Patent Publication Number: US-2022221437-A1

Title: System and method for electrical and magnetic monitoring of a material

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application of U.S. patent application Ser. No. 16/990,716, filed on Aug. 11, 2020, which claims priority to U.S. patent application Ser. No. 15/815,014, filed on Nov. 16, 2017, which claims priority to U.S. 62/422,774, filed Nov. 16, 2016, all of which is incorporated herein by reference. 
    
    
     FIELD 
     The present disclosure relates generally to material monitoring. 
     BACKGROUND 
     There are many materials used today that have characteristics that change over time, have the potential to expire, or may be contaminated. Consumers generally do not have a reliable means of monitoring the current status and characteristics of these products before or after purchasing or delivery. One such class of products is water that can be delivered by plumbing or water bottles. Potential problems with water include contamination, whether in a municipal water distribution system or in a water packaging facility. Another class of such products is beverages, especially wines, which are known to change characteristics over time, including characteristics relevant to taste of the wine. Another class of such products is foodstuffs. A common problem with beverage and foodstuff products is that these products may spoil, decompose, or proceed past their ideal period for consumption, maturity point, or peak flavor point. 
     For water, a consumer typically relies on municipal water treatment systems or quality control in the water bottle packaging facility. For beverage products and foodstuffs, some manufacturers provide an estimated “best before” date or a date on which the product was produced, which serves as a crude benchmark for estimating when a product has spoiled or passed its ideal consumption point. The typical consumer relying on these dates, however, must trust that the product contained within the packaging is still in good condition upon consumption and that it will match the characteristics advertised by the manufacturer. 
     Another class of materials that experiences relevant changes in characteristics over time are chemical products. The changes may be induced by environmental factors or they may occur spontaneously. They may be due to physical process changes such as evaporation or on-going chemical reaction processes such as ion exchange or other reactions. A chemical substance may only be useful to the purchaser when it possesses characteristics within a particular range. 
     Current solutions to monitoring water, beverages, foodstuffs, and similar materials typically involve invasive testing of the product or measurements performed on gas/vapor given off by the product. Many solutions require that the container be opened, thus altering the product&#39;s state or in many cases accelerating the spoiling process. Further, solutions that reference the gas/vapor given off by the product are indirect and may have reduced accuracy or may be incapable of measuring the desired characteristics. 
     SUMMARY 
     According to an aspect of the disclosure, a system for monitoring a characteristic of a material includes a sensor device, the sensor device including at least one electrode, the at least one electrode configured to contact the material and to apply an electrical stimulus to the material and measure an electrical response signal of the material, and at least one magnetic coil, the at least one magnetic coil configured to apply a stimulating magnetic field to the material and measure a magnetic response signal, a computing device configured to apply machine learning for determining a not directly measurable characteristic of the material based on at least the electrical response signal and the magnetic response signal, wherein at least one of the electrical response signal and the magnetic response signal is influenced by at least one of the electrical stimulus and the stimulating magnetic field altered by the material, and wherein the machine learning applied via a machine learning model trained with library data to recognize the not directly measurable characteristic of the material, the library data relating at least one of a previously measured electrical response signal and a previously measured magnetic response signal to a known not directly measurable characteristic of the material, a circuit connecting the sensor device and computing device, and a body housing the sensor device. 
     In some embodiments, the electrical stimulus is generated by transmitting an initiating electrical signal to the at least one electrode, and the stimulating magnetic field is generated by transmitting the initiating electrical signal to the at least one magnetic coil. 
     In some embodiments, the initiating electrical signal includes a varying signal profile. 
     In some embodiments, at least one of the electrical response signal and the magnetic response signal is transformed into a transformed signal profile, and the machine learning is applied to the transformed signal profile. 
     In some embodiments, the stimulating magnetic field includes a sinusoidal oscillating signal. 
     In some embodiments, the at least one electrode includes an input electrode and an output electrode, and the output electrode is configured to apply the electrical stimulus to the material, and the input electrode is configured to measure the electrical response signal. 
     In some embodiments, the at least one magnetic coil includes an input magnetic coil and an output magnetic coil, and the output magnetic coil is configured to apply the stimulating magnetic field to the material, and the input magnetic coil is configured to measure the magnetic response signal. 
     In some embodiments, the system further includes a material conduit, the material conduit defining an interior for transporting the material, the body housing the sensor device is attachable to the material conduit, and the at least one electrode of the sensor device extending into the interior of the material conduit. 
     According to another aspect of the disclosure, a system for monitoring a characteristic of a material includes a sensor device, the sensor device including at least one electrode, the at least one electrode configured to contact the material and to measure an electrical response signal, and at least one magnetic coil, the at least one magnetic coil configured to apply a stimulating magnetic field to the material and to measure a magnetic response signal, a computing device configured to apply machine learning for determining a not directly measurable characteristic of the material based on at least the electrical response signal and the magnetic response signal, wherein at least one of the electrical response signal and the magnetic response signal is influenced by the stimulating magnetic field altered by the material, and wherein the machine learning applied via a machine learning model trained with library data to recognize the not directly measurable characteristic of the material, the library data relating at least one of a previously measured electrical response signal and a previously measured magnetic response signal to a known not directly measurable characteristic of the material, a circuit connecting the sensor device and computing device, and a body housing the sensor device. 
     In some embodiments, the stimulating magnetic field is generated by transmitting an initiating electrical signal to the at least one magnetic coil, the initiating electrical signal including a varying signal profile. 
     In some embodiments, the magnetic response signal is transformed into a transformed signal profile, and the machine learning is applied to the transformed signal profile. 
     In some embodiments, the stimulating magnetic field includes an sinusoidal oscillating signal. 
     In some embodiments, the at least one magnetic coil includes an input magnetic coil and an output magnetic coil, and wherein the output magnetic coil is configured to apply the stimulating magnetic field to the material, and the input magnetic coil is configured to measure the magnetic response signal. 
     In some embodiments, the system further includes a material conduit, the material conduit defining an interior for transporting the material, wherein the body housing the sensor device is attachable to the material conduit, the at least one electrode of the sensor device extending into the interior of the material conduit. 
     According to another aspect of the disclosure, a system for monitoring a characteristic of a material includes a sensor device, the sensor device including at least one electrode, the at least one electrode configured to contact the material and to apply an electrical stimulus to the material, and at least one magnetic coil, the at least one magnetic coil configured to apply a stimulating magnetic field to the material and to measure a magnetic response signal, a computing device configured to apply machine learning for determining a not directly measurable characteristic of the material based on at least the magnetic response signal, wherein the magnetic response signal is influenced by at least one of the electrical stimulus and the stimulating magnetic field altered by the material, and wherein the machine learning applied via a machine learning model trained with library data to recognize the not directly measurable characteristic of the material, the library data relating at least one of a previously measured electrical response signal and a previously measured magnetic response signal to a known not directly measurable characteristic of the material, a circuit connecting the sensor device and computing device; and a body housing the sensor device. 
     In some embodiments, the electrical stimulus is generated by transmitting an initiating electrical signal to the at least one electrode, and the stimulating magnetic field is generated by transmitting the initiating electrical signal to the at least one magnetic coil, and wherein the initiating electrical signal comprises a varying signal profile. 
     In some embodiments, the magnetic response signal is transformed into a transformed signal profile, and the machine learning is applied to the transformed signal profile. 
     In some embodiments, the at least one magnetic coil includes an input magnetic coil and an output magnetic coil, and wherein the output magnetic coil is configured to apply the stimulating magnetic field to the material, and the input magnetic coil is configured to measure the magnetic response signal. 
     In some embodiments, the system further includes a material conduit, the material conduit defining an interior for transporting the material, the body housing the sensor device is attachable to the material conduit, the at least one electrode of the sensor device extending into the interior of the material conduit. 
     According to another aspect of the disclosure, a system for monitoring a characteristic of a material includes a sensor device, the sensor device including at least one magnetic coil, the at least one magnetic coil configured to apply a stimulating magnetic field to the material and to measure a magnetic response signal, a computing device configured to apply machine learning for determining a not directly measurable characteristic of the material based on at least the magnetic response signal, wherein at least the magnetic response signal is influenced by the stimulating magnetic field altered by the material, and wherein the machine learning applied via a machine learning model trained with library data to recognize the not directly measurable characteristic of the material, the library data relating at least one of a previously measured magnetic response signal to a known not directly measurable characteristic of the material, a circuit connecting the sensor device and computing device, and a body housing the sensor device. 
     In some embodiments, the sensor device further includes at least one electrode, the at least one electrode configured to contact the material and to measure an electrical response signal, the computing device is configured to apply machine learning for determining a not directly measurable characteristic of the material based on at least the electrical response signal and the magnetic response signal, at least one of the electrical response signal and the magnetic response signal is influenced by the stimulating magnetic field altered by the material, and the library data relates at least one of a previously measured electrical response signal and a previously measured magnetic response signal to a known not directly measurable characteristic of the material. 
     Other features and advantages are described below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Preferred embodiments of the present disclosure will now be described, by way of example only, with reference to the attached Figures, wherein: 
         FIG. 1  depicts a schematic diagram of a system for monitoring characteristics of a material, according to a non-limiting embodiment; 
         FIG. 2  depicts a perspective view of a device for monitoring characteristics of a material, according to a non-limiting embodiment; 
         FIG. 3  depicts a functional block diagram of the device of  FIG. 2 ; 
         FIG. 4  depicts a flowchart of a method for determining a characteristic of a material, according to a non-limiting embodiment; 
         FIG. 5  depicts a schematic diagram of the generation and measurement of electrical and magnetic signals for use in a machine learning model; 
         FIG. 6  depicts a flowchart of a method for initializing a device for monitoring characteristics of a material, according to a non-limiting embodiment; 
         FIG. 7  depicts a functional block diagram of a device for monitoring characteristics of a material, according to another non-limiting embodiment; 
         FIG. 8  depicts a schematic diagram of a system for monitoring characteristics of a material, according to another non-limiting embodiment; and 
         FIG. 9  depicts a perspective view of a device for monitoring characteristics of a material, according to another non-limiting embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The disclosure relates to a system and method for monitoring a characteristic of a material by measuring electrical or magnetic properties of the material. The system includes a material monitoring device having at least one electrode and at least one magnetic coil, and is in communication with a machine learning model trained to recognize characteristics of the material based on electrical and magnetic properties of the material. The material can be stimulated with an electrical stimulus or stimulating magnetic field, and an electrical response signal or magnetic response signal can be measured. This disclosure discusses applications to the monitoring of water quality, beverages, foodstuffs, and other materials. 
     The system may include a computing device hosting a database and a machine learning model, or may include a cloud computing environment having a distributed database and a machine learning model. The material monitoring device can thereby be made with minimal storage and processing capabilities, with storage and processing duties being handled by an external cloud computing device or cloud computing environment, allowing for efficient energy operation of the material monitoring device. 
     The material monitoring device can be made sufficiently compact to be able to directly take measurements inside small conduits and vessels containing materials. For example, the material monitoring device may take measurements along a water faucet or water material. As another example, the material monitoring device may be at least partly housed within a cork of a wine bottle and may take measurements from the wine in the wine bottle. 
     The material monitoring device includes at least one electrode and at least one magnetic coil for measuring electrical and magnetic signals from the material. In some implementations, an electrode may provide an electrical stimulus to the material, or a stimulating magnetic field to the material, to stimulate the electrical or magnetic signal measured from the material. In some implementations, a plurality of electrodes, or a plurality of magnetic coils, may be used, with some electrodes or magnetic coils being dedicated to providing a stimulus, with others being dedicated to measurement. The stimuli and response signals may be incorporated into the machine learning model for training and for determining a characteristic of the material. 
     Additionally, the material monitoring device can be made with electrodes that can be in direct contact with the material being monitored, improving the electrical connection with the material and thereby the accuracy of any electrical measurement taken, without disturbing the material by requiring the vessel to be opened for inspection. Similarly, the material monitoring device can be made with magnetic coils in proximity to the material being monitored. 
     A library relating previously measured electrical measurements and magnetic measurements of materials to characteristics of those materials can be developed to train a machine learning model to recognize characteristics of those materials based on electrical or magnetic signal profile measurements of those materials. A machine learning model can thereby be trained to recognized characteristics of a material which are not directly measurable by conventional or practical means. Thus, a machine learning model can be trained to recognize a not directly measurable characteristic of a material. For example, it may not be feasible to conduct sample gathering and laboratory analysis of a home&#39;s water supply on a continual basis to determine the presence of a contaminant in the water, and thus such a procedure may be sufficiently lengthy and cumbersome such that the presence of the contaminant is considered not directly measurable. However, by application of the system described herein, a machine learning model may be trained to recognize that measurement of, for example, a particular magnetic signal profile from water flowing through a water faucet, following a particular electrical stimulus, indicates the presence of a contaminant, such as a microbe contaminant, in a home&#39;s water supply. A water quality monitoring device installed on the home water faucet may thereby be configured to immediately indicate such contamination. As another example, it may become recognized that the measured electrical impedance of wine may be related to the development of a particular flavor of the wine throughout its aging process. In other examples, a beneficial characteristic of water or other material may be monitored, such as the quantity of a nutrient, a preferable level of mineral, the presence of a beneficial microbe, etc. 
     Other features and advantages of the system are described more fully below, where non-limiting embodiments of the system are described with reference to the following Figures. For convenience, reference numerals may be repeated (with or without an offset) to indicate analogous components or features. 
       FIG. 1  shows a system  100  for monitoring a material  105 , according to a non-limiting embodiment. The system  100  includes one or more material conduits  110  delivering a material  105 . The system  100  includes a material monitoring device  200  attached to a material conduit  110  monitoring the material  105  passing through material conduit  110 . 
     In the present embodiment, the material  105  being monitored comprises tap water passing through a water conduit such as a water pipe or a water faucet. The material monitoring device  200  is located at the opening  115  of the material conduit  110 . 
     The material monitoring device  200  is in communication with a wireless device  130 . The wireless device  130  is in communication over network  150  with one or more computing devices  160  storing a database  170 . The network  150  can include a wireless cellular data network, a Wi-Fi network, a local-area network, a wide-area network (WAN), a Bluetooth pairing or connection, the internet, a virtual private network (VPN), a combination of such, and similar. The database  170  stores measurement data  172  and library data  174 , discussed in greater detail below. 
     Briefly, the material monitoring device  200  measures electrical and magnetic properties of the material  105  and transmits the results as measurement data  172 , which may include other ancillary data, including data related to any electrical or magnetic stimulus, to the wireless device  130 . 
     The wireless device  130  is in communication with the computing device  160  which stores the database  170 . Measurement data  172  is periodically transmitted by the material monitoring device  200  to the wireless device  130 , which in turn transmits the measurement data  172  to the computing device  160 . The library data  174  stores existing data relating one or more electrical properties or magnetic properties of a material  105  to characteristics of the material  105 . 
     The computing device  160  is configured to compute, correlate, or otherwise determine a characteristic of the material  105  by comparing the measured electrical properties or magnetic properties of the material  105  in measured data  172  to library data  174 . The computing device  160  can communicate an indication of this characteristic or the characteristic itself to interested parties (not shown), such as a consumer, owner, retailer, or manufacturer across the network  150 , whether through the wireless device  130  or otherwise. In some embodiments, an indication that a characteristic has reached a threshold can be transmitted as an alert to the wireless device  130 . 
     Some characteristics, although not measurable directly, can be recognized by a machine learning model incorporating measurement data  172  and library data  174 , which relates electrical and magnetic properties of water to known, not directly measurable, characteristics of water. For example, a machine learning model may be trained to recognize that measurement of a particular magnetic signal profile from water flowing through a water faucet, following a particular electrical stimulus, indicates the presence of a contaminant such as a microbe contaminant, a chemical contaminant, a metal contaminant such as lead, a mineral contaminant, or other contaminant in a home&#39;s water supply. Thus, where a characteristic is not directly measurable, such as, in the case of a contaminant, where detection of the contaminant may involve a sufficiently lengthy and cumbersome process such that the presence of the contaminant is considered not directly measurable, a machine learning model may be trained to recognize the not directly measurable characteristic with library data  174  relating previously measured electrical or magnetic properties of the material to where the presence of the contaminant is known. Thus, the library data may relate previously measured electrical or magnetic properties to known not directly measurable characteristics of the material. For example, the library data  174  may include magnetic signal profiles which indicate the presence of a particular microbe contaminant, or library data  174  may include electrical signal profiles which indicate a quantity of chemical. In some examples, an electrical or magnetic signal may indicate the presence of a beneficial compound, such as a nutrient, a preferable level of mineral, a beneficial microbe, etc. The machine learning model and signal analysis are discussed in greater detail below with reference to  FIG. 5 . 
     In the present embodiment, the wireless device  130  includes a smart phone running an operating system such as, for example, Android®, iOS®, Windows® mobile, BB  10 , or similar. The wireless device  130  receives alerts and indications from the computing device  160  regarding characteristics of the material  105 , thereby serving as an end-user device for monitoring a material. 
     In other embodiments, the wireless device  130  includes a tablet computer, a personal digital assistant (PDA), computer, or other machine with communications ability within range of the material monitoring device  200 . In these embodiments, the wireless device  130  similarly serves as an end-user device for monitoring a material. 
     In still other embodiments, the wireless device  130  includes a wireless access point, wireless router, or similar network device. In these embodiments, a computing device  160  serves as an end-user device for monitoring a material. 
     In still other embodiments, a first computing device  160  is in communication with a second computing device  160 , the second computing device  160  serving as an end-user device for monitoring a material. 
     In the present embodiment, an computing device  160  includes a computing device running a server application with storage, communication, and processing means. 
     A person skilled in the art upon reading this specification will appreciate that the wireless device  130  and the computing device  160  can each be more generally referred to as external computing devices, and that in certain embodiments the responsibility of each external computing device may be interchangeable. In the present embodiment, measurement data  172  is transmitted from the material monitoring device  200 , temporarily stored on the wireless device  130 , and transmitted to a computing device  160  for permanent storage on database  170 , for computation, and for determination of a characteristic of the material with reference to library data  174 . In the present embodiment, cost, size, and energy use of the material monitoring device  200  is reduced by keeping storage and computation away from the material monitoring device  200 , and having only measurement and data transmission take place on the material monitoring device  200 , with a wireless device  130  acting as an intermediary data transport device. 
     In other embodiments, these responsibilities can be distributed arbitrarily across the material monitoring device  200 , wireless device  130 , and computing device  160 , or a cloud computing environment. For example, the database  170  comprising library data  174  may be stored on a single wireless device  130 , or may be distributed across several wireless devices  130 , eliminating the need for a computing device  160 . Alternatively, a material monitoring device  200  or a plurality of material monitoring devices  200  may be in direct communication with a computing device  160  or a plurality of computing devices  160 , eliminating the need for a wireless device  130 . Furthermore, the person skilled in the art upon reading this specification will appreciate that storage, computation, correlation, and machine learning techniques can take place directly on a single or a plurality of material monitoring devices  200 , on a single or plurality of wireless devices  130 , or on a single or plurality of computing devices  160 . In further embodiments, a plurality of material monitoring devices  200  include sufficient storage and communication capability to host a distributed database comprising library data, and sufficient processing capability to determine characteristics of materials and communicate alerts of such characteristics. 
     It is contemplated that, in some embodiments, the system  100  includes a plurality of material monitoring devices  200  monitoring a plurality of materials  105  at a plurality of material conduits  110 , a plurality of material monitoring devices  200  contributing measurement data  172  to library data  174  for contribution to a machine learning model. 
     In other applications, materials other than water are monitored. For example, it is understood that the materials  105  being monitored can comprise other fluids, liquids, gases, solids, plasmas, beverages, other alcohols, foodstuffs, chemicals, chemicals undergoing chemical reactions, or any other suitable material of interest for which electronic or magnetic monitoring would be feasible. The material  105  may include beer, liquor, another beverage, a chemical, or any other fluid. In such embodiments, the conduit  110  comprises piping, tubing, hose, spout, or any other conduit suitable to transport the fluid. 
     In still other applications, the material  105  includes a solid foodstuff that is capable of flow through a conduit and is susceptible to electrical measurements from an electrode and magnetic measurements through a magnetic coil. An example of such a solid foodstuff includes granulated sugar. In such embodiments, the conduit  110  includes a conveyer, trough, or any other mechanism suitable to transport the solid. A example of a solid or semi-solid foodstuff is tomato paste. Such a foodstuff may flow through a conduit and may be forced or extruded through a pair of electrodes that perform one or more of the electrical measurements described herein. Further applications include measurement of gas/vapor. Other examples include medical vaccine monitoring, medication monitoring, or medication authentication. 
       FIG. 2  depicts a perspective view of a material monitoring device  200 , according to a non-limiting embodiment. The material monitoring device  200  comprises a body  206  having an interior end  202  and an exterior end  204 , a sensor device  210  at the interior end  202 , and an exterior indicator  216  at the exterior end  204 . With reference to the embodiment in  FIG. 1 , the material monitoring device  200  can be incorporated into an attachment to an opening of a water faucet, with sensor device  210  oriented toward the material  105  in a manner permitting interaction of the sensor device  210  with the material  105 , and the exterior indicator  216  oriented to be visible to a user of the water faucet. 
     The sensor device  210  comprises an output electrode  212 , an input electrode  214 , and a magnetic coil  215 . The output electrode  212  and input electrode  214  extend into the material  105 . The output electrode  212  is used to apply an electrical stimulus to the material  105 . In turn, the input electrode  214  is used to measure an electrical response signal of the material  105 . The input electrode  214  thus includes a return-path electrode for completing the electrical connection allowing an electrical response signal to return from the material  105 . 
     The magnetic coil  215  is used to apply a stimulating magnetic field to the material  105 , and is also used to measure a magnetic response signal from the material  105 . 
     The output electrode  212  and input electrode  214  may include any suitable material for electrical conductivity, including gold, a gold-plated metal, platinum, a platinum-plated metal, carbon, graphite, graphene, silver, silver chloride, silicon, germanium, tin, iron, copper, or brass, or other suitable materials. Similarly, the magnetic coil may include an electromagnet of any suitable material for generating a magnetic field. 
     The exterior indicator  216  includes at least one of: a simple single color light-emitting diode (LED), a multi-color LED, a moving coil galvanometer, voltmeter or current meter, a piezoelectric transducer, a speaker, a buzzer, a siren, a relay switch, an optical bar graph, a counter such as a numerical counter or any suitable counter, liquid crystal display (LCD), or any other suitable indicator device that interfaces with the circuitry of the material monitoring device  200 , as described in greater detail below. 
     In the present embodiment of a system for monitoring characteristics of water passing through a water faucet, the exterior indicator  216  comprises a two color LED, where the color red indicates the water contains a contaminant, and the green colour indicates that no contaminants are detected. 
     Although in the embodiment of  FIG. 1 , the material monitoring device  200  is attached to opening  115  of conduit  110 , it is contemplated that the material monitoring device  200  may be located elsewhere along conduit  110 , for example, along the piping leading to the water faucet. 
     In some applications for monitoring liquids, the output electrode  212  and input electrode  214  need not extend into the liquid, but rather conducts measurements on the gas/vapor in the headspace above the liquid to infer properties of the liquid. 
     Although in the present embodiment shown in  FIG. 2 , the sensor device  210  is shown having an input electrode and an output electrode, it is contemplated that a single electrode may serve as both input and output electrode. Furthermore, it is contemplated that the sensor device  210  may include an input magnetic coil and an output magnetic coil. A purpose of sensor magnetic coil  43  is to magnetically couple with the product being monitored and optionally allow it to magnetically stimulate the product being monitored and/or optionally measure a magnetic field result from the product being monitored. Moreover, it is contemplated that the sensor device  210  may include a plurality of electrodes, some of the electrodes operating as input electrodes and some as output electrodes, and that the sensor device  210  may include a plurality of magnetic coils, some of the magnetic coils operating as input magnetic coils and some as output magnetic coils. 
     Various further embodiments of the material monitoring device  200  are contemplated. In one embodiment, the sensor device  210  includes a third electrode. In such an embodiment, the three electrodes are a working electrode, a reference electrode, and a counter electrode, thus enabling additional electro-analytical techniques. For example, the sensor device  210  includes a three-electrode potentiostat system for measuring redox reactions or other types of reactions. 
     In a further embodiment, the sensor device  210  includes only a single electrode for taking measurements without applying any electrical stimulus to the material  105 . In such an embodiment, the sensor device  210  comprises no output electrode, but only a single input electrode for taking input measurements. 
     Similarly, in a further embodiment, the magnetic coil  215  may be configured for taking magnetic measurements without applying a stimulating magnetic field to the material  105 . 
     In further variations of the material monitoring device  200 , the exterior indicator  216  may be omitted. In this variation, the status or characteristics of the material  105  may be communicated to and presented at wireless device  130  or computing device  160 . 
       FIG. 3  depicts functional blocks of the material monitoring device  200 , according to a non-limiting embodiment. The material monitoring device  200  comprises a sensor device  210  comprising an output electrode  212  an input electrode  214 , and a magnetic coil  215 . The material monitoring device  200  further comprises an exterior indicator  216 , a communication device  230 , power supply  222 , and circuit  220 . 
     The communication device  230  is configured to transmit data corresponding to measured electrical and magnetic properties of the material  105  to the wireless device  130  and/or computing device  160 , as the case may be. The communication device  230  comprises a communications antenna, or any other suitable communication device configurable to communicate directly with a wireless device  130  or computing device  160 . 
     The power supply  222  supplies power to the components of the material monitoring device  200 . In the present embodiment, the power supply  222  comprises a power harvesting circuit. The power harvesting circuit harvests electrical power from a communications field or by, in the case of a material travelling through a conduit, by kinetic power harvesting from the motion of the material  105 . In other embodiments, the power supply  222  comprises a battery, a solar cell, or external power supply connection, such as an AC or DC connection. Although in the present embodiment the power supply  222  is illustrated as being housed within the body  206  of the material monitoring device  200 , in other embodiments it is contemplated that the power supply could be exterior to the body  206 . 
     The circuit  220  comprises circuitry for providing electrical connections between the sensor device  210 , communication device  230 , power supply  222 , and exterior indicator  216 . In various embodiments, a portion of the circuit  220  forms part of the sensor device  210 . Furthermore, in some embodiments, the circuit  220  includes one or more of the following: integrated circuit device power harvesting circuit  52 , integrated circuit device communications radio circuit  54 , integrated circuit device control state-machine circuit  56 , integrated circuit device sensor output stimulator circuit  58 , integrated circuit device sensor input measurement circuit  60 , and integrated circuit device sensor magnetic stimulation and measurement circuit  61 , a processor, a microcontroller, a state machine, a logic gate array, an application-specific integrated circuit (ASIC), a system-on-a-chip (SOC), a field-programmable gate array (FPGA), or similar, capable of executing, whether by software, hardware, firmware, or a combination of such, a method for monitoring characteristics of a material as discussed in greater detail below. In the present embodiment, the circuit  220  implements a system-on-a-chip (SOC). In some embodiments, the circuit  220  includes memory, where measurement data  172  is to be stored on the material monitoring device  200 , before, or in addition to, being transmitted to the wireless device  130  or computing device  160 . 
     In various embodiments, the circuit  220  is a discrete electrical circuit made up of separate discrete electrical components. In other embodiments, the circuit  220  includes an ASIC, an FPGA, an SOC, or combinations thereof. Embodiments of the circuit  220  that include a combination of separate discrete electrical components and an ASIC, FPGA, and/or SOC are also contemplated. In various embodiments, portions of the circuit  220  that describe a logical state-machine are implemented as software and/or firmware that operate on a processor or microcontroller. In various embodiments, the circuit  220  further includes an electrode interface portion that includes circuit elements specific to the electrodes for performing electrical stimulation and electrical measurements, and such circuit elements can be considered to be part of the sensor device  210 . 
     In some embodiments, the material monitoring device  200  is configured to conduct electrical measurements of the material  105 . In such embodiments, the material monitoring device  200  may conduct impedance spectroscopy, also known as dielectric spectroscopy, for electrically stimulating the material  105  and performing a measurement on the material  105 . It is to be understood, however, that in other embodiments, other electro-analytical methodologies can be performed, such as potentiometry, coulometry, voltammetry, square wave voltammetry, stair-case voltammetry, cyclic voltammetry, alternating current voltammetry, amperometry, pulsed amperometry, galvanometry, and polarography, and other suitable electro-analytical methodologies. In various embodiments, several of the aforementioned methodologies are used in combination. 
     In some embodiments, the material monitoring device  200  further comprises a sensor capable of taking additional measurements, such as acceleration, position, temperature, pressure, color, light intensity, light phase, density, surface tension, viscosity, resistance, impedance, voltage, current, charge, quantity of mass, quantity and direction of force, quantum mechanical properties, or any other suitable property that can be measured by a sensor. In yet other embodiments, the sensor includes a gyroscope or magnetometer. 
     In some embodiments, the material monitoring device  200  comprises a sensor with a digital interface designed to perform similar measurements, with the sensor interfacing with the circuit  220  through methods such as Two Wire Interface (TWI or I2C compatible), SPI interface, Microwire, 1-Wire, Single Wire Protocol (SWP), or any other suitable digital or analog communications methodologies. 
     The circuit  220  may control operations of the material monitoring device  200 , including initializing the circuit  220  with required startup parameters, initiating and recording measurements of the sensor device  210 , packetizing the measurement data  172  into data packets, controlling the communication device  230  for the reception and transmission of data, commands, and ancillary information, any firmware or software updates, and any other suitable information being transmitted or received. 
       FIG. 4  depicts a flowchart of a method  400  for determining a characteristic of a material, according to a non-limiting embodiment. The method  400  is one way in which the characteristics of a material can be monitored. It is to be emphasized, however, that the blocks of method  400  need not be performed in the exact sequence as shown. The method  400  is described as performed by a system and device discussed herein, but this is not limiting and the method can alternatively be performed by other systems and/or devices. 
     With reference to  FIG. 5 , and with continued reference to  FIG. 4 , the generation and measurement of electrical and magnetic signals, as described in method  400 , are diagrammed schematically. 
     At block  402 , an initiating electrical signal  502  is generated and transmitted. In the present embodiment, the initiating electrical signal  502  is generated on the material monitoring device  200 , and is transmitted to the output electrode  212  and magnetic coil  215  on the material monitoring device  200 . Transmission of the initiating electrical signal  502  to the output electrode  212  generates an electrical stimulus  504 . Transmission of the initiating electrical signal  502  to the magnetic coil  215  generates a stimulating magnetic field  506 . 
     It is to be understood that in other embodiments, the two or more initiating electrical signals  502  may be generated, one for transmission to output electrode  212 , another for transmission to the magnetic coil  215 . Furthermore, it is to be understood that the initiating electrical signal  502  may be generated elsewhere in system  100 , such as from a computing device  160 , and transmitted to material monitoring device  200 . 
     The electrical stimulus  504  may be referred to as an electrical electrode stimulation signal profile (EESSP). In some embodiments, the EESSP may comprise a varying signal profile developed to excite the material  105 . Such varying signals may include a continuous, discrete, periodic, or an aperiodic signal, or combinations thereof. 
     In some embodiments, the EESSP may comprise a dynamic AC signal or a static DC signal. In embodiments in which the EESSP comprises a dynamic AC signal, the EESSP may include a sinusoidal oscillating signal. The sinusoidal oscillating signal may be continuous and periodic for a duration sufficient to stimulate the material  105  such that an electrical response signal  508  may be measured. The EESSP may be varied in amplitude, frequency, or other properties. In some embodiments, the EESSP may be generated from a voltage source. In other embodiments, the EESSP may be generated from a current source. 
     The stimulating magnetic field  506  may be referred to as a magnetic coil stimulation signal profile (MCSSP). In some embodiments, the MCSSP may comprise a varying signal developed to excite the material  105 . Such varying signals may include a continuous, discrete, periodic, or aperiodic signal, or combinations thereof. 
     In some embodiments, the MCSSP may comprise a dynamic AC signal or a static DC signal. In embodiments in which the MCSSP comprises a dynamic AC signal, the MCSSP may include a sinusoidal oscillating signal. The sinusoidal oscillating signal may be continuous and periodic for a duration sufficient to stimulate the material  105  such that a magnetic response signal  510  may be measured. The MCSSP may be varied in amplitude, frequency, or other properties. In some embodiments, the MCSSP may be generated from a voltage source. In other embodiments, the MCSSP may be generated from a current source. 
     In some embodiments, the MCSSP may comprise a uniform magnetic field. 
     At block  404 , the electrical stimulus  504  is applied to material  105  by output electrode  212 . 
     At block  406 , the stimulating magnetic field  506  is applied to material  105  by magnetic coil  215 . 
     At block  408 , an electrical response signal  508 , detected from material  105 , is measured by input electrode  214  as electrical response signal measurement  512 . The electrical response signal  508  and thus the electrical response signal measurement  512  is influenced by the electrical stimulus  504  being altered by the material  105 . The electrical response signal  508  or electrical response signal measurement  512  may be referred to as an electrical electrode receiving signal profile (EERSP). In some embodiments, the EERSP may be analyzed further in its raw form. In some embodiments, the EERSP may be processed with a mathematical transform for further use in further analysis. The mathematical transforms that may be applied to the EERSP include Fourier transform, Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT), Laplace transform, Z transform, Hilbert transform, Discrete Cosine transform, wavelet transform, discrete wavelet transform, Infinite Impulse Response (IIR), Finite Impulse Response (FIR), or their discrete or accelerated variants, or other mathematical transforms. The mathematical transform can be made in any possible domain such, as but not limited to, time and space domain, frequency domain, Z-plane analysis (Z-domain), and Wavelet analysis, and any such relevant domain or analysis methodology. 
     At block  410 , a magnetic response signal  510 , detected from material  105 , is measured by magnetic coil  215  as magnetic response signal measurement  514 . The magnetic response signal  510  and thus the magnetic response signal measurement  514  is influenced by the stimulating magnetic field  506  being altered by the material  105 . The magnetic response signal  510  or magnetic response signal measurement  514  may be referred to as a magnetic coil receiving signal profile (MCRSP). In some embodiments, the MCRSP may be analyzed further in its raw form. In some embodiments, the MCRSP may be processed with a mathematical transform for further use in further analysis. The mathematical transforms that may be applied to the MCRSP include Fourier transform, Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT), Laplace transform, Z transform, Hilbert transform, Discrete Cosine transform, wavelet transform, discrete wavelet transform, Infinite Impulse Response (IIR), Finite Impulse Response (FIR), or their discrete or accelerated variants, or other mathematical transforms. The mathematical transform can be made in any possible domain such, as but not limited to, time and space domain, frequency domain, Z-plane analysis (Z-domain), and Wavelet analysis, and any such relevant domain or analysis methodology. 
     In some embodiments, the material monitoring device  200  conducts measurements at regular intervals, as some applications require a delay time in order to perform a suitable measurement. In one such embodiment, the wireless device  130  sends instructions to material monitoring device  200  to conduct a measurement at an interval. In another such embodiment, the computing device  160  sends instructions to material monitoring device  200  to conduct a measurement at an interval. 
     In some embodiments, the electrical response signal  508  and the magnetic response signal  510  are included in measurement data  172 . In some embodiments, initiating electrical signal  502  is included in measurement data  172 . 
     At block  412 , the measurement data  172  is packetized for transmission to an external computing device. In embodiments in which the circuit  220  comprises memory, the measurement data  172  is recorded on memory before transmission. 
     At block  414 , measurement data  172  is transmitted by the communication device  230  to the wireless device  130 , which in turn transmits the measurement data  172  to the computing device  160 , which stores the measurement data  172  on database  170 . 
     At block  416 , the measurement data  172  transmitted at block  340  is contributed to the library data  174  in database  170 . In other embodiments in which the measurement data  172  is not contributed to the library data  174 , this block is omitted. 
     At block  418 , measurement data  172  is analyzed at the computing device  160 . In the present embodiment, measurement data  172  is analyzed by machine learning model  550 . 
     Although in the present embodiment, the machine learning model  550  is located at the computing device  160 , it is emphasized that machine learning, and any analysis at block  418 , can take place at a wireless device  130 , the material monitoring device  200 , or a computing device  160 , or can be arbitrarily distributed across monitoring devices  200 , wireless devices  130 , and computing devices  160 , or a cloud computing environment. 
     At block  420 , a characteristic of the material  105  is determined based on the analysis at block  418 . 
     Where a machine learning model  550  is applied in the analysis of measurement data  172  at block  418 , several machine learning techniques may be applied. In one such embodiment, a neural network algorithm that employs a Bayesian algorithm and a decision tree analysis to classify the measurement data  172  and report the classified result in order to classify the characteristics of the material  105 . 
     In another embodiment, principal component analysis (PCA) is used on the measurement data  172  to report on the status of the material  105  and also classify its characteristics. 
     In another embodiment, principal component regression (PCR) is used on the measurement data  172  to report on the status of the material  105  and also classify its characteristics. 
     In other embodiments, other suitable data analysis techniques may be used, such as clustering analysis, correlation, neural network machine learning algorithms, support vector machine algorithms, random forest algorithms, convolution neural network algorithms, deep belief networks, deep QA networks, or other appropriate algorithms. Machine learning algorithms may include supervised machine learning algorithms or unsupervised machine learning algorithms. 
     It is to be emphasized that the material monitoring device includes at least one electrode and at least one magnetic coil for measuring electrical and magnetic signals from the material. In some embodiments, an electrical stimulus  504  is applied without a stimulating magnetic field  506 , where an electrical response signal  508  may be measured alone, a magnetic response signal  510  may be measured alone, or both an electrical response signal  508  and magnetic response signal  510  may be measured. In some embodiments, a stimulating magnetic field  506  is applied without an electrical stimulus  504 , where an electrical response signal  508  may be measured alone, a magnetic response signal  510  may be measured alone, or both an electrical response signal  508  and magnetic response signal  510  may be measured. In some embodiments, both an electrical stimulus  504  and a stimulating magnetic field  506  are applied, simultaneously or sequentially in any scheme, where an electrical response signal  508  may be measured alone, a magnetic response signal  510  may be measured alone, or both an electrical response signal  508  and magnetic response signal  510  may be measured. In some embodiments, a plurality of electrodes, or a plurality of magnetic coils, may be used, with some electrodes or magnetic coils being dedicated to providing a stimulus, with others being dedicated to measurement. In still other embodiments, no electrical stimulus  504  is applied, and no stimulating magnetic field  506  is applied, where an electrical signal alone, a magnetic signal alone, or both, are measured. 
     Furthermore, it is emphasized some of the blocks of method  400  need not be performed in the exact sequence as shown. For example, the stimulus application in blocks  404  and  406  may be executed simultaneously and the measurement in blocks  408  and  410  may be executed simultaneously. 
     Furthermore, blocks of the method  400  may thus be omitted or repeated. For example, where the material monitoring device  200  comprises a single electrode, blocks  404  and  408  are replaced with a block at which a measurement is taken. 
     Although in the present embodiment, machine learning techniques are applied at block  418 , other forms of analysis may be used. For example, a polynomial regression may be used on the measurement data  172  to report on the status of the material  105  and also classify its characteristics. Linear regression and non-linear regression may also be used. 
     In some embodiments, the material monitoring device  200  may vary the electrical stimulus  504  (EESSP) or the stimulating magnetic field  506  (MCSSP) over time. In some embodiments, the EESSP and MCSSP may be varied simultaneously. In some embodiments, the EESSP or MCSSP may be varied independently. The EESSP or MCSSP may be varied through a spectrum of any property of interest. For example, the EESSP may be varied through a band of amplitude, while the MCSSP is varied through a band of amplitude. Any combination of variation of EESSP or MCSSP in any dimension, together or independently, are contemplated. A robust dataset of electrical response signals  508  (EERSP) and magnetic response signals  510  (MCRSP) can thus be gathered for inclusion into and analysis by the machine learning model  550  for determination of a particular family of materials having particular characteristics. 
     Thus, by application of method  400 , a characteristic of a material  105  being monitored is determined with reference to the electrical properties or the magnetic properties of the material  105 . These characteristics, although not measurable directly, are recognized by a machine learning algorithm incorporating measurement data  172  and library data  174 , which relates electrical properties and magnetic properties of a material to known characteristics of the material. By application of method  400 , the library data  174  is expanded with additional data relating electrical properties and magnetic properties of materials to characteristics of materials. 
       FIG. 6  depicts a flowchart of a method  600  for initializing a material monitoring device  200 , according to a non-limiting embodiment. The method  600  is one way in which a material monitoring device can be initialized. It is to be emphasized, however, that the blocks of method  600  need not be performed in the exact sequence as shown. The method  600  is described as performed by a system and device discussed herein, but this is not limiting and the method can alternatively be performed by other systems and/or devices. 
     In the present embodiment, the material monitoring device  200  remains in an idle state with low energy consumption between conducting measurements. When instructed to conduct a measurement, the material monitoring device  200  undergoes a process of initialization to prepare to conduct a measurement. Upon concluding conducting a measurement, the material monitoring device  200  returns to an idle state. 
     At block  602 , an instruction to conduct a measurement is received by the communication device  230  from an external computing device such as the wireless device  130  or computing device  160 . 
     At block  604 , it is determined whether the material monitoring device  200  has sufficient electrical power to conduct a measurement. If sufficient power is present, block  606  is executed. If sufficient power is not present, block  614  is executed. Whether sufficient electrical power is present may be determined by whether a suitable electrical connection is established with an outside power source, whether sufficient battery power is remaining, or whether the energy harvesting circuit has harvested sufficient power for operation. 
     At block  606 , circuit parameters are initialized. For example, initialization includes initializing one or more parameters such as: processor or system clock frequency, analog circuit gain, analog circuit drive strength, analog circuit termination impedance, stimulation values, delay values, filter settings, and any other suitable programmable setting in the device. The aforementioned list of parameters is non-limiting and other parameters are contemplated. 
     At block  608 , a characteristic of material  105  is determined as described with respect to method  400  in  FIG. 4  above. 
     At block  610 , it is determined whether sensor regeneration is required. If sensor regeneration is required, block  612  is executed. If sensor regeneration is not required, block  614  is executed. Some sensors require a special regeneration cycle, and others do not, as will be apparent to the person skilled in the art upon reading this specification. For example, a three-electrode potentiostat measurement system that uses very sensitive electrodes may require a regeneration cycle to free ions from the electrode that may collect on the electrode during the measurement cycle. 
     At block  614 , the material monitoring device  200  is in an idle state with low energy consumption. In the present embodiment where the power supply  222  is a power harvesting circuit, the material monitoring device  200  waits until sufficient power is harvested for a measurement to be conducted. 
     It will be understood by the person skilled in the art upon reading this specification that it is possible to add or omit blocks as necessary to execute any given measurement algorithm. 
       FIG. 7  depicts functional blocks of a material monitoring device  700 , according to a non-limiting embodiment. The material monitoring device  700  includes a sensor device  710  having an output magnetic coil  712 , an input magnetic coil  714 , and an electrode  715 . The output magnetic coil  712  is generates and applies a stimulating magnetic field to a material  105 , and the input magnetic coil  714  is dedicating to measuring a magnetic response signal. The electrode  715  operates to both apply an electrical stimulus to the material  105  and measure an electrical response signal. 
     With regard to the body  706 , communication device  730 , circuit  720 , power supply  722 , and exterior indicator  716 , reference may be had to the description of analogous components in  FIG. 3 . 
       FIG. 8  shows a system  800  for monitoring a material  805 , according to a non-limiting embodiment. System  800  includes one or more material vessels  810  having vessel openings  815  and containing a material  805 . In the present embodiment, the material  805  comprises wine, and the material vessel  810  comprises a wine bottle. System  800  includes a wireless device  830 , network  850 , computing devices  860 , database  870 , measurement data  872 , and library data  874 , for which reference may be had to the description of analogous components in  FIG. 1  and the disclosure above. 
       FIG. 9  depicts a perspective view of the material monitoring device  900 , according to a non-limiting embodiment. Material monitoring device  900  includes a body  906 , an interior end  902 , an exterior end  904 , a sensor device  910  having an output electrode  912 , an input electrode  914 , a magnetic coil  915 , and an external indicator, for which reference may be had to the description of analogous components in  FIG. 2  and the disclosure above. 
     The material monitoring device  900  can be incorporated into a wine cork plugging the vessel opening  815  of material vessel  810 . The body  906  can be sized to plug the opening  815  of the material vessel  810 . In the present embodiment for monitoring wine in a wine bottle, the body  906  comprises a wine bottle cork sized to plug the opening  815  of the wine bottle. However, in other embodiments, the body  906  comprises a barrel bung, a cap, a lid, or an attachment embedded into the side of a vessel, or any other stopper, or means for housing a material monitoring device  900  with a sensor device  910  for measurement of the material  805  being monitored. The material of the body  906  comprises any material suitable for the particular application, such as plastic, natural cork, synthetic cork, agglomerated cork, or wax for the wine bottle application. 
     In the present embodiment of a system for monitoring characteristics of wine in a wine bottle, when disposed within the opening of a wine bottle, the interior end  902  of the material monitoring device  900  is oriented toward the wine, with the sensor device  910  protruding from the interior end  902 , and with output electrode  912  and input electrode  914  extending into the wine contained within the wine bottle. 
     A sensor device of material monitoring device  900  may thereby measure electrical or magnetic properties of the material  805 , and may have electrodes in direct contact with the material  805 , or in contact with the gas/vapor in the headspace above the liquid to infer properties of the material  805 , as discussed above throughout this disclosure. 
     An advantage of housing the material monitoring device  900  within a wine bottle cork is that the wine bottle need not be opened, and thus disturbed, in order to inspect the wine for a characteristic. Further, in the present embodiment of monitoring the characteristics of wine, the system  800  could be used to monitor whether the wine is within the optimal taste window or outside of the optimal taste window. 
     In the present embodiment of a system for monitoring characteristics of wine in a wine bottle, the external indicator  916  comprises a three color LED, where the color red indicates the wine has passed its optimal point of consumption, the color yellow indicates the wine approaching the end of its optimal point of consumption, and the green colour indicates that the wine is within its optimal point of consumption. 
     In some embodiments, canonical correlation is used on the measurement data  872  to report on the status of the material  805 , including, in the case of monitoring the characteristics of wine, whether the wine is within the wine&#39;s optimal taste window or approaching its expiry point, and an estimate of how much time may be left before the wine is expected to reach its expiry point. 
     Although the present example discusses an application to monitoring wine in a wine bottle, wine in a wine bottle is merely one example. Implementations are not limited to monitoring a particular class of materials, whether the material is a fluid, liquid, gas, solid, beverage, foodstuff, chemical, and the vessel is not limited to a particular class of vessel. In addition, other types of containers and delivery conduits instead of vessels are contemplated, such as cartons, packages, kegs, water pipes, water bottles, water containers (e.g., office-style water coolers), to name a few. 
     In other embodiments, materials other than wine are monitored. For example, it is understood that the materials  805  being monitored can comprise fluids, liquids, gases, solids, plasmas, beverages, other alcohols, foodstuffs, chemicals, chemicals undergoing chemical reactions, or any other suitable material of interest for which electronic monitoring would be feasible. Other examples include medical vaccine monitoring, medication monitoring, or medication authentication. Furthermore, the material vessels  810  includes wine bottles, wine barrels, bottles or barrels of other alcohols, casks, or beverage containers of any kind which can fit a material monitoring device  900 . 
     In other embodiments, wine undergoing a fermentation process in a barrel is monitored via a material monitoring device  900  embedded within the bung of the barrel, or in another suitable location, for indicating the level of completion of the fermentation cycle. Additionally, the aging process of wine can be monitored, with an alert being sent to the wireless device  830  to indicate that the wine has completed its aging process and it is ready to ship to market. Additional characteristics of wine that could be monitored, whether in a bottle or aging in a barrel, include sweetness of flavor, acidity, tannin, fruitiness of flavor, body, aroma, or any other suitable characteristic of wine that is usually measured. These characteristics, although not measurable directly, can be inferred from comparing measurement data  872  to library data  874 , which relates electrical properties of wines to known characteristics of wines. 
     It should be apparent from the above that characteristics of a material can be monitored via the electrical and magnetic properties of the material by a low-power, compact, material monitoring device capable of direct yet non-invasive contact with a material, locatable at a conduit or a vessel, in cooperation with a machine learning model for determining a characteristic of a material using an evolving model based on machine learning techniques. 
     Characteristics of a material may also be monitored by periodically taking measurements of the material using dedicated sensor devices, such as a pH sensor, temperature sensor, humidity sensor, and the like, and correlating such measurements to a related characteristic of the material in known ways. For example, the it may be known that the pH of tap water may be related to its mineral content, and thus a determination of the mineral content of a sample of water may be made with reference to its pH. However, such monitoring techniques are limited in that they rely on known relationships between a measurement and a characteristic. In contrast, by taking measurements of a material that is not known to relate to a particular characteristic, e.g. by taking measurements related to electrical or magnetic properties of a material, which provides a broader dataset for analysis than a dedicated sensor device, it may be determined that a particular feature of an electrical signal profile, or a particular feature of a magnetic signal profile, relates to a characteristic of the material that is not directly measurable, and relates in a manner which may not have been previously known, or which may not be expressible in the form of a known relationship, such as how the pH level of water is known to be impacted by its mineral content. Further, by considering the connections between electrical properties of a material and magnetic properties of the material, a richer dataset for analysis is provided. For example, electrical stimulation of the material may have a measurable effect on the magnetic properties of the material, which can be recognized by a machine learning model to indicate a particular characteristic that would not otherwise be directly measurable. Thus, a more expansive system for monitoring the characteristics of a material is provided. 
     The scope of the claims should not be limited by the embodiments set forth in the above examples, but should be given the broadest interpretation consistent with the description as a whole.