System and method of increasing sample throughput
First Claim
1. A method for determining and improving usefulness of the curve fitting equation obtained from data from a sensor, the method comprising;
- a) receiving, from the sensor, data signals generated in response to being exposed to an analyte within a sample;
b) recording data points associated with the data signals;
c) selecting a series of data points corresponding to a portion of a kinetic region time range from the recorded data points;
d) determining a curve fitting equation that fits the series of data as a logarithmic scale of time, wherein the curve fitting equation is of the form s(t)=a*(log(t))^2−
2aV(log(t))+c, and V is a log of a time at which extremum occurs, wherein t represents time and a and c are the fit parameters for second order polynomial;
e) determining an outlier candidate with a largest residual;
f) comparing a residual of the outlier candidate with the largest residual to a predetermined residual limit;
g) classifying the outlier candidate with the largest residual as an outlier if the residual of the outlier candidate with the largest residual is greater than the predetermined residual limit;
h) obtaining a measure of effect of the outlier on the parameters of the curve fitting equation;
i) comparing the measure of the effect of the outlier to a predetermined measure limit;
j) incrementing an outlier count, if the measure of the effect of the outlier is greater than the predetermined measure limit;
k) comparing the outlier count to a predetermined outlier number limit, if the measure of the effect of the outlier is greater than the predetermined measure limit; and
l) removing the outlier from the data points, if the measure of the effect of the outlier is greater than the predetermined measure limit, resulting in an analyzed set of data points, thereby increasing sample throughput.
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Abstract
Technologies for increasing sample throughput by predicting the end point response time of a sensor for the analysis of an analyte in a sample are disclosed. In one aspect, a system includes a sensor that generates data signals associated with the measurement of an analyte within the sample. A processor records appropriate data points corresponding to the signals, converts them to a logarithmic function of time scale, and plots the converted data points. The processor then determines a curve that fits the plotted data points and determines a curve fitting equation for the curve. Once the equation is determined, the processor extrapolates an end point response of the sensor using the equation. A value, such as analyte concentration, is then calculated using the extrapolated end point response.
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Citations
9 Claims
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1. A method for determining and improving usefulness of the curve fitting equation obtained from data from a sensor, the method comprising;
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a) receiving, from the sensor, data signals generated in response to being exposed to an analyte within a sample; b) recording data points associated with the data signals; c) selecting a series of data points corresponding to a portion of a kinetic region time range from the recorded data points; d) determining a curve fitting equation that fits the series of data as a logarithmic scale of time, wherein the curve fitting equation is of the form s(t)=a*(log(t))^2−
2aV(log(t))+c, and V is a log of a time at which extremum occurs, wherein t represents time and a and c are the fit parameters for second order polynomial;e) determining an outlier candidate with a largest residual; f) comparing a residual of the outlier candidate with the largest residual to a predetermined residual limit; g) classifying the outlier candidate with the largest residual as an outlier if the residual of the outlier candidate with the largest residual is greater than the predetermined residual limit; h) obtaining a measure of effect of the outlier on the parameters of the curve fitting equation; i) comparing the measure of the effect of the outlier to a predetermined measure limit; j) incrementing an outlier count, if the measure of the effect of the outlier is greater than the predetermined measure limit; k) comparing the outlier count to a predetermined outlier number limit, if the measure of the effect of the outlier is greater than the predetermined measure limit; and l) removing the outlier from the data points, if the measure of the effect of the outlier is greater than the predetermined measure limit, resulting in an analyzed set of data points, thereby increasing sample throughput. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer-readable storage medium having computer executable instructions stored thereon, which when executed by a computer, cause the computer to:
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a) receive, from a sensor, data signals generated in response to being exposed to an analyte within a sample; b) determine a curve fitting equation that fits the series of data as a logarithmic scale of time, wherein the curve fitting equation is of the form s(t)=a*(log(t))^2−
2aV(log(t))+c, and V is a log of a time at which extremum occurs, wherein t represents time and a and c are the fit parameters for second order polynomial;c) determine an outlier candidate with a largest residual; d) compare a residual of the outlier candidate with the largest residual to a predetermined residual limit; e) classify the outlier candidate with the largest residual as an outlier if the residual of the outlier candidate with the largest residual is greater than the predetermined residual limit; f) obtain a measure of effect of the outlier on the parameters of the curve fitting equation; g) compare the measure of the effect of the outlier to a predetermined measure limit; h) increment an outlier count, if the measure of the effect of the outlier is greater than the predetermined measure limit; i) compare the outlier count to a predetermined outlier number limit, if the measure of the effect of the outlier is greater than the predetermined measure limit; and j) remove the outlier from the data points, if the measure of the effect of the outlier is greater than the predetermined measure limit, resulting in an analyzed set of data points; k) determine, if the measure of the effect of the outlier is greater than the predetermined measure limit, a curve fitting equation that fits the analyzed set of data points as a logarithmic function of time; l) repeat, if the measure of the effect of the outlier is greater than the predetermined measure limit, steps c) to j) for the analyzed set of data points; and m) identify the data points for review, if the outlier count is greater than the predetermined outlier number limit. - View Dependent Claims (8, 9)
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Specification