www.elsevier.com/locate/sab
Spectrochimica Acta Part B
Aluminum alloy analysis using microchip-laser induced
breakdown spectroscopyB
Andrew Freedman*, Frank J. Iannarilli Jr., Joda C. Wormhoudt
Center for Sensor Systems and Technologies, Aerodyne Research, Inc., 45 Manning Road Billerica, MA, 01821-3976, USA
Received 23 March 2005; accepted 28 March 2005
Available online 23 May 2005
Abstract
A laser induced breakdown spectroscopy-based apparatus for the analysis of aluminum alloys which employs a microchip laser and a
handheld spectrometer with an ungated, non-intensified CCD array has been built and tested. The microchip laser, which emits low energy
pulses (4–15 AJ) at high repetition rates (1–10 kHz) at 1064 nm, produces, when focused, an ablation crater with a radius on the order of
only 10 Am. The resulting emission is focused onto an optical fiber connected to 0.10 m focal length spectrometer with a spectral range of
275–413 nm. The apparatus was tested using 30 different aluminum alloy reference samples. Two techniques for constructing calibration
curves from the data, peak integration and partial least squares regression, were quantitatively evaluated. Results for Fe, Mg, Mn, Ni, Si, and
Zn indicated limits of detection (LOD) that ranged from 0.05 to 0.14 wt.% and overall measurement errors which varied from 0.06 to 0.18
wt.%. Higher limits of detection and overall error for Cu (>0.3 wt.%) were attributed to analysis problems associated with the presence of
optically thick lines and a spectral interference from Zn. Improvements in design and component sensitivity should increase overall
performance by at least a factor of 2, allowing for dependable aluminum alloy classification.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Breakdown spectroscopy; Microchip laser; Aluminum alloy; Partial least squares; Miniature spectrometer; Alloy classification
1. Introduction
As part of an investigation into the use of a microchip
laser as part of a laser induced breakdown spectroscopy
(LIBS) sensor for the detection of trace elements in a variety
of matrices [1], we present results of a microchip-laser based
LIBS study of aluminum alloys. The microchip laser, which
possesses a number of properties which differentiate it from
the high power lasers typically used in such studies, offers
the potential for a far more compact and inexpensive LIBS-
based sensor than is currently available. This paper will
detail the benefits and disadvantages of using such a laser in
0584-8547/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.sab.2005.03.020
i This paper was presented at the 3rd International Conference on Laser
Induced Plasma Spectroscopy and Applications (LIBS 2004), held in
Torremolinos (Malaga, Spain), 28 September – 1 October 2004, and is
published in the special issue of Spectrochimica Acta Part B, dedicated to
that conference.
* Corresponding author. Tel.: +1 978 663 9500.
E-mail address: [email protected] (A. Freedman).
conjunction with a miniature spectrometer which uses an
ungated, non-intensified CCD array detector.
The properties of microchip lasers have been described
in detail elsewhere [2–5]. In summary, these lasers deliver
low pulse energies (4–15 AJ) with very short pulse widths
(500 ps) at high repetition rates (1–10 kHz) compared to
the flashlamp-pumped Nd:YAG or excimer lasers typically
used for LIBS sensors. These latter two classes of lasers
produce 5–30 ns long pulses with pulse energies in the
tens to hundreds of millijoules range and repetition rates of
1–100 Hz. Microchip-LIBS thus differs in the details of
the ablation/plasma formation process which in turn affects
the spectral emission. The most important consequences of
the microchip laser-induced breakdown are that the
emission is virtually simultaneous with the excitation pulse
and that the broadband background signal is relatively
small compared to what is encountered with high power
lasers [6]. These emission properties allow one to use an
ungated detector without a severe degradation in the
60 (2005) 1076 – 1082
A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–1082 1077
quality of the signal with respect to signal-to-noise or
spectral discrimination.
We chose to study aluminum alloys in order to compare
our results with a number of previous studies which utilized
more conventional experimental arrangements [7–13]. The
most relevant, in terms of spirit, is the work by Cravetchi et
al. [12,13] in which laser pulse energies in the range of 0.4–
10 AJ were produced using a conventional Nd:YAG laser
(operated at its fourth harmonic at 266 nm) by attenuating
the beam intensity. After being focused by a microscope
objective, the 10 ns long pulse produced craters in an
aluminum alloy substrate with 5 to 10 Am diameters. Using
a gated, intensified array detector coupled to a 0.25 m focal
length spectrometer, this group measured trace element
concentrations of Mg, Cu, Fe and Mn in aluminum using
averages of 21 laser shots with relative standard deviations
(RSD) ranging from 5% to 18% for pure matrix regions.
They specifically note that inclusion of areas on the sample
with precipitates (which are marked by large increases in
minor element concentrations) in the data can increase the
apparent RSDs by a factor of 5 or 6. When higher power
laser pulses are employed (10–500 mJ), such as in Ref. [7],
RSD values can be reduced to ¨5% when averaged over 50
laser shots. Detection limits in Ref. [7] range from 0.5 ppm
(for Mg) to 14 ppm (for Si).
2. Experimental
The microchip laser used in these studies (Litton
Synoptics) produced 500 ps long, 14 AJ pulses at a
repetition rate of 7.8 kHz. As shown in the photograph in
Fig. 1, the laser package was bolted onto an aluminum
alignment fixture which also acted as a heat sink for the
laser. A compound lens system with a nominal 1.5 cm focal
length (CVI), which provides a 1 cm working distance
between lens and sample, was used to focus the laser onto
the sample. The calculated beam waist diameter is ¨20 Am,
yielding a laser fluence of 4.7 J cm�2 (9.4 GW cm�2). The
emission from the laser-induced plume (which is readily
Fig. 1. Photograph of LIBS apparatus.
visible to the naked eye in normal room light) is focused
onto a 600 Am diameter optical fiber (Ocean Optics) using a
single lens. The fiber is essentially focused at 1:1 ensuring
that the entire plume is imaged. The angle between the laser
and sample is kept slightly more off normal with respect to
the sample (30-) than the angle between the emission
focusing optics and surface normal (25-) to ensure that there
is no specular reflection of the laser into the spectrometer.
The spectrometer used in these studies was an f/4, 101
mm focal length instrument (HR-2000, Ocean Optics)
which utilizes a silicon CCD detector array (Sony) to record
the signal. An 1800 groove mm�1 grating in conjunction
with 25 Am wide slits provided spectral coverage over a
range of 275–413 nm with a nominal resolution of 0.2 nm.
The 2048-element CCD array is fitted with a cylindrical
collection lens to concentrate light from the 0.1 cm tall slit
onto the detector elements, and with a phosphor-coated
quartz window to enhance detection sensitivity in this
spectral range.
A total of 30 disks made from aluminum alloy reference
materials were used in these studies. Each disk was
resurfaced and carefully degreased before use, but not
polished. As a result of this treatment, the surface roughness
of these disks was noticeable and certainly large compared
to the size of the 20 Am diameter ablation crater created by
the laser. Furthermore, there is no independent measurement
of the variation in the surface elemental composition which
could have been affected by the resurfacing treatment. It is
quite possible that surface segregation effects caused by
sample heating during the resurfacing could lead to
substantial discrepancies between the surface and bulk
compositions. Reference samples (metal foils with impurity
levels below 0.01%) for the trace elements and pure
aluminum were obtained commercially (Alfa Aesar). The
sample to be measured was placed against the alignment
fixture while the laser was firing. No air breakdown could
be observed with the focused laser.
The data acquisition period for each sample totaled 2.5 s;
10 sets of spectra generated from observing the emission for
250 ms (¨2000 laser shots) were averaged to produce a
single spectrum. Each of the 30 reference disks was sampled
six times (yielding 180 spectra) using a random draw
approach. In order to maintain a LIBS spark during the data
acquisition period, each sample was manually kept in
motion while being pressed against the alignment fixture.
This phenomenon is generally observed for LIBS with
microchip lasers [6]. We have been able to establish that the
mechanism is not simply caused by ablation past the focal
plane. Even though the experimentally observed depth of
focus with respect to producing a spark for our system is on
the order of 1 mm, LIBS sparks on 0.015 mm thick
aluminum foil quickly die out without the appearance of
holes in the foil. It has been suggested that an alteration of
the heat transfer into the substrate, perhaps caused by the
formation of a liquid pool, lies behind the extinction of the
LIBS plasma above a stationary substrate [6].
400380360340320300280
Cu 4.24%Si 10.13
Cu
Si
Cu
400380360340320300280
Mg
Mg
Al
CuCu
Zn Zn
Mg
Cu 0.84%Mg 0.98Mn 0.48Zn 1.03
Mn
Alloy 7075
Wavelength (nm)
Alloy 4145
Wavelength (nm)
Fig. 3. LIBS spectra of two aluminum alloys.
A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–10821078
3. Results and discussion
In order to characterize the performance of the system
and establish a list of suitable spectral peaks for analysis,
LIBS spectra of seven trace elements (Fe, Cu, Mg, Mn, Ni,
Si, and Zn) and aluminum were obtained using pure metal
foils. Fig. 2 presents spectra of three of these trace
elements (Cu, Mg, Zn) as well as aluminum; the spectra
of the other three elements are comparatively more
congested and are not shown for clarity. Note that the
measured emission line widths are on the order of 0.5 nm
(FWHM). The aluminum spectrum alone exhibits a broad
spectral feature at approximately 358 nm which is not
observed in LIBS spectra of Al or Al alloys which have
been recorded using time-gated spectrometers. Given its
breadth and its suppression by time-gating, we tentatively
assign it to a set of unresolved emission lines from Al+
(3s3d-3s4f configurations) that involve both excited upper
and lower electronic states [14].
Raw spectra for two aluminum alloys, 4145 and 7075,
are shown in Fig. 3. The 4000 series aluminum alloys are
characterized by significant silicon concentrations; this
particular alloy contains some copper as well. The 7000
series alloys contain Cu, Mg and Zn. Note from the spectra
that Cu, Mg and Zn are relatively easy to distinguish at low
concentrations, whereas the Si peak at 288 nm is small
despite its high concentration in the 4145 alloy sample. This
is caused by a marked falloff in sensitivity at the lowest
wavelengths for this spectrometer, determined by compar-
ison to a second spectrometer with an overlapping spectral
range.
The analysis of the data is complicated by the use of an
ungated, non-intensified CCD detector array. First, the
signals, although well averaged, are frequently small in
magnitude, especially at low elemental concentrations.
Second, the recorded baseline values are a substantial
fraction of the intensities in the spectral peaks causing
Inte
nsi
ty (
arb
.)
400380360340320300280
Wavelength (nm)
Al Cu Zn Mg
328326324
0.45 nm (FWHM)
Fig. 2. LIBS emission spectra of pure Al, Cu, Zn and Mg samples. The inset
shows details of the Cu spectrum.
significant shot noise. And third, the spectral peaks are
somewhat broad, leading to possible analysis interference
effects from neighboring peaks. In order to assess the
detection sensitivity attainable with such spectra, two
techniques of data analysis were used. In the first, labeled
as peak integration (PI), a linear baseline was established for
each spectral feature used in the analysis and subtracted
from that feature. The resulting peak was then integrated
and divided by the integrated intensity from an aluminum
spectral feature, thereby forming a ratio which should be
less sensitive to the exact plasma conditions than the raw
intensity. Table 1 presents in tabular form all the elemental
emission lines used in this analysis process and their
relevant properties [15]. Those emission lines chosen were
the strongest ones from the neutral species of each element.
Note that the aluminum, iron, magnesium and manganese
features comprise unresolved spectral lines. The ratios are
then plotted against the known elemental composition of
each sample and fit with a linear (for Fe and Si) or
exponential function. This peak integration technique,
however, proved highly sensitive to both the presence of
baseline noise and interference between neighboring spec-
tral peaks; both effects tended to degrade the overall
performance (precision and accuracy) of the calibration
analysis.
400380360340320300280
400380360340320300280
Wavelength (nm)
Mn Reference Spectrum
PLS Vector
Ni Reference Spectrum
PLS Vector
Wavelength (nm)
Fig. 4. Comparison of the PLS regression vectors with measured reference
spectra for Mn (upper panel) and Ni (lower panel).
Table 1
Spectral properties of peaks used in PI analysis [15]
Element Wavelength
(nm)
A21
(108 s�1)
E1 (cm�1) g1 E2 (cm
�1) g2
Al 308.2153 0.6268 0.000 2 32,435.435 4
Al 309.2710 0.7552 112.061 4 32,436.778 6
Al 309.2839 0.1242 112.061 4 32,435.435 4
Cu 324.7537 1.370 0.000 2 30,783.686 4
Fe 373.3317 0.0620 888.129 3 27,666.346 3
Fe 373.4864 0.9014 6928.266 11 33,695.394 11
Fe 373.7131 0.141 415.932 7 27,166.819 9
Mg 382.9355 0.8902 21,850.405 1 47,957.058 3
Mg 383.2299 0.6666 21,870.464 3 47,957.058 3
Mg 383.2304 1.199 21,870.464 1 47,957.027 5
Mg 383.8292 1.594 21,911.178 5 47,957.045 7
Mg 383.8295 0.3996 21,911.178 5 47,957.027 5
Mn 403.0753 0.1738 0.000 6 24,802.250 8
Mn 403.3062 0.1646 0.000 6 24,788.050 6
Mn 403.4483 0.1582 0.000 6 24,779.320 4
Ni 352.4535 1.002 204.786 7 28,569.210 5
Si 288.1578 1.894 6298.850 5 40,991.884 3
Zn 334.5015 1.500 32,890.35 5 62,776.993 7
A21 is the Einstein A coefficient and E1 and E2 are the energies of the lower
and upper states of the emitting atom, where g1 and g2 are the multiplicities
associated with both states.
A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–1082 1079
In an attempt to remedy the aforementioned problems,
we also analyzed our data using partial least squares (PLS)
regression [16–18]. PLS regression is an analysis strategy
which optimally employs all the available spectral informa-
tion. For PLS regression, we first normalize spectra to unit
vector length in order to accommodate scale variation
caused by varying emission intensities. (The PI technique
employs spectral ratios using one particular aluminum
feature.) Performing univariate PLS regression against the
entire set of spectra to predict concentrations for a particular
alloy element yields a PLS regression vector, which when
multiplied, pixel-by-pixel, with a measured spectrum,
results (with proper accounting for mean removal) in a
predicted concentration of that element. The PLS regression
vector itself is formed from a linear combination of a
selectable number of latent vectors. Care must be used so as
to not Foverfit_ the data—that is, reproducing noise in the set
of training spectra. If the analysis is carefully done, the
regression vector for an element should be physically
reasonable—i.e., it should qualitatively reproduce that
element’s reference spectrum, with small variations in other
wavelength regions corresponding to minor coincidental
correlations or anti-correlations with element concentration.
If an element’s concentration happens to be correlated with
the concentration of some other element, its PLS regression
vector can include spectral features of the other element
(such as aluminum).
To gauge the optimal level of fitting (bias) for minimum
prediction error, we employed a 10-fold cross-validation
over the set of 180 spectra. The number of latent vectors
selected varied from 5 to 22 depending on the element
analyzed. The calculated regression vectors for Mn and Ni
are shown in Fig. 4. The agreement with the reference
spectra is remarkably good, especially in the case of Mn,
where no spectral features except for the major one at ¨404
nm could be discerned by visual inspection of the alloy
spectra.
Examples of the resulting analysis using both techniques
are shown in Figs. 5 and 6 for Cu, Mg and Zn. The two
figures differ slightly in format. The PI data in Fig. 5
represent the elemental ratios plotted versus known ele-
mental composition; the non-linearity in the plot is caused
by the presence of an optically dense plume at higher
concentrations of each of the elements shown. The PLS
analysis plot shown in Fig. 6, by definition, assumes a linear
fit between spectral intensities and concentration, and the
predicted concentration is plotted versus the known con-
centration. Note that there is a considerable reduction in
noise, especially near the origin, for the PLS analysis of the
data compared to the PI-analyzed data.
6543210
6543210
6543210
0
0
0
Cu
Mg
Zn
Weight %
Rat
io
Fig. 5. Measured ratio of integrated Cu, Mg and Zn emission peaks to that
of aluminum as a function of their concentrations in various alloy samples.
The fits to the data are exponential functions.
6
4
2
0
6420
6
4
2
0
6420
6
4
2
0
6420
Cu
Mg
Zn
Reference (Weight %)
Pre
dic
ted
(W
eig
ht
%)
Fig. 6. Predicted elemental compositions versus nominal sample concen-
trations for Cu, Mg and Zn using PLS analysis. The ideal fit is depicted as
the solid line with slope 1 and intercept 0.
A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–10821080
A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–1082 1081
Several quantitative measures of the PI and PLS
calibration curves are presented in Table 2. The RMS error
(in units of wt.%) is the root-mean-square average of the
difference between the data points representing all 180
samples and the curve fit through them, divided by the slope
of the curve at that point. The level of detection (LOD), also
in units of wt.%, is defined by 3r0 /S0, where r0and S0are
the standard deviation and slope at zero trace species
concentration. We estimate r0 /S0 by computing the stand-
ard deviation of the above-mentioned differences-divided-
by-slopes, using all concentrations below the LOD. (Only
one iteration, at most, is needed to obtain a self-consistent
value.) The relative standard deviation (RSD) is the RMS
average of ratios of the difference between the observed
points and fit values to the fit values calculated for the set of
points above the LOD. The RSD is a relative quantity. As
can be seen in Table 2, the lower variance near the origin of
the PLS calibration curves (as seen in Fig. 6) results in a
substantial reduction in LOD for all the elements except Cu
compared to the PI technique. As a consequence, the RMS
error in composition averaged over all the data points
(which is heavily weighted by the preponderance of data
near the origin) using the PLS analysis is also reduced
compared to PI, for all the elements except Cu and Fe. On
the other hand, the RSD, which is calculated using only the
points above the LOD, presents a different view of the
relative performance of PLS compared to PI analysis. Only
3 of the 7 elements show an improved RSD value using PLS
analysis; RSD values of three others are actually substan-
tially worse. This finding reflects the fact that the PLS
regression metric acts to reduce the RMS error and not RSD.
The range of RSD values measured in this study (12–
20% for PI analysis, 4–29% for PLS analysis) is similar to
the range of RSDs observed in other studies (2–10%)
[7,11,12]. But with 1 to 10 Hz laser repetition rates, the time
to acquire spectra in these other studies ranged from slightly
over 2 s, as in the present work, to more than 10 times
longer. On the other hand, all of these conventional LIBS
studies exhibit far lower limits of detection (¨1–20 ppm).
In order to understand the relatively poor fit to the Cu
data using PI analysis and the lack of improvement using the
Table 2
Results of data analysis (all values are in wt.%, except RSD values)
Element PI RMS
errora
(wt.%)
PLS RMS
errorb
(wt.%)
PI LODa
(wt.%)
PLS
LODb
(wt.%)
PI RSDa
(%)
PLS
RSDb
(%)
Cu 0.32 0.38 0.40 0.36 12 22
Fe 0.13 0.18 0.32 0.14 12 29
Mg 0.22 0.11 0.26 0.11 14 10
Mn 0.07 0.06 0.11 0.05 20 28
Ni 0.20 0.14 0.51 0.10 14 13
Si 0.66 0.16 1.87 0.14 12 4
Zn 0.52 0.14 1.36 0.10 14 7
a Calculated using the Peak Integration (PI) technique described in the
text.b Calculated using a Partial Least Squares (PLS) analysis.
PLS technique, we need to consider two issues mentioned
above: the presence of spectral overlap and optical thick-
ness. First, note that the internal measurement precision is
actually quite good over the entire measurement range for
Cu (T0.1 wt.%), showing that the problem is not one of
signal-to-noise limitations or experimental reproducibility.
In the case of the PI analysis, the source of the high RMS
error is the high variance at and near the origin resulting in a
high LOD. The fit at finite Cu concentrations is relatively
good, with RSD values in line with the values for the other
elements. Inspection of line listings [14,15] reveals a Zn line
(at 328.23 nm) immediately adjacent to one of the two main
Cu lines (at 327.40 nm) which, given the limited resolution
of the spectrometer (¨0.2 nm) and the comparatively broad
emission line widths encountered (¨0.5 nm), causes a
spectral overlap. Additional nearby Zn lines can also
contribute to the difficulty in baseline definition. If the Cu
samples are placed into two subgroups, one containing Zn
(above 0.1 wt.%) and the other not, the PI-based LOD of the
samples with Zn are a factor of 2 higher than those with no
Zn. This result is consistent with the presence of over-
lapping spectral features causing an interference.
The inability of the PLS analysis to provide an improved
fit to the Cu data illustrates the limitations of PLS regression
analysis. PLS, by definition, assumes a linear dependence
between emission intensity and concentration. However, for
a number of elements, emission intensity at high elemental
concentration shows an exponential dependence—i.e., the
plasma becomes optically thick. This situation occurs for
both Zn and Mg, where PLS analysis provides substantially
improved calibration curves. One explanation for this result
is that there are a number of emission lines within the
spectral range of the instrument that exhibit different line
strengths, providing flexibility within the PLS analysis.
However, for Cu, there are only two strong lines which also
have virtually identical spectral parameters. Thus, their
emission intensities are highly correlated. This lack of
flexibility might prevent the PLS analysis from providing a
fit which improves any of the error metrics for copper.
4. Conclusions
A comparatively simple and inexpensive LIBS detector
for the analysis of aluminum alloys has been designed, built
and tested. It employs a microchip laser and a miniature
spectrometer that uses an ungated, non-intensified CCD
detector array. A handheld commercial device using this
architecture could be designed and built. With the exception
of Cu, the important trace elements (Fe, Mg, Mn, Ni, Si, and
Zn) could be detected with limits of detection (3r) rangingfrom 0.05 to 0.14 wt.%; RMS error over the entire
concentration range varied from 0.06 to 0.18 wt.%. We note
that this level of performance is probably a factor of 2 or 3
poorer than what is required for proper alloy classification.
Improvements in both overall design and component
A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–10821082
performance, such as wider spectral range and longer
integration time, should enable one to reach the desired
sensitivity and precision. It should be noted the limits of
detection observed in this study are two orders of magnitude
greater than those observed using conventional LIBS with
high power lasers and gated detector arrays.
It is still an open question as to whether the trace element
concentration measured using low fluence laser pulses is
truly representative of the bulk composition. For instance,
the use of a comparatively weak fluence laser leads to very
shallow sampling (¨10 Am depth) where the presence of a
native oxide could influence the results. The most recent
work of Cravetchi et al. [13] suggests that, under the right
circumstances, the laser plasma-induced shock wave can be
used to clean the surrounding area of its native oxide,
allowing for accurate surface composition determination.
However, their work also focuses on the presence of regions
of precipitates whose domain dimensions are on the order of
the focused laser beam. There is also the issue of sample
history which might affect the surface composition. Vir-
tually all LIBS-based measurements face issues similar to
the ones discussed here, but a LIBS sensor using a
microchip laser, with its small spark footprint and high
repetition rate, has the unique potential to provide spatially
resolved or averaged information at a high acquisition rate.
Acknowledgments
The authors acknowledge financial support from the
National Science Foundation from a Small Business Inno-
vation Research award (Grant No. 0216309). They also thank
Dr. Ben Smith of the Chemistry Department and Dr. David
Hahn of the Mechanical and Aerospace Engineering Depart-
ment at the University of Florida for many helpful dis-
cussions. The authors also thank Niton, Inc. of Billerica, MA
for providing the aluminum reference samples.
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