Usefulness of a Curve Fitting Method in the Analysis of Overlapping Overtones and Combinations of CH Stretching Modes

2002 ◽  
Vol 10 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Yukiteru Katsumoto ◽  
Daisuke Adachi ◽  
Harumi Sato ◽  
Yukihiro Ozaki

This paper reports the usefulness of a curve fitting method in the analysis of NIR spectra. NIR spectra in the 7500–5500 cm−1 (1333–1818 nm) region were measured for water–methanol, water–ethanol and water–1-propanol mixtures with alcohol concentrations of 0–100 wt% at 25°C. The 6000–5600 cm−1 (1667–1786 nm) region, where the overtones and combinations of CH3 and CH2 stretching modes are expected to appear, shows significant band shifts with the increase in the alcohol content. To analyse the concentration-dependent spectral changes, a curve fitting method was utilised, and the results were compared with those obtained previously by a second derivative method. It was found that the first overtones of CH3 asymmetric and symmetric stretching modes of alcohols show a downward shift by about 15–30 cm−1 with the increase in the concentration of alcohols. The shifts are much larger for water–methanol mixtures than for water–ethanol and water–1-propanol mixtures. The first overtones and combinations of CH2 stretching modes of ethanol and 1-propanol also show a small downward shift. These shifts support our previous conclusion that there is an intermolecular “CH⃛O” interaction between the methyl group and water in the water–alcohol mixtures. The curve fitting method provided more feasible results for the band shifts than the second derivative method. It was revealed from the curve fitting method that the first overtone of the CH3 asymmetric stretching mode of water–methanol, water–ethanol and water–1-propanol mixtures shows different concentration-dependent plots. The first overtone of CH3 asymmetric stretching mode of water–methanol mixtures shifts more rapidly in the high methanol concentration range while that of water–1-propanol concentration shifts more markedly in the low 1-propanol concentration range. That of water–ethanol mixtures shows an intermediate trend. Based upon these differences structural differences among the three kinds of water–alcohol mixtures are discussed.

2012 ◽  
Vol 19 (2) ◽  
pp. 381-394
Author(s):  
José Pereira ◽  
Octavian Postolache ◽  
Pedro Girão

Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System PerformanceThis paper presents a voltammetric segmented voltage sweep mode that can be used to identify and measure heavy metals' concentrations. The proposed sweep mode covers a set of voltage ranges that are centered around the redox potentials of the metals that are under analysis. The heavy metal measurement system can take advantage of the historical database of measurements to identify the metals with higher concentrations in a given geographical area, and perform a segmented sweep around predefined voltage ranges or, alternatively, the system can perform a fast linear voltage sweep to identify the voltammetric current peaks and then perform a segmented voltage sweep around the set of voltages that are associated with the voltammetric current peaks. The paper also includes the presentation of two auto-calibration modes that can be used to improve system's reliability and proposes the usage of a Gaussian curve fitting of voltammetric data to identify heavy metals and to evaluate their concentrations. Several simulation and experimental results, that validate the theoretical expectations, are also presented in the paper.


2010 ◽  
Vol 26 (6-8) ◽  
pp. 801-811 ◽  
Author(s):  
Mingxiao Hu ◽  
Jieqing Feng ◽  
Jianmin Zheng

1993 ◽  
Vol 272 (1) ◽  
pp. 125-134 ◽  
Author(s):  
James M. Jordan ◽  
Michael D. Love ◽  
Harry L. Pardue

2007 ◽  
Vol 46 (13) ◽  
pp. 4549-4560 ◽  
Author(s):  
Q. Peter He ◽  
Jin Wang ◽  
Martin Pottmann ◽  
S. Joe Qin

Author(s):  
Bhavish Sushiel Agarwal ◽  
Jyoti R Desai ◽  
Snehanshu Saha

The use of hand gestures opens a wide range of application for human computer interaction. The paper makes use of haar classifiers and camShift algorithm to track the movement of hand. Parallelism is introduced at every step by segmenting the data from camshaft into an NxN grid. Every block of the grid now represents a lead point which is calculated from mean of all the points belonging to the particular grid. Now we have only N2 points to recognize the curve that was performed by the user in his action. Finally the fit that was found is compared to pre-defined curve fit data to find out the curve using Mahalanobis equation. Parallelism used in reducing the number of points to be fitted allows the recognition to be faster.


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