WO2018090142A1 - Procédé et système de traitement de données destinés à des analyseurs spectrophotométriques distribués - Google Patents

Procédé et système de traitement de données destinés à des analyseurs spectrophotométriques distribués Download PDF

Info

Publication number
WO2018090142A1
WO2018090142A1 PCT/CA2017/051374 CA2017051374W WO2018090142A1 WO 2018090142 A1 WO2018090142 A1 WO 2018090142A1 CA 2017051374 W CA2017051374 W CA 2017051374W WO 2018090142 A1 WO2018090142 A1 WO 2018090142A1
Authority
WO
WIPO (PCT)
Prior art keywords
spectrum
estimating
calibrating
data
sample
Prior art date
Application number
PCT/CA2017/051374
Other languages
English (en)
Inventor
Andrzej Barwicz
Roman Z. Morawski
Original Assignee
Andrzej Barwicz
Morawski Roman Z
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Andrzej Barwicz, Morawski Roman Z filed Critical Andrzej Barwicz
Publication of WO2018090142A1 publication Critical patent/WO2018090142A1/fr

Links

Classifications

    • 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
    • G01J3/02—Details
    • G01J3/0205—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows
    • 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
    • G01J3/02—Details
    • G01J3/0205—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows
    • G01J3/0218—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows using optical fibers
    • 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
    • G01J3/02—Details
    • G01J3/0256—Compact construction
    • 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
    • G01J3/18—Generating the spectrum; Monochromators using diffraction elements, e.g. grating
    • G01J3/1809—Echelle gratings

Definitions

  • the following relates to methods and systems for implementing data processing, for distributed spectrophotometric analyzers.
  • the following relates to measuring optical spectra and spectrum-related quantities, and more specifically to a method of enhancing spectral resolution and/or reducing uncertainty of such measurements.
  • spectrophotometric data are of increasing importance for analytical laboratories, as well as for environmental, biomedical and industrial monitoring.
  • Spectrophotometry is becoming increasingly a method of choice not only in qualitative and quantitative analyses of (bio)chemical substances, but also in identification of physical properties of various objects and their classification. To illustrate the diversity of actual and potential applications of spectrophotometry consider the following examples:
  • the French manufacturer ALPhANOV provides a compact spectrophotometer GoSpectro which is compatible with most smartphones and tablets. It is operating in the spectral range of 400-750 nm, and its spectral resolution is ca. 10 nm (Ref.
  • a resolution of 10 nm in this range is found to limit the application of such a device.
  • pharmacological applications are known to require about 1 nm resolution.
  • the Californian company Eigen Imaging Inc. provides a series of smartphone
  • spectrophotometers operating in the UV-Vis-NIR spectral ranges (Ref. [2]).
  • the MIT Media Lab from the Massachusetts Institute of Technology, has demonstrated a smartphone- based spectrophotometer operating in the spectral range of 340-780 nm, with a spectral resolution of ca. 15 nm (Ref. [3]).
  • Interdisciplinary Photonics Lab from The University of Sydney, has demonstrated a low-cost, optical-fiber-based spectrometer developed on a smartphone platform for field-portable spectral analysis. This solution is said to operate in the spectral range of 400-700 nm, with a spectral resolution of ca. 0.42 nm/screen pixel (Ref. [4]).
  • Spectrophotometry is an analytic technique concerned with the measurement and characterization of the interaction of radiant energy with matter.
  • the distribution of radiant energy, absorbed or emitted by a sample of a substance under study, is called its spectrum. If energy of ultraviolet (UV), visible (Vis) or infrared (IR) light is used, the corresponding spectrum is called a light-spectrum.
  • UV ultraviolet
  • Vis visible
  • IR infrared
  • a spectrophotometer has a resolution associated with its design or
  • a required resolution for UV and a required resolution for IR spectral imaging is different.
  • high-resolution and low-resolution are related to an imaged spectral band or to wavelengths of light within the imaged band.
  • a broadband spectrophotometer either graduated spectral resolution or a spectral resolution sufficient to properly image each band is used.
  • spectrophotometers whose external function is to provide information (chemical or biochemical) on a selected group of substances rather than their spectra. Those instruments are called spectrophotometric analyzers. Analyzers of blood glucose, analyzers of ambient air, and analyzers of chimney pollutions are just a few examples of such spectrophotometric analyzers. [0023] Consider, for example, an overview of food analyzers. Spectrophotometric analyzers for grain and fruits, milk and beer, chocolate and cheese, etc., are today manufactured by numerous companies all over the world.
  • U.S. Patent No. 9,587,982 discloses a compact spectrometer that is suitable for use in mobile devices such as cellular telephones.
  • the device can include a filter, at least one Fourier transform focusing element, a micro-lens array, and a detector, but does not use any dispersive elements.
  • Methods for using the spectrometer for performing on site determination of food quality by comparison with an updatable database are also disclosed.
  • U.S. Publication No. 2016/0109371 discloses a portable spectrometer system for more reliable and convenient on-site drug testing. It comprises a test strip having a fluorescent indicator, a fluorimeter, and a mobile computing device capable of determining the identity of an unknown substance in the sample.
  • U.S. Publication No. 2017/0160131 discloses a hand-held spectrometer operating in a reflectance mode. It is coupled to a database of spectral information that can be used to determine the attributes of the object under analysis.
  • the communication device coupled with the spectrometer, enables its user(s) to receive data related to that object.
  • a compact spectrometer to be integrated into a cellular phone or attached to a cellular phone, is disclosed.
  • the cellular phone supplies electrical power to the spectrometer, provides data storage and processing capability, as well as real-time display.
  • a system composed of the spectrometer and cellular phone allows real-time in-field spectroscopic measurements whose results can be sent out in wireless communication to a remote station or to another cellular phone user; it can fulfill many daily routine tasks encountered by ordinary civilians, for example, the blood glucose monitoring for diabetes patients.
  • U.S. Patent No. 7,420,663 discloses a spectroscopic sensor that is integrated with a mobile communication device. It is capable of measuring the optical spectra of a physical object for purposes of detection, identification, authentication, and real-time monitoring. Through the mobile communication device, the obtained spectral information can be transmitted, distributed, collected, and shared via existing wireless communication networks.
  • U.S. Patent No. 9,217,706 a handheld infrared spectroscopy device and method of its use are disclosed.
  • the device may be integrated with a mobile phone or another device that is portable and capable of performing applications.
  • the device performs infrared spectra analysis on liquid samples, allowing both portability as well as highly sophisticated and specific spectral analysis of liquid samples.
  • the device has wireless communication capability enabling transmission of data across the globe.
  • U.S. Patent No. 9,360,366 discloses a self-referencing spectrometer that simultaneously auto-calibrates and measures optical spectra of physical objects. Its integration with a mobile computing device enables the distribution of obtained spectral information through the wireless communication networks. [0034] In U.S. Publication No. 2015/0085279, systems and methods for measuring spectra (and other optical characteristics such as color, translucence, gloss, etc.) of skin and similar materials) is disclosed. An abridged spectrophotometer with improved
  • U.S. Publication No. 2016/0146726 discloses a wearable spectrometer for analyzing a chemical composition of solid, liquid and gaseous substances. While these examples illustrate a move towards providing portable and miniaturized spectro- analytics, none of these examples provides the above-described ability to maximize the benefits resulting from coupling a spectrophotometer with a mobile device and a
  • a method of performing a spectrophotometric analysis comprising: calibrating a measurement channel, the calibrating oriented on measuring optical spectra, using a low resolution spectrophotometric sensor (LRSS); and estimating an optical spectrum of a sample based on data acquired by the LRSS and results of the calibrating; wherein the calibrating and estimating are performed using one or both of a mobile application in an electronic device coupled to the LRSS, and an advanced program residing on a computing service accessible via a network by the electronic device.
  • LRSS low resolution spectrophotometric sensor
  • a computer readable medium comprising computer executable instructions for performing the method.
  • spectrophotometric analyses the system comprising: a low resolution spectrophotometric sensor (LRSS) coupled to an electronic device, the electronic device comprising a network connection and hosting a mobile application; and an advanced program hosted by a computing service accessible to the electronic device, the advanced program in
  • LRSS low resolution spectrophotometric sensor
  • FIG. 1 A is an example of a distributed system for spectrophotometric analysis
  • FIG. 1 B is another example of a distributed system for spectrophotometric analysis
  • FIG. 1 C is yet another example of a distributed system for spectrophotometric analysis
  • FIG. 2 is a flow chart illustrating operations executed in performing a
  • FIG. 3 is an example of spectra and spectral data illustrating the effectiveness of the correction of the imperfections of the LRSS
  • FIG. 4 is a flow diagram for a light-spectrum measuring instrument
  • FIG. 5 is an illustration of a measurement principle underlying an intelligent spectrophotometric transducer/sensor (IISS/T);
  • FIG. 6 is an illustration of practical gains achieved using the structure in FIG. 5;
  • FIG. 7 is a generic structure of the IISS/T
  • FIG. 8 is an illustration of the effectiveness of correction of imperfections of the spectrophotometric transducer, using a specialized digital signal processor
  • FIG. 9 is a simplified diagram of a spectrophotometric apparatus with a computing component in the form of a microprocessor, such as a digital signal processor; and
  • FIGS. 10a through 10d are flow diagrams illustrating exemplary steps for performing a spectrophotometric calibration and analysis using the structures described herein.
  • the following provides methods and systems that can apply techniques such as these to an implementation within a distributed and mobile computing platform, to allow for distributed spectrophotometric analyzers.
  • the implementations described herein can enable the remote and automatic calibration of the system using mobile access to data storage and computer resources (e.g. , in a cloud-based service); and selection of an adequate mathematical model of the particular sensor being used, from a library of models, accessible via mobile communication capabilities.
  • the method of digital signal processing can enable the circumvention of physical limitations of losing spectral performance with miniaturization, enabling the system to overcome the contradiction between requirements for the sensor specifications resulting from the needs of the applications, and physical limitations of spectrum processing related to small dimensions.
  • the system provides both a system of processing functions that enable one to enhance the performance of the final results (spectrum or spectrum-based analyses), and a physical distributed system required for distributed implementation of the above-mentioned processing, using spectrophotometric analyzers. This combination of processing and implementation enables the reduction in uncertainty of the results of spectrophotometric analyses in a synergistic manner.
  • the compensation of the hardware limitations of low-cost miniature spectrophotometers, with algorithms for data processing can now be performed not only by computing means integrated or associated with those spectrophotometers, but also by using the computing resources of a mobile-service provider or by the resources of a cloud platform.
  • the following provides methods for improving spectrum resolution of a low-resolution spectrophotometric sensor (LRSS), and for reducing the uncertainty of the result of measurement of spectrum-related quantities, by using such an LRSS and supported by distributed computing architectures.
  • LRSS low-resolution spectrophotometric sensor
  • instruments in particular, smartphone-based instruments.
  • a method for enhancing the measurement performance and the scope of applicability of the LRSS is proposed.
  • the method enables in situ spectrophotometric analyses at a significantly reduced cost.
  • the effectiveness of the proposed method allows for the manufacture of a plurality of embodiments of hand-held spectrophotometric analyzers adapted to various diversified needs, including personal needs related to in situ analysis of food products and beverages to name two.
  • the proposed method for enhancing the measurement performance and the scope of applicability of the LRSS augments its measurement performance by using data processing instead of increasing the quality of the optoelectronic hardware.
  • a mobile communication device e.g. a smartphone or tablet
  • an embedded LRSS e.g. a LRSS
  • LRSS e.g. a mobile application
  • MobApp mobile application
  • HPCM high-performance computing machine
  • the LRSS may be provided with a sensor for measuring temperature and corresponding software capabilities, included in the MobApp or AdvPrg, for correcting measurement errors induced by temperature fluctuations.
  • the combination of software components namely the MobApp and the AdvPrg, are adapted to perform the following functions:
  • HPCM a high-performance computing machine
  • LRSS - a low-resolution spectrophotometric sensor
  • MobApp - mobile application e.g. installed on a smartphone or tablet
  • D - a set of calibration data
  • D val - a set of validation data
  • FIG. 1 a illustrates an example of a distributed system for implementing spectrophotometric analyses, particularly in situ analyses using existing computing resources and additional available data enabling flexibility in calibrations and adaptability to different environments and applications.
  • an LRSS 10 that is used to measure a sample 12 is integrated with or otherwise coupled to a personal or mobile computing device 14 such as a smartphone, tablet, laptop, smart camera, other smart device, etc.
  • the computing device 14 hosts a mobile app (MobApp) 16 that utilizes network connection capabilities in the device 14 to communicate and coordinate with a computing service 20 via a data network 18.
  • the data network 18 can be wireless (e.g., cellular, WiFi, etc.), wired (Ethernet, fiber, etc.) or a combination of wired and wireless modalities.
  • the computing service 20 can be embodied by a server, hosted cloud-based application, or any other remote or distributed computing component that is accessible via the data network 18 and is connectable to the computing device 14.
  • the computing service 20 hosts or otherwise has access to an AdvPrg 22 such as that introduced above, in order to perform at least some computational operations for processing, calibrating, estimating, analyzing, etc.
  • the computing service 20 also includes or has access to a calibration model library 24, which can be made available to the AdvPrg 22 for calibrating the LRSS 10 in quasi-real-time for the purposes of performing spectrophotometric measurements and analyses.
  • This library 24 can be maintained or at least in part contributed to by one or more 3 rd party data or model sources 26. In this way, the calibration model library 24 can be kept up to date with the latest algorithms and model types for different applications, scenarios, etc.
  • FIG. 1 b is an alternative arrangement in which the computing device 14 is capable of hosting at least part of the AdvPrg capabilities (22a) thereon, with at least some other capabilities (22b) provided by the computing service 20. It can be appreciated that the arrangements shown in FIG. 1 a and 1 b can be adaptable to different devices 14 and LRSSs 10 such that the computing service 20 can service different LRSSs 10 in different ways, for different purposes in a flexible and efficient manner.
  • FIG. 1 c provides another alternative arrangement in which the computing device 14 is capable of hosting all of the operations to be performed by the AdvPrg 22, with the calibration model library 24 being accessible via the data network 18 in the same way as shown in FIGS. 1 a and 1 b.
  • the calibration models can be kept up to date while the computing device 14 in this example has the capability of executing the
  • FIGS. 1 a, 1 b, and 1 c are illustrative only, and other configurations may be possible.
  • the MobApp 16 could be hosted by the computing service 20 via a web browser rather than be installed as an app.
  • FIG. 2 presents a general flow chart of a spectrophotometric analysis that can be performed using the MobApp 16 and AdvPrg 22.
  • calibration of a spectrophotometric analysis that can be performed using the MobApp 16 and AdvPrg 22.
  • FIG. 3. presents an example of spectra and spectral data illustrating the effectiveness of the correction of the imperfections of the LRSS 10, performed according to the structures and processes described in U.S. Patent Nos. 6,002,479 and 5,991 ,023.
  • the symbol ⁇ denotes wavelength
  • the symbol ⁇ - the exact optical spectrum
  • spectrophotometry analyzer refers to various spectrophotometric instruments, devices, probes and testers designed for measuring:
  • UV ultraviolet
  • Vis visible
  • IR infrared
  • a spectrophotometric sensor (e.g., LRSS 10) is the heart of any such apparatus. It is configured to convert an optical signal into a sequence of raw data 7 representative of the spectrum X of that signal. The number of data N , together with range of wavelength
  • L min > m a xJ defines the digital resolution of spectral measurements.
  • the tilde, being a part of the symbol 7 is to indicate that the data are subject to various disturbances of external and internal origin.
  • the wavelength values cover a broader or narrower subrange of one of the following standard intervals:
  • a dispersive element (a grating or a linear variable filter) that enables separation of spectral components in space;
  • a spectrophotometric sensor is considered to have a limited ability to reproduce the most informative parts of the spectrum, viz. narrow pics whose location on the wavelength axis and relative magnitude are carrying qualitative and quantitative information on the analyzed substances or phenomena.
  • the ability of a spectrophotometric sensor to differentiate narrow neighbor pics is characterized by its spectral resolution expressed in the units of wavelength.
  • attainable spectral resolution decreases when the dimensions of a sensor are diminished.
  • the remedy for this problem applicable if miniaturization of the sensor and minimization of its price is required, can be the use of a mathematical model of the sensor hardware for compensation of its imperfections. The procedure aimed at identification of such a model is called calibration, shown as step 30 in FIG. 2.
  • the calibration of the measurement channel generally includes:
  • the estimate Z of Z may be obtained by means of the model of the mapping ⁇ Z resulting from calibration, or by means of an approximate model of that mapping, or by means of a regularized model of that mapping.
  • the model of the mapping ⁇ Z may be decomposed into two parts, a model of the mapping ⁇ X and a model of the mapping X ⁇ Z , where the latter is the definition of the spectrum-related quantity Z .
  • a spectrophotometric analysis can be implemented in a tandem by combining the LRSS 10 and a digital processor; both parts being integrated functionally and spatially.
  • the numerical processing of data can be distributed in space: partially completed by the MobApp 16, and partially by the AdvPrg 22.
  • An exemplary embodiment of the spectrophotometric analyzer can include:
  • AdvPrg 22 would be used for the following operations:
  • spectrophotometric e.g., adapted from U.S. Patent Nos. 6,002,479 and 5,991 ,023, may be implemented using not only local computing resources, but also remote computing resources whose computing power is sufficient for quasi-real-time execution of the most advanced numerical algorithms, suitable for this purpose, which are currently available and which can be updated and improved over time.
  • the adjective "quasi-real-time” means here that the final results of analysis may be obtained after a time interval acceptable for the user, e.g. , after tens of seconds rather that tens of minutes or hours.
  • a time interval acceptable for the user e.g. , after tens of seconds rather that tens of minutes or hours.
  • spectrophotometric analyzer to employ not only rich libraries 24 of sophisticated algorithms of data processing, but also large application-specific data bases, containing fully-specified spectrophotometric data necessary for calibration of the measurement channel and for interpretation of spectra.
  • those data bases would contain spectral characteristics of various kinds of olive oil originating from various regions of the world, produced at various time periods in various versions; those data bases could be periodically updated since olive oil is changing its properties in time.
  • Calibration of measurement channels is an operation safeguarding metrological traceability defined as "property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty" (see Ref. [7]). It is, thus, of key importance for measurement technology since its quality has a decisive impact on the attainable measurement uncertainty. For this reason, various aspects of calibration in spectrophotometric analyzers will be briefly outlined here.
  • the univariate calibration is, by definition, related to modelling the dependence of the concentration of a single compound of a sample on a single spectral datum (see Refs. [8], [9]), while multivariate calibration might involve determining the measurement using the spectral data acquired for tens or hundreds of wavelength values.
  • a general scheme of multivariate calibration, applied in practice, comprises the following steps:
  • D cal is diminishing.
  • To determine the optimum number of significant components in a series of spectral data one may look at how the error is decreasing when that number is growing (see Refs. [10], [11], [12], [13]). By increasing the complexity of the model, one may reduce this error almost to zero, but at the same time - make the model produce artefacts when it is applied to unknown samples.
  • the final validation of the model is an operation that consists in testing the prediction power of the chosen model: an additional set of data D val , independent of D cal but having the same structure, should be used for this purpose.
  • the PLS estimator is a relatively complex (sophisticated) mathematical tool, both from logical and algorithmic point of view - see, for example, its traditional description in the handbooks or its probabilistic interpretation in the paper. Its widespread use is most probably motivated by the availability of numerous library procedures of PLS - such as those in the PLS_Toolbox by Eigenvector Research, Inc. - rather than by the real need. As shown in a recent comparative study of five least-squares estimators of concentrations, in many cases much simpler tools - such as ridge least-squares (RiLS) estimator - or even the OLS estimator - may provide results of comparable metrological quality as those generated by the PLS estimator.
  • ridge least-squares estimator or even the OLS estimator - may provide results of comparable metrological quality as those generated by the PLS estimator.
  • the RiLS estimator has turned out in this study to be a real alternative for the PLS estimator since it provided better results than the latter in the middle-range values of concentrations. This is a one more confirmation for an earlier conclusion that it may be an effective remedy for numerical ill-conditioning of the parameter estimation problems; this conclusion applies even more to the generalized RiLS estimator where each variable gets a slightly different regularization parameter.
  • the linear-model approach may fail when considerably nonlinear relationships have to be taken into account.
  • Numerous linearization techniques have been proposed to deal with such situations: some of them work on the data and others work on the model or on some model parameters.
  • the artificial neural networks are natural tools for dealing with nonlinear models. They are used more and more frequently in spectrophotometric data processing, as a rule in combination with various data compression techniques, such as principal component analysis (PCA), to avoid excessive overfitting.
  • PCA principal component analysis
  • the multivariate calibration requires standards, i.e. samples for which the estimates of concentrations, obtained by a reference method, are known. For the cost and/or time reasons, the number of such standards cannot be too large (usually not greater than 50 or 100). Since the model has to be used for the prediction on new samples, all possible sources of variation that can be encountered later must be included in the calibration data D . This means that the chemical compounds present in the samples to be analyzed must be included in the samples used for calibration, and the range of variation in their concentrations should be at least as wide as that expected of the samples to be analyzed. There is, however, a practical limit on what is available. It is, therefore, necessary to achieve a compromise between the number of samples to be analyzed and the prediction error that can be attained. When it is possible to artificially generate a number of samples, experimental design can and should be used to decide on the composition of the calibration samples. In most cases only real samples are available, so that an experimental design is not possible. This is the case for the analysis of food products and ingredients.
  • the spectral data are recorded for many wavelength values.
  • An important opportunity to improve the numerical conditioning of the estimation problem is the proper selection of those values. It can be based on a priori knowledge of the most informative wavelength values, derived from previous experience, or may be performed by means of special techniques of selection such as the stepwise OLS estimator. A more detailed information on wavelength selection may be found in the abundant literature- to mention just a few examples.
  • FIG. 4 The block diagram shown in FIG. 4 corresponds to a spectrometer to be used for spectrum measurement. Accuracy of measurement significantly depends on
  • optical hardware of a sensor/transducer is minimized, the optical information is converted into data in an electronic form using a detector and an analogue to digital converter, and then the data is processed using digital signal processing.
  • digital signal processing In order to obtain a final measurement result, an estimate of the measured light-spectrum or parameters of the spectrum as defined by a user is performed.
  • the spectrometer is calibrated.
  • the calibration data relate to characteristics of the optical spectral imaging of the device. For example, errors and imperfections of the transducer and transforms for correction thereof and relating to low optical resolution are determined.
  • the information on the metrological imperfections of the optical component(s) is used to correct the digitized spectral data by, for example, adapting the parameters of the processing to the signal representing a measured light-spectrum after correction of the metrological imperfections of the optical component.
  • the processor determines an estimation of a measured light-spectrum or its parameters as defined by the user.
  • sample is a sample of a substance whose spectrum is being measured
  • x(l) is a result of measurement of the spectrum obtained using a high-resolution optical spectrometer
  • (l) is a result of measurement of the spectrum obtained using a low-resolution optical spectrometer-thereby permitting miniaturization-for use in a hand-held device;
  • s(l, I, a) is the measurement result from a device such as the LRSS 10 once y(l) is corrected and its resolution enhanced using a digital signal processor.
  • S (X, 1, CL) is a spectral signature, a set of characteristic peak positions and magnitudes of the sample under study.
  • the measurement result is a reconstructed spectrum having a same spectral signature.
  • the light transformed by the sample under study is transporting measurement information whose extraction, in the proposed sensor/transducer, is herein described in two steps.
  • a low-resolution spectrometric transducer performs dispersion of light, by means, for example, of a simple dispersing element. Photodetectors are used to convert the dispersed light into voltage. An A/D converter(analog-to-digital converter) is used to convert this voltage into a digital signal. This step essentially provides for creation of the spectrum by illuminating a sample, dispersing the resulting spectrum, and capturing the dispersed spectrum to provide an electronic data signal for use in the second step.
  • a digital signal processor executes methods of spectrum reconstruction and enhancement of resolution on the digital data signal in order to estimate the actual spectrum with the desired accuracy and precision.
  • the digital signal processor is a specialized processor for use in this step.
  • augmentation of spectral data is used herein to refer to the operations performed according to the present method for enhancing spectral resolution and for correcting errors of the spectral imaging transducer.
  • the specialized digital signal processor of the IISS/T is provided, during a calibration process, with information on the metrological imperfections of the spectrometric transducer's optical components. In essence, samples with known spectra are analyzed and calibration data relating to the electronic data and how it differs from known spectra for those samples is determined. This calibration data may include a selection of appropriate spectral enhancement methods that best suit the device or the type of spectrum, errors in spectral imaging such as attenuation curves, and other calibration information. The calibration data is used for spectrum reconstruction and/or for producing the final measurement result: the estimate of the spectrum or its parameters.
  • the IISS/T includes a dispersive element, a photodetector, an A/D converter, and a digital signal processor (DSP).
  • the dispersive element and the photodetector cooperate to form a spectrum having a resolution lower than a desired output resolution.
  • the DSP is used to augment the spectrum to produce an output spectrum or output data having sufficient resolution. Because much of the processing is performed within the DSP, the cost of the DSP is a significant portion of the overall sensor cost.
  • the IISS/T comprises a DSP and a miniature, low-cost and low- quality spectrometric transducer comprising, for example simple dispersive elements, photodetectors, and an analog-to-digital converter.
  • a miniature, low-cost and low- quality spectrometric transducer comprising, for example simple dispersive elements, photodetectors, and an analog-to-digital converter.
  • This fusion of the functional blocks enables a designer of IISS/T to profit from advantages of each of the optical and electrical portions.
  • reprogramming of the IISS/T is possible and software modifications that improve the overall performance are expected. It is well known that software distribution and upgrading is inexpensive relative to the costs associated with similar hardware upgrades.
  • the use of an integrated opto-electrical device provides excellent opportunity for automatic correction of temperature induced errors.
  • a small temperature sensor circuit is disposed at each of a plurality of locations within the integrated device.
  • the temperatures are determined and appropriate correction of an imaged spectrum is performed depending on the temperature of the optical components.
  • the DSP is not susceptible to errors induced by temperature fluctuations so long as it operates within a suitable temperature range. Therefore, a device according to the principles discussed herein is provided with an effective low-cost system of compensating for temperature fluctuations.
  • FIG. 6 illustrates the results of an experiment showing the practical gain in the quality of the measurement result obtained using the apparatus described herein.
  • x(l) represents data acquired by means of the reference spectrophotometer ANRITSU (MV02-Series Optical Spectrum Analyzer) set to the resolution of 0.1 nm (which is not available in today's integrated spectrometers);
  • (l) is a raw measurement acquired by means of a same reference instrument set to a resolution of 5 nm-a typical resolution of integrated spectrometers without internal specialized digital signal processors;
  • [00175] (l) is an estimate of a spectrum, whose resolution is 0.1 nm, obtained using digital signal processing according to the apparatus and method described herein.
  • the proposed method of extracting information from the optical signal is, in some ways, more efficient than sophisticated optical analog processing. Further, it is free of some troubles characteristics for this type of processing. As described below, it appears more complicated conceptually because of the use of sophisticated algorithms for digital signal processing.
  • the proposed method permits modifications and selection of different processing methods without altering a physical sensor device.
  • improvements to a spectral sensor required replacement or hardware modification of the sensor.
  • the presently described apparatus is adaptable and simple because, taking into account today's semiconductor-based integration technologies, VLSI implementation of the algorithms is easier than miniaturized integration of optical functions.
  • the increase in accuracy of electrical digital signal processing does not necessarily imply an increase in technological difficulties of its implementation, as is typical of optical analog signal processing.
  • FIG. 7 An exemplary structure of the IISS/T is shown in FIG. 7.
  • the miniature, possibly low-cost, spectrometric transducer shown in this figure includes a diffractive grating 5 and photodetectors 10 in the form of a CCD.
  • CMOS technology semiconductor-based integration technologies
  • such a device is manufactured as an integrated device.
  • the dispersive element 5 is of small size (shown within a single integrated circuit), resulting resolution of the captured spectrum is low.
  • the electronic signal is provided to a processor 20, in the form of a specialized DSP, where it is digitized and augmented to form an output spectrum or output spectral parameters.
  • the optical portion of the IISS/T is implemented in a same silicon integrated circuit (IC) as the digital processor or, alternatively, in a separate IC. Alternatively, it is mounted on an IC as an external element manufactured separately for technological reasons. Further alternatively, it is mounted separate from the IC and optically aligned therewith.
  • IC silicon integrated circuit
  • a spectrometric transducer used in an IISS/T is characterized by the following parameters:
  • [00191] - a number of diodes: 160, with total width of the detector of 4 mm, and
  • FIG. 8 illustrates the results of an experiment showing the effectiveness of the IISS/T. In this figure:
  • x(l) represents the data acquired by means of a reference spectrometer CARY- 3 (Varian) set to a resolution of 0.2 nm-a resolution not commonly available in prior art integrated spectrometers;
  • (l) is a measurement result at the output of the model of the spectrometric transducer of the IISS/T, with 15 nm resolution;
  • s(l; I, a) is an estimate of the spectrum x(l), whose resolution obtained after digital signal processing is approximately 0.1 nm. This is the resolution obtainable at the output of the IISS/T, satisfying the users' requirements for many practical applications. An enhancement of about 10 times the resolution is achieved. Of course, as spectral enhancement increases to for example 40 or 60 times the resolution of the transducer, it is expected that errors in estimation will also increase. This should be evaluated on an application by application basis to determine applicability and degree of miniaturization of the apparatus for a particular application.
  • a system comprising the following: a spectrometric apparatus, in the form of a spectrometric transducer for converting an analogue
  • sample a sample of an analyzed substance
  • the method of augmenting spectra set out below is useful in the IISS/T as a method implemented within the processor. It is described herein as an embodiment of a method of implementing spectral augmentation. Of course, the IISS/T may be provided with another suitable method as are known or may become known in the art. The method of augmenting spectra set out below is also for general application to other spectrometric devices.
  • a significant difficulty related to estimation of positions I and magnitudes a of spectrometric peaks, relates to blurring of those peaks caused by physical phenomena in a sample and by blurring of their representations in the data ⁇ 3 ⁇ 4 ⁇ caused by imperfections in spectrometric apparatus.
  • This difficulty is overcome according to the present method through application of a process for reconstruction of an idealized spectrum s(l; I, a) is assumed to be an approximation of x(l), then only the instrumental blurring is corrected.
  • the method shown generally in FIG. 10a comprises the following steps:
  • the sub-procedure ISD.. cal shown in FIG. 10b comprises the following steps:
  • a process for performing these estimations is preferably tuned for use with a specific apparatus. For example, when known variance exists in a type of dispersive element, this a priori knowledge is beneficial in determining the process for performing estimations and thereby determining a process for calibration. Of course, this is not necessary since some processes for estimation and calibration are substantially universal for spectrometric apparatuses.
  • the sub-procedure ISD_rec shown in FIG. 10c, comprises the following steps:
  • the sub-procedure ISD_est shown in FIG. 10d, comprises the following steps:
  • any module or component exemplified herein that executes instructions may include or otherwise have access to computer readable media such as storage media, computer storage media, or data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape.
  • Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.
  • Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by an application, module, or both. Any such computer storage media may be part of the LRSS 10, computing device 14, MobApp 16, computing service 20, AdvPrg 22, any component of or related thereto, etc., or accessible or connectable thereto. Any application or module herein described may be implemented using computer readable/executable instructions that may be stored or otherwise held by such computer readable media.

Landscapes

  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

L'invention concerne un procédé d'analyse spectrophotométrique. L'invention a trait en outre à un système de mesure comprenant un capteur spectrophotométrique basse résolution, un dispositif de communication mobile (tel qu'un téléphone intelligent ou une tablette) et un logiciel qui peut être installé partiellement sur ce dispositif et partiellement sur un serveur ou un service informatique distant. Le procédé comprend l'étalonnage d'un canal de mesure, orienté sur la mesure de spectres optiques ou de quantités associées aux spectres ; l'estimation du spectre optique d'un échantillon arbitraire analysé, sur la base des données provenant du capteur et des résultats de l'étalonnage ; et l'évaluation d'une quantité associée au spectre sur la base des résultats d'estimation. Ces étapes peuvent comprendre l'implication de ressources informatiques locales et/ou distantes.
PCT/CA2017/051374 2016-11-18 2017-11-17 Procédé et système de traitement de données destinés à des analyseurs spectrophotométriques distribués WO2018090142A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201662423967P 2016-11-18 2016-11-18
US62/423,967 2016-11-18

Publications (1)

Publication Number Publication Date
WO2018090142A1 true WO2018090142A1 (fr) 2018-05-24

Family

ID=62145000

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CA2017/051374 WO2018090142A1 (fr) 2016-11-18 2017-11-17 Procédé et système de traitement de données destinés à des analyseurs spectrophotométriques distribués

Country Status (1)

Country Link
WO (1) WO2018090142A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021185895A1 (fr) 2020-03-18 2021-09-23 Basf Se Système de communication, système de surveillance d'humidité et procédés associés
WO2021185892A1 (fr) 2020-03-18 2021-09-23 Trinamix Gmbh Système de communication, système de surveillance et procédés associés

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5991023A (en) * 1997-05-19 1999-11-23 Measurement Microsystems A-Z Inc. Method of interpreting spectrometric data
US8345226B2 (en) * 2007-11-30 2013-01-01 Jingyun Zhang Spectrometers miniaturized for working with cellular phones and other portable electronic devices
US20150085279A1 (en) * 2013-09-19 2015-03-26 Spectral Sensors Inc. Systems and Methods for Measuring Spectra of Skin and Other Objects and Materials and Making Predictions Based Thereon
WO2015101992A2 (fr) * 2014-01-03 2015-07-09 Verifood, Ltd. Systèmes de spectrométrie, procédés, et applications associées
US9217706B2 (en) * 2012-06-28 2015-12-22 Quick Llc Mobile smart device infrared light measuring apparatus, πmethod, and system for analyzing substances
US9360366B1 (en) * 2015-10-08 2016-06-07 Chuong Van Tran Self-referencing spectrometer on mobile computing device

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5991023A (en) * 1997-05-19 1999-11-23 Measurement Microsystems A-Z Inc. Method of interpreting spectrometric data
US8345226B2 (en) * 2007-11-30 2013-01-01 Jingyun Zhang Spectrometers miniaturized for working with cellular phones and other portable electronic devices
US9217706B2 (en) * 2012-06-28 2015-12-22 Quick Llc Mobile smart device infrared light measuring apparatus, πmethod, and system for analyzing substances
US20150085279A1 (en) * 2013-09-19 2015-03-26 Spectral Sensors Inc. Systems and Methods for Measuring Spectra of Skin and Other Objects and Materials and Making Predictions Based Thereon
WO2015101992A2 (fr) * 2014-01-03 2015-07-09 Verifood, Ltd. Systèmes de spectrométrie, procédés, et applications associées
US9360366B1 (en) * 2015-10-08 2016-06-07 Chuong Van Tran Self-referencing spectrometer on mobile computing device

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021185895A1 (fr) 2020-03-18 2021-09-23 Basf Se Système de communication, système de surveillance d'humidité et procédés associés
WO2021185892A1 (fr) 2020-03-18 2021-09-23 Trinamix Gmbh Système de communication, système de surveillance et procédés associés

Similar Documents

Publication Publication Date Title
Agelet et al. A tutorial on near infrared spectroscopy and its calibration
US6711503B2 (en) Hybrid least squares multivariate spectral analysis methods
Kartakoullis et al. Feasibility study of smartphone-based Near Infrared Spectroscopy (NIRS) for salted minced meat composition diagnostics at different temperatures
Kuang et al. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms
US8352205B2 (en) Multivariate optical elements for nonlinear calibration
Bouveresse et al. Standardization of near-infrared spectrometric instruments
Camp Jr PyMCR: A python library for multivariatecurve resolution analysis with alternating regression (MCR-AR)
EP0983490B1 (fr) Dispositif et procede de mesure d'un spectre lumineux
Chauchard et al. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes
Zamora-Rojas et al. Handheld NIRS analysis for routine meat quality control: Database transfer from at-line instruments
Sharififar et al. Evaluating a low‐cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models
Pimstein et al. Performance of three identical spectrometers in retrieving soil reflectance under laboratory conditions
De Maesschalck et al. The development of calibration models for spectroscopic data using principal component regression
Fu et al. Short‐wave near‐infrared spectrometer for alcohol determination and temperature correction
Broad et al. Guidelines for the development and validation of near-infrared spectroscopic methods in the pharmaceutical industry
Pittaki‐Chrysodonta et al. Evaluating three calibration transfer methods for predictions of soil properties using mid‐infrared spectroscopy
Wang et al. A dual model strategy to transfer multivariate calibration models for near-infrared spectral analysis
WO2018090142A1 (fr) Procédé et système de traitement de données destinés à des analyseurs spectrophotométriques distribués
Hayes et al. Improving calibration transfer between shortwave near infrared silicon photodiode array instruments
Boysworth et al. Aspects of multivariate calibration applied to near-infrared spectroscopy
González-Fernández et al. Field spectroscopy: A non-destructive technique for estimating water status in vineyards
Preys et al. Robust calibration using orthogonal projection and experimental design. Application to the correction of the light scattering effect on turbid NIR spectra
Brouckaert et al. Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale
Mou et al. Robust calibration model transfer
Cernuda et al. Hybrid adaptive calibration methods and ensemble strategy for prediction of cloud point in melamine resin production

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17872114

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17872114

Country of ref document: EP

Kind code of ref document: A1