Pathogenic sensing, differentiation and quantification platforms

Abstract

A sensor-enabled pathogen identification, differentiation, and quantification system that integrates into supply chains in an end-to-end fashion and is built on a multi-source, multi-sectoral data environment. The system uses portable sensing platforms that are easily integrated at multiple points along a supply chain and which integrate sample analysis and data processing such that all necessary work can be performed on-site and / or via wireless data transfer.

Description

PATHOGENIC SENSING, DIFFERENTIATION AND QUANTIFICATION PLATFORMSGOVERNMENT RIGHTS

[0001] This invention was made with government support under 2236622 awarded by the National Science Foundation. The government has certain rights in the invention.CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] The present application claims priority benefits from U.S. provisional patent application Ser. No. 63 / 583,603 filed September 19, 2023.FIELD

[0003] The present teachings relate to chemical and biological sensing technologies, and particular to SERS, and impedance devices fabricated to identify the presence or absence of specific pathogens, differentiate serovars of pathogens and determine the quantity of the pathogen present in a sample.BACKGROUND

[0004] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0005] Unsafe food consumption is a major global health concern, causing approximately 600 million cases of foodborne illness and 420,000 deaths annually worldwide. In the United States, foodborne diseases remain a significant problem, with Salmonella being a leading cause. Each year in the US, Salmonella is responsible for 1.35 million infections, 26,500 hospitalizations, and 420 deaths. Despite national efforts to improve food safety, the incidence of Salmonella infections has not decreased in the past three decades. The economic impact of Salmonella in the US is substantial, costing an estimated $3.7 billion annually in medical expenses, lost productivity, and deaths. This burden disproportionatelyaffects vulnerable populations, including those with lower socioeconomic status and underrepresented ethnic groups, further exacerbating financial challenges for individuals already facing difficulties.

[0006] The poultry industry plays a significant role in Salmonella transmission, with fresh and processed poultry accounting for about 23.4% of salmonellosis cases. In commercial poultry operations, Salmonella spreads through both vertical transmission (from parent chickens to offspring) and horizontal transmission (within the entire flock). These transmission routes contribute to increased food safety risks in the poultry supply chain.

[0007] As is the case with many food-borne pathogens, Salmonella detection currently relies heavily on Polymerase Chain Reaction (PCR) methods. These require 24 hours, including enrichment time, to detect Salmonella, followed by an additional 5-7 days for culture-based confirmation of positive samples. While considered gold standards, both conventional and PCR-based methods are time-consuming and expensive. The delay in obtaining results from these detection methods hinders poultry companies from implementing timely Salmonella intervention strategies to enhance food safety.

[0008] In response to these limitations, researchers have investigated various rapid foodborne pathogen detection methods over the past two decades. These include electrochemical, impedance, optical, lateral flow assay, potentiometric, and quartz crystal microbalance techniques. However, many of these methods still require significant instrumental input, sample preparation, and financial investment.

[0009] The current challenge lies in developing sensing technologies that can rapidly detect Salmonella and other pathogen contaminations directly from poultry samples at processing plants without the need for enrichment. Such technologies would enable timely, data-driven food safety decisions. This advancement would address the critical need for faster, more cost-effective pathogen detection in many industries.

[0010] Efforts to tackle Salmonella and other pathogens have also historically been siloed due to the lack of an integrated data environment across an end-to-end (farm-to-fork) supply chain, making it difficult to implement and evaluate the policies and interventions to reduce infections. The lack of integrated data on population health, poultry / food production, and environment has resulted in the absence of coordinated, evidence-based food safety decision systems along the supply chain.

[0011] In sum, existing systems for pathogen detection are slow, expensive, generally separate from key elements in supply chain processes, and in various modes can fail to adequately differentiate and quantify pathogens. The lack of portability of such systems, the fact that they often require shipping samples and / or sample data to remote labs, and the inability to sufficiently track, organize, authenticate, and contextualize such data with data from supportive sensors has made the work of ensuring safe, well-monitored supply chains very difficult. Thus, there exists a need in the art for a sensor-enabled pathogen identification, differentiation, and quantification system that integrates into supply chains in an end-to-end fashion, that is built on a multi-source, multi-sectoral data environment, and that can be housed onsite without need for sending samples to external labs.BRIEF SUMMARY

[0012] In various embodiments, presented disclosure provides a sensing system for the identification, differentiation, and quantification of at least one pathogen. In various instances the sensing system comprises at least one sensing platform configured for wireless data transfer, wherein the sensing platform comprises at least one pathogen sensor. The at least one pathogen sensor generates pathogen sensor data in response to contact with a sample of interest. The sensing platform additionally comprises at least one supportive sensor, and a computer communicatively connected to the at least one sensing platform. The computer is configured and enabled to store and process the pathogen sensor data. In various instances the at least one sensing platform is distributed at various points ina distributed sensor network.

[0013] In various other embodiments the present disclosure provides a method for the identification, differentiation, and quantification of at least one pathogen in at least one point in a supply chain. In various instances the method comprises installing a sensing system in the at least one point in the supply chain, where the sensing system comprises at least one sensing platform configured for wireless data transfer. In various instances each of the sensing platforms comprises at least one pathogen sensor, wherein the pathogen sensor generates pathogen sensor data in response to contact with a sample of interest. In various instances each of the sensing platforms additionally comprises at least one supportive sensor, and a computer configured and enabled to store and process the pathogen sensor data. The at least one sensing platform is distributed at various points in a distributed sensor network. The method additionally comprises using the computer to process the pathogen sensor data, thereby generating processed sensor data and interpreting the processed sensor data to identify, differentiate, and / or quantify the at least one pathogen.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1A exemplarily illustrates a high-level schematic of the SENSD sensing system in accordance with various embodiments of the present disclosure.

[0015] Figure 1 B exemplarily illustrates a high-level schematic of the sensing platform in accordance with various embodiments of the present disclosure.

[0016] Figure 1 C exemplarily illustrates a high-level schematic of the sensing platform including a computer in accordance with various embodiments of the present disclosure.

[0017] Figure 2A shows an exemplary detailed schematic of the SENSD sensing system comprising impedance sensors and associated circuitry in accordance with various embodiments of the present disclosure.

[0018] Figure 2B depicts an exemplary sensing platform thatcomprises multiple impedance sensors, a wireless adapter, impedance and function generation circuits, a power source, a computer, and a graphical user interface, in accordance with various embodiments of the present disclosure.

[0019] Figure 3A provides an exemplary schematic view of the SENSD sensing system comprising a SERS sensing system and associated hardware in accordance with various embodiments of the present disclosure.

[0020] Figure 3B depicts an exemplary sensing platform that comprises a SERS sensor with at least one sample chamber, a wireless adapter, a power source, a computer, and a graphical user interface, in accordance with various embodiments of the present disclosure.

[0021] Figure 3C provides an exemplary schematic view of the SENSD sensing system comprising a SERS sensing system and associated hardware with multiple sample chambers distributed in a sensor subnetwork in accordance with various embodiments of the present disclosure.

[0022] Figure 4A provides a schematic of the impedance sensing system circuit in accordance with various embodiments of the present disclosure.

[0023] Figure 4B provides a block diagram of the circuit board for impedance measurements in accordance with various embodiments of the present disclosure.

[0024] Figure 5 provides a schematic of a circuit prototype for the functional generator circuit in accordance with various embodiments of the present disclosure.

[0025] Figure 6 provides an exemplary schematic of an end-to- end poultry supply chain mapped and annotated with candidate sensor placement locations in accordance with various embodiments of the present disclosure.

[0026] Corresponding reference numerals will be used throughout the several figures of the drawings.DETAILED DESCRIPTION

[0027] The following description is merely exemplary in nature and is in no way intended to limit the present teachings, application, or uses. Throughout this specification, like reference numerals will be used to refer to like elements. Additionally, the embodiments disclosed below are not intended to be exhaustive or to limit the invention to the precise forms disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art can utilize their teachings. As well, it should be understood that the drawings are intended to illustrate and plainly disclose presently envisioned embodiments to one of skill in the art, but are not intended to be manufacturing level drawings or renditions of final products and may include simplified conceptual views to facilitate understanding or explanation. As well, the relative size and arrangement of the components may differ from that shown and still operate within the spirit of the invention.

[0028] The following detailed description illustrates the claimed invention by way of example and not by way of limitation. This description will clearly enable one skilled in the art to make and use the claimed invention, and describes several embodiments, adaptations, variations, alternatives and uses of the claimed invention, including what is presently believe is the best mode of carrying out the claimed invention. Additionally, it is to be understood that the claimed invention is not limited in its applications to the details of construction and the arrangements of components set forth in the following description or illustrated in the drawings. The claimed invention is capable of other embodiments and of being practiced or being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0029] As used herein, the word "exemplary" or "illustrative" means "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" or "illustrative" is not necessarily to be construed as preferred or advantageous over other implementations. All ofthe implementations described below are exemplary implementations provided to enable persons skilled in the art to practice the disclosure and are not intended to limit the scope of the appended claims.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is forthe purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a”, "an”, and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises", "comprising", “including”, and “having” are inclusive and therefore specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps can be employed.

[0031] When an element, object, device, apparatus, component, region or section, etc., is referred to as being "on”, “engaged to or with”, "connected to or with”, or "coupled to or with" another element, object, device, apparatus, component, region or section, etc., it can be directly on, engaged, connected or coupled to or with the other element, object, device, apparatus, component, region or section, etc., or intervening elements, objects, devices, apparatuses, components, regions or sections, etc., can be present. In contrast, when an element, object, device, apparatus, component, region or section, etc., is referred to as being "directly on”, “directly engaged to”, "directly connected to”, or "directly coupled to" another element, object, device, apparatus, component, region or section, etc., there may be no intervening elements, objects, devices, apparatuses,components, regions or sections, etc., present. Other words used to describe the relationship between elements, objects, devices, apparatuses, components, regions or sections, etc., should be interpreted in a like fashion (e.g., “between” versus “directly between”, “adjacent” versus “directly adjacent”, etc.).

[0032] As used herein the phrase “operably connected to” will be understood to mean two are more elements, objects, devices, apparatuses, components, etc., that are directly or indirectly connected to each other in an operational and / or cooperative manner such that operation or function of at least one of the elements, objects, devices, apparatuses, components, etc., imparts or causes operation or function of at least one other of the elements, objects, devices, apparatuses, components, etc. Such imparting or causing of operation or function can be unilateral or bilateral.

[0033] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. For example, A and / or B includes A alone, or B alone, or both A and B.

[0034] Although the terms first, second, third, etc. can be used herein to describe various elements, objects, devices, apparatuses, components, regions or sections, etc., these elements, objects, devices, apparatuses, components, regions or sections, etc., should not be limited by these terms. These terms may be used only to distinguish one element, object, device, apparatus, component, region or section, etc., from another element, object, device, apparatus, component, region or section, etc., and do not necessarily imply a sequence or order unless clearly indicated by the context.

[0035] Moreover, it will be understood that various directions such as "upper", "lower", "bottom", "top", "left", "right", "first", "second" and so forth are made only with respect to explanation in conjunction with the drawings, and that components may be oriented differently, for instance, during transportation and manufacturing as well as operation. Because manyvarying and different embodiments may be made within the scope of the concept(s) taught herein, and because many modifications may be made in the embodiments described herein, it is to be understood that the details herein are to be interpreted as illustrative and non-limiting.

[0036] The terms “plasmonic” and “plasmonic sensor” as used herein refer to sensors that in some way take advantage of the phenomenon of surface plasmons.

[0037] The terms “sensor” and “detector’ are used interchangeably herein to refer to a device that is capable of generating a signal to indicate the presence of a particular biological or chemical specimen.

[0038] The term “SERS” as used herein is an acronym referring to Surface Enhanced Raman Scattering.

[0039] The terms “optical fiber” and “fiber optic” as used herein refer to a broad class of fibers typically made of glass or plastic and designed to transmit light between the two ends of the fiber by means of a phenomenon known as total internal reflection.

[0040] The terms “nanoantenna” and “nanoantennae” as used herein refer to features of an array that can be either holes present in a conductive layer or discs protruding upward from a conductive layer, and whose scale is suitably measured in nanometers.

[0041] The term “microfluidic” as used herein refers to techniques or apparatuses that attempt to exert precise control over the flow of fluids and typically include small, micron-scale manipulations or features.

[0042] The term “photoresist” as used herein refers to both ‘positive’ and ‘negative’ photoresists, with necessary distinctions provided either explicitly or by context.

[0043] The term “optical detector” as used herein refers to any device known in the art to be capable of the collection of optical information, such as light. An optical detector can be but need not be independently capable of digitization or other processing of that information, and no limitson the type or content of optical information are implied herein unless otherwise stated.

[0044] The term "supply chain" as used herein refers to the comprehensive network of activities, entities, and processes involved in the production, handling, processing, transportation, distribution, and delivery of food products from origin to end consumer, including but not limited to primary production (such as farming, ranching, fishing, or cultivating), harvesting, breeding, feed production, slaughter (where applicable), processing, packaging, storage, quality control, logistics, wholesaling, and retail operations. “Points” along a supply chain are understood to refer to physical locations that are representative of the progress of a food product in a supply chain.

[0045] The term “signal” as used herein refers to a time-varying quantity that represents a measurable physical phenomenon or property detected by a sensor. The signal typically carries information corresponding to an analyte of interest, such as, in the context of the present disclosure, a pathogen. Data from a sensor may include one or more signals, each corresponding to one or more pathogens or other chemical or biological specimens of interest.

[0046] The following detailed description comprises disclosure of a sensor-enabled pathogen identification, differentiation, and quantification system that integrates into supply chains in an end-to-end fashion and is built on a multi-source, multi-sectoral data environment. The system uses portable sensing platforms that are easily integrated at multiple points along a supply chain and which integrate sample analysis and data processing such that all necessary work can be performed on-site and / or via wireless data transfer. In all embodiments, the system comprises one or more pathogen sensors, such as impedance sensors, and / or SERS sensors. The system incorporates data from the one or more pathogen sensors, operational data, including data from one or more supportive sensors, and administrative data. Provided herein is a generalized high-level descriptionof the system, followed by exemplary embodiments that detail some of the sensing technologies that it can incorporate, such as impedance sensors and SERS sensors.

[0047] Referring to FIGS. 1A-1C, the present disclosure generally provides a SENSD sensing system 100 that is structured and operable to collect sensor data related to at least one pathogens of interest and process that data. As shown in Figure 1A, the SENSD sensing system 100 generally comprises one or more sensing platforms 200, each configured and enabled for wireless data communication with other one or more sensing platforms 200 and / or with the computer 400, distributed at various points in a distributed sensor network 300. The distributed sensor network 300 is a geographical area, such as a poultry processing plant, warehouse, a factory floor, a loading zone for shipping trucks, or any other physical location defined by the locations of the one or more sensing platforms 200 that are operatively connected to one another, a wireless node, and / or a computer, as for example by wireless data connection.

[0048] Each of the one or more sensing platforms 200 comprises one or more pathogen sensors 201. Each of the one or more pathogen sensors 201 generates pathogen sensor data 201a in response to contact with a sample of interest 160. The sample of interest 160 can be any physical sample of a substance intended to be processed into a food product or in the process of being processed into a food product, such as a liquid extract from poultry, and can vary for each of the one or more pathogen sensors. For example, a sample of interest 160 can be an extract from a plant or animal treated with a liquid solvent. Each of the one or more sensing platforms 200 can also comprise one or more supportive sensors 202 configured to provide data related to the environment immediately surrounding each of the one or more sensing platforms 200. Each of the one or more supportive sensors generates supportive data 202a. In various embodiments as exemplarily depicted in FIGS. 1 B and 1 C, the one or more supportive sensors 202 can comprise a positioning sensor 203, atemperature sensor 204, a pressure sensor 205, an orientation sensor 206, and / or a humidity sensor 207.

[0049] FIGS. 1A-1 C also depict means by which pathogen sensor data 201 a acquired by the one or more pathogen sensors 201 and supportive data 202a acquired by the one or more supportive sensors 202 can be collected, stored, and processed. As exemplarily depicted in FIGS. 1 B and 1 C, all the pathogen sensor data 201 a and the supportive data 202a acquired by the one or more pathogen sensors 201 and the one or more supportive sensors 202, respectively, can be transmitted to a computer 400. In various exemplary embodiments and as shown in Figure 1 B, the computer 400 can be remote from the sensing platform 200, and the and therefore the pathogen sensor data 201 a and the supportive data 202a can be transmitted to the computer 400 either by one or more long peripheral connectors or by a wireless connection. In other exemplary embodiments and as shown in Figure 1 C, the computer 400 can be onboard each sensing platform 200. The computer 400 is configured to store the pathogen sensor data 201a and the supportive data 202a. The computer is also configured to store administrative data to correlate the pathogen sensor data 201 a with a lot number, sample date, or other administrative data related to the sample of interest 160. In various exemplary embodiments, the computer 400 is also configured and enabled to process the pathogen sensor data and / or the supportive data and to display a graphical user interface (GUI) 403 for a user to interface with the computer 400.

[0050] Additionally, as exemplarily shown in Figure 1A, in various embodiments all the pathogen sensor data and the supportive data collected by the one or more pathogen sensors 201 and the one or more supportive sensors 202 can be transmitted via communication with one or more wireless node 310 to the computer. In such an instance, the computer 400 can be a node computing system 410 communicatively connected to the wireless node(s) 310. In various embodiments, the node computing system 410 can be a local distributed system of multiple networked computersconfigured and enabled to work together to identify and quantify both known and emerging pathogen serotypes of interest in the sample of interest 160. For example, the node computing system 410 can track and identify pathogens such as Salmonella in a poultry-related sample of interest 160 from pathogen sensor data acquired by each of the one or more pathogen sensors 201. In various embodiments, the node computing system 410 can also track the incidence, growth, and / or transformation of signals in the pathogen sensor data related to pathogen serotypes that may be emerging in the environment from which the sample of interest 160 was acquired.

[0051] In various exemplary embodiments, the computer 400 and / or the node computing system 410 receive and utilize the supportive data 202a in order to validate, qualify, and or better interpret and process the pathogen sensor data 201 a. For example, in various embodiments, there can be required or optimal operating conditions defined by pressure, temperature, and humidity ranges outside of which the one or more sensing platforms 200 cannot reliably operate. In such exemplary embodiments, the supportive data 202a from the temperature sensor 204, pressure sensor 205, and humidity sensor 207 can be used to determine whether the one or more sensing platforms 200 are within the required or optimal operating conditions and thus whether the concurrently-acquired pathogen sensor data 201 a can be reliably used. In various exemplary embodiments, the supportive data 202a from the orientation sensor 206 can be used to properly process and / or contextualize the pathogen sensor data 201a. For example, the pathogen sensor 201 may be orientation-sensitive, such that pathogen data 201 a generated by the pathogen sensor 201 may need to be calibrated against the orientation of the pathogen sensor 201 in order for calculations of the quantity of a detected pathogen to be correct. In such cases, the supportive data 202a from the orientation sensor 206 can be used to ensure that the pathogen sensor 201 is properly calibrated in light of its orientation. The positioning sensor 203 provides up-to-date coordinates for a location of the sensing platform 200. In various embodiments, the supportive data 202afrom the positioning sensor 203 can be used to correlate the pathogen sensor data 201 a from each pathogen sensor 201 and determine whether any of the one or more sensing platforms 200 has been inappropriately moved. For example, if any of the one or more sensing platforms 200 has been moved to a location that conflicts with its intended location, or to a location that differs from what is described in administrative plans, then a discrepancy will arise between the coordinates provided by the positioning sensor 203 and the location described in the administrative plans.

[0052] The supportive data 202a can be securely stored for future analyses. In various exemplary embodiments, the supportive data 202a can be encrypted and stored in the computer 400 and / or the node computing system 410. In various embodiments the supportive data 202a can be used to help authenticate and validate the pathogen sensor data 201a from each of the one or more pathogen sensors 201 . For example, if the supportive data 202a indicates that a sensor platform 200 is incorrectly positioned or oriented, it can become possible for a user to invalidate that sensor platform’s pathogen sensor data 201 a on the basis that it might not have originated from the sample of interest 106 with which it is intended to be correlated. Similarly, if a user of one of the one or more sensing platforms 200 records pathogen sensor data 201a alongside supportive data 202a that comprises inaccurate time, temperature, humidity or other supportive data, this discrepancy can be used to question the authenticity of the pathogen sensor data 201 a. In various exemplary embodiments, execution of pathogen data analysis algorithms by the computer 400 and / or the node computing system 410 can determine the hierarchically rank elements and / or features of the pathogen sensor data 201 a and the supportive data 202a based on the significance of those elements and / or features for identifying, differentiating, and quantifying a pathogen of interest.

[0053] Considered in their entirety, the one or more sensing platforms 200, in conjunction with the computer 400, are capable of analyzing and processing the pathogen sensor data 201 a to a degreesufficient for it to be unnecessary for the sample of interest 160 to be removed from the distributed sensor network 300 in order to be analyzed or processed at an external laboratory with the same techniques employed by the one or more sensing platforms 200. For example, there will be no need for a sample of interest 160 that has been analyzed by an impedance sensor onboard one of the one or more sensing platforms 200 to be shipped to an external laboratory to undergo analysis by an external impedance sensor. Thus, the one or more sensing platforms 200 enable fully on-site testing and integration, and the sample of interest 160 can remain within the distributed sensor network 300.

[0054] In various exemplary embodiments, the one or more pathogen sensors 201 comprise impedance sensors 210. Figure 2A provides an exemplary schematic depiction of integration of three impedance sensors 210 into the one or more sensing platforms 200. The exemplary sensing platform 200 shown in Figure 2A generally comprises a sample pump system 250 operably connected to impedance sensors 210, one or more sub Z circuits 211 each electrically connected to the sample pump system 250 and to one of the impedance sensors 210, a main microcontroller 218 electrically connected to and configured to control the sub Z circuits 211 , and one or more computers 400 operably connected to the microcontroller 218.

[0055] In various embodiments the sample pump system 250 delivers a liquid sample at a controlled rate to the impedance sensors 210. In various exemplary embodiments, the sample pump system 250 comprises a syringe pump, although other means of conveying liquid sample that are known in the art are considered to be within the scope of the device as described. The impedance sensors 210 can be impedance sensor suitable to perform the functions described herein. For example, in various exemplary embodiments, the impedance sensors 210 can be impedance sensors such as those described in U.S. Pat. Application No. 18 / 170,815, which is incorporated herein in its entirety. Each sub Z circuit 211 that iselectrically connected to one of the impedance sensors 210 comprises three direct digital synthesizer (DDS) circuits 214, 216, 217. The DDS circuit 214 generates a sine waveform signal 215 to measure the impedance of the control and trapping electrodes in one of the impedance sensors 210. In various exemplary embodiments, the sine waveform signal can be a 500 mV signal deployed at two different frequencies, 100 Hz and 1 kHz. The DDS circuit 216 generates a focusing signal 218 and the DDS circuit 217 generates a trapping signal 219. The focusing signal 218 and the trapping signal 219 are transmitted to focusing and trapping electrodes, respectively, of one of the three impedance sensors 210. In various exemplary embodiments, the focusing signal 218 can be 5 volts peak-to-peak (Vpp) and can have a frequency of 6 MHz. In various exemplary embodiments, the trapping signal 219 can be 4 Vpp and can have a frequency of 5 MHz.

[0056] Each sub Z circuit 211 also comprises a pump controller 251 electrically connected to the sample pump system 250 as well as an automatic impedance measurement circuit 252. The pump controller 251 turns the sample pump system 250 on or off according to system requirements to ensure that the flow of sample over the electrodes of each impedance sensor 210 is correctly timed. The automatic impedance measurement circuit 252 automatically measures the impedance of the detection and control electrodes in each impedance sensor 210 using the data acquired from the sine waveform signal 215. In various exemplary embodiments, the automatic impedance measurement circuit 252 measures impedance from all impedance sensors 210 simultaneously and does so in a range between 0 and 50 megaohms (MD).

[0057] In the exemplary embodiment of Figure 2A, a main microcontroller 218 is electrically connected to and controls the sub Z circuit 211 , the one or more supportive sensors 202, a memory hardware 208, and one or more wireless adapters 301. The main microcontroller 218 is also operably connected to the one or more computers 400, whether by a peripheral connector 209 or the one or more wireless adapters 301. Thememory hardware 208 is configured and enabled to store impedance measurements acquired from the automatic impedance measurement circuit 252 as well as supportive data 202a acquired from the one or more supportive sensors 202.

[0058] In various exemplary embodiments, the one or more wireless adapters 301 can be configured and enabled to communicate according to any long or short-range wireless protocol known in the art, such as Wi-Fi and Bluetooth. The peripheral connector 209 can be a USB connector or other physical connector known in the art. The computer 400 can be any computer with a GUI 403 known in the art, including but not limited to laptop computers and mobile devices, as shown.

[0059] An exemplary sensing platform 200 comprising impedance sensors 210 that is self-contained and easily placed at a desired location in a supply chain is shown in Figure 2B. The exemplary sensing platform 200 comprises a casing 150 atop which is placed a GUI 403. The casing 150 enables the portability and ease of placement of the sensing platform 200. The casing 150 itself comprises one or more impedance sensors 210, a wireless adapter 301 , a computer 400, and a power supply 304 that is operably connected to any or all electronic components of the sensing platform 200. The exemplary sensing platform 200 shown in Figure 2B is interconnected and operates as described above and with respect to Figure 2A and serves to illustrate a convenient form factor that various embodiments of the sensing platform 200 can assume.

[0060] In various exemplary embodiments, the one or more pathogen sensors 201 comprise surface-enhanced Raman spectroscopy (SERS) sensors 220. Figure 3A provides a schematic depiction of an exemplary SERS sensor 220 into the one or more sensing platforms 200. The exemplary sensing platform 200 shown in Figure 3A generally comprises a SERS sensor 220 that is integrated into a sample chamber 226 into which sample of interest is introduced. The SERS sensor 220 is formed on the tip or the side of an optical sensing fiber 225 that is operativelycoupled to a multimode optical coupler 224. In various exemplary embodiments, the SERS sensor 220 is as known in the art and / or particularly as is described in U.S. Pat. Application No. 18 / 170,815. The multimode optical coupler 224 effectively bifurcates the optical sensing fiber 225, resulting in two optical fiber paths, a signal path 223a and a processing path 223c. The signal path 223a operatively couples the multimode optical coupler 224 to a laser source 222. The processing path 223c operatively couples the multimode optical coupler 224 to a Raman system 221. The Raman system 221 is electrically connected to the computer 400.

[0061] The operating principle of the exemplary sensing platform shown in Figure 3A is as follows. A sample of interest 160 is introduced to the sample chamber 226 and comes into contact with the SERS sensor 220. Meanwhile, the laser source 222 generates an optical signal 223b that travels along the signal path 223a, through the optical coupler 224, through the sensing fiber 225 and to the SERS sensor220. The optical signal 223b interacts with the sample of interest 160 on the surface of the SERS sensor 220, whereupon a sample signal 223d is generated. The sample signal 223d travels back along the sensing fiber 225, through the optical coupler 224, along the processing path 223c, through a notch filter 227, and into the Raman system 221. The notch filter 227 filters the sample signal 223d to improve its signal-to-noise ratio, and the Raman system 221 receives and digitizes the sample signal 223d so that it can be further processed by the computer 400.

[0062] The use of one or more SERS sensors 220 still enables the one or more sensing platforms 200 to achieve a compact, easily installed form factor without any requirements for external laboratories or sample shipments, as seen in the exemplary embodiment of Figure 3B. The exemplary sensing platform 200 comprises a casing 150 atop which is placed a GUI 403. The casing 150 itself comprises a SERS sensor 220, a wireless adapter 301 , a computer 400, and a power supply 304 that is operably connected to any or all electronic components of the sensingplatform 200. The exemplary sensing platform 200 shown in Figure 3B is interconnected and operates as described above and with respect to Figure 3A and serves to illustrate a convenient form factor that various embodiments of the sensing platform 200 can assume.

[0063] While only a single sample chamber 226 is shown in each of the exemplary sensing platforms of FIGS. 3A and 3B, it is possible to couple a single laser source 222 and Raman system 221 to multiple sample chambers 226, as shown in the exemplary sensing platform of Figure 3C. Most of the exemplary schematic shown in Figure 3C is similar to that shown in Figure 3A, but rather than having the sensing fiber 225 terminate in a single sample chamber 226, the sensing fiber 225 instead passes through multiple sample chambers 226. In this way, a single sensing platform 200 can retain a compact form factor for most of its components while still distributing its sensing capabilities across multiple sampling chambers 226 arranged at various points in a sensor subnetwork 300’. In various exemplary embodiments, the distributed sensor network 300 can thus comprise multiple sensor subnetworks 300’.

[0064] Although the exemplary sensing platforms described above have detailed the use of impedance sensors and SERS sensors, other biological and chemical sensing technologies known to one of ordinary skill in the art are also contemplated to be within the scope of the present disclosure. For example, barcoded nanopore sequencing sensors can be used for simultaneous identification of all single-nucleotide variation markers on a single target DNA molecule, thereby enabling accurate serotyping in a sample of interest.

[0065] Additionally, it is contemplated that multiple pathogen sensor types can be supported on the same sensing platform. Thus, for example, a single sensing platform 200 can comprise at least one impedance sensor 210, at least one SERS sensor 220, at least one nanopore sensor, or any combination thereof.

[0066] The following are non-limiting examples intended toillustrate the methods and devices of the present disclosure in greater detail. EXAMPLES

[0067] Example 1 : Impedance Sensor Prototype

[0068] A plan for an automated impedance sensing system was outlined as follows. The automated impedance sensing system sequentially 1 ) applies a signal to the trapping and focusing electrodes of the three sensors with a defined amplitude and frequency; 2) automatically starts the pump to provide suction to the three sensors simultaneously; and 3) measures the impedance of both the control electrode and the detection electrode of three sensors simultaneously. The sensing system measures 3 biosensors simultaneously. Six biosensors are fabricated on a single glass substrate. Each sensor has a separate set of focusing, trapping, detection, and control electrodes. The trapping and focusing electrodes trap and focus pathogens in a required area, while the detection and control electrodes are used to measure the impedance of the pathogens. The detection electrodes are antibody-coated, whereas the control electrodes are not. The change in impedance value in the case of pathogen antibody-antigen binding in the detection electrodes indicates the presence of pathogens in the sample. The change in impedance value in the control electrode should be approximately 0 Q (<200 kQ), indicating the sensor is working properly. This eliminates the possibilities of false positive. However, if there is a significant change in impedance, this indicates the measurement is not correct, and it is false negative.

[0069] The "Sub Z Circuit" performs the aforementioned three tasks sequentially. This circuit generates 4 Vpp and 5 Vpp signals with frequencies of 5 MHz and 6 MHz to the trapping and focusing electrodes of each sensor separately. The system controls the syringe pump, turning it ON and OFF according to system requirements. Furthermore, it automatically measures the impedance of all the sensors with detection and control electrodes simultaneously, ranging from 0 Q to 50 MQ. For this purpose, a sine waveform signal with an amplitude of 500 mV at two differentfrequencies, 100 Hz and 1 kHz, is used. Three biosensors on a glass slide can be measured simultaneously. The DDS circuit for generating the 100Hz / 1 kHz sinewaves is shared through separate Matched Resistor pairs for each measurement path.

[0070] A Raspberry Pi provides the main compute for the system. It is augmented by a microcontroller to provide real time control of the measurement system as required. The Raspberry Pi provides the necessary communications connectivity (WiFi, Ethernet, USB) for interfacing to external systems. It also includes a GPS receiver to aid in geolocation. The user interface includes an LCD display on the front of the unit and an external Bluetooth keyboard for data entry.

[0071] A Python application on the Raspberry Pi provides the user interface for test initiation, keeps track of the current STATE of the test, and prompts when action is required. The Python application controls the behavior of the Biosensor Controller including timing of the tests and sequence of events. This application also converts the measurement data and compute the impedance or detection results for display to the user. This software also provides data entry to capture administrative data, geospatial and time stamp information for each test. It logs this information and the test results into a database. This database is stored on board in the system in a USB drive or similar storage system with a capacity of 8 GB or more.

[0072] Example 2: Impedance Sensor Prototype Test Program

[0073] The following is an example of how the test will be sequenced by the Python application. 1 ) User clicks on “START” on GUI. 2) Python application configures the Biosensor Controller to read the baseline impedance (Antibody). 3) Python application prompts user to load the sample. 4) User clicks “Next” and Python application configures Biosensor Controller to apply focusing and trapping voltages start the Syringe Pump and sets a 45 minutes timer. 5) When the timer expires it turns the Syringe Pump off and a 10 minutes timer is started to allow Salmonella antigen to bind with antibody. 6) When timer expires the Python application promptsuser to load the water for washing the channel. 7) User clicks “Next” and Python application configures the Biosensor Controller to start Syringe Pump to wash at 3 pL / min and sets a timer for 15 minutes. This will leave antigen / antibody complex on the sensor array. 8) When the timer expires it turns off the Syringe Pump and the impedance is measured at 100Hz and 1 kHz. 9) The Python application will then subtract the baseline impedance from the total impedance to determine if the sample is positive or negative. 10) The result is provided to the user and logged into a database. A schematic of the impedance sensing system is shown in Figure 4A.

[0074] Example 3: Impedance Measurement Block Diagram

[0075] A block diagram of the circuit board for impedance measurements is shown in Figure 4B. Each block of the circuit board is detailed as follows. Messages from the Raspberry Pi instruct the Micro Controller on how to configure the circuitry during each phase of the test. The 5MHz and 6MHz Frequency Synthesizers use Direct Digital Synthesizer (DDS) ICs and will have differential outputs. Outputs are 4V 5MHz Sinewave that connect to the Focusing pads and 5V 6MHz Sinewave that connect to the T rapping pads of each slide. Output impedance is 100 ohms driven from RJ45 style connectors.

[0076] The Impedance Measurement block is the most complex and sensitive circuit and is further detailed below. A DDS circuit that can be programmed to generate either 100Hz or 1 kHz sinewaves provides the source voltage to make the impedance measurement. A buffer converts its output into a 500mV balanced differential output that connects to the sample through two 500,000 ohm.1 % resistors. The ESD Protection block helps protect the circuitry from accidental static discharge. An instrumentation amplifier acts as a high impedance buffer and convert the measurement waveform from a differential to a single ended signal.

[0077] This is followed by a precision rectification stage and filter to create a DC output that can be accurately measured. (This function might be done in software and will be determined once the project is started).The Analog to Digital Converter (ADC) measures the DC output and creates a 32 bit result. This circuit is replicated three times on the PCB to allow measurements of three slides simultaneously. A single DDS 100Hz / 1 kHz and buffer amplifiers is shared. The circuit includes a Measurement Select Relay block to connect the impedance measurement circuit to either the detection electrodes or the control electrodes.

[0078] There are three switches in the impedance circuit. These switches allow the simultaneous measurement of the impedance of the three sensors by connecting to either the detection electrodes or the control electrodes of all three sensors at once.

[0079] Additionally, the “Sub Z Circuit” includes the following. A DC external power supply provides a single DC input that will serve as the primary power supply for the board. These circuits are powered by onboard, locally regulated, and isolated power supplies. It has the capability to integrate with the Raspberry Pi microcontroller, which provides the primary computing power for real-time control of the measurement system as required. The pump Control circuit turns the pump on or off with an appropriate signal. This is a power on I power off output or a digital logic level to start or stop the pump.

[0080] Other features that integrate with the Raspberry Pi microcontroller include: Communication connectivity (Wi-Fi, Ethernet, USB) for interfacing with external systems; a GPS receiver to aid in geolocation; an LCD display on the front of the system for user interface; an external Bluetooth keyboard for data entry; and a memory section for the storage of data.

[0081] The Python programming on the Raspberry Pi 5 microcontroller provides the user interface for conducting all the tests sequentially. After conducting the experiments and measuring the impedance for all three sensors, the data is displayed to the user. This software also facilitates data entry to capture administrative data, along with geospatial and time-stamp information for each test. It logs this informationand the test results into a database. This database is stored on board the system using a USB drive or a similar storage device with a capacity of 8GB or more. Thus, the developed PCB circuit board is capable of performing three tasks: 1 ) Providing signals to the electrodes of the sensor; 2) Controlling the pump flow rate, including turning it ON and OFF; 3) Measuring the impedance of the three sensors simultaneously.

[0082] Example 4: Functional Generator Circuit for the Trapping and Focusing Electrodes

[0083] The circuit was built in the simulation software LTSpice to verify its functionality and make any necessary changes. The direct digital synthesizer (DDS) AD9851 , which will replace the function generator instrument, generates 5 MHz and 6 MHz frequencies with an amplitude of 1.06 Vpp. The AD8001 operational amplifier will be used to increase the signal amplitude to the required value by the focusing and trapping electrodes, that is 4 Vpp and 5 Vpp (See T able 1 ). The purpose of simulating the circuit in LTSpice is to determine the noise I harmonics in the circuit. One key difference when simulating in LTSpice is that it uses the signal in the Vp (peak voltage) form instead of Vpp (peak-to-peak voltage). The changes from Vpp to Vp is implemented during the simulation. The LTSpice circuit prototype for the functional generator circuit is shown in Figure 5. Where Vin1 is the signal voltage used for the focusing electrodes, while the measurement was taken across Voutl . Similarly, Vin2 is the signal used for the trapping electrodes while the measurement was taken across Vout2.Table 1. Summary of Simulation Parameters

[0084] Example 5: Noise Calculation of the Signal

[0085] There was almost very low voltage noise at 5 MHz in the signal. To determine the voltage noise in the signal, modifications wereintroduced in the circuit such as: (a) Introduce ferrite beads (a passive device that filters high frequency noise over a broad frequency range) and a low- pass filter at the output side, (b) Introduce ferrite beads and a low-pass filter at the input side.

[0086] Using these two components in the signal keep the noise level constant at 117.45 pV. The recommended noise level should be less than 0.1 % of the signal the noise level percentage is calculated using the following equation.Percentage 100Percentage 100Percentage = 0.00585%

[0087] Therefore, the noise level in the signal is less than 0.1 % of the total signal.

[0088] Example 6: Noise Calculation of the Signal

[0089] The following components were used in this integration process, (a) Arduino Mega Microcontroller, (b) RS 232 TO TTL converter, (c) Pump terminal software, (d) USB to DB9 Cable. The Arduino can be replaced by Raspberry Pi5 for multitasking procedures.

[0090] To automate the pump, several approaches were tested. The most effective method (Third option in Table 2) involved using a relay in the pump’s power cable combined with a power-up running option. This approach allows for the automation of the syringe pump with a microcontroller.Table 2. Approaches for Automating Pump

[0091] The primary requirement is to automate and control the pump operation based on the experimental needs. The Power Up Running setting was designed to allow the pump to restart automatically. The microcontroller sends a command to the relay in the power supply line of the pump. When the relay receives the command from the microcontroller, it turns on the pump, and the pump will start providing suction. Similarly, when the time expires according to the experiment needs, another command is sent to the relay to turn off the pump. In this way, one is able to automate the pump operation. The relay will turn on the 5V supply for on and off operation.

[0092] Example 7: End-to-End Supply Chain Intervention Points

[0093] An end-to-end poultry supply chain was mapped and candidate sensor placement locations were identified via site visits and expert interviews (Figure 6). Pathogen testing is needed at farms, feed sources (upstream), and during transportation and consumer stages (downstream). This allows processors to optimize testing based on Salmonella presence, like prioritizing high Salmonella loads processing toward the end of the day and implementing interventions such as acid rinses with higher PPM. To optimize sensor selection and placement for sensor- enabled supply chain configuration, a prototype mixed-integer nonlinear programming (Ml NLP) model was developed considering attributes including performance, cost, lead time, and resources. The MINLP model can maximize the system-wide food safety subject to limited budget for pathogen testing and threshold of minimum percentage of monitored nodes in the sensor-enabled supply chain. The MINLP model can be applied in a real-lifelarge-scale poultry supply chain, using efficient metaheuristic algorithms, e.g., genetic algorithm and tabu search for obtaining high-quality solutions efficiently. The integration of sensors and data analytics through the Ml NLP model promises to enhance food safety, addressing pathogen contamination points, and promoting a safer and more efficient food supply chain system.

[0094] In view of the above, it will be seen that the several objects and advantages of the present invention have been achieved and other advantageous results have been obtained. The SENSD sensing system of the present disclosure provides for single or multiplex detection of a vast variety of chemical and biological specimens, provides a variety of low-cost, portable sensing platforms with compact and easily transported form factors, obviates the need for more expensive sample transport logistics or external laboratory testing and validation, processes both pathogen sensor data and supportive data for validating, authenticating, tracking and ranking of data and results, and integrates in a facile way into every point along a product manufacturing and supply chain. The SENSD sensing system permits seamless, end-to-end encoded data transfer, collects administrative data, geospatial data, and time stamps, as well as interventions informed by end-users.

[0095] As various changes could be made in the above constructions without departing from the scope of the invention, it is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.

Claims

CLAIMSWhat is claimed is:

1. A sensing system for the identification, differentiation, and quantification of at least one pathogen, the sensing system comprising: at least one sensing platform configured for wireless data transfer, the sensing platform comprising: at least one pathogen sensor, wherein the at least one pathogen sensor generates pathogen sensor data in response to contact with a sample of interest; at least one supportive sensor; and a computer communicatively connected to the at least one sensing platform, the computer configured and enabled to store and process the pathogen sensor data; wherein the at least one sensing platform is distributed at various points in a distributed sensor network.

2. The sensing system of Claim 1 , wherein the at least one supportive sensor generates supportive data and the computer is configured to utilize the supportive data.

3. The sensing system of Claim 1 wherein the computer is further configured and enabled to store administrative data corresponding to the pathogen sensor data.

4. The sensing system of Claim 1 wherein the computer is communicatively connected to the at least one sensing platform via a wired connection and the computer comprises a plurality of processors wherein each processor is physically located on a respective one of the sensing platforms.

5. The sensing system of Claim 1 wherein the computer is communicatively connected to the at least one sensing platform via a wireless connection and the computer comprises node computing processors.

6. The sensing system of Claim 1 further comprising a graphical user interface that is operably connected to the computer, the graphical user interface configured to permit a user to interface with the computer.

7. The sensing system of Claim 6 wherein the graphical user interface is integrated into a mobile device.

8. The sensing system of Claim 7 wherein the mobile device is operably connected to the computer via a wireless connection.

9. The sensing system of Claim 1 wherein each of the at least one sensing platform comprises a casing.

10. The sensing system of Claim 9 wherein the casing is portable and comprises at least one power supply electrically connected to the at least one pathogen sensor, the at least one supportive sensor, and the computer.11 . The sensing system of Claim 1 wherein at least one of the at least one pathogen sensor comprises an impedance sensor.

12. The sensing system of Claim 11 wherein the at least one sensing platform further comprises: a sample pump system operably connected to the impedance sensors of the at least one pathogen sensor; at least one sub Z circuit, wherein each sub Z circuit is electrically connected to both the sample pump system and at least one of the impedance sensors; anda main microcontroller electrically connected to the at least one sub Z circuit.

13. The sensing system of Claim 12 wherein the at least one sub Z circuit comprises a pump controller that controls the sample pump system.

14. The sensing system of Claim 1 wherein the at least one pathogen sensor comprises at least one SERS sensor.

15. The sensing system of Claim 14 wherein the at least one sensing platform further comprises: at least one sample chamber for receiving the sample of interest; and at least one optical sensing fiber, the optical sensing fiber comprising at least one of the SERS sensors formed on the optical sensing fiber.

16. The sensing platform of Claim 15, wherein multiple SERS sensors are formed on the optical sensing fiber.

17. A method for the identification, differentiation, and quantification of at least one pathogen in at least one point in a supply chain, the method comprising: installing a sensing system in the at least one point in the supply chain, the sensing system comprising: at least one sensing platform configured for wireless data transfer, each of the sensing platforms comprising: at least one pathogen sensor, wherein the pathogen sensor generates pathogen sensor data in response to contact with a sample of interest; at least one supportive sensor; anda computer configured and enabled to store and process the pathogen sensor data; wherein the at least one sensing platform is distributed at various points in a distributed sensor network; using the computer to process the pathogen sensor data, thereby generating processed sensor data; and interpreting the processed sensor data to identify, differentiate, and / or quantify the at least one pathogen.

18. The method of Claim 17 wherein the pathogen sensor data is encoded end-to-end.

Images