Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/12—Generating the spectrum; Monochromators
  • G01J3/18—Generating the spectrum; Monochromators using diffraction elements, e.g. grating
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/12—Generating the spectrum; Monochromators
  • G01J3/26—Generating the spectrum; Monochromators using multiple reflection, e.g. Fabry-Perot interferometer, variable interference filters
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/28—Investigating the spectrum
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/28—Investigating the spectrum
  • G01J3/45—Interferometric spectrometry
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
  • G01N21/274—Calibration, base line adjustment, drift correction
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/28—Investigating the spectrum
  • G01J2003/2866—Markers; Calibrating of scan
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/3504—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing gases, e.g. multi-gas analysis
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2201/00—Features of devices classified in G01N21/00
  • G01N2201/12—Circuits of general importance; Signal processing
  • G01N2201/127—Calibration; base line adjustment; drift compensation
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2201/00—Features of devices classified in G01N21/00
  • G01N2201/12—Circuits of general importance; Signal processing
  • G01N2201/129—Using chemometrical methods

Definitions

• the technology discussed below relates generally to artificial intelligence modelers for building chemometrics models for spectral devices, and more particular to mechanisms for building generalized chemometrics models targeting ultra-wide-scale deployment of spectral devices.
• spectral sensing the interaction between electromagnetic radiation, such as light, and matter is studied.
• spectroscopy used, such as infrared/vibrational spectroscopy, atomic absorption spectroscopy, mass spectroscopy, electrochemical impedance spectroscopy, x-ray spectroscopy, in addition to others.
• infrared/vibrational spectroscopy atomic absorption spectroscopy
• mass spectroscopy mass spectroscopy
• electrochemical impedance spectroscopy electrochemical impedance spectroscopy
• x-ray spectroscopy in addition to others.
• analytical chemistry devices based on infrared spectral sensing devices have progressed quickly in the last decade.
• the development went through a paradigm shift moving from laboratory-based bench top devices to handheld devices that can be used in the field or in-line in the production facilities in a ubiquitous manner.
• the mid infrared (MIR) wavelength range (2.5 pm to 25 pm) contains spectral lines corresponding to fundamental vibrations lines.
• the IR range below 2.5 pm is the near infrared (NIR) range that includes the overtones and the combinational lines.
• NIR near infrared
• a spectral modeling system includes a spectral converter configured to receive spectral data of a plurality of samples from a subset of a plurality of spectral devices and spectral device characteristics representing spectral variations in the plurality of spectral devices.
• the spectral converter is further configured to generate a plurality of artificial spectra representing remaining spectral devices of the plurality of spectral devices based on the spectral data and the spectral device characteristics.
• the spectral modeling system further includes a chemometrics engine configured to produce a chemometrics model for one or more parameters associated with the plurality of samples based on the spectral data and the plurality of artificial spectra.
• Another example provides a method for spectral modeling.
• the method includes receiving spectral data of a plurality of samples from a subset of a plurality of spectral devices, receiving spectral device characteristics representing spectral variations in the plurality of spectral devices, and generating a plurality of artificial spectra representing remaining spectral devices of the plurality of spectral devices based on the spectral data and the spectral device characteristics.
• the method further includes producing a chemometrics model for one or more parameters associated with the plurality of samples based on the spectral data and the plurality of artificial spectra.
• FIG. 1 is a diagram illustrating an example of a spectrometer according to some aspects.
• FIG. 2 is a diagram illustrating another example of a spectrometer according to some aspect.
• FIG. 3 is a diagram illustrating another example of a spectrometer according to some aspects.
• FIG. 4 is a diagram illustrating an example of a chemometrics model according to some aspects.
• FIGs. 5A-5E are graphs illustrating various specifications of spectral devices according to some aspects.
• FIGs. 6A and 6B are graphs illustrating variations in self-apodization of a spectral device and corresponding spectral variations according to some aspects.
• FIG. 7 is a diagram illustrating an example of a spectral modeling system according to some aspects.
• FIG. 8 is a diagram illustrating an example of a spectral modeling system for two spectral devices and according to some aspects.
• FIG. 9 is a diagram illustrating an example of a characteristics extractor according to some aspects.
• FIGs. 10A and 10B illustrate an example of a spectral device configured for Stage 1 characteristic extraction according to some aspects.
• FIG. 11A-11D illustrate an example of a spectral device configured for Stage 2 or Stage 3 characteristic extraction according to some aspects.
• FIG. 12 is a diagram illustrating an example extractor setup for Stage 4 according to some aspects.
• FIG. 13 is a diagram illustrating another example of a spectral modeling system for two spectral devices and according to some aspects.
• FIGs. 14A and 14B are diagrams illustrating an example of a global spectral converter according to some aspects.
• FIGs. 15A and 15B are graphs representing the impact on the bias and slope errors in the prediction according to some aspects.
• FIG. 16 is a diagram illustrating another example of a global spectral converter according to some aspects.
• FIG. 17 is a diagram illustrating another example of a global spectral converter according to some aspects.
• FIG. 18 is a diagram illustrating another example of a global spectral converter according to some aspects.
• FIGs. 19A-19D are diagrams illustrating examples of pre-processing of the spectral data according to some aspects.
• FIG. 20 is a diagram illustrating another example of a global spectral converter according to some aspects.
• FIGs. 21A-21C are diagrams illustrating an example of spectral correction according to some aspects.
• FIG. 22 is a diagram illustrating an example of an algorithm for selection of the subset of spectral devices to collect the spectral data according to some aspects.
• FIG. 23 is a diagram illustrating another example of spectral correction according to some aspects.
• FIG. 24 is a diagram illustrating another example of spectral correction according to some aspects.
• FIG. 25 is a diagram illustrating examples of optical head configuration generalization according to some examples.
• FIG. 26 is a diagram illustrating another example of spectral correction according to some aspects.
• FIG. 27 is a diagram illustrating an example of a process flow for generating artificial spectra according to some aspects.
• FIGs. 28A-28C illustrate an example of generalization of spectral data based on self-apodization and resolution variations according to some aspects.
• FIGs. 29A-29C illustrate an example of generalization of spectral data based on wavenumber/wavelength errors according to some aspects.
• FIGs. 30A-30C illustrate an example of generalization of spectral data based on SNR across the wavelength range according to some aspects.
• FIGs. 31A-31C illustrate an example of generalization of spectral data based on absorbance scaling according to some aspects.
• FIGs. 32A and 32B illustrate an example of generalization of spectral data based on thermal drift according to some aspects.
• FIGs. 34A-34C illustrate an example of generalization of spectral data based on sample interface effects according to some aspects.
• FIG. 39 illustrates an example of alteration of the distribution of the number of spectra versus the value of the analyte in the sample according to some aspects.
• FIG. 40 is a process flow illustrating an example of optimization of the chemometrics model according to some aspects.
• FIG. 43 illustrates an example of optimizing the chemometrics model by applying wavelength selection according to some aspects.
• FIG. 44 is a diagram illustraing an example of a spectral modeling adjustment process for applying model adjustement to further generalize the chemometrics model according to some aspects.
• FIGs. 45A ⁇ 15C illustrate adjustment of the chemometrics model to account for deviant spectral devices according to some aspects.
• FIG. 46 is a diagram illustrating an example of unifying the spectral data coming from various spectral devices according to some aspects.
• FIG. 47 is a diagram illustrating another example of unifying the spectral data coming from various spectral devices according to some aspects.
• FIG. 49 is a diagram illustrating an example of a globalization process for transferring a chemometrics model to a child spectral device corresponding to a different type of spectral device according to some aspects.
• FIG. 50 is a diagram illustrating an example of testing child spectral devices according to some aspects.
• FIG. 51 is a diagram illustrating a cloud-based spectral modeling system according to some aspects.
• FIGs. 52A-52C are diagrams illustrating examples of generalized chemometrics model building using a generalizer spectral converter according to some aspects.
• FIG. 53 is a diagram illustrating another example of a spectral modeling system according to some aspects.
• FIG. 54 is a block diagram illustrating an example of a hardware implementation for a computing device employing a processing system according to some aspects.
• FIG. 55 is a flow chart illustrating an exemplary process for producing a generalized chemometrics model according to some aspects.
• Various aspects of the disclosure relate to techniques for building chemometrics (calibration) models for spectral devices targeting ultra-wide-scale deployment.
• a plurality of samples are measured on a subset of a plurality of spectral devices to generate corresponding spectral data.
• the subset may include a single spectral device or several spectral devices, but less than all of the plurality of spectral devices.
• the spectral data may include measurements of phantom samples corresponding to the plurality of samples.
• the phantom samples may be formed of a stable substance having a same absorbance spectra as the plurality of samples.
• a characteristics extractor generates a set of spectral device characteristics representing spectral variations in the plurality of spectral devices.
• the extracted spectral device characteristics may include, for example, one or more of signal- to-noise ratio (SNR), wavelength repeatability, wavelength error, absorbance scaling, self-apodization function, baseline shift, back reflection, thermal drift, environmental drift, optical path difference (OPD) variation, Etalon effect, or other suitable characteristic.
• SNR signal- to-noise ratio
• OPD optical path difference
• Etalon effect or other suitable characteristic.
• the characteristics may be generated in various manners.
• the characteristics extractor may include a plurality of stages for extracting the different spectral device characteristics. The stages may include, for example, background measurements of the plurality of spectral devices, reference material measurements of the plurality of spectral devices, narrowband emission measurements of the plurality of spectral devices, and temperature-control measurements of the plurality of spectral devices.
• the spectral device characteristics may be extracted by measuring universal samples on at least a portion of the plurality of spectral devices that are different than the samples used to obtain the spectral data on the subset of spectral devices.
• the portion of the plurality of spectral devices may include all of the plurality of spectral devices.
• the portion of the spectral devices may include selected spectral devices having corresponding spectral device characteristics covering a space of variations including corners of production line characteristics of a production line including the plurality of spectral devices.
• the measured spectra of the universal samples may be fed to a signal processor in the characteristics extractor to extract the spectral device characteristics of the plurality of spectral devices.
• the spectral device characteristics may be generated based on statistical information related to the production line of the plurality of spectral devices.
• the characteristics extractor may extract the spectral device characteristics based on an understanding of production line variations and histograms.
• the characteristics extractor may derive various statistical parameters, such as the mean value, standard deviation, skewness, or kurtosis, based on the statistical information and determine a probability distribution of each of the statistical parameters.
• the characteristics extractor may then generate the spectral device characteristics based on the statistical parameters and the respective probability distribution of each of the statistical parameters.
• the spectral device characteristics generated by the characteristics extractor and the spectral data produced by the subset of the plurality of spectral devices may then be fed into a spectral converter to produce a plurality of artificial spectra representing remaining spectral devices of the plurality of spectral devices (e.g., the spectral devices not included in the subset).
• the artificial spectra represent the expected spectra to be generated on the remaining spectral devices based on the spectral device characteristics thereof.
• the spectral converter further applies pre-processing to the spectral data produced by the subset of the plurality of spectral devices.
• the spectral converter may apply a spectral variance function, a spectral correction function, a spectral modulation and perturbation function, or an optical head variance function to account for different variances in the spectral data.
• the resulting artificial spectra, together with the original spectral data, may then be input to an artificial intelligence (Al) engine (also referred to herein as a chemometrics engine) to build a chemometrics model for one or more parameters associated with the samples.
• Al artificial intelligence
• the Al engine may be a cloud-based Al engine.
• the Al engine may then adjust the chemometrics model to account for deviant spectral devices that deviate in performance from other regular spectral devices.
• the Al engine may further generalize the chemometrics model to be appliable to different types of spectral device(s) using a transfer function generated based on measurements obtained from one or more of the plurality of spectral devices and the different types of spectral device(s).
• the different types of spectral device(s) may use a different light modulator, optical head, or other spectral device configuration.
• the chemometrics model may be used to calibrate one or more of the plurality of spectral devices.
• the chemometrics model may be used to characterize a sample under test measured on a test spectral device.
• the spectral device characteristics of the test spectral device, along with a sample measurement of the sample under test, may be fed to the Al engine to generate a result (e.g., measured value of the sample under test) using the chemometrics model.
• the new generation includes diffraction-grating spectrometers using a digital micro-mirror device (DMD) together with a diffraction grating, or a scanning micro-electro-mechanical-systems (MEMS) diffraction grating rotated by a rotary MEMS actuator with a spectral range of 950 nm to 1.9 pm.
• DMD digital micro-mirror device
• MEMS scanning micro-electro-mechanical-systems
• the new generation further includes MEMS tunable Fabry-Perot devices covering wavelength ranges 1.5 - 2.0 pm or 1.9 - 2.5 pm, and MEMS Fourier Transform Infrared (FTIR) spectrometers based on self-aligned and highly integrated architectures, such as Michelson interferometers, multimode interference MMI, or spatially-shifted Fabry- Perot.
• FTIR Fourier Transform Infrared
• the new generation further includes hand-held spectral sensing devices using any of the above core spectral sensor designs.
• FIG. 1 is a diagram illustrating an example of a spectrometer 100 according to some aspects.
• the spectrometer 100 may be, for example, a Fourier Transform infrared (FTIR) spectrometer that exploits light interference and Fourier transform to calculate the spectral content of an infrared light beam.
• FTIR Fourier Transform infrared
• the spectrometer 100 is a Michelson FTIR interferometer.
• FTIR spectrometers measure a single-beam spectrum (power spectral density (PSD)), where the intensity of the single-beam spectrum is proportional to the power of the radiation reaching the detector.
• PSD power spectral density
• the background spectrum i.e., the single-beam spectrum in absence of a sample
• the absorbance of the sample 112 may be calculated from the transmittance, reflectance, or trans-reflectance of the sample 112, the former being illustrated.
• the absorbance of the sample 112 may be calculated as the ratio of the spectrum of transmitted light, reflected light, or trans-reflected light from the sample to the background spectrum.
• the FT-IR spectrometer 100 includes a fixed mirror 106, a moveable mirror 108, a beam splitter 104, and a detector 114 (e.g., a photodetector).
• a light source 102 associated with the spectrometer 100 is configured to emit an input beam and to direct the input beam towards the beam splitter 104.
• the light source 102 may include, for example, a laser source, one or more wideband thermal radiation sources, or a quantum source with an array of light emitting devices that cover the wavelength range of interest.
• the beam splitter 104 is configured to split the input beam into two beams. One beam is reflected off of the fixed mirror 106 back towards the beam splitter 104, while the other beam is reflected off of the moveable mirror 108 back towards the beam splitter 104.
• the moveable mirror 108 may be coupled to an actuator 110 to displace the movable mirror 108 to the desired position for reflection of the beam. An optical path length difference (OPD) is then created between the reflected beams that is substantially equal to twice the mirror 108 displacement.
• the actuator 110 may include a micro-electro-mechanical systems (MEMS) actuator, a thermal actuator, or other type of actuator.
• MEMS micro-electro-mechanical systems
• the reflected beams interfere at the beam splitter 104 to produce an output light beam, allowing the temporal coherence of the light to be measured at each different Optical Path Difference (OPD) offered by the moveable mirror 108.
• OPD Optical Path Difference
• the signal corresponding to the output light beam may be detected and measured by the detector 114 at many discrete positions of the moveable mirror 108 to produce an interferogram.
• the detector 114 may include a detector array or a single pixel detector.
• the interferogram data verses the OPD may then be input to a processor (not shown, for simplicity).
• the spectrum may then be retrieved, for example, using a Fourier transform carried out by the processor.
• the spectrometer 100 may be implemented as a MEMS interferometer (e.g., a MEMS chip).
• the MEMS chip may be attached to a printed circuit board (PCB) that may include, for example, one or more processors, memory devices, buses, and/or other components.
• PCB printed circuit board
• MEMS refers to an actuator, a sensor, or the integration of sensors, actuators and electronics on a common silicon substrate through microfabrication technology to build a functional system.
• Microelectronics are typically fabricated using an integrated circuit (IC) process, while the micromechanical components are fabricated using compatible micromachining processes that selectively etch away parts of the silicon wafer or add new structural layers to form the mechanical and electromechanical components.
• IC integrated circuit
• MEMS element is a micro-optical component having a dielectric or metallized surface working in a reflection or refraction mode.
• Other examples of MEMS elements include actuators, detector grooves and fiber grooves.
• the MEMS interferometer may be fabricated using a Deep Reactive Ion Etching (DRIE) process on a Silicon On Insulator (SOI) wafer in order to produce the micro-optical components and other MEMS elements that are able to process free-space optical beams propagating parallel to the SOI substrate.
• DRIE Deep Reactive Ion Etching
• SOI Silicon On Insulator
• the electro-mechanical designs may be printed on masks and the masks may be used to pattern the design over the silicon or SOI wafer by photolithography. The patterns may then be etched (e.g., by DRIE) using batch processes, and the resulting chips (e.g., MEMS chip) may be diced and packaged (e.g., attached to the PCB).
• the beam splitter 104 may be a silicon/air interface beam splitter (e.g., a half-plane beam splitter) positioned at an angle (e.g., 45 degrees) from the input beam.
• the input beam may then be split into two beams LI and L2, where LI propagates in air towards the moveable mirror 108 and L2 propagates in silicon towards the fixed mirror 106.
• LI originates from the partial reflection of the input beam from the half-plane beam splitter 104, and thus has a reflection angle equal to the beam incidence angle.
• L2 originates from the partial transmission of the input beam through the half-plane beam splitter 104 and propagates in silicon at an angle determined by Snell’s Law.
• the fixed and moveable mirrors 106 and 108 are metallic mirrors, where selective metallization (e.g., using a shadow mask during a metallization step) is used to protect the beam splitter 104.
• the mirrors 106 and 108 are vertical Bragg mirrors that can be realized using, for example, DRIE.
• the MEMS actuator 110 may be an electrostatic actuator formed of a comb drive and spring. For example, by applying a voltage to the comb drive, a potential difference results across the actuator 110, which induces a capacitance therein, causing a driving force to be generated as well as a restoring force from the spring, thereby causing a displacement of moveable mirror 108 to the desired position for reflection of the beam back towards the beam splitter 104.
• FIG. 2 is a diagram illustrating another example of a spectrometer 200 according to some aspects.
• the spectrometer 200 may be, for example, a Fabry-Perot spectrometer that includes a fixed mirror 206, a moveable mirror 208, and a detector 212 (e.g., a photodetector).
• a light source 202 associated with the spectrometer 200 is configured to emit an input beam and to direct the input beam towards the fixed mirror 206.
• the light source 202 may include, for example, a laser source, one or more wideband thermal radiation sources, or a quantum source with an array of light emitting devices that cover the wavelength range of interest.
• d is the periodicity of the grating
• 0 m is the angle of diffracted beam
• m is the order of diffraction.
• the detector 308 may be a multi-pixel detector, as shown in FIG. 3, to detect the different intensity of light on every point on the detector 308 and convert that to an image that can then be processed to produce the light spectrum.
• a single detector can be used.
• a movable mirror or slit may be needed to direct each wavelength separately to the detector.
• FIG. 4 is a diagram illustrating an example of a chemometrics model 400 according to some aspects.
• the chemometrics model 400 (also referred to herein as a calibration model) shown in FIG. 4 may be built for a certain device (denoted as device j or unit j) by measuring samples spanning the range of variation in the analyzed parameters. For example, M samples (numbered from 1 to i to M) are measured each with a spectrum Sij produced by the device j. Reference values for the different parameters of the sample i are denoted by the vector Ri, where the vector length is the number of parameters analyzed per sample.
• the samples can also be measured by conventional methods and the values of various parameters associated with the samples may be recorded as reference values.
• the resulting chemometrics model 400 may then be applied to a spectrum Stest of a sample under test to produce a result 402 (e.g., a prediction of a parameter) associated with the sample.
• the chemometrics model 400 is included within an Al engine 404 and the spectrum may be input to the Al engine 404 for analysis and processing.
• the Al engine 404 is configured to process the spectrum to generate a result 402 indicative of at least one parameter associated with the sample from the spectrum.
• the Al engine 404 may include one or more processors for processing the spectrum and a memory configured to store one or more calibration (chemometrics) models utilized by the processor in processing the spectrum.
• the Al engine 404 can include, for example, one or more calibration models, each built for a respective type of analyte under test. Validation and outliers detection of the test results may then be performed to refine the chemometrics model 400.
• the spectrum includes a measured absorption spectra and the Al engine 404 is configured to detect one or more analytes from absorption signals of the measured absorption spectra in the near-infrared frequency range.
• absorption signals in the near-infrared region (frequency range) can be used to detect the analyte based on overtones and combinations of the fundamental vibrational modes.
• the SNR is usually extracted using a 100% line method, in which several measurements (typically 50-100) are carried out consecutively and each measurement is normalized to the preceding one, as shown in FIG. 5E.
• the noise can then be extracted from the root mean square variation N rm s of the 100 % line.
• the SNR is calculated as 100/ N ms .
• Variations in the self-apodization envelope can lead to variations in the absorbance spectrum, as shown in in FIG. 6B.
• Different effects can also arise from variations related to the photodetector response, light source, optical coupling elements between the light source, interferometer and photodetector, background samples, distance between the sample and the optical windows, actuation and sensing electronics.
• SEPj The standard error of prediction (SEPj) is calculated as the standard deviation of predicted residuals:
• DS Direct standardization
• PDS piecewise direct standardization
• DS uses the whole slave spectrum while in PDS, a small window from the slave spectrum is used instead.
• mathematical operations are applied to convert or correct the regression coefficient of the model of unit j so that it can provide correct results with unit N.
• Data processing may also be used, such as baseline removal, de-trending and derivatives, multiplicative scatter correction, orthogonal signal correction and generalized least squares.
• the standardization samples have to be of the same type as the samples being modeled and contain analytes spanning the same range of analyzed parameters.
• the samples have to be measured on each and every new spectral device the calibration (chemometrics) model is being transferred to.
• the measurements on both devices should be performed at the same time to avoid unexpected changes in the standardization samples.
• a different chemometrics model has to be installed on each different spectral device produced. This is not compatible with the mass production of chemometrics models and the ultra-wide-scale deployment of the spectral devices for ubiquitous chemical analysis.
• A b[£ 1 (A)c 1 + £ 2 (A)c 2 + -- £ n (A)c n ] (6)
• A the absorbance
• £ the substance absorptivity
• c the substance concentration in the sample.
• Equation 6 the prediction of the spectra based on Equation 6 above can be satisfactory for liquids or gases since the physical nature of the sample (e.g., in terms of shape, path length, and scattering) are well controlled, for solids and inhomogeneous or heterogeneous samples, this may not be able to be represented by Equation 6.
• a different approach is to measure a very large data set using many different spectral devices to build a global calibration model that can be used on all of them.
• pre-processing of the data set should be carefully done to reduce the variation between the spectral devices to reduce the global prediction errors.
• measuring large datasets on large numbers of spectral devices can be very expensive and not practical in some cases.
• various aspects relate to techniques for building chemometrics (calibration) models for spectral devices targeting ultra-wide-scale deployment.
• a calibration transfer from a first spectral device to a second spectral device may be carried out without measuring the same samples on both spectral devices, thus reducing the cost of the sample measurement process and enabling globalization of chemometric models.
• spectral device characteristics of the first and second spectral devices may be generated by a characteristics extractor and the output thereof fed to a spectral converter to produce artificial spectra for the second spectral device based on spectral data obtained by the first spectral device.
• the artificial spectra and spectral data may then be fed into to a chemometrics modeler (e.g., Al engine) to produce the chemometrics model for the second spectral device.
• a chemometrics modeler e.g., Al engine
• the chemometrics model may further be generalized for a plurality of spectral devices based on the respective spectral device characteristics thereof.
• FIG. 7 is a diagram illustrating an example of a spectral modeling system 700 according to some aspects.
• the spectral modeling system 700 includes a characteristics extractor 710, a spectral converter 714, and a machine learning/ Al engine 718.
• the characteristics extractor 710 is configured to receive spectral device information 708 related to a plurality of spectral devices 702 (e.g., N spectral devices).
• Each spectral device 702 may correspond, for example, to one of the spectral devices shown in FIGs. 1, 2, or 3.
• the N spectral devices 702 represent a production line of spectral devices having a same or similar configuration.
• the spectral device information 708 may indicate specification (characteristic) variations between the plurality of spectral devices 702.
• the spectral device information 708 may include measured spectra of background or reference material or measured spectra of universal samples from at least a portion of the spectral devices 702.
• the spectral device information 708 may include other spectral device information, such as statistical information related to the production line of spectral devices 702.
• the characteristics extractor 710 is configured to generate spectral device characteristics 712 representing spectral variations in the plurality of spectral devices 702 based on the spectral device information 708 and to input the spectral device characteristics 712 to the spectral converter 714.
• the spectral device characteristics 712 may include one or more of signal-to-noise ratio (SNR), wavelength repeatability, wavelength error, absorbance scaling, self-apodization function, baseline shift, back reflection, temperature variation (e.g., thermal drift), environmental drift, optical path difference (OPD) variations, or Etalon effect.
• a subset of the spectral devices 704 may be used to measure a plurality of samples (e.g., M samples).
• the resulting spectral data 706 of the measured samples may be fed into the spectral converter 714.
• the spectral data 706 may include a respective spectrum S li, S2i, . . . SMi from each of the i spectral devices 704 for each of the M samples.
• the spectral converter 714 may apply multiple mathematical transformations and spectral effects to the spectral data 706 using the spectral device characteristics 712 to generate a plurality of artificial spectra 716 representing remaining spectral devices (e.g., spectral devices not included in the subset 704) the plurality of spectral devices 702.
• the resulting artificial spectra may include Si l, S21, . . ., SMI for spectral device 1, S12, S22, . . ., SM2 for spectral device 2, and so on through SIN, S2N, ..., SMN for spectral device N.
• the artificial spectra 716 should resemble the spectra expected to be produced by the corresponding spectral device 702 (e.g., with certain spectral device characteristics).
• the artificial spectra 716, along with the spectral data 706 (e.g., Sli, S2i, . . ., SMi) obtained by the subset of spectral devices, may be fed into the Al engine 718 to produce a respective chemometrics model 720 for one or more parameters associated with the samples.
• the chemometrics models 720 may be built using reference values for different parameters of the plurality of samples.
• the chemometrics model may then be used by the plurality of spectral devices in predicting sample parameters based on actual measured samples, as shown in FIG. 4.
• FIG. 4 FIG.
• a first spectral device (Device 1) 802 is configured to measure a plurality of samples (e.g., M samples) 806 and to produced spectral data 808 (e.g., Si l, S21, . . ., SMI) representative of the plurality of samples.
• a plurality of samples e.g., M samples
• spectral data 808 e.g., Si l, S21, . . ., SMI
• the spectral data 808 together with references values 812 (e.g., Rl, R2, ..., RM) of analyzed parameters in the samples 806, obtained by a reference method 810, are fed to a chemometrics engine 814a (e.g., Al engine) to build a chemometrics model 816a for Device 1 802.
• a chemometrics engine 814a e.g., Al engine
• a chemometrics (calibration) transfer may be performed without using any of the M samples 806.
• a characteristics extractor 818 (which may correspond, for example, to the characteristics extractor 710 shown in FIG. 7) may be configured to generate spectral device characteristics 822 representing spectral variations in Device 1 802 and Device 2 804 based on spectral device information 818a and 818b associated with each of Device 1 802 and Device 2 804.
• the spectral device information 818a and 818b may include measured spectra of background or reference material or measured spectra of universal samples different than the samples 806.
• the spectral device information 818a and 818b may include other spectral device information, such as statistical information related to the spectral devices 802 and 804.
• the spectral device characteristics 822 may include one or more of signal-to- noise ratio (SNR), wavelength repeatability, wavelength error, self-apodization function, baseline shift, back reflection, or temperature variation (e.g., thermal drift).
• the spectral device characteristics 822, together with the spectral data 808 may then be fed into a spectral converter 824.
• the spectral converter 824 can then produce generated (artificial) spectra 826 for Device 2 804 resembling the spectra expected to be produced by Device 2 804 if Device 2 804 had measured the samples 806.
• the artificial spectra 826 may then be fed to a chemometrics engine 814b to produce the chemometrics model 816b for Device 2 804.
• the chemometrics engines 814a and 814b may be combined into a single chemometrics engine.
• FIG. 9 is a diagram illustrating an example of a characteristics extractor 900 according to some aspects.
• the characteristics extractor 900 includes a plurality of stages (Stage 1 902, Stage 2 916, Stage 3 932, and Stage 4 944).
• Stage 1 902, Stage 2 916, Stage 3 932, and Stage 4 944 may include a combination of one or more of the shown stages.
• the characteristics extractor 900 may be configured to produce a plurality of spectral device characteristics, such as the characteristics shown in Table 1 below.
• the spectral converter 1700 is further fed by the raw spectral data 1704 of a plurality of samples (e.g., M samples) measured by a single spectral device (device i), along with the spectral device characteristics 1706 of device i. Based on the spectral device characteristics 1702 and 1706 and the spectral data 1704, the spectral converter 1700 can produce the artificial spectra 1708 that may be used to build a global chemometrics model based on the production line statistics. In this example, the artificial spectra 1708 generated by the spectral converter 1700 does not correspond in a one-to- one manner to any of the spectral devices produced on the production line. However, the artificial spectra 1708 does correspond to virtual spectral devices that span across the characteristics of the production line.
• the artificial spectra 1708 does not correspond in a one-to- one manner to any of the spectral devices produced on the production line. However, the artificial spectra 1708 does correspond to virtual spectral devices that span across
• the spectral statistical converter can add controlled variations on the spectral data (e.g., based on the statistical spectral device characteristics) to cover the whole space of variations expected from the production line of the spectral devices, denoted as M-+ to indicate the two steps (spectral correction and spectral conversion) applied.
• M-+ the two steps (spectral correction and spectral conversion) applied.
• the modulation on the D2 axis is small, since the M- devices are distributed on this axis.
• the modulation is double sided on the DI axis in the space.
• FIG. 25 is a diagram illustrating examples of optical head configuration generalization according to some examples.
• FIG. 25 illustrates an example of a spectral device 2500 including an optical head 2502 and a spectral sensor 2504 (e.g., light modulator and detector).
• the optical head 2502 may include a plurality of light sources 2506 configured to generate incident light that is directed towards a transparent window 2508 of the optical head 2502 to interact with a sample 2510 on the top surface of the transparent window 2508.
• An optical head configuration generalizer 2512 e.g., which may correspond, for example, to the optical head configurations processor 2402 shown in FIG. 24
• the optical head configuration generalizer 2512 may account for scanning/rotating mechanisms 2514 for rotating the sample 2510, different number of light sources 2502, different optical spot sizes, different sampling distances, or other variations in the optical head configuration.
• FIG. 26 is a diagram illustrating another example of spectral correction according to some aspects.
• a generalizer statistical spectral converter 2600 can be used to generate artificial spectra of many spectral devices from one or a subset of the spectral devices.
• the subset of spectral devices can be selected to be of high performance, with minimal wavelength error, minimal resolution error, minimal photometric error, high SNR and minimal baseline shifts and minimal thermal drift effects.
• the spectral data 2604 produced by the subset of spectral devices may first be corrected by a spectral corrector 2602 based on production line testing data 2606 to produce processed spectral data 2608.
• baseline variations may be added to absorbance of the spectral data.
• the spectral data may be multiplied by a thermal/environmental drift factor across wavelengths.
• sample interface back reflection spectra may be added to the spectral data.
• the spectral data may be multiplied with a ghost image/Etalon effect.
• optical path difference (OPD) errors may be applied to the spectral data.
• a set of apodization functions may be applied to the spectral data to account for self-apodization variations.
• noise across a spectral range corresponding to a SNR distribution may be applied to the spectral data.
• a spectral convolution processor 2806 extracts the interferograms from the input spectral data 2802 and multiplies the extracted interferograms by stored self-apodization functions 2804 that are based on spectral resolution variations 2808 (e.g., resolution changes) covering a range of resolution distributed across the plurality of artificial spectra.
• the spectral convolution processor 2806 may convolute the input spectral data 2802 with a set of apodization functions 2804 to produce the output artificial spectra (S o ) 2810.
• the self-apodization effect is generally wavelength dependent, so the spectral data 2802 can be convoluted with wavelength dependent apodization functions 2804 that represent the variation of self-apodization across the spectral range.
• the spectral converter 2800 may then choose an apodization functions set 2804 based on the resolution change values 2808 and multiply the extracted interferogram by the set of apodization functions 2804 to produce the artificial spectra (S o ) 2810 as follows:
• Wavelength error can vary from wavelength position to wavelength position along the spectrum, as shown in FIG. 29B, and therefore, is a non-linear trend that may happen due to errors in calibration of retardation or the off-axis movements of the moving mirror or other non-linear interference/diffraction effects.
• a higher order correction function can be used as follows:
• the output artificial spectra (So) 2908 can be generated through interpolation of the shifted spectra via spectral interpolation block 2904 onto the input wavenumber vector, as shown in FIG. 29C.
• FIGs. 30A-30C illustrate an example of generalization of spectral data based on SNR across the wavelength range according to some aspects.
• FIGs. 30A-30C may correspond to block 2720 shown in FIG. 27.
• FIG. 30A is a diagram illustrating a spectral converter 3000 for decreasing the SNR of the spectral data according to some aspects.
• the spectral converter 3000 can directly decrease the SNR with specific value(s) based on the spectral device characteristics.
• spectral devices may have different SNRs with mean SNR and standard deviation.
• Inputs to the spectral converter 3000 may include the input spectral data Si 3004 to be processed, the SNR of the subset of spectral devices and new (target) SNR(s) (mean and standard deviation), which may correspond to the spectral device characteristics.
• the noise standard deviation 3006 can then be calculated as:
• the noise standard deviation can then be added to the input spectral data Si 3004 by addition block 3002 to produce output artificial spectra So 3008 as follows: s No ise
• PSDsackground covering device to device variations can be used to scale the SNR across wavelength.
• FIG. 30C added noise with different PSDs shaping scaling produces different SNR at short wavelengths using different apodization functions.
• FIGs. 31A-31C illustrate an example of generalization of spectral data based on absorbance scaling according to some aspects.
• FIGs. 31A-31C may correspond to block 2706 shown in FIG. 27.
• FIG. 31A is a diagram illustrating a spectral converter 3100 for emulating the variations resulted from penetration depth variations associated with sample to spectral device spacing variations, sample heterogeneity nature, and sampling accessory/method variations, each of which may correspond to spectral device characteristics.
• the spectral converter 3100 may include an absorbance scaling block 3102 configured to scale the whole absorbance spectrum 3104, as shown in FIG. 31C, by a wavelength dependent scaling factor SF(A) 3106, such that the output absorbance spectrum A o 3108 is given by:
• the spectral converter 3100 may scale absorption line intensities by first subtracting the baseline A i BL through a baseline extraction method performed by baseline subtraction block 3110, adjusted and optimized to the spectra under analysis, and then adding the baseline A i BL back by a baseline addition block 3112 after scaling.
• Baseline extraction methods may include, for example, moving average detection techniques, partial least squares PLS-based techniques, wavelet transform techniques, etc.
• the output absorbance spectrum 3108 is generated from the input absorbance spectrum 3104 as:
• FIGs. 33A and 33B illustrate an example of generalization of spectral data based on baseline shift according to some aspects.
• FIGs. 33A and 33B may correspond to block 2708 shown in FIG. 27.
• FIG. 33A is a diagram illustrating a spectral converter 3300 including an addition block 3306 for adding baseline variations 3302 to input absorbance Ai 3304 in different shapes, as shown in FIG. 33B to produce output absorbance A o 3308 according to some aspects.
• the spectral converter 3300 may add random baseline variations with mean and a standard deviation that scale with wavelength, multiplicative baseline errors that have a higher effect on absorbance peaks, and/or offset baseline variations with mean and a standard deviation.
• Inputs to the spectral converter 3300 include the spectrums Si to be processed and various spectral device characteristics, such as baseline absorbance shift E, scaled absorbance error with wavelength, and multiplicative baseline errors that may happens due to samples inhomogeneity E scattrin g .
• the output (artificial) spectrum S o can be generated from the input spectrum Si as follows:
• FIGs. 34A-34C illustrate an example of generalization of spectral data based on sample interface effects (e.g., window back reflection and Etalon fringe) according to some aspects.
• FIGs. 34A-34C may correspond to blocks 2712 and 2714 shown in FIG. 27.
• FIG. 34A is a diagram illustrating a spectral converter 3400 for emulating sample interface effects according to some aspects.
• Sample interface effects may include, for example, spectral device window back reflection (e.g., the transmission coefficient never reaches unity and there is always finite reflection from the window that adds to the measured spectrum), as shown in FIGs. 34B and 34C, and/or Etalon fringes associated with samples inside of a container or bag, as shown in FIG. 34D.
• the spectral converter 3400 can include an addition block 3406 configured to add back reflection spectra 3404 to the input spectral data Si 3402 to generate the artificial spectra S o 3412.
• the back reflection effect can be applied to the input reflectance spectrum to generate the output reflectance spectrum as: where r BR is the backreflection ratio of the background spectrum.
• the spectral converter 3400 may further include a multiplication block 3410 configured to multiply an Etalon (Fabry Perot) effect 3408 (e.g., air gap/reference coefficients) to the spectral data S[ 3402 to generate the artificial spectra S o 3412.
• Etalon effect can be further applied as:
• FIG. 35 is a graph illustrating an example of high-reflectance background reference materials variations that may also to be fed to the spectral converter to decrease its susceptibility to such variations in the spectral device characteristics (e.g., generated based on measurements of background reference materials) according to some aspects.
• an artificial spectrum S art f can be generated from a measured spectrum Smeas using the background material reflectance variations Rv (A) as follows:
• Multiple artificial spectra can be generated from one measured spectrum using a set of Rv (A) spectral variations that covers the variations of the background reference material.
• Rv (A) spectral variations that covers the variations of the background reference material.
• the maximum movement distance of the movable mirror or what is known as full travel range FTR is the factor that controls the resolution of the spectrometer.
• the maximum optical path difference OPD of an interferometer is twice the FTR. Mirrors are moved using actuators and actuators have variations. Hence, the maximum OPD is not constant across the different spectrometers. The differences in maximum OPD will not only introduce differences in resolution and wavelength accuracy, but will also introduce differences in the device line shape function and, hence, change line shape ripples position and amplitude.
• FIGs. 36A-36C illustrate the effect of changing the OPD by ⁇ 1 pm on the spectra of a wavelength reference material.
• the spectral converter may further apply an OPD variations effect (e.g., OPD errors) such that a boxcar window W/ BC (OPD) of variable size can be applied to the measured interferogram as:
• OPD boxcar window W/ BC
• Mirror positioning is usually associated with measurement and post processing errors, which lead to OPD error that consequently affects the spectral accuracy.
• the moveable mirror is driven by a comb drive actuator, where the mirror position is measured by capacitive sensing technique.
• the capacitance to OPD relation is measured on the production line within the calibration flow of each spectrometer unit.
• there is still a calibration residual error that changes from unit-to-unit.
• a residual delay can exist between the measured detector signal versus time and the corresponding capacitance (consequently OPD) signal, which adds to the OPD errors.
• Such OPD errors can be applied on the measured spectral data to generate artificial spectra that covers these errors and variations from unit-to-unit, such that the artificial units OPD, OPD art f, is a function of the actual unit OPD, 0PD actua i
• FIGs. 37A and 37B illustrate examples of calibration transfer permutation trees according to some aspects.
• the calibration transfer function can be seen as an error extraction method between different spectral devices.
• the calibration transfer model extracts the error function between a first spectral device and a second spectral device using a few samples. After that, the error function can be used to transfer any other samples spectra measured by the second spectral device to be similar to samples measured by the first spectral device.
• the extracted transfer function can be applied to other spectral devices to generate new artificial spectral devices. If every two spectral devices are permuted therebetween, they can generate two calibration transfer functions. In this manner, an N number of spectral devices can generate more spectral devices according to the following equation:
• FIG. 37A illustrates an example of a one-level tree, where six artificial spectral devices 3702 are generated from three original spectral devices.
• FIG. 37B illustrates an example of an extended one-level tree, where twelve artificial spectral devices 3702 are generated from three original spectral devices 3700.
• the generated artificial spectral devices 3702 have a naturalistic instrumental error, not a calculated or emulated error.
• the generated transfer function for different materials and applications can be stored in a library, to be accessible to different users to transfer and generalize their measured samples.
• the transfer functions can be specific to some materials or they can be generally used to transfer any material measured by a single spectral device to emulate measuring it by many spectral devices.
• the spectral converter may access the library of pre-calculated stored transfer functions and select which transfer functions should be applied based on the spectral device characteristics.
• Adding variations that do not exist to the spectral data used for building chemometrics models may weaken the chemometrics models.
• optimization of the generalizer spectral converter parameters for general purposes or for specific applications may be performed. This can be done by integrating the generalizer spectral converter with a chemometrics engine to select the generalizer spectral effects (based on spectral device characteristics), order them according to impact, and tune their parameters according to the chemometric model’s performance.
• FIG. 38 is an example of a process flow 3800 for optimizing the spectral device characteristics according to some aspects.
• parameters of the spectral device characteristics 3802 e.g., statistical parameters, such as standard deviation and mean values for the different characteristics
• the optimized parameters may then be used to generate of the plurality of artificial spectra.
• the generated artificial spectra may then be passed to the chemometrics engine 3806, which may then calculate the standard error of prediction (SEP) and their prediction bias (results reporting 3808) based on sample measurements of testing spectral devices 3812.
• SEP standard error of prediction
• results reporting 3808 prediction bias
• the results 3808 may then be compared against target values at block 3814, and if the results 3808 are less than the target values, the parameters of the characteristics 3802 may be optimized based on the feedback from block 3814. Different parameters may be tested to optimize the prediction performance of different test spectral devices with respect to the subset of spectral devices.
• FIG. 39 illustrates an example of alteration of the distribution of the number of spectra versus the value of the analyte in the sample.
• the histogram on the left shows the distribution of the spectra collected using a sample measurement spectral device (e.g., one of the subset of spectral devices) versus the protein value in a feed sample.
• the peak of the histogram is y2 at a value of protein x2 in the center of the protein range, while the minimum of the histogram is yl at the extremes of the protein ranges.
• N virtual spectral devices are intended to be produced by the spectral converter, then for each spectrum measured on the sample measurement spectral device, N spectra are produced.
• the new histogram will have a of N multiplied by y2 while the minimum of the histogram is N multiplied by yl, as shown in the middle histogram. Therefore, the difference between the maximum and minimum is magnified by a factor of N. This may cause biasing of the chemometrics model towards improving the accuracy of prediction around x2 values of the sample, while degrading the prediction towards the extremes of the protein range. Therefore, the data augmentation by the spectral converter can take into account such effects.
• the spectra coming from the subset of spectral devices corresponding to the peak of the histogram can be augmented by a factor of N repressing N remaining spectral devices of the plurality of spectral devices on the production line while the spectra coming from the subset of spectral devices corresponding to the edge of the histogram can be augmented by a factor of M repressing M remaining spectral devices, where M is larger than N, as shown in the histogram on the right.
• the chemometrics model calibration may undergo several steps of adjustments and refinements to optimize the model performance across all different situations.
• FIG. 40 is a process flow illustrating an example of optimization of the chemometrics model according to some aspects.
• optimization is based on the selection of the number of latent variables related to regression algorithms used to produce the chemometrics model.
• the number of latent variables is set to an initial value, for example 1.
• the model is applied, and at block 4006, the cost function is calculated, for example the cross validation (CV) RMSE.
• the bias is also calculated by evaluating the model on remaining (test) spectral devices of the plurality of spectral devices.
• a number of samples (N) 5304 of varying conditions and constituents’ concentrations are collected and prepared for measurement.
• Spectral measurements of the N samples are performed at block 5306 using M spectral devices (the main kit) corresponding to a subset of the plurality of spectral devices to produce a main spectral dataset 5308.
• M spectral devices the main kit
• Each of the M spectral devices measures a different set of samples.
• Each spectral device measures a set of samples with high distribution across the range of the parameters of interest (for example, samples with varying protein values across the full range of protein for this material).
• the samples are referenced using wet chemistry or NIR benchtop devices.
• the sample condition should be maintained during spectral measurement on the spectral devices and during referencing to ensure consistency.
• a subset of the samples (e.g., 20 samples) 5312 are further measured by at least one of the M spectral devices and preferably measured by multiple of the M spectral devices for extra data augmentation.
• the subset of samples 5312 shall be preserved and sealed carefully and shipped to the closest development center to developer/customer location. References for these samples shall be provided.
• Development centers are dedicated for model augmentation, validation and maintenance.
• the subset of samples 5312 received from the model developer is measured by a development kit composed of a larger number of spectral devices (D spectral devices, where D > M and D ⁇ T, where T it the total number of spectral devices) to cover more regions on the space of variations.
• the generated dataset is referred to as the development dataset 5316 and is used to augment the main dataset 5308 to introduce more data to the built model.
• the main dataset 5308 and the development dataset 5316 are merged at block 5320 forming a high coverage dataset 5322.
• the merged dataset 5322 is used to generate an initial model to check for any outlier readings.
• the cleaned dataset is fed to a generalizer module 5324.
• the generalizer module 5324 generates a plurality of artificial spectra from the cleaned dataset and spectral device characteristics of the production line (T) spectral devices.
• the artificial spectra represent virtual spectral devices that map to the production line distribution of spectral devices.
• the output of the generalizer module 5324 is an augmented dataset containing the merged dataset and artificial data.
• Model developing is performed at block 5326 on the final generalized dataset resulting in a scalable model that performs uniformly on any new spectral device.
• spectral device out cross validation may be performed at block 5328 during model building to optimize the model parameters such that they best perform on new spectral devices.
• FIG. 54 is a block diagram illustrating an example of a hardware implementation for a computing device 5400 employing a processing system 5414 according to some aspects.
• the computing device 5400 may correspond to a personal computer, server, handheld device (e.g., cell phone or tablet), cloud-based device, or any other suitable computing device.
• the computing device 5400 may be implemented with a processing system 5414 that includes one or more processors 5404.
• processors 5404 include microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure.
• DSPs digital signal processors
• FPGAs field programmable gate arrays
• PLDs programmable logic devices
• state machines gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure.
• the computing device 5400 may be configured to perform any one or more of the functions described herein. That is, the processor 5404, as utilized in the computing device 5400, may be used to implement any one or more of the processes and procedures described herein.
• the processing system 5414 may be distributed among various entities, which may be coupled via a direct or indirect connection (e.g., wired or wireless).
• the processor 5404 may in some instances be implemented via a baseband or modem chip and in other implementations, the processor 5404 may include a number of devices distinct and different from a baseband or modem chip (e.g., in such scenarios as may work in concert to achieve examples discussed herein). And as mentioned above, various hardware arrangements and components outside of a baseband modem processor can be used in implementations, including RF-chains, power amplifiers, modulators, buffers, interleavers, adders/summers, etc.
• the processing system 5414 may be implemented with a bus architecture, represented generally by the bus 5402.
• the bus 5402 may include any number of interconnecting buses and bridges depending on the specific application of the processing system 5414 and the overall design constraints.
• the bus 5402 links together various circuits including one or more processors (represented generally by the processor 5404), a memory 5405, and computer-readable media (represented generally by the computer-readable medium 5406).
• the bus 5402 may also link various other circuits such as timing sources, peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further.
• a bus interface 5408 provides an interface between the bus 5402, a network interface 5410, and a power source 5432.
• the network interface 5410 provides a means for communicating with various other apparatus over a transmission medium (e.g., wireline or wireless)
• the power source 5432 provides a means for supplying power to various components in the computing device 5400.
• a user interface 5412 e.g., keypad, display, touch screen, speaker, microphone, control knobs, etc.
• a user interface 5412 may also be provided. Of course, such a user interface 5412 is optional, and may be omitted in some examples.
• the processor 5404 is responsible for managing the bus 5402 and general processing, including the execution of software stored on the computer-readable medium 5406.
• Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.
• the software when executed by the processor 5404, causes the processing system 5414 to perform the various functions described below for any particular apparatus.
• the computer-readable medium 5406 and the memory 5405 may also be used for storing data that is utilized by the processor 5404 when executing software.
• the memory 5405 may store one or more of spectral data 5416, spectral device characteristics 5418, and/or a chemometrics model 5420.
• the computer-readable medium 5406 may be a non-transitory computer-readable medium.
• a non-transitory computer-readable medium includes, by way of example, a magnetic storage device (e.g., hard disk, floppy disk, magnetic strip), an optical disk (e.g., a compact disc (CD) or a digital versatile disc (DVD)), a smart card, a flash memory device (e.g., a card, a stick, or a key drive), a random access memory (RAM), a read only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), a register, a removable disk, and any other suitable medium for storing software and/or instructions that may be accessed and read by a computer.
• a magnetic storage device e.g., hard disk, floppy disk, magnetic strip
• an optical disk e.g., a compact disc (CD) or a digital versatile disc (DVD
• the computer-readable medium 5406 may reside in the processing system 5414, external to the processing system 5414, or distributed across multiple entities including the processing system 5414.
• the computer-readable medium 5406 may be embodied in a computer program product.
• a computer program product may include a computer-readable medium in packaging materials.
• the computer-readable medium 5406 may be part of the memory 5405.
• the processor 5404 may include circuitry configured for various functions.
• the processor 5404 may include characteristics extractor circuitry 5442, configured to generate spectral device characteristics 5418 representing spectral variations in a plurality of spectral devices (e.g., that form a production line).
• the spectral device characteristics 5418 may include at least one of signal-to-noise ratio (SNR), wavelength repeatability, wavelength error, absorbance scaling, self-apodization function, baseline shift, back reflection, thermal drift, environmental drift, optical path difference (OPD) variation, or Etalon effect.
• SNR signal-to-noise ratio
• OPD optical path difference
• the characteristics extractor circuitry 5442 may be configured to receive background spectra from at least one spectral device using a reference tile or transmission sampling accessory and to extract the SNR based on the background spectra. In some examples, the characteristics extractor circuitry 5442 may be configured to receive measured spectra from at least one spectral device measured using a wavelength reference material and to extract at least one of the wavelength repeatability or the wavelength error based on the measured spectra. In some examples, the characteristics extractor circuitry 5442 may be configured to receive at least one interferogram from at least one spectral device measured using a narrowband optical filter and to extract the self-apodization function based on the at least one interferogram. In some examples, the characteristics extractor circuitry 5442 may be configured to receive measured spectra from at least one spectral device of the remaining spectral devices measured with variable temperature and to extract the thermal drift based on the measured spectra.
• the characteristics extractor circuitry 5442 may be configured to receive measured spectra of universal samples different than the plurality of samples from at least a portion of the plurality of spectral devices and to extract the spectral device characteristics of the plurality of spectral devices using measured spectra.
• the portion includes all of the plurality of spectral devices.
• the portion includes selected spectral devices of the plurality of spectral devices having corresponding spectral device characteristics covering a space of variations including corners of production line characteristics of the production line.
• the characteristics extractor circuitry 5442 may be configured to generate the spectral device characteristics 5418 based on statistical information related to the production line.
• the statistical information may include various statistical parameters, such as the mean value, standard deviation, skewness, or kurtosis, and a probability distribution (histogram) of each of the statistical parameters.
• the characteristics extractor circuitry 5442 may further be configured to execute characteristics extractor instructions (software) 5452 stored in the computer-readable medium 5406 to implement one or more of the functions described herein.
• the processor 5404 may further include spectral converter circuitry 5444, configured to receive spectral data 5416 of a plurality of samples from a subset of a plurality of spectral devices and to further receive the spectral device characteristics 5418 representing spectral variations in the plurality of spectral devices.
• the spectral converter circuitry 5444 may further be configured to generate a plurality of artificial spectra representing remaining spectral devices of the plurality of spectral devices based on the spectral data 5416 and the spectral device characteristics 5418.
• the spectral data 5416 includes measurements of phantom samples corresponding to the plurality of samples, where each of the phantom samples includes a stable substance having a same absorbance spectra as one of the one or more samples.
• the spectral converter circuitry 5444 may be configured to apply a spectral variance function to the spectral data to produce processed spectral data representative of variances in the subset of the plurality of spectral devices. In some examples, the spectral converter circuitry 5444 may be configured to apply a spectral correction function to the spectral data to produce processed spectral data that removes uncontrolled variances in the subset of the plurality of spectral devices. In some examples, the spectral converter circuitry 5444 may be configured to apply a spectral modulation and perturbation function to the spectral data to produce processed spectral data spanning different levels of aging and environmental conditions variations. In some examples, the spectral converter circuitry 5444 may be configured to apply an optical head variance function to the spectral data to produce processed spectral data that accounts for different optical head configurations in the subset of the plurality of spectral devices.
• the spectral converter circuitry 5444 may be configured to apply a set of apodization functions to the spectral data (or the processed spectral data) to produce the plurality of artificial spectra. In some examples, the spectral converter circuitry 5444 may be configured to add wavelength errors to the spectral data (or the processed spectral data) to produce the plurality of artificial data. In some examples, the spectral converter circuitry 5444 may be configured to add noise across a spectral range corresponding to a signal-to-noise ratio (SNR) distribution to the spectral data (or the processed spectral data) to produce the plurality of artificial spectra.
• SNR signal-to-noise ratio
• the spectral converter circuitry 5444 may be configured to scale an absorbance spectrum of the spectral data (or the processed spectral data) using a wavelength dependent scaling factor to produce the plurality of artificial spectra. In some examples, the spectral converter circuitry 5444 may be configured to multiply the spectral data (or processed spectral data) by a thermal drift factor across wavelength to produce the plurality of artificial spectra.
• the spectral converter circuitry 5444 may be configured to add baseline variations to absorbance of the spectral data (or processed spectral data) to produce the plurality of artificial spectra. In some examples, the spectral converter circuitry 5444 may be configured to add back reflection spectra to the spectral data (or processed spectral data) to produce the plurality of artificial spectra. In some examples, the spectral converter circuitry 5444 may be configured to multiply an Etalon effect to the spectral data (or the processed spectral data) to produce the plurality of artificial spectra.
• the spectral converter circuitry 5444 may be configured to multiply background material reflectance variations associated with background materials used to produce the spectral device characteristics to the spectral data to produce the plurality of artificial spectra. In some examples, the spectral converter circuitry 5444 may be configured to apply optical path difference (OPD) errors to the spectral data to produce the plurality of artificial spectra.
• OPD optical path difference
• the spectral converter circuitry 5444 may be configured to optimize the spectral device characteristics based on measured values from test spectral devices. In some examples, the spectral converter circuitry 5444 may be configured to alter a distribution of the plurality of artificial spectra with respect to a corresponding measured value of the plurality of samples. In some examples, the spectral converter circuitry 5444 may be configured to extract difference spectra between the plurality of artificial spectra and the spectral data, where the difference spectra corresponds to clutter signals indicative of device variations between the plurality of devices. The spectral converter circuitry 5444 may then be configured to filter the clutter signals from the spectral data and the plurality of artificial data to produce processed spectral data used to generate the chemometrics model.
• the spectral converter circuitry 5444 may be configured to receive a development dataset of a subset of the plurality of samples measured by a development kit including an additional subset of the plurality of spectral devices larger than the subset of the plurality of spectral devices. The spectral converter circuitry 5444 may then be configured to merge the spectral data and the development dataset to produce a merged dataset and to use the merged dataset to generate the plurality of artificial spectra.
• the spectral converter circuitry 5444 may be configured to access a library of pre-calculated stored transfer functions, select one or more selected transfer functions of the pre-calculated stored transfer functions based on the spectral device characteristics, and use the one or more selected transfer functions to generate the artificial spectra.
• the spectral converter circuitry 5444 may further be configured to extract difference spectra between the plurality of artificial spectra and the spectral data, where the difference spectra corresponds to a repeatability file indicative of device variations between the plurality of devices.
• the spectral converter circuitry 5444 may further be configured to execute spectral converter instructions (software) 5454 stored in the computer-readable medium 5406 to implement one or more of the functions described herein.
• the processor 5404 may further include chemometrics engine circuitry 5446 configured to produce a chemometrics model for one or more parameters associated with the plurality of samples based on the spectral data and the plurality of artificial spectra.
• the chemometrics engine circuitry 5446 may be configured to select the subset of the plurality of spectral devices, select one or more wavelength ranges for the spectral data, removing fluctuations in the spectral data resulting from improper measurement or variations in the subset of the plurality of spectral devices, and train the chemometrics model based on the spectral data and the plurality of artificial spectra.
• the chemometrics engine circuitry 5446 may be configured to adjust the chemometrics model using additional spectral data from deviant spectral devices of the plurality of spectral devices that deviate in performance from regular spectral devices of the plurality of spectral devices. In some examples, the chemometrics engine circuitry 5446 may be configured to optimize a number of latent variables used to produce the chemometrics model to minimize a bias between test spectral devices of the remaining spectral devices and produce a root mean squared error within a specified range from a target minimum value.
• the chemometrics engine circuitry 5446 may be configured to identify a unified spectral dataset for the subset of spectral devices based on the spectral data by projecting the spectral data onto a space that is uncorrelated with a subspace of spectral device specification discrepancies. In some examples, the chemometrics engine circuitry 5446 may be configured to form a matrix describing discrepancies between the subset of the plurality of spectral devices for each measurement in the spectral data and to apply a conditional dimensionality reduction on the sensor data using the matrix.
• the chemometrics engine circuitry 5446 may be configured to calibrate additional spectral devices using the phantom samples and the chemometrics model. In some examples, the chemometrics engine circuitry 5446 may be configured to generate a transfer function using a set of samples measured on one or more of the plurality of spectral devices and a different spectral device comprising a different configuration than any of the plurality of spectral devices. The chemometrics engine circuitry 5446 may then be configured to generalize the chemometrics model to include the different spectral device based on the transfer function.
• the chemometrics engine circuitry 5446 may be configured to producing the chemometrics model for the one or more samples based on the spectral data, the plurality of artificial spectra, and additional spectral device characteristics of the subset of the plurality of spectral devices. In some examples, the chemometrics engine circuitry 5446 may be configured to receive a sample measurement of a sample under test from a test spectral device of the plurality of test devices, where the sample under test corresponding to one of the one or more samples, receive test spectral device characteristics of the test spectral device and generate a result using the chemometrics model, the sample measurement, and the test spectral device characteristics.
• the chemometrics engine circuitry 5446 may be a cloud-based artificial intelligence engine configured to store the chemometrics model and test spectral device characteristics and other test spectral device characteristics of other test spectral devices of the plurality of spectral devices.
• the chemometrics model 5420 may be a cloud-based chemometrics model accessible to the plurality of spectral devices.
• the chemometrics engine circuitry 5446 may be configured to use the repeatability file together with corresponding zero reference values to generate the chemometrics model.
• the chemometrics engine circuitry 5446 may further be configured to execute chemometrics engine instructions (software) 5456 stored in the computer-readable medium 5406 to implement one or more of the functions described herein.
• FIG. 55 is a flow chart illustrating an exemplary process 5500 for producing a generalized chemometrics model according to some aspects. As described below, some or all illustrated features may be omitted in a particular implementation within the scope of the present disclosure, and some illustrated features may not be required for implementation of all embodiments.
• the process 5500 may be carried out by the computing device 5400 illustrated in FIG. 54 or a spectral modeling system including one or more computing devices. In some examples, the process 5500 may be carried out by any suitable apparatus or means for carrying out the functions or algorithm described below.
• the spectral modeling system may receive spectral data of a plurality of samples from a subset of a plurality of spectral devices.
• the spectral modeling system may receive spectral device characteristics representing spectral variations in the plurality of spectral devices.
• the spectral modeling system may generate a plurality of artificial spectra representing remaining spectral devices of the plurality of spectral devices based on the spectral data and the spectral device characteristics.
• the spectral modeling system may produce a chemometrics model for one or more parameters associated with the plurality of samples based on the spectral data and the plurality of artificial spectra.
• the word “exemplary” is used to mean “serving as an example, instance, or illustration.” Any implementation or aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects of the disclosure. Likewise, the term “aspects” does not require that all aspects of the disclosure include the discussed feature, advantage or mode of operation.
• the term “coupled” is used herein to refer to the direct or indirect coupling between two objects. For example, if object A physically touches object B, and object B touches object C, then objects A and C may still be considered coupled to one another — even if they do not directly physically touch each other. For instance, a first object may be coupled to a second object even though the first object is never directly physically in contact with the second object.
• circuit and “circuitry” are used broadly, and intended to include both hardware implementations of electrical devices and conductors that, when connected and configured, enable the performance of the functions described in the present disclosure, without limitation as to the type of electronic circuits, as well as software implementations of information and instructions that, when executed by a processor, enable the performance of the functions described in the present disclosure.
• One or more of the components, steps, features and/or functions illustrated in FIGs. 1-55 may be rearranged and/or combined into a single component, step, feature or function or embodied in several components, steps, or functions. Additional elements, components, steps, and/or functions may also be added without departing from novel features disclosed herein.
• the apparatus, devices, and/or components illustrated in FIGs. 1-54 may be configured to perform one or more of the methods, features, or steps described herein.
• the novel algorithms described herein may also be efficiently implemented in software and/or embedded in hardware.
• “at least one of: a, b, or c” is intended to cover: a; b; c; a and b; a and c; b and c; and a, b and c.
• All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims.
• nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. ⁇ 112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.”

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