Signal processing method and apparatus

Abstract: A method and apparatus for the determination of spectral samples is disclosed wherein spectral measurements are taken, normalization of the spectral measurements takes place, and a bilinear modeling is performed to extract spectral data. Once this data is derived, the interference quantitization levels are determined using multiple linear regression analysis, and are then removed from the sample readings in order to determine a more precise level of analyte spectra, such as analyte levels of glucose in serum or whole blood.

Description: The present invention relates generally to processing of data signals to reduce undesired variations or noise present in the data. Specifically, the present invention relates to an instrument or method for processing of data signals to reduce undesired variations or noise present in data. Most specifically, the present invention relates to an instrument, method or process to provide measurements of analytes reduced from data by removing undesired variations in noise present in that data. Data signals may be considered as either one dimensional streams or multidimensional structures. For example, a single measurement, such as temperature, made at a sequence of times or on a series of specimens yields a one dimensional data stream. Two dimensional data structures result when each measurement in a sequence produces a "spectrum" consisting of multiple interrelated data points, such as an optical spectrum of a chromatogram. Noise and variation in such data structures are often assumed to be random and unrelated among the data points. With such random data, the most direct approach to reducing variation and noise is in a weighted averaging of such data points. In the two dimensional case, this is typically done by averaging of several spectra from the sequence of spectra, for instance, in combining measurements having the same spectral data point index, or "wavelength". Another typical approach is the weighted averaging of several adjacent data points within each spectrum. In many instances, both types of averaging are often combined, to produce more precise measurements of data, and to further reduce effects of any variations or random noise in the data. Previously, there have also been a large number of multivariate methods of analyzing data. These techniques have included, for example, such methods as least mean square methods of curve fitting various spectra. With this technique, each spectrum from the data set is approximated as a linear combination of the spectra of known constituents or components within the data. These approximations satisfy a least mean square criterion. The appropriate coefficients of these linear combinations are then linearly related with, typically, analyte concentrations in the specimens. Methods such as these have been extended in various respects. First, multilinear correlation of several of the derived curve-fit coefficients with analyte values is accomplished to reduce the errors. Second, measured spectra from specimens of known compositions, rather than pure constituent spectra, are used as components. Errors are reduced using these techniques such that they become comparable to those obtained by multilinear regresion using unmodified or derivative type data at selected wavelengths. Yet, such methods assume a previous knowledge of the reference spectra. As well, these methods are not applicable to situations where the interfering spectra have variable characteristics. Other methods have included techniques such as spectral subtraction, where interferences are reduced by subtracting previously known or estimated reference spectra based on prior information about such spectra. For instance, in cases in which optical absorption spectra are used, it is known that such spectra will never be negative. Accordingly, once an absorption difference spectrum is estimated to be approximately zero at one or more data points, it is no longer desirable to subtract a greater interference magnitude from this spectrum. At that point, the combined interference spectrum is set, and the estimate of the analyte spectrum is obtained. Other higher level techniques have included such methods as latent variable analysis or bilinear modeling. In these methods, underlying sets of data points, or latent spectra, are extracted statistically from a data set. This method includes, among others, factor analyses, principal component analysis (PCA) and partial least square (PLS) methods. In these systems, a priori knowledge of the latent reference spectra are used throughout later analyses. In other words, once a latent reference spectral estimate is made, these higher level techniques inflexibility set the latent reference spectra. All these previous techniques become inaccurate if the measuring instrument or conditions change significantly. In addition, these previous techniques are incapable of differentiating between mimicked analyte spectral shapes and interference shapes. For instance, where interference shapes bear a correlation to the analyte spectral shapes, in many of the previous methods it is possible for the interference shapes to be confused with the analyte spectra, producing serious errors in the approximation techniques. Various methods have been proposed to correct these errors, but only after they have occurred. From APPLIED SPECTROSCOPY, vol. 42, no. 6, 1 June 1988, pages 1015-1019 a method is known for extracting NIR discriminant spectral patterns without any reference to chemical values consisting of two steps: first , groups of samples are defined a priori and second, Principal Component Analysis (PCA) is applied on spectral data and Factorial Discriminant Analysis (FDA) is applied on the PC scores. Discriminant spectral patterns are assesses as linear combinations of PCA eigenvectors with weights determined by FDA. Summary of the Invention Accordingly, it is an object of the present invention to determine the spectral components which interfere with measured data as these components are actually present in the data, rather than using a priori knowledge of these interfering components to fit the measured data for interference estimates and reduction. It is a second object of the present invention to improve estimates in the interference spectra by removing intercorrelation between analyte spectral shape and interference spectral shape in the data. It is a third object of the present invention to improve estimates of the degree of interference, independent of analyte and interference concentrations in the data, by removing intercorrelations between the analyte spectral shape and shapes of interference spectra. It is yet another object of the present invention to correct interferences within the data by a more accurate determination of the character and magnitude of these interferences. It is an additional object of the present invention to subtract the improved estimates of the character and magnitude of the interference measurements and eliminate these interferences substantially completely from the measured data to improve the ultimate determination of analytes within the originally measured data. Finally, it is an object of the present invention to incorporate the more accurate measurements of data through these interference spectral component measurements in a method and apparatus for accurate measurements of analytes. It is most desireable to incorporate these methods and apparatus in an apparatus to measure analyte levels, including such methods as taking near infrared measurements of analyte levels in blood, especially the measurement of glucose levels in whole blood or blood products. These and other objects of the present invention are accomplished in a method wherein spectral shapes of interferences are not considered a priori information, but are derived from signals as they are being measured, using bilinear modeling, on a data set consisting of one or more groups of replicate spectra. The average spectrum of each replicate group is subtracted from each spectrum within the group. within each replicate group, the analyte spectral signal is constant, and will be contained in the average spectrum which is subtracted. Once the appropriate replicate group average spectrum is subtracted, multiple replicate groups may be combined into a larger data set for the bilinear modeling to determine latent variable interference spectra. Thus, the components of the bilinear model are derived from the set of modified spectra, after removal of the replicate group average spectrum from each spectrum in each replicate group. These components will be used to explain variance in the measured data. Even if the analyte concentration changes between the replicate groups, the analyte variance is zero, because the analyte signal has been removed by subtracting the replicate group average spectrum. Thus, the bilinear modeled components represent the spectra of variable interferences and do not include significant analyte information. Despite the removal of the analyte signal from the data prior to bilinear modeling, the shape of the resultant latent variable interference spectra will usually be correlated to some degree to the analyte spectral shape. This correlation could cause errors in estimating the magnitude of each interference. Therefore, the spectrum of each of the interferences, whether known a priori or derived, is projected on the analyte spectrum to find the portion of each interference which is unique, i.e., orthogonal to the analyte spectrum. Each interference spectrum will be equal to the sum of its unique portion and a certain amount matching or mimicking the analyte spectrum in spectral characteristics. Projection of each measured spectrum on the unique portions of the interferences (for instance by regression analysis) provides coefficients to represent an estimate of the amount of each interference present in each measured spectrum. Each unique interference portion is orthogonal to the analyte spectrum. Thus, these estimates of interference magnitude are not biased by the analyte concentration. For each interference, the coefficients of the unique portion can be applied to the original spectrum of the spectrum to subtract from the measured spectrum in order to eliminate that portion of the interference. To insure that the measured spectra are replicates with respect to the analyte, the present invention may include a step of normalizing the measured spectral intensity so that the analytes' spectral signal is kept constant for constant analyte concentration. These and other objects of the present invention are accomplished in connection with the following discussion of the drawings as well as a detailed description of the inventions in which: Detailed Description of the Drawings Fig. 1 is a perspective view of a spectrophotometric sensor system for use in performance of the methods and apparatus of the present invention; Fig. 2 is a general block diagram of the apparatuses used as components in conjunction with the present invention as shown in Fig. 1; and Fig. 3 is a block diagram of the processor unit as used in the present invention. Detailed Description of the Present Invention The present invention has general applicability in the field of signal and data processing, wherever a sequence of "spectra" or data structures consisting of multiple interrelated data points, are obtained and the desired variability in the data can be described as "spectral" patterns. Either dimension of the two dimensional data structure can be considered a "sequence", with the other dimension representing the spectra. In these cases, which are the rule rather than the exception, this method is effective in reducing unwanted variability and noise. As an example, optical spectroscopy, for instance, in the determination of analyte levels in blood using near infrared techniques, often exhibits interferences which change from measurement to measurement due to unavoidable changes in the measurement geometry, the composition of the matrix surrounding the analyte, or physical factors such as bulk density, scattering, temperature, bubbles, cavitation, other flow effects, and similar phenomena, such problems are common in regular transmission, diffuse reflection, diffuse transmission and interaction measurements. The precision of on-line process control measurement of clear liquids is usually limited by variable compositional or physical interferences rather than truly random noise. As can be seen from Fig. 1, for instance, a spectrophotometric sensor system 10 as used in the present invention is described. This system can be used, for instance, in the determination of analyte levels in blood or, for instance, in the display of glucose levels in blood. This sensor system 10 comprises a measuring device 20 with an optical sensor 30 which will give optical readings of data, for instance, spectrophotometric measurements of specimens or materials such as blood. From this measuring device 20 the processor 100 used in conjunction with the method of the present invention is attached. This can be seen in Fig. 2. From the processing unit 100, a controller/analyzer 200 is attached, which allows the processed data to be analyzed and the processing unit 100 is to be controlled based on the data analysis results. The data, after analysis, is sent to display 40. The processor 100 of the present system follows a five-step approach: a) The processor 100 takes the specimen-derived data from the measuring sensor device 20 and normalizes this data. b) After This optional normalization, the processor extracts the variable interference spectral components from the data. c) These components are then separated by regression with the analyte spectrum to remove the interference portion which mimicks the analyte. d) The magnitudes of the interferences are determined by multiple regression of each measured spectrum on these analyte-orthogonal interferences. Finally, e) the originally extracted interferences are scaled by these magnitudes and subtracted from the original normalized data in order to finally provide a corrected spectrum for use in controller/analyzer 200 to determine the analyte levels. It is these which are ultimately displayed in the display unit 40 of the system 10. If desired, steps c) through e) can be repeated for additional analytes. The present application now turns to the five distinct functions taking place in the processing unit 100 of the system. Each of the measured spectra, S n , is sequentially stored in the group storage area 112 of the normalizing portion 110 of the processor 100. From this group storage area 112, each of the spectra S n from a replicate group is added together and divided by the number of measurements in the group in order to determine the average spectra, S at averager 115. Thus, the spectra can be described as in equation (1) below: S n = b on + b ln S + S′ n In equation (1), S′ n is described as variations in individual spectra from the average spectrum, S . The coefficient b on represents an additive offset in the measured spectral data, and the coefficient b ln corresponds to a multiplicative scale factor relating spectrum S n to the average spectrum. A normalized or corrected spectrum S and n is then derived using equation (3) in the adder/divider element 116 of the normalization unit 110: S n = S n - b on b ln From equations (3) and (1), S and = S + S n' / b ln showing that all the differences, S' n , from the average have been normalized by the factor b ln . In order to further proceed toward the ultimate determination of analyte levels, spectra describing the interferences should be extracted, but only on variable components, and not on components to be determined, such as analyte levels. Therefore, the newly derived S and n are once again grouped in the group storage unit 132 of the interference characterization section 130 of the processor 100. From this replicate group an average, S and avg is taken. Then, each S and n has subtracted from it the S and avg to arrive at a newly derived S and' n as in equation (6) : S ' n = S n - S avg Now each of the newly derived S and' n will not contain any contributions from the desired analyte spectra. As each individual S and' n is derived, it is stored in the combined group storage unit 134 in the interference characterization portion 130. Once a desired number of replicate groups of S and' n are stored, they are used as a set within the components analyzer 136. In using known methods of bilinear modeling such as PCA, the set of spectra S and' n are analyzed and broken into components P l -P j . It is known, however, that each of these components P l -P j are mutually orthogonal. Thus, the derived P j are now ready for input into the interference quantization and removal area 140 of processor 100. In most instances, the spectrum of the derived components P j , will have some similarity to the known spectrum of the analyte, for instance, glucose. In these cases, the correlation between the two spectra will not equal zero, and regressing of the spectra S and n on the interfering components P j would produce incorrect coefficients influenced by analyte concentration. Thus, the derived components must be modified in order to appropriately orthogonalize these components with respect to the analyte. This is accomplished by removing the entire portion of the interference (component spectrum P j ) which mimics the analyte. Thus, the individual derived components, P j , are put into a simple linear regression analyzer 142. Each component P j is projected on a known analyte reference spectrum, A from reference area 141. After the linear regression is accomplished, coefficients quantifying the analyte like portion C 1j are derived for each individual component P j . These coefficients C 1j are multiplied by the known analyte spectrum A to determine the portion of each component P j which mimics the analyte spectrum A. Thus, a modified spectral component Q 1j can be derived at combiner 143 at for each P 1j as in equation (7): Q j = P j - c 1j A Importantly, each Q j will now be orthogonal to the analyte spectrum A. As a check on these operations, coefficient C oj should be derived, where C oj should equal zero. Now, the previously stored corrected individual spectra S and n can be analyzed using the modified components Q j in order to determine the magnitude of each component Q j contained in each group corrected individual spectrum S and n . As seen in the interference quantization and removal section 140, a multiple linear regression analysis at analyzer 144 is performed regressing each S and n on the Q j components to arrive at coefficients m jn where each of the m jn conforms to equation (8): As seen in equation (8), the m on are described as offsets of the spectral data. Now the newly derived factors m jn can be combined with the components P j at multiplier 145 in order to determine the actual interference component spectra, I jn , in each specimen, according to equation (9): I jn = m jn P j One remaining step is necessary to determine the entire interference spectrum contained in each sample. Thus, each individual interference component, I jn , is placed into a vector adder 150 as seen in Fig. 3. Each of the interference components is summed. The offset m on is added on to this factor according to equation (10): Now, the interference spectra derived for each sample, I n , can be removed from the corrected individual spectra S n . At combiner 155, as in equation (11), the sum of the interference components, I n , is subtracted from the corrected individual spectra S n to arrive at the final corrected spectrum : Of course, in order to more clearly approximate the S and n and reduce residual random errors, an average can be taken at averager 160 in the final step of the interference quantitization and removal process. Thus, the newly arrived average final corrected spectrum will most accurately parallel the level of analyte that is present in the sample. Optionally, it is possible to use a buffer 138 as seen in the same step in order to prevent changes in analyte levels within a replicate group from causing errors. For instance, in cases where a presumed constant analyte level actually changes, these changes can be monitored by controller/analyzer 200 using the output . If the analyte variation within a replicate group larger than a predetermined amount, the components derived during the present derivation are determined to be inaccurate, and previously stored ones are used.

Claims: A method for analyzing data described as spectra comprising: finding the average spectrum within groups of said spectra; subtracting said average spectra from said individual spectra to produce modified spectra comprising the variability of each spectrum from said group average spectrum; taking one or more of said groups of said modified spectra and performing a bilinear model analysis to determine component spectra comprising said modified spectra; combining said components and a reference spectrum by means of linear modeling to modify said components such that they are orthogonal with respect to said reference spectrum; analyzing each individual spectrum by linear modeling with respect to these modified, reference-orthogonal components to determine the magnitude of each said modified component comprising said individual spectrum; applying said magnitudes to the original, unmodified bilinear model components, and subtracting the resulting interference spectra from said individual spectrum. A method for analyzing data described as spectra in order to remove the effect of spectral components which interfere with the measured data, comprising the steps of: removing the effect of constant analyte contribution by subtracting the average spectra of replicate groups of said spectra to form modified spectra representing variable interference information; determining component spectra of said modified spectra by bilinear model analysis; determining the magnitude of each said component spectra contained in each original spectrum and applying these magnitudes to the component spectra to develop interference spectra; and subtracting the resulting interference spectra from said original spectra. The method of claim 2 wherein the magnitude of each component spectra contained in each original spectrum is combined with known reference spectra to modify the component spectra such that they are orthogonal to the known reference spectra and wherein each original spectrum is analyzed to determine the magnitdues of each modified, reference-orthogonal component contained in them and then applying these magnitudes to the original component spectra to develop interference spectra. The method of claim 3 wherein said combining step and said analyzing step are performed by linear modeling. The method of claim 2 wherein the magnitude of each component spectrum contained in each original spectrum is determined using known reference spectra and the component spectra in a bilinear model. The method of claim 2 including the step of normalizing the measured spectra by correcting for offsets and multiplicative errors using coefficients determined by bilinear modeling. A method as in claim 6 wherein the groups of spectra are measured spectra, S n , and the method includes the steps of normalizing the measured spectra by adding the measured spectra, S n , together; dividing the added spectra by the number of measurements required to develop the spectra to form an average spectra S ; and forming a modified (corrected) spectra S ˙ n based on the relationship S n = S + S n ' b 1n where b 1n corresponds to a multiplicative scale factor relating S n save to the average spectrum, S , and where S n ' represents variations in individual spectra from the average spectrum, S . The method of claim 3 wherein the groups of spectra are designated as S ˙ n , the component spectra determined by bilinear analysis are designated P j and wherein said combining step includes performing linear regression analysis by projecting each component on a known analyte spectrum A and developing coefficients quantifying the analyte-like portion c 1j for each individual component P j ; multiplying the coefficients c 1j by the known analyte spectrum A to determine the portion of each component P j which mimics the analyte spectrum A to develop a modified spectral component Q j for each P j so that Q j = P j - c 1j A The method of claim 8 wherein the analyzing step employs the use of the Q j components to determine the magnitude of the Q j components in each group of spectrum S ˙ n by performing multiple linear regression analysis by regressing each S ˙ n on the Q j components to develop coefficients of m jn , where each m jn is related by m on being the offsets of the spectral data; and by combining the m jn coefficients with components P j to determine the actual interference component spectra, I jn , in each specimen according to the relationship I jn = m jn P j The method according to claim 9 wherein the method includes the step of adding the individual interference components with the offset m on to develop an interference spectra in accordance with the relationship and wherein the final corrected spectrum S ˙ n is developed by removing the interference components according to the relationship S n = S n -I n Apparatus for analyzing data described as spectra for removing the effects of spectral components which interfere With the measured data comprising: means for removing the effect of constant analyte contribution by subtracting the average spectra of replicate groups of said spectra to form modified spectra representing variable interference information; means for determining component spectra of said modified spectra by bilinear model analysis; means for determining the magnitude of each said component spectra contained in each original spectrum and applying these magnitudes to the component spectra to develop interference spectra; and means for subtracting the resulting interference spectra from said original spectra. The apparatus of claim 10 wherein said means for determining the magnitude of each said component spectra includes means for combining each original spectrum with known reference spectra to modify the component spectra such that they are orthogonal to the known reference spectra and means for analyzing each original spectrum to determine the magnitudes of each modified, reference-orthogonal component contained in them and for then applying these magnitudes to the original component spectra to develop interference spectra.

Inventors: STARK EDWARD W, STARK, EDWARD W.
Applicants: STARK EDWARD W, STARK, EDWARD W.
Application year: 1990
Main classification: G01N21
IPC codes: G01J3, G01N21, G06F17