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Chapter: Modern Analytical Chemistry: Spectroscopic Methods of Analysis

Qualitative Applications - Ultraviolet-Visible and Infrared Spectrophotometry

As discussed earlier, ultraviolet, visible and infrared absorption bands result from the absorption of electromagnetic radiation by specific valence electrons or bonds.

Qualitative Applications

As discussed earlier, ultraviolet, visible and infrared absorption bands result from the absorption of electromagnetic radiation by specific valence electrons or bonds. The energy at which the absorption occurs, as well as the inten- sity of the absorption, is determined by the chemical environment of the absorbing moiety. For example, benzene has several ultraviolet absorption bands due to π - > π*  transitions. The position and intensity of two of these bands, 203.5 nm (ε = 7400) and 254 nm = 204), are very sensitive to substitution. For benzoic acid, in which a carboxylic acid group replaces one of the aromatic hydrogens, the two bands shift to 230 nm = 11,600) and 273 nm = 970). Several rules have been developed to aid in correlating UV/Vis absorption bands to chemical struc- ture. Similar correlations have been developed for determining structures using in- frared absorption bands. For example the carbonyl, C=O, stretch is very sensitive to adjacent functional groups, occurring at 1650 cm–1 for acids, 1700 cm–1 for ke- tones, and 1800 cm–1 for acid chlorides. The qualitative manual interpretation of UV/Vis and IR spectra receives adequate coverage elsewhere in the chemistry cur- riculum, notably in organic chemistry and is therefore not considered further in this text.

With the availability of computerized data acquisition and storage it is possible to build database libraries of standard reference spectra. When a spectrum of an un- known compound is obtained, its identity can often be determined by searching through a library of reference spectra. This process is known as spectral searching. Comparisons are made by an algorithm that calculates the cumulative difference between the absorbances of the sample and reference spectra. For example, one simple algorithm uses the following equation


where D is the cumulative difference, As is the absorbance of the sample at wave- length or wavenumber i, Ar is the absorbance of the reference compound at the same wavelength or wavenumber, and n is the number of points for which the spec- tra were digitized. The cumulative difference is calculated for each reference spec- trum. The reference compound with the smallest value of D provides the closest match to the unknown compound. The accuracy of spectral searching is limited by the number and type of compounds included in the library and by the effect of the sample’s matrix on the spectrum.

Another advantage of computerized data acquisition is the ability to subtract one spectrum from another. When coupled with spectral searching it may be pos- sible, by repeatedly searching and subtracting reference spectra, to determine the identity of several components in a sample without the need of a prior separation step. An example is shown in Figure 10.33 in which the composition of a two- component mixture consisting of mannitol and cocaine hydrochloride was identi- fied by successive searching and subtraction. Figure 10.33a shows the spectrum of the mixture. A search of the spectral library selects mannitol (Figure 10.33b) as a likely component of the mixture. Subtracting mannitol’s spectrum from the mix- ture’s spectrum leaves a result (Figure 10.33c) that closely matches the spectrum of cocaine hydrochloride (Figure 10.33d) in the spectral library. Subtracting leaves only a small residual signal (Figure 10.33e).


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