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Optica Publishing Group

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

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Abstract

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

© 2022 Optica Publishing Group

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References

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2021 (2)

A. P. Rao, P. R. Jenkins, D. M. Vu, J. D. Auxier, A. K. Patnaik, and M. B. Shattan, “Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning,” Anal. Methods 13, 3368–3378 (2021).
[Crossref]

A. P. Rao, P. R. Jenkins, J. D. Auxier, and M. B. Shattan, “Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device,” J. Anal. At. Spectrom. 36, 399–406 (2021).
[Crossref]

2020 (2)

E. Bellou, N. Gyftokostas, D. Stefas, O. Gazeli, and S. Couris, “Laser-induced breakdown spectroscopy assisted by machine learning for olive oils classification: the effect of the experimental parameters,” Spectrochim. Acta B 163, 105746 (2020).
[Crossref]

H. Cho, Y. Kim, E. Lee, D. Choi, Y. Lee, and W. Rhee, “Basic enhancement strategies when using Bayesian optimization for hyperparameter tuning of deep neural networks,” IEEE Access 8, 52588–52608 (2020).
[Crossref]

2019 (3)

B. T. Manard, M. F. Schappert, E. M. Wylie, and G. E. McMath, “Investigation of handheld laser induced breakdown spectroscopy (HH LIBS) for the analysis of beryllium on swipe surfaces,” Anal. Methods 11, 752–759 (2019).
[Crossref]

J. Guezenoc, A. Gallet-Budynek, and B. Bousquet, “Critical review and advices on spectral-based normalization methods for LIBS quantitative analysis,” Spectrochim. Acta B 160, 105688 (2019).
[Crossref]

W. Mo, X. Luo, Y. Zhong, and W. Jiang, “Image recognition using convolutional neural network combined with ensemble learning algorithm,” J. Phys. Conf. Ser. 1237, 022026 (2019).
[Crossref]

2018 (2)

P. K. Tiwari, S. Awasthi, R. Kumar, R. K. Anand, P. K. Rai, and A. K. Rai, “Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis,” Lasers Med. Sci. 33, 263–270 (2018).
[Crossref]

B. Bhatt, K. Hudson Angeyo, and A. Dehayem-Kamadjeu, “LIBS development methodology for forensic nuclear materials analysis,” Anal. Methods 10, 791–798 (2018).
[Crossref]

2017 (1)

2016 (2)

J. Klus, P. Mikysek, D. Prochazka, P. Porizka, P. Prochazková, J. Novotny, T. Trojek, K. Novotný, M. Slobodník, and J. Kaiser, “Multivariate approach to the chemical mapping of uranium in sandstone-hosted uranium ores analyzed using double pulse laser-induced breakdown spectroscopy,” Spectrochim. Acta B 123, 143–149 (2016).
[Crossref]

Y. Guo, Y. Ni, and S. Kokot, “Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics,” Spectrochim. Acta B 153, 79–86 (2016).
[Crossref]

2015 (3)

T. F. Boucher, M. V. Ozanne, M. L. Carmosino, M. D. Dyar, S. Mahadevan, E. A. Breves, K. H. Lepore, and S. M. Clegg, “A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy,” Spectrochim. Acta B 107, 1–10 (2015).
[Crossref]

D. Syvilay, N. Wilkie-Chancellier, B. Trichereau, A. Texier, L. Martinez, S. Serfaty, and V. Detalle, “Evaluation of the standard normal variate method for laser-induced breakdown spectroscopy data treatment applied to the discrimination of painting layers,” Spectrochim. Acta B 114, 38–45 (2015).
[Crossref]

P. Söderlind, F. Zhou, A. Landa, and J. Klepeis, “Phonon and magnetic structure in δ-plutonium from density-functional theory,” Sci. Rep. 5, 15958 (2015).
[Crossref]

2014 (2)

E. D’Andrea, S. Pagnotta, E. Grifoni, G. Lorenzetti, S. Legnaioli, V. Palleschi, and B. Lazzerini, “An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis,” Spectrochim. Acta B 99, 52–58 (2014).
[Crossref]

T. Zhang, L. Liang, K. Wang, H. Tang, X. Yang, Y. Duan, and H. Li, “A novel approach for the quantitative analysis of multiple elements in steel based on laser-induced breakdown spectroscopy (LIBS) and random forest regression (RFR),” J. Anal. At. Spectrom. 29, 2323–2329 (2014).
[Crossref]

2013 (3)

X. Li, Z. Wang, S.-L. Lui, Y. Fu, Z. Li, J. Liu, and W. Ni, “A partial least squares based spectrum normalization method for uncertainty reduction for laser-induced breakdown spectroscopy measurements,” Spectrochim. Acta B 88, 180–185 (2013).
[Crossref]

I. James, E. Barefield, E. J. Judge, J. M. Berg, S. P. Willson, L. A. Le, and L. N. Lopez, “Analysis and spectral assignments of mixed actinide oxide samples using laser-induced breakdown spectroscopy (LIBS),” Appl. Spectrosc. 67, 433–440 (2013).
[Crossref]

J. El Haddad, M. Villot-Kadri, A. Ismael, G. Gallou, K. Michel, D. Bruyère, V. Laperche, L. Canioni, and B. Bousquet, “Artificial neural network for on-site quantitative analysis of soils using laser induced breakdown spectroscopy,” Spectrochim. Acta B 78–79, 51–57 (2013).
[Crossref]

2012 (4)

R. B. Anderson, J. F. Bell, R. C. Wiens, R. V. Morris, and S. M. Clegg, “Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy,” Spectrochim. Acta B 70, 24–32 (2012).
[Crossref]

D. W. Hahn and N. Omenetto, “Laser-induced breakdown spectroscopy (LIBS), part II: review of instrumental and methodological approaches to material analysis and applications to different fields,” Appl. Spectrosc. 66, 347–419 (2012).
[Crossref]

Y. Kim, B. Han, H. S. Shin, H. D. Kim, E. C. Jung, J. H. Jung, and S. H. Na, “Determination of uranium concentration in an ore sample using laser-induced breakdown spectroscopy,” Spectrochim. Acta B 75, 190–193 (2012).
[Crossref]

J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian optimization of machine learning algorithms,” Adv. Neural Inf. Process. Syst. 25, 2960–2968 (2012).

2011 (1)

2010 (2)

D. Hahn and N. Omenetto, “Laser-induced breakdown spectroscopy (LIBS), part I: review of basic diagnostics and plasma-particle interactions: still-challenging issues within the analytical plasma community,” Appl. Spectrosc. 64, 335–366 (2010).
[Crossref]

R. Chinni, D. A. Cremers, and R. Multari, “Analysis of material collected on swipes using laser-induced breakdown spectroscopy,” Appl. Opt. 49, C143–C152 (2010).
[Crossref]

2009 (2)

J. Sirven, A. Pailloux, Y. Baye, N. Coulon, T. Alpettaz, and S. Gosse, “Towards the determination of the geographical origin of yellow cake samples by laser-induced breakdown spectroscopy and chemometrics,” J. Anal. At. Spectrom. 24, 451–459 (2009).
[Crossref]

S. M. Clegg, E. Sklute, M. D. Dyar, J. E. Barefield, and R. C. Wiens, “Multivariate analysis of remote laser-induced breakdown spectroscopy spectra using partial least squares, principal component analysis, and related techniques,” Spectrochim. Acta B 64, 79–88 (2009).
[Crossref]

2008 (1)

E. C. Ferreira, D. M. Milori, E. J. Ferreira, R. M. Da Silva, and L. Martin-Neto, “Artificial neural network for Cu quantitative determination in soil using a portable laser induced breakdown spectroscopy system,” Spectrochim. Acta B 63, 1216–1220 (2008).
[Crossref]

2007 (1)

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process. 16, 349–366 (2007).
[Crossref]

2006 (1)

P. Heraud, B. R. Wood, J. Beardall, and D. McNaughton, “Effects of pre-processing of Raman spectra on in vivo classification of nutrient status of microalgal cells,” J. Chemom. 20, 193–197 (2006).
[Crossref]

2005 (1)

2003 (1)

S. S. Hecker, “Plutonium: coping with instability,” J. Miner. Metal Mater. Soc. 55, 13–19 (2003).
[Crossref]

2000 (1)

F. E. Gibbs, D. L. Olson, and W. Hutchinson, “Identification of a physical metallurgy surrogate for the plutonium–1 wt.% gallium alloy,” AIP Conf. Proc. 532, 98–101 (2000).
[Crossref]

1999 (1)

M. Steinzig and F. H. Harlow, “Characterization of cast metals with probability distribution functions,” MRS Proc. 538, 185–190 (1999).
[Crossref]

1998 (1)

R. Blundell and A. Duncan, “Kernel regression in empirical microeconomics,” J. Human Resour. 33, 62–87 (1998).
[Crossref]

1995 (1)

K. J. Cios and I. Shin, “Image recognition neural network: IRNN,” Neurocomputing 7, 159–185 (1995).
[Crossref]

1989 (1)

D. C. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Math. Program. 45, 503–528 (1989).
[Crossref]

1983 (1)

G. Long and J. Winefordner, “Limit of detection. A closer look at the IUPAC definition,” Anal. Chem. 55, 712A–724A (1983).
[Crossref]

Adams, R.

J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. Patwary, M. Prabhat, and R. Adams, “Scalable Bayesian optimization using deep neural networks,” in International Conference on Machine Learning (PMLR) (2015), pp. 2171–2180.

Adams, R. P.

J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian optimization of machine learning algorithms,” Adv. Neural Inf. Process. Syst. 25, 2960–2968 (2012).

Alpettaz, T.

J. Sirven, A. Pailloux, Y. Baye, N. Coulon, T. Alpettaz, and S. Gosse, “Towards the determination of the geographical origin of yellow cake samples by laser-induced breakdown spectroscopy and chemometrics,” J. Anal. At. Spectrom. 24, 451–459 (2009).
[Crossref]

Anand, R. K.

P. K. Tiwari, S. Awasthi, R. Kumar, R. K. Anand, P. K. Rai, and A. K. Rai, “Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis,” Lasers Med. Sci. 33, 263–270 (2018).
[Crossref]

Anderson, R. B.

R. B. Anderson, J. F. Bell, R. C. Wiens, R. V. Morris, and S. M. Clegg, “Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy,” Spectrochim. Acta B 70, 24–32 (2012).
[Crossref]

Auxier, J.

A. Rao, J. Auxier, D. Vu, and M. Shattan, “Applications of portable LIBS for actinide analysis,” in Laser Applications to Chemical, Security and Environmental Analysis (2020), paper LM1A.2.

Auxier, J. D.

A. P. Rao, P. R. Jenkins, J. D. Auxier, and M. B. Shattan, “Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device,” J. Anal. At. Spectrom. 36, 399–406 (2021).
[Crossref]

A. P. Rao, P. R. Jenkins, D. M. Vu, J. D. Auxier, A. K. Patnaik, and M. B. Shattan, “Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning,” Anal. Methods 13, 3368–3378 (2021).
[Crossref]

M. B. Shattan, D. J. Miller, M. T. Cook, A. C. Stowe, J. D. Auxier, C. Parigger, and H. L. Hall, “Detection of uranyl fluoride and sand surface contamination on metal substrates by hand-held laser-induced breakdown spectroscopy,” Appl. Opt. 56, 9868–9875 (2017).
[Crossref]

Awasthi, S.

P. K. Tiwari, S. Awasthi, R. Kumar, R. K. Anand, P. K. Rai, and A. K. Rai, “Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis,” Lasers Med. Sci. 33, 263–270 (2018).
[Crossref]

Barefield, E.

Barefield, J. E.

S. M. Clegg, E. Sklute, M. D. Dyar, J. E. Barefield, and R. C. Wiens, “Multivariate analysis of remote laser-induced breakdown spectroscopy spectra using partial least squares, principal component analysis, and related techniques,” Spectrochim. Acta B 64, 79–88 (2009).
[Crossref]

Baye, Y.

J. Sirven, A. Pailloux, Y. Baye, N. Coulon, T. Alpettaz, and S. Gosse, “Towards the determination of the geographical origin of yellow cake samples by laser-induced breakdown spectroscopy and chemometrics,” J. Anal. At. Spectrom. 24, 451–459 (2009).
[Crossref]

Beardall, J.

P. Heraud, B. R. Wood, J. Beardall, and D. McNaughton, “Effects of pre-processing of Raman spectra on in vivo classification of nutrient status of microalgal cells,” J. Chemom. 20, 193–197 (2006).
[Crossref]

Bell, J. F.

R. B. Anderson, J. F. Bell, R. C. Wiens, R. V. Morris, and S. M. Clegg, “Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy,” Spectrochim. Acta B 70, 24–32 (2012).
[Crossref]

Bellou, E.

E. Bellou, N. Gyftokostas, D. Stefas, O. Gazeli, and S. Couris, “Laser-induced breakdown spectroscopy assisted by machine learning for olive oils classification: the effect of the experimental parameters,” Spectrochim. Acta B 163, 105746 (2020).
[Crossref]

Berg, J. M.

Bhatt, B.

B. Bhatt, K. Hudson Angeyo, and A. Dehayem-Kamadjeu, “LIBS development methodology for forensic nuclear materials analysis,” Anal. Methods 10, 791–798 (2018).
[Crossref]

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Blundell, R.

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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Figures (10)

Fig. 1.
Fig. 1. Pellet press die and equipment used to create samples.
Fig. 2.
Fig. 2. Experimental LIBS setup using Everbright 1064 nm Nd:YAG laser to induce ablations. Optical emissions were collected with an $f = + {150}\;{\rm mm}$ lens and a collimator coupled to an optical fiber. This directed light to the spectrometer (Echelle) allowing the Andor camera (CCD) to record the spectra. Timing parameters for the laser and camera were set with the digital delay generator (DG).
Fig. 3.
Fig. 3. Flowchart describing development of predictive ML models for spectral analysis implemented in this study.
Fig. 4.
Fig. 4. Loading values of each wavelength in the spectra from principal component 1 (PC 1). The two wavelengths corresponding to the strongest Ga I emissions load the highest and therefore contribute to most of the total variance of the data.
Fig. 5.
Fig. 5. Application of Savitzky–Golay and noise median filters to spectra, zoomed in on the Ga I 417.2 nm peak.
Fig. 6.
Fig. 6. Comparison of bagging and boosting ensemble methods. Squares denoted by “S” and “M” represent data subsets and individual learner models trained on those subsets, respectively.
Fig. 7.
Fig. 7. Illustration of the support vector regression method.
Fig. 8.
Fig. 8. ANN architecture diagram; each circular node represents a single neuron, and each arrow represents the connection of the output of one neuron to the input of another.
Fig. 9.
Fig. 9. Ensemble test regressions using (a) bagging and (b) boosting algorithms.
Fig. 10.
Fig. 10. Test regressions of the (a) SVR, (b) GKR, and (c) ANN models.

Tables (2)

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Table 1. Hyperparameter Optimization Options for All Models

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Table 2. Regression Model Error and Sensitivity Results

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

I k s n v = I k μ I σ I , k .
y ^ s = i = 1 n K h ( x s , x i ) y i i = 1 n K h ( x s , x i ) .
K h ( x s , x i ) = exp ( x s x i 2 h ) .
R M S E P = i n ( y i y ^ i ) 2 n ,
L o D = 3 σ a b .

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