Oleksiy Shulika. Victor V.
Claudia S. Alan G. Sergio Musazzi. Sebastien Forget. Valerii V. Von Bally. Juan Jimenez. Home Contact us Help Free delivery worldwide. Free delivery worldwide. Bestselling Series. Harry Potter. Popular Features. New Releases. Description This book deals with the Laser-Induced Breakdown Spectroscopy LIBS a widely used atomic emission spectroscopy technique for elemental analysis of materials. It is based on the use of a high-power, short pulse laser excitation. Numerous examples of state of the art applications provide the readers an almost complete scenario of the LIBS technique.
The LIBS theoretical aspects are reviewed. The book helps the readers who are less familiar with the technique to understand the basic principles. Numerous examples of state of the art applications give an almost complete scenario of the LIBS technique potentiality. These examples of applications may have a strong impact on future industrial utilization. The authors made important contributions to the development of this field.
Product details Format Hardback pages Dimensions x x Other books in this series. Add to basket. Nonimaging Fresnel Lenses Ralf Leutz. Laser Measurement Technology Axel Donges. In laser-induced breakdown spectroscopy one utilises the high power densities obtained by focusing the radiation from a pulsed laser normally operating at a single, fixed wavelength , to generate a luminous micro plasma from the analyte solid, liquid and gaseous samples.
To a good approximation, the plasma composition is representative of the analyte's elemental composition. In the thirty years or so since its inception the potential of LIBS as an analytical tool has been realised, leading to an ever increasing list of applications, both for analysis in the laboratory and industrial environments [ 19 , 20 ]. We would like to note that the ablation of dental tissue using pulsed lasers, and simultaneously monitoring the plasma emission closely mimics the principle behind the technique of LIBS [ 21 ].
Standard LIBS analysis systems comprise typical major component units, namely a the laser source; b the laser light delivery and plasma emission collection system; and c the system for spectral analysis. For the experimental study described here, i. The laser used for plasma generation was a standard pulsed Nd:YAG laser Quantel Brilliant or BigSky , operating at its fundamental wavelength of nm, at 20 Hz repetition rate. Individual laser pulses had a pulse length of 4—8 ns depending on the Q-switch timing adjustment of the laser power supply.
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The pulse energy was precisely controlled using a half-wave retardation plate and Glan-laser polarizer in the beam path. It was measured using a calibrated energy meter Coherent LabMaster ; typically, pulse energies in the range 10—30 mJ were utilised. We would like to note here, that near IR lasers of nanosecond pulse duration most likely will not be the lasers of choice in practical applications for dental drilling, but should be viewed as a proof-of-principle scenario.
The overall optical arrangement used in our experiments is shown in Figure 1. This radiation was passed through a centre hole of 2 mm diameter in the light-collection mirror.source
What is Laser-Induced Breakdown Spectroscopy?
The laser light pulses exiting the far end of the fibre distal-end were directed onto the target material. Note that for a large fraction of our experiments no optical components were used between the fibre end and the tooth. A fraction of the light emitted from the target surface was collected via the same fibre; this re-emerges at the launch end proximal-end , with a divergence relating to the numerical aperture of the fibre.
The mirror with an UV-enhanced metallic coating was used to separate this diverging light from the in-coming Nd:YAG laser pulses. This diverging plasma fluorescence light was re-focused onto the spectrograph fibre bundle. Note that the fibre assembly can be used to just collect the light from the plasma simply by placing the distal end close to the target, in case that the laser pulses are not delivered through the same optical fibre for the drilling of dental tissue.
For optional assistance in precise pointing of the near-IR ablation radiation onto particular areas of the tooth, light from a HeNe laser could be introduced collinearly via the Nd:YAG beam steering mirror high transmission at nm. The system used for spectral analysis consisted of a standard spectrograph ACR, Acton Research with a gateable, intensified photodiode array detector IRY, Princeton Instruments attached to it.
The gating of the detector and the timing for spectral accumulation were controlled by a PC via a pulse delay generator PG, Princeton Instruments. We would like to note that the experimental results presented here were obtained using laser pulses of a few nanosecond duration. Precise time gating of the system for plasma analysis is normally needed, to avoid the strong, broadband spectral contribution from Bremsstrahlung during the early phase of the plasma evolution [ 23 ] largely due to plasma — laser radiation interaction.
Exploiting the pattern recognition algorithm, described later in this paper, the time-gating of the detection system does not have to be overly critical: unwanted broadband background contributions are automatically accounted for. Thus the acquisition time can be set as high as a few milliseconds [ 24 ], rather than the usual microsecond intervals; only proper synchronisation to the laser pulse is required, in principle. Furthermore, we like to note that for laser pulses of picosecond or sub-picosecond duration the plasma — laser radiation interaction is much shorter, and normally Bremsstrahlung does not play a very significant role on the time scale of the spectrum used for elemental analysis.
Normally, lower Bremsstrahlung backgrounds are also encountered when using UV laser radiation to generate the ablation plasma. Each spectrum collected using a LIBS instrument is a "finger-print" of the material being analysed and the conditions under which it was collected. Most of the efforts in quantitative LIBS research have been aimed at normalising the spectrum collection conditions and procedures, so that the spectra are sufficiently reproducible for precise quantitative analysis, down to detection sensitivities of a few parts-per-million.
In the monitoring process described here, this sophistication is not really required. Provided that the relative intensity fluctuations related to the reproducibility in the measurement technique itself are smaller then the expected signal variations associated with the element distributions in the sample, the spectra allow for conclusive distinction between specific sample compositions. This is due to the fact that overall irregularities in the spectrum collection procedure can be included in the "finger-print" tolerance of the sample. The limit of this hypothesis is approached when the sample groups to be identified are very similar, i.
However, this does not pose a problem in the case presented here; only differentiation between carious and healthy tissue has to be achieved. This is easy to realise using the pattern recognition algorithm considered here. As a note of caution it should be added that, caries in its early stages might pose a challenge to the recognition algorithm because the difference to healthy tissue may not be very large.
However, we have shown for a range of matrices with only subtle compositional differences that our method is still successful [ 25 ]. More commonly known as Discriminant Analysis in spectroscopy, the aim of any pattern recognition algorithm is to unambiguously determine the identity, or quality of an unknown sample in comparison to a reference database. In this work we have focused on the latter point, since — to emphasise this again — only unambiguous identification is an issue for monitoring the difference between healthy and carious tissue.
It will become evident from the discussion further below that even recognising caries in its early stages of development should be possible in principle. When discriminant analysis is used in product-identification, or product-screening mode, the spectrum of the "unknown" sample may be compared against multiple discriminant models.
Each model is constructed from the spectra collected from samples representative of various material groups, as defined by the composition of the samples. An indication for the likelihood of the spectrum matching one of these groups emerges from the analysis, and any sample can therefore be classified as a "match", or as a "no-match" see Figure 2. This identification can be displayed visually on a monitor e.
We like to note here that, in principle, only one discriminant model with its related database training set would suffice for caries identification. Said data base has to enclose spectra from a wide selection of healthy teeth to provide a statistical means of element concentration scatter; any deviation outside that statistical limit may then be associated with carious tissue.
On the other hand, the algorithm has to be "trained" to include only spectral features which are potentially associated with caries since other elemental concentrations may also change, due to other causes e. To take into account causes like the one mentioned additional discriminant models might need to be added to unambiguously identify the effect of caries. Numerous algorithms do exist that can be used to assess the similarity of a measured spectrum with the training set.
Here, the description has been restricted to the algorithm of interest, i. In order to calculate the Mahalanobis Distance M. PCA is a typical analytical approach, which normally forms part of any spectral data analysis software package, and thus we abstain here from providing extensive details of such analysis methods and algorithms but only provide a general procedural picture. Dist analysis training sets of spectra are decomposed into a series of mathematical spectra called factors which, when added together, reconstruct the original spectrum.
The contribution any factor makes to each spectrum is represented by a scaling coefficient , or score , which is calculated for all factors identified from the training set. Thus, by knowing the set of factors for the whole training set, the scores will represent the spectra as accurately as the original responses at all wavelengths [ 25 ].
In this study, we investigated different tooth samples with and without caries — predominantly molar and canine teeth of adults. No special sample treatment was carried out; extracted teeth were just washed out in distilled water and air-dried. One-hundred fifty-nine extracted teeth with different extent of obvious caries molars and 36 canine teeth of adults , which were assessed using visual examination by trained examiners, were used for the results discussed in this publication. The difficult-to-detect early caries lesions, such as in pits or fissures, which are generally non-pigmented or white spot lesions were not included in the study.
This was because histopathologic analysis, for correct distinction between carious and healthy tissue in these non-evident cases, was not available at the time of study. Such an extended investigation is now in preparation, in collaboration with two dental practices and a hospital. Most experiments were carried out in vitro. In addition, one test experiment was also conducted in vivo on a molar tooth of an adult volunteer. The latter experiment was performed at very low laser irradiance, just above the ablation threshold where the power density is not sufficient to cause noticeable damage to the tooth but nevertheless a luminous plasma is created.
Six distinct spectral ranges covering a range of matrix and non-matrix elements were used; the relevant spectra are shown in Figure 3. As pointed our earlier, in principle a single model would probably suffice but having more than one decider naturally improves on the identification accuracy.
Selected LIBS spectra from an enamel part of the tooth, recorded at a location affected by caries full line trace and at a sound, unaffected location dotted line trace. In the caries-affected section a Ca diminishes at the expense of Mg ; b Ca diminishes at the expense of Li ; c Ca diminishes at the expense of Ba and Sr ; d Ca and P diminish at the expense of Zn and C ; e Ca diminishes at the expense of Na ; and f Ca diminishes at the expense of K and Mg.
The program generated a Discriminant Analysis model for each sample, using the methods outlined in the previous section, against which test spectra were matched. When checking the identity of "unknown" spectra collected from a range of tooth samples, all were either identified as definite or possible matches to the healthy or diseased tissue discriminant analysis models, even if only one out of the six identifier spectral regions was used.
The major constituent of the tooth's crystalline enamel and dentine matrix structure is hydroxyapatite, Ca 10 PO 4 6 OH 2 whose absolute abundance is distinctly different for healthy dental tissue, and tissue affected by caries. For affected teeth the relative concentrations of the matrix elements Ca and P decrease severely. On the other hand, non-mineralising non-matrix elements, e. A similar indicator for the effect of caries attack is the substantial increase of strontium, Sr , and barium, Ba , in relation to the matrix element Ca ; see Figure 3c.
In the Discriminant Analysis models utilised here the important result is the M.
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Dist value. Depending on this, a pass P — healthy tissue, possible? Tests carried out on hundreds of spectra recorded from a multitude of different teeth showed conclusive evidence that by using the M. The M. Dist value is effectively a measure of the similarity of an "unknown" spectrum to a group of training spectra.
Thus the M. Dist value in Discriminant Analysis models reporting a " FAIL " result is normally high, indicating that the spectral contributions from individual elements are very different for e. The smaller the M. Dist values for a model giving a " FAIL " result the less elemental variations are encountered.
On this basis statistical fluctuations in the spectra, caused by inevitable pulse-to-pulse intensity variations, can also be accounted for in the Prediction Module by adjusting the M. As is the case in all Multivariate Quantitative Analysis approaches, careful application is required if the technique is to be applied both correctly and successfully. For example, the limits within which the M.
For example, Raman spectroscopists often use values greater than these, e. Therefore, these limits always have to be determined prior to a practical application, such as distinguishing between healthy and caries-affected tooth material. The factors, which dictate these limits in LIBS analysis are i spectrum reproducibility and ii the sample-to-sample homogeneity.
Ebook Laser Induced Breakdown Spectroscopy Theory And Applications
By testing the models produced with randomly collected spectra from samples of the material that they represent carious or sound dental tissue , the range of M. Dist values, which gives a positive identification can be found. If this is not done then the model might incorrectly miss-identify materials. In addition, by carefully adjusting the M. Dist limits, poor reproducibility can in principle be accounted for, provided there are sufficient elemental differences in the samples being sorted, such that clear changes in the spectral responses can be observed.
With reference to Figure 3 indeed large differences in the spectral signature of healthy and carious-affected tissue are encountered, and we have shown that in contrast to these obvious cases, subtle differences can also be distinguished meaning that even the detection of early caries lesions should be feasible.
Journal of the European Optical Society - Rapid publications, Vol 11 (2016)
This will be discussed further below, also with reference to the choice of M. Dist limit values. Finally, we like to note that multivariate analysis is rather unintuitive for the non-expert since a simple graphical representation of the statistical model can not normally be given, as is the case in univariate analysis. In multivariate analysis, there are many variables x i , and the function would require a multidimensional plot. For a spectrum with up to a few hundreds of data points variables this can not be perceived. In order to clarify this point, an example for univariate analysis is given further below.
Note that in most analytical cases of well-behaved data multivariate algorithms will provide reduced errors when compared to univariate algorithms. This result is quite remarkable, since the spectra collected in this study were recorded for non-optimised settings. The distal-end of the optical fibre was just mounted at the distance of about 2 mm from the sample for in vitro applications, and for the in vivo measurements the fibre was simply held by hand while ablating the tooth.
Each spectrum was accumulated for only ten laser-induced plasma events. Fewer laser pulses per spectrum were used at times to speed up the analysis process, but this was at the expense of the spectrum reproducibility, and hence slightly reduced identification probability. The training spectra utilised in this study were obtained from a range of tooth samples in vitro — namely from extracted tooth supplied by dentists. An example for the strength of the analysis method can be seen in Figure 4.
Here an in vitro measurement was carried out on a caries-affected tooth to map the areas of "healthy" and "diseased" tissue.
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