Patent Publication Number: US-11035797-B2

Title: Hybrid time-resolved and time-shifted spectroscopy for measuring biological analytes

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a continuation-in-part of application Ser. No. 15/847,876, filed Dec. 19, 2017, which is a continuation-in-part of application Ser. No. 15/582,428, filed Apr. 28, 2017, the disclosures of which are incorporated by reference for all purposes. 
    
    
     TECHNICAL FIELD 
     The present technology relates generally to spectral imaging, and more specifically to measurement of biological analytes. 
     BACKGROUND 
     The approaches described in this section could be pursued but are not necessarily approaches that have previously been conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. 
     Spectroscopy (or spectrography) refers to techniques that employ radiation in order to obtain data on the structure and properties of matter. Spectroscopy involves measuring and interpreting spectra that arise from the interaction of electromagnetic radiation (e.g., a form of energy propagated in the form of electromagnetic waves) with matter. Spectroscopy is concerned with the absorption, emission, or scattering of electromagnetic radiation by atoms or molecules. 
     Spectroscopy can include shining a beam of electromagnetic radiation onto a desired sample in order to observe how it responds to such stimulus. The response can be recorded as a function of radiation wavelength, and a plot of such responses can represent a spectrum. The energy of light (e.g., from low-energy radio waves to high-energy gamma-rays) can result in producing a spectrum. 
     SUMMARY 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described in the Detailed Description below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
     The present disclosure is related to various systems and methods for hybrid time-resolved and time-shifted spectroscopy. Specifically, a method for hybrid time-resolved and time-shifted spectroscopy for measuring biological analytes may comprise: illuminating an analyte using first light from an excitation source, the first light having a first excitation wavelength; detecting a first spectrum from the analyte illuminated by the first light using a time-resolved spectroscopy technique, the first spectrum including a first Raman signal and fluorescence; illuminating the analyte using second light from the excitation source, the second light having a second excitation wavelength, the second excitation wavelength being larger than the first excitation wavelength by a first predetermined increment; detecting a second spectrum from the analyte illuminated by the second light using a time-resolved spectroscopy technique, the second spectrum including a second Raman signal and the fluorescence, the detecting using a Raman spectrometer, the second Raman signal being shifted from the first Raman signal by a second predetermined increment; illuminating the analyte using third light from the excitation source, the third light having a third excitation wavelength, the third excitation wavelength being larger than the second excitation wavelength by the first predetermined increment; detecting a third spectrum from the analyte illuminated by the third light using a time-resolved spectroscopy technique, the third spectrum including a third Raman signal and the fluorescence, the third Raman signal being shifted from the second Raman signal by the second predetermined increment; recovering the first Raman signal using the first spectrum, the second spectrum, and the third spectrum using an inverse transform; and using the first Raman signal to identify and measure at least one molecule of the analyte using a database of identified Raman signals. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments are illustrated by way of example, and not by limitation, in the figures of the accompanying drawings, in which like references indicate similar elements and in which: 
         FIG. 1  is a simplified representation of a system for hybrid time-resolved and time-shifted spectroscopy for measuring biological analytes, according to some embodiments. 
         FIG. 2  is an alternate view of a system for spatial optimization for measuring biological analytes, according to various embodiments. 
         FIG. 3  is a cross-sectional view of the system of  FIG. 2 , in accordance with some embodiments. 
         FIGS. 4A and 4B  are graphical representations of penetration depth into liquid water and absorption spectra of biological tissues, respectively, in accordance with various embodiments. 
         FIG. 5  is a simplified representation of spectra, according to some embodiments. 
         FIGS. 6A and 6B  illustrate fluorescence, according to various embodiments 
         FIG. 7  is a simplified graphical representation of intensity, in accordance with some embodiments. 
         FIG. 8  is a simplified graphical representation of intensity for more than one excitation wavelength, in accordance with various embodiments. 
         FIG. 9  is a simplified flow diagram of a method for hybrid time-gated and time-resolved spectroscopy, according to some embodiments 
         FIG. 10  is a simplified flow diagram of a method for time resolved spectroscopy, according to various embodiments. 
         FIG. 11  is a simplified flow diagram of a method for recovering a Raman spectrum, in accordance with some embodiments. 
         FIG. 12  is a table of molecules, in accordance with various embodiments. 
         FIG. 13  is a simplified block diagram of a computing system, according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     While this technology is susceptible of embodiment in many different forms, there is shown in the drawings and will herein be described in detail several specific embodiments with the understanding that the present disclosure is to be considered as an exemplification of the principles of the technology and is not intended to limit the technology to the embodiments illustrated. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the technology. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used in this specification, 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. It will be understood that like or analogous elements and/or components, referred to herein, may be identified throughout the drawings with like reference characters. It will be further understood that several of the figures are merely schematic representations of the present technology. As such, some of the components may have been distorted from their actual scale for pictorial clarity. 
       FIG. 1  illustrates system  100  for hybrid time-resolved and time-shifted spectroscopy for measuring biological analytes, according to some embodiments. System  100  can include spectrometer  110 A, analyte  150 A, and computing system  190 . 
     According to some embodiments, analyte  150  is at least one of solid, liquid, plant tissue, human tissue, and animal tissue. For example, animal tissue is one or more of epithelial, nerve, connective, muscle, and vascular tissues. By way of further non-limiting example, plant tissue is one or more of meristematic (e.g., apical meristem and cambium), protective (e.g., epidermis and cork), fundamental (e.g., parenchyma, collenchyma and sclerenchyma), and vascular (e.g., xylem and phloem) tissues. 
     According to some embodiments, spectrometer  110 A comprises excitation light source  120 , optical bench  130 , sampling apparatus  140 A, stepper motor  142  for motion control, and delay  180 . Excitation light source  120  is a monochromatic light source, such as a laser, in accordance with some embodiments. For example, excitation light source  120  is at least one of an Nd:YAG (neodymium-doped yttrium aluminium garnet; Nd:Y3Al5O12), Argon-ion, He—Ne, and diode laser. By way of further non-limiting example, excitation light source  120  can provide light (electromagnetic waves) in a range between ultra-violet (UV) light (e.g., electromagnetic radiation with a wavelength from 10 nm to 400 nm) and shortwave near-infrared (NIR) (1.4 μm to 3 μm), including portions of the electromagnetic spectrum in-between, such as visible light (e.g., 380 nm-760 nm) and NIR light (e.g., 0.75 μm to 1.4 μm). 
     In various embodiments, excitation light source  120  is tunable—a wavelength of the light from excitation light source  120  is changed by one or more (predetermined) increments and/or to one or more (predetermined) values—such as by using heat control (e.g., from a heating element), electrical control (e.g., using microelectromechanical systems (MEMS)), and mechanical control (e.g., using a mechanism to turn a mirror). Preferably, excitation light source  120  provides high spectral purity, high wavelength stability, and/or high power stability output. 
     Sampling apparatus  140 A performs various combinations and permutations of directing light  160  from excitation light source  120 , collecting the resulting Raman scattered light or Raman scatter (among others)  170 , filtering out radiation at the wavelength corresponding to the laser line (e.g., Rayleigh scattering), and providing the Raman scatter (among others)  170  to optical bench  130 , according to some embodiments. For example, sampling apparatus  140 A includes a microscope and/or an optical probe. By way of further non-limiting example, sampling apparatus  140 A includes optical fiber, one or more filters (e.g., notch filter, edge-pass filter, and band-pass filter), and the like. Raman scatter (among others)  170  includes, for example, at least one of Raman scatter, fluorescence, and Rayleigh scattering (which can be filtered out by sampling apparatus  140 A). 
     Sampling apparatus  140 A can be attached to or mounted on translation stage (or linear stage)  146 . Translation stage  146  can restrict the motion of sampling apparatus  140 A to a single axis of motion (or one degree of freedom out of six degrees of freedom). In various embodiments, translation stage  146  can include a (moving) platform and a (fixed) base (not depicted in  FIG. 1 ), where the platform moves relative to the base. The platform and base can be joined by some form of guide which restricts motion of the platform to only one dimension. For example, guide types can be rollers, recirculating ball bearing, flexure, cylindrical sleeve, dovetail, and the like. 
     The position of the (moving) platform relative to the (fixed) base is typically controlled by a linear actuator. For example, a lead screw can pass through a lead nut in the platform. Rotation of the lead screw can be controlled by a motor, such as stepper motor  142 . In this way, translation stage  146  can move sampling apparatus  140 A (e.g., a probe) in spatial relationship to analyte  150 A in a controlled manner. 
     Stepper motor  142  can move translation stage  146  to precisely change the distance between sampling apparatus  140 A and analyte  150 A in steps (increments) ranging from 2 μm to 0.5 nm. Stepper motor  142  can be a brushless DC electric motor that divides a full rotation into a number of equal steps. Stepper motor  142 &#39;s rotational position can then be controlled (e.g., by computing system  190 ) to move and hold at one of these steps without needing a position sensor for feedback. Although translation stage  146  is shown including sampling apparatus  140 A and stepper motor  142 , in various embodiments translation stage  146  can include other constituent parts of spectrometer  110 A and computing system  190 . 
     In accordance with some embodiments, optical bench  130  is a spectrograph. For example, optical bench  130  includes slit  132 , spectral dispersion element  134 , and detector  136 . By way of non-limiting example, optical bench  130  measures wavelengths in one or more of the UV spectrum (10 nm to 400 nm), visible spectrum (e.g., 380 nm-760 nm), visible to near-infrared (e.g., 400 nm-1000 nm), short-wave infrared (e.g., 950 nm-1700 nm), and infrared (e.g., 1 μm-5 μm). 
     Slit  132 , spectral dispersion element  134 , and detector  136  can be arranged in optical bench  138 , along with other components (e.g., monochromator—which transmits a mechanically selectable narrow band of wavelengths of light or other radiation chosen from a wider range of wavelengths available at an input—including one or more of a mirror, prism, collimator, holographic grating, diffraction grating, blazed grating, and the like), according to different configurations. For example, different configurations include: crossed Czerny-Turner, unfolded Czerny-Turner, transmission, and concave holographic optical benches. 
     Slit  132  can determine the amount of light (e.g., photon flux, such as Raman scatter (among others)  170 ) that enters optical bench  138 . Dimensions (e.g., height and width, not shown in  FIG. 1 ) of slit  132  can determine the spectral resolution of optical bench  130 . By way of non-limiting example, a height of slit  132  can range from 1 mm to 20 mm. By way of further non-limiting example, a width of slit  132  can range from 5 μm to 800 μm. 
     Spectral dispersion element  134  can determine a wavelength range of optical bench  130  and can partially determine an optical resolution of optical bench  130 . For example, spectral dispersion element  134  is a ruled diffraction grating or a holographic diffraction grating, in the form of a reflective or transmission package. Spectral dispersion element  134  can include a groove frequency and a blaze angle. 
     Detector  136  receives light and measures the intensity of scattered light. Detector  136  can be a one- or two-dimensional detector array comprised of a semiconductor material such as silicon (Si) and indium gallium arsenide (InGaAs). In some embodiments, a bandgap energy of the semiconductor determines an upper wavelength limit of detector  136 . An array of detector  136  can be in different configurations, such as charged coupled devices (CCDs), back-thinned charge coupled devices (BT-CCDs), complementary metal-oxide-semiconductor (CMOS) devices, and photodiode arrays (PDAs). CCDs can be one or more of intensified CCDs (ICCDs) with photocathodes, back illuminated CCDs, and CCDs with light enhancing coatings (e.g., Lumogen® from BASF®). Detector  136  has a resolution of 8-15 wavenumbers, according to some embodiments. Detector  136  can be used to detect concentrations of molecules in a range of 1-1,000 mg per deciliter (mg/dL). 
     By way of further non-limiting example, detector  136  is a single pixel time-gated detector such as single-photon avalanche diode (SPAD), micro-channel plate (MCP), photomultiplier tube (PMT), silicon photomultiplier (SiPM), or avalanche photodiode (APD) that sits on a scanning motor driven rail, or detector arrays such as a single-photon avalanche diode (SPAD) array, or an intensified CCD (ICCD). A SPAD is a solid-state photodetector in which a photon-generated carrier (via the internal photoelectric effect) can trigger a short-duration but relatively large avalanche current. The leading edge of the avalanche pulse marks the arrival (time) of the detected photon. The avalanche current can continue until the avalanche is quenched (e.g., by lowering a bias voltage down to a breakdown voltage). According to various embodiments, each pixel in some SPAD arrays can count a single photon and the SPAD array can provide a digital output (e.g., a 1 or 0 to denote the presence or absence of a photon for each pixel). 
     To detect another photon, a control circuit(s) (not depicted in  FIG. 1 ) integrated in and/or external to the SPAD can be used to read out measurements and quench the SPAD. For example, the control circuit can sense the leading edge of the avalanche current, generate a (standard) output pulse synchronous with the avalanche build up, quench the avalanche, and restore the diode to an operative level. The control circuit can provide passive quenching (e.g., passive quenching passive reset (PQPR), passive quench active reset (PQAR), and the like) and/or active quenching (e.g., active quench active reset (AQAR), active quenching passive reset (AQPR), and the like). In various embodiments, detector  136 A is a complementary metal-oxide semiconductor (CMOS) SPAD array. 
     A micro-channel plate (MCP) is a planar component used for detection of single particles, such as photons. An MCP can intensify photons by the multiplication of electrons via secondary emission. Since a microchannel plate detector has many separate channels, it can also provide spatial resolution. 
     A photomultiplier tube (PMT) is a photoemissive device which can detect weak light signals. In a PMT, absorption of a photon results in the emission of an electron, where the electrons generated by a photocathode exposed to a photon flux are amplified. A PMT can acquire light through a glass or quartz window that covers a photosensitive surface, called a photocathode, which then releases electrons that are multiplied by electrodes known as metal channel dynodes. At the end of the dynode chain is an anode or collection electrode. Over a very large range, the current flowing from the anode to ground is directly proportional to the photoelectron flux generated by the photocathode. 
     Silicon photomultipliers (SiPM) are solid-state single-photon-sensitive devices based on Single-photon avalanche diode (SPAD) implemented on a common silicon substrate. Each SPAD in an SiPM can be coupled with the others by a metal or polysilicon quenching resistor. 
     Avalanche photodiodes (APDs) are semiconductor photodiodes with an internal gain mechanism. In an APD, absorption of incident photons creates electron-hole pairs. A high reverse bias voltage creates a strong internal electric field, which accelerates the electrons through the semiconductor crystal lattice and produces secondary electrons by impact ionization. The resulting electron avalanche can produce gain factors up to several hundred. 
     An intensified charge-coupled device (ICCD) is a CCD that is optically connected to an image intensifier that is mounted in front of the CCD. An image intensifier can include three functional elements: a photocathode, a micro-channel plate (MCP) and a phosphor screen. These three elements can be mounted one close behind the other. The photons which are coming from the light source fall onto the photocathode, thereby generating photoelectrons. The photoelectrons are accelerated towards the MCP by an electrical control voltage, applied between photocathode and MCP. The electrons are multiplied inside of the MCP and thereafter accelerated towards the phosphor screen. The phosphor screen converts the multiplied electrons back to photons which are guided to the CCD by a fiber optic or a lens. An image intensifier inherently includes shutter functionality. For example, when the control voltage between the photocathode and the MCP is reversed, the emitted photoelectrons are not accelerated towards the MCP but return to the photocathode. In this way, no electrons are multiplied and emitted by the MCP, no electrons are going to the phosphor screen, and no light is emitted from the image intensifier. In this case no light falls onto the CCD, which means that the shutter is closed. 
     Detector  136  can be other photodetectors having a time resolution of about one nanosecond or less. By way of further non-limiting example, detector  136  is a streak camera array, which can have a time-resolution of around 180 femtoseconds. A streak camera measures the variation in a pulse of light&#39;s intensity with time. A streak camera can transform the time variations of a light pulse into a spatial profile on a detector, by causing a time-varying deflection of the light across the width of the detector. 
     A spectral resolution of a spectrum measured by detector  136  can depend on the number of pixels (e.g., discrete photodetectors) in detector  136 . A greater number of pixels can provide a higher spectral resolution. Detector  136  can comprise a one-dimensional and/or two-dimensional array of pixels. For example, detector  136  has in a range of 32 to 1,048,576 pixels. According to some embodiments, detector  136  has in a range of 512 to 1,024 pixels. 
     In some embodiments, the output (e.g., measurements) from detector  136  is provided to an analog-to-digital converter (ADC) (not shown in  FIG. 1 ). The ADC can be integrated into detector  136  or separate from detector  136 , such as in at least one of optical bench  130 , spectrometer  110 A, and computing system  190 . The ADC can convert the measurements before the next measurements are received. For example, when measurements are received at 20 KHz, the ADC can convert at 20 KHz or faster. When the output of detector  136  is already a digital spectrum, analog-to-digital conversion is not needed. 
     Spectrometer  110 A can provide information about molecular vibrations to identify and quantify characteristics (e.g., molecules) of analyte  150 . Spectrometer  110 A can direct light (electromagnetic waves)  160  from excitation light source  120  (optionally through sampling apparatus  140 A) onto analyte  150 . Light  160  from excitation light source  120  can be said to be shone on analyte  150  and/or analyte  150  can be said to be illuminated by excitation light source  120  and/or light  160 . When (incident) light from excitation light source  120  hits analyte  150 , the (incident) light scatters. A majority (e.g., 99.999999%) of the scattered light is the same frequency as the light from excitation light source  120  (e.g., Rayleigh or elastic scattering). 
     A small amount of the scattered light (e.g., on the order of 10 −6  to 10 −8  of the intensity of the (incident) light from excitation light source  120 ) is shifted in energy from the frequency of light  160  from excitation light source  120 . The shift is due to interactions between (incident) light  160  from excitation light source  120  and the vibrational energy levels of molecules in analyte  150 . (Incident) Light  160  interacts with molecular vibrations, phonons, or other excitations in analyte  150 , causing the energy of the photons (of light  160  from excitation light source  120 ) to shift up or down (e.g., Raman or inelastic scattering). The shift in energy (e.g., of Raman scatter  170  from analyte  150 ) can be used to identify and quantify characteristics (e.g., molecules) of analyte  150 . 
     Optical bench  130  detects (an intensity of) the Raman scatter  170  using detector  136  (optionally received through sampling apparatus  140 A). 
     Spectrometer  110 A can further include delay  180  for gating, according to some embodiments. Delay  180  can be communicatively coupled to excitation light source  120  and detector  136  through communications  185 . In various embodiments, delay  180  can detect when excitation light source  120  provides light  160  (e.g., a laser pulse is emitted). For example, delay  180  can have a sensor (not depicted in  FIG. 1 ) which detects light  160  being emitted from excitation light source  120 . By way of further non-limiting example, excitation light source  120  can provide a (electronic) signal to delay  180  when excitation light source  120  provides light  160  (e.g., fires laser pulse). A predetermined amount of time after light  160  is detected/signaled, delay  180  can provide a signal indicating to detector  136  to (effectively) stop detecting and provide measurements (e.g., report a photon count at that time). The predetermined amount of time can be a gate. For example, the predetermined amount of time (e.g., gate or time window) can be selected using the duration of light  160  (e.g., a laser pulse), characteristics of the analyte being measured (e.g., duration/lifetime of fluorescence), and the like. 
     Delay  180  can be an (programmable) analog (e.g., continuous time) and/or digital (e.g., discrete time) delay line. In some embodiments, delay  180  is a network of electrical components connected in series, where each individual element creates a time difference between its input signal and its output signal. In various embodiments, delay  180  comprises one or more delay elements (e.g., forming a (circular) buffer) such as in discrete logic (e.g., flip flops, inverters, digital (or voltage) buffer, and the like), (general purpose) microprocessor, digital signal processor, application specific standard product (ASSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), and the like. Although depicted as a part of spectrometer  110 A, delay  180  can alternatively be external to spectrometer  110 A, such as part of computing system  190 . 
     Spectrometer  110 A can be communicatively coupled to computing system  190  through communications  195 . Communications  195  can be various combinations and permutations of wired and wireless communications (e.g., networks) described below in relation to  FIG. 9 . Computing system  190  can include a database of Raman spectra associated with known molecules and/or remotely access the database over a communications network (not shown in  FIG. 1 ). In some embodiments, computing system receives intensity measurements from spectrometer  110 A, produces at least one Raman spectrum using data (e.g., intensity measurements) from spectrometer  110 A, and identifies and/or quantifies molecules in analyte  150  using the at least one Raman spectrum and a database of Raman spectra associated with known molecules. 
     In some embodiments, computing system  190  is a single computing device. For example, computing system  190  is a desktop or notebook computer communicatively coupled to Spectrometer  110 A through a Universal Serial Bus (USB) connection, a WiFi connection, and the like. In various embodiments, computing system  190  can be various combinations and permutations of stand-alone computers (e.g., smart phone, phablet, tablet computer, notebook computer, desktop computer, etc.) and resources in a cloud-based computing environment. For example, computing system  190  is a smart phone and a cloud-based computing system. The smart phone can receive data (e.g., intensity measurements) from spectrometer  110 A using USB, Wi-Fi, Bluetooth, and the like. The smart phone can optionally produce at least one Raman spectrum using the data. The smart phone can transmit the data and/or at least one Raman spectrum to a cloud-based computing system over the Internet using a wireless network (e.g., cellular network). The cloud-based computing system can produce at least one Raman spectrum using the data and/or quantify and/or identify molecules in analyte  150  using the recovered Raman spectrograph. Although depicted as outside of spectrometer  110 A, additionally or alternatively at least part of computing system  190  can be integrated into spectrometer  110 A. Computing system  190  is described further in relation to  FIG. 9 . 
     According to some embodiments, spectrometer  110 A offers at least some of the advantages of: differentiating chemical structures (even if they contain the same atoms in different arrangements), physical contact with analyte  150  not required, no damage to analyte  150  (e.g., non-destructive testing), preparation of analyte  150  is not required, analyte  150  can be in a transparent container (e.g., when light  160  is in the visible or near-visible light spectrum), sensitivity to small changes in material structure (e.g., detection of molecular vibrations is very sensitive to changes in chemistry and structure), analyzing samples in aqueous solutions (e.g., suspensions, biological samples, etc.), and the like. 
       FIG. 2  is a simplified representation of system  200  of a system for hybrid time-resolved and time-shifted spectroscopy for measuring biological analytes, according to various embodiments. System  200  can be an alternative view of System  100  ( FIG. 1 ). System  200  includes Raman instrument  110 B and analyte  150 B. Analyte  150 B has at least some of the characteristics of analyte  150 A ( FIG. 1 ). Raman instrument  110 E is depicted as being directed to a surface  250 A of analyte  150 B purely for illustrative purposes. Raman instrument  110 E can be oriented toward other surfaces of analyte  150 B, such as surface  250 B. Moreover, analyte  150 B is depicted as a (human) finger purely for illustrative purposes. Other plant or animal tissue can be used. Alternatively or additionally, other parts of a human body (e.g., including a blood vessel, such as an earlobe, neck, face, back, chest, arm, leg, toe, and the like) may be used. 
     Raman instrument  110 E has at least some of the characteristics of Raman instrument  110 A ( FIG. 1 ). Raman instrument  110 E can include aperture  210 A. Aperture  210 A can be an opening through which light  160 A from excitation light source  120  ( FIG. 1 ) exits Raman instrument  110 B and/or through which Raman scatter (among others)  170 A enters Raman instrument  110 B. For example, analyte  150 B is illuminated by excitation light source  120  through aperture  210 A and the Raman scatter (among others)  170 A ( FIG. 1 ) from analyte  150 B is received by detector  130  ( FIG. 1 ) through aperture  210 A. Aperture  210 A can include at least some of the features of optional sampling apparatus  140 A ( FIG. 1 ). Although aperture  210 A is shown as one opening, aperture  210 A can be more than one opening. 
     Raman instrument  110 E can optionally include surface  220 . In some embodiments, surface  220  is a surface on which analyte  150 B is placed so that analyte  150 B is positioned for measurement by Raman instrument  110 E and/or analyte  150 B does not substantially move during operation of Raman instrument  110 E (e.g., substantial movement would cause a sample to change between measurements). 
     Raman instrument  110 E can be a portable, handheld, or compact unit which can operate on battery power. Raman instrument  110 E can be communicatively coupled to computing system  240  through communications  230 . Communications  230  can be various combinations and permutations of wired and wireless communications (e.g., networks, busses, and the like) described below in relation to  FIG. 10 . Computing system  240  can include a database of Raman spectrographs associated with known molecules and/or remotely access the database over a communications network (not shown in  FIG. 2 ). In some embodiments, computing system receives intensity measurements from Raman instrument  110 B, produces at least one Raman spectrograph using data (e.g., intensity measurements) from Raman instrument  110 B, and identifies and/or quantifies molecules in analyte  150 B using the at least one Raman spectrograph and a database of Raman spectrographs associated with known molecules. Computing system  240  is described further below in relation to  FIG. 10 . 
     In some embodiments, computing system  240  is a single computing device. For example, computing system  240  is a desktop or notebook computer communicatively coupled to Raman instrument  110 B through a Universal Serial Bus (USB) connection, a WiFi connection, and the like. 
     In various embodiments, computing system  240  is more than one (physical) computing device. For example, computing system  240  is a smart phone and a cloud-based computing system. The smart phone can receive data (e.g., intensity measurements) from Raman instrument  110 E using USB, WiFi, Bluetooth, and the like. The smart phone can optionally produce at least one Raman spectrum (e.g., including the Raman signal and fluorescence, for each excitation wavelength) using the data. The smart phone can transmit the data and/or at least one Raman spectrum to a cloud-based computing system over the Internet using a wireless network (e.g., cellular network). The cloud-based computing system can produce at least one Raman spectrum using the data, recover a Raman spectrograph (e.g., without fluorescence) from the at least one received/produced Raman spectrum, and/or quantify and/or identify molecules in analyte  150 B using the recovered Raman spectrograph. 
     By way of further non-limiting example, communications  230  and at least some of computing system  240  can be in a dock (or cradle or pad) (not depicted in  FIG. 2 ) in (or on or adjacent to) which Raman instrument  110 E is placed. When Raman instrument  110 E is placed in (or on or adjacent to) the dock, communications  230  between Raman instrument  110 E and computing system  240  can be various combinations and permutations of wired and/or wireless communications. Alternatively or additionally, the dock can charge a rechargeable battery (e.g., lithium ion battery) of Raman instrument  110 E using wired and/or wireless charging. For example, the dock can include a connector (or plug or socket or other electrical contacts) which mates with a connector (or socket or plug or other electrical contacts) of Raman instrument  110 E (not depicted in  FIG. 2 ) for communications and/or charging. By way of further non-limiting example, the dock (and Raman instrument  110 B) can include at least one antenna, coil, and the like for wireless communications and/or charging. Other combinations and permutations of communications  230  and computing system  240  (e.g., as described below in relation to  FIG. 10 ) may be used. 
       FIG. 3  shows system  300 , which is a simplified cross-sectional view of system  200  ( FIG. 2 ) for hybrid time-resolved and time-shifted spectroscopy for measuring biological analytes, in accordance with some embodiments. System  300  includes spectrometer  110 C and analyte  150 C. Spectrometer  110 C has at least some of the characteristics of spectrometer  110 A ( FIG. 1 ) and spectrometer  110 B ( FIG. 2 ). Analyte  150 C has at least some of the characteristics of analyte  150 A ( FIG. 1 ) and analyte  150 B ( FIG. 2 ). 
     Analyte  150 C can include layers, such as epidermis  310 , dermis  330 , and subcutaneous (fatty) tissue  340 . Dermis  330  includes blood vessel  320  (e.g., vein and/or artery). For pictorial clarity, some features of epidermis  310 , dermis  330 , and subcutaneous (fatty) tissue  340  (e.g., hair shaft, sweat pore and duct, sensory nerve ending, sebaceous gland, pressure sensor, hair follicle, stratum, and the like) are not shown in  FIG. 3 . 
     Light  160 B can have at least some of the characteristics of light  160 A ( FIG. 1 ). Light  160 B (e.g., from excitation light source  120  ( FIG. 1 )) illuminates analyte  150 C. Light  160 B can pass through epidermis  310  to dermis  330 . Photons of light  160 B can bounce off molecules inside blood vessel  320 . (Resulting) Raman scatter (among others)  170 B is received by detector  130  ( FIG. 1 ). Raman scatter (among others)  170 B can have at least some of the characteristics of Raman scatter (among others)  170 A ( FIG. 1 ). 
     An optimal location for taking blood measurements is where the blood is, for example, blood vessel  320 . Measurement accuracy can be compromised when light  160 B overshoots or undershoots blood vessel  320 . In human beings, blood vessel  320  is on the order of 80 μm thick and epidermis  310  is on the order of 200 μm, so it is easy to overshoot and/or undershoot blood vessel  320  (e.g., misses blood vessel  320 ). Spectrometer  110 C can be precisely positioned relative to blood vessel  320 , to ensure light  160 B bounces off of blood vessel  320  and a quality measurement can be taken. The proper distance from spectrometer  110 C to blood vessel  320  to ensure accurate blood measurement can vary, though. For example, the thickness of epidermis  310  can vary depending on where it is on the body. In addition, the thickness of epidermis  310  varies from person to person. Accordingly, embodiments of the present invention advantageously move sampling apparatus  140 A to an optimal position for taking spectrographic measurements. 
     Details of analyte  150 C, such as epidermis  310 , dermis  330 , and subcutaneous (fatty) tissue  340 , are provided purely by way of example and not limitation. Analyte  150 C can include other, more, and/or fewer details than those illustrated in  FIG. 3 . Analyte  150 C is depicted as (human) tissue purely for illustrative purposes and other plant or animal tissue can be used. 
       FIG. 4A  is a graphical representation (e.g., plot, graph, and the like)  400 A of penetration depth  410 A into liquid water of light over excitation wavelength. By way of non-limiting example, an epidermis (e.g., epidermis  310  in  FIG. 3 ) can have a thickness on the order of 100 μm, so an excitation wavelength of light (e.g., light  160 A and light  160 B in  FIGS. 1 and 3 , respectfully) can be advantageously selected such that a penetration depth is at least 100 μm (e.g., approximately 190 nm to 2400 nm). In some embodiments, the excitation wavelength of light is in a range of 670 nm-900 nm for (human) tissue. Other ranges for the excitation wavelength of light can be used (e.g., depending on the depth of the tissue to be studied). 
       FIG. 4B  is a graphical representation (e.g., plot, graph, and the like)  400 B of absorption spectra of various tissues over excitation wavelength. By way of non-limiting example, an excitation wavelength of light (e.g., light  160 A and light  160 B in  FIGS. 1 and 3 , respectfully) can be advantageously selected to minimize the absorption coefficient so as to minimize absorption of the light by the tissue to be studied (e.g., so the light can scatter and be detected). When the tissue substantially absorbs light and/or Raman scatter (among others) (e.g.,  170 A and  170 B in  FIGS. 1 and 3 , respectively), there can be insufficient Raman electromagnetic radiation for detector  130  to detect. For example, in skin tissue that has highly fluorescent chromophores, the increased absorption amplifies the emitted fluorescence and masks the weaker Raman signal. In various embodiments, the excitation wavelength of light is in a range of 670 nm-900 nm for (human) tissue. Other ranges for the excitation wavelength of light can be used (e.g., depending on the absorption coefficient of the tissue to be studied). 
     In embodiments where analyte (e.g.,  150 A-C ( FIGS. 1-3 )) is a live (and not dead) animal (e.g., living, alive, etc.), blood flows through blood vessel  320  ( FIG. 3 ). Blood flow through blood vessel  320  in animals (e.g., humans) is caused by a heart (not shown in  FIG. 4 ) pumping blood (e.g., beating heart). When measurements are taken at a rate slower than blood flows, different samples of blood are measured instead of the same sample and fluorescence will change with each sample. 
     When Raman instrument  110 C takes multiple measurements, the measurements can be taken before the molecules in blood illuminated in one measurement (e.g., blood sample) flow away and are not available for the next measurement. For example, a resting adult human heart can beat at approximately 60 to 100 beats a minute (˜1 Hz). Raman instrument  110 C can take measurements within a tenth of a second (˜0.1 KHz) or less, such that measurements are taken faster than blood flows (e.g., multiple measurements are taken from the same (instead of different) sample). Slower and/or faster sampling rates (e.g., frequency at which measurements are taken) can be used depending on the heart rate associated with analyte  150 C ( FIG. 3 ). In various embodiments, the sampling rate is 10 Hz-1 KHz. 
       FIG. 5  illustrates example spectrum  500  produced using system  100  ( FIG. 1 ), system  200  ( FIG. 2 ), system  300  ( FIG. 3 ), in some embodiments. A Raman spectrum—a plot/graph of an intensity of the Raman scattering (shifted light) against frequency—can be produced by a computing system  190  using intensity measurements from optical bench  130  ( FIG. 1 ). Spectrum  500  (and  550 A) can reliably be used to identify molecules in analyte  150 A ( FIG. 1 ), analyte  150 B ( FIG. 2 ), and analyte  150 C ( FIG. 3 ). In this way, Raman spectra (e.g., spectra  550 A) can be said to produce a “fingerprint” of molecules in analyte  150 . For example, Raman spectra (e.g., spectra  550 A) of analyte  150  can be compared to a database (e.g., in the same or another computing system) of Raman spectra associated with known molecules to identify and quantify molecules in analyte  150 A ( FIG. 1 ), analyte  150 B ( FIG. 2 ), and analyte  150 C ( FIG. 3 ). 
     Spectrum  500  is plotted/graphed along three axes: intensity  510 A, time  520 A, and wavelength λ (or wavenumber)  530 A. As shown in  FIG. 5 , intensity (axis  510 A) can be power (light intensity) in a.u. (arbitrary units of intensity); other units can be milliwatts (mW) or photon count. Time (axis  520 A) can be in nanoseconds (ns). Wavelength (axis  530 A) can be a Raman shift in units such as nanometers (nm) or as a wavenumber in cm −1 . System  100  ( FIG. 1 ), system  200  ( FIG. 2 ), and system  300  ( FIG. 3 ) can measure an intensity of Raman scatter having wavelength λ. For example, measurements taken at three wavelengths λ 1 , λ 2 , and λ 3  result in measurements  540 λ 1 ,  540 λ 2 , and  540 λ 3 , respectively. Measurements  540 λ 1 ,  540 λ 2 , and  540 λ 3  show an intensity of Raman scattered light (the light having a particular wavelength λ 1 , λ 2 , and λ 3 ) over time. Measurements  540 λ 1 - 540 λ 3  can be collectively viewed when plotted/graphed along two axes: intensity  510 A and wavelength λ (or wavenumber)  530 A, which results in spectrum  550 A (which can be referred to as a Raman spectrum). Spectrum  550 A shows the peak intensity of Raman scatter at a range of wavelengths λ, such as wavelengths λ 1 , λ 2 , and λ 3  (or wavenumber). 
       FIG. 6A  shows graphical representation (e.g., plot, graph, and the like)  600 A of (relative) (received) light intensity or power (e.g., in arbitrary units of intensity (a.u.), in milliwatts (mW), or photon count) along axis  510 E over time (e.g., in nanoseconds) along axis  520 B. Graphical representation  600 A includes Raman signal  610 A, fluorescence  620 A, and total signal  540 B, according to some embodiments. Raman signal  610 A is, by way of non-limiting example, an intensity of a particular wavelength of Raman scatter for a material to be measured (e.g., analyte  150  in  FIG. 1 ). Total signal  540 B can have at least some of the characteristics of spectra  540 λ 1 - 540 λ 3  ( FIG. 5 ) and be an intensity measured by a detector (e.g., optical bench  130  in  FIG. 1 ) from (approximately) time t 0  to time t 4 . In contrast to Raman scattering, fluorescence emission (fluorescence  620 A) follows an absorption process. Fluorescence  620 A can be several orders of magnitude (e.g., 10 5 -10 6 ) higher in intensity than Raman signal  610 A and can overwhelm or obscure Raman signal  610 A, such that Raman signal  610 A is difficult to measure. 
     When light (e.g., light  160  in  FIG. 1 ) from an excitation source (e.g., excitation light source  120  in  FIG. 1 ) illuminates a material to be measured (e.g., analyte  150 A ( FIG. 1 ), analyte  150 B ( FIG. 2 ), and analyte  150 C ( FIG. 3 )), receipt of the Raman signal (also called Raman scatter or return signal)  610 A by a detector (e.g., detector  136  in  FIG. 1 ) is almost instantaneous (e.g., ≤1 ps, depending on the distance travelled by the light and the Raman signal) (e.g., at time t 0 ). For this reason, Raman signal  610 A can also be thought of as (approximately) representing the light from the excitation source, such as a laser pulse. In contrast, fluorescence  620 A is received/occurs after Raman signal  610 A (e.g., at time t 1 ). When light from the excitation source illuminates the material to be measured (e.g., at time t 0 ), receipt of fluorescence  620 A by the detector occurs later (e.g., at time t 1 , which can be hundreds of nanoseconds or even milliseconds later). 
     When the detector (e.g., detector  136  in  FIG. 1 ) is active (e.g., measuring light, detecting photons, and the like) while Raman signal  610 A is present and before fluorescence  620 A obscures/interferes with Raman signal  610 A (e.g., from time t 0  to time t 2 ), Raman signal  610 A can be measured by the detector without being completely overwhelmed or obscured by fluorescence  620 A. Time window  630  is ideally narrow (relative to time window  640 ) and the time during which most (90%-100%) of the Raman photons are present and can be collected, although in practice time window  630  can be broader to include time when Raman photons are not present. For example, time window  630  is as wide (time-wise) as a laser pulse from excitation light source  120  ( FIG. 1 ) (e.g., t 0 -t 3 ). By way of further example, time window  630  is the time during which Raman signal  610 A is present (e.g., approximately 80%-100% of peak intensity) and fluorescence  620 A is mostly not present (e.g., from time t 0  to time t 2 , time t 0  to time t 3 , and the like) or is present. 
     As shown in  FIG. 6A , although fluorescence  620 A begins being received at time t 1 , an intensity of fluorescence  620 A may not be high enough to begin overwhelm or obscure Raman signal  610 A until at or after time t 2 . Control of the detector such that the detector is substantially active only during time window  630  can be referred to as gating. Moreover, time window  630  can also be referred to as gate  630 . Gating can be used to reject a significant portion of fluorescence  620 A. 
     In some embodiments, the detector (e.g., detector  136  in  FIG. 1 ) is active (e.g., gate  630  in  FIG. 6A ) prior to the excitation source (e.g., excitation light source  120  in  FIG. 1 ) providing light. By way of non-limiting example, (ideal) gate  630  is 1 ns (1,000 ps). The time resolution of the detector using the 1 ns (ideal) gate  630  is approximately equal to the laser pulse duration (e.g., 600 ps). 
     Time window  640  is a second time window or gate which is ideally broad/wide (relative to time window  630 ) and during which Raman photons are ideally not present and not detected, and fluorescence is present. In practice, Raman photons may be present during time window  640 . For example, during time window  640 , little of Raman signal  610 A is present (e.g., 0%-20% of peak intensity). 
     As shown in  FIG. 6A , time window  640  can partially (or completely) overlap with time window  630 . Alternatively, time window  630  and time window  640  can be contiguous. In other words, time window  630  and time window  640  occur one after the other sequentially. For example, time window  640  can begin (almost immediately) after time window  630  ends, and can end before the intensity of total signal  540 B drops to zero (e.g., at time t 4 ). For example, time window  640  can extend out to time t 4 . In various embodiments, time window  630  ends and time window  640  begins before or after t 1  (or t 2 ). Generally, time window  630  is shorter in duration than time window  640 , although time window  630  can be greater-than-or-equal-to time window  640 . 
     The spectrometer (e.g., spectrometer  110 A ( FIG. 1 ), spectrometer  110 E ( FIG. 2 ), and spectrometer  110 C ( FIG. 3 )) can be controlled such that measurements can be taken during both time window  630  and time window  640  using one pulse (e.g., of light from excitation light source  120 ). Alternatively or additionally, two pulses (e.g., of light from excitation light source  120 ), one pulse for measurements in time window  630  and another pulse during time window  640 . 
       FIG. 6B  depicts graphical representation (e.g., plot, graph, and the like)  600 B of (relative) (received) light intensity or power (e.g., in arbitrary units of intensity (a.u.), in milliwatts (mW), or photon count) (along axis  510 C) over time (e.g., in nanoseconds) along axis  520 C from a (e.g., 600 ps) laser pulse, in accordance with some embodiments. Graphical representation  600 B can include Raman signal  610 B and fluorescence  620 B 1 - 620 B 3 . Graphical representation  600 B can show relative intensities and/or lifetimes/durations of Raman signal  610 B and fluorescence  620 B 1 - 620 B 3 . Raman signal  610 B has at least some of the characteristics of Raman signal  610 A described above in relation to  FIG. 6A . Fluorescence  620 B 1 - 620 B 3  can have at least some of the characteristics of fluorescence  620 A ( FIG. 6A ). Since Raman scattering occurs almost immediately (e.g., ≤1 ps, depending on the distance travelled by the light and the Raman signal) after an excitation light pulse from the excitation source (e.g., excitation light source  120  in  FIG. 1 ), Raman signal  610 B can also (approximately) represent the excitation light pulse. 
     Graphical representation  600 B illustrates the relative intensities and/or the relative lifetimes/durations among fluorescence  620 B 1 - 620 B 3 , according to various embodiments. Raman signal  610 B can have at least some of the characteristics of Raman signal  610 A ( FIG. 3A ). Fluorescence  620 B 1 - 620 B 3  can have at least some of the characteristics of fluorescence  620 A ( FIG. 6A ). In some embodiments, fluorescence  620 B 1 - 620 B 3  results when light (e.g., light  160  in  FIG. 1 ) from an excitation source (e.g., excitation light source  120  in  FIG. 1 ) illuminates a material to be measured (e.g., analyte  150 A ( FIG. 1 ), analyte  150 B ( FIG. 2 ), and analyte  150 C ( FIG. 3 )), where the wavelength of the light used varies. In other words, fluorescence  620 B 1 - 620 B 3  can be from the same material, but the wavelength of the light used is different. 
     As shown in  FIG. 6B , each of fluorescence  620 B 1 - 620 B 3  can have a different lifetime/duration, with fluorescence  620 B 1  having the shortest and fluorescence  620 B 3  having the longest. By way of non-limiting example, fluorescence  320 B 1  has a 1 ns lifetime/duration, fluorescence  320 B 2  has a 5 ns lifetime/duration, and fluorescence  320 B 3  has a 10 ns lifetime/duration. Depending upon the material, a fluorescence can have other lifetimes/durations (e.g., 100 ps-10 ms). As shown in  FIG. 6B , the longer the lifetime/duration of a respective one of fluorescence  620 B 1 - 620 B 3 , the lower the intensity of a respective one of fluorescence  620 B 1 - 620 B 3  can be. Moreover, the decay rate of fluorescence  620 B 1 - 620 B 3  is different at each frequency. 
       FIG. 7  illustrates graphical representation (e.g., plot, graph, and the like)  700  of (relative) (received) light intensity or power (e.g., in arbitrary units of intensity (a.u.), in milliwatts (mW), or photon count) along axis  510 D over received light wavelength (e.g., in nanometers (nm)) along axis  530 B. Graphical representation  700  includes Raman signal  730  ( 730 A- 730 D). Raman signal  730  can be obscured by fluorescence (e.g., fluorescence  620 A ( FIG. 6A ) and fluorescence  620 B 1 - 620 B 3  ( FIG. 6B )), resulting in spectrum  550 B. Raman signal  730  is a Raman spectrograph for an analyte (e.g., analyte  150 A-C ( FIGS. 1-3 ) that would be measured if it were not overwhelmed/obscured by fluorescence. Although Raman signal  730  is shown having four peaks at regular intervals, Raman signal  730  may have any number of peaks having different intensities and occurring at different/irregular frequencies. The peaks of Raman signal  730  can indicate information about different molecular bonds. 
     When light (e.g., light  160 A and  160 B in  FIGS. 1 and 3 , respectively) illuminates analyte (e.g., analyte  150 A-C in  FIGS. 1-3 , respectively), fluorescence (e.g., fluorescence  620 A ( FIG. 6A ) and fluorescence  620 B 1 - 620 B 3  ( FIG. 6B )) (in addition to Raman signal  530  ( 530 A- 530 D)) can result. Fluorescence can be several orders of magnitude (e.g. 10 5 -10 6 ) higher in intensity than Raman signal  730 . Fluorescence can overwhelm or obscure Raman signal  730 , such that Raman signal  730  is difficult to actually measure. 
     An intensity measured by detector  130  ( FIG. 1 ) includes an intensity (I) of the Raman signal (I R ) and intensity of fluorescence (I F ) at each wavelength (e.g., I=I R +I F ). For example, the intensity measured by detector  130  ( FIG. 1 ) would look like fluorescence (e.g., fluorescence  620 A ( FIG. 6A ) and fluorescence  620 B 1 - 620 B 3  ( FIG. 6B )) with very small contributions  750 A- 750 D from Raman signal  530  ( 530 A- 530 D), resulting in spectrum  550 B. Contributions  750 A- 750 D are provided for illustrative purposes and are not drawn to scale. Fluorescence is several orders of magnitude (e.g. 10 5 -10 6 ) larger than Raman signal  730  and contributions  750 A- 750 D may not be visible if shown to scale. 
     An intensity of the Raman signal is inversely proportional to the excitation wavelength (λ) of light (e.g., light  160 A and  160 B in  FIGS. 1 and 3 , respectively) (e.g., Raman signal strength α λ −4 ). In contrast, an intensity of the fluorescence is proportional to the excitation wavelength (λ). Generally, when a longer excitation wavelength (λ) is used to illuminate tissue, there is less fluorescence but the Raman signal strength becomes smaller and difficult to measure. Likewise, when a shorter excitation wavelength (λ) is used (e.g., in the near infrared (NR) spectrum) to illuminate tissue, too much fluorescence is produced making it difficult to measure the Raman signal. 
       FIG. 8  depicts graphical representation (e.g., plot, graph, and the like)  800  of (relative) (received) light intensity or power (e.g., in arbitrary units of intensity (a.u.), in milliwatts (mW), or photon count) along axis  510 E over received light wavelength (e.g., in nanometers (nm)) along axis  530 C, according to some embodiments. Graphical representation  800  includes Raman signal  730  ( 730 A- 730 D), Raman signal  810  ( 810 A- 810 D), Raman signal  820  ( 820 A- 820 D), and spectrum  550 C. Raman signal  730  was described above in relation to  FIG. 7 . Raman signals  810  and  820  are Raman spectrographs for analyte (e.g., analyte  150 A-C in  FIGS. 1-3 , respectively) that would be measured if it were not overwhelmed/obscured by fluorescence (e.g., fluorescence  620 A ( FIG. 6A ) and fluorescence  620 B 1 - 620 B 3  ( FIG. 6B )). Although Raman signals  810  and  820  are shown each having four peaks at regular intervals, Raman signals  810  and  820  may have any number of peaks having different intensities and occurring at different/irregular frequencies (e.g., corresponding to or following Raman signal  730 ). Raman signals  730 ,  810 , and  820  can result from different excitation wavelengths (λ). 
     As described above, excitation light source  120  ( FIG. 1 ) can be tunable, such that an excitation wavelength can change (e.g., by a predetermined increment, to one or more predetermined wavelengths, etc.). When measurements are (sequentially) taken at different excitation wavelengths (λ) (e.g., λ=λ 0 , λ 1 , λ 2 , . . . ), a Raman signal for each excitation wavelength can be produced. For example, Raman signal  730  ( 730 A- 730 D) is measured at λ=λ 0 , Raman signal  810  ( 810 A- 810 D) at λ=λ 1 , and Raman signal  820  ( 820 A- 820 D) λ=λ 2 . Although three different excitation wavelengths (e.g., λ=λ 0 , λ 1 , λ 2 ) are used, any number N of different excitation wavelengths can be used (e.g., λ=λ 0 , λ 1 , . . . λ N ). N can be a function of a sampling rate of Raman instrument (e.g., Raman instrument  110 A ( FIG. 1 ),  110 E ( FIG. 2 ), and  110 C ( FIG. 3 )), a molecule to be detected and/or quantified, and the like. The excitation wavelength can be incremented/decremented by a predetermined amount Δλ, such that λ 1 =λ 0 +Δλ, λ 2 =λ 1 +Δλ, λ 3 =λ 2 +Δλ, etc. As shown in  FIG. 8 , Raman signals  810  and  820  can be shifted from an adjacent Raman signal (e.g., Raman signals  730  and  810 , respectively) by Δλ. Although Raman signals  730 ,  810 , and  820  are shifted (e.g., by Δλ), the envelopes (e.g., amplitude and frequency of the peaks) of Raman signals  730 ,  810 , and  820  are consistent. At each of λ=λ 0 , λ 1 , λ 2 , . . . , fluorescence (e.g., fluorescence  620 A ( FIG. 6A ) and fluorescence  620 B 1 - 620 B 3  ( FIG. 6B )) remains the same (e.g., as long as the analyte (e.g., analyte  150 A ( FIG. 1 ), analyte  150 B ( FIG. 2 ), and analyte  150 C ( FIG. 3 )), (blood) sample, and the like does not change (enough to change the spectrum)). 
     An intensity measured by detector  130  ( FIG. 1 ) includes an intensity (I) of the Raman signal (I R ) and intensity of fluorescence (I F ) at each wavelength (e.g., I=I R +I F ), as described above in relation to  FIG. 7 . For example, for excitation wavelength λ=λ 1 , spectrum  550 C would look like fluorescence (e.g., fluorescence  620 A ( FIG. 6A ) and fluorescence  620 B 1 - 620 B 3  ( FIG. 6B )) with very small contributions (e.g., contributions  830 A- 830 D) from Raman signal  810  ( 810 A- 810 D). By way of further non-limiting example, for excitation wavelength λ=λ 2 , spectrum  550 C would look like fluorescence with very small contributions (e.g.,  840 A- 840 D) from Raman signal  820  ( 820 A- 820 D). Contributions  830 A-D and  840 A-D are provided for illustrative purposes and are not drawn to scale. 
     As described below in relation to  FIGS. 9-11 , a Raman spectrograph for analyte (e.g., analyte  150 A ( FIG. 1 ), analyte  150 B ( FIG. 2 ), and analyte  150 C ( FIG. 3 ) (e.g., compensating for fluorescence) can be produced using Raman signals  730  ( 730 A- 730 D),  810  ( 810 A- 810 D),  820  ( 820 A- 820 D), etc. 
       FIG. 9  illustrates method  900  for hybrid time-resolved and time-shifted spectroscopy of biological analytes, according to some embodiments. Method  900  can be performed by a Raman instrument and/or a computing system. The Raman instrument can have at least some of the characteristics of Raman instrument  110 A ( FIG. 1 ), Raman instrument  110 E ( FIG. 2 ), and Raman instrument  110 C ( FIG. 3 ). The computing system can have at least some of the characteristics of computing system  240  ( FIG. 2 ) and computing system  1300  ( FIG. 13 ). 
     Method  900  can commence at step  910 , where an analyte can be illuminated using light having an initial excitation wavelength. For example, the analyte has at least some of the characteristics of analyte  150 A ( FIG. 1 ), analyte  150 B ( FIG. 2 ), and analyte  150 C ( FIG. 3 ). By way of further non-limiting example, the light can be provided by the Raman instrument, for example, using excitation light source  120  ( FIG. 1 ). For illustrative purposes, the initial excitation wavelength can referred to as λ 0  and can have a value of 670 nm (e.g., λ 0 =670 nm). Other values for λ 0  can be used. 
     In some embodiments, the provided light has a predetermined wavelength and/or duration. For example, the predetermined wavelength (also called an excitation wavelength) can depend on the material to be measured and be selected to minimize absorption (by the material) of the provided light and maximize the Raman signal, such as described above in relation to  FIGS. 6A-6B . In addition, a shorter excitation wavelength can provide a stronger Raman signal than a longer excitation wavelength. In some embodiments, the excitation wavelength is in a range of 450 nm-650 nm. 
     By way of further non-limiting example, the predetermined duration can be selected so as to at least provide a Raman signal of sufficient duration to be measured by detector  136 , such as the gate (e.g., time window  630  in  FIG. 6A ). At or after the receipt or occurrence of fluorescence, the provided light is not needed and may stop. In various embodiments, the predetermined duration is in a range of 200 ps-2 ns. For example, the predetermined duration is on the order of 600 ps. 
     At step  920 , a spectrum (e.g., including Raman scattering (or Raman signal) and fluorescence) can be detected from the illuminated analyte. In some embodiments, the light hitting the analyte results in Raman scattering (or Raman signal) and fluorescence. For example, the Raman scattering (e.g., contributions  750 A-D,  830 A-D, and  840 A-D) and fluorescence can be detected by the Raman instrument (e.g., using detector  130  optionally through optional sampling apparatus  140 A ( FIG. 1 )). By way of further non-limiting example, the detected Raman scattering (e.g., contributions  750 A-D) and fluorescence may appear (e.g., when graphed, plotted, and the like) as shown in spectrum  550 B (where the excitation wavelength is λ 0 ). The detected spectrum (e.g., data, graphical representation, and the like) can be stored in the Raman instrument and/or the computing system. 
     According to various embodiments, the spectrum can be detected at step  920  using a time-resolving technique described below in relation to  FIG. 10 . 
     At step  930 , the preceding excitation wavelength can be increased or decreased by a predetermined increment or decrement, respectively. For illustrative purposes, the predetermined increment/decrement can be referred to as Δλ. For example, when the preceding excitation wavelength is λ 0 , an increased/decreased excitation wavelength is λ 1 , where λ 1 =λ 0 +Δλ. By way of further non-limiting example, when the preceding excitation wavelength is λ 1 , an increased/decreased excitation wavelength is λ 2 , where λ 2 =λ 1 +Δλ. By way of additional non-limiting example, when N spectra are to be detected, λ A =λ 0 +(A*Δλ), where A={0, 1, . . . (N−1)}. 
     For illustrative purposes, the predetermined increment/decrement can have a value of 0.5 nm. To illustrate embodiments where the excitation wavelength is increased, when λ 0 =670 nm, λ 1 =670.5 nm, λ 2 =671 nm, and so on according to the number of spectra to be detected (N). In some embodiments, the excitation wavelength is decreased by a decrement. 
     At step  940 , the analyte can be illuminated using light having the increased or decreased wavelength. To illustrate embodiments where the excitation wavelength is increased, the light can have a wavelength λ 1 =670.5 nm, λ 2 =671 nm, or so on according to the number of spectra to be detected (N). 
     At step  950 , a spectrum (e.g., including Raman scattering (or Raman signal) and fluorescence) can be detected from the illuminated analyte. In some embodiments, the light (having the increased/decreased excitation wavelength) hitting the analyte results in Raman scattering (or Raman signal) and fluorescence. For example, the Raman scattering and fluorescence can be detected by the Raman instrument (e.g., using detector  130  optionally through optional sampling apparatus  140 A ( FIG. 1 )). The detected Raman scattering and fluorescence may appear (e.g., when graphed/plotted) as shown in graphical representation  700  ( FIG. 7 ) (where the excitation wavelength is the increased/decreased excitation wavelength, for example, λ 1 , λ 2 , and so on according to the number of spectra to be detected). Each detected spectrum (e.g., data, graphical representation, and the like) can be stored by (and/or in) the Raman instrument and/or the computing system. 
     According to various embodiments, the spectrum can be detected at step  950  using a time-resolving technique described below in relation to  FIG. 10 . 
     At step  960 , a determination is made as to whether another spectrum is to be detected. In some embodiments, the predetermined number of spectra to be detected (N) is compared to the number of spectra (actually) detected. When the predetermined number of spectra to be detected (N) is less than the number of spectra detected, method  900  can proceed to step  930 . When the predetermined number of spectra to be detected (N) is equal to the number of spectra actually detected, method  900  can proceed to step  970 . For example, when N=6 and spectra are already detected for λ 0 , λ 1 , λ 2 , λ 3 , λ 4 , and λ 5 , method  900  can proceed to step  970 . By way of further non-limiting example, when N=3 the detected Raman scattering and fluorescence (e.g., detected for each of λ 0 , λ 1 , and λ 2 ) may appear (e.g., when graphed/plotted together) as shown in graphical representation  800  ( FIG. 8 ). 
     Optionally at step  970 , a Raman spectrum of the analyte can be recovered using the detected spectra (e.g., N detected spectra). In some embodiments, the Raman spectrum of the analyte can be recovered using expectation maximization techniques. The recovered Raman spectrum may appear (e.g., when graphed/plotted) as shown in graphical representation  700  ( FIG. 7 ) (e.g., Raman signal  730  ( 730 A-D) without fluorescence). Recovering the Raman spectrum of the analyte is described further below in relation to  FIG. 11 . 
     Optionally at step  980 , a molecule can be identified using the recovered Raman spectrum. For example, a database of known Raman spectrum for certain molecules can be searched using (e.g., compared to) the recovered Raman spectrum to find a match. 
       FIG. 10  illustrates method  1000  for time-resolved spectroscopy, according to some embodiments. Method  1000  can be performed by a Raman instrument and/or a computing system. The Raman instrument can have at least some of the characteristics of Raman instrument  110 A ( FIG. 1 ), Raman instrument  110 B ( FIG. 2 ), and Raman instrument  110 C ( FIG. 3 ). The computing system can have at least some of the characteristics of computing system  240  ( FIG. 2 ) and computing system  1300  ( FIG. 13 ). 
     Steps  1010 - 1060 , in whole or in part, provide further detail of step  920  and/or  950  in  FIG. 9 . Following step  910  and/or  940 , method  1000  can commence at step  1010 , where there is a wait or pause for a predetermined delay (e.g., after the analyte is illuminated). In some embodiments, the predetermined delay can be controlled by delay  180  in  FIG. 1 . For example, the predetermined delay can be substantially the duration of the gate (e.g., time window  630  in  FIG. 6A ), which can depend on the material to be measured. Additionally or alternatively, the predetermined delay can also take into account latency (delays) arising from detection of the light being provided (e.g., laser firing), detector  136 A de-activating after receipt of the instruction or control signal, characteristics of the material, and the like. For example, the preceding example latencies in system  100  ( FIG. 1 ) can be characterized and delay  180  calibrated to take into them account (or otherwise compensate for them). 
     At step  1020 , the detector (e.g., detector  136 A in  FIG. 12 ) can be signaled to stop collecting returned light and/or provide measurements. In some embodiments, an instruction or control signal can be provided to the detector (e.g., detector  136 A and/or a control circuit(s) for detector  136 A), which de-activates the detector (e.g., detector  136 A stops measuring light/photons, outputs the light measurements, and/or optionally resets detector  136 A to detect further photons such as by quenching). 
     At step  1030 , the provided (received) measurements can be (optionally) converted to a digital spectra (e.g., using an ADC) and/or the digital spectra can be stored. In some embodiments, when detector  136 A is a SPAD array which provides a digital output, the measurements (e.g., spectra) from detector  136 A are already digital spectra and do not need conversion, but can still be stored. 
     At step  1040 , a determination is made as to whether another spectrum is to be detected. In some embodiments, the predetermined number of spectra to be detected (P) is compared to the number of spectra (actually) detected. For example, when the predetermined number of spectra to be detected (P) is less than the number of spectra detected, method  1000  can proceed to step  1050 . When the predetermined number of spectra to be detected (P) is equal to the number of spectra actually detected, method  1000  can proceed to steps  930  (e.g., when method  1000  is performed at step  920  in  FIG. 9 ) and/or  960  (e.g., when method  1000  is performed at step  950  in  FIG. 9 ). 
     In some embodiments, steps  1010 - 1040  can be repeated in a range of 9-9,999,999 times (e.g., P=10-1,000,000,000). For example, P can be in a range of 1,000-10,000 times. By way of further non-limiting example, P can be 1,000,000 samples taken in 50 seconds at a sample rate (e.g., steps  1010 - 1040  are repeated) of 20 kHz. In 50 seconds, some measurable characteristics of the material to be measured (e.g., analyte  150 A-C in  FIGS. 1-3 ) do not appreciably change (e.g., an accurate reading can be performed). In some embodiments, there is latency (a delay) between when detector  136 A receives an instruction or control signal to de-activate and when detector  136 A actually de-activates. This latency can be, for example, 10 ps-1,000 ps. In some embodiments, this latency is on the order of 100 ps. For example, when detector  136 A continues measuring after the end of the gate (e.g., time window  1170 A in  FIG. 11A , at time t 2 ), detector  136 A will measure at least some fluorescence. 
     In addition, detector  136 A may detect ambient/background radiation. Ambient/background radiation can include one or more of: Ultraviolet C (UVC) light (e.g., 100 nm-280 nm wavelength), Ultraviolet B (UVB) light (e.g., 280 nm-315 nm wavelength), Ultraviolet A (UVA) light (e.g., 315 nm-400 nm wavelength), visible light (e.g., 380 nm-780 nm wavelength), and infrared (e.g., 700 nm-1 mm wavelength). To reduce the distortion (to the measured spectra) introduced by fluorescence and/or ambient/background radiation, multiple measurements can be taken, since the measured fluorescence and/or ambient/background radiation can vary across multiple measurements. 
     At step  1050 , method  1000  can wait or pause for another predetermined delay before proceeding to step  1060 . The another predetermined delay determines at least partially a frequency at which light is provided to (e.g., a laser fires at) the material and the returned light measured. The provided light (e.g., laser pulses) can be temporally spaced, such that at least the fluorescence from the material dies out (e.g., the end of the fluorescence lifetime is reached) before the next laser pulse is sent out. In other words, the time between laser pulses (e.g., the another predetermined delay) can be longer than the fluorescence lifetime/duration (e.g.,  FIG. 11B ). Additionally or alternatively, the another predetermined delay can be selected such that when detector  136 A is a SPAD array, each pixel in the SPAD array can be quenched (and ready to detect a photon) before light is provided again at step  1060 . 
     In some embodiments, the frequency at which the light is provided (e.g., the laser fires) can be in the range of 1 KHz-100 KHz. For example, the frequency is on the order of tens of kilohertz, such as 20 KHz (e.g., the another predetermined delay (uncompensated) is 50 ms). The another predetermined delay can be adjusted to compensate for latency (delays) incurred by at least some of steps  1010 - 1050  (e.g., the time is takes to perform at least some of steps  1010 - 1050 ). The another predetermined delay can be different from the predetermined delay. 
     At step  1060 , the analyte is illuminated. For example, step  1060  is a repeat of step  910  (e.g., when method  1000  is performed at step  920  in  FIG. 9 ) and/or  940  (e.g., when method  1000  is performed at step  950  in  FIG. 9 ). 
     According to various embodiments, steps  1010 - 1060  can be applied (one or more times) to optical phantoms, each optical phantom having/mimicking a different concentration of a particular molecule ( FIG. 9 ). To perform a calibration, instead of step  930  and/or  960 , a (Raman) spectrum or spectrograph (e.g., intensity at one or more wavelengths) of the material can be recovered using the detected spectra (e.g., P detected spectra). In some embodiments, the (Raman) spectrum or spectrograph (e.g., intensity at one or more wavelengths) of the material can be recovered by summing the detected spectra. Since the measured fluorescence and noise introduced by ambient light can vary across multiple measurements, summing multiple measurements can reduce/eliminate distortions introduced by fluorescence and/or ambient light. Additionally or alternatively, statistical methods (e.g., arithmetic mean, rolling average, and the like) can be used to recover the (Raman) spectrum or spectrograph. 
     The recovered (Raman) spectrum or spectrograph may appear (e.g., when graphed/plotted) as shown in graphical representation  700  ( FIG. 7 ) (e.g., Raman signal  730  ( 730 A-D) substantially without fluorescence). A molecule (and optionally a concentration of the molecule) can be identified using the recovered (Raman) spectrum. In some embodiments, the recovered (Raman) spectrum or spectrograph (e.g., intensity at one or more wavelengths) can be calibrated using one or more optical phantoms. For example, steps  1010 - 1060  can be applied to an optical phantom which mimics the material to be tested. In the case of biological analytes, optical phantoms are tissue-simulating objects used to mimic light propagation in living tissue. Optical phantoms can be designed with absorption and scattering properties matching optical characteristics of living human and animal tissues. 
     During calibration, the resulting recovered spectrum from each phantom/concentration can be correlated with the molecule (and concentration) of that optical phantom. Using calibration, the correlation between the recovered (Raman) spectrum or spectrograph (e.g., intensity at one or more wavelengths) of the material to be measured and the presence/concentration of a certain molecule can be established. In some embodiments, the spectra generated during the calibration process are stored in a database and the actual spectrum produced when taking real measurements can be compared to the stored spectra. The characteristics of a matching stored spectrum can be associated with the actual spectrum. 
     Additionally, calibration using optical phantoms for other molecules at different concentrations can be performed. Although a calibration process for detecting a range of concentrations is described, calibration can be performed for detecting the presence of a molecule (e.g., using a phantom having a minimum, threshold, or maximum concentration of the molecule). 
       FIG. 11  shows method  1100  for recovering a Raman spectrum of an analyte using expectation maximization techniques and the detected spectra, according to some embodiments. Method  1100  can commence at Step  1110 , where the detected spectra (e.g., N detected spectra from method  900  in  FIG. 9 ) can be received. By way of non-limiting example, the detected spectra are referred to as vector X. The detected intensity in vector X includes the intensity of fluorescence and the Raman signal (e.g., I=IR+IF). According to some embodiments, vector X (e.g., detected spectra) can be represented by: 
                   X   =     [           Y     1   ,   1                 Y     1   ,   2               ⋮             Y     1   ,   N                 Y     2   ,   1                 Y     2   ,   2               ⋮             Y     2   ,   N               ⋮             Y       K   -   1     ,   N                 Y     K   ,   1                 Y     K   ,   2               ⋮             Y     K   ,   N             ]             (   1   )               
where each Y i  (where i={1, 2, . . . K}) is a measured spectra using a different excitation wavelength.
 
     By way of further non-limiting example, the (separate) values of the fluorescence and the Raman signal are referred to as vector Z. Vector Z (e.g., (separate) values of the fluorescence and the Raman signal) can be represented by a vector have 2N dimensions: 
                   Z   =     [           S   1   F               S   2   F             ⋮             S   N   F               S   1   R               S   2   R             ⋮             S   N   R           ]             (   2   )               
where the fluorescence spectrum is S F  and the Raman spectrum is S R .
 
     A relationship between vector X and vector Z can be represented as a matrix of (predetermined) parameters, matrix H. By way of non-limiting example, a relationship between vector X, vector Z, and matrix H can be:
 
 H×Z=X   (3)
 
where matrix H can be represented by a KN×2N matrix having predetermined values, such as:
 
     
       
         
           
             
               
                 
                   H 
                   = 
                   
                     [ 
                     
                       
                         
                           
                             1 
                             , 
                             0 
                             , 
                             0 
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                             , 
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                             0 
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                             0 
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                             ⁢ 
                             
                                 
                             
                             , 
                             1 
                           
                         
                       
                       
                         
                           
                             0 
                             , 
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                             ⁢ 
                             
                                 
                             
                             , 
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                             0 
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                     ] 
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
     The relationship depicted in equation 3 is an inverse problem: using a known vector X to determine vector Z, where matrix H is a large matrix which cannot be inverted. In various embodiments, the inverse problem in equation 3 is solved using Maximum Likelihood-Expectation Maximization (ML-EM) iterative methods included in methods discussed herein. For example, among all possible values for vector Z, one that maximizes the probability of producing vector X is selected. The maximization can be performed using the Expectation Maximization (EM) techniques included in method  1100 . 
     At step  1120 , an initial guess vector Z (n=0)  can be used for vector Z (e.g., S F  and S R ). In some embodiments, vector Z (n=0)  can be arbitrary, a prior calculated estimate of vector Z (e.g., using method  1100 ), combinations thereof, and the like. 
     At step  1130 , an estimate for vector Z (e.g., Z (n+1) ) can be determined. For example, Z can be estimated using: 
     
       
         
           
             
               
                 
                   
                     z 
                     i 
                     
                       ( 
                       
                         n 
                         + 
                         1 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       z 
                       i 
                       n 
                     
                     * 
                     
                       ( 
                       
                         1 
                         
                           
                             ∑ 
                             j 
                           
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                             H 
                             ji 
                           
                         
                       
                       ) 
                     
                     * 
                     
                       ( 
                       
                         
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                           j 
                         
                         ⁢ 
                         
                           H 
                           ji 
                         
                       
                       ) 
                     
                     * 
                     
                       ( 
                       
                         
                           X 
                           j 
                         
                         
                           
                             ∑ 
                             k 
                           
                           ⁢ 
                           
                             
                               H 
                               jk 
                             
                             ⁢ 
                             
                               Z 
                               k 
                               n 
                             
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     At step  1140 , the estimate for vector Z (e.g., vector Z (n+1) ) can be evaluated. In some embodiments, the estimate for vector Z is evaluated for convergence. For example, when a change between successive iterations (e.g., between vector Z n  and vector Z n+k , where k can be a number in the range of 0-10,000) is smaller than a predetermined amount (e.g., tolerance, such as 1%-10% change), then vector Z can be said to converge. The change can be determined between an iteration early in the method (e.g., vector Z j  (where j can be a number in the range of 5-10,000) and a latest iteration. Additionally or alternatively, vector Z can be said to have converged after a predetermined number (e.g., 10-50,000) of iterations. In various embodiments, for some spectra having different fluorescence levels, changes in the estimate for vector Z are negligible (e.g., smaller than a predetermined amount) after around 2,000 iterations (e.g., 1,000-3,000 iterations). When vector Z has not converged or immediately after the first iteration (e.g., using vector Z (n=0) ), method  1100  can proceed to step  1150 . When vector Z is determined to have converged, method  1100  can proceed to step  1160 . 
     At step  1150 , n can be incremented (e.g., n←n+1), Z can be incremented (e.g., Z (n) ←Z (n=1) ) and method  1100  can perform another iteration by proceeding to step  830 . 
     At step  1160 , a next estimate for vector Z can be determined using vector X, matrix H, and the estimate for vector Z calculated in the prior iteration. 
     In various embodiments, method  1100  can be performed multiple times, each repetition using a different initial guess Z (n=0) . For example, the initial guesses can be various combinations and permutations of arbitrary, prior calculated estimate of Z (e.g., using method  1100 ), and the like. A vector Z can be selected from among the repetitions of method  1100 . 
       FIG. 12  depicts a table  1200  of example molecules  1210  which may be detected by the systems (e.g., system  100  ( FIG. 1 ), system  200  ( FIG. 2 ), and system)  300  ( FIG. 3 )), and detected using methods (e.g., method  900  ( FIG. 9 ) and method  1000  ( FIG. 10 )) described herein. Conditions  1220  associated with each molecule  1210  are shown for illustrative purposes. 
       FIG. 13  illustrates an exemplary computer system  1300  that may be used to implement some embodiments of the present invention. The computer system  1300  in  FIG. 13  may be implemented in the contexts of the likes of computing systems, networks, servers, or combinations thereof. The computer system  1300  in  FIG. 13  includes one or more processor unit(s)  1310  and main memory  1320 . Main memory  1320  stores, in part, instructions and data for execution by processor unit(s)  1310 . Main memory  1320  stores the executable code when in operation, in this example. The computer system  1300  in  FIG. 13  further includes a mass data storage  1330 , portable storage device  1340 , output devices  1350 , user input devices  1360 , a graphics display system  1370 , and peripheral device(s)  1380 . 
     The components shown in  FIG. 13  are depicted as being connected via a single bus  1390 . The components may be connected through one or more data transport means. Processor unit(s)  1310  and main memory  1320  are connected via a local microprocessor bus, and the mass data storage  1330 , peripheral device(s)  1380 , portable storage device  1340 , and graphics display system  1370  are connected via one or more input/output (I/O) buses. 
     Mass data storage  1330 , which can be implemented with a magnetic disk drive, solid state drive, or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit(s)  1310 . Mass data storage  1330  stores the system software for implementing embodiments of the present disclosure for purposes of loading that software into main memory  1320 . 
     Portable storage device  1340  operates in conjunction with a portable non-volatile storage medium, such as a flash drive, floppy disk, compact disk, digital video disc, or Universal Serial Bus (USB) storage device, to input and output data and code to and from the computer system  1300  in  FIG. 13 . The system software for implementing embodiments of the present disclosure is stored on such a portable medium and input to the computer system  1300  via the portable storage device  1340 . 
     User input devices  1360  can provide a portion of a user interface. User input devices  1360  may include one or more microphones, an alphanumeric keypad, such as a keyboard, for inputting alphanumeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. User input devices  1360  can also include a touchscreen. Additionally, the computer system  1300  as shown in  FIG. 13  includes output devices  1350 . Suitable output devices  1350  include speakers, printers, network interfaces, and monitors. 
     Graphics display system  1370  include a liquid crystal display (LCD) or other suitable display device. Graphics display system  1370  is configurable to receive textual and graphical information and processes the information for output to the display device. 
     Peripheral device(s)  1380  may include any type of computer support device to add additional functionality to the computer system. 
     The components provided in the computer system  1300  in  FIG. 13  are those typically found in computer systems that may be suitable for use with embodiments of the present disclosure and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system  1300  in  FIG. 13  can be a personal computer (PC), hand held computer system, telephone, mobile computer system, workstation, tablet, phablet, mobile phone, server, minicomputer, mainframe computer, wearable, or any other computer system. The computer may also include different bus configurations, networked platforms, multi-processor platforms, and the like. Various operating systems may be used including UNIX, LINUX, WINDOWS, MAC OS, PALM OS, QNX, ANDROID, IOS, CHROME, and other suitable operating systems. 
     Some of the above-described functions may be composed of instructions that are stored on storage media (e.g., computer-readable medium). The instructions may be retrieved and executed by the processor. Some examples of storage media are memory devices, tapes, disks, and the like. The instructions are operational when executed by the processor to direct the processor to operate in accord with the technology. Those skilled in the art are familiar with instructions, processor(s), and storage media. 
     In some embodiments, the computer system  1300  may be implemented as a cloud-based computing environment, such as a virtual machine operating within a computing cloud. In other embodiments, the computer system  1300  may itself include a cloud-based computing environment, where the functionalities of the computer system  1300  are executed in a distributed fashion. Thus, the computer system  1300 , when configured as a computing cloud, may include pluralities of computing devices in various forms, as will be described in greater detail below. 
     In general, a cloud-based computing environment is a resource that typically combines the computational power of a large grouping of processors (such as within web servers) and/or that combines the storage capacity of a large grouping of computer memories or storage devices. Systems that provide cloud-based resources may be utilized exclusively by their owners or such systems may be accessible to outside users who deploy applications within the computing infrastructure to obtain the benefit of large computational or storage resources. 
     The cloud is formed, for example, by a network of web servers that comprise a plurality of computing devices, such as the computing system  600 , with each server (or at least a plurality thereof) providing processor and/or storage resources. These servers manage workloads provided by multiple users (e.g., cloud resource customers or other users). Typically, each user places workload demands upon the cloud that vary in real-time, sometimes dramatically. The nature and extent of these variations typically depends on the type of business associated with the user. 
     It is noteworthy that any hardware platform suitable for performing the processing described herein is suitable for use with the technology. The terms “computer-readable storage medium” and “computer-readable storage media” as used herein refer to any medium or media that participate in providing instructions to a CPU for execution. Such media can take many forms, including, but not limited to, non-volatile media, volatile media and transmission media. Non-volatile media include, for example, optical, magnetic, and solid-state disks, such as a fixed disk. Volatile media include dynamic memory, such as system random-access memory (RAM). 
     Transmission media include coaxial cables, copper wire and fiber optics, among others, including the wires that comprise one embodiment of a bus. Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM disk, digital video disk (DVD), any other optical medium, any other physical medium with patterns of marks or holes, a RAM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a Flash memory, any other memory chip or data exchange adapter, a carrier wave, or any other medium from which a computer can read. 
     Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to a CPU for execution. A bus carries the data to system RAM, from which a CPU retrieves and executes the instructions. The instructions received by system RAM can optionally be stored on a fixed disk either before or after execution by a CPU. 
     Computer program code for carrying out operations for aspects of the present technology may be written in any combination of one or more programming languages, including an object oriented programming language such as JAVA, SMALLTALK, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of wired and/or wireless network, including a (wireless) local area network (LAN/WLAN) or a (wireless) wide area network (WAN/WWAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider, wireless Internet provider, and the like). 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 
     Aspects of the present technology are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present technology. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 
     Aspects of the present technology are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present technology. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.