Resolution of light absorption during interferometric testing by algorithm deconvolution and calculation

Abstract

Described herein is a method of programmatically addressing the absorption of light by an analyte molecule that is monitored by an interferometric sensing system, for example, in combination with an interferometric sensor. The first light signal may impinge on the biological layer during biochemical testing, and light reflected by the biological layer may form a second light signal detectable by a detector of the interferometric sensing system. By analyzing the second optical signal, the second optical signal can be deconvolved into a reflection component and an absorption component. If the principal component of the second optical signal is a reflected component, an algorithm may be employed to establish the bonding amplitude. If the principal component of the second optical signal is an absorption component, another algorithm may be employed to establish the binding amplitude.

Description

Technical Field

[0001] Various embodiments are directed to methods of programmatically resolving the absorption of light by an analyte molecule, for example, whose binding to an interferometric sensor is being monitored by an interferometric sensing system and an associated computer program. Background Art

[0002] Diagnostic tests based on the binding events between analyte molecules and analyte binding molecules are widely used in medicine, veterinary medicine, agriculture and research applications. These diagnostic tests can be used to detect whether there is an analyte molecule in a sample, the amount of analyte molecules in a sample, or the rate at which an analyte molecule binds to an analyte binding molecule. The analyte binding molecule forms an analyte-antianalyte binding pair (or simply referred to as a "binding pair") with its corresponding analyte molecule. Examples of binding pairs include complementary strands of nucleic acids, antigen-antibody pairs, and receptor-receptor binding agents. The analyte can be any member of the binding pair, and the anti-analyte can be another member of the binding pair.

[0003] In history, diagnostic tests have used solid flat surfaces on which analyte binding molecules are fixed. The analyte molecules in the sample will bind to these analyte binding molecules with high affinity in the detection zone defined. In this type of assay (referred to as "solid phase assay"), the solid surface is exposed to the sample under conditions that promote the analyte molecules to bind to the analyte binding molecules. Typically, the binding events are directly detected by measuring the change in mass, reflectivity, thickness, color or another characteristic of the binding event. For example, when the analyte molecules are labeled with chromophores, fluorescent markers or radioactive markers, it is possible to detect the binding events based on how many (if any) markers can be detected in the detection zone. Alternatively, the analyte molecules can be labeled after they have been bound to the analyte binding molecules in the detection zone.

[0004] U.S. Patent No. 5,804,453 discloses a method for determining the concentration of a substance in a sample solution using an optical fiber having a reagent (i.e., a capture molecule) coated on its distal end that binds to the substance. The distal end is then immersed in a sample solution containing the substance. The substance combines with the reagent to generate an interference pattern that is detected by a spectrometer.

[0005] U.S. Patent No. 7,394,547 discloses a biosensor having a first optically transparent element mechanically attached to an optical fiber tip with an air gap therebetween. A second optical element acting as an interference layer having a thickness greater than 50 nanometers (nm) is then attached to the distal end of the first optical element. A biolayer is formed on the peripheral surface of the second optical element. An additional reflective surface layer having a thickness between 5nm and 50nm and a refractive index greater than 1.8 is coated between the interference layer and the first optical element. The principle of detecting an analyte in a sample based on a change in spectral interference is described in the reference, which is incorporated herein by reference in its entirety.

[0006] U.S. Patent No. 7,319,525 discloses a different configuration in which a portion of an optical fiber is mechanically attached to a tip connector consisting of one or more optical fibers with an air gap between the proximal end of the optical fiber portion and the tip connector. An interference layer is constructed on the distal surface of the optical fiber portion, and then a biological layer is constructed.

[0007] Although the prior art provides functionality utilizing thin film interferometer based biosensors, there exists a need to improve the performance of these interferometers. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1A An example of a binding signal is shown.

[0009] Figure 1B It is shown how, in some cases, the combined signal may experience a downward shift in amplitude rather than an upward shift.

[0010] Figure 2A A biosensor interferometer including a light source, detector, waveguide, and optical components (also referred to as "probes") is depicted.

[0011] Figure 2B An example of a probe is depicted.

[0012] Figure 3 Examples of probes according to various embodiments are depicted.

[0013] Figure 4 Another example of a probe according to various embodiments is depicted.

[0014] FIG. 5A to FIG. 5B The detection principle in a thin film interferometer is shown.

[0015] Figure 6 Examples of slides according to various embodiments are depicted.

[0016] Figure 7 Another example of a slide according to various embodiments is depicted.

[0017] Figure 8 A flow chart depicting a process for resolving the absorption of light by an analyte molecule whose binding activity is monitored by an interferometric sensing system.

[0018] Fig. 9 It is shown how the amplitude of the complete signal (indicated with R1 ) and half the amplitude of the antisymmetric signal (indicated with R2 ) can be determined by analysis of the subtraction curve.

[0019] Fig.10 A network environment including an analytics platform executed by a computing device is shown.

[0020] Fig.11 An example of a communication environment including an analytics platform configured to acquire data from one or more sources is depicted.

[0021] Fig.12 is a block diagram illustrating an example of a processing system in which at least some of the operations described herein may be implemented.

[0022] Fig.13 Included are graphs with binding curves calculated for the lipid particle loading phase and the antibody binding and dissociation phases for experiments involving CXCR4 lipid particles.

[0023] Fig.14 Included are graphs with binding curves calculated for the lipid particle loading phase and the antibody binding and dissociation phases for experiments involving CD20 lipid particles.

[0024] Embodiments are shown in the accompanying drawings by way of example and not limitation. Although the accompanying drawings depict various embodiments for illustrative purposes, those skilled in the art will recognize that alternative embodiments may be employed without departing from the principles of the present technology. Therefore, although specific embodiments are shown in the accompanying drawings, the present technology is susceptible to various modifications. DETAILED DESCRIPTION

[0025] As part of a diagnostic test, light can be shone onto an interferometric sensor on which a biolayer is formed. Typically, the formation of the biolayer is facilitated by depositing analyte binding molecules along one side of the interferometric sensor and then exposing the interferometric sensor to a liquid sample. Analyte molecules in the liquid sample bind to the analyte binding molecules over time to form the biolayer, and these binding activities are evidenced by interference patterns detectable by the detector of the interferometric sensing system.

[0026] As discussed further below, an incident light signal (also referred to as a "first light signal") is directed toward the biological layer, and light reflected by the biological layer results in the generation of a reflected light signal (also referred to as a "second light signal"). The first light signal and the second light signal form a spectral interference pattern. When analyte molecules bind to analyte binding molecules, thereby increasing the thickness of the biological layer, the optical path of the second light signal becomes longer. Therefore, the spectral interference pattern shifts. By measuring the phase shift in real time, the binding signal (also referred to as a "binding curve") can be plotted as the shift versus time. Figure 1A An example of a binding signal is provided in. The binding signal helps establish not only the rate of association of the analyte molecules with the analyte binding molecules, but also the total amount of analyte molecules in the fluid sample being examined.

[0027] This method of generating a binding signal works well when the analyte molecules are small (e.g., when the analyte molecules are proteins or antibodies). Simply put, when a small analyte molecule binds to a corresponding analyte binding molecule, the spectral interference pattern shifts consistently and the amplitude of the binding signal increases consistently. However, there are some situations, namely, situations where the analyte molecules are large and / or complex, where the amplitude of the binding signal will experience a downward shift instead of an upward shift as shown. Figure 1B This is problematic because the binding signal will indicate that the biolayer has actually decreased in size rather than increased in size. Examples of large analyte molecules include some cells, viruses, bacteriophages, nanoparticles (e.g., lipid nanoparticles), and artificial structures (e.g., magnetic particles).

[0028] Historically, these situations have been addressed by “flipping” the combined signal, using Figure 1B , which plots absolute values ​​rather than actual values. However, this temporary solution does not actually solve the underlying problem that has not been well understood until recently. At a high level, the underlying problem is that large analyte molecules can absorb a significant amount of the first light signal that is impinged on the biolayer. While the biolayer formed by small analyte molecules will reflect almost all of the first light signal, the biolayer formed by large analyte molecules is more susceptible to absorption and in some respects essentially acts like a crystal. This absorption can affect the second light signal to the extent that the combined signal cannot be correctly calculated by the aforementioned algorithm.

[0029] Therefore, what is presented here is a method for programmatically solving the absorption of light by an analyte molecule, the binding of which, for example, to an interferometric sensor is monitored by an interferometric sensing system. As mentioned above, a first light signal can be irradiated on a biological layer during a biochemical test, and the light reflected by the biological layer can form a second light signal that can be detected by a detector of the interferometric sensing system. By analyzing the second light signal, the second light signal can be deconvoluted into a reflection component and an absorption component. If the main component of the second light signal is the reflection component, the aforementioned algorithm (also referred to as a "conventional algorithm") of the "flip" binding signal can be used if necessary. If the main component of the second light signal is the absorption component, another algorithm (also referred to as an "absorption algorithm") can be used. The absorption algorithm is discussed in more detail below.

[0030] definition

[0031] The term "about" means within ±10% of the stated value.

[0032] The term "analyte binding molecule" refers to any molecule that is capable of participating in a binding reaction with an analyte molecule. Examples of analyte binding molecules include, but are not limited to: (i) antigen molecules, used to detect the presence of antibodies specific for the antigen; (ii) antibody molecules, used to detect the presence of the antigen; (iii) protein molecules, used to detect the presence of a binding partner for the protein; (iv) ligands, used to detect the presence of a binding partner; and (v) single-stranded nucleic acid molecules, used to detect the presence of nucleic acid molecules.

[0033] The term "interferometry sensor" refers to any sensing device on which a biolayer is formed to produce an interference pattern. One example of an interferometry sensor is a probe designed to be suspended in a solution containing a sample with analyte molecules. Another example of an interferometry sensor is a glass slide with a flat surface on which a biolayer can be formed during a biochemical test.

[0034] The term "probe" refers to a monolithic substrate having an aspect ratio (length to width) of at least 2 to 1 and coated with a thin film layer on the sensing side.

[0035] The term "monolithic substrate" refers to a solid piece of material having a uniform composition, such as glass, quartz or plastic, with one refractive index.

[0036] The term "waveguide" refers to a device designed to confine and guide the propagation of electromagnetic waves (such as light). One example of a waveguide is a flexible, transparent fiber made by stretching glass, plastic, or another transparent material to a small diameter (e.g., approximately the diameter of a human hair). These waveguides are often referred to as "optical fibers." Another example of a waveguide is a metal tube used to transmit ultra-high frequency waves. A waveguide can also take the form of a pipe or a coaxial cable.

[0037] introduction

[0038] A. Overview of Interferometry Sensing Systems

[0039] Several entities have developed interferometric sensing systems (also referred to as "interferometers" or simply "systems") designed to perform biochemical testing. FIG. 2A to FIG. 2B An example of such a system is shown. Specifically, Figure 2A An interferometer 200 is depicted that includes a light source 202, a detector 204, a waveguide 206, and an optical component 208 (also referred to as a "probe"). The probe 208 may be connected to the waveguide 206 via a coupling medium.

[0040] The light source 202 can emit light that is guided by the waveguide 206 toward the probe 208. For example, the light source 202 can be a light emitting diode (LED) configured to generate light within a range of at least 50 nanometers (nm), 200 nm, or 150 nm within a given spectrum (e.g., 400 nm or less to 700 nm or greater). Alternatively, the interferometer 200 can employ multiple light sources with different characteristic wavelengths, such as LEDs designed to emit light of different wavelengths in the visible range. The same functionality can be achieved by a single light source with appropriate filters for guiding light with different wavelengths onto the probe 208.

[0041] The detector 204 is preferably a spectrometer, such as an Ocean Optics USB4000, capable of recording the spectrum of the interfering light received from the probe 208. Alternatively, if the light source 202 operates to direct different wavelengths onto the probe 208, the detector 204 can be a simple photodetector capable of recording the intensity at each wavelength. In another embodiment, the detector 204 can include multiple filters that allow detection of the intensity at each of the multiple wavelengths.

[0042] Waveguide 206 can be configured to transmit light emitted by light source 202 to probe 208, and then transmit light reflected by surfaces within probe 208 to detector 204. In some embodiments, waveguide 206 is a bundle of optical fibers (e.g., a single-mode fiber optic cable), while in other embodiments, waveguide 206 is a multi-mode fiber optic cable.

[0043] The probe 208 may include a monolithic substrate 214, a thin film layer (also referred to as an "interference layer"), and a biomolecule layer (also referred to as a "biolayer") including analyte molecules 222 that have been bound to analyte binding molecules 220. The monolithic substrate 214 includes a transparent material through which light can travel. The interference layer also includes a transparent material. When light is impinged on the probe 208, the proximal surface of the interference layer can act as a first reflective surface, and the biolayer can act as a second reflective surface. As further described below, the light reflected by the first reflective surface and the second reflective surface can form an interference pattern that can be monitored by the interferometer 200.

[0044] The interference layer typically includes multiple layers combined in a manner that improves the detectability of the interference pattern. Here, for example, the interference layer includes a tantalum pentoxide (Ta2O5) layer 216 and a silicon dioxide (SiO2) layer 218. The tantalum pentoxide layer 216 can be thin (e.g., about 10nm to 40nm) because its main purpose is to increase the reflectivity at the near-side surface of the interference layer. At the same time, the silicon dioxide layer 218 can be relatively thick (e.g., about 650nm to 900nm) because its main purpose is to increase the distance between the first reflective surface and the second reflective surface.

[0045] To perform a biochemical test, the probe 208 may be suspended in a microwell 210 (or simply referred to as a "well") containing a sample 212. During the biochemical test, analyte molecules 222 in the sample 212 will bind to the analyte binding molecules 220 along the distal end of the probe 208, and these binding events will cause an interference pattern that can be observed by the detector 204. The interferometer 200 can monitor the thickness of the biological layer formed along the distal end of the probe 208 by detecting the shift in the phase characteristics of the interference pattern. Figure 1B As shown, the waveguide 206 can be directly coupled to the probe 208 to eliminate any gap between them. For example, in an embodiment where the waveguide 206 includes an optical fiber, the proximal end of the probe 208 can be directly coupled to the optical fiber. As mentioned above, the interferometer 200 is responsible for monitoring the interference pattern caused by the light reflected at the first and second reflective surfaces of the probe 208.

[0046] Note that for purposes of illustration, embodiments of the interferometric sensing system may be described in the context of a probe designed to be suspended within a solution containing a sample. However, one skilled in the art will recognize that these features are equally applicable to other sensing surfaces, such as a flat surface (e.g., a glass slide) over which a biolayer is formed by flowing a solution during a biochemical test.

[0047] Overview of B Probe

[0048] Figure 3An example of a probe 300 according to various embodiments is depicted. The probe 300 includes an interference layer 304 fixed along the distal end of a monolithic substrate 302. Analyte binding molecules 306 can be deposited along the distal surface of the interference layer 304. During a biochemical test, as analyte molecules 308 in a sample bind to the analyte binding molecules 306, a biolayer will be formed.

[0049] like Figure 3 As shown, the monolithic substrate 302 has a proximal surface (also referred to as the "coupling side") that can be coupled to a waveguide of, for example, an interferometer, and a distal surface (also referred to as the "sensing side") on which additional layers are deposited. Typically, the monolithic substrate 302 has a length of at least 3 millimeters (mm), 5 mm, 10 mm, or 15 mm. In a preferred embodiment, the aspect ratio (length to width) of the monolithic substrate 302 is at least 5 to 1. In such embodiments, the monolithic substrate 302 can be said to have a cylindrical form. The cross-section of the monolithic substrate 302 can be circular, elliptical, square, rectangular, triangular, pentagonal, etc. The monolithic substrate 302 preferably has a refractive index that is substantially higher than the refractive index of the interference layer 304, so that the proximal surface of the interference layer 304 effectively reflects light directed onto the probe 300. The preferred refractive index of the monolithic substrate can be higher than 1.5, 1.8, or 2.0. Thus, monolithic substrate 302 may include a high refractive index material such as glass (refractive index 2.0), while some embodiments of monolithic substrate 302 may include a low refractive index material such as quartz (refractive index 1.46) or plastic (refractive index 1.32 to 1.49).

[0050] The interference layer 304 is composed of at least one transparent material, which is coated on the distal surface of the monolithic substrate 302. These transparent materials are deposited on the distal surface of the monolithic substrate 302 in the form of a thin film with a thickness ranging from a fraction of a nanometer (e.g., a monolayer) to a few microns. The interference layer 304 can have a thickness of at least 600nm, 700nm, or 900nm. Exemplary thicknesses are between 600nm and 5,000nm (and preferably between 800nm ​​and 1,300nm). In this article, for example, the interference layer 304 has a thickness of approximately 900nm to 1,000nm or 940nm.

[0051] Compared to conventional probes, the interference layer 304 has a substantially similar refractive index to the biolayer. This ensures that reflections from the distal end of the probe 300 are primarily due to the analyte molecules 308 rather than the interface between the interference layer 304 and the analyte binding molecules 306. Typically, the biolayer has a refractive index of about 1.36, although this may vary depending on the type of analyte binding molecules (and therefore analyte molecules) along the distal end of the probe 300.

[0052] In some embodiments, the interference layer 304 is composed of magnesium fluoride (MgF2), while in other embodiments, the interference layer 304 is composed of potassium fluoride (KF), lithium fluoride (LiF), sodium fluoride (NaF), lithium calcium aluminum fluoride (LiCaAlF6), strontium fluoride (SrF2), aluminum fluoride (AlF3), sulfur hexafluoride (SF6), etc. Magnesium fluoride has a refractive index of 1.38, which is substantially the same as the refractive index of the biological layer formed along the distal end of the probe 300. For comparison, the interference layer of a conventional probe is typically composed of silicon dioxide, and the refractive index of silicon dioxide is approximately 1.4 to 1.5 in the visible light range. Since the interference layer 304 and the biological layer have similar refractive indices, the light will experience minimal scattering when it travels from the interference layer 304 into the biological layer and then back from the biological layer into the interference layer 304.

[0053] During biochemical testing, probe 300 can be suspended in a cavity (e.g., hole) including a sample. During biochemical testing, as analyte molecules 308 combine with analyte binding molecules 306, a biolayer will be formed along the distal end of probe 300. When light is irradiated on probe 300, the proximal surface of interference layer 304 can serve as a first reflecting surface, and the distal surface of the biolayer can serve as a second reflecting surface. The presence, concentration, or binding rate of analyte molecules 308 and probe 300 can be estimated based on the interference of the light beams reflected by these two reflecting surfaces. When analyte molecules 308 are attached to (or detached from) analyte binding molecules 306, the distance between the first reflecting surface and the second reflecting surface will change. Since the size of all other components in probe 300 remains the same, the interference pattern formed by the light reflected by the first reflecting surface and the second reflecting surface is phase-shifted according to the change in the biolayer thickness caused by the binding event.

[0054] In operation, an incident light signal 310 emitted by a light source is transmitted toward the biological layer through the monolithic substrate 302. Within the probe 300, the light will be reflected at the first reflective surface, thereby generating a first reflected light signal 312. The light will also be reflected at the second reflective surface, thereby generating a second reflected light signal 314. The second reflective surface initially corresponds to the interface between the analyte binding molecule 306 and the sample, and the sample is immersed in the probe 300. When binding occurs during a biochemical test, the second reflective surface becomes the interface between the analyte molecule 308 and the sample.

[0055] The first reflected light signal 312 and the second reflected light signal 314 form a spectral interference pattern, such as Figure 5A When the analyte molecule 308 binds to the analyte binding molecule 306 on the distal surface of the interference layer 304, the optical path of the second reflected light signal 314 becomes longer. Figure 5BAs shown, the spectral interference pattern shifts from T0 to T1. By continuously measuring the phase shift in real time, a kinetic binding curve can be plotted with the shift versus time. The binding rate of the analyte molecule to the analyte binding molecule fixed on the far side surface of the interference layer 304 can be used to calculate the analyte concentration in the sample. Therefore, the measurement of the phase shift is the detection principle of the thin film interferometer.

[0056] Figure 4 Another example of a probe 400 according to various embodiments is depicted. Figure 4 The probe 400 may be substantially similar to Figure 3 Here, however, the probe 400 includes an adhesion layer 410 deposited along a distal surface of an interference layer 404 affixed to the monolithic substrate 402. While the interference layer 404 is present in most embodiments, the adhesion layer 404 is generally optional and thus may only be included when greater adhesion of the analyte binding molecules 406 is desired or required.

[0057] Adhesion layer 410 may include a material that promotes adhesion of analyte binding molecules 406. An example of such a material is silicon dioxide. Adhesion layer 410 is typically very thin compared to interference layer 404 so that its effect on light traveling toward or returning from the biolayer will be minimal. For example, adhesion layer 410 may have a thickness of approximately 3 nm to 10 nm, while interference layer 404 may have a thickness of approximately 800 nm to 1,000 nm. The biolayer formed by analyte binding molecules 406 and analyte molecules 408 will typically have a thickness of several nm. Figure 3 The probe 300 is very similar, Figure 4 The probe 400 may also have a reflective layer (not shown) deposited along the distal end of the monolithic substrate 402 such that the reflective layer is positioned between the monolithic substrate 402 and the interference layer 404. The thickness of the reflective layer may be approximately the same as the thickness of the adhesion layer 410.

[0058] As mentioned above, these features are equally applicable to sensing surfaces having other forms. An example of such a sensing surface is a glass slide with a flat surface (also called a "chip") on which a biolayer is formed by flowing a solution over the flat surface during a biochemical test. Figure 6 to Figure 7 Let's discuss a few examples of flat surfaces.

[0059] Figure 6An example of a glass slide 600 according to various embodiments is depicted. The glass slide 600 includes a substrate 602 on which an interference layer 604 is deposited. In some embodiments, the interference layer 604 is deposited along the entire upper surface of the substrate 602, while in other embodiments, the interference layer 604 is deposited along a portion of the upper surface of the substrate 602. For example, the interference layer 604 can be deposited within a channel or hole formed in the upper surface of the substrate 602. As discussed above, FIGS. 2 to 3 may be used to describe the interference layer 604. Figure 3 The height of the monolithic substrate 302, 402 is usually much greater than the width. However, the opposite can also be the case here. In fact, the width of the substrate 602 can be greater than the length by a factor of 5, 7.5, 10, or 20. As an example, the substrate can be approximately 75 mm by 26 mm, with a height / thickness of approximately 1 mm.

[0060] During a biochemical test, analyte molecules 608 may bind to analyte binding molecules 606 that have been immobilized along the upper surface of interference layer 604 to form a biolayer. To establish the thickness of the biolayer, light may be directed to the upper surface of slide 600, such as Figure 6 600. More specifically, an incident light signal 20610 emitted by a light source may be shown at a biological layer that has been formed along the upper surface of the slide 600. This may require the incident light signal 20610 to travel through an ambient medium 616, which may be a vacuum, air, or a solution. The incident light signal 20610 will be reflected at a first reflective surface, thereby generating a first reflected light signal 612. The first reflective surface may represent an interface between the biological layer and the ambient medium 616. The incident light signal 20610 will also be reflected at a second reflective surface, thereby generating a second reflected light signal 614. The second reflective surface may represent an interface between the interference layer 604 and the substrate 602. As discussed above, the first reflected light signal 612 and the second reflected light signal 614 form a spectral interference pattern, which may be analyzed to establish the thickness of the biological layer. Note that because the incident light signal 20610 is not transmitted through the substrate 602, the substrate 602 may be transparent or non-transparent (e.g., opaque).

[0061] Figure 7 Another example of a slide 700 is depicted according to various embodiments. Figure 7 The slide 700 can be largely similar to Figure 6 The slide 600 can include a substrate 702 on which an interference layer 704 and an analyte binding molecule 706 are deposited. During a biochemical test, an analyte molecule 708 can bind to the analyte binding molecule 706 to form a biolayer.

[0062] Here, however, the incident light signal 710 is shown at the lower surface of the slide 700. In operation, the incident light signal 710 is transmitted through the substrate 702 toward the biological layer. Within the slide 700, the light will be reflected at the first reflective surface, thereby generating a first reflected light signal 712. The first reflective surface can represent the interface between the interference layer 704 and the substrate 702. The light will also be reflected at the second reflective surface, thereby generating a second reflected light signal 714. The second reflective surface can represent the interface between the biological layer and the ambient medium 716. As discussed above, the first reflected light signal 712 and the second reflected light signal 714 form a spectral interference pattern, which can be analyzed to establish the thickness of the biological layer.

[0063] Although in Figure 6 to Figure 7 Although not shown, the slides 600, 700 may include a reflective layer disposed between the substrate 602, 702 and the interference layer 604, 704 to increase reflectivity along the interface and / or an adhesion layer disposed along the upper surface of the interference layer 604, 704 to immobilize the analytical binding molecules 606, 706.

[0064] Resolving Light Absorption During Interferometry Testing

[0065] Figure 8 A flow chart of process 800 is depicted for resolving the absorption of light by an analyte molecule whose binding activity is monitored by an interferometric sensing system. Typically, process 800 is performed by a computer program executed on the interferometric sensing system. Thus, process 800 can be implemented by executing corresponding instructions by a processor included in the interferometric sensing system.

[0066] Alternatively, process 800 may be performed by a computer program that is executed by a processor that is external to the interferometric sensing system. Figure 10 to Figure 11 This implementation is discussed further below. This "external processor" may be included in a computing device that is communicatively connected to the interferometric sensing system. Examples of computing devices include mobile phones, tablet computers, laptop computers, and network accessible server systems consisting of one or more computer servers.

[0067] Initially, the computer program may obtain a data set generated by an interferometric sensing system (step 801). Specifically, the data set is generated by a detector of the interferometric sensing system, and therefore the data contained therein may be referred to as "detector data" or "range data". Figure 5AAs shown, the detector can measure the intensity across a set of wavelengths at a given point in time. This set of measurements can be referred to as a "signal" or "frame". Because the growth of a biolayer along the surface of an interferometric sensor is typically evidenced by a phase shift in the optical interference pattern, the detector can generate frames corresponding to different points in time. These frames can be generated by the detector continuously or periodically.

[0068] In some embodiments, the data set obtained by the computer program includes measurements associated with more than one frame. For example, measurements can be obtained in batches, wherein each batch includes measurements generated for at least two frames, so as to save computing resources. Therefore, the computer program can periodically obtain the measurements generated by the detector. In other embodiments, the measurements are obtained in real time by the computer program, for example, in the form of a signal waveform (or simply "signal" or "waveform"), which represents the sequential order of discrete values ​​corresponding to different wavelengths. Therefore, the computer program can obtain the measurements on a rolling basis, because these measurements are generated by the detector.

[0069] In some embodiments, the computer program applies a moving average filter to the data set in order to smooth its values ​​(step 802). Specifically, the computer program can generate a moving average filter by selecting a boxcar function as the impulse response of the filter, and then the computer program can apply the moving average filter to the data set in order to calculate a moving average of the measurements. The term "moving average" refers to a calculation that analyzes data points (i.e., measurements associated with a given frame) by creating a series of averages of different subsets of those data points. At a high level, calculating a moving average can help smooth short-term fluctuations and highlight longer-term trends because the goal can be to produce a moving average that is consistent with the average value of the data point. Figure 5A The signals are shown in comparable form to allow easier determination of the phase offset.

[0070] After this, for at least some "frames", the computer program can generate a subtraction curve (step 803) by comparing the corresponding measurement result with the measurement result of the "reference frame". Therefore, the computer program can generate a series of subtraction curves, each of which represents the difference between the corresponding frame and the "reference frame". Although the "reference frame" can be established as the beginning of a biochemical test, it does not have to be the first frame generated by the detector. For example, the "reference frame" can be the first frame generated after the end of the startup phase, allowing the signal to become less noisy in the startup phase. The startup phase can last for a predetermined number of frames (e.g., 100, 500, 1000 or 2500 frames), or the startup phase can last until, for example, the noise is determined to be low enough by the analysis of the frame by the computer program. Alternatively, the startup phase can last for a predetermined amount of time (e.g., 0.25, 0.50, 1.00 or 2.50 seconds). At a high level, the "reference frame" can indicate the beginning of a biochemical test, wherein it is assumed that minimal binding has occurred.

[0071] For each subtraction curve included in the series of subtraction curves, the computer program can then calculate an absorption ratio based on an analysis of the subtraction curve (step 804). To achieve this, the computer program can decompose the subtraction curve into its antisymmetric component and asymmetric component, as discussed further below. The asymmetric component can represent an absorption component, while the antisymmetric component can represent a reflection component. To calculate the absorption ratio, the computer program can compare the absorption component to the measurable signal as a whole, as follows:

[0072]

[0073] The computer program may then compare the absorption ratio to a threshold to determine on a per frame basis whether the principal component is an absorption component or a reflection component (step 805). As an example, the computer program may determine whether the absorption ratio indicates that the absorption component is at least 50% of the measurable signal overall.

[0074] In the case where a given absorbance ratio is less than a threshold value, the computer program can determine that the principal component is a reflection component representing a phase shift. In this case, the computer program can employ a first algorithm that calculates appropriate values ​​for the binding curve so that the absolute value of the phase shift is used as the binding amplitude (step 806). Thus, the first algorithm can calculate the binding curve in a conventional manner.

[0075] In case a given absorbance ratio is greater than a threshold, the computer program may determine that the principal component is an absorbance component. In this case, the computer program may employ a second algorithm that calculates an appropriate value for the binding curve based on the asymmetric component of the subtraction curve (step 807).

[0076] Thereafter, the computer program may cause the combined amplitude to be displayed on a graph visible on the interface (step 808). Typically, the interface is presented by and viewed on the interferometric interferometric sensing system. However, the interface may be presented by and viewed on another computing device. The computer program may be executed on the other computing device, or the computer program may send the necessary data to the other computing device for display. Examples of computing devices include mobile phones, tablet computers, laptop computers, etc.

[0077] Note that in some embodiments, these calculations are performed on a per-frame basis in order to maximize accuracy. In other embodiments, these calculations may be performed on a group of frames to save computing resources or to more quickly calculate a binding curve. The execution of process 800 may result in the generation of two outputs. The first output is the corresponding ratio of the absorption component and the reflection component in the measurable signal, as determined based on the subtraction curve calculated for the current frame relative to the reference frame. The second output is the quantitative amplitude of absorption, which is related to the amount of binding on the interferometric sensor. As mentioned above, the computer program may determine whether the principal component of the measurable signal is an absorption component or a reflection component. As part of the experimental operation, the computer program may do so for a group of frames rather than on a per-frame basis. For example, the computer program may determine whether the absorption component or the reflection component is the principal component across 25, 50, 100 or 500 "frames". Then, the binding curve may be calculated based on the decomposed principal component of the measured signal, and more specifically, the binding amplitude over time.

[0078] Additional information about absorption calculations

[0079] For the purpose of illustration, the following further discusses Figure 8 Aspects of the process described. This illustrative example is not intended to limit the process in any way.

[0080] One of the core responsibilities of the computer program is to calculate the subtraction curve of the frames generated by the detector. As mentioned above, the subtraction curve can be calculated by analyzing two frames (i.e., the frame of interest and the reference frame) as follows:

[0081] Amplitude n (i)=frame n (i)-frame 参考 (i) Equation 2

[0082] where i extends from 1 to an integer N (e.g., 3648), which represents the resolution of the detector, and n is the index of the frame of interest. Note that because the detector is typically a spectrometer, the term "frame" can be used interchangeably with the term "range." Therefore, Equation 2 can also be written as:

[0083] Amplitude n (i) = Scopen (i)- Scope 参考 (i) Equation 3

[0084] The computer program can then decompose the subtraction curve, which is usually in the form of a sine wave, into its antisymmetric and asymmetric components. The antisymmetric component represents the phase shift due to reflection, while the asymmetric component represents absorption. Using these components, the computer program can obtain the ratio of absorption and phase shift from the measured signal. Fig. 9 It is shown how the amplitude of the full signal (indicated by R1) and half the amplitude of the antisymmetric signal (indicated by R2) can be determined by analysis of the subtraction curve. Fig. 9 In the example, the x-axis unit is pixel, which indicates wavelength, where 1 pixel is roughly equal to 0.05nm to 0.08nm, and the y-axis unit indicates intensity. Each amplitude along the y-axis indicates the relative intensity of the corresponding wavelength. After determining R1 and R2, the absorption ratio and phase shift ratio can be calculated as follows

[0085]

[0086] ratio 相位偏移 =1-ratio 吸收 Equation 5

[0087] These operations can be performed repeatedly by the computer program on a set of frames as part of an experimental operation. For example, the computer program can evaluate 50, 100, or 250 frames after the start-up phase of a reaction is completed to avoid noise. The computer program can then compare the average absorption ratio calculated for those frames in the window of interest to a threshold. If the average absorption ratio (AAR) is greater than the threshold, the computer program can assign the experimental operation to an absorption category indicated as follows:

[0088]

[0089] In the case where the experimental procedure is assigned to the absorption category, the computer program can calculate the binding amplitude at a given time point based on the asymmetric component in the subtraction curve, which shows the difference between the corresponding frame and the reference frame. For example, the reference frame can be the first frame generated as part of the experimental procedure. Fig. 9 , the combined amplitude can be calculated as follows:

[0090]

[0091] Here, the asymmetric component (i.e., R1-2×R2) is initially divided by the average value of at least some pixels of the current frame to reduce noise. Specifically, the asymmetric component can be divided by the average value of the frame to make the intensity dimensionless, and then the value is transferred to the variation factor. The computer program can select a certain range of pixels to minimize randomness, in fact, imitating the rectangular wave train principle. Thereafter, the computer program can find the root of the value and then multiply it by a constant coefficient. The computer program can determine the coefficient based on the absorption of the immobilized antibody molecule. Typically, the coefficient is selected to be very consistent among most biomolecules, although the coefficient can be adjusted for extreme cases.

[0092] In the case where the experimental operation is assigned to the phase shift category (also called the "reflection category"), the computer program can calculate the cross-correlation value to establish the combined amplitude. At a high level, the cross-correlation captures the wavelength shift along the x-axis for a given measurement by monitoring the peak of the sinusoid.

[0093] Overview of Centrally Hosted Computer Programs

[0094] As mentioned above, aspects of the methods described herein may be implemented by a computer program executed by an interferometric sensing system. As mentioned above, the computer program may alternatively be executed by a processor located outside the interferometric sensing system. This "external processor" may be included in a computing device that is communicatively connected to the interferometric sensing system. Regardless of whether the computer program is inside or outside the interferometric sensing system, the computer program may be part of an interferometric analysis platform (or simply "analysis platform"). In addition to processing data generated by the interferometric sensing system, the analysis platform may be responsible for facilitating the design, conduct, or recording of biochemical tests.

[0095] Fig.10 A network environment 1000 is shown that includes an analysis platform 1002 executed by a computing device 1004. An individual (also referred to as a "user") may be able to interact with the analysis platform 1002 via an interface 1006. For example, a user may be able to access an interface through which the characteristics of a biochemical test (e.g., type of analyte binding molecule or analyte molecule, run time, reagents) are specified. As another example, a user may be able to access an interface through which data generated by an interferometric sensing system or an analysis of the data may be reviewed.

[0096] like Fig.10As shown, the analysis platform 1002 can reside in the network environment 1000. Therefore, the computing device 1004 on which the analysis platform 1002 resides can be connected to one or more networks 1008A to 1008B. Depending on its nature, the computing device 1004 can be connected to a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or a cellular network. For example, if the computing device 1004 is a computer server, the user can access the computing device 1004 through a corresponding computing device connected to the Internet via a LAN.

[0097] Interface 1006 can be accessed via a web browser, desktop application or mobile application. For example, in order to interact with analysis platform 1002, the user can start the web browser on computing device 1004, and then navigate to the webpage address associated with analysis platform 1002. As another example, the user can access the interface generated by analysis platform 1002 via desktop application, through which she can select the data for analysis, review the analysis of the data, etc. Therefore, the interface generated by analysis platform 1002 can be accessed by various computing devices, including mobile phones, tablet computers, desktop computers, etc. The interface generated by analysis platform 1002 can even be accessed on the interferometry sensing system responsible for generating data. In such embodiments, the interferometry sensing system can transmit the data generated in the biochemical test process to another computing device for analysis platform 1002 to process, and then analysis platform 1002 can transmit the processed data or the analysis of processed data to the interferometry sensing system for display.

[0098] Typically, the analysis platform 1002 is implemented by a cloud computing service provided by, for example, Amazon Web Services. Google Cloud Platform TM or Microsoft operation. Thus, computing device 1004 can represent a computer server that is part of server system 1010. Typically, server system 1010 is composed of multiple computer servers. These computer servers may include different types of data (e.g., information about patients, such as demographic information and health information), algorithms for processing, presenting and analyzing data, and other assets. Those skilled in the art will recognize that the data can also be distributed among server system 1010 and computing devices. For example, sensitive information associated with a patient whose sample is being examined can be stored on an interferometric sensing system and initially processed by the interferometric sensing system so that the sensitive information is obfuscated or removed before the data is transmitted to server system 1010 for further processing.

[0099] As mentioned above, aspects of the analysis platform 1002 may be hosted locally, for example, in the form of a computer program executed on an interferometric sensing system, a mobile phone, a laptop computer, or a desktop computer. Depending on the intended use, several different versions of the analysis platform 1002 may be available. For example, suppose a user wants to actively guide or record a biochemical test in which data is generated by an interferometric sensing system. In this case, the computer program may allow selection or specification of a patient, the type of biochemical test, the length of different test phases, the type of reagent, the type of analyte binding molecule, the type of analyte molecule, etc. Alternatively, if the user is interested in simply reviewing the analysis of the data generated by the interferometric sensing system, the analysis platform 1002 may be "simpler".

[0100] Fig.11 An example of a communication environment 1100 including an analysis platform 1102 configured to acquire data from one or more sources is depicted. Here, the analysis platform 1102 may receive data from an interferometric sensing system 1106, a laptop computer 1104, or a network-accessible server system 1110 (collectively referred to as "networked devices"). For example, the analysis platform 1102 may obtain data generated by its detector (e.g., a spectrometer) during a biochemical test from the interferometric sensing system 1106, and obtain information about the biochemical test or a corresponding patient from the network-accessible server system 1110 or the laptop computer 1108. Note that the analysis platform 1102 may and typically will obtain data from more than one interferometric sensing system. For example, the analysis platform 1102 may obtain data from interferometric sensing systems located in different geographic locations (e.g., in different healthcare facilities, research facilities, etc.).

[0101] The networked devices may be connected to the analysis platform 1102 via one or more networks 1104A to 1104C. Networks 1104A to 1104C may include PAN, LAN, WAN, MAN, cellular network, Internet, etc. Additionally or alternatively, the networked devices may communicate with each other via short-range wireless connection technology. For example, if the analysis platform 1102 resides on a network-accessible server system 1110, data received from the network-accessible server system 1110 does not need to traverse any network. However, the network-accessible server system 1110 may be connected to the interferometry sensing system 1106 and the laptop computer 1108 via a separate Wi-Fi communication channel. As another example, if the analysis platform 1102 resides on the interferometry sensing system 1106, data generated by the interferometry sensing system 1106 may not need to traverse any network. However, the interferometry sensing system 1106 may be connected to the network-accessible server system 1110 via a Wi-Fi communication channel and communicate with each other via a short-range wireless connection technology according to the present invention. Communication protocol, Wi-Fi A short-range communication channel established by a communication protocol, a near field communication (NFC) communication protocol, or the like is connected to the laptop computer 1108 .

[0102] Embodiments of the communication environment 1100 may include a subset of networked devices. For example, some embodiments of the communication environment 1100 include an analysis platform 1102 that receives data from an interferometric sensing system 1106 and receives additional data from a network-accessible server system 1110 on which it resides. In such embodiments, a user may be able to interact with the analysis platform 1102 via a display and corresponding control device that is part of or connected to the interferometric sensing system 1106. As another example, some embodiments of the communication environment 1100 include an analysis platform 1102 that receives data from a series of interferometric sensing systems located in different environments (e.g., different clinics, research facilities, testing facilities, etc.).

[0103] Processing system

[0104] Fig.12 1 is a block diagram illustrating an example of a processing system 1200 in which at least some of the operations described herein may be implemented. For example, components of the processing system 1200 may be hosted on an interferometric sensing system, or components of the processing system 1200 may be hosted on a computing device that is communicatively connected to the interferometric sensing system or a storage medium in which data generated by the interferometric sensing system is at least temporarily stored.

[0105] The processing system 1200 may include a processor 1202, a main memory 1206, a non-volatile memory 1210, a network adapter 1212, a video display 1218, an input / output device 1220, a control device 1222 (e.g., a keyboard or pointing device), a drive unit 1124 including a storage medium 1226, and a signal generating device 1230 communicatively connected to a bus 1216. The bus 1216 is shown as an abstraction representing one or more physical buses or point-to-point connections connected by appropriate bridges, adapters, or controllers. Thus, the bus 1216 may include a system bus, a peripheral component interconnect (PCI) bus or a PCI-Express bus, a HyperTransport bus, an Industry Standard Architecture (ISA) bus, a Small Computer System Interface (SCSI) bus, a Universal Serial Bus (USB), an Inter-Integrated Circuit (I 2 C) bus or the Institute of Electrical and Electronics Engineers (IEEE) standard 1394 bus (also known as "FireWire").

[0106] Although main memory 1206, non-volatile memory 1210, and storage medium 1226 are illustrated as a single medium, the terms "machine-readable medium" and "storage medium" should be taken to include a single medium or multiple media (e.g., a centralized / distributed database and / or associated caches and servers) storing one or more sets of instructions 1228. The terms "machine-readable medium" and "storage medium" should also be taken to include any medium capable of storing, encoding, or carrying a set of instructions to be executed by processing system 1200.

[0107] Generally speaking, the routines executed to implement the embodiments of the present disclosure may be implemented as part of an operating system or a specific application, component, program, object, module, or sequence of instructions (collectively referred to as a "computer program"). A computer program typically includes one or more instructions (e.g., instructions 1204, 1208, 1228) set in various memories and storage devices in a computing device at various times. When read and executed by the processor 1202, the instructions cause the processing system 1200 to perform operations to perform elements related to various aspects of the present disclosure.

[0108] Additional examples of machine and computer readable media include recordable media such as volatile and nonvolatile memory devices 1210, removable disks, hard drives, and optical disks (e.g., compact disk read only memory (CD-ROM) and digital versatile disk (DVD)), and transmission-type media such as digital and analog communications links.

[0109] The network adapter 1212 enables the processing system 1200 to coordinate data in the network 1214 with external entities through any communication protocols supported by the processing system 1200 and the entities external to the processing system 1200. The network adapter 1212 may include a network adapter card, a wireless network interface card, a router, an access point, a wireless router, a switch, a multilayer switch, a protocol converter, a gateway, a bridge, a bridge router, a hub, a digital media receiver, a repeater, or any combination thereof.

[0110] Example

[0111] Example 1. Data output for CXCR4 lipid particles

[0112] The streptavidin-coated probes were functionalized with 50 μg / mL biotin-WGA lectin (Vector Laboratories B-1025-5) in PBS. CXCR4 lipid particles (Integral Molecular LEV-101) were then loaded onto the WGA-functionalized probes at 8 μg / mL for 30 minutes. After a brief wash step, the CXCR4-loaded probes were exposed to 15 nM CXCR4 antibody (R&D Systems MAB170) for 5 minutes, followed by a 5-minute dissociation step. Binding data were collected on a GatorPrime (GatorBio Inc.) instrument and the results were processed using CC or the new absorption algorithm. Results are shown in Fig.13 shown.

[0113] Fig.13 Included is a graph with two binding curves calculated for the lipid particle loading phase of the above experiment. These binding curves include a first binding curve calculated entirely using a conventional algorithm and a second binding curve calculated using an absorption algorithm. Fig.13 As can be seen in FIG. 1 , the second binding curve more clearly indicates continued growth of the biolayer, whereas the first binding curve appears to indicate that the thickness of the biolayer is decreasing.

[0114] Fig.13 Also included is a graph with two binding curves calculated for the CXCR4 antibody binding and dissociation phases. Again, these binding curves include a first binding curve calculated entirely using the traditional algorithm and a second binding curve calculated using the absorption algorithm. As shown in Fig.13 As can be seen in FIG. 1 , the second binding curve more clearly indicates growth and stabilization of the biolayer, whereas the first binding curve appears to indicate that the thickness of the biolayer continues to decrease—initially faster, and then slower.

[0115] Example 2. Data Output for CD20 Lipid Particles

[0116] The streptavidin-coated probes were functionalized with 50 μg / mL biotin-WGA lectin (Vector Laboratories B-1025-5) in PBS. CD20 lipid particles (Integral Molecular LEV-103) were then loaded onto the WGA-functionalized probes at 20 μg / mL for 30 minutes. After a brief wash step, the CXCR 4-loaded probes were exposed to 100 nM CD20 antibody (R&D Systems MAB4225) for 5 minutes, followed by a 5-minute dissociation step. Binding data were collected on a GatorPrime (GatorBio Inc.) instrument and the results were processed using CC or the new absorption algorithm. Results are shown in Table 1. Fig.14 shown.

[0117] Fig.14 Included is a graph of two binding curves calculated for the lipid particle loading phase of the above experiment. These binding curves include a first binding curve calculated entirely using the traditional algorithm and a second binding curve calculated using the absorption algorithm. As shown in Fig.14 As can be seen, the second binding curve indicates more growth of the biolayer than detected by the traditional algorithm.

[0118] Fig.14 Also included is a graph with two binding curves calculated for the CD20 antibody binding and dissociation phases. Again, these binding curves include a first binding curve calculated entirely using the traditional algorithm and a second binding curve calculated using the absorption algorithm. Fig.14 As can be seen in FIG. 1 , the second binding curve more clearly indicates binding and dissociation resulting in variations in biolayer thickness compared to the first binding curve which simply indicates a roughly constant growth and subsequent rapid disintegration of the biolayer.

[0119] Remark

[0120] The foregoing description of various embodiments of the present technology has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the claimed subject matter to the precise form disclosed.

[0121] Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to best describe the principles of the technology and its practical application, thereby enabling others skilled in the relevant art to understand the claimed subject matter, various embodiments and various modifications suitable for the particular use contemplated.

Claims

1. A method implemented by a computer program executed on an interferometric sensing system, the interferometric sensing system measuring the binding of an analyte molecule in a liquid sample to a probe, the method comprising: Get a data set representing the sequential order of the signal, wherein each signal represents a series of values ​​indicative of the intensity of light across a series of wavelengths at corresponding points in time, the light being received from a probe suspended in the liquid sample; For each of the signals, generating a subtraction curve by comparing the corresponding series of values ​​with another series of values ​​associated with a reference signal; calculating an absorbance ratio based on analysis of the subtraction curve; determining whether a main component of the signal is reflection or absorption based on the absorption ratio; In response to a determination that the principal component is reflective, calculating a combined amplitude so that the absolute value of the signal is used as the combined amplitude; In response to a determination that the principal component is absorbent, calculating a binding amplitude based on an asymmetric component of the subtraction curve; and This causes a combined amplitude calculated for the signal to be displayed on a graph visible on the interferometric sensing system.

2. The method of claim 1, wherein the reference signal is a first signal generated after a startup phase, wherein the startup phase allows measurements generated by the interferometric sensing system to become less noisy.

3. The method of claim 1, wherein the reference signal is generated by the interferometric sensing system immediately prior to a signal included in the data set.

4. The method according to claim 1, further comprising: The subtraction curve is decomposed into (i) an antisymmetric component corresponding to the phase shift and (ii) an asymmetric component corresponding to the absorption.

5. The method of claim 4, wherein the combined amplitude is calculated when the principal component is absorption, (i) dividing the asymmetric component by an average value of at least some pixels of a frame of the signal so as to produce an asymmetric component with less noise, (ii) taking the root of the less noisy asymmetric component, and (iii) Multiplying the root of the less noisy asymmetric component by a coefficient.

6. The method according to claim 1, further comprising: A moving average filter is applied to the data set to calculate a moving average for each of the signals.

7. The method of claim 1, wherein said generating, said calculating, and said determining are performed when said signal is obtained such that said combined amplitude is calculated in real time.

8. A non-transitory medium having stored thereon instructions, which when executed by a processor cause the processor to perform operations comprising: obtaining a data set comprising (i) a reference signal and (ii) a series of signals generated after the reference signal, wherein each signal included in the data set is generated by an interferometric sensing system measuring light received from a probe suspended in a liquid sample; comparing each of said signals to said reference signal to produce a series of subtraction curves; For each of the signals, The absorbance ratios were calculated based on analysis of the corresponding subtraction curves; determining whether a main component of the signal is reflection or absorption based on the absorption ratio; and A combined magnitude is calculated based on the principal components.

9. The non-transitory medium of claim 8, wherein the processor is part of the interferometric sensing system.

10. The non-transitory medium of claim 8, wherein the operations further comprise: calculating an average absorption ratio of the series of signals based on the absorption ratio calculated for each of the signals; comparing the average absorption ratio with a threshold value; In response to a determination that the average absorption ratio is greater than the threshold, The series of signals are assigned to absorption classes.

11. The non-transitory medium of claim 10, wherein the calculating comprises: A combined amplitude for each of the signals is established based on the asymmetric components in the corresponding subtraction curves.

12. The non-transitory medium of claim 8, wherein the operations further comprise: calculating an average absorption ratio of the series of signals based on the absorption ratio calculated for each of the signals; comparing the average absorption ratio with a threshold value; In response to a determination that the average absorption ratio is less than the threshold, The series of signals are assigned to reflection classes.

13. The non-transitory medium of claim 12, wherein the calculating comprises: The combined amplitude of each of the signals is established by calculating the cross-correlation value.

14. The non-transitory medium of claim 8, wherein the operations further comprise: The binding amplitude calculated for the series of signals is published on a graph visible on the interface.

15. The non-transitory medium of claim 14, wherein the interface is visible on the interferometric sensing system.

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