e d i to ri a l
Think Carefully, and Take More Data T
he art of analysis has long consisted of creating measurement strategies that have the greatest possible selectivity and sensitivity. Although this approach has well served science, society, and the discipline, it increasingly fails to exploit two of the greatest assets available to modern measurement science: cheap data and fast computers. More than two decades after their commercial introduction, learning set-based analytical methods continue to impress me, with near-IR (NIR) reflectance being the most widely known. These methods thrive on the opposite of selectivity, measuring everything and carefully ignoring most of it. The value of this strategy seems to have only partially penetrated the research community. Learning set methods now include such remarkable measurements as blood glucose through skin (also NIR) and automated fruit and vegetable recognition for supermarket checkouts (visible spectral reflectance). Their success depends on learning sets that encompass samples across the full concentration range for the target analyte as well as multiple examples of all possible interfering components. There is a bit of a “chicken and egg” problem with this approach, in that the learning set samples must be “known” for the target species— easy for apples, harder for glucose. However, time, expense, separation requirements, and sensitivity can yield methods that are impractical for most measurement problems but appropriate for learning set creation. The neglect of the opportunity in academic circles likely stems from an opinion that this “anti-intellectual” approach is unworthy of the scholarly environment. A similar reaction to combinatorial synthesis was and still is strong in the medicinal chemistry community, although it continues to revolutionize industrial pharmaceutical practice. Such an attitude may be understandable to those of us who expect to make our livings through careful thinking; however, a second look reveals this attitude to be shortsighted. Learning set-based methods have analogies in other areas of technology. They constitute a decision to make certain activities historically performed serially, highly parallel. We ignore them at our peril. Supercomputing is now dominated by massively parallel, modest-performance processor arrays, which are making obsolete companies that pursued high-performance serial strategies. Structure optimization in the pharmaceutical industry
is universally pursued by synthesizing many compounds in parallel rather than a few serially. These trends are driven by technology and competition. Modest processors are cheap; parallel synthesis makes test compounds cheap. A comparable situation exists for measurement science. Modern, automated methods have made measurement data cheap. Fast computing and enormous data storage are also cheap. The exploitation of these resources opens a measurement pathway that runs counter to the cultivated instincts of many practitioners. These ideas meet in measurements on highly complex systems. Among the most complex and interesting samples are living organisms. Biologists have prospered by incrementally expanding understanding by using samples that are too hopelessly complex to grasp in complete detail. To date, much of this knowledge has remained qualitative. We physical scientists can benefit from the biologist’s model and still contribute our own quantitative rigor. Optical imaging offers an extreme example of large data volume (cheap data). Without much difficulty, megabytes of data per second can be generated with near single molecule sensitivity. The accelerating effort to monitor complete protein expression (the proteome) will provide learning set data relevant to a variety of signaling, enzyme activity, and gene activity functions. Consumer electronics technology has created fast, inexpensive image processing capabilities, which only the military could have afforded a few years ago. These capabilities are the ingredients of methods that could be used to monitor activity in living cells. Such tools would have a significant impact in research and commercial labs. The challenge of creating robust learning sets and the mathematics of extracting information from these mountains of data are far from anti-intellectual. New technologies are opening exciting and powerful measurement methods for those who have the insight to exploit them. Opportunities abound for those who think carefully and take more data.
Timothy Harris Praelux, Inc.
[email protected] N O V E M B E R 1 , 2 0 0 0 / A N A LY T I C A L C H E M I S T R Y
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