includes a heating block with an opening for holding the open container to maintain the sample at a particular temperature.
  • the present invention further provides an apparatus for identifying a microorganism in a biological material by detecting volatile by-products produced by the microorganism which comprises (a) a circuit comprising one or more transducer means mounted on an inner surface of a wall or walls defining a confined space having therein an open container for containing the biological material with the microorganism which produces the volatile products in the confined space, the open container having a bottom with sidewalls extending upwards therefrom to form the open container and wherein the sidewall has two openings positioned to be opposed to each other, each opening having a collimating lens therein, and wherein the one or more transducer means in the circuit detects one or more volatile by-products of the volatile by-products produced in the confined space by the microorganism in the biological material to produce an analog signal in the circuit; (b) an analog to digital converter means in the circuit for converting the analog signal from the transducer means to a digital signal; (c) an acquisition means in the circuit which stores the digital signal
  • the transducer means detects a fermentation by-product produced by the microorganism.
  • the biological material is selected from the group consisting of vegetables, fruits, meats, grains, herbs, spices, and legumes.
  • the circuit further comprises a transducer means which detects temperature.
  • the circuit further comprises a transducer means which detects humidity.
  • the acquisition means is in a computer with a video screen for visualizing the gas signature and spectral footprint. In a further embodiment of the above embodiments of the apparatus, the acquisition means is in a computer with a video screen for visualizing the gas signature and spectral footprint and wherein the gas signature and spectral footprint are recorded in a graph.
  • the acquisition means is a computer with an artificial neural network to identify the microorganism by comparing the gas signature and the spectral footprint to a library including a plurality of gas signatures and spectral footprints produced by a plurality of microorganisms stored in the computer.
  • the transducer means in addition detects temperature, wherein the transducer means in addition detects humidity; and wherein the acquisition means is in a computer which detects each of the humidity, the temperature and the volatile byproducts to produce a series of first detectable signals which are reproducible over a series of detections.
  • the one or more transducer means are mounted in the confined space wherein at least a part of the wall or walls is removable for sealing and unsealing.
  • the transducer means is mounted on a cover for the sealable container.
  • At least one resistor is provided in the circuit with the transducer means to enable reproducible results from the transducer means. In a further embodiment of the apparatus, at least one resistor is provided in the circuit with the transducer means to enable reproducible results from the transducer means and wherein the resistor is mounted outside of the container. In a further embodiment of the apparatus, the converter means is a 12-bit multiple channel analog to digital converter.
  • the apparatus further includes a heating block with an opening for holding the open container to maintain the biological material at a particular temperature.
  • the present invention further provides a method for identifying a microorganism in a biological material by detecting a multiple of volatile by-products produced by the microorganism which comprises (a) providing an apparatus for the detection of the pattern of the multiple of volatile by-products produced by a microorganism which comprises: a circuit comprising one or more transducer means mounted on an inner surface of a wall or walls defining a confined space having therein an open container for containing the biological material with the microorganism which produces the volatile products in the confined space, the open container having a bottom with sidewalls extending upwards therefrom to form the open container and wherein the sidewall has two openings positioned to be opposed to each other, each opening having a collimating lens therein, and wherein the one or more transducer means in the circuit detects one or more volatile by-products of the by-products produced by the microorganism in the biological material to produce an analog signal; an analog to digital converter means in the circuit for converting the analog signal from the transducer
  • the biological material is a food positioned adjacent to the transducer means .
  • the volatile by-product is an alcohol.
  • the biological material is selected from the group consisting of vegetables, fruits, meats, grains, herbs, spices, and legumes and the volatile by-product is an alcohol .
  • the biological material in the open container is placed in the confined space which is sealable which is then sealed, and wherein the one or more transducer means are adjacent to the sample in the sealed container.
  • the acquisition means is a computer with an artificial neural network to compare the gas signature and the spectral footprint to the library of gas signatures and spectral footprints .
  • the apparatus further includes a heating block with an opening for holding the open container to maintain the sample at a particular temperature.
  • the present invention provides a detecting apparatus which uses artificial intelligence in the form of a neural network to detect a signature showing the presence of the volatile material in a sample. Further still, it is an object of the present invention to provide an apparatus and method for the detection of harmful microorganisms by measuring volatile products produced by the microorganism. Further, it is an object of the present invention to provide an apparatus and method for the detection of spoilage by-products produced by microorganisms or degradative oxidation in foods and other biological materials .
  • FIGS 1 and IA are schematics of the gas sensor component of the apparatus of the present invention for microorganism detection.
  • Figure 2 is a schematic showing the sensor circuit.
  • Figure 2A is a schematic diagram showing the data acquisition circuit of Figure 1.
  • Figure 3 is a graph of a representative gas signature generated by non-0157 :H7 E. coli in BHI broth.
  • Figure 4 is a graph of a representative gas signature generated by E. coli 0157 :H7 in BHI broth.
  • Figure 5 is a graph of a representative gas signature generated by E. coli 0157 :H7 in nutrient broth.
  • Figure 6 is a graph of a representative gas signature generated by non-0157 :H7 E. coli in nutrient broth.
  • Figure 7 is a graph showing an average time in hours (+/- standard deviation) for initial increase in gas concentration to occur, as measured by voltage increase in gas sensors, at different initial bacteria concentrations .
  • Figure 8 is a graph of a control gas signature from monitoring dry block heater.
  • Figure 9 is a graph of control gas signature from monitoring nutrient broth.
  • Figure 10 is a graph of a representative growth curve for E. coli 0157 :H7 plotted against a typical gas signature.
  • Figure 11 is a graph of a representative growth curve for non-0157 :H7 E. coli plotted against a typical gas signature.
  • Figure 12 is a graph of a representative gas signature generated by E. coli 0157 :H7.
  • Figure 13 is a graph of representative gas signature generated by non-0157 :H7 E. coli .
  • Figure 14 is a graph of gas signatures from the ammonia sensor for each of the E. coll 0157 :H7 field isolates.
  • Figure 15 is a graph of gas signatures from the ammonia sensor for each of the non-0157 :H7 E. coli field isolates.
  • Figure 16 is a graph of gas signatures from the ammonia sensor for E. coli 0157 :H7 field isolates from outbreak of human illness.
  • Figure 17 is a graph of gas signatures from the ammonia sensor for E. coli 0157 :H7 isolates from the same feed yard.
  • Figure 18 is a schematic diagram of the apparatus used as a rot sniffer.
  • Figure 18A shows the diagram for the TGS 822 (Figaro, Japan) sensor (transducer) and B&B 232 SDA12 A to D converter.
  • Figure 18B shows the heater circuit.
  • Figures 19 to 24 are graphs showing results with a potato sniffer apparatus.
  • Figure 25 is a schematic diagram of the transducer apparatus with a spectrophotometer apparatus.
  • Figure 26 is a graph showing the typical gas pattern and cell count of S. typhymurium over a 24 hour time period.
  • Figure 27 is a graph showing the gas patterns of S. typhymurium in alfalfa sprouts.
  • Figure 28 is a graph showing the spectral footprints of S. typhymuri um in TSB and alfalfa sprouts.
  • Figure 29 is a graph showing a gas pattern comparison between S. typhymuri um and E. coli 0157 :H7 in
  • Transducers such as gas sensors can detect and identify specific compounds or products instantaneously and monitor them over time.
  • the gas sensors provide a convenient and inexpensive monitoring tool for certain compounds or volatiles gases, such as volatile breakdown products of bacterial metabolism that include, but are not limited to, ammonia, ammonium compounds, sulfides, amines, ketones, alcohols, methane, butanes, oxides, carbon dioxide, and other gaseous compounds that are a product of bacterial metabolism.
  • gaseous compounds have characteristic light absorption and/or scattering patterns.
  • This spectral pattern which is referred to as "spectral footprint,” together with the gas signatures generated from the gas sensors, would make possible a complete recognition of a microorganism by the volatile breakdown products it produces.
  • the present invention enables the detection and identification of microorganisms that cause disease or cause spoilage by the gas signature and spectral footprint of the volatile breakdown products produced by the microorganism.
  • the present invention provides an apparatus and method for "seeing” and “sniffing” volatile compounds emitted from microorganisms that cause disease or spoilage.
  • the invention uses transducers such as gas sensors to "sniff" the volatile compounds that are produced by the microorganisms in the sample to generate a gas signature of the volatile compounds produced and uses a ultraviolet/visible/near infrared (UV/vis/NIR) fiber optic spectrometer with an excitation source from the ultraviolet to the near infrared regions of the light spectrum to "see” the spectral footprint of the volatile compounds produced.
  • UV/vis/NIR ultraviolet/visible/near infrared
  • the combination of the gas signature and spectral footprint provides a definitive identification of the microorganism in the sample, which enables the determination of whether the microorganism is a microorganism that causes disease or spoilage.
  • the transducer- and spectrophotometer-based apparatus of the present invention further includes an artificial neural network (ANN) , which is capable by pattern recognition analysis of distinguishing microorganisms that cause disease or spoilage from microorganisms that do not cause disease or spoilage by detecting the volatile compounds that are emitted by the microorganism. Because each microorganism species differs in the volatile compounds it produces, the gas signature and spectral footprint for each microorganism species is unique to that species.
  • ANN artificial neural network
  • the gas signatures and the spectral footprints produced by microorganisms in a sample are analyzed by ANN and compared to a library of standard gas signatures and spectral footprints for a plurality of microorganisms.
  • the microorganism in the sample is identified.
  • the present invention is more specific than either a gas sensor or spectrophotometer alone.
  • the present invention enabled the human pathogen E. coli 0157 :H7 to be distinguished from non-0157 :H7 E. coli isolates by detecting and measuring the volatile compounds that were emitted by the bacteria.
  • the present invention is further able to identify food products that have spoiled.
  • Figure 25 shows a diagram of the apparatus of the present invention.
  • the figure shows open container or cuvette 10 containing sample 12 in heating block 14.
  • chamber 16 Positioned over the open container 10 in heating block 14 is chamber 16 with transducers 18 mounted on circuit board 11 wall 17 to define a confined space 15 which encloses open container 10.
  • the confined space 15 allows the volatile products 41 from sample 12 to accumulate to an amount which is detectable.
  • the chamber can be under a reduced atmosphere.
  • the transducers 18 are gas sensors which include, but are not limited to, alcohol sensors, H 2 S sensors, amine sensors, methane sensors, hydrogen sensors, alcohol vapor sensors, and air contaminants sensors.
  • a schematic of a typical sensor circuit is shown in Figure 2.
  • Preferably, further included are temperature and relative humidity transducers (not shown) .
  • Power supply 26 provides current 27 to heating block 14 to maintain heating block 14 at a particular temperature and to provide operating current to gas sensors 18.
  • Lens 22 is operably connected by fiber optic cable 28 to a UV/vis/NIR lamp 30.
  • Fiber optic cable 32 is operably connected to fiber optic spectrophotometer 34.
  • Light is transmitted from the UV/vis/NIR lamp 30 through optic fiber cable 28 to collimating lens 22.
  • the light is transmitted through volatile vapors 41 and collected by collimating lens 24 which transmits the light through fiber optic cable 32 to spectrophotometer 34.
  • Spectrophotometer 34 converts the light to a digital signal which the spectrophotometer 34 then transmits to computer 36.
  • FIGS 822 sensor shows schematic diagrams of an alcohol sensor (TGS 822 sensor) in circuit with data acquisition system B&B 232 SDA12.
  • Computer 36 converts the digital output from the transducers 18 to a gas signature and converts the digital signal from the spectrophotometer 34 to a spectral footprint.
  • Computer 36 further contains artificial neural network software 40 which compares the gas signature and spectral footprint obtained from the volatile products 41 from the sample 12 to a library of gas signatures and spectral footprints to produce output 42 which is the identification of the source for the volatile products.
  • the output 42 is visually displayed as a graph or text on a monitor (not shown) or the graph or text is printed from a printer (not shown) .
  • the artificial olfactory component or electronic nose of the present invention allows differentiation of odors and various volatile products
  • An electronic nose is a device usually consisting of transducers comprising metal oxide gas sensors coupled with an artificial neural network. Analysis of compounds using this technology has been shown to be rapid, nondestructive, economical and continuous
  • the metal oxide sensors are based on the principle that the electrical resistance established in a gas sensor is decreased in the presence of specific volatile compounds.
  • the specificity of the gas sensor is determined by the metal oxide used in the gas sensor. Gas sensor resistance drops very quickly in the presence of a specific gas and recovers to its original level in the absence of the gas.
  • a simple electrical circuit can convert the change in conductivity to an output signal that corresponds to the gas concentration (Figaro USA, 1996) .
  • the output signal is reported as a voltage reading that is transferred to a computer software program for continuous plotting, and generating a gas signature or pattern, which is analyzed by the ANN.
  • the transducers or gas sensors that can be included in the apparatus of the present invention include, but are not limited to, alcohol sensors, H 2 S sensors, amine sensors, methane sensors, hydrogen sensors, alcohol vapor sensors, and air contaminants sensors.
  • Figure 1 shows a diagram of the gas sensor component of the apparatus of the present invention.
  • the figure shows open container or cuvette 110 containing sample 112 in heating block 114.
  • chamber 116 Positioned over the open container 110 in heating block 114 is chamber 116 with transducers 118 mounted on circuit board 111 on wall 117 to define a confined space 115 which encloses open container 110.
  • the confined space 115 allows the volatile products 141 from sample 112 to accumulate to an amount which is detectable.
  • the chamber can be under a reduced atmosphere.
  • the transducers 118 are gas sensors which include, but are not limited to, alcohol sensors, H 2 S sensors, amine sensors, methane sensors, hydrogen sensors, alcohol vapor sensors, and air contaminants sensors.
  • FIG. 1 A schematic of a typical sensor circuit it shown in Figure 2.
  • Power supply 126 provides current 127 to heating block 114 to maintain heating block 114 at a particular temperature and to provide operating current to gas sensors 118.
  • FIGS 2A and 18B show schematic diagrams of a data acquisition circuit for converting the analog signal from the transducers 118 to a digital signal.
  • Figure 18B shows a schematic diagram of an alcohol sensor (TGS 822 sensor) in circuit with data acquisition system B&B 232 SDA12.
  • Computer 136 converts the digital output from the transducers 118 to a gas signature.
  • Computer 136 further contains artificial neural network software 140 which compares the gas signature obtained from the volatile products 141 from the sample 112 to a library of gas signatures to produce output 142 which is the identification of the source for the volatile products.
  • the output 142 is visually displayed as a graph or text on a monitor (not shown) or the graph or text is printed from a printer (not shown) .
  • Figure IA shows a diagram of the chamber for the gas sensor component of the apparatus of the present invention.
  • the figure shows open container or cuvette
  • chamber 216 is chamber 216 with transducers 218 mounted ' " on circuit board 211 wall 217 to define a confined space
  • the transducers 218 are gas sensors which include, but are not limited to, alcohol sensors, H 2 S sensors, amine sensors, methane sensors, hydrogen " sensors, alcohol vapor sensors, and air contaminants sensors.
  • circuit 220 which transmits the signal from the transducers to the data acquisition system (not shown) .
  • Gaseous compounds have characteristic light absorption, reflection, and scattering patterns. This spectral pattern or spectral footprint is used to make a microorganism recognition. The spectral pattern is digitized and transferred to a computer software program for continuous plotting generating a spectral pattern, which is analyzed by the ANN.
  • the artificial neural network (ANN) used for data analysis or pattern recognition of the gas signature and spectral footprints is an information processing system that functions similar to the way the brain and nervous system process information (Tuang et al . , FEMS Microbiol. Letts. 177: 249-256 (1999)).
  • the ANN is trained for the analysis and then tested to validate the method.
  • the ANN is configured for pattern recognition, data classification, and forecasting.
  • Commercial software programs are available for this type of data analysis. Recent advances with electronic nose technology have found applications in the food industry for enhancing traditional quality control techniques, based on the ability to detect rancidity, spoilage, and "off" odors (Bartlett et al . , Food Tech. 51: 44-48 (1997)).
  • EXAMPLE 1 This example shows the development and evaluation of the gas sensing component of the present invention for identifying E. coli 0157 :H7 in a laboratory setting.
  • Keshri et al (Letts. Appl. Microbiol. 27: 261-264 (1998)) used an electronic nose to monitor the patterns of volatile gas production to detect activity of spoilage fungi, prior to visible growth, and differentiate between species. Six different fungi were monitored and good replication was seen among the gas patterns generated by the same species. The results indicated that early detection and differentiation of fungi species was possible using electronic nose technology to monitor the patterns of gas emissions. The potential for field use of electronic nose technology in animal production was demonstrated in a study by Lane and Wathes (J. Dairy Sci. 81: 2145-2150 (1998) ) .
  • An electronic nose was used to monitor the perineal odors and predict estrus in the cow. Detectable differences in the perineal odors of cows in the midluteal phase and cows in estrous were observed. However, more research was needed to find sensors more sensitive to the specific emitted volatile compounds to enhance prediction of stage in estrous. The goal of ongoing studies is to develop an electronic nose device for use in cattle operations to enhance estrus detection.
  • Enterobacteriaceae including E. coli , carry out mixed acid fermentation resulting in the end product formation of ethanol, acetate, succinate, formate, molecular hydrogen, and carbon dioxide (Atlas, Principles of Microbiology. Mosby Year Book, Inc. St. Louis, Missouri (1995) ) .
  • an electronic nose could be used to detect the volatile compounds produced by various E. coli strains and differentiate serotype 0157 :H7 based on a unique pattern of gas emissions.
  • Electronic nose technology has the potential to enhance efforts addressing pre-harvest food safety concerns involving E. coli 0157 :H7, by providing a convenient, economically feasible, and less labor intensive tool for identifying carrier cattle or other environmental sources/reservoirs of the organism.
  • Advantages of an electronic nose as a diagnostic tool include the identification of live bacteria and monitoring of their growth, no requirement for reagents, and the capability of being automated.
  • the objective of this research was to develop a gas sensor based instrument, coupled with an artificial neural network (ANN) , which is capable of differentiating the human pathogen E. coli 0157 :H7 from non-0157 :H7 E. coli isolates.
  • ANN artificial neural network
  • the production of gases from eight laboratory isolates and 20 field isolates of E. coli were monitored during growth in laboratory conditions, and a unique gas signature for each isolate was generated.
  • An ANN was used to analyze the gas signatures, and classify the bacteria as 0157 :H7 or non-0157 :H7 E. coli . Detectable differences were observed between the gas signatures of the E.
  • gas sensor based technology has promise as a diagnostic tool for pathogen detection on pre-harvest and post-harvest food safety.
  • An apparatus was assembled comprising the gas sensing component of the present invention for collecting, monitoring, and recording the gas emissions from various growing E. coli cultures.
  • the first consideration was the need for a culturing system or a way to grow and maintain bacteria within the apparatus.
  • the next consideration was a method to capture or collect the gas emissions in a confined space. Detection of the presence of the gas and identification of the type of volatile compounds being emitted must be available.
  • the final consideration was a means of recording the data or gas measurements automatically. Construction involved assembly of a chamber and interconnections between chamber and data collection system (computer) .
  • a chamber was designed to sit on a dry-block heater, which ( Figure IA) could hold a culture vial and maintain a temperature supportive of bacterial culture growth.
  • the chamber was rectangular in shape, approximately 10 cm in height x 12.5 cm in length x 10 cm in width.
  • the chamber was constructed out of PLEXIGLASS and sealed to capture or contain the volatile compounds and prevent permeation of odors from the outside environment into the sensor chamber.
  • Gas sensors for detecting the presence of specific compounds, were mounted in the ceiling of the chamber, directly above the opening of the culture vial in the dry block heater. The gas sensors were linked to a circuit board placed on the top of the chamber, which was connected to the power source .
  • Metal oxide gas sensors were acquired from a proprietary vendor (Figaro USA, Inc., Glenview, IL) to detect, measure, and monitor the volatile gases released from the bacterial cultures. The following description of the sensor operating principle was obtained from the "General Information for TGS Sensors" (Figaro USA, 1996) .
  • a chemical reaction occurs between the metal oxide, usually Sn0 2 , in the sensor and the volatile gas it is designed to detect.
  • An electrical current flows between connected micro crystals of metal oxide within the sensor.
  • the sensing material, metal oxide has a negative charge on the surface and absorbs oxygen, which accepts electrons, leading to a positive charge.
  • the resulting surface potential can act as a potential barrier against electron transfer, increasing the electrical resistance within the sensor.
  • the volatile compound for which the sensor is specifically designed to detect serves as a reducing gas.
  • the negatively charged oxygen density on the surface between the metal oxide crystals is decreased.
  • the height of the barrier against electron transfer is reduced and there is a decrease in sensor resistance.
  • the amount of decrease in sensor resistance is proportional to gas concentration; the higher the gas concentration the greater the increase in electron flow.
  • the decrease in sensor resistance, or increase in electrical conductivity is converted to a change in voltage by the circuit board. The voltage readings are fed to the data acquisition board and transferred to the computer for continuous plotting.
  • the sensors employed in the apparatus were chosen based on their ability to detect volatile metabolites known to be produced from bacterial metabolism (Moat and Foster, Microbial Physiology.
  • the amine sensor is very sensitive to ammonia and amine compounds; the alcohol sensor to methane, iso-butane, and ethanol; and the air contaminants sensor to similar alcohol compounds at lower concentration (Figaro USA, 1996) .
  • Two additional sensors were used to monitor the ambient temperature (Figaro D Thermistor) and relative humidity (Figaro NHU- 3) within the apparatus. Monitoring the stability of temperature and humidity is critical due to their effects on the sensitivity of the sensors. A change in temperature or relative humidity can affect the rate of the chemical reaction as it occurs within each sensor
  • Figure IA shows a diagram of the sensor chamber and sampling platform for the apparatus.
  • a data acquisition module (model 232SDA12, B & B Electronics, Ottawa, IL) was used to convert the output from the gas sensors to digital output for recording by a computer containing software for data collection. This module was positioned on the chamber and was directly connected to a computer housing the software for data collection.
  • Figure 2A shows a diagram of the data acquisition module for converting the output from the sensors to digital output for recording by a computer. Ports for tubing were drilled into either side of the chamber; on one side the tubing was connected to a vacuum pump and the other side had tubing open to the outside. These tubes were used to evacuate and draw air through the chamber between experiments .
  • Figures 1 and IC show the overall system and Figure 2 shows a diagram of the gas sensors which are placed in the chamber ceiling.
  • An artificial neural network was chosen for the analysis and interpretation of the gas signatures.
  • An ANN is an information processing system that is patterned after the way the brain and nervous system process information (Tuang et al . , FEMS Microbiol. Letts. 177: 249-256 (1999)).
  • BPN back-propagation neural network
  • the desired output vector is the classification of the organism, "0" for non-0157 :H7 E. coli and "1" for E. coli 0157 :H7.
  • Training is accomplished by using a standardized data set (standard gas signatures) and associating the input or gas signature with the desired output or classification.
  • the program compares the data and computes network output with the desired output until an acceptable level of recognition is achieved.
  • Another set of data is used for testing the predictive capability of the trained BPN.
  • the BPN In testing, the BPN is exposed to the input vectors not labeled with the bacterial type or desired output classification. Evaluation of the training is based on the ability of the BPN to recognize and accurately classify the bacteria type from the input gas signature. The efficacy of the apparatus for differentiating E. coli 0157 :H7 from non-0157 :H7 isolates is determined by the ability of the BPN to distinguish between gas signatures and correctly classify the bacterial type.
  • coli isolates were assayed at the different inoculum concentrations to determine the time each concentration required to reach the initial voltage increase. Gas patterns or signatures were identified starting at the initial voltage increase and ending with the voltage readings decreased to levels equivalent or less than those prior to the initial increase.
  • the dry block heater and uninoculated media were monitored over time to determine if detectable volatile compounds, not associated with bacterial growth, were being released.
  • the chamber was placed over the dry block heater with nothing in it. The sensor readings were taken at a one minute sampling rate for 20 hours.
  • 10 ml of nutrient broth was placed in a sterile 14 ml polystyrene vial. The vial was placed in the dry block heater at 37+0.2°C with the chamber in place and monitored at a one minute sampling rate for twenty hours .
  • the growth activity of the microorganisms in nutrient broth within the apparatus was monitored to investigate the relationship between bacterial growth and gas emissions. All eight isolates of E. coli were used in this experiment. Cultures were grown and maintained in nutrient broth to establish a stock culture of each isolate. There were two separate experimental runs on each isolate, making a set of 16 growth curves. For each isolate, a predetermined concentration of 10 5 CFU/ml, was introduced to a sterile polystyrene vial containing 10 ml of nutrient broth. The vial was then placed in the dry block heater within the chamber. At 2-hour intervals the chamber was lifted and 100 ⁇ l of the sample culture was drawn out of the vial using a pipette over a 16 hour period. The 100 ⁇ l samples were serially diluted and viable plate counts were performed. The results from the plate counts were plotted over the 16 hour time period to establish standard growth curves for each isolate.
  • FIG. 10 Representative growth curves plotted against gas signatures for E. coli 0157 :H7 and for non-0157 :H7 E. coli are shown in Figures 10 and 11, respectively.
  • the Figures demonstrate the relationship between the lag, log, and stationary phases of microbial growth and the occurrence of gas emissions within the sensing system. It was repeatedly observed that the initial voltage change or detection of gases occurred during the mid to late log phase of bacterial growth. It was also observed that the voltage stabilized during the stationary growth phase.
  • the apparatus was capable of detecting the gas emissions from growing E. coli cultures. Differences in the gas patterns were seen based on the media and bacteria concentration employed. The variations in gas patterns based on the type of media used are most likely due to differences n the nutrient composition of the media that resulted in different metabolic breakdown products. No obvious visual differences in the gas patterns produced by E. coli 0157 :H7 and non-0157 :H7 isolates were observed when cultured in BHI broth. However, recognizable differences were observed in the gas patterns when cultured in nutrient broth.
  • EXAMPLE 2 This example illustrates differentiation of E. coli 0157 :H7 from non-0157 :H7 E. coli serotypes using the gas sensor component of the present invention. Characterized strains of E. coli , four isolates of E. coli 0157 :H7 and four non-0157 :H7 serotypes, as shown in Example 1 were used. Four standardized experimental runs were performed on each isolate making a total set of 32 experimental runs or gas signatures. First, 10 ml of nutrient broth was placed into a sterile 14 ml polystyrene vial. A set concentration of bacteria, 10 5 CFU/ml, was introduced into the vial from culture stocks of the bacteria.
  • the vial was centrally placed in a 37° C dry-block heater and grown within the chamber. Each experiment ran for 16 hours with gas sampling every five minutes. The gas readings or voltage measurements were continuously plotted, generating a gas signature. Preliminary studies identified the initial cell concentration and time interval most appropriate for experimental standardization.
  • the chamber used is as described in Example 1.
  • Each of the four experimental runs on every E. coli isolate generated a standardized gas signature for that isolate, providing four gas signatures for each isolate.
  • Data set "1" consisted of the signatures from the first experimental run on each isolate.
  • Data sets "2,” “3,” and "4" were made up of gas signatures from each subsequent experimental run.
  • the data was divided equally into training and testing sets for the neural network analysis.
  • the ANN was configured for data classification.
  • the data sets were used in different combinations as part of the training and testing of the ANN. For example, data sets 1 and 2 were used as the training set and sets 3 and 4 were used as the testing set for one train-test scenario.
  • the next scenario used data sets 3 and 4 for training and 1 and 2 for testing.
  • the third scenario involved data sets 1 and 3 for training and sets 2 and 4 for testing. There were a total of six scenarios for each responding sensor type as shown in Table 4.
  • the recognition/classification by the ANN is based on the shape of the gas pattern, not specific time-data points. Although the shapes of the gas signatures are similar there is fluctuation in the voltage readings at a specific time due to differences in gas concentration intensity. This fluctuation in voltage level affects the ability of the ANN to recognize unseen patterns and accurately classify them. By dividing the data into testing and training sets, the specific patterns used to "test" the ANN analysis have not been seen before.
  • the ANN is programmed to recognize a gas pattern shape based on the training set.
  • the ANN calculates the probability that the previously unseen patterns in the testing set are indicative of a desired classification. For example, the ANN compares each gas signature in the testing set with the patterns it was "trained” to recognize from the training set. The resulting output from the ANN is the probability for each testing pattern, or isolate gas signature, that it is E. coli 0157 :H7 or non-0157 :H7 E. coli .
  • the previous training/testing scenario was deleted and the ANN was retrained and tested. The sensitivity and specificity of detecting E. coli 0157 :H7 for each scenario was calculated and then averaged together.
  • the apparatus was evaluated for its value as a screening test for E. coli 0157 :H7. Based on the differences in the gas patterns of the two E. coli groups, 0157 :H7 and non-0157 :H7, the ANN generated probabilities that individual gas signatures were representative of E. coli 0157 :H7 or not. Based on the correctness of the classification from the probabilities, the sensitivity and specificity of the apparatus were calculated (Smith, In Veterinary Clinical Epidemiology: A Problem Oriented Approach. CRC Press, Ann Arbor, MI. (1995), pp 31-52). Detectable differences were observed between the gas signatures of the E. coli 0157 :H7 and the non- 0157 :H7 isolates. Figure 12 shows that the gas pattern observed for the E.
  • coli 0157 :H7 showed an initial increase and a period of stabilization followed by a gradual decrease in the voltage readings.
  • Figure 13 shows that a binary increase in voltage was observed with the non 0157 :H7 E. coli isolate followed again by a period of tapering off.
  • the outputs of the three sensors were used to train and test the neural network classifying E. coli 0157 :H7. Based on the evaluation of test accuracy (Smith (Smith, In Veterinary Clinical Epidemiology: A Problem Oriented Approach. CRC Press, Ann Arbor, MI. (1995), pp 31-52), the ANN had high predictive capability for accurately classifying the bacteria based on the output of individual sensors.
  • the results of the sensitivity and specificity analysis for the three sensors and scenarios are presented in Tables 5, 6, and 7
  • Sensitivity and specificity varies depending on the probability cut-off used to classify the gas signatures as 0157 :H7 and non-0157 :H7 E. coli. For example, for the first cut-off point, any signature with a 50% or greater probability of being E. coli 0157 :H7 was considered "positive.” For all sensors, as the probability cut-off point was reduced, the ability to correctly classify E. coli 0157 :H7 increased, however, the rate mis-classification of non 0157 :H7 E. coli also increased.
  • This example shows that the apparatus is capable of detecting and differentiating E. coli 0157 :H7 from non-0157 :H7 E. coli isolates in a laboratory setting.
  • Gas-specific sensors were used to detect volatile compounds produced by bacteria during normal metabolic activity. The gas patterns generated are due to the presence of amines, nitrogenous compounds, and alcohols, which are common metabolic breakdown products known to be associated with E. coli (Moat and Foster, Microbial Physiology (3 rd ). Wiley-Liss. New York, New York (1995) ) .
  • the hydrogen sulfide sensor did not show a response over time because hydrogen sulfide is not a normal by-product of E. coli metabolism.
  • the hydrogen sulfide sensor may be important for detecting organisms that emit hydrogen sulfide.
  • the differences observed between the gas patterns of the E. coli 0157 :H7 isolates and the non- 0157 :H7 isolate is likely due to genetically encoded differences between their metabolic pathways. Differences in E. coli metabolism are already taken advantage of in routine differentiation of E. coli
  • coli 0157:H7 from non-0157 :H7 E. coli could be altered depending on what probability level was used as the cut-off point. For each gas sensor, as the probability cut-off point was lowered, the sensitivity of detecting E. coli 0157 :H7 increased (See Tables 5, 6, and 7) . However, the specificity decreased, which resulted in more non-0157 :H7 E. coli being mis- classified as E. coli 0157 :H7. Sensitivity is the number of true positives, i.e., signatures from E.
  • EXAMPLE 3 This example illustrates the ability of the gas sensor component of the present invention to differentiate between E. coli 0157 :H7 and non-0157 :H7 E. coli field isolates.
  • E. coli isolates were obtained from the Bacteriology Laboratory at the Veterinary Diagnostic Center, University of Kansas. Most of the isolates were collected as part of an ongoing animal production food safety investigation in Midwestern feedyards. Additional isolates were obtained from an outbreak of human illness caused by E. coli 0157 :H7 that had contaminated venison. These isolates had been characterized using biochemical reactions in selective culturing, latex agglutination, and polymerase chain reaction. Of the 20 isolates, 12 were confirmed as E. coli 0157:H7. Procedures for the bacteria culturing and collection of gas signatures were performed as in Example 1. All isolates were grown in nutrient broth to create stock cultures. The bacteria concentration in the stock cultures was determined by viable plate count procedures.
  • ANN artificial neural network
  • BRAINMAKER California Scientific Software (1998) interpretation
  • the 32 E. coli gas signatures were generated in Example 2 were used to train the ANN for pattern recognition.
  • the ANN was configured for pattern recognition and data classification.
  • Gas signatures from all 20 field isolates were subject to interpretation and classification by the trained ANN.
  • Xi is the voltage data point
  • i is 1, ..., n for all data for each sensor
  • Xmax is the highest voltage point
  • Xmin is the lowest voltage point. This method of normalization was used to reduce variation in the gas patterns caused by background voltage levels or pattern height.
  • the ANN was retrained with the original 32 gas signatures and then tested with the 20 field samples.
  • the ANN determined a probability that the isolate being tested was an E. coli 0157 :H7 or a non-0157 :H7 E. coli .
  • an isolate was classified as E. coli 0157 :H7 or non-0157 :H7 E. coli based on which probability was higher. For example, if the isolate being tested had a greater probability of being E. coli 0157 :H7 than non-0157 :H7 E. coli , it was classified as E. coli 0157 :H7.
  • the ammonia, air contaminants, and alcohol sensors detected gases over time which were indicative of volatile breakdown products of bacterial growth and metabolism.
  • Many of the gas signatures shared shape characteristics similar to either the standard E. coli 0157 :H7 or non-0157 :H7 E. coli isolated tested supra.
  • there was greater variation in the overall form of the gas signatures which may have been a result of strain variations between the field isolates.
  • the greatest variation in gas signatures was observed among the non-0157 :H7 E. coli isolates. All of the gas signatures from E. coli
  • 0157 :H7 isolates shared some general characteristics.
  • Figure 14 shows the gas signatures from the ammonia sensor for each of the E. coli 0157 :H7 field isolates and Figure 15 shows the gas signatures from the ammonia sensor for each of the non-0157 :H7 E. coli field isolates.
  • E. coli 0157 :H7 isolates obtained from similar sources produced gas signatures that were visually most closely alike. For example,
  • Figure 16 shows that the isolates obtained from the outbreak of human illness had very similar signatures and Figure 17 shows that isolates that were obtained from the same feedlots but at different times and locations showed the same pattern of gas signatures.
  • Gas sensor-based technology in conjunction with ANN, has been previously used to differentiate between classes of bacteria (Gardner et al . , Measurement Sci. Technol. 9: 120-127 (1998)).
  • a gas sensor apparatus was developed to differentiate E. coli 0157 :H7 from non-0157 :H7 E. coli based upon unique gas signatures generated during bacterial growth under laboratory conditions.
  • gas signatures were generated and analyzed by ANN. The sensitivity and specificity of this system ranged from 81-92% and 63-71%, respectively, depending on the types of gas signature analyzed.
  • the apparatus was able to distinguish E. coli 0157 :H7 and non-0157 :H7 E. coli isolates obtained from various field situations, including those associated with an outbreak of clinical human illness and from multiple cattle production systems. Greater variation in the bacterial strains and patterns of gas emissions made the correct classification of the field isolates using the ANN less accurate. Although the overall shape of the gas signatures showed some variation, the isolates of E. coli 0157 :H7 shared some general visual characteristics. Greater conformity of the gas signatures of the E. coli 0157 :H7 isolates was seen when the isolates were sorted by source. For example, isolates originating from an outbreak of human illness had virtually identical gas signatures.
  • Additional methods of data normalization for the output from the gas sensors will further improve the accuracy of the ANN.
  • These means of normalizing the data can serve to further reduce the specific types of differences between the gas sensors, which would make pattern recognition by the ANN easier.
  • the analytical program that is used for the ANN analysis would allow for greater variation in the gas patterns that were observed for the numerous non- 0157 :H7 E. coli serotypes.
  • the diagnostic value of the apparatus will be enhanced.
  • EXAMPLE 4 The current method of detecting soft-rot in potato storage bins consists of visual inspection and odor detection by bin managers. This type of inspection is greatly limited by the thresholds of human senses; often infections are not discovered until considerable damage has been done. These limitations can be overcome by the use of an electronic nose. This example shows the effectiveness of the gas sensing component of the present invention as a potential early-warning diagnostic tool for soft-rot disease.
  • FIG. 18A shows the diagram for the heater circuit.
  • Figure 18B shows the diagram for the TGS 822 (Figaro, Japan) sensor (transducer) and B&B 232 SDA12 A to D converter.
  • the sensors are connected to a data acquisition system. When exposed to a volatile, the outputs of the sensors were changed to electronic signals and sent to the data acquisition system. These signals were read and quantified as a number between 0 and 5 volts.
  • the system allows projection of a real-time graph of all sensor outputs, which gives a constantly updated picture of the relative concentration of volatiles, relative humidity, and temperature of the headspace gas.
  • S is the concentration of the alcohol solution in ppm and T is the temperature in degrees Celsius.
  • T is the temperature in degrees Celsius.
  • standard solutions were made by adding 57.9 mg of 99.9% liquid acetone to 100 ml distilled water.
  • the standard solutions was placed in glass jars and positioned inside the sampling units for about one hour to enable headspace equilibrium to be achieved before the voltage readings were taken.
  • One of the sampling units was set up as a control, which measured the output from distilled water.
  • the data was logged and analyzed for correlation between headspace concentration and voltage output.
  • a gas chromatograph (GC) was used to corroborate the calculated and measured concentrations of the ethanol standards.
  • Inoculum of Erwinia carotovora var. carotovora was prepared from overnight cultures of the bacterium grown in LB broth. The culture was diluted with 0.1 M MgS0 and adjusted to a concentration of about 10 8 CFU/ml by measuring the absorbance of the bacterial suspension at 610 nm with a spectrophotometer. Assortments of Snowden potatoes were then inoculated with the bacteria by pipetting 0.1 ml of the fluid culture and stabbing it 1.5 cm into the potatoes with a disposable pipette. Control potatoes were similarly stabbed with empty pipettes. For both, the pipette tips were left in place after stabbing. Once inoculated, the infected potatoes were placed in the sampling units.
  • the gas sensing component of the present invention showed that there was a linear relationship between ethanol, acetone, and isopropanol headspace concentrations and voltage output, which are shown in Figures 19, 20, and 21, respectively.
  • Figure 19 shows the sample GC validation for the ethanol standard solution. The zero of the volatile sensor averaged about 0.2 volts. A 5 volt reading was achieved for about 300 ppm of the ethanol standard and the isopropanol standard ( Figure 20) and about 150 ppm for the acetone standard ( Figure 21) .
  • Figure 22 shows that there was a linear relationship between GC spike length and ethanol concentration. The real-time gas sensor voltage readings of the infected potatoes versus the non-infected potatoes revealed a significant difference in the amount of headspace volatiles produced.
  • GC/MS Gas chromatography/ mass spectrometry
  • This example shows that the voltage readings of the gas sensing component of the present invention correlated highly with volatile concentrations and rotted tissues in potatoes.
  • GC/MS analysis of the headspace gases revealed high levels of dimethyl disulfide, acetone, isopropanol, 1-butanol, and carbon dioxide from E. carotovora-infected potatoes.
  • the differences that were observed in the voltage readings for a gradient of potato infection illustrates that the gas sensing component of the present invention is able to identify the degree of soft-rot infection in potatoes by emission of volatiles.
  • Use of the gas sensing component of the present invention is simple to use and does not require special training or experience.
  • the gas sensing component is useful to potato farmers and potato distributors.
  • the gas sensing component can save money, time, and waste to the potato industry by providing a low-cost system for detecting volatiles in real-time that allows for the rapid and continuous monitoring of volatiles produced by infected potatoes in potato storage bins.
  • EXAMPLE 5 This example describes the apparatus of the present invention and illustrates its use in detecting Salmonella grown on Alfalfa sprouts.
  • FIG. 25 shows a schematic diagram of the apparatus of the present invention.
  • the apparatus comprises the gas sensor chamber comprising the gas sensors, a 35 ml open container (FUV cuvette available from Spectrocell, Inc.), PC plug-in UV-vis spectrometer master channel with grating of 200-850 nm and 25 micron slit and with OFLV detector, spectrometer slave board with 650-1000 nm grating and 25 micron slit, collimating lens, 2 meters each of 300 and 400 micron patch fibers, an ultraviolet (UV) , visible (vis) , and near infrared
  • UV cuvette available from Spectrocell, Inc.
  • PC plug-in UV-vis spectrometer master channel with grating of 200-850 nm and 25 micron slit and with OFLV detector
  • spectrometer slave board with 650-1000 nm grating and 25 micron slit
  • collimating lens 2 meters each of 300 and 400 micron patch fibers
  • NIR deuterium tungsten light source
  • collimating lens holder collimating lens holder
  • personal computer and block heater
  • Spectrometer, lenses, and patch fibers can be purchased from Ocean Optics, (Dunedin, Florida) .
  • the apparatus rapidly detects foodborne and waterborne pathogens, such as Salmonella spp., E. coli 0157 :H7 and other E. coli species, and Listeria spp., in packaged plant food products, other food matrices, and environmental samples .
  • the invention is an improvement over conventional methods for detecting volatile compounds.
  • the conventional method of "seeing" volatile compounds use gas chromatography (GC) , which require extensive sample preparation, expensive, and bulky. In some methods, the gases are trapped in appropriate reagents and then analyzed using high-performance liquid chromatography (HPLC) . This method requires skilled personnel.
  • GC has not been used to detect pathogen contamination.
  • the apparatus of the present invention is portable; it uses a fiber optic PC plug-in spectrometer with an excitation source from the ultraviolet to the near infrared regions of the light spectrum; sample preparation is simple (no technical personnel are required, the user merely places the sample in the cuvette) ; and the apparatus is used with industries concerned with food safety.
  • a sample is placed inside the 35 ml, 10 cm cuvette, which sits on the heating block inside the chamber.
  • the cuvette is connected to two collimating lenses for illumination.
  • the heating block is maintained at 37° C (optimum temperature for bacterial growth) .
  • Gaseous compounds emitted from microbial growth accumulate above the sample inside the headspace of the cuvette.
  • the volatile compounds are detected by the gas sensors and absorption, reflectance, and scattering of the volatile compounds are measured using the UV/vis/NIR fiber optic spectrophotometer. Gas sampling is every five minutes and spectral sampling is every two hours.
  • the gas signatures or patterns and the spectral footprints are analyzed by ANN and compared to standard gas signatures and spectral footprints indicative of the presence of a particular microorganism.
  • the present invention is more specific than either a gas sensor or spectrophotometer alone.
  • Figure 26 shows the typical gas pattern and cell count of Salmonella typhymuri um.
  • the graph shows the gas signatures for S. typhymurium produced using an alcohol sensor, an H 2 S sensor, an amine sensor, a methane sensor, a hydrogen sensor, an alcohol vapor sensor, and an air contaminants sensor.
  • Figure 27 shows the gas patterns of S. typhymurium grown in alfalfa sprouts.
  • the graph shows the gas signatures for S. typhymuri um produced using an alcohol sensor, an H2S sensor, an amine sensor, a methane sensor, a hydrogen sensor, an alcohol vapor sensor, and an air contaminants sensor.
  • Figure 28 shows the spectral footprints of the volatile compounds emitted by S.
  • S. typhymurium grown on alfalfa sprouts produces a spectral footprint that is distinguishable from the spectral footprint produced by alfalfa sprouts alone or S. typhymurium grown in TSB.
  • Figure 29 shows that using the amine sensor, the gas pattern of Salmonella is distinguishable from that of E. coli 0157 :H7, both grown in TSB. While the present invention is described herein with reference to illustrated embodiments, it should be understood that the invention is not limited hereto. Those having ordinary skill in the art and access to the teachings herein will recognize additional modifications and embodiments within the scope thereof. Therefore, the present invention is limited only by the claims attached herein.

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Abstract

L'invention concerne un procédé et un appareil de détection de produits volatiles dans un échantillon à l'aide d'un transducteur changeant de tension en fonction du contact des produits volatiles avec le transducteur, afin de produire une signature de gaz des produits volatiles, et d'un spectrophotomètre permettant d'analyser les produits volatiles afin de produire une empreinte spectrale des produits volatiles. L'appareil et le procédé sont utilisés pour détecter l'altération d'une matière biologique, telle qu'un aliment. L'appareil est aussi utilisé pour détecter des micro-organismes contaminant un produit alimentaire ou un échantillon environnemental et par comparaison de la signature de gaz et de l'empreinte spectrale à une banque de signatures de gaz et à des empreintes spectrales, l'appareil permet l'identification des micro-organismes et en particulier l'identification de micro-organismes pathogènes.
PCT/US2001/021031 2000-07-03 2001-07-02 Procede et appareil de detection de produits volatiles dans un echantillon WO2002003047A2 (fr)

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AU2001271760A AU2001271760A1 (en) 2000-07-03 2001-07-02 Method and apparatus for the detection of volatile products in a sample

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US21592400P 2000-07-03 2000-07-03
US60/215,924 2000-07-03

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6905316B2 (en) * 2000-10-13 2005-06-14 Holset Engineering Company, Ltd. Turbine
CN103399046A (zh) * 2013-08-09 2013-11-20 中国农业科学院农产品加工研究所 一种辅助鉴别梨产地的方法
CN104730140A (zh) * 2013-07-30 2015-06-24 中国标准化研究院 一种电子鼻检测蜂蜜中的参数优化方法
CN104849321A (zh) * 2015-05-06 2015-08-19 浙江大学 一种基于嗅觉指纹图谱快速检测柑橘品质的方法
WO2017178774A1 (fr) * 2016-04-15 2017-10-19 Ethera Système de contrôle de la qualité de l'air dans un environnement clos
CN108195895A (zh) * 2017-12-26 2018-06-22 山东农业大学 一种基于电子鼻和分光测色仪的茶树叶片氮含量快速检测方法
CN109187861A (zh) * 2018-08-31 2019-01-11 苏州出入境检验检疫局检验检疫综合技术中心 一种基于载气式电子鼻的猪肉新鲜度检测方法
CN109631790A (zh) * 2019-01-09 2019-04-16 中国科学院新疆天文台 一种天线副反射面支撑腿变形在线测量装置及测量方法
CN110441267A (zh) * 2019-08-09 2019-11-12 张建会 一种食品质量检测的食品组成元素光谱检测装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5479815A (en) * 1994-02-24 1996-01-02 Kraft Foods, Inc. Method and apparatus for measuring volatiles released from food products
US5807701A (en) * 1994-06-09 1998-09-15 Aromascan Plc Method and apparatus for detecting microorganisms
US6244096B1 (en) * 1998-06-19 2001-06-12 California Institute Of Technology Trace level detection of analytes using artificial olfactometry

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5479815A (en) * 1994-02-24 1996-01-02 Kraft Foods, Inc. Method and apparatus for measuring volatiles released from food products
US5807701A (en) * 1994-06-09 1998-09-15 Aromascan Plc Method and apparatus for detecting microorganisms
US6244096B1 (en) * 1998-06-19 2001-06-12 California Institute Of Technology Trace level detection of analytes using artificial olfactometry

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6905316B2 (en) * 2000-10-13 2005-06-14 Holset Engineering Company, Ltd. Turbine
CN104730140A (zh) * 2013-07-30 2015-06-24 中国标准化研究院 一种电子鼻检测蜂蜜中的参数优化方法
CN103399046A (zh) * 2013-08-09 2013-11-20 中国农业科学院农产品加工研究所 一种辅助鉴别梨产地的方法
CN104849321A (zh) * 2015-05-06 2015-08-19 浙江大学 一种基于嗅觉指纹图谱快速检测柑橘品质的方法
CN104849321B (zh) * 2015-05-06 2017-10-20 浙江大学 一种基于嗅觉指纹图谱快速检测柑橘品质的方法
WO2017178774A1 (fr) * 2016-04-15 2017-10-19 Ethera Système de contrôle de la qualité de l'air dans un environnement clos
CN108195895A (zh) * 2017-12-26 2018-06-22 山东农业大学 一种基于电子鼻和分光测色仪的茶树叶片氮含量快速检测方法
CN108195895B (zh) * 2017-12-26 2020-04-24 山东农业大学 基于电子鼻和分光测色仪的茶树叶片氮含量快速检测方法
CN109187861A (zh) * 2018-08-31 2019-01-11 苏州出入境检验检疫局检验检疫综合技术中心 一种基于载气式电子鼻的猪肉新鲜度检测方法
CN109631790A (zh) * 2019-01-09 2019-04-16 中国科学院新疆天文台 一种天线副反射面支撑腿变形在线测量装置及测量方法
CN110441267A (zh) * 2019-08-09 2019-11-12 张建会 一种食品质量检测的食品组成元素光谱检测装置

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