(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"), which reduces the labor and cost of preparing and running large numbers of standard solutions and safely disposing of them afterwards;
(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 and reduced the effect of photon and detector noise).
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.
(d) requires that the absorbances of the components vary with wavelength, and, for multi-component analysis, that the absorption spectra of the components be sufficiently different. Black or grey absorbers do not work with this method.
The disadvantages of the Tfit method are:
(a) it makes the computer work harder 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 close to 1.0 (see #8 on http://terpconnect.umd.edu/~toh/models/AbsSlitWidth.html#BestAbsorbance). The range of absorbances below 1.0 is easily accessible down to at least 0.001, but the range above 1.0 is limited. Even an absorbance of 10 is unreachable on most instruments and 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.
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.
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, and possibly a better
approach would be 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: 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 compensates for the
non-linearity caused by unabsorbed stray light and the
polychromatic light effect. Other potential sources of
non-linearity remain, in particular chemical effects, such as photolysis,
equilibrium shifts, temperature and pH effects, binding,
dimerization, polymerization, molecular phototropism,
Bottom line: The TFit method is based on the Beer-Lambert Law; it simply 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, requiring fewer standards to properly define the calibration curve and avoiding the need for non-linear calibration models. 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, and by the mathematical skills of the college students to whom this subject is typically first presented, 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. That was then; this is now. Tfit is the 21st century way to do absorption spectroscopy.
Tfit method can even 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
for more info about setting up macros and solver on
your version of Excel).
is an empty template for a single isolated peak; TransmissionFittingTemplateExample.xls
the same template with example data entered. TransmissionFittingDemoGaussian.xls
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) is a demonstration spreadsheet that includes another Excel macro that constructs calibration curves 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 Ctrl-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 ideally be a straight flat line with zero slope).
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
Here is a specific numerical example,
where the true absorbance is 1.00, using only 4-point spectra
for illustrative simplicity (normally an array-detector system
would acquire many more wavelengths than that). In this particular
case the instrument width (InstFun) is twice the absorption width,
the stray light is 0.01 (1%). The conventional single-wavelength
estimate of absorbance is far too low: log10(1/.38696)=0.4123.
In contrast, the TFit method using fitM:
yobsd=[0.56529 0.38696 0.56529 0.73496]';
TrueSpectrum=[0.2 1 0.2 0.058824]';
InstFun=[1 0.5 0.0625 0.5]';
absorbance=([weight weight].*[Background RefSpec])\(-log10(yobsd).*weight)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.
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 Absorbance display.
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
0.0010 0.0004 0.0005 0.0006 0.0010
0.0000 1.0318 1.4230 0.0152 0.0140
0.0000 -60.1090 -45.1035 -38.6300 0.4898
100.0000 2.0038 3.7013 57.1530 99.9967
0 0.2252 0.2318 0.0784 0.0682
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. TFit's linearity is especially important in a regulated lab where quadratic least-squares fits are discouraged.
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
InstWidth G/B Increase/decrease width of instrument function
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 January, 2017.
© Tom O'Haver
Department of Chemistry and Biochemistry
The University of Maryland at College Park
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 email@example.com. Number of unique visits since May 17, 2008: