A/C INTERFACE
ROBOTICS
INTHELABORATOR
A GENERIC APPROACH Robert L. Sharp Richard G. Whitfield Lloyd E. Fox The Upjohn Company Portage Road Kalamazoo, Ml 49002
The introduction of robot technology to the analytical laboratory has allowed alternatives in the design of laboratory automation systems to be considered. Just as a series of workstations is used to manufacture automobiles in lieu of using one robot for all tasks {1-5), laboratory automation cells can be dedicated to specialized sample preparation steps and integrated to achieve a flexible sample preparation assembly line. Progress is being made in the pharmaceutical industry (6, 7), where the diversity of product lines makes singleprocedure automation (8-10) impractical except for a few very high-volume products or tests, such as dissolution (11) or Karl Fischer titrations, which are applied to many different products. Rapid changeover between different procedures with minimal human intervention is also being used to improve the practicality of automated sample preparation (12). In this A/C INTERFACE, we describe a flexible approach to laboratory sample preparation that takes advantage of the assembly line concept of manufacturing automation (13-17) and removes the high-volume or manual changeover requirements of single-procedure automation. This task is accomplished by breaking all of the targeted procedures down into classes and subclasses of generic analytical steps, and then automating and networking these individual steps—which are common to many different procedures—into a single integrated system. One of the analytical modules, a liquid-handling
workstation, will be described in detail. Workstations for other analytical steps and some of the technical barriers that must be overcome to achieve them will also be discussed. Generic sample preparation system Sample preparations are composed of subsets of a finite list of generic steps. For instance, any chromatographic assay will use some combination of the following steps: aliquot measurement, dilution, mixing, extraction, separation, concentration, and prepared sample transfer. For different sample types, these steps differ in the amount of sample, the solvent, the dilution volume, the mixing time, the extraction process, and the separation method. Therefore one versatile module can be designed to weigh a sample. The process of weighing a sample is basically the same regardless of the sample type. The variations from sample to sample, such as
Table I. Trial no
a 6 c
target weight, volume, or sample-handling differences, are easily accommodated by software or automated hardware modifications. Such modules can be designed for each preparation step and operated as stand-alone workstations or integrated with other modules to form a system capable of preparing samples for several laboratory tests with minimal human intervention. We have named our integrated group of modules the Generic Automated Sample Preparation (GASP) system. The modules being developed and integrated to form GASP are outlined below along with the current developmental status. Aliquot measurement. Three modules are being developed for solid, liquid, and ointment/gel-type samples. The function of each is to transfer a specific sample amount from one container to a standardized robotic container. During transfer, a target weight within a specified weight range or vol-
Weights obtained for powder samples Powder 1 a % difference mg obtained
1 2 3 4 5 6 7 8 9 10
465.9 482.5 486.1 475.9 493.1 479.0 476.0 483.0 488.9 487.0
mean RSDC
481.7 1.6%
1.3 4.9 5.7 3.5 7.2 4.1 3.5 5.0 6.3 5.9
Powder 2" % difference mg obtained 61.6 58.6 57.7 61.4 61.6 61.2 57.3 60.4 58.3 61.7
-3.7 -8.4 -9.8 -4.1 -3.8 -4.4 -10.5 -5.6 -8.9 -3.6
60.0 3.0%
Target weight is 460 mg. Target weight is 64 mg. RSD stands for relative standard deviation.
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0003-2700/88/A360-1056/$01.50/0 © 1988 American Chemical Society
urne can be verified using an analytical balance that is part of the workstation. After transfer, the measured quantities of samples in standard containers are either placed in storage racks or auto matically forwarded to another module where further manipulations are per formed. The three sampling modules are described below. The solids-handling module works with tablets, suppositories, and pow dered samples. The devices used by the workstation include a powder-dispens ing device and an air-actuated tablet
manipulator. An automated tabletgrinding device is also being developed. The results obtained with this system for dispensing specific weights of pow dered samples are listed in Table I. The robotic cycle time for each sample is approximately 5 min. Reweighing, or adjustment of the quantity being weighed, is programmed to ensure that all samples are within a specified per centage of the target weight. The liquids-handling module is de signed to obtain specific volumes and/ or weights of solutions, suspensions, lo
Table II. Weights observed for volumetric aliquots of syrup, suspension, and oil blend Liquid type Syrup
Suspension
Oil blend
Sample A A Β Β C C
A A A A A
A Β C
Trial no.
Weight (g)
1 2 1 2 1 2
5.5931 5.6170 5.7218 5.6739 5.7806 5.6628
mean RSD
5.6749 1.23 %
1 2 3 4 5
1.0143 1.0132 1.0113 1.0123 1.0214
mean RSD
1.0145 0.40%
1 1 1
1.2471 1.2914 1.3015
mean RSD
1.2800 2.26%
Aliquot volume (mL) 5
1
1.3
Density (g/mL)
Target range (g)
~1.210
5.44-6.65
1.0129
~0.930
0.91-1.11
1.09-1.33
tions, or syrups. An integrated pipet ting system is used to obtain the ali quot. The performance observed for three different types of liquid samples is outlined in Table II. Ointments, creams, and gels are sam pled using the ointments/gel-handling module. A pneumatic gel dispenser currently in use on another robotic set up (18) has been modified to allow for the transfer of many different types of viscous samples. Dilution. Dilution, which typically follows the sampling step, occurs in the standardized robotic containers via ad dition of metered amounts of multiple solvents that may include various mo bile-phase and internal standard solu tions. More than one dilution may oc cur for a particular sample. A variety of commercially available autodilutors are being evaluated to achieve this. Mixing. Typical mixing methods, including sonication, vortex mixing, platform shaking, and wrist shaking, are achieved by integration of commer cially available mixing equipment. Separation. This workstation is composed of a variety of modules for extraction, filtration, derivatization, centrifugation, heating, aspiration, and concentration. Prepared sample transfer. When required by the instrumentation used for the sample analysis, the prepared samples are transferred from the stan dard vials to vials specific to the test for which the sample was prepared. This may be followed by the loading of autosamplers to complete the sample prep aration procedure. Design considerations Because laboratory robotics is a devel oping discipline, continual changes in hardware, software, and technical ap proaches must be expected if not en couraged. These changes bring about the needed progress but create prob-
ANALYTICAL CHEMISTRY, VOL. 60, NO. 18, SEPTEMBER 15, 1988 · 1057 A
lems in communications, the availability and compatibility of workstation hardware, methods of data entry, and hardware-software maturity. Flexibility is a key element in system design; it makes the inevitable upgrading or upward integration possible. The generic approach allows one to use previously developed chemical methods for which procedures already exist, without the manual limitations but with the likely need for some revalidation. Total human emulation is seldom desirable because of differences in dexterity between humans and robots, so revalidation is a reasonable price to pay for added flexibility. Either approach requires adaptation of equipment that was not designed for use with robots. Because the generic approach encourages integration of equipment from multiple vendors, it should foster further developments in "robot-ready" instrumentation, which should further decrease the advantages of human emulation. The ability to perform concurrent sample preparations allows one to optimize the use of time. This ability can be important to the chemistry when factors such as hygroscopicity, reaction rates, or equilibration times are important, and these needs are usually met through scheduling of individual sequences. The robot's cycle time for any specific operation has more of an impact on economic considerations. Throughput will always be a function of hours of operation, sequence scheduling, and robotic cycle time. Robots require large amounts of laboratory space. The use of common access areas and workstations (Figure 1) improves space efficiency, particularly when many robots are involved. Another way to increase space efficiency is to consider volume rather than area. The volume accessibility of the robot, which
is well defined, and the additional space that can be made accessible to the robot through the use of vertically inclined storage racks (e.g., glassware, pipet tip, and filter dispensers) should be considered. The adaptability of the generic sample preparation system to a varied and changing population of samples and to a developing inventory of ancillary devices is very computer and software dependent. Standard communication protocols must be established, and a common database must be available for procedural instructions and for interim and final results. It is preferable to use a local area network (LAN) for this communication. Other desirable features include the ability to upgrade without revalidating the entire system, automatic sample identification and data entry, and compatibility with a laboratory information management system (LIMS). The physical layout of a GASP system is dependent on its location and available space. With the modular design of GASP, flexibility in layout is achieved through mechanical transfers between workstations using some combination of common temporary storage areas, track-mounted robots, and linear displacement devices. Electronic interfacing within modules is typically with RS232C links; interfacing between modules and with common databases is achieved with a LAN. Sample information must be readily available to each workstation from a sample database and updated upon completion of the workstation's task. An example layout is shown in Figure 1. Clearly, the goal of GASP is to provide for complete preparation of all laboratory samples. Development of each workstation is dependent on the capabilities of the other workstations of the system. The first module we de-
veloped was the liquid-handling workstation. Designed to transfer liquids, this module has sufficient flexibility to facilitate incorporation of future modules. Design of data input and output, robotic envelope use, device communication, and standardization of containers required consideration of future modules. Although some workstations are still being developed, the completed liquid-handling workstation and integration factors are described. Also included are data to demonstrate workstation performance. Liquid-handling workstation
Description. The liquid-handling workstation is computer controlled via a dual-floppy-drive IBM PC (International Business Machines, Boca Raton, FL) equipped with an AST Sixpack card (AST Research, Inc., Irvine, CA)
Figure 2.
The vial dispenser.
Figure 3. The two fiber-optic photosensors used in the workstation.
Figure 1.
Possible Generic Automated Sample Preparation layout.
1058 A · ANALYTICAL CHEMISTRY, VOL. 60, NO. 18, SEPTEMBER 15, 1988
Figure 4. station.
The liquid-handling work-
and an 8087 math coprocessor chip (Intel Corp., Hillsboro, OR). Device interfacing is done via RS232C communication. Perkin-Elmer Robot Language (PERL) software (PerkinElmer, Norwalk, CT) is used for all programming, and a Perkin-Elmer Masterlab robot arm (19) transports samples to the various ancillary devices used in the workstation. The arm is equipped with hand sense, which allows for grip-width control, and with a remote pipet tip adapter for sample acquisition. Polypropylene tubing connects the tip adapter to the Masterlab syringe unit (Perkin-Elmer) for sample volume measurements. Sample weights are determined using a Mettler AE163 analytical balance (Mettler Instrument Corp., Highstown, NJ) equipped with a pneumatic cylinder (Perkin-Elmer) to open and close the balance chamber door. Necessary mixing of samples just prior to sample acquisition is performed with a Perkin-Elmer Mastermixer. A device interface controller (Perkin-Elmer) monitors AC and DC power as well as inputs from various sensors used in the workstation. Vial
Filename: slhtest Date: 11/8/1987 Time 16:58:46 SAMPLE ID # VOL (mL) WT. (G) TOT. WT. 1 1AB 1 0.959100 21.367900 vial 1 was completed at: 17:05:09 2 2AA 1 0.960400 23.230300 vial 2 was completed at: 17:13:13 Have attempted to obtain target wetght 5 times!! 3 XXXXX 0.9 0.900000 21.222500 vial 3 was completed at: 17:25:27 4 YYYPPP 8 7.607800 28.101000 vial 4 was completed at: 17:34:55 5 222 1.024170 1.004900 21.445100 vial 5 was completed at: 17:43:08 5.020600 25.359900 6 6 5 vial 6 was completed at: 17:51:30 Figure 6.
22.269900 20.364800 20.493200 20.440200 20.339300
Example report of log of operations for a robotic run.
caps are removed or replaced using a Perkin-Elmer capping station. The vial storage racks, pipet tip adapter arm, and a platform adapter for the balance pan were designed and produced internally. The vial dispenser (Pennies Applied Technology, Raleigh, NC), shown in Figure 2, can house 108 vials but requires less work space than
Main Menu:
ROBOTIC LIQUID SAMPLE TRANSFER PROGRAM - Ver. 1.1; RLS READ FROM A PREVIOUSLY CREATED FILE = 1 CREATE A FILE « 2 DISPLAY AVAILABLE FILES = 3 INITIATION OF ROBOTIC RUN = 4 EXIT FROM THE PROGRAM = 5 ENTER MENU ITEM DESIRED then
File Creation Menu:
FOR ENTRIES THE SAME AS THE PREVIOUS SAMPLE ENTER A < CR > YOU ARE STORING DATA FOR SAMPLE NO: 1 after entry hit SAMPLE TITLE ? test sample SAMPLE ID ? 1234 TO WEIGH THE SAMPLE - ENTER 1 CHOICE = ? 1 TO TAKE AN ALIQUOT - ENTER 2 WEIGH A SAMPLE Use specific gravity to adjust volume ? If y e s - 1 ? If no - 2 Figure 5.
TAREWT 20.408800
Examples of liquid-handling workstation menus.
a typical horizontal test tube rack. Samples are weighed into a 100- X 28-mm Wheaton screw-cap vial, which was chosen as the standard vial to be used with GASP. This vial has a capacity of 40 mL, permitting the sample transfer and further manipulations, such as dilutions, to be done in the same vial. The samples are mixed thoroughly before manual transfer to the sample vial, a Wheaton 60- X 28-mm screw-cap vial. Takenaka fiber-optic photosensors (Pulnix America Inc., Sunnyvale, CA), shown in Figure 3, are used for object verification. Operation. Figure 4 shows a photograph of the liquid workstation. Programming the module consists of linking operator-created commands, such as robot positions or sensor and device control statements, with PERL commands to create a procedure. Each procedure is a subroutine of commands that either directs the execution of a specific task or allows for data input and output. All individual procedures are nested within a master procedure. Data input to the workstation is accomplished using a menu-driven query-and-response session. Sample menus are shown in Figure 5. The input includes such parameters as sample identifiers, volumes, density corrections, and desired weights, which results in a data file array used by the devices to perform correct manipulation of the samples. The operator is also given the option of using previously created files to direct sample preparation. The program is written to allow the data to come from an outside source, such as a bar code reader or a LIMS database. Data generated by the workstation are appended to the operator-created file, and the combined information is stored on disk. Optionally, reports that list pertinent preparation data and a log of operations and error messages are provided for the operator. An example of the log is shown in Figure 6.
ANALYTICAL CHEMISTRY, VOL. 60, NO. 18, SEPTEMBER 15, 1988 · 1059 A
The sequence of events for the work station manipulation of liquid samples is shown in Figure 7. The data array created by the operator allows for dif ferent manipulations (e.g., 1 mL for sample a, 2 mL for sample β, etc.) to be performed. Current hardware limits the exact volume transfers to 5 mL and weighed volumes to 10 mL. This ac commodates our targeted procedures, . but can be changed if needed. Error detection and resolution con stitute a major portion of the program ming of the workstation. Any error from an object verification or position sensor must be analyzed relative to other activities in progress. Proper res olution of the error conditions is essen tial to prevent damage, sample loss, or stoppage of an unattended run. Detec tors used in the workstation include fiber-optic photosensors for object ver ification, such as the presence or ab sence of vial caps and pipet tips; prox imity sensors located on the air cylin der used to open and close the balance chamber door; a contact switch on the capping station to indicate whether the capper grips are opened or closed; and hand sense on the robot's gripper to read the gripper width. Capabilities. The workstation is ca pable of preparing aliquots of suspen sions or solutions. All suspension sam ples are delivered by weight to avoid errors from entrapped air, then record ed either by weight directly or by calcu lated volume using the density input during the data entry session. Solution samples are transferred and recorded as exact volumes. Accurate sample weights to within 10% of the specified target weight are obtained for suspension samples. The weights of aliquots of a group of lotion samples obtained and transferred to the standard robotic container are list ed in Table III. Approximately 2 g of
Table III. Workstation weighing of lotion samples Sample no.
Weight obtained (g)
1 2 3 4 5 6 7 8 9 10
2.0645 2.0855 2.0294 2.0859 2.0276 2.0225 1.8908 1.9996 2.0719 2.0282
mean RSD
2.0306 2.82 %
Note. Desired sample weight is 2 g;g.target range for sample weight is 1.8-2.2
Figure 7.
Sequence of events for the workstation manipulation of the samples.
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the lotion was required for this sample preparation; a weight range of 1.8-2.2 g was acceptable. All obtained weights fall within the acceptable limits. A comparison between manual and automated pipetting of distilled water is shown in Table IV. Our three most commonly used sample volumes were weighed. The precision of this method was then compared with the precision of manual pipetting. A maximum dif ference of 5 /iL between the two meth ods was found, and the means of the two methods were not statistically dif ferent. Furthermore, for the 2-mL and 5-mL comparisons, the precision of the delivery was better by the automated method. These data indicate that the automated method could be intro duced as part of the sample prepara tion process without a significant change in the laboratory test precision. Adjustments for physical differences such as density, viscosity, and surface tension may need to be incorporated for other solutions. Conclusion Manufacturing organizations have tak en the lead in using the flexible fea tures of robotics technology. This ap proach is also satisfactory for a labora tory in which a particular test is repeated again and again. But the vari ability of analytical samples and tests does not easily lend itself to such a pat tern. Integration of a series of robotic workstations can circumvent these shortcomings. The liquid-handling workstation presented in this article is an important component of a com pletely automated sample preparation system for the laboratory. The flexibil ity of the approach is a strong indicator that robots will play an increasingly important role in the analytical lab in years to come. References (1) Flexible Automotive Manufacturing Takes Shape; Automotive Industries 1985,12(2), 94. (2) Anderson, L. Robotics World 1987,5(6), 32-34. (3) Krauskopf, B. Manufacturing Engi neering 1984,93(5), 61-63. (4) Bennington, R. J. Welding Journal 1986,65(11), 27-30. (5) Alderson, C E . Robotics World 1987, 5(8), 19-20. (6) Johnson, E. L.; Pachla, L. A. In Ad vances in Laboratory Automation Robot ics; Zymark Corporation: Hopkinton, MA, 1986, p. 37. (7) Wheeler, G. P.; Littler, P. Α.; Gustin, G. M.; Cardone, M. J. In Advances in Laboratory Automation Robotics; Zy mark Corporation: Hopkinton, MA, 1986, p. 125. (8) Cerino, Α.; Barrett, P.; Fisher, C; McGrattan, B. In Proceedings of the Con ference on Analytical Chemistry and En ergy Technology; 1985, p. 63. (9) Dong, M. W. J. Liquid Chromatogr. 1986,9, 3063. (10) Wolfram, L. E. Research and Develop
Table IV. Comparison of automated vs distilled water manual delivery of Mean weight (g)
Standard deviation (g)
Relative standard deviation (%)
Automated (10 samples) Manual (30 samples)
0.9825
0.0068
0.6893
0.9877
0.0097
0.9799
Automated (10 samples) Manual (20 samples)
1.9790
0.0045
0.2269
1.9806
0.0112
0.5643
Automated (10 samples) Manual (20 samples)
4.9832
0.0116
0.2323
4.9853
0.0225
0.4510
Amount of sample
Type of delivery
1-mL volume
2-mL volume
5-mL volume
ment 1986, 74. (11) Kostek, L. J.; Brown, Β. Α.; Curley, J. E. In Advances in Laboratory Automa tion Robotics; Zymark Corporation: Hop kinton, MA, 1985; p. 701. (12) "Pyetechnology," Zymate Laboratory Automation Systems, Zymark Corpora tion; March 1,1987. (13) Muller, S. Presented at Robots in the Automotive Industry International Con ference, Birmingham, U.K., April 1982. (14) Automotive Industries 1983, 163, 1720. (15) Anderson, L. Robotics World 1987, 5(8), 24-25.
(16) Petronis, T. Robotics World 1987,5(5), 20-21. (17) Design News 1984, 40,18-19. (18) Pesheck, V. V., The Upjohn Co., per sonal communication, 1987. (19) Schmidt, G. J.; Dong, M. W. American Laboratory 1987, 79(2), 62. The authors acknowledge Mark Gerger for statis tical assistance with data interpretation, Gary Gaunnac for suggestions that led to an improved remote pipet system, Michael Golden for review of and suggestions for the manuscript, and John Shabushnig for assistance with the photosensors and computer upgrades.
Robert L. Sharp (center) received his B.S. degree in chemistry from Clarion University of Pennsylvania in 1983. Richard G. Whitfield (left) did his undergrad uate work in chemistry at Glassboro State College and was awarded a Ph.D. degree in physical chemistry in 1977 from Michigan State University. Lloyd E. Fox (right) received both his B.S. and M.S. degrees from Central Michigan Uni versity and was granted a Ph.D degree in analytical chemistry from the Universi ty of North Carolina in 1972. All are members of the Quality Assurance Technol ogy Development Group at the Upjohn Company. Their interests include labora tory automation and automated in-process measurements.
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