Conclusion Get more connected.

effects. The number of factors was
set between 2 and 6. The validation
option was set to Full Cross
Validation (see later), and the Expert
Assist (see later) was selected for the
calibration. The first method was built
using the full range (20 – 270 OH
number) of samples. However, the
second method was built covering a
much smaller range of OH number
(25 – 60). The second method was
able to predict more accurately
values within its low OH range. The
first method however, was able to
predict values better for the samples
lying higher in the range, but was not
as accurate for the mid-range values.
Results and discussion
The absorbance spectra for the
polyol standards and the unknowns
were collected and two examples,
representing extremes of OH number,
are shown in Figure 1. The spectral
variations at approximately 7200 –
6670 and 5300 – 4760 cm -1 (1389 –
1499 and 1887 – 2100 nm) clearly
show differing OH number, and
correspond to polymeric O-H first
overtone and O-H combination bands
respectively. Temperature variation is
important to consider in the analysis
of polyols and other NIR applications.
This application did not require
temperature control, and heating
was not necessary as all the samples
were liquid at room temperature.
However, care was taken to ensure
that the sample temperatures were
allowed to equilibrate and the
samples were left in the beam for the
same length of time before each
measurement was made.
The QUANT+ software is capable
of modeling the concentrations in
terms of the spectral contributions
relevant to OH determination. This
algorithm can extract the factors in
the calibration that are not responsible
for the specific OH value (the
chemical constituent of interest)
by eliminating the corresponding
dimensions (spectral absorptions)
from the model.
The error present in an OH
evaluation due to hydroxyl band of
moisture absorbed in the sample can
then therefore be minimized by
Factors Standard Error of Prediction % Variance
Limited Range 2 0.91 99.86
Figure 2: Estimated vs. Specified – limited range method.
digitally separating the different OH
bands using this feature of the
software. The calibration equation is
modeled to exclude irrelevant bands
from the analysis, and minimize the
error in the prediction results.
The Full Cross Validation feature
used in this application further
improves the reliability of results.
The software drops one sample from
the calibration set and performs an
entire calibration with the remaining
samples, then makes a prediction for
the sample left. In normal
applications the sample being
predicted is not present in the
calibration set, this gives a more
realistic if less accurate result. The
Expert Assist option in the software
makes intelligent decisions
regarding the spectra in the
calibration set. The Expert will
decide whether to reject any spectra
or specific regions of spectra in the
calibration set, or disregard any
property values on the basis of a set
of rules developed for IR quantitative
analysis. It will also identify any
outliers in the set. Once these
decisions have been made to exclude
some data in the set, the software
will decide whether or not the
calibration needs to be repeated.
The procedure continues iteratively
until all the rules are satisfied.
The method built over the narrow
range of OH number was used to
predict the samples of OH value
ranging from 20 to 50. The error
estimates are considerably better
than the results obtained using the
full calibration set. By looking at the
results tabulated in the table in Figure
2, 99.86 % of the data can be
explained using 2 factors with a
standard estimate of prediction (SEP)
of 0.91 OH number. Figure 2 illustrates
the accuracy of the method in the
form of an Estimated value versus
Specified value plot. Prediction of
OH number on the 8 independent
samples using this calibrated method
gave OH numbers that were found to
lie within ca. +/– 1 OH number of
the value obtained from the
reference method (titration).
The full range method uses a larger
calibration set to predict OH number
over a much wider range (20 – 275).
The values recorded in the table in
Figure 3 show that in this method,
99.98% of the data can be explained
using three factors in this analysis.
The SEP is much larger in this
method (4.60 OH number) and was
found to be better at predicting
OH number of the higher valued
unknowns.