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and chemical properties of molecules to their biological activity (path 3). Others use regression analysis to fit structural information to biological activity (path 4). Recently, pattern recognition has been used to relate structural information to biological activity (22), and the future will no doubt see an increase in this activity. In describing the philosophy and current chemical applications of pat tern recognition, I was forced, for rea sons of brevity, to ignore several very interesting and high-quality studies conducted by chemists. Additionally, there are even more excellent applica tions being conducted at the time of this writing. Future
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In some ways, chemistry has fallen behind other fields in the application of new methods to extract useful in formation from raw data. Analytical chemists and spectroscopists buried under the burden of interpreting the enormous quantities of data produced by the laboratory computer are well aware of this lag, and many are cur rently working on solutions. Chemists using pattern recognition methods, as well as optimization, factor analysis, and several other chemometric meth ods, are demonstrating that the com puter is capable of aiding significantly in providing better and more useful chemical information with less effort expended by the chemist. To many chemists, it is painfully obvious that mathematicians, statisticians, and computer scientists cannot and will not solve our problems for us. It is the chemist who must accept the responsi bility of infusing the tools of these in formation scientists into chemistry and even contributing to method de velopment when and if necessary. This interfacing task is clearly a ser vice to chemistry if the end result is a demonstrated enhancement in the ac quisition and extraction of chemical information. With this in mind, what research in pattern recognition is needed in the future? New and improved chemometric methods are needed to perform the mappings indicated in Figure 6. The greatest emphasis in method develop ment should be on preprocessing. The chemical literature will no doubt see improved methods of display, super vised and unsupervised learning that will enhance the results of the various mappings shown in the last section. Preprocessing method improvements will provide better information repre sentations and will therefore provide features more related to the informa tion desired. The scientist is some what biased in thinking that if a spec trum or other datum is optimal for human analysis, then it should be
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1160 A · ANALYTICAL CHEMISTRY, VOL. 47, NO. 13, NOVEMBER 1975
ideal for computer analysis. This may not be the case. The scientist seeks simplified spectra to make analysis easier. The computer can take advan tage of complex spectra. For instance, many of the successful applications of pattern recognition to spectral analy sis use preprocessing methods that transform spectra into waveforms that look much more complicated and are of little use to the chemist. Probably the most important area of future development is the incorpo ration of pattern recognition methods into on-line measurement systems. In this way, the power of the methods can be used for real problems, and the methods will become familiar tools to chemists and spectroscopists. Data from several instruments can be com bined to attack more difficult prob lems involving high-dimensional data spaces. In much the same way as the chemist routinely uses various spec trometry tools, pattern recognition and other chemometric methods should be available to the chemist on the same basis. The use of interactive computer graphics, the cost of which has been falling sharply in recent years, will be an integral part of on line measurement acquisition and analysis systems. As pattern recognition programs be come more available to chemists, the breadth and depth of applications will certainly increase. This process is al ready under way as more than 50 chemical laboratories have received pattern recognition systems from our laboratory (ARTHUR) or from C. F. Bender of the Lawrence Livermore Laboratory (RECOG). Applications should either demon strate a clear improvement over exist ing techniques to solve chemical prob lems or open new fields of investiga tion which were previously untouched because the tools were not available. Pattern recognition methods oper ate with defined criteria and attempt to distill useful information from raw data. If the criteria used by the meth ods and their limitations and pitfalls are not clearly understood by chem ists, the dangers are incorrect inter pretation and a misuse of costly mea surements. It is the author's opinion that they should be used to extend the ability of human pattern recognition and rely heavily on graphics for the presentation of results. The computer can assimilate many more numbers at one time than can the chemist, but it is the chemist who, in the end* must do the chemistry. References (1) N. J. Nilsson, "Learning Machines," McGraw-Hill, New York, N.Y., 1965. (2) P. C. Jurs, B. R. Kowalski, T. L. Isenhour, and C. N. Reilley, Anal. Chem., 41,
