WO2018090142A1

WO2018090142A1 - Method and system for data processing for distributed spectrophotometric analyzers

Info

Publication number: WO2018090142A1
Application number: PCT/CA2017/051374
Authority: WO
Inventors: Andrzej Barwicz; Roman Z. Morawski
Original assignee: Andrzej Barwicz; Morawski Roman Z
Priority date: 2016-11-18
Filing date: 2017-11-17
Publication date: 2018-05-24

Abstract

A method of spectrophotometric analysis is disclosed. There is provided a measuring system including a low-resolution spectrophotometric sensor, a device of mobile communication (such as smartphone or tablet) and software which may be installed partially on that device and partially on a remote computing server or service. The method includes calibration of a measurement channel, oriented on measuring optical spectra or spectrum- related quantities; estimation of the optical spectrum of an arbitrary, analyzed sample, on the basis of the data from the sensor and the results of calibration; and evaluation of a spectrum-related quantity on the basis of the results of estimation. These steps may include involvement of local and/or remote computing resources.

Description

METHOD AND SYSTEM FOR DATA PROCESSING FOR DISTRIBUTED

SPECTROPHOTOMETRIC ANALYZERS

CROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This application claims priority to U.S. Provisional Patent Application No.

62/423,967 filed on November 18, 2016, the contents of which are incorporated herein by reference.

TECHNICAL FIELD

[0002] The following relates to methods and systems for implementing data processing, for distributed spectrophotometric analyzers. In particular, 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.

BACKGROUND

[0003] Spectrophotometric tools, and thus the methods for interpretation of

spectrophotometric data, are of increasing importance for analytical laboratories, as well as for environmental, biomedical and industrial monitoring.

[0004] On the one hand, the development of corresponding applications is driven by a growing demand for this kind of tools. This demand can be implied, inter alia, by the advancement of standards related to environmental protection, health care, individual and collective security, as well as by the widespread use of optical means for inspection of industrial processes, and in various internet of things (loT) applications.

[0005] However, on the other hand, this development can be considered as a result of the growing availability of miniature spectrophotometers, that is mini- and micro- spectrophotometers. In addition to general-purpose miniature devices, the

spectrophotometers designed for functional and physical coupling with smartphones are beginning to emerge.

[0006] 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:

[0007] - monitoring of optical telecommunications links,

[0008] - assessment of eating quality of stonefruits,

[0009] - classification of the floral origin of honey samples, [0010] - determination of meat tenderness,

[0011] - forensic classification of paper,

[0012] - biometric identification or verification of individuals,

[0013] - detection of internal insect infestation in seed lots,

[0014] - quantitative analysis of textile moisture and textile classification, and

[0015] - hydroxyl and acid number prediction in polyester resins.

[0016] For example, 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.

[1]). A resolution of 10 nm in this range is found to limit the application of such a device. For example, 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]). These are examples of low-resolution spectrophotometnc sensors designed to be coupled with mobile devices. However, their spectral resolution (i.e. R = 10-15 nm) is found to be insufficient for solving many problems identified herein as potential mass applications of spectrophotometry. For example, it is considered impossible to detect, by way of those sensors, the difference between original and adulterated olive oil, or even between olive oil and corn oil.

[0017] The above examples illustrate that current MOEMS, VLSI and assembly technologies are mature enough for manufacturing a miniature low-cost spectrophotometnc chip being a key hardware element of any spectrophotometnc sensor, but all those technologies - in one way or another - are facing the physical limitations of losing spectral performance with miniaturization. This is a major challenge in spectrophotometnc sensor development, namely to overcome the contradiction between requirements for the sensor specifications resulting from the needs of its applications and physical limitations of spectrum processing related to small dimensions. [0018] That is, these examples illustrate solutions which make only partial use of the benefits resulting from coupling a spectrophotometer with a mobile device and a telecommunications network. That is, they cover the exchange and storage of data, but not the advanced numerical data processing that would be required to make full use of these benefits, as herein described.

[0019] 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. Herein, the term spectrum is used in the sense of light-spectrum and the term spectrophotometer is used as commonly known.

[0020] A spectrophotometer has a resolution associated with its design or

implementation affecting the resolution of measured spectra. As is well understood by those of skill in the art of spectrophotometry, a required resolution for UV and a required resolution for IR spectral imaging is different. Further, the terms high-resolution and low-resolution are related to an imaged spectral band or to wavelengths of light within the imaged band. For a broadband spectrophotometer, either graduated spectral resolution or a spectral resolution sufficient to properly image each band is used.

[0021] Interpretation of spectra provides fundamental information at atomic and molecular energy levels. For example, 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 are all studied using spectrum information. Practically, comparisons of spectra provide a basis for the determination of qualitative chemical composition and chemical structure, and for quantitative chemical analysis as disclosed in 2016 Edition of Encyclopedia of Spectroscopy and Spectrometry (Eds.: J. Lindon, G. E. Tranter and D. Koppenaal, Pub.: Academic Press) which is hereby incorporated by reference.

[0022] This is the theoretical basis for prolific development of specialized

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. Their development is possible due to the intensive research on chemometric methods dedicated to the analysis of such raw materials and products as grape fruits and wine, olive fruits and olive oil, other fruits and juices, vinegars, cheeses and other milk products, eggs, meat, honey, chocolate, and tea. Further examples can be found in Ref. [5]. The demand for spectrophotometric analyzers of food is expressed by the main actors of the broadly understood food business. Although those actors are potential users of such analyzers, the measurement needs are today better defined, and the use of analyzers is more common, among the food producers and supervisors of food business than among the providers of raw materials, food distributors, food vendors and individual consumers. It may be predicted, however, that the significant lowering of the prices of spectrophotometric analyzers could quickly increase the demand among the members of the latter group.

[0024] The main reasons for using analyzers in food business may be summarized as follows: checking the quality of food, monitoring of the food production process, providing data necessary for production control, specification of food products necessary for their labelling, and precise classification of food products enabling their better pricing. There are more-or-less evident economic benefits behind each of them. Precise classification, for example, enables one to minimize losses due to aging of food by replacing a worst-case approach with a realistic-case approach since the selection and grading of food may be based on the objective measurement results rather than on the "best-before" date.

[0025] Existing spectrophotometers, which could be adapted to in situ measurements, are relatively large and expensive. Although intensive research activity, directed towards developing spectrophotometers for sensing applications, has been carried out for at least 30 last years, a low-cost but performant miniature solution is still lacking. In existing solutions, either the bandwidth of the spectrophotometer is too narrow, or the quality of the spectral imaging is poor, or the optical processing components are large and costly.

[0026] Several example attempts are summarized below, each lacking in an ability to make full use of the benefits resulting from coupling a spectrophotometer with a mobile device and a telecommunications network, consistent with the above discussion.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] In U.S. Patent No. 8,345,226 , 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.

[0031] 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.

[0032] In 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.

[0033] 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

calibration/normalization methods is used for this purpose.

[0035] In other example, 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

telecommunications network.

SUMMARY

[0036] In one aspect, there is provided 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.

[0037] In another aspect, there is provided a computer readable medium comprising computer executable instructions for performing the method.

[0038] In yet another aspect, there is provided a system for performing

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

communication with the mobile application for calibrating a measurement channel, the calibrating oriented on measuring optical spectra, using the LRSS, and estimating an optical spectrum of a sample based on data acquired by the LRSS and results of the calibrating.

BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Embodiments will now be described with reference to the appended drawings wherein:

[0040] FIG. 1 A is an example of a distributed system for spectrophotometric analysis; [0041] FIG. 1 B is another example of a distributed system for spectrophotometric analysis;

[0042] FIG. 1 C is yet another example of a distributed system for spectrophotometric analysis;

[0043] FIG. 2 is a flow chart illustrating operations executed in performing a

spectrophotometric analysis using the systems and methods described herein;

[0044] FIG. 3 is an example of spectra and spectral data illustrating the effectiveness of the correction of the imperfections of the LRSS;

[0045] FIG. 4 is a flow diagram for a light-spectrum measuring instrument;

[0046] FIG. 5 is an illustration of a measurement principle underlying an intelligent spectrophotometric transducer/sensor (IISS/T);

[0047] FIG. 6 is an illustration of practical gains achieved using the structure in FIG. 5;

[0048] FIG. 7 is a generic structure of the IISS/T;

[0049] FIG. 8 is an illustration of the effectiveness of correction of imperfections of the spectrophotometric transducer, using a specialized digital signal processor;

[0050] 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

[0051] FIGS. 10a through 10d are flow diagrams illustrating exemplary steps for performing a spectrophotometric calibration and analysis using the structures described herein.

DETAILED DESCRIPTION

[0052] It has been recognized that spectrophotometers may generate significant amounts of data that are highly correlated from one wavelength value to the next, and from one sample to another. This highly serial correlation decreases the apparent information in the data. However, if an approach is used that takes into account this serial correlation, it can be possible to turn the correlation to a benefit, by taking advantage of the redundancy.

[0053] There are numerous numerical methods that are efficient at extracting useful information from spectral data, namely methods that are designed to improve the precision of inference based on those data. Various computational techniques may be used for their implementation, from microprocessors integrated with the optical hardware, to networks of supercomputers reached via wireless telecommunications channels.

[0054] The practical implementation of many spectrophotometric solutions have been found to depend on the availability of low-cost miniature spectrophotometric sensors and of the access to high-performance computing resources. For example, in environmental applications, there is a need for integrated and miniature measurement tools which can be deployed directly at sites where measurements are important (e.g., factory exits, waste, dumps, etc.), and to transmit continually, without cable connection, the information necessary for real-time monitoring to the centers for pollution prevention or waste management.

[0055] It has also been recognized that the apparatus, methods, and processing techniques described in U.S. Patent Nos. 6,002,479 and 5,991 ,023 provide a particularly advantageous design for compensation of the hardware limitations of low-cost miniature spectrophotometers with advanced algorithms for data processing. An overview of these algorithms may be found in Ref. [6].

[0056] 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. Among other things, 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.

[0057] In this way, 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. Moreover, 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.

[0058] Accordingly, 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. Moreover, 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.

[0059] The resolution limitations imposed by the physical size of a spectrophotometer are well understood. These limitations can be circumvented, as herein described, with the use of sophisticated technologies for implementing a method of resolution enhancement for use with an LRSS. These methods allow for design and manufacture of portable

instruments, in particular, smartphone-based instruments.

[0060] In accordance with the principles described herein, 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.

[0061] 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.

[0062] By utilizing the proposed apparatus, there are provided methods for measuring:

[0063] - the spectra of UV-Vis-NIR radiation;

[0064] - the reflectance or transmittance or absorbance spectra in the UV-Vis-NIR range of radiation;

[0065] - the quantities related to the reflectance or transmittance or absorbance spectra in the UV-Vis-NIR range of radiation;

[0066] by means of the LRSS being a source of raw measurement data.

[0067] The processing of those data by both local and remote computing resources is an integral part of the proposed method. In this way, there is also provided:

[0068] - a mobile communication device (e.g. a smartphone or tablet) with an embedded LRSS, or LRSS otherwise coupled thereto; [0069] - a mobile application (MobApp), selected by the user and installed on that device;

[0070] - a high-performance computing machine (HPCM), offered by a mobile-service or other cloud or network-based provider; and

[0071] - an advanced program of data processing (AdvPrg) installed on the HPCM.

[0072] The above-described embodiment would be of particular interest for personal users. It could be customized, by appropriate choice of MobApp and AdvPrg, to serve various user's needs, such as analysis of his/her skin surface, analysis of selected kinds of beverages, analysis of air purity, etc.

[0073] In accordance with another embodiment, 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.

[0074] According to another aspect, the combination of software components, namely the MobApp and the AdvPrg, are adapted to perform the following functions:

[0075] - preprocessing of raw data provided by the LRSS, possibly including the improvement of their spectral resolution;

[0076] - calibration of the LRSS, oriented on a selected class of quantities to be measured, possibly including the spectrum;

[0077] - estimation of the spectrum-related quantities on the basis of data from the LRSS and results of its calibration; and

[0078] - coordination of all the above-listed operations, including exchange of data among the components of the system and its user.

[0079] The following acronyms are used herein:

[0080] AdvPrg - advanced program of data processing;

[0081] HPCM - a high-performance computing machine;

[0082] LRSS - a low-resolution spectrophotometric sensor;

[0083] MobApp - mobile application (e.g. installed on a smartphone or tablet);

[0084] OLS - ordinary least squares;

[0085] PLS - partial least squares;

[0086] RiLS - ridge least squares; and [0087] PCA - principal component analysis.

[0088] The following mathematical symbols are used for the description:

[0089] ta representative of the exact optical spectrum χ(λ) ;

[0090] measurement data acquired by means of the LRSS;

[0091] imate of X , obtained on the basis of the data Y ;

[0092] ( (λ)) z₂ (x(/l)) ...J - the vector of quantities related to the

spectrum x (λ) , which may be estimated on the basis of the data Ϋ ;

[0093] Z - an estimate of Z , obtained on the basis of the estimate X or of data Y ;

[0094] P^≡V = [Pi P₂ - ] - the vector of calibration parameters;

[0095] P≡ p - an estimate of P ;

[0096] D - a set of calibration data; and [0097] D^val - a set of validation data.

[0098] Turning now to the figures, 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.

[0099] In FIG. 1 a 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. It can be appreciated that the data network 18 can be wireless (e.g., cellular, WiFi, etc.), wired (Ethernet, fiber, etc.) or a combination of wired and wireless modalities.

[00100] 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.

[00101] 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.

[00102] 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. In this way, the calibration models can be kept up to date while the computing device 14 in this example has the capability of executing the

spectrophotometric operations.

[00103] It can be appreciated that the configurations shown in FIGS. 1 a, 1 b, and 1 c are illustrative only, and other configurations may be possible. For example, the MobApp 16 could be hosted by the computing service 20 via a web browser rather than be installed as an app.

[00104] FIG. 2 presents a general flow chart of a spectrophotometric analysis that can be performed using the MobApp 16 and AdvPrg 22. At step 30 calibration of a

measurement channel is performed, followed by a spectrophotometric analysis at step 32. The system then determines if recalibration is necessary at step 34. If not, the process continues at step 32. If so, the calibration step 30 is repeated.

[00105] 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. In this figure, the symbol ^ denotes wavelength, the symbol ^ - the exact optical spectrum, and - its estimate obtained on the basis of the raw measurement data from the LRSS, whose continuous envelope is represented by .

[00106] The wavelength distribution of radiant energy in an optical signal is called its spectrum. The term "spectrophotometry analyzer", as used herein, refers to various spectrophotometric instruments, devices, probes and testers designed for measuring:

[00107] - the spectrum of ultraviolet (UV) and/or visible (Vis) and/or infrared (IR) radiation;

[00108] - or selected parameters of such spectrum;

[00109] - or physical and/or chemical parameters characterizing a pre-defined class of chemical or biochemical substances, the parameters related to such spectrum.

[00110] 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^axJ 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:

[00111] - 200-300 nm - middle-ultraviolet radiation (MUV),

[00112] - 300-380 nm - near-ultraviolet radiation (UV),

[00113] - 380-750 nm - visible radiation (Vis), 750-2,500 nm - near-infrared radiation (NIR)

[00114] - or 2.5-10 μηι - middle-infrared radiation (MIR).

[00115] Several physical principles and corresponding devices may be used for designing spectrophotometric sensors:

[00116] - a dispersive element (a grating or a linear variable filter) that enables separation of spectral components in space;

[00117] - a tunable filter that enables separation of spectral components in time;

[00118] - an optical heterodyne that enables shifting the spectrum in a wavelength range where its analysis is easier; [00119] - an interferometer providing the data whose Fourier transform is representative of the spectrum.

[00120] Regardless of the principle of operation, 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. As a rule, due to known physical constraints, 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.

[00121] In general, the concept of calibration applies to the relationship between Ϋ and

Z . The reconstruction of the spectrum X on the basis of the data Ϋ may be viewed as a special case of this general approach. For this reason, it is more appropriate to speak about calibration of the measurement channel, linking Z and Ϋ , rather than about calibration of the LRSS 10. The calibration of the measurement channel generally includes:

[00122] - selection of a mathematical structure (a system of algebraic and or difference equations) having the potential to model the mapping Ϋ→Z for a sufficiently broad class of Ϋ realizations; and

[00123] - estimation of the parameters of this structure P≡ [ρ_λ p₂ ...f using the reference data, representative of Ϋ and Z , acquired for a sample or a set of samples considered to be the measurement standard(s).

[00124] As shown in FIG. 2, when having calibrated the measurement channel, one can estimate Z corresponding to any Ϋ using an estimate P of P , also in the special case when Z = X . The estimate Z of Z may be obtained by means of the model of the mapping Ϋ→Z resulting from calibration, or by means of an approximate model of that mapping, or by means of a regularized model of that mapping. Alternatively, the model of the mapping Ϋ→Z may be decomposed into two parts, a model of the mapping Ϋ→X and a model of the mapping X→ Z , where the latter is the definition of the spectrum-related quantity Z . [00125] In this case:

[00126] - calibration is aimed at estimation of the parameters of the model of the mapping Ϋ→X (underlying spectrum reconstruction);

[00127] - an estimate X of X is computed on the basis of Ϋ using an estimate P of P , resulting from calibration; and

[00128] - an estimate Z of Z is determined on the basis of X using an exact, approximate regularized model of the mapping X→ Z .

[00129] Further detail of the algorithmic basis for such an implementation may be found, inter alia, in Ref. [6].

[00130] According to the implementations described in U.S. Patent Nos. 6,002,479 and 5,991 ,023 (illustrated in FIGS. 4 to 10 and described below), 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. According to the presently described implementation, 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:

[00131] - a smartphone or a tablet with an embedded LRSS 10 and an installed MobApp 16 as shown in FIG. 1 a; or

[00132] - an HPCM with an installed AdvPrg 22, offered by a mobile-service provider (e.g. computing service 20).

[00133] The minimum functional load of the MobApp 16 in this example would be:

[00134] - the acquisition of raw spectral data from the LRSS 10, and their transmission (via wireless communication means) to the HPCM on the service 20; and

[00135] - the reception of the results of analysis from the HPCM, and their presentation to the user.

[00136] In this case, the AdvPrg 22 would be used for the following operations:

[00137] - preprocessing of raw spectral data from the LRSS 10, possibly including the improvement of their spectral resolution;

[00138] - calibration of the LRSS 10, oriented on a selected class of quantities to be measured, possibly including the spectrum; [00139] - estimation of the spectrum-related quantities on the basis of data from the LRSS 10 and results of its calibration; and

[00140] - exchange of data with the MobApp 16.

[00141] Other options for sharing tasks between the MobApp 16 and the AdvPrg 22 are disclosed herein, e.g., as shown in FIGS. 1 a, 1 b, and 1 c.

[00142] The results of an analysis, performed by means of a spectrophotometric analyzer designed according to the principles discussed herein, are less uncertain than those obtained by means of other (existing) analyzers with the same LRSS 10. This is due to the fact that the methods for resolution enhancement and for interpretation of

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.

[00143] 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. The use of the HPCM enables the designer of a

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. For example, in the case of an analyzer designed for authentication of olive oil, 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.

[00144] 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.

[00145] 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.

[00146] A general scheme of multivariate calibration, applied in practice, comprises the following steps:

[00147] - selection of the reference samples (mixtures) to be used for identification of the model;

[00148] - acquisition and visual evaluation of the spectral data, used for calibration, before and after preprocessing;

[00149] - a first modelling trial to decide whether it is possible to attain the expected quality of the model and whether non-linearity should be introduced in this model;

[00150] - iterative refinement of the model, e.g. by considering elimination of possible outliers with respect to the model, selecting the model complexity; and

[00151] - final validation of the model.

[00152] The optimum complexity of the model, resulting from calibration, is a key issue. As the complexity of the model is increasing, the prediction error assessed for the data set

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.

[00153] As already mentioned, the problem of estimation of the calibration parameters P is, as a rule, numerically ill-conditioned. Two kinds of measures are applied to remediate this difficulty, viz. : selection of samples and selection of wavelength values used for calibration (which are discussed in the next paragraphs), and so-called soft modelling. There are numerous references containing reviews of the methodology of soft modelling (see Refs.

[14], [15], [16], [17]). Regardless of whether linear or non-linear model is chosen, the numerical methods for solving linear algebraic equations (of various categories) play a key role in building he solution. No single method for solving such equations has turned out the best in all spectrophotometric applications. Sometimes, the simplest tool, i.e. the ordinary least-squares (OLS) estimator is sufficient, much more frequently, however - more sophisticated one must be applied. Therefore, several alternative methods have been developed; among them the partial least-squares (PLS) estimator is probably the most commonly acknowledged. It has gained much popularity among the researchers in chemometrics, and - consequently - has been enhanced and modified in various ways, including the incorporation of some nonlinearity and constraints, such as the positivity of spectral data and the positivity of concentrations of compounds - to mention only a few of them.

[00154] 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. 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.

[00155] It should be noted that 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. In general, 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.

[00156] 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.

[00157] There are several strategies available for selection of the calibration samples representative of the problem to be solved. The simplest of them is random selection, but it is open to the possibility that some sources of variation may be lost. Another strategy refers to a priori knowledge about the problem under study: if all the sources of variation are known, then the calibration samples can be selected on the basis of that knowledge. A more detailed information on selection of samples may be found in the literature (see Ref. [18]).

[00158] For each sample, 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.

[00159] The exemplary embodiments, presented above are not intended to limit the applicability of the method to the presented examples. Neither is it intended to limit the variety of algorithms that may be used to embody the operations of performed by the MobApp 16 and the AdvPrg 22. Numerous other embodiments may be envisaged without departing from the scope of what is recited in the appended claims.

[00160] To further illustrate details of an example configuration, the follow provides details of an apparatus and method for light spectrum measurement, further details of which can be found in U.S. Patent Nos. 6,002,479 and 5,991 ,023, the contents of which are incorporated herein by reference.

[00161] The block diagram shown in FIG. 4 corresponds to a spectrometer to be used for spectrum measurement. Accuracy of measurement significantly depends on

performance of digital signal processing. According to the principles discussed herein, 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. 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. For example, using a digital signal processor with suitable programming, 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. During use, once a spectrum is imaged and digitized, 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 then determines an estimation of a measured light-spectrum or its parameters as defined by the user.

[00162] Referring to FIG . 5, measurement principles underlying the corresponding sensor/transducer are illustrated. In this figure, «sample» is a sample of a substance whose spectrum is being measured;

[00163] x(l) is a result of measurement of the spectrum obtained using a high-resolution optical spectrometer;

[00164] (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; and,

[00165] 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. In the figure, S (X, 1, CL) is a spectral signature, a set of characteristic peak positions and magnitudes of the sample under study. Alternatively, the measurement result is a reconstructed spectrum having a same spectral signature.

[00166] 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.

[00167] First, 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.

[00168] 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. Preferably, the digital signal processor is a specialized processor for use in this step. The term 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.

[00169] 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.

[00170] 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.

[00171] Preferably, 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. This fusion of the functional blocks enables a designer of IISS/T to profit from advantages of each of the optical and electrical portions. In fact, 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. Further, 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. Of course, 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.

[00172] 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. In this figure

[00173] 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);

[00174] (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; and

[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.

[00176] As is evident from a review of FIG. 6, a low resolution y(l) is enhanced to form an excellent approximation of the spectrum measured using a higher resolution

spectrometer.

[00177] Comparison of the signals s(l), y(l) and £ (A) gives an idea of practical gains obtained using the apparatus, namely the gain in resolution shown is of the order of 10. Therefore, the experiment demonstrates that using a low-resolution dispersive element and a DSP, results are typical of a spectrometer having significantly better resolution. Since size of spectrometers is at least partially related to resolution, a device according to what is shown herein permits spectrometers of significantly reduced size for use in similar applications. Of course, the reduced size and cost of the device permit many new applications heretofore prohibited by size, cost, and/or resolution of prior art spectrometers.

[00178] 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. Heretofore, improvements to a spectral sensor required replacement or hardware modification of the sensor. Technologically, 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. Moreover, 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.

[00179] 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. Optionally, using semiconductor-based integration technologies such as CMOS technology, such a device is manufactured as an integrated device.

[00180] Light from a sample and representing a spectrum requiring analysis is received at port D. The light is characterized by the x(l). The light is provided to dispersive element 5 through which it is dispersed to photodetectors 10 which provide an electronic signal corresponding to a captured spectrum, (l) .

[00181] Since 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.

[00182] Depending on a targeted wavelength range of the IISS/T, the optical portion of the IISS/T, shown in FIG. 7, 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.

[00183] As mentioned above, the miniaturization of spectrometric instruments is limited by the required accuracy of measurement and limitations of integrated devices.

Implementation of optical functions using semiconductor-based integration technologies does not provide similar performance to classical discrete optical instrumentation. This is an important motivation for the device, which allows for miniaturization of spectrum- measurement-based instrumentation.

[00184] As a further example, let us assume that a spectrometric transducer used in an IISS/T is characterized by the following parameters:

[00185] - range of wavelength from 450 to 650 nm (Vis), [00186] - total surface of the spectrometric transducer; 1 cm. sup.2,

[00187] - Litrow configuration of a diffractive grating

[00188] - photodetector composed of semiconductor diodes whose diameter is 25 .mu.m;

[00189] then using the developed model of the spectrometric transducer of the IISS/T, we obtain the following:

[00190] - a diffractive grating with 1200 steps/mm,

[00191] - a number of diodes: 160, with total width of the detector of 4 mm, and

[00192] - optical resolution of obtained optical transducer .DELTA.. lambda. =1 1 nm.

[00193] These results follow from the above assumptions, therefore a higher resolution detector requires either different assumptions or processing of the obtained-imaged-- spectra according to the configuration shown herein.

[00194] FIG. 8 illustrates the results of an experiment showing the effectiveness of the IISS/T. In this figure:

[00195] 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;

[00196] (l) is a measurement result at the output of the model of the spectrometric transducer of the IISS/T, with 15 nm resolution; and,

[00197] 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.

[00198] Referring to FIG. 9, a system is shown comprising the following: a spectrometric apparatus, in the form of a spectrometric transducer for converting an analogue

electromagnetic signal, such as light containing information of a measured spectrum, into a digital electrical signal representing the spectrum; a computing means in the form of a microprocessor, a general-purpose digital signal processor, or an application-specific digital signal processor; and, other functional elements necessary for measuring a spectrum of a sample of an analyzed substance (hereinafter referred to as sample).

[00199] 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.

[00200] The main objective of the method of enhancing resolution and correction of spectral data-augmenting spectra-is estimation of the positions I and magnitudes a of the peaks contained in the spectrum of a sample under study x(l) on the basis of the acquired spectrometric data The feasibility of this operation is critically conditioned by an auxiliary operation on the reference data {γ ^α1} and corresponding reference spectrum x^cal( ), referred to as calibration of the spectrometric apparatus. This operation is aimed at the acquisition of information on a mathematical model of a relationship between spectrometric data and an idealized spectrum, which underlies the method according to the present embodiment for estimation of the parameters I and a. Although calibration does not necessarily directly precede augmentation of a sequence of spectrometric data {>¾}, valid calibration results should be available during this process.

[00201] 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 {¾} 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.

[00202] In accordance with the above general functional requirements and referring to FIGS. 10a through 10d, the method shown generally in FIG. 10a, comprises the following steps:

[00203] - calibration of a spectrometer (the sub-procedure ISD__ cal),

[00204] - reconstruction of a spectrum s(A;l,a) (the sub-procedure ISD__ rec),

[00205] - estimation of parameters I and a on the basis of an estimate s(A) of s(A;l,a) (the sub-procedure ISD__ est). sub-procedure ISD_cal

[00206] The sub-procedure ISD.. cal shown in FIG. 10b, comprises the following steps:

[00207] a) choosing a form of ideal peak v_s (λ,Ι) and of operators G and R;

[00208] b) choosing a calibration sample whose spectrum x^cal (λ) is known;

[00209] c) setting measurement parameters of the spectrometric apparatus;

[00210] d) acquiring data {y_n ^cal } representative of the calibration sample whose spectrum x^cal (λ) is known;

[00211] e) pre-processing of the data {y_n ^cal } to eliminate outliers, to perform baseline correction, smoothing, acquiring a priori information in the form of a pre-estimate of the variance of errors in the calibration data, and normalization; and

[00212] f) determining parameters P_G of the projection operator G, and parameters P_R of the reconstruction operator R. 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.

sub-procedure ISD_rec

[00213] The sub-procedure ISD_rec, shown in FIG. 10c, comprises the following steps:

[00214] a) setting measurement parameters substantially the same as those above;

[00215] b) acquiring data {y_n } representative of a sample under study;

[00216] c) pre-processing of the data {y_n } in a similar fashion to the preprocessing for determining the calibration data;

[00217] d) estimating an idealized spectrum s(A;l,a) on the basis of the data {y_n }, by means of the predetermined operator R and the parameters P_R ;

sub-procedure ISD_est

[00218] The sub-procedure ISD_est, shown in FIG. 10d, comprises the following steps:

[00219] a) estimating positions I of peaks within a spectrum on the basis of the estimate s(A) of s(A;l,a) by means of a maximum-detection algorithm; [00220] b) estimating magnitudes a of the peaks, by means of a curve fitting algorithm using one of the following methods:

[00221] - the data {y_n }, v_s (λ,Ι), the operator G with parameters P_G, and the estimate I;

[00222] - the estimate s(A), v_s (λ,Ι), and the estimate I.

[00223] c) iteratively correcting the estimates of the parameters of peaks obtained in (a) and (b);

[00224] d) adapting the results of parameter estimation in accordance with user requirements, such as transformation of parameters into some pre-defined parameters of an analyzed substance.

[00225]

[00226] For simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the examples described herein. However, it will be understood by those of ordinary skill in the art that the examples described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the examples described herein. Also, the description is not to be considered as limiting the scope of the examples described herein.

[00227] It will be appreciated that the examples and corresponding diagrams used herein are for illustrative purposes only. Different configurations and terminology can be used without departing from the principles expressed herein. For instance, components and modules can be added, deleted, modified, or arranged with differing connections without departing from these principles.

[00228] It will also be appreciated that 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.

[00229] The steps or operations in the flow charts and diagrams described herein are just for example. There may be many variations to these steps or operations without departing from the principles discussed above. For instance, the steps may be performed in a differing order, or steps may be added, deleted, or modified.

[00230] Although the above principles have been described with reference to certain specific examples, various modifications thereof will be apparent to those skilled in the art as outlined in the appended claims.

References:

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2004, Vol. 74, pp. 283-292.

[9]: R. G. Brereton, Chemometrics - Data Analysis for the Laboratory and Chemical Plant, J. Wiley & Sons, Chichester (UK) 2005.Section 5.2.

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The entire contents of the references are incorporated herein by reference.

Claims

Claims:

1. 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.

2. The method of claim 1 , wherein the calibrating comprises accessing one or more calibration models from a library accessible via the network by the electronic device.

3. The method of claim 1 or claim 2, wherein the LRSS comprises:

a low resolution transducer comprising a port for receiving electromagnetic radiation for measuring a spectrum thereof; a dispersive element for receiving the electromagnetic radiation received at the port, dispersing the received electromagnetic radiation, and providing the dispersed electromagnetic radiation; a photodetector for receiving the dispersed electromagnetic radiation from the dispersive element and for converting the dispersed electromagnetic radiation into an electrical signal representative of spectral data;

an analog to digital converter for converting the electrical signal representative of spectral data into a digital signal representative of the spectral data; and

a processor for receiving the digital signal, for enhancing the resolution of the spectral data, and for correcting at least some error within the spectral data using stored data, the stored data relating to a captured spectrum of a sample to a known spectrum of the sample, the known spectrum of the sample having higher resolution.

4. The method of claim 3, wherein the low-resolution transducer has a resolution R > 2 nm.

5. The method of any one of claims 1 to 4, wherein the calibrating and estimating comprises:

capturing data representative of a first spectrum of a first sample using the transducer;

comparing the data representative of the first spectrum to data representative of a known spectrum for a sample substantially the same as the sample and determining calibration data for transforming, according to a determined transformation, the data representative of the first spectrum into an approximation of the data representative a spectrum of a second sample using the transducer; and

estimating an ideal spectrum for the second sample using the calibration data, the estimating being performed using the determined transformation;

6. The method of claim 1 , wherein the calibrating and estimating are performed by the mobile application.

7. The method of claim 1 , wherein the calibrating is performed by the advanced program, and the estimating is performed by the mobile application.

8. The method of claim 1 , wherein the calibrating and estimating are performed by the advanced program.

9. The method of claim 1 , wherein the calibrating and estimating are each performed partially by the mobile application and partially by the advanced program.

10. The method of claim 1 , further comprising:

evaluating a spectrum-related quantity based on results of the estimating;

wherein the evaluating is performed by either or both the mobile application and the advanced program.

1 1. The method of claim 10, wherein the calibrating is performed by the advanced program, and the estimating and evaluating are performed by the mobile application.

12. The method of claim 10, wherein the calibrating and estimating are performed by the advanced program, and the evaluating is performed by the mobile application.

13. The method of claim 10, wherein the calibrating, the estimating, and the evaluating are performed by the advanced program.

14. The method of claim 10, wherein the calibrating, estimating, and evaluating are each performed partially by the mobile application and partially by the advanced program.

15. The method of any one of claims 10, 1 1 , 13, and 14, wherein the estimating and evaluation are performed together.

16. A computer readable medium comprising computer executable instructions for performing the method of any one of claims 1 to 15.

17. A system for performing 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 communication with the mobile application for calibrating a measurement channel, the calibrating oriented on measuring optical spectra, using the LRSS, and estimating an optical spectrum of a sample based on data acquired by the LRSS and results of the calibrating.

18. The system of claim 17, wherein the calibrating comprises accessing one or more calibration models from a library accessible via the network by the electronic device.

19. The system of claim 17 or 18, wherein the LRSS comprises:

20. The system of claim 19, wherein the low-resolution transducer has a resolution R > 2 nm.

21. The system of any one of claims 17 to 20, wherein the calibrating and estimating comprises:

22. The system of claim 17, wherein the calibrating and estimating are performed by the mobile application.

23. The system of claim 17, wherein the calibrating is performed by the advanced program, and the estimating is performed by the mobile application.

24. The system of claim 17, wherein the calibrating and estimating are performed by the advanced program.

25. The system of claim 17, wherein the calibrating and estimating are each performed partially by the mobile application and partially by the advanced program.

26. The system of claim 17, further comprising:

evaluating a spectrum-related quantity based on results of the estimating;

27. The system of claim 26, wherein the calibrating is performed by the advanced program, and the estimating and evaluating are performed by the mobile application.

28. The system of claim 26, wherein the calibrating and estimating are performed by the advanced program, and the evaluating is performed by the mobile application.

29. The system of claim 26, wherein the calibrating, the estimating, and the evaluating are performed by the advanced program.

30. The system of claim 26, wherein the calibrating, estimating, and evaluating are each performed partially by the mobile application and partially by the advanced program.

31. The system of any one of claims 16, 17, 19, and 20, wherein the estimating and evaluation are performed together.