A Fast-Neutron Diagnostic Probe

[2] Nozaki, T., J. Radioanal. Chem. 72, 527 (1982). (3] Itoh, Y., Nozaki, T., Masui, T., and Abe, T., Appl. Phys. Lett, 47, 488 (1985). [4] lizuka, T., Takasu, S., Tajima, M., Arai, T., Nozaki, T., Inoue, N., and Watanabe, M., J. Electrochem. Soc. 132, 1707 (1985). [51 Nagai, H., Hayashi, S., Aratani, M., Nozaki, T., Yanokura, M., and Kohno, I., Nucl. Instr. Methods B28, 59 (1987). [6] Nozaki, T., Itoh, Y., Hayashi, S., and Qui, Q., Proc. 7th Modern Trends in Activ. Anal., Copenhagen (June 1986) p. 47.

[2] Nozaki, T., J. Radioanal. Chem. 72, 527 (1982). (3] Itoh, Y., Nozaki, T., Masui, T., and Abe, T., Appl. Phys. Lett, 47, 488 (1985). Consolidated Controls Corporation Lockport Place Lorton, VA 22079 A novel, nuclear method for instrumental analysis of elements has been developed. The system, called a "neutron diagnostic probe (NDP)," has several unusual capabilities that are especially applicable to nondestructive, 3-dimensional analysis in solid materials or inaccessible spaces. The prototype instruments that have been assembled and tested demonstrate that the NDP system can penetrate several inches of solids, locate inaccessible inhomogeneities by time-of-flight ranging, and perform elemental analysis of the located volume of interest. This neutron diagnostic probe utilizes a combination of associated-particle, neutron timeof-flight spectroscopy; inelastic gamma-ray spectroscopy; and a recently developed sealed-tube neutron generator. The NDP interrogates a material or inaccessible space with an electronically collimated and timed beam of fast neutrons which have great penetrating power. Highly penetrating, inelastic gamma rays produced in the interrogated volume by the neutrons are detected and analyzed. The spatial production of these gamma rays is analyzed to "image" the distribution of materials within the volume of interest. The energies of the gamma rays from each substance within the space examined are analyzed to determine the concentrations of the elements in each volume element. An inelastic gamma-ray energy spectrum characteristic of every element except hydrogen can be obtained.
The name of this associated-particle technique [1] is derived from the alpha particle "associated" with a 14-MeV neutron produced by the T(d,n)He 4 reaction in the neutron generator. Because the associated particle is produced simultaneously with the fast neutron and is emitted in the opposite direction, its detection specifies the subsequent trajectory of the individual neutron. This capability makes it possible to define a timed and directed "beam" of 14-MeV neutrons traveling away from a continuous T(d,n)He 4 generator at about 5 cm per nanosecond. By using time-of-flight, range gating, only gamma rays generated by neutron reactions within a known sensitive volume at a predetermined distance from the generator are recorded.
The key element in the instrumentation is the sealed-tube neutron generator (STNG) shown schematically in figure 1. The basic design of the neutron generator is similar to that described by Reifenschweiler [2,3], using the T(d,n)He 4 reaction, a getter-controllable mixture of deuterium and tritium gases and a self-loading target. The innovations in the STNG used for this work are: 1) the inclusion of an internal alpha detector to supply time and direction information by the associatedparticle technique and 2) provision for focusing the ion beam on the target to ensure a "point source" of 14-MeV neutrons. In operation, the radiation detectors and nuclear electronics are arranged as shown in the block diagram of figure 2. Light signals from the ZnS alpha scintillator are amplified by a photomultiplier (PM) and preamplifier, shaped by a constant-fraction discriminator (CFD) and routed to the "start" input of a time-to-amplitude converter (TAC). The alpha signals are also counted and stored in the multichannel analyzer (MCA) data acquisition system for subsequent normalization of the spectral information. The gamma rays generated by inelastic neutron interactions in the interrogated sample are converted to energy spectra with a 15 cm diameter by 36 cm long Nal(TI) scintillation spectrometer. These signals are shaped (CFD) and routed to the "stop" input of the TAC to provide a range-proportional, time-of-flight spectrum at the TAC output. The time-of-flight spectrum is proportional to range because the 14-MeV neutrons travel from the STNG to the interrogated sample at a constant 5 cm/ns and the gamma rays produced in the sample travel at 30 cm/ns to the Nal(TI) detector. Signals from the portion of the time-of-flight spectrum that corresponds to the range position of the sample are selected to open a gate circuit (linear gate) and pass only gamma-ray signals that occur within the appropriate delayed-coincidence, time gate.
The gamma-ray pulses are also linearly amplified and because of the time gate (range gate) only those analog signals of gamma rays originating in the selected sample volume are digitally converted (ADC) and recorded in the data acquisition system. This range-gating technique (time gate of about 3 ns) eliminates nearly all background. In general the background count rate (N.) is expressed as where T is the duration of the time gate while N. and N, are the random count rates in the alpha detector and gamma detector respectively. In the system described here NZ 3 X l03 s5' and rz3X 10-9 s, therefore a random rate N,= I x 104 s-' in the gamma detector only generates Ncz 10' s-' background counts. As with all nuclear meth-ods, the ultimate precision of the measurements will depend on signal-to-noise ratio and counting statistics, i.e., a standard deviation of approximately ±VK where N is the total count. In circumstances requiring background subtractions or other data manipulations the usual rules for propagation of precision indices apply.
In an application that might be called "remote trace analysis" the timed beam of 14-MeV neutrons was directed through about I inch of steel to monitor Na 2 SO 4 corrosive agent on turbine blades. Measurements at less than 1/10,000 Na were made. Thus far the neutron diagnostic probe has been used mostly in areas of bulk analysis of oil shale, coal and sandstone and detection of concealed explosives or contraband, however it is expected to find additional applications for special-purpose instrumental trace analysis where requirements of nondestructive imaging and inaccessibility are paramount.


extfieldsofstudy:
Computer Science
Medicine

text:
Acquisition and analysis of GFAAS data

Since its inception as an analytical technique some 30 years ago atomic absorption spectrometry has become a firmly established method for the analysis of trace metals. Graphite furnace atomic absorption spectrometry provides the analyst with the capability of analysis of solutions containing μg l-1 levels of the analyte, but, because of the transient nature of the signals, a sophisticated approach to the data aquisition and handling of data is required. Most modern commercial graphite furnace atomic absorption spectrometers have built in microprocessors for this purpose but they often have limited capability for extensible user programs and limited data storage facilities. In this communication we describe the use of an Apple IIe microcomputer for the acquisition of data from a Pye Unicam SP9 graphite furnace atomic absorption spectrometer. Details of the interface which utilizes an in-house designed AD converter, and an overview of the Pascal and assembler programs employed are given. The system allows the user to record, store and dump the graphical display of the furnace signalsfor all analyses performed. Files containing details of peak height, and area are formatted on an eight-column spreadsheet. Details of sample type, concentrations of standards, dilutions and replication are entered from the keyboard. The calibration graph is constructed using a moving quadratic fit routine and the concentrations of the analyte in unknown solutions calculated. In addition to this, greater processing power and integration of the data into other analytical schemes can be achieved by exporting the data to other software packages and computers. Details of data transfer between the Apple IIe and an Amstrad PC 1512 are given. Some examples of the use of the system in the development of an analytical methodfor silver in plant material are given.

Introduction
Atomic absorption spectrometry (AAS) is an established analytical technique, which is routinely employed in many analytical laboratories for the determination of trace quantities of metals in solutions [1]. Since its development by Walsh over 30 years ago the growth in the application of AAS has become part of the trend towards increasing use of instrumentation in chemical analysis. The first commercial AAS instruments were introduced in the early 1960s and generally employed flames as cells for the production of analyte atoms. The flame is still the most common atom cell for routine analyses and, in general, analytical sensitivities range from g 1-1 to mg -. If greater sensitivity is required, or the sample size is inadequate for nebulization into a Present address: Dr M. J. Adams, School of Applied Sciences, The Polytechnic, Wulfruma Street, Wolverhampton WV1 1SB, UK. (C) 1988 The Macaulay Land Use Research Institute. conventional flame system, then non-flame techniques can be employed. Of these, the most widely used is graphite furnace atomic absorption spectrometry (GFAAS). An increase in sensitivity of between one and three orders of magnitude can be realized for most elements using GFAAS compared with flame AAS procedures. GFAAS has some disadvantages compared with flame AAS. The duration of the analytical signal from a flame system can be extended to any convenient period, provided there is sufficient sample, and the signal can be integrated over this time to achieve a high measurement precision. The signal produced by electrothermal excitation in GFAAS, however, is transitory and requires a more sophisticated approach to the techniques of data capture and recording. Continued improvements and refinements of instrumentation, in particular the application of microelectronic technology, have removed or reduced many of the problems associated with early GFAAS techniques. Most modern commercial instruments used for GFAAS have some built-in microprocessor-controlled unit for recording the analytical signal and many have sophisticated graphic display output facilities and video screen to show the signal trace, the shape of which can be a useful aid in optimizing and monitoring the analytical procedure.
The built-in microprocessors associated with most commercial analytical instruments are programmed to attend to routine tasks and have only limited capability for extensible user programs and, usually, they provide minimal data storage facilities. Where greater data processing facilities are required and there is a need to store data and provide an integrated data management scheme within a laboratory environment, then interfacing the instrument to a personal microcomputer can present the user with a more flexible and extensible analytical system. A wide variety ofinexpensive personal computers are available and most are capable of being interfaced to analytical instrumentation. One such machine, which has found extensive application in many laboratories, is the 8-bit Apple II microcomputer.
In this communication we describe the use of an Apple IIe microcomputer for the acquisition and recording of analytical data directly from a Pye Unicam SP9 Video Furnace AAS system (Pye Unicam Ltd, Cambridge, UK). The details of the computer-spectrometer interface are provided and the use of the system is illustrated with typical results obtained from an optimization study ofthe determination of silver in plant material. An overview of the Pascal and assembler programs employed in the data logging and manipulation procedures is provided. The transfer of data acquired by the Apple IIe from the spectrometer to a second microcomputer operating a commercial spreadsheet and graphics programs is also described.

Instrumentation
The commercial atomic absorption spectrometer employed is a Pye Unicam SP9 system complete with a PU9095 video furnace programmer, a PU9090 data graphics system and SP9 furnace autosampler. The SP9 is a single-beam spectrometer in which background correction for non-specific absorption is achieved with the aid of a deuterium-filled hollow cathode continuum source which is pulsed alternately with the analyte's hollow cathode lamp. The intensity of the analytical signal output from the photomultiplier is converted to absorbance using a logarithmic amplifier and this signal is output to the data graphics system. In conjunction with the video furnace programmer this data processing system can display 'cookbook' details of analytical conditions for the elements determinable by AAS. In addition, the graphics facilities of the video programmer permit the display of the transient peaks observed in GFAAS studies and the calibration plots for the analytical standards employed. Autosamplers are a desirable addition to any GFAAS system both to improve measurement precision and enable automation of the analytical procedure to be achieved. The SP9 furnace autosampler employed here accepts up to 38 sample cups and two wash cups.
The microcomputer employed to interface to the SP9 system was an Apple IIe. This microcomputer has proved to be a popular choice for laboratory applications largely because of the ease with which interface and expansion circuits can be accommodated within the microcomputer. The Apple IIe machine uses a 8-bit microprocessor (type 6502) as the central processing unit and can directly access up to 64K of memory, which can be expanded to 128K using page-switching techniques. As standard, the computer was equipped with dual floppy disk drive units, a video monitor and an Epson FX-80 printer. Interface circuit cards were installed in the computer using its backplane bus structure; several edge connector slots are available for these boards. All the computer software was written under the Pascal UCSD Operating System.

Interfacing
The general interfacing scheme is illustrated in figure 1. The .spectrometer was connected to the microcomputer via an analogue-to-digital (AD) converter within the computer and its so-called games socket which served as a TTL trigger detector. The AD converter was designed and constructed in-house and has been described in detail elsewhere [2 and 3]. It comprises a precision 12-bit CMOS AD574 integrated circuit (Analog Devices) which can complete a 12-bit data conversion in about 25 ts. This device and all other components, including the X10 signal amplifier, were mounted on a single circuit board for direct connection to the computer using one of its input/output expansion slots. The AD converter was operated in a unipolar mode, 0-10V FSD. The analogue analytical signal was taken directly from the spectrometer. Normally, this signal is supplied as a voltage in the range 0-10 mV, corresponding to 0-1 Absorbance Units, for use with a standard analogue chart recorder. To eliminate potential problems due to electrical pick-up on the spectrometer-computer cable the absorbance and scale expansion circuit board within the spectrometer was modified to provide a 0-1V analogue output. This was achieved by bypassing an internal signal attenuator circuit.
In employing analogue signals for digital data acquisition programs, special care must be taken to control the initiation of the data logging procedure and the rate at which data is accumulated. In the application discussed here the cycle start pulse was obtained from the recorder socket of the video furnace programmer. This.signal is conventionally used to activate a high-speed chart recorder, which can be employed to monitor the transient analytical trace. This signal (0/5V TTL) was led to the games socket of the Apple computer. The rate of data acquisition was under program control and selected to achieve about 450 readings in the maximum 9s analytical measurement period.
The microcomputer interfacing and data acquisition software was written in Pascal with the AD conversion routine being prepared as assembly code and called from the main program as an external function. Once initiated, this program strobes the computer keyboard and the TTL (games) socket to test if a quit key has been pressed by the operator or if an analytical program has been started from the video programmer. If an analytical cycle is initiated then digitization and recording begins. The spectrometer system is programmed to provide a 7s autozeroing period prior to firing the graphite furnace and during this period 350 digital readings are recorded and averaged by the Apple computer to provide a mean background reading which is subtracted from each point of the resultant analytical AAS trace. The AAS data is recorded until the trigger signal is deactivated as programmed at the video furnace programmer. Following each analytical cycle the Apple computer scales the AAS data and presents the complete trace on its graphics display unit along with the maximum signal recorded (peak absorbance) and the signal area (absorbances). The trace data is stored in a disk file after each analysis to minimize data loss should there be a program crash or any loss of electrical power to the computing system. The whole procedure is repeated until the quit key is pressed by the user. When the data acquisition program is exited the analytical data in the disk file is re-formatted to provide a data file containing the peak height and area data and a graphics file containing the original data for the profile of the trace. These files can be used for subsequent calibration and manipulation programs within the Apple computer or exported to other computer systems. A second computer, an Amstrad PC1512, was employed for off-line processing and management of the analytical data. The Apple microcomputer was connected to the PC via a serial, RS232C, interface link. Data transferred to the PC could be studied using a wide range of commercial software packages and integrated with other analytical and management data in this second microcomputer.

Data processing and analysis
The principal data processing software for manipulating and analysing the pre-recorded GFAAS data is an in-house program, DataCalc. DataCalc loads the analytical data into computer memory and displays it a screen 'page' at a time, on the video monitor. The format of the display is similar to many of the common spreadsheet programs, viz. the analytical data occupies discrete rows and columns in the screen. The user can move a cursor from cell to cell between columns in a record and between records or rows. The data can be edited, listed on the printer and saved back on floppy disk for storage and future processing operations. Data processing commands include the determination of the analytical calibration curve and fitting the sample absorbance data to this 132  As well as the date and filename, displayed on the top row of the screen, up to 16 records (rows) of analytical data, formatted in eight columns are displayed at any time. These data columns provide the record (sample) number, class, replicate code, dilution factor, analytical signal peak height and area and the computed, or user-entered, concentrations. The record class defines the nature of the sample as being a standard, unknown, blank or null. Any record marked by the user as being null is ignored by the computer in subsequent calculations. Analyses by GFAAS are rarely performed singly. Sample duplication is usually encountered and to indicate this replication, the program permits an integer replicate code to be used to indicate similar solutions, the absorbance or concentration values of which will be automatically averaged. The peak height and area fields contain integer values corresponding to the analytical instrument's signal output, as digitized by the computer (these values can be entered by the user ifthe program is used in isolation from the spectrometer). The concentration column contains either values for standards keyed-in by the user or values computed following calibration.
The major function of the DataCalc processing package is to compute an analytical calibration curve from the standards' data and calculate previously unknown sample concentrations by interpolating their measured signal absorbance values on the standard curve. The set of standards' records is sorted into an order of increasing concentration, using a simple bubble-sort routine, and the curve-fitting coefficients are calculated. The procedure adopted to compute the calibration graph is similar to that employed by the spectrometer manufacturers and detailed by Whiteside et al. [4]. The average absorbance value determined from all designated blank solutions is subtracted from all other absorbance data and a straight line is fitted between zero and the first standard. A quadratic function is then fitted between every subsequent pair of data points, a third fitting point being calculated using extrapolated slopes. This 'moving-quad- ratic' curve-fitting procedure ensures the curve follows the standards and can be used for complexed shaped calibration graphs. Once calculated, the general form of the calibration curve is displayed on the high-resolution graphics screen of the monitor. If any standard data point is obviously in error, the procedure may be repeated after editing the data by classifying the offending data as a null record. If the curve is accepted then the unknown data points are fitted automatically to the curve and the sample concentration values computed and the results displayed on the screen. The calibration procedure can be undertaken with peak height or area data, as selected by the user.
In addition to these data processing functions the software allows the user to examine the GFAAS profile of any of the records by loading the appropriate data from the disk-based graphics file. The display of this data, or the displays of the analytical calibration curves, can be output to the printer. A printed 'screen-dump' of the calibration curve for the determination of silver by AAS is illustrated in figure 3. The facility to recall and display absorbance profiles on screen allows the operator to quickly examine results from the unattended analysis of a batch of samples to check, for example, that the absorbance profiles of samples and standards are closely similar and that there are no spurious signals. This provides the analyst with a degree of confidence that cannot be obtained from a simple numerical value.
The functions performed by the DataCalc software on the Apple computer are sufficient for most of the routine tasks performed with the GFAAS system. Greater processing power and integration of the AAS data into a more comprehensive analytical scheme can be achieved by exporting the data and results to other software packages and computers. This can be readily achieved by transmitting the data as an ASCII file via a serial RS232C interface from the Apple to a second computer. For most of the time this host machine can be operated independently of the Apple system and will, typically, be running commercial spreadsheet, database, word processing and statistics packages.
A Pascal program can be selected to transmit the analytical data from the Apple machine to the PC system. The user.is prompted to select the transfer of absorbance profile graphics, data or the analytical spreadsheet data.
Communication is achieved between the two microcomputers using 'Kermit', a de facto standard protocol for inter-computer information transfer, operating on the PC. Controlled by the PC the data is output from the Apple and recorded as a text file on a PC disk. This data is available for subsequent analysis.

Applications
The main function of the computerized system is to collect raw data from the atomic absorption instrument, compute the analytical calibration curve from the standards and calculate the concentration of the analyte in unknown samples. In addition to this routine operation, however, the facilities offered by the microcomputer are valuable during the development of new furnace analytical programs. An example of the use of the system in the development and application of a method for the analysis of silver in nitric acid extracts of ashed sphagnum moss is described.
The first stage in the development of GFAAS methods often involves a series of measurements with standard solutions of the analyte to determine the optimum conditions for the pyrolysis and atomisation steps of the furnace program. Figure 4 shows the profiles for the atomization of 0.1 ng Ag (applied as AgNO3 in 2% HNO3) at 1800 C from a totally pyrolytic graphite tube and platform combination following pyrolysis at different temperatures.
A comparison of the profile obtained at a pyrolysis temperature of 340 C with that at 490 C shows the appearance of an additional early peak. This is indicative of a slight loss of silver from the platform to the tube wall during pyrolysis. This deduction was confirmed by the atomization of Ag from the wall alone, see figure 4(d). As the pyrolysis temperature is increased further, to 685 C, there is in addition to loss from the wall to the platform, a substantial loss ofanalyte from the atom cell resulting in a decrease in peak height andarea. The pyrolysis temperature selected for further work on Ag, applied as nitrate, was 340 C.
The use of a host computer running a commercial spreadsheet program 'Symphony' is illustrated in figure  5. This shows an overlay of the absorbance profiles for the atomization of0" ng Ag at different temperatures. In this case the data has been re-scaled (DataCalc scales all points to fill the page) using the spreadsheet commands of Symphony. The efect of increasing the atomization temperature on the relative positions and shapes of the signals is clearly illustrated. As the atomization temperature is increased peak height increases sharply and no plateau is reached in the range studied. Peak area measurements exhibit a slight decrease as the temperature is increased, probably due to increased diffusion losses. For subsequent analyses a compromise atomization temperature of 1800 C held for a period of 5s was 0.34 lme (s) Figure 4. Absorbance profiles for atomization of 0"1 ng Ag at 1800 C. (a) following pyrolysis at 340; (b) 490; and (c) 680 C using a totally pyrolytic tube and platform; and (d) following pyrolysis at 340 C using a tube alone.
used. This example of the use of a powerful spreadsheet program such as Symphony serves to illustrate the principle of data transfer and use within a laboratory and has many potential applications. Where a detailed study or mathematical treatment of absorbance profiles is to be conducted, for example kinetic modelling of the atomization process, a variety of commercial programs are available. Similarly, the range of data-base packages commercially available for microcomputers makes the in-house development of such programs largely unnecessary. They can form the basis of solphisticated laboratory data management systems which can readily link to the dedicated microcomputer application as detailed here.

Conclusion
A microcomputer system has been described which interfaces to a commercial electrothermal atomic absorption spectrometer for the direct acquisition and subse- quent manipulation of the analytical data. As described above, an important advantage of this computerized system is that a full record can be maintained of the absorbance versus time profiles for all the solutions analysed. This information is invaluable in designing new analytical procedures when it is necessary to monitor the effects on the analytical signal of, for example, different ashing and atomizing temperatures. The computerized storage of the graphic trace information also permits the user to check an analysis where a fault is suspected to have occurred in the graphite tube system.
The absorbance peak height and area data are recorded in the computer system for calibration and analysis using spreadsheet-type software. In many cases no further manipulation of this data is required other than that supplied by the in-house designed programs. Where the AAS analysis, however, forms a part of a wider analytical scheme involving other techniques and other laboratories, the GFAAS data recorded by the microcomputer system can be transferred via a simple asynchronous serial interface to other computer systems. An example of GFAAS data manipulation using a commercial spreadsheet package on a PC computer has been presented.


Chemometrics and Standards

Standards are central to the achievement and maintenance of accuracy in trace analysis. This fact is well-known and well-accepted in the international analytical chemical community, where "standards" are generally considered to be Standard Reference Materials (SRMs) or Certified Reference Materials (CRMs). The term, standards, however, is multivalued, as noted recently by a former Director of the National Bureau of Standards [1]. That is, even in our more conventional view of trace analysis, we must consider in addition to standard materials: standard procedures (protocols), standard data (reference data), standard units (SI), standard nomenclature, standard (certified) instruments, and standard tolerances (regulatory standards, specifications, norms) [2]. It is interesting, in light of these several types of "standards" which have some bearing oil accuracy in trace analysis, to consider the possible significance of standards in and for Chemometrics. To pursue this objective, we first must have a common understanding of the meaning of the term, chemometrics, and what significance it may have for accurate trace analysis. A concise definition is given by the subtitle of the volume which resulted from the first NATO Advanced Study Institute on Chemometrics, i.e., "Mathematics and Statistics in Chemistry" [3]. Implications for accuracy, especially accuracy in trace analysis, are immediately evident. That is, wherever mathematical or statistical operations contribute to the experimental design, data evaluation, assumption testing, or quality control for accurate chemical analysis, "chemometric standards" are at least implicitly relevant. The major part of this paper will be devoted to an explicit discussion of such chemometric standards, including case studies drawn from recent research at the National Bureau of Standards. The discussion will be placed in the framework of the Analytical System, or Chemical Measurement Process (CMP), for such a perspective makes it possible to consider logically a "theory of analytical chemistry"; and certainly chemometrics is a very important part of such a theory [4,5]. To set the stage, the next section will include a brief view of the current content of Chemometrics, together with a summary of its history and literature. This article will conclude with a glimpse at the future of chemometrics, with special emphasis on means to achieve increased accuracy in our chemical measurements and increased understanding of the external (physical, biological, geochemical) systems which provide the driving forces for analytical chemistry.

Introduction
Standards are central to the achievement and maintenance of accuracy in trace analysis. This fact is well-known and well-accepted in the international analytical chemical community, where "standards" are generally considered to be Standard Reference Materials (SRMs) or Certified Reference Materials (CRMs). The term, standards, however, is multivalued, as noted recently by a former Director of the National Bureau of Standards [1]. That is, even in our more conventional view of trace analysis, we must consider in addition to standard materials: standard procedures (protocols), standard data (reference data), standard units (SI), standard nomenclature, standard (certified) instruments, and standard tolerances (regulatory standards, specifications, norms) [2]. It is interesting, in light of these several types of "standards" which have some bearing oil accuracy in trace analysis, to consider the possible significance of standards in and for Chemometrics.
To pursue this objective, we first must have a common understanding of the meaning of the term, chemometrics, and what significance it may have for accurate trace analysis. A concise definition is given by the subtitle of the volume which resulted from the first NATO Advanced Study Institute on Chemometrics, i.e., "Mathematics and Statistics in Chemistry" [3]. Implications for accuracy, especially accuracy in trace analysis, are immediately evident. That is, wherever mathematical or statistical operations contribute to the experimental design, data evaluation, assumption testing, or quality control for accurate chemical analysis, "chemometric standards" are at least implicitly relevant.
The major part of this paper will be devoted to an explicit discussion of such chemometric standards, including case studies drawn from recent research at the National Bureau of Standards. The discussion will be placed in the framework of the Analytical System, or Chemical Measurement Process (CMP), for such a perspective makes it possible to consider logically a "theory of analytical chemistry"; and certainly chemometrics is a very important part of such a theory [4,5]. To set the stage, the next section will include a brief view of the current content of Chemometrics, together with a summary of its history and literature. This article will conclude with a glimpse at the future of chemometrics, with special emphasis on means to achieve increased accuracy in our chemical measurements and increased understanding of the external (physical, biological, geochemical) systems which provide the driving forces for analytical chemistry.

A Brief History
The content of Chemometrics, as viewed by the "Working Party on Chemometrics" of the Union of Pure and Applied Chemistry (ILTPAC), is given in table 1 [6]. Included in the second, major portion of the table are titles for some 30 chapters which comprise an overview document being prepared for IUPAC. Two points are evident from the list of titles: (1) the scope of chemometrics is very broad indeed, encompassing significant portions of applied mathematics; (2) as implied by the name, major emphasis is given to measurement, specifically chemical measurement. In a narrower sense, chemometrics is sometimes viewed as the intersection of statistics and analytical chemistry, as seen by the emphasis on experimental design, control, and the analysis of signals and analytical data. The several chapters on signal and data analysis include such topics as filtering, deconvolution, time series analysis, exploratory data analysis, clustering, pattern recognition, factor analysis, and (multivariate) Accuracy in Trace Analysis regression. Standards and analytical accuracy have special relevance to the chapters on terminology, precision and accuracy, performance characteristics, calibration, analysis, and quality control.

Accuracy in Trace Analysis
To complete this brief look at the content, history and literature of chemometrics, it is fitting to refer to the Chemometrics Conference held at NBS just 3 years ago. It was a special meeting in many respects, for it epitomized the interdisciplinary nature and increasing scope of chemometrics; and it was "probably the first (such meeting) in the United States by that title" [14]. The meeting was jointly planned by an interdisciplinary team, consisting of a chemist and two statisticians. It was jointly sponsored by two national chemical and two national mathematical societies. Finally, it contained an extremely effective and balanced blend of experts from the two disciplines: mathematicians (and statisticians) providing critiques of chemometrics presentations by chemists, and chemists providing critiques of the presentations by mathematicians. The synergism resulting from this approach is evident from examining the proceedings [14]. It is appropriate to conclude with reference to this volume, for it was dedicated to W. J. Youden, our first chemometrician in table 2.

Standards
The agenda for chemometrics, from the perspective of standards, is outlined in table 3. First, we must deal with the issue of nomenclature. Because of the relatively recent formal emergence of chemometrics, and because of its interdisciplinary character, this is a very important matter for our early attention. Nomenclature, in this context, refers to much more than terminology. That is, it includes basic meaning and explicit formulation of concepts falling within the scope of mathematics and chemistry. The efforts of IUPAC, both in the Commission on Analytical Nomenclature [15] and as outlined in table 1 [6] , will be extremely helpful in this fundamental task for chemometrics-to assure that chemists and mathematicians "speak the same language" where that language maintains as much self consistency as possible with the slightly diverse languages of the separate disciplines. (To some extent, we shall have to accept a bilingual dictionary. For example, "efficiency," "consistency," and "sample," have somewhat different implications in statistics and analytical chemistry.) Most important is a rigorous approach to the specification and evaluation of the fundamental characteristics of analytical methods and analytical results, such as detection, identification, and quantification (estimates and uncertainties). A combination of chemical knowledge (or "chemical intuition") and statistical expertise in this effort is the best means to assure validity and control through the specification and testing of assumptions. A second level of control which represents a special responsibility for chemometrics is the production and evaluation of quality software and algorithms-a responsibility which is being met in both chemometrics journals. The logical extension of chemical software standards is found in chemometric validation, or Standard Test Data (STD), designed to guarantee quality for the Evaluation step of the CMP. STD

Accuracy in Trace Analysis
The last "standard" indicated in table 3 relates to design. Design of the sampling, measurement, and data evaluation steps of the CMP to meet specified needs, is really thefirst responsibility of chemometrics. A careful blend of statistical expertise and chemical knowledge once again is the best means for meeting the accuracy or information requirements of the CMP. Inadequate attention to design is perhaps the most serious fault in ordinary chemical analysis. Either inconclusive or inadequate chemical results are obtained, using the samples and methods at hand, or costs are needlessly high in obtaining the relevant information. This area constitutes one of the greatest opportunities for chemometrics for attaining requisite accuracy at minimal cost; appropriate methods include information and decision theory, statistical design and optimization techniques, and exploratory multivariate approaches such as pattern recognition and cluster analysis [3].

The Analytical System
A "systems perspective" for the CMP has been promulgated by a number of eminent analytical chemists over the past 2 decades. One of the earliest and most noteworthy efforts was made by the Arbeitskreis "Automation in der Analyse" beginning in the early 70s [4]. The systems and information theoretic view, which was pioneered by members of this circle, such as Gottschalk, Kaiser, and Malissa, is even more relevant today, and it offers perhaps the best model for an integrated chemometric approach to accuracy. Considering a simplified representation of the CMP or analytical system presented for this purpose in reference [16] (fig. 2), for example, it is clear that not only is there material flow through the system, in terms of sampling, sample preparation, and measurement, but there is also the flow of information, and unfortunately noise. Treating the CMP as an integrated system is essential for the optimal application (cost vs accuracy) of chemometric tools for design, control, calibration, and evaluation. Interfaces between the several steps of the CMP must be astutely matched to prevent information loss, and data evaluation and reporting techniques must be recognized as part of the overall measurement process, capable of preserving or distorting information just like the chemical and instrumental steps. The CMP or analytical system model can be especially helpful in planning for accuracy through appropriate points of introduction of SRMs and STD, and for explicit treatment of feedback, where initial results are utilized for improved, on-line redesign ("learning") of the CMP.
Extended discussion of the analytical system is beyond the scope of this paper, but its introduction is essential for a meaningful consideration of the relationship of chemometrics to accuracy and standards, as indicated above. The system view is obviously important for designing or investigating overall performance characteristics, such as the blank, recovery, specificity, and systematic and random error-through propagation techniques [17]. That is, if one wishes to achieve an overall precision, or detection limit, or identification capability, then the design of an optimal system must take into consideration the corresponding parameters for each step of the CMP, from sampling through data evaluation. Such an integrated approach to design, with the help of chemometric techniques, is as relevant to the design of selfcontained automated and intelligent analytical instruments, as it is to the design of an integrated analytical approach of an entire organization (such as CAC) to a broader analytical question, such as the selection and certification of Standard Reference Materials [4,12].
the "space of analytical methods" with the "space of analytical problems." Some of the ways in which hypothesis testing impacts analytical accuracy, in terms of the fundamental parameters of analytical chemistry, are presented in table 4. Figures I and 2 convey the elements of this theory together with its application to detection and univariate identification, respectively [19]. Further details cannot be presented here, but it should be noted that accuracy in trace analysis demands quantitative chemometric approaches to detection, identification, and quantification (uncertainty evaluation), plus model and assumption validation. Inadequate attention to this matter, and imperfect understanding of the fundamental (a, /3 errors) limitations of hypothesis testing, i.e., chemical measurement, continue to produce very erroneous conclusions regarding the results or power of our analytical techniques [19].
It is especially interesting and important to consider this in terms of the final, data evaluation step of the CMP, in view of the expanding use of "intelligent" and automated instrumentation, which generally includes "black box" data evaluation. Monitoring the accuracy of such internal algorithms is clearly one of the critical tasks of chemometrics in the near future, one for which Standard Test Data (STD) may play an important role. The need is exhibited in figure 3, where perfectly visible gamma ray peaks remain "undetected" by a widely used instrumental gamma ray analytical system [20]. Table 4. Analytical accuracy and hypothesis teting 3 Hypothesis formation (external system model) Design of the measurement process-external (x, 1, t) -internal (MP, EP) Hypotheses to be tested: model (simplest internal: y =B +-Ax +±e,) detection, discrimination (estimation) no. of components (knowledge, "fit," constraints) identification (informing variable; pattern) error structure (stationary, white, cdl, variance, bias) Some diagnostics-z, 1, t', K-S, X, X', F. residual patterns,... 'Symbol explanation: x, t, t=sampling species, location(s), time(s), MP, EP=maeasurement and evaluation steps of the CMP, t, tX2'=noncentral tand X statistics; K-S=Kolmogorov-Smirnov statistic.
Before leaving this survey of fundamentals, we must emphasize the importance of the first syllable. Chemometrics differs from statistics and mathe-matics in that chemical intuition or expertise forms an essential part of the activity. As mentioned above, hypothesis formation, which is necessarily the first step in designing a scientific experiment, requires disciplinary expertise. Accuracy in data evaluation or experiment control, for example, can only be expected when the chemometric techniques employed recognize the range of possible alternative hypotheses (models or assumptions). This is the crux of setting reliable bounds for systematic error, or in establishing "definitive" analytical methods. Empirical rules or heuristic techniques adapted to this purpose should be viewed with some caution. Examples of problems demanding chemical expertise for alternative hypotheses are identification, and the assessment of blank and matrix effects [17, 19 (ch. 16)]. In figure  2, for example, knowledge of the alternative was essential to compute the identification power of the test. In the more general case, where chemical species are identified on the basis of spectral or chromatographic patterns, we must know the locations and uncertainty characteristics of all "nearby" patterns to assess the identification power for a given null pattern, or to design a measurement process meeting prescribed identification capabilities. In moving from the universe of all possible neighboring spectral patterns, to the universe of possible interferences [21] or calibration models, for example, chemometrics faces a considerable challenge.

Selected Illustrations
To illustrate the relevance of chemometrics to the assurance of accuracy in trace analysis, we shall examine three recent and continuing investigations from our laboratory. The first has been selected as an example where quantitative hypothesis testing techniques have been applied to one of the fundamental elements of any analytical system: the noise. The second relates to an exploratory research study which seeks to relate patterns of laser microprobe mass spectra to sources of combustion particles ("soot") in the atmosphere. It illustrates the importance of chemical information (or "intu- The primary objective of our investigation of noise was to test the validity of the Poisson hypothesis for very low-level counting data, with special emphasis on background counts. The validity of the Poisson assumption has long been one of the more intriguing questions in nuclear physics and chemistry, and it has therefore been the subject of some notable experiments [22]. Our experimental system was uniquely designed to permit a much more stringent test of this hypothesis, as it provided individual arrival times for more than a million events. A second objective, if the Poisson assumption proves valid, is to provide a physical random number generator-a device operating by the laws of physics, to generate random numbers for use in numerical simulations, as an alternative to numerical pseudo-random numbers. A practical objective for investigating the lowlevel counting noise distribution derives from our physical knowledge of the measurement system, i.e., our knowledge of potential alternative hypotheses. Perhaps the most important such alternative is the possibility of correlated events in the radiation detector, which could have a profound influence on the magnitude and variability of our background noise. As indicated in figure 4, the effective background is reduced by about a factor of 100 through anticoincidence shielding. If, due to wall or gas impurity effects, just 1% of the electronically canceled events were to produce a sec-ondary, time delayed event in the central detector, the effective background would be doubled! Time series and distributional analysis of the background noise thus allows us to investigate this alternative process. Knowledge of the statistical power of the null (Poisson) hypothesis test against this particular alternative is therefore vital both for the construction of valid uncertainty intervals, and for understanding the basic physics and chemistry of the background events. One illustration of the distributional analysis is given in figure 5, where X' is used to test deviations from the expected exponential distribution of time intervals between events. Further discussion of this investigation, including a tabulation of six alternative hypotheses, is given in reference [23]. Further investigation of sources of background noise is currently underway, using multivariate exploration of pulse shape characteristics.

Origins of Atmospheric Soot Particles
Perhaps the best known applications of chemometrics involve multivariate techniques such as principal component analysis (PCA) and cluster analysis. Such techniques have reached a high degree of sophistication, as exploratory tools for the classification of samples which may be characterized by multivariable patterns or "spectra." An excellent introduction to the principles and methods of the "soft" or empirical multivariate modeling techniques is given in reference [24]. PCA and related techniques are especially useful for data exploration, in that they permit ready visualization of sample relationships, provided there are not too many independent components in the system under investigation. Thus, a collection of mixtures of two components having quite complex, yet different, spectra or chemical patterns, can be represented by a set of points in a plane, or on a line if the mixtures are normalized. If the pure components are represented, they appear as the end points. Two dimensional PCA plots thus allow us to display relations among mixtures of three normalized components; and three dimensions increases the display capability to four components. Beyond exploratory display capability, several methods of multivariate chemical analysis may be employed for quantitative estimates for the number and identity of components, and for the analysis of mixtures [25]. These are outgrowths of the seminal work of Lawton and Sylvestre [26].

Accuracy in Trace Analysis
The interplay between the multivariate display techniques and chemical "intuition" (experience, knowledge) is exhibited in our investigation of laser microprobe mass spectra (LAMMS) of individual soot particles formed from the combustion of wood and fossil fuel. The scientific basis for our interest in this problem derives from the potential health effects of combustion particles, which often carry mutagens, on the one hand, and the geochemical and climatic implications, on the other. The ability to infer combustion sources for individual soot particles could add greatly to our understanding of climatic perturbations and perhaps even such phenomena as the Tertiary-Cretaceous Extinction [27]. PCA data exploration was attractive for this study because the system was relatively simple in terms of intrinsic structure (two components), but relatively complex in terms of both the graphitic soot formation and laser plume ion formation processes. The work demonstrates an extremely important point with respect to accuracy, however. That is, the importance of having thoroughly reliable chemical information for validation of the exploratory techniqres. This is shown in figure 6. The upper part of the figure shows the successful classification of wood vs hydrocarbon fuel soot particles on the basis of their positive ion laser microprobe mass spectra. Application of this model, which was developed for laboratory-generated particles, to soot particles collected in the field (urban atmosphere), however, would lead to erroneous conclusions (misclassification). The failed classification shown in the lower part of the figure was discovered through the use of an independent tracer of known accuracy, `4C, for source discrimination [28]. Subsequent research on this very important basic and practical problem has led to some understanding of the reason for the difference between laboratory and field particles, a basic issue being sensitivity of certain species (features) to deviations from the two-source, linear model. This example illustrates one of the more important cautions in the use of multivariate techniques, such as PCA and factor (FA) analysis: namely, the influential character of outliers and departures from assumptions. Further investigation of the atmospheric particles has shown the utility and relative robustness of selected negative ion carbon clusters for combustion source discrimination, as shown in figure 7. Unlike PCA and FA approaches to exploratory multivariate data analysis, the coordinates of the "bi-plot" of figure 7 are not per-turbed by outliers. Also, they are often more readily interpretable chemically than eigenvectors, though clearly they do not possess the dimension reduction efficiency of PCA.

Standard Test Data
A special task for chemometrics is guaranteeing the accuracy of the data evaluation phase of the chemical measurement process. An important element in the task is the development of representative, reference data sets having known characteristics, for testing the validity of data evaluation. Such "standard test data" (STD) thus play the same role for data evaluation that SRMs do for procedure evaluation. STD are likely to become increasingly important as the data evaluation step becomes more complex, and as it becomes less accessible to the user, as in automated analytical systems. The nature and importance of STD for assessing interlaboratory precision and accuracy have been well demonstrated by exercises based on univariate gamma ray spectral data created by the International Atomic Emergy Agency (IAEA) [29] and multivariate atmospheric data created by NBS [30]. The parallelism with SRMs has been further established for the former STD through incorporation into the catalog of the IAEA's Analytical Quality Control Service Program [31]. A brief description of the objectives and outcome of the multivariate STD exercise follows. (A more extended review of both exercises may be found in reference [16].) The objective of the multivariate STD exercise was to evaluate the resolving power, and precision and accuracy of all major mathematical techniques employed for aerosol source apportionment, based on linear models incorporating chemical "fingerprints" or spectra. To adequately test these techniques, which comprised various forms of multivariate factor or regression analysis, it was necessary to generate data matrices which were realistic simulations of the variations in source mixes found in an urban airshed. Also important was a realistic injection of random errors characterizing pure source profiles as well as "measured" ambient samples. This was accomplished by means of the linear equation given below, where the S 1 , were generated by applying a dispersion model incorporating real meteorological data to two urban (geographic) models. The STD generation scheme is illustrated for one of these urban models in figure 8. The outcome of the exercise was instructive. Though results for the several techniques were generally correlated, and agreement with the "truth" was generally within a factor of two, some important differences and discrepancies were observed. For example, FA methods in contrast to weighted least squares estimation ("chemical mass balance") could not provide estimates for all components. They were limited to a collection of four or five component classes. Also, presumably identical methods, operating on strictly identical data resulted in differing component estimates as well as different standard error (SE) estimates. Comparing the actual distributions of residuals to the quoted SEs, we found the latter to vary from gross underestimates to gross overestimates. It was clear from this exercise that results depended heavily on "operator judgment," i.e., unique solutions could not be obtained without the use of certain, often implicit assumptions or decisions. It can be shown that problems of this sort, and in fact a large fraction of the multivariate problems in chemistry, are underdetermined or heavily dependent on assumptions. This is a challenge to chemometrics. Chemical knowledge combined with astute design should eliminate some of the inaccuracy connected with model selection, error treatment, and incautious use of criteria such as non-negativity.
Just as with SRMs, the above intercomparison was not the last word with this data set. Rather it has served as a test bed for additional and newlydeveloped methods of multivariate chemical data analysis [16], the most recent of which involves a new, more accurate representation of multivariate data by "parallel coordinate" systems [33]. In the future, we would expect STD to continue to serve the mutiple purposes of chemometric quality control for both conventional and automated analytical systems, assessment of interlaboratory or interalgorithmic accuracy, and as stimuli for chemometric research on complex, multicomponent systems.

Summary and Forecast
In conclusion, let us consider for a moment the matter of forecast, as viewed from two perspectives: (1) What may be forecast for the future of chemometrics in relation to standards and accuracy? (2) What directions are envisioned if we are to use chemometrics to improve our ability to understand and forecast the behavior of external systems, such as the environment? Key issues which comprise the answer to the first question are: * Nomenclature, including rigorous terminology and formulation of the performance characteristics of the CMP, plus standard nomenclature for methods of CMP design, control and evaluation derived from applied mathematics.
* Optimal design of the overall analytical system to meet prescribed analytical needs and accuracy limits, utilizing detailed chemical knowledge of the characteristics of the individual CMP steps.
* Attention to the validity of the analytical model, both the functional relationship and the noise models; specification of hypotheses and tests having adequate power with respect chemically significant alternative hypotheses. * Assessment of the accuracy of mathematical techniques as applied to chemical data, via algorithm or software evaluation, or overall data reduction evaluation using STD.
* Development of new methods of increased accuracy by iteratively linking CMP design, chemical separation, instrumental measurement and data evaluation, to reduce dependence on unverified assumptions, and to improve precision through interference reduction and application of expert knowledge.
The second question relates to the fact that databased, empirical models cannot be relied upon to provide information beyond their immediate domain. That is, if we wish to be in a position to make 200 Accuracy in Trace Analysis accurate forecasts, or even accurate interpolations, for a given system, there is no substitute for a detailed mechanistic understanding of the properties (model) of that system. It is in this area that chemometrics, and analytical chemistry, have their greatest promise for the future. This prospect is best viewed in terms of a pair of interacting systems. The first system represents the raison d 'tre or driving force for analytical chemistry; it is the external system which depends on chemical analyses for its elucidation or control. The second system is the analytical system or CMP. Chemometrics has long recognized the linkage between these two systems, but much of the work has been based on sampling and measurements designed to establish empirical patterns, or "soft modeling" [34].
Soft modeling, which might be viewed as an outgrowth of empirical, statistical modeling, is extremely important for exploratory studies, and for providing statistical descriptions of empirical relationships in complex chemical or biological systems. In contrast, "hard global models... have great advantages both in their far-reaching predictions and their interpretation in terms of fundamental quantities." And, unlike soft models, "the deviation between the hard model and the measured data must not be larger than the errors of measurement" (Wbld and Sj6strom, pp. 243ff [34]). Increased movement in chemometrics toward hard modeling is clearly attractive because of the potential for increased basic understanding and increased accuracy; it is realistic in view of the enormous advances during the last decade in sampling and measurement capabilities, and especially in computational capacity.
The transition toward more accurate representation of the external physical, chemical or biological systems which analytical chemistry must serve is outlined in table 5. To complement Wold's basic categories, we present the "musical" classification of Douglas Hofstadter [35], and the mechanistic model categories often used to describe biological or environmental systems [36]. Hofstadter's descriptors are apt. They convey succinctly the increasing sophistication of models ("analogies") in an area of enormous intrinsic complexity-artificial intelligence. The flow of models for the environmental system brings us immediately back to analytical chemistry and chemometrics. That is, the linear model, such as that described in section 4.3 is our simplest representation for an environmental system. Consistency and accuracy, governed by measurement error alone, cannot be generally ex-pected with so simple a model. Improvements may be gained through: (1) combined chemometric techniques, such factor analysis followed by time series analysis, to explore the dynamics of the system [37J; and (2) "hybrid" modeling to take into account certain non-linearities such as homogeneous and heterogeneous reactions 1381. Major progress in understanding and monitoring an environmental system comes when natural "compartments" may be defined, with differential equations describing transfers between compartments [39]. When the compartmental description is inadequate, one must consider an even more detailed description of the system, generally by taking into consideration its full dynamic space-time character through the use of coupled equations representing transport and reaction [40]. These last two categories of modeling and measurement are important for assessing the potential impact of human activities on climate, in connection with the "CO," problem, and the coupled reactive system CO-OH-CH 4 , respectively [41]. Multivariate source apportionment (conservative tracers) [32]. Particle-sulfate system apportionment (37,38]. C02 system: troposphere biosphere-ocean; biological systems [36,39]. eCO-OH.CH 4 system (production, transport, reaction) [40,41].
We face very important opportunities to gain increased fundamental knowledge of the nature (mechanistic models) and state of external (environmental, biological) systems through the use of hard, or at least harder, models to guide the sampling and measurement designs for these systems.
By working closely with expert theoretical geochemists or biochemists, for example, chemometricians have the opportunity to design the analytical measurement process to optimally test alternative external models, to better estimate their parameters, and to more accurately evaluate their present state and future course [42].  Figure 2. Hypothesis testing formulation for identification in analytical chemistry. Probability density functions are given for the difference in composition (Si) for particles emanating from the same source vs two different sources [19].  Figure 3. Clearly visible gamma ray peaks ( 2 0 Hg, 31 Cr), which were not detected above a uCo background in the IAEA practi. cal examination of commercial software [20].

Ambient
Event Inteval (s) Figure 5. Chi-square test of the empirical equal probability histogram for low-level counting data [23]. 0.V Figure 6. Isometric PCA projections of Lab and Ambient particle LAMMS positive ion spectra on the first three eigenvectors. Soot particles from wood are denoted "W" and "C"; those from hydrocarbon fuel are denoted "H" and "A." Feature (mass) selection on the basis of "characteristicity" preceded the principal component analysis [28].  ., e , E',Aitkh~i,.i{Egi.0 0