EP1183505A1 - Procede et systeme pour l'interpretation adaptive de donnees spectrometriques, combinee a un reetalonnage continu - Google Patents

Procede et systeme pour l'interpretation adaptive de donnees spectrometriques, combinee a un reetalonnage continu

Info

Publication number
EP1183505A1
EP1183505A1 EP00930902A EP00930902A EP1183505A1 EP 1183505 A1 EP1183505 A1 EP 1183505A1 EP 00930902 A EP00930902 A EP 00930902A EP 00930902 A EP00930902 A EP 00930902A EP 1183505 A1 EP1183505 A1 EP 1183505A1
Authority
EP
European Patent Office
Prior art keywords
data
spectrum
calibration
spectrometric
light
Prior art date
Legal status (The legal status 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 status listed.)
Withdrawn
Application number
EP00930902A
Other languages
German (de)
English (en)
Inventor
Andrzej Barwicz
Michael P. Wisniewski
Roman Z. Morawski
Mohamed Ben Slima
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Measurement Microsystems A-Z Inc
Original Assignee
MEASUREMENT MICROSYSTEMS A-Z Inc
Bookham Technology PLC
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 MEASUREMENT MICROSYSTEMS A-Z Inc, Bookham Technology PLC filed Critical MEASUREMENT MICROSYSTEMS A-Z Inc
Publication of EP1183505A1 publication Critical patent/EP1183505A1/fr
Withdrawn legal-status Critical Current

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
    • 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

Definitions

  • This invention relates generally to light-spectrum-measurement and more specifically to compact systems for light-spectrum-measurement for portability and for in situ applications.
  • Spectroscopy is an analytic technique concerned with the measurement characterization of the interaction of radiant energy with matter, including the instruments designed for this purpose, called spectrometers, and corresponding means of the interpretation of the interaction both at the fundamental level and for practical analysis.
  • the distribution of radiant energy, absorbed or emitted by a sample of a substance under study, is called its spectrum.
  • Interpretation of spectra provides fundamental information at atomic and molecular energy levels, the distribution of species within those levels, the nature of processes involving change from one level to another, molecular geometries, chemical bonding, and interaction of molecules in solution.
  • comparisons of spectra provide a basis for the determination of qualitative chemical composition and chemical structure, and for quantitative chemical analysis.
  • the spectra to be measured have two common features: they are non-negative, and they may be decomposed into relatively flat regions and peaks of various widths.
  • the parameters of the peaks are used, in particular, for qualitative and quantitative analysis of complex chemical substances:
  • Ultraviolet Sample Illumination describes a spectrometer with an automatic calibration of the photodetectors, based on the use of a pivotable standard white sample positioned in the path of the light and controlled by a computer system.
  • a calibration factor is computed and recalculated for each photodetector whenever the temperature changes within the spectrometer, and then used to multiply the output signal of the photodetectors.
  • this is a partial static calibration, since it corrects only the effect of the photodetectors and doesn't take into account the effect of the dynamical errors introduced by the light diffraction element and the slits.
  • This latter is used for the continual calibration of the device in order to correct the thermal drifts, the nonlinearities of the actuator and the voltage variations.
  • a deconvolution algorithm is also used to remove the effects of the tunable filter spectral response in order to enhance the resolution of the measured light spectrum.
  • the major drawback of this device is that it measures one wavelength or path length difference at a time, thus necessitating a long time to process all the optical channels.
  • a need remains for a low-cost miniaturized spectrometric device, self-calibrated, with a spectral resolution comparable to that of conventional optical spectrum analyzer, and capable of determining the spectral signature of a wide variety of light spectra in situ.
  • the demand for an adaptive reconstruction and interpretation procedures combined with a continual re-calibration is generated by the need for a spectrometric device that can be compact, autonomous with a higher degree of reliability, self-calibrated, operating under a broader range of environmental conditions, and be cost-effective for wide deployment.
  • the main objective of the invention is to respond to this demand.
  • the invention also provides a method for calibration and continual re-calibration of the spectrometric device, i.e. for identification of a numerical relation between the measurement results produced by this spectrometric device and established measurement standards of spectrum.
  • the invention also provides a method for continual re-calibration of the spectrometric device allowing for automatic adaptation of the method to possible variation of the instrumental imperfections and a shape of spectrometric data due to the aging of the device and/or thermal drifts and nonlinearities of the detector.
  • the invention also provides a method for numerical adaptive reconstruction of the spectrum under study, combined with the continual re-calibration of the spectrometric device, i.e. for partial elimination of the effects of noise and blur corrupting the spectrometric data.
  • the invention also provides a method for numerical adaptive interpretation, i.e. estimation of the positions and magnitudes of peaks the spectrum of the light is composed of.
  • the calibration of the spectrometer is accomplished using an auxiliary light whose spectrum is assumed to be known and representative of the spectra to be measured in the series of experiments following the calibration.
  • the result of calibration has the form of two operators defining the following numerical algorithms: • an algorithm for simulation of the spectrometric data, given the spectrum to be measured (operator of projection ⁇ );
  • the first algorithm is an algorithm of model identification including choice or estimation of the structural parameters of the model and estimation of its functional parameters.
  • the second algorithm is an algorithm of deconvolution, or an algorithm of generalized deconvolution, or an algorithm for numerically solving the first-kind integral equations.
  • the continual re-calibration of the spectrometer is accomplished using an external reference light spectrum and the digital reference data representing this reference light, stored in a computer or an internal memory, when available, of the spectrometric device.
  • the re-calibration of the device is aimed at updating the parameters of the above mentioned operators.
  • the processing of the data representative of the light under study is comprising two main operations:
  • the method of adaptive interpretation of spectrometric data is useful for providing the quality light-spectrum measurement especially in the case of miniature integrated intelligent spectrometric devices.
  • the spectrometric device keeps optimum performance, in particular, for telecommunication applications.
  • the proposed method allows for automatic calibration of especially miniature integrated spectrometric devices during a manufacturing process and absent physical tuning of individual devices.
  • the calibration will compensate for many forms of fabrication discrepancies without modification of the physical devices.
  • FIG. 1. presents an exemplary measuring system
  • Fig 2. presents the flow diagram of the procedures of adaptive reconstruction and interpretation a) the procedure AI-RC, b) the sub-procedure AI-RC_cal, c) the sub-procedure AI-RC_recal, d) the sub-procedure AI-RC_rec, e) the sub-procedure AI-RC_int;
  • Fig. 3. presents the exemplary measuring system
  • Fig. 4. presents the spectrum of the test sample: a) real spectrum x( ⁇ ) , b) the test data ⁇ ygro ⁇ ;
  • Fig. 5. presents the spectrum of the sample used for calibration: a) real spectrum x cal ⁇ ⁇ ) ,
  • Fig. 6. presents the output of: a) the rational filter, b) the spline-based Kalman filter;
  • Fig. 7. presents the final result of spectrometric data interpretation
  • Fig. 8. presents the exemplary measuring system - MM microOSA
  • Fig. 9. shows possible applications of MM microOSATM in Dense Wavelength Division Multiplexing (DWDM) system
  • Fig. 10. presents the spectrum of the test light: a) the idealized spectrum s( ⁇ ) of the light at the input of the DWDM transmitter,
  • Fig. 11. presents the emission spectrum of the laser
  • Fig. 12. illustrates the final result of adaptive reconstruction and inte ⁇ retation of telecommunication data.
  • the procedure of adaptive reconstruction combined with continual re-calibration of a device providing spectrometric data and adaptive inte ⁇ retation of spectra is designed for a measuring system, for example the system shown in Fig.l, comprising:
  • a spectrometric device in the form of a spectrometer or its essential part, in the form of a micro-optical spectrum analyzer converting an optical signal carrying the information on the measured spectrum into a digital code representing this spectrum;
  • a processing means for processing the digital representation in the form of a general- pu ⁇ ose computer, a microprocessor, a general-pu ⁇ ose digital signal processor, or an application-specific digital signal processor; and • other functional elements necessary for measuring the spectrum of light.
  • m e real spectrum of an external reference light used for the re-calibration of the spectrometric device;
  • n ⁇ Ca j the spectrometric data measured using the spectrometric device, representative of x recal ( ⁇ ) used for the re-calibration of this device;
  • R - an operator (transform) of reconstruction and inte ⁇ retation:
  • R r - an operator (transform) of reconstruction, in the form of a generalized deconvolution method, for transforming the data into an estimate ⁇ x n ⁇ of x( ⁇ ) :
  • R ' an operator (transform) of inte ⁇ retation, in the form of a generalized deconvolution method, for transforming the data ⁇ x n ⁇ into an estimate s( ⁇ ) of s( ⁇ ; I, a) :
  • the main objective of the procedure AI-RC is estimation of the positions 1 and magnitudes a of the peaks contained in a spectrum of a light under study x( ⁇ ) on the basis of the acquired spectrometric data ⁇ .
  • the feasibility of this operation is critically conditioned by an auxiliary operation on the reference data and corresponding reference spectrum x cal ( ⁇ ) , referred to as calibration of the spectrometric device.
  • This operation is aimed at the acquisition of information relating to a mathematical model of a relationship between spectrometric data and idealized spectrometric data known to be measured, which underlies the method chosen for estimation of the parameters 1 and a.
  • calibration does not necessarily immediately precede processing of each sequence or sample of spectrometric data [y n j , preferably valid calibration results are always available during spectral processing.
  • the main difficulty relating to estimation of the positions 1 and magnitudes a of spectrometric peaks within a sensed spectrum is implied by blurring of those peaks, caused by physical phenomena in a sample, and by blurring of their representations in the data caused by an imperfect spectrometric apparatus.
  • This difficulty is overcome in the procedure AI-RC through application of a series of steps for adaptive reconstruction and adaptive inte ⁇ retation of an idealized - hypothetical - spectrum s( ⁇ ;l,a) in order to correct the sensed data to reduce both types of blurring; if s( ⁇ ; ⁇ ,a) is assumed to be an approximation of x( ⁇ ) , then only the instrumental blurring needs to be corrected.
  • AI-RC is composed of the following broadly defined steps:
  • j l,...,J] to be determined during initial calibration, and updated during continual re-calibration; d) choosing reference light signals for the initial calibration: ⁇ x ⁇ ⁇ ( )j and related
  • % e j representative of ]xTM cal ⁇ j)f for j ⁇ ,...,J ; c) pre-processing of the data n recal ) including elimination of outliers, baseline subtraction, smoothing, acquiring a priori information for the operation 3.d (e.g. a pre- estimate of the variance of errors in the calibration data), normalization, etc.; d) updating of the vectors of parameters
  • the sub-procedure AI-RC_rec comprises the following operations: a) acquiring data ly n representative of the spectrum under study ⁇ x n ⁇ ;
  • a particular version of the procedure AI-RC has been designed for a measuring system shown in Fig. 3 and including: the abso ⁇ tion mini spectrophotometer - model S 1000 by Ocean Optics®; and a personal computer PC.
  • test data were acquired for a standard holmium perchlorate solution sample; its real spectrum x( ⁇ ) is shown in Fig. 4a.
  • the parameters of this spectrum are the following:
  • the vector of the parameters p e of the operator G contains the discrete values of g sx ⁇ ) and the parameter ⁇ .
  • the vector p R , - [p R , l p R , ...J of the parameters of the operator R r contains the coefficients of the rational filter, as well as the discrete values of the function g sx ( ⁇ ) as described in Ben Slima M., Szczecinski L., Massicotte D., Morawski R. Z., Barwicz A.: "Algorithmic Specification of a Specialized Processor for Spectrometric Applictions", Proc. IEEE Instrum. & Meas. Technology Conf. (Ottawa, Canada, May 19- 21, 1997), pp. 90-95 and in Ben Slima M., Morawski R.
  • AI-RC was designed for a measuring system which is represented by the integrated MM microOSA (Optical Spectrum Analyser) as shown in Fig. 8.
  • the analyser is shown used for optical channel monitoring in DWDM networks in Fig. 9.
  • the following DWDM system parameters have been selected both for calibration and for acquisition of test data:
  • the test data were acquired for a telecommunication bandwidth; its real spectrum x( ⁇ ) is shown in Fig. 10a.
  • the adaptation of the MM ⁇ OSA for DWDM applications means that the model of the data takes on the form:
  • ASE amplified spontaneous emission
  • the a priori information about emission light of a laser is used for reconstruction of the idealized light spectrum s( ⁇ ;l,a).
  • the continual re-calibration sub-procedure allows the measurement of the spectrum of laser emission in Is time intervals to continually update parameters p R of reconstruction sub-procedure.
  • the function g TM ( l) has been assumed to have the form of the Gauss function:
  • the vector of the parameters p e of the operator G contains the discrete values of g ⁇ ( ⁇ ) , shown in Fig. 11 and the parameter ⁇ , equal to the spectral bandwidth of the MM ⁇ OS A.
  • the update of coefficients of the rational filter is achieved during the continual re- calibration on the basis of the measured light emission spectrum of a laser.
  • RRMSE relative root-mean-square error
  • the proposed procedure AI-RC may be applied in various light spectrum measuring instruments and systems, especially in miniaturized integrated spectrometers for in situ applications such as environmental probes, optical spectrum analyzers and optical performance monitors in telecommunication etc.
  • the motivation for its application in a given measurement situation is founded on expected gains in performance and resolution such as the following examples:
  • the exemplary embodiment of the invention, presented in section 3 is not intended to limit the applicability of AI-RC to abso ⁇ tion spectrophotometry and the monitoring of optical telecommunication channels. Neither is it intended to limit the variety of algorithms that may be used to embody the operations the procedure is composed of. On the contrary, the invention is intended to cover alternatives, modifications and. Some practical options for methods embodying the operations of AI- RC are briefly characterized.
  • ⁇ * ⁇ ⁇ g( ⁇ , ⁇ ') F s [s( ⁇ '; ⁇ ,a)] d ⁇ '
  • g ⁇ and g( ⁇ , ⁇ ') are the apparatus functions of the spectrometric apparatus; F s and F y are non-linear functions.
  • the corresponding operators G may have the following forms: a) the operator corresponding to the stationary linear model:
  • the following methods are useful for determining the regularization parameters of the operator R : a) the discrepancy principle with a pre-estimate of the variance of measurement errors in the data as described in Tikhonov A. N., Goncharsky A. V., Stepanov V. V., Yagola A. G.: Numerical Methods for the Solution of Ill-Posed Problems, Kluwer 1995; b) the method of the L-curve as described in Hansen P. C, O'Leary D. P.: "The Use of the L-curve in the Regularization of the Discrete Ill-posed Problems", SIAMJ. Sci. Comput. l. 14, No. 6, 1993, pp. 1487-1503; c) the method of additional set of calibration data as described in Szcqueln ski L., Morawski R. Z., Barwicz A.: "Numerical Correction of Spectrometric Data Using a
  • the isolated peak v. ( ⁇ ,l) has the following forms: a) the Dirac distribution ⁇ ( ⁇ ) for all values of / ; b) a triangle whose width is constant or varying versus / ; c) a rectangle whose width is constant or varying versus / ; d) a Gauss function whose width is constant or varying versus / ; and e) a Lorenz function whose width is constant or varying versus / .
  • the following methods are useful in estimation of the apparatus function g ⁇ ) : a) smoothing approximation applied directly to the data if the isolated peak v s ( ⁇ ,l) is assumed to have the form the Dirac distribution ⁇ ( ⁇ ) ; b) deconvolution of the data ⁇ y n cal ⁇ with respect to s( ⁇ ; ⁇ cal ,a cal ) ; and c) subsequent use of deconvolution and smoothing approximation.
  • the following methods may be used for determining other parameters of the operator R : a) a direct transformation of the parameters of the operator G ; b) the minimization of any norm of the solution
  • algorithmic solutions are given in Morawski R. Z., Mi? kina A., Barwicz A.: "Combined Use of Tikhonov Deconvolution and Curve Fitting for Spectrogram Inte ⁇ retation", Instrum. Science & Technology, Vol. 24, No. 3, August 1996, pp. 155-167 and Morawski R.
  • the methods for estimation of the magnitudes a are used for iterative correction of the estimates of the magnitudes a and positions 1 of the peaks interchangeably with the following methods:
  • the following methods are useful for normalization of the data: a) the linear or nonlinear transformation of the ⁇ -axis, aimed at diminishing the non- stationarity effects in the data; b) the linear or nonlinear transformation of the ⁇ -axis, aimed at diminishing the non- linearity effects in the data; and c) the linear or nonlinear transformation of the A-axis and ⁇ -axis, aimed at diminishing the non-stationarity and non-linearity effects in the data.
  • the following methods are useful for smoothing the data: a) the linear, FIR-type or IIR-type, filtering; b) the median filtering; c) the smoothing approximation by cubic splines; and d) the deconvolution with respect to an identity operator.

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  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
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  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Spectrometry And Color Measurement (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

L'invention concerne un procédé pour la reconstruction et l'interprétation adaptatives de spectres, combinées au réétalonnnage d'un dispositif produisant des données spectrométriques. Ledit procédé consiste à assurer un étalonnage automatique au moyen du spectre lumineux externe de référence et des données de références numériques correspondantes mémorisées dans la mémoire interne du dispositif. La procédure de réétalonnage continu permet l'adaptation automatique des valeurs des coefficients dans une sous-procédure de reconstruction, ainsi que l'estimation des valeurs des coefficients dans une sous-procédure d'interprétation, en fonction de la forme ponctuelle des données spectrométriques.
EP00930902A 1999-05-21 2000-05-23 Procede et systeme pour l'interpretation adaptive de donnees spectrometriques, combinee a un reetalonnage continu Withdrawn EP1183505A1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
CA2273132 1999-05-21
CA2273132 1999-05-21
PCT/CA2000/000583 WO2000071980A1 (fr) 1999-05-21 2000-05-23 Procede et systeme pour l'interpretation adaptative de donnees spectrometriques, combinee a un reetalonnage continu

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EP1183505A1 true EP1183505A1 (fr) 2002-03-06

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EP (1) EP1183505A1 (fr)
JP (1) JP2003500639A (fr)
CN (1) CN1361861A (fr)
AU (1) AU4903000A (fr)
WO (1) WO2000071980A1 (fr)

Cited By (1)

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Publication number Priority date Publication date Assignee Title
CN106872146A (zh) * 2017-02-24 2017-06-20 中国测试技术研究院 一种光源相关色温和显色指数分析方法

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JP4533527B2 (ja) * 2000-12-05 2010-09-01 アンリツ株式会社 光スペクトラムアナライザおよびその波長校正方法
CA2413218C (fr) 2002-11-29 2015-01-27 Measurement Microsystems A-Z Inc. Moniteur de performance optique instantanee
CN103424185B (zh) * 2012-05-21 2015-10-28 敦宏科技股份有限公司 自动校正误差的检测系统及方法
CN102998824B (zh) * 2012-12-10 2016-01-13 深圳市华星光电技术有限公司 彩色滤光片穿透频谱量测偏差补正系统和方法
CN106053430B (zh) * 2016-06-16 2019-02-05 重庆大学 用于微量气体拉曼光谱检测基线校正的包络线迭代方法
US10429240B2 (en) * 2016-07-29 2019-10-01 Viavi Solutions Inc. Transfer of a calibration model using a sparse transfer set
CN109724695B (zh) * 2018-12-14 2020-07-14 执鼎医疗科技（杭州）有限公司 一种光谱仪波长标定装置及方法
EP3674675A1 (fr) * 2018-12-27 2020-07-01 INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência Procédé d étalonnage d'un dispositif de spectroscopie comprenant une pluralité de capteurs et de transfert d'information
CN109991181B (zh) * 2019-03-19 2020-08-18 谱诉光电科技（苏州）有限公司 自适应表面吸收光谱分析方法、系统、存储介质、设备
TWI708197B (zh) * 2019-04-26 2020-10-21 國立成功大學 生產機台組件的預測保養方法與其電腦程式產品
CN113567357B (zh) * 2021-07-26 2024-05-24 杭州海康威视数字技术股份有限公司 一种光谱数据的融合方法、以及装置

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US5040889A (en) * 1986-05-30 1991-08-20 Pacific Scientific Company Spectrometer with combined visible and ultraviolet sample illumination
US5303165A (en) * 1992-02-12 1994-04-12 The Perkin-Elmer Corporation Standardizing and calibrating a spectrometric instrument
CA2212776A1 (fr) * 1997-08-08 1999-02-08 Andrzej Barwicz Transducteur a microcapteur spectrometrique et procedure d'interpretation des donnees spectrometriques

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CN106872146A (zh) * 2017-02-24 2017-06-20 中国测试技术研究院 一种光源相关色温和显色指数分析方法
CN106872146B (zh) * 2017-02-24 2019-02-19 中国测试技术研究院 一种光源相关色温和显色指数分析方法

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WO2000071980A9 (fr) 2002-01-17
WO2000071980A1 (fr) 2000-11-30
AU4903000A (en) 2000-12-12
JP2003500639A (ja) 2003-01-07
CN1361861A (zh) 2002-07-31

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