Advantages of the TFit method compared to conventional methods are:

(a) much wider

dynamic range(i.e., the concentration range over which one calibration curve can be expected to give good results) ;(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
File Exchange.

The following sections give the background of the method and a description of the main function and demonstration programs:

Background

In optical absorption spectroscopy, the intensity I of light passing through an absorbing sample is given (in Matlab notation) by the Beer-Lambert Law:

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. It
also reduces the number of atoms or molecules that are actually
measured. Here's why: UV/visible absorption spectroscopy is based
on the the absorption of photons of light by molecules or atoms
resulting from transitions between electronic energy states. It's
well known that the absorption peaks of molecules are more-or-less
wide bands, not monochromatic lines, because the molecules are
undergoing vibrational and rotational transitions as well and are
under the perturbing influence of their environment. This is the
case also in atomic absorption spectroscopy: the absorption
"lines" of gas-phase free atoms, although much narrower that
molecular bands, have a finite non-zero width, mainly due to their
velocity (temperature or Doppler broadening) and collisions with
the matrix gas (pressure broadening). A macroscopic collection of
molecules or atoms, therefore, presents to the incident light beam
a *distribution *of energy states and absorption
wavelengths. Absorption results from the interaction of individual
atoms or molecules with individual photons. A purely monochromatic
incident light beam would have photons all of the same energy,
presumably corresponding to the average in the energy distribution
of the collection of atoms or molecules being measured. But many,
actually most, of the atoms or molecules would have a energy
greater or less than the average and* would thus not be measured*.
If the bandwidth of the incident beam is increased, more of those
non-average atoms or molecules would be available to be measured,
but then the simple calculation of absorbance as log10(Izero/I)
would result in a non-linear response to concentration.

Numerical simulations
show that the optimum signal-to-noise ratio is typically achieved
when the spectral 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 and 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. Keep in mind that the
log(Izero/I) absorbance calculation is a 160-year-old
simplification that was driven by the desire for mathematical
convenience, not by the need for detection sensitivity and
signal-to-noise ratio. It dates from the time before electronics
and computers, when the only computational tools were pen and
paper and slide rules, and when a method such as described here
would have been unthinkably impractical.

The
Tfit method can be made to work in a spreadsheet. The
shift-and-multiply method is used for the convolution
of the reference spectrum with the slit function, and
the "Solver" add-in for Excel and Calc is used for the
iterative fitting of the model to the observed
transmission spectrum. It's handy, but not essential,
to have a "macro" capability to automate the process
(See http://peltiertech.com/Excel/SolverVBA.html#Solver2
for more info about setting up macros and solver on
your version of Excel).

TransmissionFittingTemplate.xls
(screen image)
is an empty template for a single isolated peak; TransmissionFittingTemplateExample.xls
(screen
image) is
the same template with example data entered. TransmissionFittingDemoGaussian.xls
(screen
image) is a demonstration with a simulated
Gaussian absorption peak
with variable peak position, width, and height, plus
added stray light, photon noise, and detector noise,
as viewed by a spectrometer with a triangular slit
function. You can vary all the parameters and compare
the best-fit absorbance to the true peak height and to
the conventional log(1/T) absorbance. All
of these spreadsheets include a macro,
activated by pressing **Ctrl-f****,** that uses
the Solver function to perform the iterative
least-squares calculation (see CaseStudies.html#Using_Macros).
But if you prefer not to use macros, you can do it
manually by clicking the **Data **tab, **Solver**,
**Solve**, and then **OK**.

TransmissionFittingCalibrationCurve.xls
(screen
image) includes another Excel macro that demonstrates
the construction of a calibration curve
comparing the TFit and conventional log(1/T)
methods, for a series of 9 standard
concentrations that you can specify.
To create a calibration curve, enter the
standard concentrations in AF10 - AF18 (or just
use the ones already
there, which cover
a 10,000-fold
concentration
range from 0.01 to 100), then press **C****trl-f
**to run the macro. In this spreadsheet the macro does a lot more than in the
previous example:
it automatically goes through the
first row of the little table in
AF10 - AH18, extracts each
concentration value in turn,
places it in the
concentration cell A6, recalculates the
spreadsheet, takes the resulting
conventional absorbance (cell J6) and places
it as the first guess in cell I6, brings up
the Solver to compute the best-fit
absorbance for that peak height, places both
the conventional
absorbance and the
best-fit
absorbance in
the
table in
AF10 - AH18,
then goes to the next concentration and
repeats for each concentration value. Then it
constructs and
plots the log-log calibration curve (shown
on the right) for both the TFit method
(blue dots) and the
conventional (red
dots)
and computes the
trend-line equation and
the R2 value for the TFit
method, in the upper right
corner of graph. Each time
you press **Ctrl-f **it
repeats the whole
calibration curve with
another set of random
noise samples. (Note: you
can also use this
spreadsheet to compare the
precision and reproducibility
of the two methods by entering
the *same *concentration
9 times in AF10
- AF18.
The result should be a straight
flat line with
zero slope).

fitM is a fitting function for the Tfit method, for use with Matlab's or Octave's fminsearch function. The input arguments of fitM are:

absorbance=
vector of absorbances that are calculated to give the best fit to
the transmission spectrum.

**yobsd** = observed transmission spectrum of the mixture
sample over the spectral range (column vector)

**Spectra** = reference spectra for each component, over the
same spectral range, one column/component, normalized to 1.00.

**InstFun** = Zero-centered instrument function or slit
function (column vector)

**StrayLigh**t = fractional stray light (scalar or column
vector, if it varies with wavelength)

Note: Typical use:

`absorbance=fminsearch(@(lambda)(fitM(lambda,yobsd,TrueSpectrum,InstFun,straylight)),start);
`

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
addition, etc.).

Here is a specific numerical example,
where the true absorbance is 1.00, using only 4-point spectra for
simplicity (normally an array-detector system would acquire many
more wavelengths than that). In this case the instrument width
(InstFun) is twice the absorption width, the stray light is 0.01
(1%), and the conventional single-wavelength estimate of
absorbance is far too low: `log10(1/.38696)=``0.4123`.
In contrast, the TFit method using fitM:

`fminsearch(@(lambda)(fitM(lambda,[0.56529 0.38696 0.56529
0.73496]',[0.2 1 0.2 0.058824]',[1 0.5 0.0625 0.5]',.01)),.4)`

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. The expression for absorbance given above for the TFit method can be compared to that for the

where RefSpec is the matrix of reference spectra of all of the pure components. You can see that, in addition to the RefSpec 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.absorbance=([weight weight].*[BackgroundRefSpec])\(-log10(yobsd).*weight)

>> tfit(1)

width = 10

InstWidth = 20

noise = 0.01

straylight = 0.01

IzeroShift = 0.01

True A SingleW SimpleR WeightR TFit

1 0.38292 0.54536 0.86839 1.0002

>> tfit(10)

10 1.4858 2.2244 9.5123 9.9979

>> tfit(100)

100 2.0011 3.6962 57.123 99.951

>> tfit(200)

200 2.0049 3.7836 56.137 200.01

>> tfit(.001)

0.001 0.00327 0.00633 0.000520 0.000976

In the example shown above, the true peak absorbance is shown varying from 0.0027 to 57 absorbance units, the absorption widths and instrument function widths are equal (which results in the optimum signal-to-noise ratio), the unabsorbed stray light is 0.5%., and the photon noise is 5%. (For demonstration purposes, the lowest 6 absorption peak shapes are

KEYBOARD COMMANDS

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

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.

True A SingleW SimpleR WeightR TFit

MeanResult =

0.0010 0.0004 0.0005 0.0006 0.0010

PercentRelativeStandardDeviation =

1.0e+003 *

0.0000 1.0318 1.4230 0.0152 0.0140

PercentAccuracy =

0.0000 -60.1090 -45.1035 -38.6300 0.4898

MeanResult =

100.0000 2.0038 3.7013 57.1530 99.9967

PercentRelativeStandardDeviation =

0 0.2252 0.2318 0.0784 0.0682

PercentAccuracy =

0 -97.9962 -96.2987 -42.8470 -0.0033

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.

Demo of the T-Fit method applied to the multi-component absorption spectroscopy of a mixture of three absorbers. The adjustable parameters are: the absorbances of the three components (A1, A2, and A3), spectral overlap between the component spectra ("Sepn"), width of the instrument function ("InstWidth"), and the noise level ("Noise"). Compares quantitative measurement by weighted regression and TFit methods. Simulates photon noise, unabsorbed stray light and random background intensity shifts. Note: After executing this m-file, slide the "Figure No. 1" and "Figure No.2" windows side-by-side so that they don't overlap. Figure 1 shows a log-log scatter plot of the true vs. measured absorbances, with the three absorbers plotted in different colors and symbols. Figure 2 shows the transmission spectra of the three absorbers plotted in the corresponding colors. As you use the keyboard commands (below) in Figure No. 1, both graphs change accordingly.

In the sample calculation shown above, component 2 (shown in blue) is almost completely buried by the stronger absorption bands of components 1 and 3 on either side, giving a much weaker absorbance (0.1) than the other two components (3 and 5, respectively). Even in this case, the TFit method gives a result (T2=0.101) within 1% of the correct value (A2=0.1). In fact, over most combinations of the three concentrations, the TFit methods works better (although of course nothing works if the spectral differences between the components is too small). Note: in this program, as in all of the above, when you change InstWidth, the photon noise is automatically changed accordingly just as it would in a real variable-slit dispersive spectrophotometer.

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:

KEYBOARD COMMANDS

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 July, 2016.

© Tom O'Haver

Professor Emeritus

Department of Chemistry and Biochemistry

The University of Maryland at College Park

toh@umd.edu

http://terpconnect.umd.edu/~toh/

This page is part of "**A
Pragmatic Introduction to Signal Processing**", created
and maintained by Prof.
Tom O'Haver , Department of Chemistry and Biochemistry, The
University of Maryland at College Park. Comments, suggestions and
questions should be directed to Prof. O'Haver at toh@umd.edu.
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