Peak cluster area

Peak cluster area is a measure of the size of the peak cluster. 

Peak area is calculated (by the instrument) using a mathematical technique called integration. For this reason peak cluster area is often referred to as a peak s integral, integration, or integrated area. 

Although the decomposition of a data table yields the elution profiles of the individual compounds, a calibration step is still required to transform peak areas into concentrations. Essentially we can follow two approaches. The first one is to start with a decomposition of the peak cluster by one of the techniques described before, followed by the integration of the peak of the analyte. By comparing the peak area with those obtained for a number of standards we obtain the amount. One should realize that the decomposition step is necessary because the interfering compound is unknown. The second approach is to directly calibrate the method by RAFA, RBL or GRAFA or to decompose the three-way table by Parafac. A serious problem with these methods is that the data sets measured for the sample and for the standard solution should be perfectly synchronized. 

The racemate of 1,3,2-benzodithiazole 1-oxide 42 was separated by supercritical fluid chromatography on the (A j )-Whelk-( )l column with supercritical carbon dioxide containing 20% methanol as a mobile phase. Peak areas of enantiomers prior to and after the separation, used for the calculation of the enantiomerization barrier, were detected by computer-assisted peak deconvolution of peak clusters registered on chromatograms using computer software <2002CH1334>. 

This method can be extended to fairly complex peak clusters, as presented in Figure 6.26 for die data in Table 6.2. Ignoring die noise at die beginning, it is fairly clear diat there are three components in die data. Note diat die central component eluting approximately between times 10 and 15 does not have a true composition 1 region because die correlation coefficient only reaches approximately 0.9, whereas the odier two compounds have well established selective areas. It is possible to determine the purest point for each component in die mixture successively by extending the approach illustrated above. 

The green Mo3 cluster shows three H resonances in the ratio 1 10 5. The peak of area one (58.9, CDC13) is in the range associated with metal formyls (34) and there are two equivalent and one different Cp groups. The carbonyl stretches of the M03 cluster occur at 1763 and 1713 cm-1. The mass spectrum shows a series of Mo3 patterns starting at m/e = 580 with consecutive loss of 5 carbonyls, and a series of strong peaks due to dipositive ions corresponding to the monopositive series 524, 496, 468, and 440. 

The method is subject to some errors if peaks appear frequently (for instance, as peak clusters) in areas far from the diagonal of the separation space, and does not take into account the operation in the programmed temperature mode. 

Figure 5.20 shows the periodogram for the once-differenced summer temperature time series. Unlike in the previous periodogram, there are now a series of peaks clustered in the area around 2.5-3 years/cycle. Also, there is a secondary peak around 4 years/cycle followed by a rather weak peak in the 8 years/cycle region. All these values seem to be multiples of each other suggesting that they represent a single feature rather than separate features. 

The number listed below a peak cluster gives you the area of the peak cluster. 

The area of each peak cluster is often represented by a number written above or below a peak cluster. 

Be careful Peak area (not peak height) tells you the number of H s associated with a peak cluster. That is, sometimes a tall, thin peak will have a smaller area than a short, broad peak. 

Peak area, not peak height, tells you the relative number of H s associated with a peak cluster. 

The practical peak capacity of these spaces can be characterized by 2D statistical-overlap theory. Consider a relatively simple problem of probability. If, on average, m circles of diameter d() are distributed randomly in a large area A, then the average number p of clusters of isolated and overlapping circles approaches (Roach, 1968)... 

It is important to realize that statistical-overlap theory is not constrained by the contour of area A, which does not have to be rectangular as in earlier studies (in addition to previous references, see Davis, 1991 Martin, 1991,1992). In other words, Equations 3.2 and 3.3 should apply to the spaces WEG, FAN, and PAR. In this chapter, the number of clusters of randomly distributed circles in such areas is compared to the predictions of Equations 3.2 and 3.3a to assess the relationship between nP and practical peak capacity. Similarly, the number of peak maxima formed by randomly distributed bi-Gaussians in such areas is compared to the predictions of Equations 3.2 and 3.3b, and to Fig. 3.2, to make another assessment. 

Again we remember that each pulse of the line chromatogram (Fig. 4.6a), may be either a singlet or a multiplet peak, and the recorded peak height is proportional to the area value of the detected cluster. 

To highlight and explain the quantitative chemical differences between the pitches found in the two archaeological sites, a chemometric evaluation of the GC/MS data (normalized peak areas) by means of principal component analysis (PCA) was performed. The PCA scatter plot of the first two principal components (Figure 8.6) highlights that the samples from Pisa and Fayum are almost completely separated into two clusters and that samples from Fayum form a relatively compact cluster, while the Pisa samples are... 

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