Progress in LIBS for Land Mine Detection 

by Jennifer L. Gottfried, Russell S. Harmon, and Aaron LaPointe 

ARL-TR-5127 April 2010 

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Army Research Laboratory 

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ARL-TR-5127 April 2010 

Progress in LIBS for Land Mine Detection 

Jennifer L. Gottfried 

Weapons and Materials Research Directorate, ARL 

Russell S. Harmon

U.S. Army Research Office 

Aaron LaPointe

CECOM Night Vision and Electronic and Sensors Directorate 

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Progress in LIBS for Land Mine Detection 

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Countermine Technology Team, CECOM Night Vision and Electronic and Sensors Directorate, Ft. Belvoir, VA 22060 

14. ABSTRACT 

The ability to interrogate objects buried in soil and ascertain their chemical composition in-situ would be an important 

capability enhancement for both military and humanitarian demining. Laser-induced breakdown spectroscopy (LIBS) is a 

simple spark spectrochemical technique using a pulsed laser. Recent developments in broadband and man-portable LIBS 

provide the capability for the real-time detection of all elements at very high sensitivity in any target material. This 

technological advance offers a unique potential for the development of a rugged and reliable man-portable or robot-deployable 

chemical sensor that would be capable of both in-situ point probing and chemical sensing for land mine detection. In this study, 

broadband LIBS spectra were acquired under laboratory conditions for more than a dozen different types of antipersonnel and 

antitank land mine casings from four countries. A set of antitank land mine simulants was also acquired. Subsequently, a 

statistical classification technique (partial least-squares discriminant analysis) was used to discriminate land mine casings from 

the simulants and to assign “unknown” spectra to a mine type based upon a library classification approach. Overall, a correct 

classification success of 99.0% was achieved, with a misclassification rate of only 1.8%. 

15. SUBJECT TERMS

LIBS, land mine detection, laser spectroscopy, chemometric analysis, demining 

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iii

Contents 

List of Figures iv

List of Tables iv

1. Introduction 1

2. LIBS for Land Mine Discrimination 1

3. LIBS as a Confirmatory Sensor for Demining 12

4. References 14 

Distribution List 17 
iv

List of Figures 

Figure 1. Photo of custom LIBS setup for land mine identification. ..............................................4

Figure 2. Single-shot LIBS spectra of antipersonnel land mine casings. .......................................4

Figure 3. Single-shot LIBS spectra of antitank land mine casings. ................................................5

Figure 4. Single-shot LIBS spectra of land mine simulants. ..........................................................5

Figure 5. Loadings plot for PC1 for the PCA model built using 1000 LIBS spectra from 36 

different land mines and 5 mine simulants. ...............................................................................7

Figure 6. Scores plot of PC1 vs. PC2 for the PCA model built using 1000 LIBS spectra from 

36 different land mines and 5 mine simulants. ..........................................................................7

Figure 7. Scores plot of PC1 vs. PC2 for the PCA model built using 220 LIBS spectra from 

6 different PMN-1 land mines (top and bottom). The cap for mine no. 19 was not 

available. ....................................................................................................................................8

Figure 8. LIBS spectra of the top cap and bottom casing of two PMN-1 land mines. ...................8

Figure 9. Loadings plot for PC1 vs. PC2 (PCA model based on the 6 PMN-1 mines). 

Comparison with the scores plot in figure 7 shows which spectral features contribute to 

the separation between the PMN-1 mines. ................................................................................9

Figure 10. VIP scores for 3 of the classes in the 17-class PLS-DA model: (a) TNG-M80, 

(b) TMA-3, and (c) VS-HCT 2. ...............................................................................................10

List of Tables 

Table 1. Initial land mine casing results. ........................................................................................2

Table 2. Description of land mines. ................................................................................................2

Table 3. PLS-DA results for validation test set consisting of 1500 additional LIBS spectra of 

the mines and simulants included in the model. ......................................................................11

Table 4. PLS-DA results for second test set of mines and simulants not included in the 

model........................................................................................................................................12
1

1. Introduction 

Land mines are a direct threat on the battlefield and remain a significant problem for civilians in 

areas of current and former conflict. Some 15,000–20,000 people in more than 90 countries fall 

victim to land mines each year (1, 2). These explosive hazards cannot be efficiently removed 

using current humanitarian demining approaches (3). At present, land mine detection is 

generally conducted in a manner similar to that employed half a century ago. A handheld metal 

detector is used to identify a subsurface anomaly that may be a buried land mine. In 

humanitarian demining, meter-wide lanes are searched for anomalies by swinging a metal 

detector back and forth just above the ground surface. When the metal detector receives a signal, 

the suspected area is probed to determine whether or not it contains a buried land mine. This is 

also the procedure used for military postoperational land mine clearance, although the new 

AN/PSS14 handheld mine detection system that combines a metal detector with a groundpenetrating radar was deployed for the first time in 2003 on a limited basis by U.S. forces in 

Afghanistan. To investigate the anomaly detected by a land mine sensor system, a human 

deminer uses a thin, tapered prodding device to determine if the anomaly is a solid object and, if 

so, whether or not it is a land mine. This is a very tedious and sometimes uncertain approach. 

Thus, there is a particular need for a deminer’s soil prodder that will differentiate between buried 

antipersonnel mines and other subsurface objects (4). 

2. LIBS for Land Mine Discrimination 

Laser-induced breakdown spectroscopy (LIBS) is a simple spark spectrochemical technique that 

uses a pulsed laser to create the spark. The technique has many attributes that make it an 

attractive tool for chemical analysis, particularly its potential as a field-portable sensor for 

geochemical analysis (5, 6). LIBS is relatively simple and straightforward, so skilled analysts 

are not required. Little to no sample preparation is required, which eliminates the possibility of 

sample adulteration through improper handling or storage, or cross-contamination during sample 

preparation. LIBS provides a real-time response and a simultaneous detection and analysis of all 

elements. The laser plasma is formed over a very limited spatial area so that only a small sample 

(pg-ng) is engaged in each laser microplasma event. All instrument components can be made 

small and rugged for field use, and LIBS sensors can be operated either as a point sensor or in a 

standoff detection mode.
2

Broadband LIBS, which captures the spectral region from 200 to 940 nm (in which all elements 

emit light), is a sensor technology capable of discriminating among different types of plastic land 

mine casings through a statistical approach that compares the LIBS spectrum acquired for an 

unknown sample against a spectral library (7–11). The discrimination of different explosive and 

energetic materials using LIBS has also been demonstrated (12–18). Thus, LIBS has the 

potential to be a single-sensor technology that can detect and discriminate both the casing of a 

land mine and its explosive contents. The recent development of man-portable/backpack LIBS 

sensor systems (7, 9–11, 16, 19, 20) that utilize small, rugged lasers (which are very suited for 

field use) has opened up the possibility for LIBS to be used as a confirmatory sensor technology 

for land mine detection. 

More than a dozen different types of antipersonnel and antitank mines and a broad range of 

natural and anthropogenic materials (“clutter”) of the kind likely found within the soil of a 

conflict area or former conflict area were analyzed previously in our lab using a LIBS system (9, 

11). A spectral library was created, and a simple linear correlation was used to classify the LIBS 

spectra. The results of that study are summarized in table 1. Although LIBS was able to 

distinguish the mines and clutter objects very well (94.6% correct mine identification), it was not 

able to identify specific mine types (80.4% correct identification). The goal of this study was to 

apply recent advances in the chemometric analysis of LIBS data (16) to the problem of land mine 

detection to determine if specific mine types could be identified with greater accuracy. 

Table 1. Initial land mine casing results (9). 

Target 

(No. of Samples) 

Correct Mine/No-Mine ID 

(%) 

Correct Sample ID 

(%) 

Land mine casings (56) 94.6 80.4 

Plastics (24) 83.3 79.2 

Wood and cardboard (5) 100 100 

Rocks (5) 100 100 

Metal (10) 100 80.0 

Total (100) 93.0 82.0 

For this study, 3000 single-shot spectra of 41 inert mine casings (see table 2 for description of 

mines) and 7 mine simulants (1 wood, 2 black plastic, and 4 nylon) were acquired with a custom 

laboratory LIBS setup designed to accommodate large antitank mines (see figure 1). This U.S. 

Army Research Laboratory (ARL) system consisted of a Big Sky CFR400 laser (1064 nm, 

90–150 mJ, 10-ns pulse) and an Ocean Optics LIBS2500+ broadband spectrometer that recorded 

LIBS plasma light emission over the spectral range from 200 to 940 nm. The light from the 

laser-induced plasma was collected and focused with a parabolic mirror onto the seven-fiber 

optic bundle delivering the light to the spectrometer. For each land mine casing and simulant, 1 

cleaning shot was taken followed by 25 successive shots in two different locations (for a total of 

50 single-shot spectra per sample). Single-shot spectra of the land mines are shown in figures 2 

and 3, and spectra of the mine simulants are shown in figure 4. The strongest emission lines 

observed in the spectra have been identified and labeled. Atomic emission from the atmosphere 

contributes to the oxygen and nitrogen lines in all the spectra.
3

Table 2. Description of land mines. 

Mine Type Country of Origin Quantity AP/AT Photoa

PMA-1A Yugoslavia 14 AP 

M14 USA 9 AP 

PMN-1 b Russia 6 AP 

TS-50 Italy 2 AP 

VS-MK2b Italy 2 AP 

TNG-M80 USA 1 AT 

VS-HCT2 Italy 1 AT 

TMA-3 Yugoslavia 1 AT 

TM-62P3 Russia 1 AT 

POM Unknown 1 AT Not available 

Note: Antipersonnel (AP) and antitank (AT) land mines were provided by Aaron LaPointe (U.S. Army Night Vision 

and Electronic Sensors Directorate). a

Photos obtained from ORDATA online (21). b

Spectra of both the rubber top and the plastic bottom of the mine casing were acquired.
4

Figure 1. Photo of custom LIBS setup for land mine identification. 

Figure 2. Single-shot LIBS spectra of antipersonnel land mine casings.
5

Figure 3. Single-shot LIBS spectra of antitank land mine casings. 

Figure 4. Single-shot LIBS spectra of land mine simulants. 
6

As shown in this study (figures 2 and 3) and previous work (7–11), broadband LIBS analysis has 

demonstrated observable differences in land mine casings composition. To extend the LIBS 

approach to the problem of land mines at an operational level, the identification issue must be 

addressed. The big advantage of broadband LIBS, i.e., capturing the entire portion of the LIBS 

spectrum from 200 to 940 nm, is based on the idea that every material yields a unique LIBS 

spectrum. This being the case, a LIBS spectrum should provide a “fingerprint” of the material 

analyzed, and LIBS spectra of different materials should be distinguishable one from another by 

chemometric analysis. 

Principal components analysis (PCA) is a chemometric technique that finds linear combinations 

of variables, i.e., principal components (PCs), which describe major trends in the data. PCA 

enables data compression and information extraction, and has previously been applied to LIBS 

spectra of biological and chemical warfare agent simulants (22, 23) and explosive residue 

samples (13–16) at ARL. Although PCA is not a classification technique, it provides a useful 

tool for identifying whether samples are the same or different and what variables are responsible 

for the differences. Twenty single-shot broadband spectra for each of 36 land mines (including 

spectra from the top and bottom of mines with separate cases and caps) and 5 mine simulants 

were used to build a PCA model. Spectral preprocessing involved normalization followed by 

mean-centering. The PCA scores are linear combinations of the original variables and contain 

information on how the samples relate to each other. The loadings contain information on how 

the variables (i.e., spectral wavelengths) relate to each other; mathematically they are the 

eigenvectors of the covariance matrix describing the variables. By considering the scores and 

loadings simultaneously, the variables responsible for the differences between samples can be 

identified. 

The loadings for PC1 (describing 60.58% of the variance in the data set) show that the most 

important peaks in the model correspond primarily to the C, CN, C2, O, N, and Ca content of the 

land mines (figure 5). Because most of the land mines contain similar amounts of C, H, N, O, 

and Ca, the PCA model is unable to distinguish between the types of land mines and simulants 

(figure 6). When different types of land mines are considered individually, however, some 

interesting patterns emerge. For example, spectra of six PMN-1 mines were acquired. A plot of 

scores for the PMN-1 mines (figure 7) shows that mine nos. 16 and 17 are grouped together, 

separate from the other four PMN-1 mines (sample nos. 18–21). A comparison of the LIBS 

spectra (figure 8) reveals that mine no. 16 (and 17) has a stronger LIBS signal than mine no. 18 

(and 19–21). In addition, spectra of both the rubber top of the mine and the bottom plastic case 

show distinct differences that are captured by the PCA model (figure 7). The rubber-like top 

parts of the PMN-1 mines have more Ca, CaO, and CaOH emission lines than the bottom 

(plastic-like) parts. Comparison of the scores plot (figure 7) with the loadings plot for PC1 vs. 

PC2 (figure 9) reveals that the separation between the top and bottom portions of the mine in the 

PCA scores plot is due to the higher Ca content in the top material, and the separation between 

mine nos. 16 and 17 and 18–21 likely reflects differences in the plasma temperature (since the 

separation is caused by the relative intensities of Ca atomic and ionic emission lines).
7

Figure 5. Loadings plot for PC1 for the PCA model built using 1000 LIBS spectra 

from 36 different land mines and 5 mine simulants. 

Figure 6. Scores plot of PC1 vs. PC2 for the PCA model built using 1000 LIBS spectra from 

36 different land mines and 5 mine simulants.
8

Figure 7. Scores plot of PC1 vs. PC2 for the PCA model built using 220 LIBS 

spectra from 6 different PMN-1 land mines (top and bottom). The cap 

for mine no. 19 was not available. 

Figure 8. LIBS spectra of the top cap and bottom casing of two PMN-1 land mines.
9

Figure 9. Loadings plot for PC1 vs. PC2 (PCA model based on the 6 PMN-1 mines). 

Comparison with the scores plot in figure 7 shows which spectral features 

contribute to the separation between the PMN-1 mines. 

Partial least-squares discriminant analysis (PLS-DA) is a supervised, multivariate least-squares 

analysis technique that has shown to be extremely useful for hazardous materials classification 

with LIBS (14–17). In PLS-DA, the predictor variables (called “latent variables” [LVs]) are 

generated while simultaneously considering both intraclass and interclass variance. This is in 

contrast to PCA, which only considers the total variance of the entire data set. By maximizing 

the differences between sample classes (i.e., mine types), more subtle differences in the LIBS 

spectra are described by the PLS-DA model. A PLS-DA model was built using 17 sample 

classes consisting of 20 single-shot spectra each of 12 PMA-1A mines, 7 M14 mines, 6 PMN-1 

cases, 5 PMN-1 caps, 2 VS-MK2 cases, 2 VS-MK2 caps, 2 TS-50 cases, 2 TS-50 caps, 1 POMAT mine, 1 TNG-M80, 1 VS-HCT 2, 1 VS 1.6, 1 TMA-3, 1 TM-62P3, 1 wood mine simulant, 1 

black plastic mine simulant, and 4 white nylon mine simulants. Twenty latent variables were 

chosen to describe the model spectra based on cross-validation (contiguous block method). 

Because the model uses 20 LVs for optimal class discrimination, interpreting the loadings for 

each LV (in order to determine which wavelengths in the model are most useful for class 

separation) is complicated. The variable importance in projection (VIP) scores for each class are 

essentially a weighting of the regression vector and show which variables are important for 

separation between the classes in the model (24, 25). A variable with a VIP score close to or >1 

can be considered important in a given model. As figure 10 shows, the VIP scores (which 

combine the LVs that contribute to each class) reflect the contributions of minor impurities to the 

LIBS spectra of the mines. Instead of trying to separate the different mines on the basis of 

common emission lines C, CN, C2, and Ca (see figure 5), the PLS-DA model is able to 

discriminate the mines based on differences in their trace metal content (Cu, Ba, Ti, Cs, Mg, 

etc.).
10

Figure 10. VIP scores for 3 of the classes in the 17-class PLS-DA model: (a) TNG-M80, (b) TMA-3, and (c) 

VS-HCT 2. 

For each target class in the model, the likelihood that a given sample spectrum belongs to that 

class is predicted based on a value of 0 to 1. A value closer to 0 indicates that the sample is not 

in the modeled class, while a value of one indicates that the sample is in the modeled class. A 

threshold between 0 and 1 (above which a sample is considered in the class) is automatically 

calculated by the software using Bayesian statistics in order to minimize the number of false 

positives and false negatives (24). A validation set consisting of 30 additional spectra from each 

mine/simulant in the model was tested against the model. Based on the calculated thresholds for 

each sample class, 1485 out of 1500 test spectra fell above the threshold in the appropriate class, 

resulting in 99.0% correct classification for the validation set (table 3). The misclassification 

rate for the validation set was 1.8%. Of the misidentifications, 2.6% resulted in a mine simulant 

being identified as a mine (false positives), and 1.6% resulted in a mine being identified as a 

simulant (false negatives). The majority of the misidentifications (95.8%) were between mine 

types. 
11

Table 3. PLS-DA results for validation test set consisting of 1500 additional LIBS spectra of the mines and 

simulants included in the model.

Test Spectra 

(No.)

Model Classes 

(No. Above Bayesian Threshold) 

PMA-1A (360) 352 1 0 0 0 2 3 3 15 2 0 15 10 14 0 6 11 

M14 (210) 0 209 0 0 0 0 2 54 0 0 0 13 0 0 3 12 0 

PMN-1 case (180) 2 0 180 0 1 0 1 0 4 5 3 2 0 1 0 1 0 

PMN-1 cap (150) 0 0 0 150 0 0 0 0 0 0 0 1 0 0 0 0 0 

VS-MK2 case (60) 0 0 0 0 59 0 8 1 0 0 0 3 0 0 0 15 0 

VS-MK2 cap (60) 0 0 1 0 0 60 0 0 0 0 0 0 0 0 0 0 0 

TS-50 case (60) 0 0 0 0 21 0 59 3 0 0 0 0 0 0 1 1 0 

TS-50 cap (60) 0 60 0 0 0 0 0 60 0 0 0 4 0 0 0 1 0 

POM AT (30) 1 0 0 0 0 2 0 0 28 1 0 0 0 0 0 0 11 

TNG-M80 (30) 5 0 0 0 0 0 0 0 1 30 0 0 0 0 0 0 0 

VS-HCT 2 (30) 0 0 4 0 3 0 0 0 2 0 30 0 0 0 0 0 0 

VS 1.6 (30) 0 0 0 1 0 0 0 1 0 0 0 30 0 0 0 0 0 

TMA-3 (30) 0 0 0 0 0 0 0 0 0 0 0 0 30 0 0 1 0 

TM-62P3 (30) 22 0 0 0 0 0 0 0 0 0 0 4 3 30 0 0 2 

Wood simulant (30) 0 5 0 0 0 0 0 0 0 0 0 14 0 0 29 0 0 

Black plastic simulant (30) 3 3 0 0 13 0 0 0 0 0 0 0 0 0 0 29 0 

White nylon simulant (120) 0 0 0 0 0 0 0 0 16 1 0 7 0 0 0 0 120

A second test set of 50 spectra each for 6 mines and 2 simulants not included in the model, as 

well as spectra of the POM-AT mine acquired at a lower laser energy (90 mJ compared to the 

150 mJ used for the model spectra), were tested against the PLS-DA model. When the Bayesian 

threshold was used to determine classification, the test set resulted in 98.6% correct classification 

and 4.6% misclassification. Of the misclassified spectra, 8.6% represented false positives and 

0.1% represented false negatives. When the Bayesian threshold was used as a criterion for class 

membership, each single-shot spectrum could classify with more than one sample class (since the 

sample may lie above the threshold for more than one class). The percentage of correct 

classifications therefore refers to the diagonal elements of the confusion matrix (test sample class 

vs. model class), while the percentage misclassification refers to the off-diagonal elements (see 

table 3). For the second test set, an alternate method for determining class membership was 

applied. If the predicted probability for a test sample is >50% for a particular class, the sample is 

considered part of that class; however, if a test sample does not have a predicted probability 

>50% for any class in the model (or for more than one class), it is considered unclassified. When 

only the Bayesian threshold is used, the decision to classify a test sample with a particular model 

class is much less restrictive (resulting in more true positives as well as more false positives). 

The second method for class determination allows for the possibility that the sample does not 

match any of the classes in the model and prevents a sample from being grouped with more than 

one class. The results from the classification of the second test set are shown in table 4. The 

percentage of correct classification is now only 45.2% with 54.4% of the spectra unclassifiable; 

however, only 0.4% of the spectra are misclassified. Despite the difficulty in classifying two
12

M14 mines and the PMN-1 mine with their respective classes in the model, they were correctly 

identified as mines by the model 100% of the time. Overall, 99.3% of the mines in the test set 

were correctly identified as mines (regardless of type), but 35 of the black mine simulant spectra 

and all 50 of the wood mine simulant spectra were incorrectly identified as mines. Although the 

PLS-DA model includes 14 mine types, only three simulant types are included; increasing the 

number and type of clutter objects in the model will improve the predictive ability of the model 

to determine mine/no mine classification for sample types not included in the model. 

Table 4. PLS-DA results for second test set of mines and simulants not included in the model.

Sample No. Correct No. Misidentified No. Unclassified Mine/No Mine ID 

Three PMA-1A mines 141/150 0/150 9/140 149/150 

Two M14 mines 8/100 1/100 91/100 100/100 

One PMN-1 minea 24/100 0/100 76/100 100/100 

POM mine (low energy) 38/50 0/50 12/50 48/50 

Black mine simulant 15/50 0/50 35/50 15/50 

Wood mine simulant 0/50 1/50 49/50 0/50 a

Spectra of both the casing and cap were acquired. 

3. LIBS as a Confirmatory Sensor for Demining 

An ideal land mine detector would detect and identify both the exterior casing and the contained 

explosive charge. LIBS has demonstrated such a dual capability, with unique broadband spectra 

successfully acquired under laboratory conditions for explosive materials and both metal and 

plastic antipersonnel and antitank land mines. With the recent progress made toward realizing 

man-portable LIBS, the concept of a LIBS demining probe for a confirmation land mine 

detection sensor now seems within reach. The idea is a backpack-size system that would contain 

a minilaser in the handle of a deminer’s probe, with light delivered to and collected from the 

tapered tip of the probe and analysis made by touching the buried object. Such a capability could 

also be deployed by a robot for unmanned land mine detection (19). 

The LIBS-based deminer’s probe would be similar in form and material composition to the 

current generation of prodders in use within the humanitarian demining community, i.e., having 

a thin, ~30- to 40-cm-long end-tapered probe that joins a barrel-style handle. The probe would 

also allow for the delivery of an inert gas (e.g., He or Ar) to the tip of the probe, with the tapered 

tip of the probe designed to protect the ends of the optical fiber from both damage and soil 

obstruction during insertion. The probe handle would contain a pulsed laser capable of 

delivering >30-mJ pulse power. The laser signal would be directed through a set of optics (also
13

contained in the probe handle) to an optical fiber contained in the hollow probe that would 

transmit both the laser signal and the returned plasma light to the LIBS-spectrometer system that 

would be contained either in a backpack worn by the user or in a “pelican-type” case carried by 

the user. An analysis could be made rapidly and reliably by touching the buried object. Then, a 

laser spark would be created and a plasma generated on the surface of the object after 

preliminary “cleaning” shots to remove surface soil particles and expose a fresh material surface. 

The resultant plasma light would be captured by the fiber optic in the probe tip and transmitted 

through the fiber optic in the prodder and handle to the broadband spectrometer. The broadband 

LIBS spectrum obtained would be background corrected by software in the onboard computer in 

real time and then compared with a spectral library of land mine casings, explosives, and 

common environmental clutter objects to determine if there was a positive correlation. A 

spectral match with a reference spectrum in either the land mine casings or explosives library 

would be declared a positive response that required marking and excavation. This specially 

designed LIBS system for land mine detection could be used as a confirmatory sensor for both 

humanitarian and military demining, but it also has the potential to be used in a variety of 

noncountermine applications (e.g., homeland security, forensic, environmental cleanup, 

geoscience, and bioscience applications) that would benefit from chemical analysis of an 

undisturbed substance in the field in real time.
14

4. References 

1. Jeffrey, S. J. Antipersonnel Mines: Who Are the Victims? J. Accid. Emerg. Med. 1996, 13

(5), 343–346. 

2. Kingsbury, N. Land Mine Detection: DOD’s Research Program Needs a Comprehensive 

Evaluation Strategy; GAO-01-239; U.S. General Accounting Office: Washington, DC, 

2001. 

3. Krstelj, V.; Stepanic, J., Jr. Non-Destructive Testing in Antipersonnel Landmines Detection. 

Proceedings of the Int. Conference MATEST '99, Cavtat, Croatia, 22–24 September 1999. 

4. Krstelj, V.; Stepanic, J., Jr. Humanitarian De-Mining Detection Equipment and Working 

Group for Antipersonnel Landmines Detection. Insight 2000, 42 (3), 187–190. 

5. Harmon, R. S.; DeLucia, F. C.; McManus, C. E.; McMillan, N. J.; Jenkins, T. F.; Walsh, 

M. E.; Miziolek, A. Laser-Induced Breakdown Spectroscopy - An Emerging Chemical 

Sensor Technology for Real-Time Field-Portable, Geochemical, Mineralogical, and 

Environmental Applications. Appl. Geochem. 2006, 21 (5), 730–747. 

6. Harmon, R. S.; Remus, J.; McMillan, N. J.; McManus, C.; Collins, L.; Gottfried, J. L.; 

DeLucia, F. C.; Miziolek, A. W. LIBS Analysis of Geomaterials: Geochemical 

Fingerprinting for the Rapid Analysis and Discrimination of Minerals. Appl. Geochem. 

2008, 24 (6), 1125–1141. 

7. Harmon, R. S.; DeLucia, F. C.; Winkel, J. R. J.; LaPointe, A.; Grossman, S. L.; McNesby, 

K. L.; Miziolek, A. W. LIBS: A New Versatile Field-Deployable Real-Time Detector 

System With Potential for Landmine Detection. In Detection and Remediation Technologies 

for Mines and Minelike Targets VIII; Proc. SPIE-Int. Soc. Opt. Eng., Vol. 5089; Harmon, R. 

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