Table 1 - uploaded by Dragan A Cirovic
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UV NIS spectrometer characteristics
Source publication
The instrumental noise distribution in electronic absorption spectrometry was studied by recording 27 instrumental replicates at five concentration levels of a series of dilutions of pyrimidine. It was found that the dependence of noise on concentration was weak, especially at the base of peaks; heteroscedasticity where noise is greatest at the top...
Citations
... It saves the data in text format. Since the spectra are registered in the SDBS website in transmittance unit, and logarithmic transformation affects on noise distribution (e.g., the homoscedastic transmittance noise becomes strongly dependent on the concentration after undergoing logarithmic transformation to absorbance [19, 20] , or multiplicative errors become additive after logarithmic transformation [21, 22]) we preferred to use the transmittance data firstly. However, as it will be explained in next sections, it was found that both transmittance and absorbance spectra resulted in the similar results. ...
... However, as it was noted previously, digital transformation of the pictured spectra added some noises to the transmittance data. Logarithmic transformation of the transmittance data affects the noise distribution (e.g., changing homoscedastic noise in transmittance into heteroscedastic noise [19][20][21][22]) and consequently affects the prediction results. Interval ECVA (i-ECVA) was also used to see whether selecting of some spectral regions from the whole spectral region will increase the prediction quality of the ECVA or not. ...
Although there are some distinctive peaks in mid-IR region of the electromagnetic spectrum for carbonyl compounds, it is very difficult to assign a FT-IR spectrum to a specific carbonyl functional group due to the presence of other functional groups, which change the position of the distinctive peaks. Here, we analyzed the FT-IR spectra of a large set of carbonyl compounds by chemometrics methods to differentiate between the different carbonyl functional groups. FT-IR spectra of 370 carbonyl compounds (149 carboxylic acids, 47 aldehydes, 110 esters and 64 ketones) were collected from the Spectral Database for Organic Compounds and then were converted to digital data using a home-made program. The extended canonical variate analysis combined with partial least squares discriminate analysis method (ECVA-PLS-DA) was employed as a supervised classification method. Classification analysis by ECVA-PLS-DA resulted in a suitable classification model such that one can discriminate between the different carbonyl compounds using FT-IR spectra with a small error. The classification errors (reported as percentage of misclassified compounds) were 1.8% and 7.8% for training and test sets, respectively. Considering high structural diversity of the studied compounds and the employment of different methods for acquiring the spectra (i.e., KBr disk, CCl4 solution, liquid film and Nujol moll) there are acceptable errors. Thus, it is concluded that with the help of chemometrics methods, one is able to differentiate the carbonyl compounds using their IR spectra without need to extra spectroscopic information. This can be considered as a significant improvement in structural characterization of organic compounds using only IR spectroscopy.
Univariate calibration and two-way and three-way partial least squares
(PLS) were applied to a series of GC–MS results for 21 mixtures of
two closely eluting compounds, salbutamol and clenbuterol. Steps in the
analysis, including baseline correction, alignment of chromatograms, mass
selection, unfolding (for three-way data), standardizing and centring, are
described, appropriately modified for the problem in hand. Both mass
spectral and, for three-way data, time dependent loadings can be
calculated. The quality of quantitative predictions was determined using a
leave one out cross validation method. For PLS slightly better predictions
were obtained compared with the best predictions for univariate single ion
monitoring. Three-way PLS provides a wealth of extra information.
The wavelet power spectral density is a low-resolution equivalent to the traditional power spectral density based on the Fourier transform. The time information obtained by the wavelet transform is utilized to eliminate high frequency components of the analytical signal that would interfere with the analysis of the baseline noise. The median absolute deviation is used as a robust estimator of the standard deviation, because it is not affected by the aforementioned problems. In the case of a mixed random process, consisting of a first-order auto-regressive random process and additive white noise, the ability of the F-test to detect the presence of correlated noise depends on the length of the signal and the ratio of the variances of both noise components. The exponent of /f noise is estimated by weighted least squares. Signals from flow injection analysis demonstrate how the method can be applied to time varying systems.
The ability of the wavelet transform (WT) to detect and estimate
heteroscedastic noise was determined. The WT provides information in both
the time and the frequency domains which is necessary for the detection of
heteroscedastic noise. By a simple F-test applied to
non-overlapping intervals of wavelet coefficients differences in the
variance can be detected. By fitting a smoothed version of the signal
versus a smoothed estimate of the local variance the
proportionality factor can be estimated.
