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1.1.3. Multidimensional Dataset
Many analytical instruments generate one-dimensional (1D) data. Very often, even if they can produce multidimensional signals, 1D datasets are still selected for data treatment and interpretation because it is easier and less time-consuming to manipulate them. Also, most investigators are used to handle 1D data. Yet, valuable information may be lost in this approach.
Figure 1.1 shows the spectrochromatogram obtained in a study of the herb Danggui (Radixangeliciae sinensis)  by using the Hewlett-Packard (HP) HPLC-DAD model 1100 instrument. Methanol was utilized for sample extraction. In carrying out the experiment, a Sep-Pak C18 column was used and the runtime was 90 min. The two-dimensional (2D) spectrochromatogram shown in Figure 1.1 contains 2.862 million data points . It looks very complicated and cannot be interpreted easily just by visual inspection. As mentioned earlier, many workers simplify the job by selecting a good or an acceptable 1D chromatogram(s) from Fig. 1.1 for analysis. Figure 1.2
Figure 1.1. The 2D HPLC chromatogram of Danggui.
shows the 1D chromatograms selected with the measured wavelengths of 225, 280, and 320 nm, respectively. However, which one should be chosen as the fingerprint of Danggui is not an easy question to answer since the profiles look very different from one another. The variation in these chromatographic profiles is due mainly to different extents of ultraviolet absorption of the components within the herb at different wavelengths. From an information analysis , Figure 1.2b is found to be the best chromatogram. Yet, the two other chromatograms may be useful in certain aspects.
Methods for processing 1D data have been developed and applied by chemists for a long time. As previously mentioned, noise removal, background correction, differentiation, data smoothing and filtering, and calibration are examples of this type of data processing. Chemometrics is considered to be the discipline that does this kind of job. With the growing popularity of hyphenated instruments, chemometric methods for manipulating 2D data have been developing. The increasing computing power and memory capacity of the current computer further expedites the process. The major aim is to extract more useful information from mountainous 2D data. In the following section, the basic fundamentals of chemometrics are briefly introduced. More details will be provided in the following chapters.
Figure 1.2. The HPLC chromatogram of Danggui measured at (a) 225 nm, (b) 280 nm, and (c) 320 nm.
1.2.1. Introduction to Chemometrics
The term chemometrics was introduced by Svante Wold  and Bruce R. Kowalski in the early 1970s . Terms like biometrics and econometrics were also introduced into the fields of biological science and economics. Afterward, the International Chemometrics Society was established. Since then, chemometrics has been developing and is now widely applied to different fields of chemistry, especially analytical chemistry in view of the
numbers of papers published, conferences and workshops being organized, and related activities. ‘‘A reasonable definition of chemometrics remains as how do we get chemical relevant information out of measured chemical data, how do we represent and display this information, and how do we get such information into data?’’ as mentioned by Wold . Chemometrics is considered by some chemists to be a subdiscipline that provides the basic theory and methodology for modern analytical chemistry. Yet, the chemometricans themselves consider chemometrics is a new discipline of chemistry . Both the academic and industrial sectors have benefited greatly in employing this new tool in different areas.
Howery and Hirsch  in the early 1980s classified the development of the chemometrics discipline into different stages. The first stage is before 1970. A number of mathematical methodologies were developed and standardized in different fields of mathematics, behavioral science, and engineering sciences. In this period, chemists limited themselves mainly to data analysis, including computation of statistical parameters such as the mean, standard deviation, and level of confidence. Howery and Hirsch, in particular, appreciated the research on correlating vast amounts of chemical data to relevant molecular properties. These pioneering works form the basis of an important area of the quantitative structure-activity relationship (QSAR) developed more recently.
The second stage of chemometrics falls in the 1970s, when the term chemometrics was coined. This new discipline of chemistry (or subdiscipline of analytical chemistry by some) caught the attention of chemists, especially analytical chemists, who not only applied the methods available for data analysis but also developed new methodologies to meet their needs. There are two main reasons why chemometrics developed so rapidly at that time: (1) large piles of data not available before could be acquired from advanced chemical instruments (for the first time, chemists faced bottlenecks similar to those encountered by social scientists or economists years before on how to obtain useful information from these large amounts of data) and (2) advancements in microelectronics technology within that period. The abilities of chemists in signal processing and data interpretation were enhanced with the increasing computer power.