(a) much wider dynamic range;
(b) greatly improved calibration linearity ("hyperlinearily");
(c) ability to operate under conditions that are optimized for signal-to-noise ratio rather than for Beer's Law ideality (e.g. small spectrometers with lower dispersion and larger slit widths to increase light throughput).
Just like the multilinear
regression (classical least squares) methods conventionally
used in absorption spectroscopy, the Tfit method
(a) requires an accurate reference spectrum of each analyte,
(b) utilizes multiwavelength data such as would be acquired on diode-array, Fourier transform, or automated scanning spectrometers, and
(c) applies both to single-component and multicomponent mixture analysis.
The disadvantages of the Tfit method are:
(a) it is computationally more intensive than the multilinear regression methods (but, on a typical personal computer, calculations take only a fraction of a second, even for the analysis of a mixture of three components);
(b) it requires knowledge of the instrument function, i.e., the slit function or the resolution function of an optical spectrometer (however, this is a fixed characteristic of the instrument and is easily measured beforehand by scanning the spectrum of a narrow atomic line source such as a hollow cathode lamp); and
(c) it is an iterative method that can under unfavorable circumstances converge on a local optimum (but this is handled by proper selection of the starting values, based on preliminary approximations calculated by conventional methods).
Click here to download an interactive
self-contained demo m-file that works in recent versions of
Matlab. You can also download a ZIP file
"TFit.zip" that contains both the interactive version for
Matlab and a command-line version for Octave.
You can also download it from the Matlab
The following sections give the background of the method and a description of the main function and demonstration programs:
I = Izero.*10^-(alpha*L*c)
where “Izero” is the intensity of the light incident on the sample, “alpha” is the absorption coefficient of the absorber, “L” is the distance that the light travels through the material (the path length), and “c” is the concentration of absorber in the sample. The variables I, Izero, and alpha are all functions of wavelength; L and c are scalar.
In conventional applications, measured values of I and Izero are used to compute the quantity called "absorbance", defined as
A = log10(Izero./I)
Absorbance is defined in this way so that, when you combine this
definition with the Beer-Lambert law, you get:
A = alpha*L*c
So, absorbance is proportional to concentration, ideally, which simplifies analytical calibration. However, any real spectrometer has a finite spectral resolution, meaning that the light beam passing through the sample is not truly monochromatic, with the result that an intensity reading at one wavelength setting is actually an average over a small spectral interval. More exactly, what is actually measured is a convolution of the true spectrum of the absorber and the instrument function. If the absorption coefficient "alpha" varies over that interval, then the calculated absorbance will no longer be linearly proportional to concentration (this is called the “polychromicity” error). The effect is most noticeable at high absorbances. In practice, many instruments will become non-linear starting at an absorbance of 2 (~1% Transmission). As the absorbance increases, the effect of unabsorbed stray light and instrument noise also becomes more significant.
The theoretical best signal-to-noise ratio and absorbance precision for a photon-noise limited optical absorption instrument can be shown to be close to an absorbance of 1.0 (see #8 on http://terpconnect.umd.edu/~toh/models/AbsSlitWidth.html#BestAbsorbance). However, if one attempts to arrange sample dilutions and absorption cell path lengths to obtain a working range centered on an absorbance of 1.0, for example over the range .1 – 10, or 0.01 – 100, the measurements will obviously fail at the high end. (Clearly, the direct measurement of an absorbance of 100 is unthinkable, as it implies the measurement of light attenuation of 100 powers of ten - no real measurement system has a dynamic range even close to that). In practice, it is difficult to achieve an dynamic range even as high as 5 or 6 absorbance, so that much of the theoretically optimum absorbance range is actually unusable. (c.f. http://en.wikipedia.org/wiki/Absorbance). So, in conventional practice, greater sample dilutions and shorter path lengths are required to force the absorbance range to lower values, even if this means poorer signal-to-noise ratio and measurement precision at the low end.
It is true that the non-linearity caused by polychromicity can
be reduced by operating the instrument at the highest resolution
setting (reducing the instrumental slit width). However, this has
a serious undesired side effect: in dispersive
instruments, reducing the slit width to increase the
spectral resolution degrades the signal-to-noise substantially.
The optimum signal-to-noise
ratio is often achieved when the resolution of the instrument approximately matches
the width of the analyte absorption, but operating the
instrument in that way would result in very substantial
non-linearity over most of the absorbance range because of the “polychromicity” error. This
non-linearity has its origin in the spectral domain
(intensity vs wavelength), not in the calibration domain
(absorbance vs concentration). Therefore it should be no surprise
that curve fitting in the calibration domain, for example fitting
the calibration data with a quadratic or cubic fit, might not be
the best solution. Perhaps a better approach is to perform the
curve fitting in the spectral domain. This is possible
with modern absorption spectrophotometers that use array detectors
that have many tiny detector elements that slice up the spectrum
of the transmitted beam into many small wavelength segments,
rather than detecting the sum of all those segments with one big
photo-tube detector as older instruments do.
The TFit method sidesteps the above problems by calculating the
absorbance in a completely different way: it starts with the
reference spectra (an accurate absorption spectrum for each
analyte, also required by the multilinear regression methods),
normalizes them to unit height, multiplies each by an adjustable
coefficient, adds them up, computes the antilog, and convolutes it
with the previously-measured slit function. The result,
representing the instrumentally broadened transmission spectrum,
is compared to the observed transmission spectrum. The
coefficients (one for each unknown component in the mixture) are
adjusted by the program until the computed transmission model is a
least-squares best fit to the observed transmission spectrum. The
best-fit coefficients are then equal to the absorbances determined
under ideal optical conditions. Provision is also made to
compensate for unabsorbed stray light and changes in background
intensity (background absorption). These calculations are
performed by the function fitM, which is
used as a fitting function for Matlab's iterative non-linear
fitting function fminsearch. The
TFit method gives measurements of absorbance that are much closer
to the "true" peak absorbance (in the absence of stray light and
polychromatic light errors) and it allows linear and wide dynamic
range measurements to be made, even if the slit width of the
instrument is increased to optimize the signal-to-noise ratio.
Note: It's important to understand that the use of the TFit
method does not guarantee a perfectly linear analytical curve at
all absorbances, despite the impression given by these
simulations. The TFit method simply compensates for the
non-linearity caused by unabsorbed stray light and the
polychromatic light effect. Other sources of non-linearity remain,
in particular chemical effects,
such as photolysis, equilibrium shifts, temperature effects, pH
effects, binding, dimerization, polymerization, molecular
phototropism, fluorescence, etc.
Bottom line: The TFit method is based on the Beer-Lambert Law, but it calculates the absorbance in a different way that does not require the assumption that stray light and polychromatic radiation effects are zero. Because it allows larger slit widths to be used, it is possible to obtain greater signal-to-noise ratios, while still achieving a much wider linear dynamic range than previous methods.
where start is the first guess (or
guesses) of the absorbance(s) of the analyte(s); it's
convenient to use the conventional log10(Izero/I) estimate of
absorbance for start. The other arguments
(described above) are passed on to FitM. In this example, fminsearch
returns the value of absorbance that would have been measured in
the absence of stray light and polychromatic light errors (which
is either a single value or a vector of absorbances, if it is a
multi-component analysis). The absorbance can then be converted
into concentration by any of the usual
calibration procedures (Beer's Law, external standards, standard
Iterative least-squares methods
are ordinarily considered to be more difficult and less reliable
than multilinear regression methods,
and this can be true if there are more than one nonlinear variable
that must be iterated, especially if those variables are
correlated. However, in the TFit method, there is only one iterated variable
(absorbance) per measured component, and reasonable first guesses
are readily available from the conventional single-wavelength
absorbance calculation or multiwavelength regression methods. As a
result, an iterative least-squares method works very well in this
case. Comparing the expression for absorbance given above
for the TFit method to that for the weighted regression method:
absorbance=([weight weight].*[Background ReferenceSpectra])\(-log10(yobsd).*weight)you can see that, in addition to the ReferenceSpectra and observed transmission spectrum (yobsd), the TFit method also requires a measurement of the Instrument function (spectral bandpass) and the stray light (which the linear regression methods assume to be zero), but these are characteristics of the spectrometer and need be done only once for a given spectrometer. Finally, although the TFit method does make the computer work harder, the computation time on a typical laboratory personal computer is only a fraction of a second (roughly 25 µsec per spectral data point per component analyzed), using Matlab as the computational environment.
|Keypress-operated interactive explorer for the Tfit method
(for Matlab only), applied to the measurement of a
single component with a Lorentzian (or Gaussian) absorption
peak, with controls that allow you to adjust the true
absorbance (Peak A), spectral width of the absorption peak
(AbsWidth), spectral width of the instrument function
(InstWidth), stray light, and the noise level (Noise)
continuously while observing the effects graphically and
numerically. Simulates the effect of photon noise,
unabsorbed stray light, and random background intensity
shifts (light source flicker). Compares observed absorbances
by the single-wavelength, weighted multilinear regression
(sometimes called Classical Least Squares in the
chemometrics literature), and the TFit methods. To run this
file, right-click TFitDemo.m click
"Save link as...", save it in a folder in the Matlab path,
then type "TFitDemo" at the Matlab command prompt.
With the figure window topmost on the screen, press K to get a list of the
keypress functions. Version 2.1, November 2011, adds
SNR calculation; W key to Switch between Transmission and
In the example shown on the left, the true peak absorbance is exactly 1.000, the absorption widths and instrument function widths are equal, the unabsorbed stray light is 0.5%., and the photon noise is 5%. The results below the graphs show that the TFit method gives a much more accurate measurement (1.0046) than the single-wavelength method (0.617) or weighted multilinear regression method (0.954).
Note: The original version of this demo, which uses sliders, works only on Matlab 6.5, but I recommend that you use TFitDemo.m instead, as it is more up-to-date and includes the statistics and calibration curve.
Peak shape....Q Toggles between Gaussian and Lorentzian absorption peak shape
True peak A...A/Z True absorbance of analyte at peak center, without
instrumental broadening, stray light, or noise.
AbsWidth......S/X Width of absorption peak
SlitWidth.....D/C Width of instrument function (spectral bandpass)
Straylight....F/V Fractional unabsorbed stray light.
Noise.........G/B Random noise level
Re-measure....Spacebar Re-measure signal
Switch mode...W Switch between Transmission and Absorbance display
Statistics....Tab Prints table of statistics of 50 repeats
Cal. Curve....M Displays analytical calibration curve in Figure 2
Keys..........K Print this list of keyboard commands
Why does the noise on the graph change if I change the instrument function (slit width or InstWidth)? In the most common type of absorption spectrometer, using a continuum light source and a dispersive spectrometer, the spectrometer's spectral bandwidth ("InstWidth") is changed by changing the monochromator slit width, which also effects the light intensity transmitted through the monochromator and thus the signal-to-noise ratio. Therefore, in all these programs, when you change InstWidth, the photon noise is automatically changed accordingly just as it would in a real spectrophotometer.
True A SingleW SimpleR WeightR TFit
| Simple script that computes the statistics of the TFit
method compared to single- wavelength (SingleW), simple
regression (SimpleR), and weighted regression (WeightR)
methods. Simulates photon noise, unabsorbed stray light and
random background intensity shifts. Estimates the precision
and accuracy of the four methods by repeating the
calculations 50 times with different random noise samples.
Computes the mean, relative percent standard deviation, and
relative percent deviation from true absorbance. Parameters
are easily changed in lines 19 - 26. Results are displayed
in the MATLAB command window.
In the sample output shown on the left, results for true absorbances of 0.001 and 100 are computed, demonstrating that the accuracy and the precision of the TFit method is superior to the other methods over a 10,000-fold range.
This statistics function is included as a keypress command (Tab key) in TFitDemo.m.
|Function that compares the analytical curves for
single-wavelength, simple regression, weighted regression,
and the TFit method over any specified absorbance range
(specified by the vector “absorbancelist” in line 20).
Simulates photon noise, unabsorbed stray light and random
background intensity shifts. Plots a log-log scatter plot
with each repeat measurement plotted as a separate point (so
you can see the scatter of points at low absorbances). The
parameters can be changed in lines 20 - 27.
In the sample result shown on the left, analytical curves for the four methods are computed over a 10,000-fold range, up to a peak absorbance of 100, demonstrating that the TFit method (shown by the green circles) is much more nearly linear over the whole range than the single-wavelength, simple regression, or weighted regression methods.
This calibration curve function is included as a keypress command (M key) in TFitDemo.m.
The original version of this demo, which uses sliders, works only on Matlab 6.5, but you can also download the newer self-contained keyboard-operated version that works in recent versions of Matlab:
A1 A/Z Increase/decrease true absorbance of component 1
A2 S/X Increase/decrease true absorbance of component 2
A3 D/C Increase/decrease true absorbance of component 3
Sepn F/V Increase/decrease spectral separation of the components
InstWidth G/B Increase/decrease width of instrument function (spectral bandpass)
Noise H/N Increase/decrease random noise level when InstWidth = 1
Peak shape Q Toggles between Gaussian and Lorentzian absorption peak shape
Table Tab Print table of results
K Print this list of keyboard commands
Sample table of results (by pressing the Tab key):
True Weighted TFit
Absorbance Regression method
Component 1 3 2.06 3.001
Component 2 0.1 0.4316 0.09829
Component 3 5 2.464 4.998
Created October 03, 2006. Revised September, 2014.
© Tom O'Haver
Department of Chemistry and Biochemistry
The University of Maryland at College Park
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