Biochemical detection using an ultra-broadband coherent optical comb

Abstract

To provide systems and methods for biochemical detection using ultra wideband coherent optical combs.SOLUTION: A biosensor includes an excitation light source configured to generate an optical excitation signal, a programmable optical filter, an optical detector, and a detection circuit configured to apply coherent mixing detection. During biosensing operation, the optical excitation signal is applied to a biochemical sample including at least one target biochemical agent, and the optical detector captures a first signal including spectral responses corresponding to applying the optical excitation signal. The programmable optical filter applies optical filtering to a copy of the optical excitation signal on the basis of a matching target reference signal for the target biochemical agent, to generate a second signal. The detection circuit applies coherent mixing detection to the first signal and the generated second signal. Applying the coherent mixing detection includes combining the first signal and the second signal line-by-line.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001]

[0001] Aspects of the present disclosure relate to sensing-related solutions, particularly to biochemical detection. More specifically, some implementations of the present disclosure relate to methods and systems for performing and utilizing biochemical detection using an ultra-wideband coherent optical comb. [Background technology]

[0002]

[0002] The limitations and drawbacks of conventional devices and solutions for biochemical detection will become apparent to those skilled in the art by comparing such systems with certain aspects of the present disclosure described in the remainder of this application with reference to the drawings. Summary of the Invention

[0003]

[0003] A system and method for biochemical detection using an ultra-wideband coherent optical comb is provided, substantially as shown in and / or described in connection with at least one of the drawings and more fully defined in the claims.

[0004] These and other advantages, aspects and novel features of the present disclosure, as well as details of illustrated embodiments thereof, will be more fully understood from the following description and drawings. [Brief explanation of the drawings]

[0005] [Figure 1]

[0005] FIG. 1 is a diagram of an exemplary biofluid-based sensor. [Figure 2]

[0006] FIG. 1 is a graph illustrating a spectral response that may be used in a coincidence response for optical (photonic) detection. [Figure 3]

[0007] FIG. 1 is a block diagram illustrating an exemplary biosensor system according to the present disclosure. [Figure 4A]

[0008] 1 is a diagram of an example use case utilizing a biosensor system according to an example implementation according to the present disclosure. [Figure 4B]

[0008] FIG. 1 is a diagram of an exemplary use case for utilizing a biosensor system according to an exemplary implementation based on the present disclosure. [Figure 5]

[0009] 1 is a flowchart illustrating biochemical detection using an ultra-wideband coherent optical comb according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0006]

[0010] Figure 1 is a diagram of exemplary biofluid-based sensors, including a biosensor 100, a patch biosensor 110, an eyeglass biosensor 120, and a wristwatch biosensor .

[0007]

[0011] In this regard, a biosensor is an analytical sensing device configured for use in detecting specific biochemicals or agents (e.g., biochemical compounds or molecules). Biosensors generally combine a biological component with a physicochemical detection component. For example, a biosensor may be composed of a bioreceptor (e.g., an enzyme, antibody, cell, nucleic acid, etc.), a transducer (e.g., a semiconducting material / nanomaterial), and electronic components for achieving one or more functions (e.g., signal amplification, signal processing, etc.). In some cases, a biosensor may also incorporate additional components to provide additional functionality, such as a transceiver for communicating data to and from the biosensor, an input / output (I / O) component for enabling user input and output, etc. Where applicable, the components of a biosensor may include appropriate circuitry and other physical subcomponents for implementing various aspects of the functions performed by these components.

[0008]

[0012] One exemplary type of biosensor is a glucose biosensor, which is used to sense (measure) glucose in the human body. In this regard, biosensor 100, patch biosensor 110, eyeglass biosensor 120, and wristwatch biosensor 130 may correspond to different exemplary implementations of such a glucose biosensor. Each of these biosensors can be configured to sense and operate on a particular biological fluid to facilitate measuring glucose in the body.

[0009]

[0013] For example, biosensor 100 may represent the most common implementation for biofluid sensing, including a physical sensor in a suitable housing configured to measure glucose in a fluid such as sweat or blood. In this regard, in some cases, biosensor 100 may be configured to enable glucose measurement by pricking or otherwise penetrating the skin. Other biosensors represent alternative solutions for sensing (and measuring) without the need to pricking the skin. For example, wristwatch biosensor 130 may be an electrochemical glucometer configured to sense interstitial fluid (ISF), i.e., to sense and measure glucose in sweat exuding to the skin surface in an area beneath wristwatch biosensor 130. Similarly, wearable patch biosensor 110 may be a glucose monitor configured for ISF sensing, i.e., to sense and measure glucose in sweat exuding to the skin surface in an area beneath patch biosensor 110. Eyeglass biosensor 120 may be a glucose monitor configured to capture the optical properties of ISF in the eye or tears and sense and measure glucose therein. Other types of glucose monitors may also be used, such as integrated contact lens glucose sensors for tear sensing, temporary tattoo-based glucose monitors configured for ISF sensing, etc.

[0010]

[0014] As mentioned above, some biosensors, such as biofluid-based sensors, may need to be used invasively (e.g., by pricking the skin), but non-invasive biosensing is preferred. Accordingly, some biosensors incorporate detection technologies that can function non-invasively. In some cases, biosensing may be performed using, for example, optical-based detection, such as by measuring and evaluating a coincidence response to detect the presence of a particular biochemical or agent (e.g., a particular biochemical compound, molecule, etc.) and, optionally, measuring the amount thereof. For example, as shown in FIG. 1 , biosensor 100, patch biosensor 110, eyeglass biosensor 120, and wristwatch biosensor 130 may each be configured to perform their characteristic glucose biosensing function using optical-based detection. Nevertheless, the present disclosure is not limited to glucose biosensors, and solutions based on the present disclosure may be used in any suitable biosensor in which optical detection may be utilized.

[0011]

[0015] Optical-based detection may be performed using coincidence response-based photonic detection. This can be done by directing a light source, such as a laser light source, at a particular biofluid or any suitable biochemical material and detecting and measuring the spectral response. The spectral response can then be matched with available known response data (e.g., absorbance characteristics of identified biochemicals over specific wavelengths) to identify biochemical compounds or molecules that may be present. Coincidence response-based photonic detection is described in further detail with respect to FIG. 2.

[0012]

[0016] 2 is a graph illustrating spectral responses that may be used in coincidence responses for optical (photonic) detection. Graphs 200 and 210 are shown in FIG.

[0017] In this regard, graph 200 includes data representing near-infrared spectroscopy (NIR)-based absorption response (y-axis) as a function of wavelength (x-axis). In particular, the data captured in graph 200 corresponds to different biochemical materials / molecules, such as urea, lactic acid, triacetin, ascorbic acid (vitamin C), alanine, and glucose. As shown in graph 200, there are few or no clear resonances in the first overtone region, but across the spectrum, various molecules exhibit different absorption behaviors. Graph 210 includes data representing mid-infrared (Mid-IR) spectroscopy-based absorption response (y-axis) as a function of wavelength (x-axis). In particular, the data captured in graph 200 shows the absorbance of human blood, specifically the absorbance due to (unoxygenated) hemoglobin and oxygenated hemoglobin. As shown in graph 210, the data is acquired using illumination in a specific wavelength range (e.g., 2.5-25 μm or 2.5-10 μm), such as with a quantum cascade laser. As shown in graph 210, hemoglobin exhibits an absorption response with peaks at about 3.3 μm and about 7.1 μm, with a stronger signal absorbance at about 8.3-10 μm. Nevertheless, non-oxygenated hemoglobin exhibits different absorption behavior than oxygenated hemoglobin across the spectrum.

[0013]

[0018] As discussed above, biosensors incorporating match-response-based detection can utilize known spectral responses (e.g., as shown in graphs 200 and 210). In this regard, in solutions utilizing response-matching-based optical (photonic) detection, laser light is projected onto the sample under test, and the spectral response is then measured and analyzed, for example, with respect to such known spectral response. By doing this across a broad band of the spectrum, a spectral response can be constructed that can be matched to the spectral responses of known molecules.

[0014]

[0019] Conventional solutions, which may utilize a coincidence response based on detected amplitudes across multiple wavelengths, may have several limitations and / or drawbacks. In this regard, various designs can be used to implement response-coincidence-based optical (photonic) detection. For example, in some implementations, multiple narrow-linewidth lasers can be used to precisely map intensity responses across a wide spectral range (at different frequencies or wavelengths) to identify a target biochemical agent based on the predetermined absorption spectral response of a reference agent. In this regard, a target (e.g., a sample of biological fluid) can be illuminated, such as by using an array of lasers with known wavelengths. Array-based implementations can incorporate an array of (high-power) lasers, each fixed at a specified wavelength. Alternatively, a tunable laser can be used to sample spectral responses at different wavelengths, such as by scanning at least one portion of the spectrum line-by-line (e.g., at different frequencies or wavelengths) over a period of time. However, this can present several challenges because the target's absorption intensity at each wavelength point can vary with time, temperature, size, etc. Because agents may also be mobile, the longer the scanning and detection time, the more uncertainty may be introduced. Furthermore, measuring a spectral response typically requires measuring both phase and amplitude. However, conventional solutions may not be able to capture both phase and amplitude data simultaneously. In this regard, capturing the phase response of a target biochemical agent (e.g., a molecule) may be impossible because the target's absorption spectrum may be obscured by many surrounding materials with similar ensemble responses.

[0015]

[0020] Solutions based on the present disclosure may address some of the limitations and drawbacks of conventional solutions. In this regard, implementations based on the present disclosure may incorporate various features to enable improved detection of biochemical agents (e.g., compounds, molecules, etc.) even in the presence of surrounding materials with similar responses. For example, by utilizing a highly correlated, high-density optical frequency laser comb source, the correlated intensity and phase responses of a target agent can be recorded over a wide spectral range, which can be performed simultaneously in short snapshots to avoid agent movement. Coherent detection may be achieved, for example, by mixing spectral comb lines of the absorption optical response from an unknown sample with a pre-trained reference optical comb response having the spectral response of the target biochemical agent (e.g., molecule), which may be obtained via a reference path. In particular, line-by-line spectral mixing of the sample signal and the spectral response reference signal may be performed, such as with a photodetector, thereby highlighting the correlated spectral product of the target agent's response and the reference while discriminating against background and noise. An optical frequency laser comb source, such as a mode-locked or gain-switched laser, can be used as the excitation source to illuminate both the target agent and a pre-trained reference filter in the reference path, which will then be mixed into the detector. Alternatively, an actual sample of the target agent can be used instead of using a pre-programmed / trained optical filter in the reference path. Because the line-by-line frequency responses from the target agent and reference filter come from the same source, coherent mixing can be performed with a single detector to collect ensemble similarities in amplitude and phase for each comb line across the spectral range. This single-detector coherent detection avoids the need for locking the oscillation frequency to a known frequency standard and the need for a bank of detectors to collect the line-by-line frequency responses, as in older methods.

[0016]

[0021] A spectral response reference signal can be coherently created by passing a portion of the same excitation laser comb source through a defined optical filter programmed to emulate the spectral shape and response (also referred to as a "reference filter response") of a target biochemical agent (e.g., molecule). The reference filter response may be constructed to emulate collected library data (e.g., trained using a validated data set) or trained to fit a response to a known agent as the target sample (e.g., trained using a known physical reference material). In some cases, advanced processing techniques, such as artificial intelligence (AI) (e.g., machine learning (ML))-based processing, may be used to control various aspects of the detection process, such as constructing the reference filter response to improve the accuracy of the emulation under controlled environmental noise. An exemplary implementation of a biosensor according to the present disclosure is shown in and described with reference to FIG. 3.

[0017]

[0022] 3 is a block diagram illustrating an exemplary biosensor system according to the present disclosure. In FIG. 3, a biosensor system (or portion thereof) 300 is shown.

[0023] The biosensor system 300 may include appropriate circuitry for performing optical biosensing-based functions according to the present disclosure. In particular, the biosensor system 300 may be configured to perform enhanced biochemical detection using an optical excitation signal (e.g., a laser signal), such as using a set of ultra-wideband coherent optical combs, as described herein. As shown in the exemplary implementation illustrated in FIG. 3, the biosensor system 300 includes an excitation light source 310, a control and spectral library block 320, a programmable optical filter 330, an optical filter 340, and a correlated coherent mixing detection block 350.

[0018]

[0024] The excitation light source 310 may include appropriate circuitry for generating and outputting an excitation signal that meets specific predetermined characteristics. In particular, the excitation light source 310 is configured to generate an optical excitation signal having an emission spectrum consisting of a series of discrete frequency lines. In this regard, the comb lines need not be equally spaced. In one exemplary implementation, two responses for each frequency line can be acquired for each single line, and the line-by-line results can then be summed using a single detector. This is sometimes referred to as self-referencing line-by-line mixing. In one exemplary implementation, the excitation light source 310 may comprise a pulsed comb laser source configured to emit a laser excitation signal that meets specific predetermined characteristics, such as from a mode-locked laser, a gain-switched laser, or a set of coherent comb lines created by a nonlinear medium.

[0019]

[0025] The control and spectral library block 320 may be configured with appropriate circuitry to provide control-related light shaping functions for optical filters and to maintain a spectral library of reference signals emulating various known agents for use during processing in the biosensor system 300. In this regard, the control and spectral library block 320 may be configured to generate and transmit control signals for controlling the operation of the optical responses of one or more other reference components within the biosensor system 300. Additionally, the control and spectral library block 320 may provide a reference optical response of a comb light source ready for coherent detection of a target biochemical agent. In this regard, the control and spectral library block 320 may maintain a spectral library for one or more agents. The control and spectral library block 320 may be configured to generate and / or update spectral-related data (e.g., including reference signals) emulating one or more agents. In some cases, the control and spectral library block 320 may be configured to update the spectral library based on feedback data obtained from processing a particular agent learned in the biosensor system 300.

[0020]

[0026] In some cases, the control and spectral library block 320 may be configured to incorporate the use of advanced processing techniques, such as AI / ML-based processing. In this regard, AI / ML-based processing may be used, for example, to construct and / or modify data (e.g., reference signals) in the spectral library. For example, constructing and updating the reference-matched response spectral signals may be performed using machine learning (ML) algorithms. Such machine learning (ML) algorithms may be, for example, linear regression-based algorithms, nonlinear regression-based algorithms, logistic regression-based algorithms, decision tree ensembles (e.g., gradient boosting or random forests)-based algorithms, neural network-based algorithms, recurrent neural network-based algorithms, long-short-term memory networks, Gaussian process algorithms, Bayesian algorithms, graph neural networks, hyperdimensional computing, generative adversarial networks, transformers, chat GPT, large-scale language models, etc.

[0021]

[0027] The programmable optical filter 330 may include appropriate circuitry for performing optical filtering. In this regard, the programmable optical filter 330 may be configured to filter an input signal based on a preprogrammed filtering spectral shape and response, i.e., based on a reference filter response of the programmable optical filter 330. The reference filter response may be programmed based on a reference signal provided by the control and spectral library block 320.

[0022]

[0028] The optical amplifier 340 can be configured with appropriate circuitry to provide optical amplification to the input signal. In this regard, the optical amplifier 340 can be configured to apply a predefined gain and / or time delay to the input signal to calibrate and optimize coherent detection due to hardware differences in the test setup between the two signal paths through the sample and reference filters. In some cases, the optical amplifier 340 can be programmable, and the gain and delay applied by the optical amplifier 340 can be calibrated (e.g., in coordination with the control and spectral library block 320 or any other controller in the system).

[0023]

[0029] The correlated coherent mixing detection block 350 may be comprised of suitable circuitry for performing optical detection, which may be specifically based on correlated coherent mixing as described herein.

[0024]

[0030] In operation, the biosensor system 300 can be used to perform biochemical detection. In this regard, the excitation light source 310 can provide an excitation signal source (e.g., a laser) for use in biochemical sensing. The excitation signal can be adaptively configured to record both intensity and phase responses across a wide spectral range and to pass through both the target agent and a reference optical filter in parallel, simultaneously (or at least simultaneously). In particular, the excitation signal generated by the excitation light source 310 can include a signal (e.g., a laser pulse signal) having a spectrum consisting of a series of discretely spaced combs (frequency lines). A portion of the excitation signal passes through the target (e.g., physical contact) 360, and the corresponding spectral response is captured by the photodetector 352, or the like. In this regard, the spectral response may include the spectral response of the target biochemical agent as well as other spectral responses corresponding to surrounding materials (and thus effectively constituting noise).

[0025]

[0031] Another portion of the excitation signal is also provided as an input signal to the programmable optical filter 330, which can be configured to apply a pre-trained optical filtering response based on preset criteria for the reference agent. Specifically, the programmable optical filter 330 can be programmed by the control and spectral library block 320. In this regard, to facilitate programming, the control and spectral library block 320 can control and program the filter to create a spectral response emulating the reference agent through the programmable optical filter 330. In this manner, the programmable optical filter 330 generates, as an output, a signal at the output of the programmed optical filter that represents the spectral response of the matching target spectrum reference signal. The output of the programmable optical filter 330 is then amplified via the optical amplifier 340, and the output of the optical amplifier 340 is then input to the correlation coherent mixing detection block 350 for coherent mixing with the simultaneously captured spectral response. An exemplary use case for the biosensor system 300 is shown in and described in more detail with reference to FIGS. 4A-4B.

[0026]

[0032] 4A-4B are diagrams of example use cases utilizing a biosensor system according to example implementations of the present disclosure, illustrating signals that may be generated by biosensor system 300 during example use case scenarios.

[0027]

[0033] In this regard, excitation light source 310 generates excitation signal 400 adaptively configured to record both the intensity and phase responses of sets of frequency combs covering a wide spectral range, and to do so simultaneously (or at least simultaneously). In particular, as shown in FIG. 4A , excitation signal 400 may be a single or set of pulsed signals covering multiple frequency bands (as shown in the time-domain graph with one set of pulses per frequency band), with each pulse processing a spectrum consisting of a series of correlated, discrete, equally spaced combs (correlated frequency lines), as shown in the frequency-domain graph. Excitation signal 400 is applied to a target (e.g., physical contact), which may include both target biochemical materials (drugs) and background (non-target) materials. Exemplary spectral profiles of the target biochemical material and background material are shown in graphs 420 and 430, respectively. Thus, applying a portion of excitation signal 400 to a target results in spectral responses corresponding to both the target biochemical material and the background material. In this regard, the spectral responses of the target biochemical material and the background material are shown in graphs 440 and 450, respectively. As shown, each of the spectral responses includes multiple lines of corresponding amplitude and phase, which are set (adjusted) based on the corresponding spectral profiles of the target biochemical material and background material (graphs 420 and 430). In this manner, composite spectral response signal 470 (captured by photodetector 352) is composed of the spectral responses of both the target biochemical material and the background material.

[0028]

[0034] Concurrently, a remaining portion of the excitation signal 400 is used to generate a spectral response reference signal 480. In this regard, the spectral response reference signal 480 is simultaneously created by applying the excitation signal 400 to an optical filter configured to produce a prescribed filtering spectral shape and response, such as by using a pre-trained matching spectral reference signal 460 of the target drug (which may be provided by control parameters from the pre-trained spectral library block 320). In this regard, the matching target spectral reference signal 460 corresponds to a pre-trained spectral representation of the target material stored (e.g., constructed) in the system. Thus, the optical filtering allows for configuring the excitation signal 400, i.e., defining the amplitude and phase at each of the discrete frequency lines, according to the pre-trained spectral characteristics corresponding to the target biochemical. The output of the optical filtering then undergoes optical amplification (e.g., via optical amplifier 340) to balance path loss and delay differences in the test setup, providing the spectral response reference signal 480. Thus, the spectral response reference signal 480 essentially represents an amplified replica of the spectral response of the target material when subjected to the excitation signal 400 without the background material.

[0029]

[0035] Next, the spectral response reference signal 480 and the composite spectral response signal 470 undergo correlated coherent mixing detection (e.g., via correlated coherent mixing detection block 350). In this regard, correlated coherent mixing detection involves coherently combining (e.g., by multiplication) the spectral response reference signal 480 and the composite spectral response signal 470 line by line, resulting in a larger response (pulse) when the frequency response of the target agent is similar to that coming from the reference filter of each comb line compared to the uncorrelated frequency response from the background material when the ensemble from all comb lines adds constructively across the spectral range. This allows for filtering out the background material. In this regard, due to the mismatch in amplitude and phase of each comb line across the spectral range, the ensemble response corresponding to the background material combines destructively and is significantly reduced.

[0030]

[0036] In some cases, the results of the correlated coherent mixing detection can be used to update the spectral profile of the target material or to establish a new type of target material. For example, if a known material is used as the target material, the results of the correlated coherent mixing detection can be fed back to optimize the control parameters of the optical filter to update the existing reference element library in the spectral library block 320.

[0031]

[0037] In some implementations, the same processing pathways described herein can be used to build and / or calibrate a spectral library. For example, a sample of a particular biochemical agent can be subjected to detection in a controlled environment, i.e., in the absence of background materials. The corresponding captured spectral response can then be used to create new reference signals or update existing reference signals in the spectral library.

[0032]

[0038] 5 is a flowchart illustrating biochemical detection using an ultra-wideband coherent optical comb according to the present disclosure. In FIG. 5, a flowchart 500 is shown that includes several example steps (represented as blocks 502-512) that may be performed in a system suitable for providing enhanced biochemical detection using an ultra-wideband coherent optical comb (e.g., biosensor system 300 of FIG. 3).

[0033]

[0039] In step 502, an excitation signal is generated (e.g., by one or more pulsed comb laser sources). The number of laser sources with associated frequency lines is selected to achieve a spectral range or band in which a particular material or agent of interest exhibits distinctive spectral characteristics. A portion of the excitation signal is then applied to an object containing the target biochemical agent / material and, possibly, a different type of background material. Another portion of the excitation signal is simultaneously applied to an optical filter configured based on a matched response spectral reference (signal) corresponding to the target biochemical agent / material.

[0034]

[0040] In step 504, the spectral response from application of the excitation signal to the target is captured, for example, a composite spectral response signal is obtained that includes spectral responses corresponding to both the target biochemical agent / material and background materials.

[0035]

[0041] In step 506, the optical filtering response from the replica of the excitation signal is shaped to a response that matches the pre-trained spectral reference of the target drug, and in step 508, optical amplification is applied to the output of the optical filtering to generate a reference comb signal having the reference spectral response of the target drug.

[0036]

[0042] In step 510, the spectral response reference signal is coherently mixed with the captured spectral response from the sample (e.g., into a composite spectral response for each frequency line), and in step 512, detection is performed based on the ensemble result of the coherently mixed frequency lines.

[0037]

[0043] Thus, solutions based on the present disclosure offer many advantages over conventional solutions. In particular, implementations based on the present disclosure enable reliable discrimination based on both amplitude and phase information. This can enable more accurate correlation spectral responses, frequency line by frequency line, across broad spectral coverage when using multiple independent excitation laser sources, without knowledge of or locking to a specific frequency grid. These implementations can also have high sensitivity. In this regard, matched filtering can reduce chaotic noise and responses from unwanted background materials and / or the use of coherent gain provided by amplified and shaped reference light from the same excitation laser source. Additionally, the need for a frequency scale, such as the use of a frequency grid or frequency locking, may be eliminated in these implementations. This is due to the accuracy of coherent, self-referenced detection within the optical lifetime of the source laser. Furthermore, a wide spectral range can be covered using a small number of low-cost gain-switched or mode-locked pulsed lasers. Furthermore, these implementations can incorporate advanced processing techniques, such as the use of AI / ML-based cognitive learning and tracking. In this regard, such techniques may enable multi-domain identification of multiple biological agents with complex spectral responses, and may also allow adaptation to variations in physical channels due to contact and environmental changes.

[0038]

[0044] According to the present disclosure, an exemplary system for biosensing includes an excitation light source configured to generate an optical excitation signal, a programmable optical filter configured to apply optical filtering, a photodetector, and a detection circuit configured to apply coherent mixing detection, wherein the system is configured such that during a biosensing operation, the optical excitation signal is applied to a biochemical sample containing at least one target biochemical agent, the photodetector captures a first signal including a spectral response corresponding to applying the optical excitation signal to the biochemical sample, the programmable optical filter applies optical filtering to a copy of the optical excitation signal based on a matching target reference signal including a spectral response profile of the at least one target biochemical agent, the detection circuit applies coherent mixing detection of the first signal and a second signal generated based on applying optical filtering to the copy of the optical excitation signal, and applying coherent mixing detection includes combining the first signal and the second signal line by line.

[0039]

[0045] In one exemplary embodiment, the excitation light source comprises a pulsed comb laser source.

[0046] In an exemplary embodiment, the system further comprises an optical amplifier configured to apply optical amplification to increase the coherent mixing gain at the detector, the optical amplifier applying optical amplification to the output of the programmable optical filter to generate a second signal.

[0040]

[0047] In one exemplary embodiment, the system further comprises a control and spectral library circuit configured to provide a reference signal for use in detecting one or more target biochemical agents.

[0041]

[0048] In one exemplary embodiment, the control and spectral library circuitry is configured to provide a matching target reference signal to the programmable optical filter.

[0049] In one exemplary embodiment, the control and spectral library circuitry is configured to provide control data to the detection circuitry for applying coherent mixing detection.

[0042]

[0050] In one exemplary embodiment, the control and spectral library circuitry is configured to maintain a spectral library that includes one or more reference signals corresponding to one or more target biochemical agents.

[0043]

[0051] In one exemplary embodiment, the control and spectral library circuitry is configured to generate or update at least one of the reference signals in the spectral library.

[0044]

[0052] In one exemplary embodiment, the control and spectral library circuitry is configured to generate or update the at least one reference signal using artificial intelligence (AL)-based processing.

[0045]

[0053] In one exemplary embodiment, the control and spectral library circuitry is configured to apply machine learning (ML) when using artificial intelligence (AL) based processing.

[0054] According to the present disclosure, an exemplary method for biosensing includes generating an optical excitation signal by an excitation light source; applying the optical excitation signal to a biochemical sample containing at least one target biochemical agent; capturing a first signal by an optical detector, the first signal including a spectral response corresponding to applying the optical excitation signal to the biochemical sample; applying optical filtering by a programmable optical filter to a copy of the optical excitation signal based on a matching target reference signal including a spectral response profile of the at least one target biochemical agent; and applying coherent mixing detection to the first signal and a second signal generated based on applying optical filtering to the copy of the optical excitation signal, wherein applying coherent mixing detection includes combining the first signal and the second signal line by line.

[0046]

[0055] In one exemplary embodiment, the optical excitation signal comprises a pulsed comb laser signal.

[0056] In one exemplary embodiment, the method further includes applying optical amplification to the output of the optical filtering to generate a second signal.

[0047]

[0057] In an exemplary embodiment, the method further includes configuring optical filtering based on the matching target reference signal.

[0058] In an exemplary embodiment, the method further includes configuring optical filtering to reshape the replica of the optical pump signal based on the matching target reference signal.

[0048]

[0059] In one exemplary embodiment, the method further includes generating control data for controlling the applying coherent mixing detection.

[0060] In one exemplary embodiment, the method further includes maintaining a spectral library including one or more reference signals corresponding to one or more target biochemical agents.

[0049]

[0061] In an exemplary embodiment, the method further comprises generating or updating at least one of the reference signals in a spectral library.

[0050]

[0062] In one exemplary embodiment, the method further includes generating or updating at least one reference signal using artificial intelligence (AL) based processing.

[0063] In one exemplary embodiment, the method further comprises applying machine learning (ML) when using artificial intelligence (AL) based processing.

[0051]

[0064] As used herein, "and / or" means any one or more of the items in the list connected by "and / or." As an example, "x and / or y" means any element of the 3-element set {(x),(y),(x,y)}. In other words, "x and / or y" means "one or both of x and y." As another example, "x, y, and / or z" means any element of the 7-element set {(x),(y),(z),(x,y),(x,z),(y,z),(x,y,z)}. In other words, "x, y, and / or z" means "one or more of x, y, and z." As used herein, the term "exemplary" means serving as a non-limiting example, instance, or illustration. As used herein, the terms "for example" and "eg" begin a list of one or more non-limiting examples, instances, or illustrations.

[0052]

[0065] As used herein, the terms “circuits” and “circuitry” refer to physical electronic components (e.g., hardware) as well as any software and / or firmware (“code”) that may comprise, be executed by, and otherwise be associated with the hardware. As used herein, for example, a particular processor and memory (e.g., a volatile or non-volatile memory device, a general computer-readable medium, etc.) may comprise a first “circuit” when executing a first one or more lines of code, and may comprise a second “circuit” when executing a second one or more lines of code. In addition, a circuit may be comprised of analog and / or digital circuits. Such circuits may, for example, operate on analog and / or digital signals. It should be understood that a circuit may reside within a single device or chip, on a single motherboard, in a single chassis, in multiple enclosures in a single geographic location, in multiple enclosures distributed across multiple geographic locations, etc. Similarly, the term "module" may refer, for example, to physical electronic components (e.g., hardware) as well as any software and / or firmware ("code") that may comprise, be executed by, and otherwise be associated with the hardware.

[0053]

[0066] As used herein, a circuit or module is "operable" to perform a function whenever the circuit or module includes the necessary hardware and code (if necessary) to perform the function, regardless of whether performance of the function has been disabled (e.g., by a user-configurable setting, a factory setting, etc.) or not enabled.

[0054]

[0067] Other embodiments of the present invention may provide a non-transitory computer-readable medium and / or storage medium having stored thereon machine code and / or a computer program having at least one code section executable by a machine and / or computer, the machine code and / or computer program causing a machine and / or computer to perform the processes described herein.

[0055]

[0068] Accordingly, various embodiments according to the present invention may be implemented as hardware, software, or a combination of hardware and software. The present invention may be implemented in a centralized manner in at least one computing system, or in a distributed manner where different elements are distributed across multiple interconnected computing systems. Any type of computing system or other device adapted for performing the methods described herein is suitable. A typical implementation may consist of one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and / or one or more processors (e.g., x86, x64, ARM, PIC, and / or any other suitable processor architecture), as well as associated support circuitry (e.g., storage, DRAM, FLASH, bus interface circuitry, etc.). Each separate ASIC, FPGA, processor, or other circuit may be referred to as a "chip," and multiple such circuits may be referred to as a "chipset." Another implementation may include a non-transitory machine-readable (e.g., computer-readable) medium (e.g., a FLASH drive, an optical disk, a magnetic storage disk, etc.) having stored thereon one or more lines of code that, when executed by the machine, cause the machine to perform the processes as described in this disclosure. Another implementation may consist of a non-transitory machine-readable (e.g., computer-readable) medium (e.g., a FLASH drive, an optical disk, a magnetic storage disk, etc.) having stored thereon one or more lines of code that, when executed by the machine, cause the machine to be configured to operate as a system as described in this disclosure (e.g., loading software and / or firmware into its circuitry).

[0056]

[0069] Various embodiments according to the invention may also be embedded in a computer program product, which contains all the features enabling the implementation of the methods described herein and which, when loaded into a computer system, is capable of performing these methods. A computer program in this context means any expression in any language, code or notation of a set of instructions intended to cause a system having information processing capabilities to perform certain functions, either directly or following either or both of the following: a) translation into another language, code or notation; b) reproduction in a different material form.

[0057]

[0070] While the present methods and / or systems have been described with reference to particular implementations, those skilled in the art will recognize that various modifications may be made and equivalents may be substituted without departing from the scope of the present methods and / or systems. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present methods and / or systems not be limited to the particular implementations disclosed, but rather to include all implementations falling within the scope of the appended claims. [Explanation of symbols]

[0058] 100 Biosensors 110 Patch-type biosensor 120 Eyeglass-type biosensor 130 Wristwatch-type biosensor 300 Biosensor System 310 Excitation Light Source 320 Control and Spectral Library Blocks 330 Programmable Optical Filter 340 Optical Amplifier 350 Correlated Coherent Mixture Detection Block 352 Photodetector 360 goals 400 excitation signal 460 Spectral Reference Signal 470 Composite Spectral Response Signal 480 Spectral Response Reference Signal 500 Flowchart

Claims

1. 1. A system for biosensing, comprising: an excitation light source configured to generate an optical excitation signal; a programmable optical filter configured to apply optical filtering; a photodetector; a detection circuit configured to apply coherent mixing detection; and during biosensing operation, the optical excitation signal is applied to a biochemical sample containing at least one target biochemical agent; the photodetector capturing a first signal comprising a spectral response corresponding to applying the optical excitation signal to the biochemical sample; the programmable optical filter applies optical filtering to the copy of the optical excitation signal based on a matching target reference signal that includes a spectral response profile of the at least one target biochemical agent; The detection circuit applies coherent mixing detection to the first signal and a second signal generated based on applying the optical filtering to the copy of the optical excitation signal, the optical excitation signal having a spectrum consisting of a series of discretely spaced frequency lines, and applying the coherent mixing detection includes coherently combining the spectral response of the first signal and the spectral response of the second signal, frequency line by frequency line. A system configured as follows:

2. 10. The system of claim 1, wherein the excitation light source comprises a pulsed comb laser source.

3. 10. The system of claim 1, further comprising an optical amplifier configured to apply optical amplification to an output of the programmable optical filter to generate the second signal.

4. 10. The system of claim 1, further comprising a control and spectral library circuit configured to provide a reference signal for use in detecting one or more target biochemical agents.

5. 5. The system of claim 4, wherein the control and spectral library circuitry is configured to provide the matching target reference signal to the programmable optical filter.

6. 5. The system of claim 4, wherein the control and spectral library circuitry is configured to provide control data to the detection circuitry for applying the coherent mixing detection.

7. 5. The system of claim 4, wherein the control and spectral library circuitry is configured to maintain a spectral library including one or more reference signals corresponding to the one or more target biochemical agents.

8. 8. The system of claim 7, wherein the control and spectral library circuitry is configured to generate or update at least one of the reference signals in the spectral library.

9. 10. The system of claim 8, wherein the control and spectral library circuitry is configured to generate or update the at least one reference signal using artificial intelligence (AL) based processing.

10. 10. The system of claim 9, wherein the control and spectral library circuitry is configured to apply machine learning (ML) when using the artificial intelligence (AL) based processing.

11. 1. A method for biosensing, comprising: generating an optical excitation signal by an excitation light source; applying the optical excitation signal to a biochemical sample containing at least one target biochemical agent; capturing, by a photodetector, a first signal comprising a spectral response corresponding to applying the optical excitation signal to the biochemical sample; applying optical filtering to the copy of the optical excitation signal based on a matching target reference signal containing a spectral response profile of the at least one target biochemical agent by a programmable optical filter; applying coherent mixing detection to the first signal and a second signal generated based on applying the optical filtering to the copy of the optical excitation signal; Including, the optical excitation signal has a spectrum consisting of a series of discretely spaced frequency lines, and applying coherent mixing detection comprises coherently combining the spectral response of the first signal with the spectral response of the second signal, frequency line by frequency line. method.

12. 12. The method of claim 11, wherein the optical excitation signal comprises a pulsed comb laser signal.

13. 12. The method of claim 11, further comprising applying optical amplification to an output of the optical filtering to generate the second signal.

14. 12. The method of claim 11, further comprising configuring the optical filtering based on the matching target reference signal.

15. 15. The method of claim 14, further comprising configuring the optical filtering to reshape the replica of the optical pump signal based on the matching target reference signal.

16. The method of claim 11 , further comprising generating control data for controlling the step of applying coherent mixing detection.

17. 12. The method of claim 11, further comprising the step of maintaining a spectral library containing one or more reference signals corresponding to one or more target biochemical agents.

18. 18. The method of claim 17, further comprising generating or updating at least one of the reference signals in the spectral library.

19. 20. The method of claim 18, further comprising generating or updating the at least one reference signal using artificial intelligence (AL) based processing.

20. 20. The method of claim 19, wherein when the artificial intelligence (AL) based processing is used, the method further comprises applying machine learning (ML).