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Implementation of Traditional and Real-World Cooperative Learning Techniques in Quantitative Analysis Including Near Infrared Spectroscopy for Analysis of Live Fish Tracy P. Houghton and John H. Kalivas* Department of Chemistry, Idaho State University, Pocatello, ID 83209; *
[email protected] Laboratory courses offering classical methods of chemical analysis need to be maintained and taught at the university level (1). With the advent of instrumental analysis methods, there has been a gradual shift from teaching classical methods in quantitative analysis (1, 2). However, classical methods are still needed to establish standards to calibrate instruments. For example, analysis of the protein and fat content of food products is required to meet food labeling regulations. The Kjeldahl method, involving a titration, and solvent extraction coupled with gravimetry are typically used to develop reference standards for protein and fat analysis, respectively. These reference standards can then be used to build spectroscopic calibration models. Whether instrumental or classical methods are used, traditional quantitative analysis laboratory courses need to be modified (3). Specifically, it has recently been reported that as undergraduates graduate and enter the work force, they lack technical skills required for jobs in industry (3–8). Deviations from the traditional approach are needed to emphasize analyses that require critical thinking and problemsolving skills, provide topics relevant to students’ lives and real-world experiences with links to other disciplines, offer analytical processes and concepts, allow students to work independently as well as in groups, generate opportunities for data interpretation, and develop written and oral communication skills. Many of these objectives are necessary because teams of scientists and engineers must be able collaborate with other teams to develop cooperative goals and objectives (3, 4, 8). Additionally, scientists and engineers spend a large amount of time writing reports summarizing their work, proposals justifying purchase of instruments, etc. A cooperative learning approach for quantitative analysis laboratory courses can provide students with many of these experiences. Overview This paper reports changes made to a traditional onesemester sophomore quantitative analysis laboratory course at Idaho State University in an attempt to educate undergraduates in the skills important to successful scientists. The course combines classical and instrumental methods of analyses with real-world cooperative-learning approaches. This is accomplished by dividing the laboratory course into two sessions. The first session is devoted to individually performed traditional unknown analyses based on supplied unknowns. During the second segment students work in groups to carry out an ecosystem study of a cold-water trout environment. Trout were chosen because they are native to Idaho and provide a valuable economical resource for the state in the form of trout farms, aquaculture, and sport fishing. In the Ecosystem section of this paper, new teaching tools are described. These
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consist of analysis of a live organism using modern technology combining near infrared (NIR) spectroscopy with multivariate regression, costs analysis, and quality control/quality assurance (QC/QA). Also described in this paper is a summary of course assessment results. Fundamentals Traditional In traditional quantitative analysis laboratory courses, students obtain unknowns from the laboratory instructor and the course grade is determined by the accuracy of the results. These unknowns are usually certified samples from commercial sources. This structure allows students to work independently and build confidence in their ability to obtain accurate and precise results. Other benefits gained from this format are familiarity with methods of data collection, classical methods of analysis, basic statistical concepts, and some critical thinking and problem-solving skills. Thus, accomplishing independent analyses on unknowns is an important learning experience and should be maintained as part of a new laboratory course protocol. However, the traditional approach does not furnish a climate in which to build inquiry, develop problem-solving skills as a team, cultivate leadership responsibilities, or improve oral and written communication skills, all of which can be obtained by exploiting real-world analyses.
Real-World The real-world approach has been demonstrated as an effective way to teach quantitative analysis (9–16 ). One strategy is to separate students into groups and assign group members real-world industrial research laboratory roles. At the end of the semester students write and present project reports. Another successful real-world approach uses an ecosystem provided by an aquarium (13, 14, 16 ). In ref 13, a tropical freshwater aquarium was used and potentiometric analyses were implemented. A tropical marine aquarium was used to teach gravimetry, titrimetry, electrometry, and spectrophotometry in ref 14. Rather than setting up an aquarium in the laboratory, a strontium analysis by atomic absorption has been detailed in which students obtain marine aquarium samples from local pet shops (16 ). Many of the skills desired by industry can be met by using cooperative learning with real-world samples. In this way, students experience the industrial team approach to solving problems. Working in groups allows students to share knowledge and explain concepts to peers. This can motivate students to learn because they are accountable to the group members. However, a course designed on only a real-world basis lacks analysis of traditional unknowns. Most importantly, because real-world samples are true unknowns it is difficult to
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directly grade students on accuracy. Thus, essential laboratory skills are probably not firmly established, nor is confidence developed in one’s ability to conduct a real-world analysis. An indirect measure of accuracy used by industry is cost. If an analysis is inaccurate, various costs become involved. Another approach to assess accuracy of real-world analyses is the use of recovery of known additions. This is further discussed in the Ecosystem section.
A Combined Approach A new course has been designed to incorporate ideas from both the traditional approach and real-world approaches. The two approaches are complementary, thereby eliminating their respective deficiencies. Additionally, three new features have been incorporated into the real-world portion of the course design. One involves the use spectroscopy for analysis of a live organism. Another deals with budget preparation. The third is concerned with QC/QA. Ecosystem Study Aquarium Water Because many physical and chemical factors affect trout, there is an abundance of studies for students to conduct. Some important factors are heavy metals, toxic organic compounds, water temperature, the nitrogen cycle, dissolved oxygen (DO), and alkalinity (17, 18). A vital part of any aquatic system is the nitrogen cycle, with a focus on ammonia, nitrite, and nitrate concentrations. Ammonia concentration greater than 0.5 ppm damages fish gills and poses a serious threat to trout health. Nitrite is also toxic to trout, as it competes for oxygen binding sites in hemoglobin and needs to be maintained below 0.5 ppm. Nitrate concentration can reach levels of several hundred ppm before it poses a threat to trout. The nitrogen cycle is present in both the natural environment of trout and in an aquarium. Ammonia and nitrate analyses are achieved using an ion-selective electrode and nitrite is determined spectrophotometrically (13, 19). One of the most important variables of a living aquatic system is DO. The concentration of DO should be 5 ppm or higher for trout survival. Dissolved oxygen levels in the natural environment as well as in an aquarium are directly affected by atmospheric pressure, salinity, and dissolved solids and inversely related to temperature. Of these variables, temperature is the primary factor affecting DO concentrations in the aquarium. Two methods are used for DO analysis. One involves redox titrations and the other makes use of a DO meter (19). Water fertility, the ability of water to produce primary and secondary food sources, can be determined by alkalinity (20). Alkalinity serves as a pH buffer and source of inorganic carbon, and hence helps to determine the ability of water to support algal growth and other aquatic life. Primary chemical species responsible for alkalinity are bicarbonate ion, carbonate ion, and hydroxide ion (20). Thus, units of alkalinity are mg CaCO3/L. Alkalinity should be maintained in excess of 40 mg CaCO3/L to provide adequate levels of food production. An acid–base titration is used for alkalinity analysis (19).
Trout In addition to the ammonia, nitrite, nitrate, DO, and alkalinity studies on aquarium water, trout are also analyzed.
Typical analyses used to characterize the overall health of farm and wild trout are moisture, lipid, and protein concentrations. Fish generally contain these major constituents at levels of 70–80% water, 13–23% protein, and 2–12% lipid (21). Determinations of these analytes in living fish have had numerous applications in fields such as genetics, physiology, ecology, evolutionary studies, and toxicology (22). In contrast, traditional approaches for determining moisture, lipid, and protein may require the death of the fish. Moisture content is obtained by drying a fresh trout tissue sample (21). Lipid and protein contents are determined by solvent extraction and a Kjeldahl procedure, respectively (22). Both methods generate hazardous waste. Recent research with NIR spectroscopy has demonstrated its applicability in determining moisture, lipid, and protein in dead rainbow trout (23–26 ). In the new course, students determine lipid and moisture concentrations in live trout using NIR spectroscopy. In this way, lipid and moisture determinations are accomplished in minutes and the trout do not have to be destroyed. While many papers concerned with spectroscopy have been published in this Journal (27–38), most describe univariate applications for analysis of one component with a single wavelength and have used UV–visible spectroscopy. Fewer papers report exposing students to multivariate forms of calibration and prediction (36–38). A recent publication on industry trends discussed the important use of spectroscopic methods with computers in conjunction with sophisticated multivariate data analysis procedures (39). More and more problems are being solved by the use of NIR spectroscopy and multivariate calibration; this method stands out for its speed, lack of requirement for sample preparation, and resource savings. In the new course, students’ determinations of lipid and moisture content of live rainbow trout are based on a predeveloped predictive model created using principal component regression (PCR). The reader is referred to ref 40 for additional information on PCR. During the course, students are acquainted with the basic principles of multivariate regression, including PCR, and how multivariate regression relates to the univariate case. Completing analyses using multivariate regression allows students to realize that complex sample matrices can be analyzed with the use of spectroscopy. It is also revealed that more than one analyte concentration can be predicted from the same spectrum. A principal component plot is utilized to show students where their trout lies with respect to calibration concentrations used to create the model. Through examination of this plot students determine if their result has been obtained by interpolation or extrapolation. Further details of the fish analysis are provided in the Experimental section.
Budgets Local industry counseled that students should gain an understanding of and experience in preparing budget proposals as another aspect of a real-world situation (personal communication with W. T. Hopkins, J. R. Simplot Research and Development, P.O. Box 912, Pocatello, ID 83204, May 1998). Many undergraduates leave college with no idea of how much it actually costs to execute an analysis, and when they enter an industrial setting they may be required to write budget proposals. Thus, it would be beneficial for students to
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have experience preparing a budget and estimating the cost of items required to perform an analysis. An added benefit is that students gain an understanding of how much it costs to set up an analysis for a course. Implementation details are provided in the Course Design section.
QC/QA Quality control/quality assurance documents that analyses are providing creditable results. Quality control/quality assurance practices are commonly used in industrial laboratories where analysts must demonstrate competence in an analytic procedure before reportable work can be performed. Because real-world samples represent true unknowns (i.e., actual analyte concentrations are not known or certified), implementation of QC/QA becomes critical. With QC/QA, students ascertain the credibility of their various analyses (41–44). For the new course described in this paper, students gain practice in the recovery of known additions. Experimental Procedures Because the aquatic analyses are well described in the literature (19), details are provided only for the NIR analysis of trout.
Spectral Measurements To build the calibration model, 96 freshly killed rainbow trout (Oncorhynchus mykiss) ranging from 87 g (20 cm) to 885 g (39 cm) were obtained from Rangen Incorporated, located in Pocatello, Idaho. Trout were immediately stored on ice and spectra were measured at Idaho State University within 6 hours. Spectra were measured from 800 to 1000 nm at 0.5-nm increments using a ceramic disk as reference and a DSquared Development DPA20 NIR spectrometer equipped with a bifurcated fiber optic probe of 1.3-cm diameter. After the surface of a trout was blotted dry, three spectra were obtained midway between the dorsal fin and the adipose fin above the lateral line. Each spectrum was the average of 64 scans. Spectra were measured as reflectance and converted to absorbance. Figure 1a shows a representative spectrum. As noted in refs 23, 24, and 26, the prominent lipid absorption band is at 934 nm, which is the third overtone of a C–H stretch of a methylene group. A second band for lipid at 893 nm is due to the third overtone of a C–H stretch of a methyl group. An overtone combination band for O–H in water can be seen at 960 nm. These bands are more pronounced in the second derivative spectrum shown in Figure 1b. Trout were frozen at ᎑20 °C immediately after acquisition of spectra. Six frozen trout were selected per day and reference moisture and lipid analysis procedures were followed. Moisture and Lipid Determination A tissue section positioned where NIR spectra were measured was removed from each trout. For a 26-cm trout, this section is approximately 5 cm long and 1 cm deep; widths taper from 3 cm next to the dorsal fin to 2 cm next to the adipose fin. Each flesh sample was weighed and then dried to constant weight in a 50-mm aluminum weighing boat in a 100 °C oven (24 h is adequate). The weight of the dried sample was measured and the percent moisture was computed (45). A procedure based on the method in ref 46 was modified for lipid analysis of trout. Using a mortar and pestle, an oven1316
dried sample was ground until grains about 1 mm in diameter were obtained. Three portions of the sample weighing approximately 0.25 g were placed in test tubes. Five milliliters of 3:2 hexane–isopropanol mixture was added. Portions were homogenized for approximately one minute using a tissue homogenizer. After homogenizing, test tubes were centrifuged for 10 min at 3200 rpm. The supernate was removed and placed into a test tube of known weight. Solid residue was washed three times with 1 mL of the hexane–isopropanol mixture. Washings were added to the weighed test tube. The hexane–isopropanol was evaporated from the test tubes by placing them in a dry bath incubator set at 60 °C. A light stream of nitrogen flowed into each test tube to assist in solvent evaporation. After the hexane–isopropanol evaporated, percent lipid was determined on a dry weight basis.
Calibration Model Data were processed using built-in functions of MatLab 5.2 (The MathWorks, Natick, MA) on a PC equipped with a Pentium II microprocessor. The three spectra recorded for each trout were averaged to produce one spectrum for each trout. A Savitsky-Golay algorithm with a 15-point window and a second-order polynomial was used to smooth spectra and every other data point was then removed (40, 47 ). Smoothing was performed using the PLS_Toolbox 2.01c (Eigenvector Research Inc., Manson, WA). Spectra were mean-centered, and PCR with leave-one-out cross validations was used (see ref 40 for details of the PCR). Root-mean-square error of cross validation values indicated that two-factor PCR
Figure 1. (a) Representative smoothed NIR spectrum of a trout measured midway between the dorsal fin and the adipose fin above the lateral line. (b) The corresponding second derivative spectrum.
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models were best. These values are 2.22 and 0.93 for percent lipid and moisture, respectively.
Analysis of Live Trout by Students Students use the same instrument and parameters used to record calibration spectra. A fish is netted from the tank and placed in a vessel containing an anaesthetizing agent (3-aminobenzoic acid ethyl ester) dissolved in fresh water that has been chilled, using ice, to within 2 °C of the aquarium water temperature. The water in the vessel is undergoing constant aeration with an air stone attached to an oxygen tank. Once the trout is anaesthetized, it is removed and spectra are recorded in the same manner as calibration spectra. The trout is then placed in another vessel containing oxygenated chilled water for recovery from the anaesthetization, after which it is returned to the tank. Students then use the predetermined PCR model with the averaged trout spectrum to obtain lipid and moisture estimates of the trout. Regression diagnostics are provided. Hazards The Material Safety Data Sheet associated with every chemical used should be read before use of that chemical to assess hazards. There are no other significant hazards associated with this laboratory. Course Design and Assessment Format During the first ten weeks of the course, students perform individual analyses consisting of gravimetric determination of sulfate, EDTA titration for calcium, a self-designed calcium and magnesium EDTA titration, spectrophotometric analysis for copper, and determination of lipid concentration in certified pork meat by solvent extraction and gravimetric analysis. The final six weeks of the course are devoted to seven analyses of the trout aquarium ecosystem: ammonia, nitrite, nitrate, DO, and alkalinity in aquarium water, and lipid and moisture content in live trout muscle. The course convenes twice a week for three hours each meeting. For the ecosystem portion of the course, students are divided into fixed groups of four students each. Group members rotate through a manager role responsible for organization of a particular analysis; that is, each student in the group manages at least one analysis. The manager prepares a budget proposal and an outline of individual task assignments, which must be given to the instructor before an analysis can begin. To assist students in preparing the proposed budget, instructors provide information on managers and technicians’ wages with fringe benefits, and costs for waste disposal and any specialty items not found in a Fisher Scientific catalog. While implementing analyses, managers keep track of actual costs and prepare final budgets. When all groups have completed the ecosystem studies, group results are compared and graphed by the managers of respective analyses. Oral and written reports on the ecosystem are required from each group. To assist students in preparing reports, required discussion topics are furnished in the laboratory manual. The course was taught in the spring 1998 and 1999 semesters to three sections each containing 16 students. Budgets, QC/QA, and analysis of live trout were new to the spring 1999 semester.
Grading During the first portion of the course, students are graded on the accuracy of their individual unknown analyses based on certified values. They can receive a maximum of 100 points for each unknown, resulting in 600 points being allotted for the unknown section of the course. The written laboratory report is worth 200 points. The instructor can give up to 100 points based on evaluation of student efforts for ecosystem analyses and the oral report. Group peers contribute up to 100 points for evaluations of each group member. Assessment To assist in determining if students actually benefit from the combined approach for teaching quantitative analysis laboratory compared to only a traditional approach or only a real-world tactic, assessments were implemented. There are many forms of assessment, such as examinations, evaluations of written and oral reports, self-reflection surveys, and peer evaluation (48). For the course described here, students were primarily assessed by surveys designed and statistically analyzed by the Office of Institutional Research at Idaho State University. Students complete three survey assessments during the course. Assessment 1 was conducted the first day. Assessment 2 was administered after completion of the unknowns. Assessment 3 was performed when the course ended. In Assessment 3, students were asked to comment on strengths of the course and to suggest changes to improve it. This paper presents a synopsis of the results; we will provide assessment forms with tabulated results upon request. Results from Assessments 1, 2, and 3 provide insight into what skills were learned and not learned from the traditional quantitative analysis course structure and the real-world cooperative learning techniques. We found that using traditional unknowns did introduce some important skills and abilities and the ecosystem study provided further advancement and confidence in accomplishing certain tasks. Areas where the ecosystem study was found to be effective were active learning, working as a team, leading a team, communicating the results of scientific research, and ability to integrate knowledge from other disciplines with knowledge about chemistry. The assessments also suggest that the traditional and ecosystem portions of the course contribute equally to development of critical thinking skills and finding solutions for new problems. As in the traditional portion of the course, analyses in the ecosystem section of the course are rather “cookbookish”. Thus, students are not necessarily compelled to operate at a high level of critical thinking or find new solutions to new types of problems. If they are given less information about their project or asked to design each analysis, both critical thinking and finding new solutions to new types of problem should improve. Assessment 3 asked students to identify strengths for the unknown portion of the course. Typical comments were: helped me learn basic techniques required for the course turned me into a more careful chemist work on my own graded on accuracy learned problem solving skills had to trust my results developed skills to understand where error in an analysis may occur
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Common improvements suggested by students were: need to know what the unknown actual values are work in groups quality control practices introduced before the ecosystem study
Students were also asked in Assessment 3 to comment on strengths associated with the ecosystem study. Characteristic comments were: hands-on learning
10. 11. 12. 13. 14. 15. 16. 17. 18.
made you feel like you learned something from class direct relationship between chemical principles and real life
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working in a group setting developed leadership skills gain scientific writing skills gaining an understanding of how an aquarium works practical use of and study of chemistry
Suggested improvements focused on the clarity of the lab manual and the need for more structure. Conclusion According to survey assessments, a quantitative analysis course designed as described in this paper provides opportunities for sophomore students to learn new skills and reinforce developing skills. By conducting unknown analyses, students gain an understanding of how close their results are to actual values and thereby build confidence. Unknowns along with the ecosystem study expose students to classical methods of analysis and students work independently as well as in groups. Because analyses conducted on the trout ecosystem link biology and chemistry, students realize interdisciplinary connections. Quality control practices and determining costs of analyses were successfully incorporated into the course. Finally, students realize that modern technology provides a technique for chemical analysis of live organisms. Acknowledgments We would like to thank the Camille and Henry Dreyfus Foundation for partial funding. Kenneth Rodnick is acknowledged for his valuable input on rainbow trout. Rangen Inc. is thanked for donating the trout and trout food. Literature Cited 1. Beck, C. M. II. Anal. Chem. 1991, 63, 993A–1003A. 2. Perone, S. P.; Englert, P.; Pesek, J.; Stone, C. J. Chem. Educ. 1993, 70, 847. 3. Curricular Developments in the Analytical Sciences; a report from NSF workshops in 1997; direct inquires to Kuwana, T., Department of Chemistry, University of Kansas, Lawrence, KS 66045. 4. DePalma, R. A.; Ullman, A. H. J. Chem. Educ. 1991, 68, 383– 384. 5. Thorpe, T. M.; Ullman, A. H. Anal. Chem. 1996, 68, 477A– 480A. 6. Wenzel, T. J. Anal. Chem. 1998, 790A–795A. 7. Shaping the Future: New Expectations for Undergraduate Education in Science, Mathematics, Engineering and Technology; NSF96139; National Science Foundation: Washington, DC, 1996. 8. Thorpe, T. M. J. Chem. Educ. 1986, 63, 237. 9. Walters, J. P. Anal. Chem. 1991, 63, 977A–985A.
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20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48.
Walters, J. P. Anal. Chem. 1991, 63, 1077A–1087A. Walters, J. P. Anal. Chem. 1991, 63, 1179A–1191A. Wenzel, T. J. Anal. Chem. 1995, 67, 470A–475A. Harris, T. M. J. Chem. Educ. 1993, 70, 340–341. Hughes, K. D. Anal. Chem. 1993, 65, 883A–889A. Sherren, A. T. J. Chem. Educ. 1991, 68, 598–599. Gilles de Pelichy, L. D.; Adams, C.; Smith, T. J. Chem. Educ. 1997, 74, 1192–1194. Alabaster, J. A.; Lloyd, R. Water Quality Criteria for Freshwater Fish; Butterworth: Boston, 1980. Stickney, R. R.; Kohler C. C. In Methods for Fish Biology; Schreck, C. B.; Moyle, P. B., Eds.; American Fisheries Society: Bethesda, MD, 1990. Standard Methods for the Examination of Water and Wastewater, 19th ed.; Eaton, A. D.; Clesceri, L. S.; Greenberg, A. E., Eds.: American Public Health Assn.: Washington, DC, 1995. Manahan, S. E. Environmental Chemistry, 6th ed.; Lewis: Boca Raton, FL, 1994. Love, R. M. The Chemical Biology of Fishes; Academic: New York, 1970. Mathias, J. A.; Williams, P. C.; Sobering, D. C. Aquaculture 1987, 61, 303–311. Rasco, B. A.; Miller, C. E.; King, T. L. J. Agric. Food Chem. 1991, 39, 67–72. Lee, M. H.; Cavinato, A. G.; Mayes, D. M.; Rasco, B. A. J. Agric. Food Chem. 1992, 40, 2176 –2181. Gjerde, B.; Martens, H. J. Anim. Breed. Garnet. 1987, 104, 137– 148. Clark, M. M.; Cavinato, A. G. East. Oregon Sci. J. 1997, 13, 139. Cappas, C.; Hoffman, N.; Jones, J.; Young, S. J. Chem. Educ. 1991, 68, 300–303. Murchia, N. S.; Lundquist, E. G.; Russo, S. O.; Peters, D. G. J. Chem. Educ. 1990, 67, 608–611. Dado, G.; Rosenthal, J. J. Chem. Educ. 1990, 67, 797–800. Parody-Morreale, A.; Camera-Artigas, A.; Sanchez-Ruiz, J. J. Chem. Educ. 1990, 67, 988–990. Walmsley, F. J. Chem. Educ. 1992, 69, 583. Bruneau, E.; Lavabre, D.; Levy, G.; Micheau, J. C. J. Chem. Educ. 1992, 69, 833–837. Thomsen, M. W. J. Chem. Educ. 1992, 69, 328–329. Simmonds, R. J. J. Chem. Educ. 1987, 64, 966–967. Cruywagen, J.; Heyns, J. B. B. J. Chem. Educ. 1989, 66, 861– 863. Blanco, M.; Iturriaga, H.; Maspoch, S.; Tarin, P. J. Chem. Educ. 1989, 66, 178–180. Charles, M. J.; Martin, N. W.; Msimanga, H. Z. J. Chem. Educ. 1997, 74, 1114–1117. Pandey, S.; McHale, M. E. R.; Coym, K. S.; Acree, W. E. Jr. J. Chem. Educ. 1998, 75, 878–880. Goldman, D. S. Spectroscopy 1997, 12 (3), 38. Beebe, K. R.; Pell, R. J.; Seasholtz, M. B. Chemometrics: A Practical Guide; Wiley: New York, 1998. Bell, S. C.; Moore, J. J. Chem. Educ. 1998, 75, 874–877. Marcos, J.; Ríos, A.; Valeárcel, M. J. Chem. Educ. 1995, 72, 947– 949. Laquer, F. C. J. Chem. Educ. 1990, 67, 900–902. Paselk, R. A. J. Chem. Educ. 1980, 57, 441. Weatherley, A. H.; Gill, H. S. J. Fish Biol. 1983, 23, 653–673. Radin, N. S. Methods Enzymol. 1981, 72, 5–7. Skoog, D. A.; Holler, F. J.; Nieman, T. A. Principles of Instrumental Analysis, 5th ed.; Saunders: Philadelphia, PA, 1998. Stevens, F.; Lawrenz, F.; Sharp, L. User-Friendly Handbook for Project Evaluation: Science, Mathematics, Engineering, and Technology Education; NSF93-152; National Science Foundation: Washington, DC, 1997.
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