M. L. Sali
The Perkin-Elmer Corporation Norwalk. Conn. 06859 As analytical chemists have gathered more data for every analysis, the demands for data reduction and reporting have increased to burdensome levels. In many circumstances, commercially available spreadsheet software can provide solutions that alleviate some of the burden. Spreadsheet softwareoffers an alternative to conventional, linear programming in a language such as BASIC. This alternative can allow solutions to be developed and used with greater ease. Persons unfamiliar with conventional programming can often develop their own solutions using spreadsheet software, without outside help. Modern spreadsheet software contains powerful graphics, data formatting, and data manipulation features that are not easily accessed within most programming environments. The ability to present carefully formatted tabular and graphical representations of data should be considered when comparing the spreadsheet and conventional programming approaches to solving problems. Formatting output can be the most difficult part of conventional programming, and much of this is done automatically by spreadsheet programs. What is a spreadsheet program? A spreadsheet program is a rectangular array of cells; each cell is a discrete region that may contain data, alphanumeric labels, “macro” commands, or formulas. Formulas are built from mathematical operators (+, -, *, I), constants (e.&, 12.341, references to other cells in the array, and built-in functions. Built-in functions include those for financial, scientific, and statistical calculations that allow complex manipulations to be performed with a single formula. It is useful to define spreadsheet software with a metaphor (Le., using a 0003-2700/88/0360-731A/501.50/0 @ 1988 American Chemical Society
familiar situation to describe an unfamiliar one). The “spreadsheet metaphor” is based on data tables constructed on sheets of grid paper. This metaphor was first widely embraced for business calculations in the form of
community does. Spreadsheets have become recognized as powerful tools for data handling, and their application to report generation and routine data analysis for analytical results has become popular. In addition to their
A/C INrERFACE an “electronic accounting book.” The power of being able to make a single change in an input value and having an entire worksheet recalculated in seconds was intoxicating when compared with the hours it would take to perform such “what i f . . .” analyses manually. The spreadsheet put the capability for data analysis into the hands of the person responsible for the decision makinp. This efficiency “as perhaps the most significant factor in gaining the business community’s wide acceptance of sprradsheet programs and personal comwtine. . 1 Spreadsheets and analytical chemistry Analytical chemists need effective data analysis tools as much as the business
strength for calculation and presentation, the capability of spreadsheet programs to perform logical or database operations on the data is critical to the generation of effective reports. Data.. base operations simplify the generation of reports through the extraction, analysis, and sorting of the data. For example, all data for a single element in a multielement data set can easilv he extracted intoaseparate table by using these database functions. This capability to “prtdigest” information allows the analyst to communicate results more effecti.r,el\,. Spreadsheet-use in analytical chemistry was originally inspired by singleuse, unique applications in research environments. These were eenerallv investigative applications -whereLy the
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creation of a dedicated program to handle the data would have required a much greater effort than the simple building of tables of input data and results in a spreadsheet. The spreadsheet approach was more efficient in terms of the cost of development, and the flexibility of the approach allowed modification of the calculations in an interactive environment. This flexibility proved to be valuable in research applications; the editlcompileldebug requirements of a conventional programmatic approach impede the data modification process. The more avenues of data manipulation that are explored in a research problem, the better the understanding of that problem. Any approach that encourages the exploration of different data-handling techniques is valuable in a research application. These unique research applications still exist, and spreadsheets are being used profitably in them. However, the newer areas of application in routine reporting of results are responsible for the broad acceptance of spreadsheets in the analytical community. Spreadsheets and the analytkal process Figure 1 presents the five steps of the analytical process: sampling, sample preparation, measurement, data reduction, and data reporting. The sampling and sample preparation phases generate information that will later be collated with the analytical results to provide a complete report. The sampling-phase information includes sample identification, origin, and time of sampling. The sample preparation phase produces information such as sample weights or dilution ratios that will be used for the calculation of final results.
Flgure 1. The analytical pr 732A
The measurement phase of analysis is the least exploited spreadsheet application t o date. Several recently introduced commercial products (e.g., Lotus Measure, Palantir Windows InTalk) allow a spreadsheet to interact with an analytical instrument in real time; some of these products support both the transmission of commands to the instrument and the acquisition of data from the instrument. Such applications may be as simple as recording a titration curve from an autotitrator or they may be as complex as synthesizing and transmitting control commands to an instrument in addition to providing real-time acquisition of data. Several products developed specifically for laboratory applications are available, including Asyst, Lahtech Notebook, and DADiSP. These packages generally support real-time communications with instruments and offer some scientific functions not built in to the general-purpose spreadsheets. Some “integrated” software packages, such as Lotus Symphony, Microsoft Works, and Innovative Software’s Smart System, offer a communications module that will allow easy movement of data acquired in real time to the spreadsheet workspace. Using these hybrid products can eliminate data formatting and data transfer steps from the spreadsheet solution. The data reduction phase of analysis is the most natural area of spreadsheet application. Data reduction is calculation intensive, and data rearrangement and graphical presentation are also important. These are areas in which spreadsheet software excels, and solutions are easily created. The data-reporting phase can be simplified and enhanced with spreadsheet software. Modern programs offer many data formatting options, including both tabular and graphical options. The ease of creating graphics within the spreadsheet environment gives the analyst a wider range of reporting capabilities. The proper presentation of an analytical report is critical to the complete understanding of the results. Spreadsheet models Spreadsheet programs alone offer the user a “clean sheet of paper” environment in which to work. The flexibility to do many things is there, but no functionality exists. A spreadsheet program, like a blank sheet of paper, offers little intrinsic value. As the writing on the sheet of paper defines its functionality, spreadsheet functionality is defined by a model. This model is the set of rules by which operations take place in the spreadsheet workspace. Different models cause different actions to occur in the workspace. Models are composed of the formulas, references, data, and macro commands en-
ANALYTICAL CHEMISTRY, VOL. 80, NO. 11, JUNE 1. 1988
tered in the workspace. Most spreadsheet models usually include format instructions for output and for user interaction, such as data entry. The rules that make up a model are generally simple formulas within a familiar, easy-to-understand algebraic syntax. This allows users who are inexperienced with computer programming to develop models without learning a complex set of commands and structures. The results of a model are displayed immediately, allowing the user to develop the model interactively and to make changes as the results are examined. Interactive model development encourages the nonprogrammer to evaluate approaches that might not otherwise be examined. A better understanding of results comes with careful examination and presentation of the data analysis. Using spreadsheet software, there is no need to formally specify a program for development by a software engineer or to wait for that program to be developed. Ease of model development is critical to the utility of spreadsheet software. Models are generally built from a prototype set of rules and formats. Once the prototype is developed, its application to the data set is made by copying it through the spreadsheet. Spreadsheet programs are carefully developed to make this copying and editing easy for the user. A simple example can be used to illustrate the creation of a model using relative and absolute references. This example will calculate the molecular weight of a gas using the ideal gas law. Figure 2 shows the spreadsheetmodel. The experimental data set contains measured pressures and temperatures for a given mass of an ideal gas a t constant volume. The spreadsheet in Figure 2 uses references in the form of &Cy, where x is the horizontal row number and y is the vertical column number. The molecular weight of the gas is calculated a t each data point, and an average of the results is calculated as well. A spreadsheet is constructed with a header section, the measured pressures in the first column of the data table, and the temperatures in the second column. Cells in the header region contain the mass of the gas, the volume of the container, and the gas constant. A prototype formula was constructed in row 13, column 3. This formula multiplies the two constants (mass of gas and gas constant), referring to them as absolute references. The use of R7C2 in the formula refers to the contents of the cell in the seventh row of column 2, the mass of the gas sample. Similarly, R9C2 refers to the gas constant. The product of the mass and the (contmued on p . 735A3
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manageable subsections. This structured approach is especially important for the development of reliable models and for the development of models hy persons inexperienced in computer programming.
Macro commands An extension to the data manipulation capabilities of a spreadsheet, macro commands are sequences of suhcommands that allow automation of the spreadsheet model. Macros are actually small programs that operate within the model. They may be very simple, such as formatting the active cell to contain three significant figures, or they may he complex, operating the entire model, prompting the user for data as necessary, and generating the final report. Spreadsheet software often provides executable macro programs for recording the commands that are executed manually. Such programs allow the nonprogramming user to build these small programs by example. Some macro “languages” are programming languages in their own right, allowing controlled looping, conditional testing, file system access, and input/ output control. These specialized languages are limited only in that they function within the spreadsheet environment and generally cannot extend their support beyond these limitations.
. .
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ing letter-number referencing.
gas constant is then multiplied by the temperature. The temperature is referred to as RC[-11, a relatiue rejere w e ; the brackets contain an offset from the current position. This reference refers to the contents of the cell in the same row, one column to the left. No offset is required for the row hecause the offset is zero. Negative offsets for columns locate cells to the left, and positive offsets locate cells to the right. Negative offsets for rows locate cells toward the top of the worksheet, and positive offsets locate cells toward the bottom. The product of mass and temperature is divided by the product of the pressure and volume to calculate the molecular weight. The pressure is pointed to with a relative reference, RC[-2]. The volume is addressed as a constant, with an absolute reference R8C2. The prototype formula was copied down the third column, into cells
R14C3, R15C3, R16C3, and R17C3. These formulas calculate a molecular weight for each set of measurements. The built-in function “Average” is used with relative references to calculate the average of the molecular weights. This function is used in the formula in R18C3. Results of the model are displayed in Figure 2a; formulas are shown in Figure 2h. An alternative reference style uses letters as column designators and numhers as row designators. In this style, the cell on the far left in the first row is called Al. A special character is used as a prefix to indicate absolute or relative referencing. The formulas for this example are shown in the letter-number format in Figure 2c. As demonstrated with the ideal gas law example, a prototype can he created and quickly applied to a full data set. In a similar fashion, functionality can he added to the model, thus allowing a complex model to he developed in
Spreadsheet graphics Figure 3 contains three examples of different chart formats available in the Microsoft Excel spreadsheet program. Figure 3a, a simplified energy level diagram for several atomic emission transitions, was created from a worksheet containing the energy levels and ionization potentials for the species. Figure 3h is a graph of analytical recovery for barium in an internal standardization study. Such control charts can easily he created and their generation automated. Figure 3c is a histogram representing the frequency distrihution of error Occurrence for different signal-processing methods. These three examples of spreadsheet graphics are only a sample of the ways in which tabular data within a model can he presented in a graphical format.
Moving data into spreData importation Many analytical techniques generate huge amounts of data with every analysis. A good example is in the application of multiwavelength detectors in liquid chromatography. Previously, the absorbance a t a single wavelength was monitored over time; now, a complete spectrum may be recorded for each time slice. Such techniques help the analyst gather more information about a sample, hut to use that information to better characterize and understand
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igure 3. Spreadsheet graphics in Microsoft Excel. 1 A simplilied energy level diagram. Ib) A control chart showing recOverieS lor barium in an internal andardization study. (c)A lrequency distribution histogram 01 errors.
ANALYTICAL CHEMISTRY, VOL. 60. NO. 11. JUNE 1. 1988
the sample, one must manipulate and examine the data. Spreadsheets can provide a tool for this better understanding. T o exploit the spreadsheet environment for manipulation of instrumental data, the analyst must move the data into the spreadsheet workspace. The transfer of externally generated data into a spreadsheet, referred to as data importation, requires two steps: data transfer and data formatting. D a t a transfer is the process of moving the data from the instrument computer to the computer on which manipulation will be performed. The method used i s dependent on the type of computer on which the data reside as well as the type of computer used for manipulation, when they differ. Data formatting i s the process of putting the data into a form that the soreadsheet oroeram can recognize. Data transfer. Data transfer methods include communication via removable storage media (diskette or tape), intercomputer communication. and direct, real-time. program-to-program communication. The first two methods arerommonlyemployed. hut theappli. cation of interprogram data exchange with spreadsheets is i n its infancy. Older instrumentation does not rou. tinely have the capability to store resultsinuser.accessibledatafiles,orthe format used for storage may be proprietary.Insituationslikethese,it isoften possible to capture the data that were tohesenttoaprinterwiththecomputer on which the data handling is t o be performed. Data formatting. Often the data files as transferred from an instrument computer are not in a format that can be read by the spreadsheet program. Some sort of reformatting utility may be available, or one may be written. This type of utility usually provides an output f ile that has special characters hetween fields ("delimi1ers"J and has text fields enclosed in quotes. Most spreadsheet programs can easily import such formats. An alternative to a conversion utility is to "parse" the data in the spreadsheet or a word processor. Par.sing is the separation of data lines into discrete fields, based on key characters or spacing. This is usually a less desirable approach because i t only works well when each data line is in identical for. mat. This approach works well, however, when capturing printed reports because they are most often rigidly formatted. Although not always elegant, a data importation architecture can be developed for most applications.
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Figure 4. Plasma 40 CLP reporting procedurc
reporting for the Inorganics Analysis Section of the U.S.Environmental Protection Agency. Contract Laboratory Program (CLP) has been partially automated. The wastewater analysis is performed on a Perkin-Elmer Plasma 40 inductively coupled plasma emission spectrometer. Data are imported via a conversion utility that allows a delimited, quoted text file to be created from an instrument data file. Both the instrument computer and the data manipulation computer are IBM PCs. This application is particularly well suited to the demonstration of spreadsheet capabilities. The EPA reporting scheme is complex and inflexible, and manual report preparation is time consuming and expensive. The contract requires report submission in both printed (hardcopy) and computer-readable format. These considerations mandate the use of a computer for report generation. A manual approach to report generation would be subject to errors in transcription, calculation, and in the carefully specified format. Importing the results directly from the instrument data file into a report generation system is important for the credibility of the final report. The section automated was the generation of the cover page for the Inorganic Analysis Data Package and Form I, the Inorganic Analysis Data Sheet. Preparation of Form I requires flagging of results based on detection limits and decision making for format. The procedure followed is represented in Figure 4. The steps involved are as follows: data transfer; data formatting; data importation; and macro execution, which includes data extraction, data copying, data flagging, and report output. The last four steps have been automated with a macro procedure and are represented in Figure 4 by the arrow indicating “macro execution.” The data transfer is accomplished by using floppy disk data files. The conversion of the fdes is performed, and the spreadsheet model is activated. When the model is activated, a macro is executed that prompts the user to select a data file to report. After the selection is made, the file is opened into a
spreadsheet. A region of the spreadsheet is established as a database, and each record corresponds to a line of Plasma 40 data. The database is then purged of data not required for the report, using logical comparisons to retain the data that are required. The database is stored as a temporary work file, leaving both the original and converted data files intact. Prototype worksheets for the forms are opened, and preestablished links move some of the data, such as comment lines, into place immediately. Some data are conditionally copied with the looping constructs of the macro language and the database functions of the spreadsheet. Conditional formatting of the results presents them to two significant figures if less than 10 and to three significant figures if greater than 10. This formatting is achieved with a conditional test in the macro language. The extraction functions of the spreadsheet select results by sample and by element. Extractions are performed on the Plasma 40 database to gather the data for each Form I that must be submitted. Each sample requires a Form I, and each Form I requires 24 analytical results. Samples are analyzed for 22 of the 24 elements with Plasma 40. The flags for the results have been preestablished in the prototype report forms by using conditional formulas. There are tables containing the detection limit data for comparisons in a hidden region of the prototype files. The conditional formulas for the flags contain functions that use these tables to look up information. Based on the element name, the instrumental- and contract-required detection limits are “fetched” for comparison to the data reported. These fetched values are then used in the conditional formulas to determine the value of the flag. The header, footer, and data sections of the forms are then filled in with the macro procedure. The forms generated are fully acceptable under the July 1987 Statement of Work. This application was designed as a demonstration of the utility of spreadsheet software in the analytical chemis-
try environment. It is not a total solution to CLP reporting requirements. However, fully developing the model would be a feasible approach to completely automating the CLP reporting system. Even if data for a given sample were stored in different files from different instruments (e.g., ICP, graphite furnace AAS), the data from these files could be readily merged in the model to provide complete reports. The CLP reporting application is representative of the many strictly specified regulatory analyses; such strict formats are increasingly being reported for nonregulatory analyses as well. As reporting demands for analytical chemistry grow in terms of volume and rigidity, analysts cannot depend on instrument manufacturers to provide adequate reporting schemes wholly within an instrument software package. We can expect to see more support for spreadsheet reporting applications to fill these needs. Spreadsheet reliability The reliability of spreadsheet models is a critical issue for the chemist. As model complexity grows, the likelihood of an error being introduced also grows. Errors may be very subtle (e.g., an incorrect reference to a data table), extremely difficult to detect, and easily introduced when changes are made during development of the model. Errors often arise from using the wrong reference type (absolute, mixed, or relative). Errors can also be difficult to detect in a conventional program. However, the conventional program generally applies the same formulas to all data, using a loop structure. Thus all results to which the error is applied would be in error. This is easier to detect than an error in a single cell of a large spreadsheet data table. Standard test approaches can be used to verify spreadsheet models. Simple input data (e.g., a table of all 1s) can be created and the results examined to determine what happens to those data as they propagate through the model. If one has a good understanding of the model, creating the proper test data sets and examining (eontimedonp. 741A)
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their results can be the most effective way of validating the model. If a calibration curve is to be used in the model, producing a test set that yields a straight line of zero slope should provide the same result for all applications of that curve. If this is not the case, the error usually stands out and can be detected. Other sections of the model can be tested with other sets of conditions. The key to being able to verify performance in this manner is to have a complete understanding of the model. Another approach to the reliability issue is called auditing. Some spreadsheet or external packages include such auditing functions as the listing of cell interrelations and their type, cell formulas, and comments attached to cells. Using such information to document and examine a model can go a long way toward increasing the credibility of the results as well as providing tools for debugging a model when an error has been detected.
Spreadsheel performance The performance of spreadsheet models is usually slow when compared with a dedicated progrsmmatic solution. However, performance is becoming less and less important as computers become faster and more powerful. Nonetheless, as a spreadsheet model becomes performance hound, there are often techniques that can be applied to enhance performance without affecting functionality. Complex models being designed for routine use should be developed with performance as a goal. Iterative or recursive calculations should be avoided whenever explicit solutions are available. Performance of a spreadsheet can often be enhanced simply by grouping dependent cells in the same order that recalculation occurs. This allows the cells that are depended on to he recalculated first, avoiding multiple calcuIations of the same cells. Modern spreadsheet programs further enhance performance using minimal recalculation techniques. These recalculation algorithms are reliable because their metbod is based on a table of cell dependencies available from the model definition. Another alternative is to enhance the computer hardware with a math coprocessor, which can have a significant impact on calculation-intensive models.
spreadsheet sottwam IimHations Spreadsheet software limitations may be functional: What types of built-in functions exist? What types of cell interrelationships may exist? What types of data are supported? What verification is available to lend Credibility to the model? Other limitations are practical: How large a model can be created? How fast can sheet recalculation be
performed? What output formats are available? Understanding these limitations is important when developing a model, when selecting which spreadsheet to use, and when determining whether to use a spreadsheet approach a t all. To aid the reader in selecting spreadsheet software, several reviews of popular spreadsheet programs (1-3) are available. These reviews provide comparison tables of spreadsheet features, which help to determine which package best meets the application requirements. Commercially available products are continually being upgraded, so research should be done a t the time of selection to confirm the validity of these comparison reviews. CanclUSlOns Between the flexibility required in a research environment and the rigid demands for reporting in a regulatory analytical environment, a continuous spectrum of applications exists. Spreadsheet software can be applied throughout this spectrum of applications, be it through the interactive model development in a research application or through the ease of developing a complex, customized reporting scheme. Spreadsheet software is becoming an important tool in the scientist's hands for the manipulation, analysis, and presentation of experimental data. Wielding this tool, one bas capabilities that were not available before a t reasonable costs and levels of complexity. These capabilities will undoubtedly be exploited to perform new tasks that would not otherwise be performed.
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References (1) Warner, E. InjoWorld 1987,9(6), 43. (2) MaUoy, R.B te 1987! 12(7),69. (3) Taylor, J. P8Magaztne 1987,6(18),94.
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