LABORATORY EXPERIMENT pubs.acs.org/jchemeduc
To See the World in a Grain of Sand: Recognizing the Origin of Sand Specimens by Diffuse Reflectance Infrared Fourier Transform Spectroscopy and Multivariate Exploratory Data Analysis† Alessandra De Lorenzi Pezzolo* Dipartimento di Scienze Molecolari e Nanosistemi, Universita Ca’ Foscari Venezia, Dorsoduro 2137, 30123 Venezia, Italia
bS Supporting Information ABSTRACT: The diffuse reflectance infrared Fourier transform (DRIFT) spectra of sand samples exhibit features reflecting their composition. Basic multivariate analysis (MVA) can be used to effectively sort subsets of homogeneous specimens collected from nearby locations, as well as pointing out similarities in composition among sands of different origins. This experiment is designed for graduate students and provides a practical application of both solid-state spectroscopy and chemometrics. It is helpful to students acquainted with MVA and willing to develop some spectroscopic skills. Conversely, the technique can be used by students familiar with spectroscopy who wish to investigate multivariate exploratory data analysis. The spectra can also illustrate a discussion of the spectroscopy of minerals (ad-hoc flowcharts for the identification of the main constituents of sands through their spectral features are proposed). KEYWORDS: Graduate Education/Research, Upper-Division Undergraduate, Environmental Chemistry, Laboratory Instruction, Physical Chemistry, Hands-On Learning/Manipulatives, Chemometrics, IR Spectroscopy, Materials Science, Spectroscopy
couple of ad-hoc flowcharts are proposed in the appendix in the Supporting Information.
U
sing diffuse reflectance infrared Fourier transform (DRIFT) spectra, this experiment exploits basic multivariate analysis (MVA) to identify the spectra corresponding to samples of similar compositions. This study examines 30 sand samples from different locations whose DRIFT spectra exhibit features reflecting their composition. MVA is demonstrated to effectively sort subsets of homogeneous specimens collected in the same area, as well as automatically pointing out similarities in composition of sand from different origins. The analysis can be carried out for any collection of sand samples, provided that a sufficient number of specimens are from the same area or are known to be of similar composition. Students can either work with new spectra or contribute to the development of a spectral database against which the spectra recorded during the current lab course can be evaluated (all the data of the present work are available in the Supporting Information). For good results, uniform sample preparation procedure is mandatory. This experiment is designed for upper-level undergraduate students or graduate students and can be used to demonstrate the applications of techniques from solid-state spectroscopy and chemometrics. It will be helpful to students acquainted with MVA and wishing to develop some spectroscopic competency. Conversely, the experiment can be used by students familiar with spectroscopy willing to investigate multivariate exploratory data analysis. The spectra can also be used for a discussion on the spectroscopy of minerals. To help in the identification of the main constituents of sands through their spectral features, a Copyright r 2011 American Chemical Society and Division of Chemical Education, Inc.
’ EXPERIMENT Sample Preparation
Students were given several raw sand specimens whose origins are shown in Figure 1 and asked to process the material following a uniform procedure, explained in detail in the Supporting Information. The sand samples were repeatedly washed in running tap water to eliminate dirt and mud and dried in an oven (at about 170 °C). To prepare the samples for DRIFTS measurements, small quantities of sand were processed in a vibrating mill (Madatec mod. TAC 400/MS) to obtain a fine powder. Because of the strong influence of grain size on spectral features13 all sand samples were subjected to the same grinding time to ensure comparable results.4 It is also important that the grinding vial is thoroughly cleaned before the next sample is processed; thorough cleaning of the inside of the vials with a steel brush, scrubbing with a descaler agent, and rinsing thoroughly after an ultrasonic bath gives satisfactory results. However, before processing the next sample, a grinding cycle with some pure KBr followed by a check of the corresponding spectrum is strongly recommended to Published: July 13, 2011 1304
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LABORATORY EXPERIMENT
respectively. A Bruker Optik Vertex 70 interferometer equipped with a Pike DiffusIR accessory was employed to measure DRIFT spectra that were recorded from 370 to 4000 cm1 with a nominal resolution of 4 cm1. Five hundred scans were averaged for each run and subjected to NortonBeer (medium) apodization before Fourier transformation. To ensure direct linearity with concentration, all spectra were converted into Kubelka Munk (KM) units,5 f ðR∞ Þ ¼
Figure 1. Approximate collection sites of the sand samples discussed in the present work.
Figure 2. DRIFT spectrum of a sample (sand collected at the Ca’ Pasquali beach near Venezia, Italy) and residuals after two subsequent cleaning cycles (resolution 4 cm1, 500-scan average). Spectra have been shifted from one another by 0.5 KM units for clarity.
ensure that the removal of residuals has been performed to an acceptable extent (see, for instance, Figure 2). All samples were kept in oven before measurements to avoid interferences from water absorption lines in the spectra as well as stored in desiccator whenever possible. DRIFTS Measurements
All diffuse reflectance spectra were measured in samples prepared at a ground sand concentration of about 3%4 in a KBr powder matrix (Pike Technologies, reagent grade). The weighing procedure was carried out directly into the mixing vials and all the weighed samples were subjected to an adequate vibrating mill cycle to ensure good homogeneity (in the case of disposable polythene vials being employed a few possible very-weak to weak lines ascribable to plastic erosion can be found in the spectra). Two mini-cup sample holders were filled with the diluted sand sample and with pure KBr and placed in the DRIFT attachment of the spectrometer to give sample and reference spectra,
ð1 R∞ Þ2 k ¼ s 2R∞
ð1Þ
relating the absolute reflectance R∞, which can be evaluated by the ratio of the reflectance of the sample to that of a nonabsorbing medium (i.e., the KBr matrix), to the ratio k/s between the absorption and the scattering coefficients of the sample. Equation 1 is valid for optically thick samples (no light transmitted through) where absorbing particles are diluted in a reflecting matrix to a certain extent.4,5 Thirty sand samples are examined in this experiment. The sand samples, the corresponding labels, and their masses are listed in Table 1. The recorded spectra of all the sand samples are shown in Figure 3 allowing a first appraisal of their different features. It is evident that a number of spectral characteristics appear to be common to some or most of the curves; this behavior is obviously related to the corresponding sand composition. In particular, spectral features ascribable to carbonates (at about 700730, 860880, and 14451480 cm1) and silicates (mainly at about 370400, 430, 470, 520, 585, 775800, and 10001160 cm1)68 together with minor different contributions are recognized. Details about the mineralogical characteristics of the samples can be found in the appendix of the Supporting Information, and the band assignment for the spectra in Figure 3 are reported in the Supporting Information.
’ HAZARDS No particular hazard is connected to this experiment; nonetheless, when dealing with particulate matter that can be irritants,9 students must be careful not to inhale powders and should wear protective rubber gloves. In the case (advisable) that the grinding vials are cleaned by means of an electric screwdriver fitted with a steel brush, students should wear a leather glove for protection while holding the vial pieces. KBr ingestion can produce nausea, vomiting, and abdominal pain; if repeated, ingestion can also cause central nervous system depression. Prolonged exposure to KBr by any route can bring on bromaderma (skin rashes).10 ’ MULTIVARIATE ANALYSIS AND DISCUSSION Generally speaking, multivariate analysis (MVA) addresses complex sets of data made up of more than one variable measured on many samples. By means of statistical tools, MVA identifies relationships and properties that are often blurred either by noise or correlation between variables or redundancy.11 As such, MVA is being applied to an increasingly wide range of fields including biology,12 chemistry,13 environment and cultural heritage science,14 food science,15 materials science,16 process control,17 and space science.18 Principal component analysis (PCA) is a basic and effective multivariate algorithm that drastically reduces the number of significant variables, thus uncovering the relevant information 1305
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Table 1. Data for the 30 Sand Samples Collection Area Venetian Coast (North Adriatic Sea)
Istria (North Adriatic Sea)
Campania-Calabria Coast (Southern Tirrenian Sea)
Pelion (Aegean Sea) Different Places
a
Site
Label
Sand Mass/
KBr Mass/
Total Mass/
Sand
mg
mg
mg
%
Alberoni
alb
1.85
60.30
62.15
2.98
Bibione
bib
1.83
60.11
61.94
2.95
Ca’ Pasquali
cpa
1.84
60.23
62.07
2.96
Lido di Jesolo
ldj
1.81
60.10
61.91
2.92
Lido di Venezia
ldv
1.83
60.13
61.96
2.95
Murazzi
mur
1.84
60.20
62.04
2.97
San Nicolo San Pietro in Volta
sni spv
1.85 1.85
60.35 60.25
62.20 62.20
2.97 2.98
Sottomarina
sot
1.85
60.15
62.00
2.98
Icicia
ici
1.85
60.12
61.97
2.99
Izolab
izo
1.80
60.19
61.99
2.90
Koper
kop
1.86
60.32
62.18
2.99
Losinij
los
1.87
60.39
62.26
3.00
Capo Vaticanoa
cva
1.87
60.37
62.24
3.00
Pontecagnano Litoranea Salerno
pcl sal
1.85 1.83
60.19 60.18
62.04 62.01
2.98 2.95
Tropeaa
tro
1.86
60.27
62.13
2.99
Devsteni
dev
1.85
60.29
62.14
2.98
Milina
mil
1.86
60.23
62.09
3.00
Capo Hombre (Galitzia, Spain)a
cho
1.87
60.16
62.03
3.01
Fiume Po (c/o Pavia, Italy)
fpo
1.86
60.19
62.05
3.00
Isola Superiore (Lago Maggiore, Italy)
isu
1.83
60.16
61.99
2.95
Lichnos (Epirus - Ionian Sea, Greece)b Marsa Matrouh (Mediterranean Egypt)
lic mma
1.84 1.84
60.44 60.19
62.28 62.03
2.95 2.97
Merzouga (Sahara Desert, Morocco)
mer
1.83
60.23
62.06
2.95
Pinarello (Corsica Island)
pin
1.83
60.12
61.95
2.95
Riccione (Rn - Middle Adriatic Sea, Italy)
ric
1.80
60.13
61.93
2.91
Te Pukatea Bay (South Island, New Zealand)a
tpb
1.85
60.24
62.09
2.98
Torrente Canali (c/o Tonadico, Tn, Italy)
tca
1.84
60.15
61.99
2.97
Zaansche Schans (Netherlands)
zas
1.83
60.15
61.98
2.95
Coarse grained. b Small pebbles.
contained in the original data.11,19,20 Typically, the data to be analyzed consist of a large number of measurements (variables, of number “n”) collected for different subjects (observations, of number “m”); in this case n = 570, corresponding to the number of KM intensity values of the DRIFT spectrum in the most significant 5001600 cm1 range, and m = 30, the number of the sand samples. After subtraction of the average of each variable (centering, necessary to avoid biasing the results), the measured data are collected in the X matrix (dimensions m n) whose columns are the Xi vectors representing the measurements on the i-th sample. Each observation can thus be represented by a point in the n-dimensional space scanned by the initial variables and the distance between these points is considered representative of differences between the corresponding samples. A linear function is then fitted to the data to best account for the variance of the observations in that space. This is the first principal component (PC1). The influence of each variable on the PC1 is measured by its loading value P1j (j = 1 to n) corresponding to the angle between the PC1 and the axis corresponding to the variable. For each observation, the score value T1i (i = 1 to m) is defined by the distance of its projection on the PC1 to the axis origin. These two vectors allow the calculation of the degree of the variance explained by the first PC, that is T1P1T. The remaining
unexplained variance is left in the residual matrix E1 = X T1P1T on which the second PC2 is determined, and so on to higher-order PCs until all the variance of the data is accounted for. The first (and highest in variance explained) PC describes the relevant part of the signal variations, whereas the following ones progressively describe noise. As a consequence, representing each observation in the new space scanned by the most significant principal components allows a simpler but meaningful description of the original measurements. A score plot of two (or three) PCs at a time can thus be useful to reveal relationships between samples. It follows quite clearly from the above discussion that PCA can give reliable results as long as the investigated systems can be described by linear combinations of the original basis;20 in addition, poor results are to be expected whenever a high noise level is present in the data. To uncover similarities in the DRIFT spectra of the sand samples, a simple PCA employing Umetrics AB Simca-P software v 8.021 was performed. The analysis of the raw spectra truncated to the most significant 5001600 cm1 range shows that the first two PCs account for 70.1 and 14.9% of the overall variance, respectively, whereas the third and fourth PCs account for further 6.3 and 2.9%. Among the different score plots, the PC1 versus 1306
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Figure 3. DRIFT spectra of the sand samples discussed in this work (range 3701600 cm1, resolution 4 cm1, 500-scan average). The spectra have been shifted from one another by 4 KM units for clarity.
Figure 4. Scores plot of the first versus fourth principal components. Raw spectra in the 5001600 cm1 range. Points representative of specimens collected in the same area are colored (red = Venetian coast; blue = Istrian coast; green = South Tirrenian Sea Coast; pink = Pelion Peninsula coast).
PC4 plot (Figure 4) best shows clustering of samples collected in the same areas (colored labels in the figure) albeit with some exceptions, whereas specimens of different origin (black labels) do not exhibit a pattern. By examining the points in the score plot against the corresponding spectra, apart from the expected clusters of the specimens of common origin, it is found that the different positions reflect similarities in composition. It is possible to identify a “carbonates-rich” area on the left-hand side and a “silicates-rich” area on the right-hand side, even if most of the samples contain in different proportions both types of constituents. Furthermore,
LABORATORY EXPERIMENT
the lower part of the carbonates area contains samples rich in dolomite (a double carbonate of calcium and magnesium),22 whereas samples rich in calcium carbonate with no magnesium carbonate component are located in the upper-middle (calcite form predominant) and uppermost part (aragonite form predominant). For silicates, a larger variability is found; specimens rich in quartz fall in the upper part of the area, whereas those with increasing feldspars content are located downward. As for the samples of common origin, the following trends are observed. For the sands collected along the Venetian coast, eight out of the nine sands are clearly bunched together, indicating their homogeneity. The sot sample coming from the seashore of Sottomarina, however, shows a “stray” position falling well into the silicates area; this behavior reflects the fact that the rivers south of the lagoon are rich in silicates as opposed to the northern rivers whose sands are mainly composed of dolomite.23 For the sands of Istrian origin, three out of the four points of this family fall close to each other. The kop sample coming from the sea bottom in the Koper Harbor, however, falls quite distant from the others, likely due to a higher content in aragonite (an unusual high fraction of shell fragments was actually discernible at naked eye). The two samples collected in two sea resorts of the Pelion Peninsula fall close in the score plot. The four samples collected in different marine places of the Campania and Calabria coast (Southern Tirrenian Sea origin) fall far from each another indicating the inhomogeneity of the territory. The feasibility of tracing samples collected in the same areas indicates the possibility of more sophisticated data treatments, such as a SIMCA (soft independent modeling of class analogies) classification. For such a task, a wider data collection is advisable, preferably exhibiting more homogeneous subsets.
’ CONCLUSIONS A combined spectroscopic and chemometric study of 30 sand specimens collected in different sites was reported, offering at least three different approaches to the discussion: (i) sample preparation and DRIFTS measurements of solid samples, for which a detailed procedure is given; (ii) spectral analysis, for which some guidelines and two ad-hoc flowcharts for the identification of carbonates and silicates main constituents are given in the appendix of the Supporting Information, allowing a possible discussion of the mineralogical and environmental side of the subject; and (iii) multivariate analysis of the data consisting of a simple principal component analysis that allows the identification of homogeneous groups of specimens. If required, on a further, more sophisticated level, a SIMCA model could be developed to describe quantitatively such groups. The proposed experiment can be carried out neglecting some of the points discussed according to the pedagogical intents (e.g., no spectral analysis is necessary if a chemometric activity is required or no MVA exploratory analysis is necessary in the case of a spectroscopic experiment). ’ ASSOCIATED CONTENT
bS
Supporting Information Sample preparation instructions and data sheets to collect experimental details and results; an appendix containing a short summary of the main characteristics of DRIFT spectra from carbonate and silicate sands and flowcharts to help in their interpretation; all raw DRIFT spectra employed in the present
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’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected]. Notes †
The title of the article is from “To see a world in a grain of sand And a heaven in a wild flower - Hold infinity in the palm of your hand - And eternity in an hour” from “The Auguries of Innocence” by William Blake (composed 18001803; 1st published 1863).
’ ACKNOWLEDGMENT The author wishes to thank Joe Buford Parse for helping to improve her English and the merry brigade of seekers and collectors (Lori Antonello and Danilo Puppato; Andrea Battistella; Silvia Bocus and Paolo de Luigi; Olaf M. Engedahl; Caterina and Ylenia Fasulo; Checco Gonella; Anna Griggio, Lisa and Sergio Colotti; Alessandra Luise and Nivea Galli; Alvise Miotti; Piergiovanni Mometto and Francesca Cerrina Feroni; Marino Pedrali; Marco Vittorio and Lucio Pezzolo; Elena Schiavon; Alessandro Tassan; Raffaella Visinoni and Leonardo Franco) for supplying most of the sand samples. ’ REFERENCES
LABORATORY EXPERIMENT
Martin, S. D. J. Chem. Educ. 2003, 80 (5), 541–543. Cazar, R. A. J. Chem. Educ. 2003, 80 (9), 1026–1029. (14) Simeonov, V.; Massart, D. L.; Andreev, G.; Tsakovski, S. Chemosphere 2000, 41, 1411–1417. Mirti, P.; Gulmini, M.; Pace, M.; Elia, D. Archaeometry 2004, 46, 183–200. (15) Al-Jowder, O.; Defernez, M.; Kemsley, E. K.; Wilson, R. H. J. Agric. Food Chem. 1999, 47, 3210–3218. Rodriguez-Saona, L. E.; Koca, N.; Harper, W. J.; Alvarez, V. B. J. Dairy Sci. 2006, 89, 1407–1412. (16) Ndiaye, I.; Maslouhi, A.; Denault, J. Polym. Compos. 2000, 21 (n. 4), 595–604. Elomaa, M.; Lochm€uller, C. H.; Kudrjashova, M.; Kaljurand, M. Termochim. Acta 2000, 362, 137–144. (17) Lewis, E.; Sheridan, C.; O’Farrell, M. O.; Flanagan, C.; Kerry, J. F.; Jackman, N. Nonlinear Anal.: Hybrid Syst. 2008, 2, 51–57. Rius, A.; Callao, M. P.; Rius, F. X. Analyst 1997, 122, 737–741. (18) Jayarajah, C. N.; Thompson, M. Microgravity Sci. Technol. 2005, XVI, 348–352. Sirven, J. B.; Salle, B.; Mauchien, P.; Lacour, J. L.; Maurice, S.; Manhes, G. J. Anal. At. Spectrom. 2007, 22, 1471–1480. (19) Brown S. A Short Primer on Chemometrics for Spectroscopists; John Wiley and Sons: Chichester, U.K., 2007; http://www.spectroscopynow. com/coi/cda/detail.cda?id=10348&type=EducationFeature&chId=9&page=1 (accessed Jun 2011). (20) Shiens J. A Tutorial on Principal Component Analysis, 2009, vers. 3.01; www.snl.salk.edu/~shlens/pca.pdf (accessed Jun 2011). (21) Umetrics Home Page. http://www.umetrics.com/simca (accessed Jun 2011). (22) Minerals Zone. http://www.mineralszone.com/minerals/dolomite.html (accessed Jun 2011). (23) Jobstraibizer, P.; Malesani, P. I sedimenti dei fiumi veneti. Mem. Soc. Geol. Ital. 1973, XII, 411–452. Guerzoni, S.; Tagliapietra, D. Atlante della Laguna; Marsilio: Venezia, Italy, 2006; pp 2425, table 10.
(1) Mayo, D. W.; Miller, F. A.; Hannah, R. W. Course Notes on the Interpretation of Infrared and Raman Spectra; John Wiley & Sons: Hoboken, NJ, 2004; p 445. (2) Nagarajan, R.; Gupta, D.; Varma, S. P. Infrared Phys. Technol. 2002, 43, 377–382. (3) Clark, R. N. Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In Manual of Remote Sensing; Renez, A. N., Ed.; John Wiley and Sons: New York, 1999; Vol.3, pp 358; http://speclab.cr. usgs.gov/PAPERS.refl-mrs/refl4.html (accessed Jun 2011), par. 6.2. (4) De Lorenzi Pezzolo, A. J. Chem. Educ. 2011, 88, DOI: 10.1021/ ed100832e. (5) Fuller, M. P.; Griffiths, P. R. Anal. Chem. 1978, 50 (13), 1906–1910. (6) Mayo, D. W.; Miller, F. A.; Hannah, R. W. Course Notes on the Interpretation of Infrared and Raman spectra; John Wiley & Sons: Hoboken, NJ, 2004; p 311. (7) Farmer, V. C., Ed. The Infrared Spectra of Minerals; Mineralogical Society: London, 1974. (8) Clark, R. N. Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy. In Manual of Remote Sensing; Renez, A. N., Ed.; John Wiley and Sons: New York, 1999; Vol.3, pp 358; http://speclab.cr. usgs.gov/PAPERS.refl-mrs/refl4.html (accessed Jun 2011), par. 3.2. (9) Kirk-Othmer Enciclopedia of Chemical Technology, 3rd ed.; Wiley & Sons: New York, 1982; Vol.18, p 63. (10) University of Vermont’s MSDS electronic library, http:// hazard.com/msds/ (accessed Jun 2011). (11) Lavine, B. K. Clustering and Classification of Analytical Data. In Encyclopedia of Analytical Chemistry: Instrumentation and Applications; Meyers, R. A., Ed.; John Wiley & Sons: Chichester, 2000; pp 96899710; www.wiley.com/legacy/wileychi/eac/pdf/A5204-W. PDF (accessed Jun 2011). (12) Winder, C. L.; Goodacre, R. Analyst 2004, 129, 1118–1122. German, M. J.; Hammiche, A.; Ragavan, N.; Tobin, M. J.; Cooper, L. J.; Matanhelia, S. S.; Hindley, A. C.; Nicholson, C. M.; Fullwood, N. J.; Pollock, H. M.; Martin, F. L. Biophys. J. 2006, 90, 3783–3795. (13) Ribone, M. E.; Pagani, A. P.; Olivieri, A. C.; Goicoechea, H. C. J. Chem. Educ. 2000, 77 (10), 1330–1333. Rusak, D. A.; Brown, L. M.; 1308
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