{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:19:01Z","timestamp":1771957141029,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T00:00:00Z","timestamp":1637712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>A major goal in pre-detonation nuclear forensics is to infer the processing conditions and\/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (\u201cmorphology\u201d) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and\/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate t tests (Hotelling\u2019s T2), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.<\/jats:p>","DOI":"10.3390\/a14120340","type":"journal-article","created":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T04:01:28Z","timestamp":1637812888000},"page":"340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Overview of Algorithms for Using Particle Morphology in Pre-Detonation Nuclear Forensics"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7298-9706","authenticated-orcid":false,"given":"Tom","family":"Burr","sequence":"first","affiliation":[{"name":"Statistical Science, Material Recovery, Information Sciences, Chemistry Groups, Los Alamos National Laboratory, Los Alamos, NM 87545, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian","family":"Schwerdt","sequence":"additional","affiliation":[{"name":"Statistical Science, Material Recovery, Information Sciences, Chemistry Groups, Los Alamos National Laboratory, Los Alamos, NM 87545, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1530-1952","authenticated-orcid":false,"given":"Kari","family":"Sentz","sequence":"additional","affiliation":[{"name":"Statistical Science, Material Recovery, Information Sciences, Chemistry Groups, Los Alamos National Laboratory, Los Alamos, NM 87545, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6735-5410","authenticated-orcid":false,"given":"Luther","family":"McDonald","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marianne","family":"Wilkerson","sequence":"additional","affiliation":[{"name":"Statistical Science, Material Recovery, Information Sciences, Chemistry Groups, Los Alamos National Laboratory, Los Alamos, NM 87545, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"152983","DOI":"10.1016\/j.jnucmat.2021.152983","article-title":"Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis","volume":"552","author":"Girard","year":"2021","journal-title":"J. Nucl. Mater."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1515\/ract-2018-3033","article-title":"Uranium Oxide Synthetic Pathway Discernment Through Thermal Decomposition and Morphological Analysis","volume":"107","author":"Schwerdt","year":"2019","journal-title":"Radiochim. Acta"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8605","DOI":"10.1021\/acsomega.1c00435","article-title":"Impact of Controlled Storage Conditions on the Hydrolysis and Surface Morphology of Amorphous-UO3","volume":"6","author":"Hanson","year":"2021","journal-title":"ACS Omega"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.talanta.2017.08.020","article-title":"Nuclear Forensics Investigation of Morphological Signatures in the Thermal Decomposition of Uranyl Peroxide","volume":"176","author":"Schwerdt","year":"2018","journal-title":"Talanta"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jhazmat.2011.07.032","article-title":"Effects of the Different Conditions of Uranyl and Hydrogen Peroxide Solutions on the Behavior of the Uranium Peroxide Precipitation","volume":"193","author":"Kim","year":"2011","journal-title":"J. Hazard. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1021\/acs.analchem.6b05020","article-title":"Quantifying Morphological Features of \u03b1-U3O8 with Image Analysis for Nuclear Forensics","volume":"89","author":"Olsen","year":"2017","journal-title":"Anal. Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.talanta.2018.04.092","article-title":"Nuclear Proliferomics: A New Field of Study to Identify Signatures of Nuclear Materials as Demonstrated on alpha-UO3","volume":"186","author":"Schwerdt","year":"2018","journal-title":"Talanta"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.jnucmat.2019.01.042","article-title":"A New Approach for Quantifying Morphological Features of U3O8 for Nuclear Forensics Using a Deep Learning Model","volume":"517","author":"Ly","year":"2019","journal-title":"J. Nucl. Mater."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Schwartz, D., and Tandon, L. (2017). Uncertainty in the USE of MAMA Software to Measured Particle Morphological Parameters from SEM Images, LAUR-17-24503.","DOI":"10.2172\/1361474"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gaschen, B.K., Bloch, J.J., Porter, R., Ruggiero, C.E., Oyen, D.A., and Schaffer, K.M. (2016). MAMA User Guide V2.0.1, LA-UR-16-25116.","DOI":"10.2172\/1291192"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/TII.2016.2542043","article-title":"A New Approach for Segmentation and Quantification of Cells or Nanoparticles","volume":"12","author":"Wang","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_12","unstructured":"R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Venables, W., and Ripley, B. (1999). Modern Applied Statistics with S-Plus, Springer.","DOI":"10.1007\/978-1-4757-3121-7"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Miller, R. (1997). Beyond ANOVA: The Basics of Applied Statistics, CRC Press.","DOI":"10.1201\/b15236"},{"key":"ref_16","unstructured":"Johnson, R., and Wichern, D. (1992). Applied Multivariate Statistical Analysis, Prentice Hall. [2nd ed.]."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.3390\/s6111587","article-title":"Characterizing Clutter in the Context of Detecting Weak Gaseous Plumes in Hyperspectral Imagery","volume":"6","author":"Burr","year":"2006","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104224","DOI":"10.1016\/j.chemolab.2020.104224","article-title":"Bottom-up and Top-Down Uncertainty Quantification for Measurements","volume":"209","author":"Burr","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Burr, T., Favalli, A., Lombardi, M., and Stinnett, J. (2020). Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy. Algorithms, 13.","DOI":"10.3390\/a13100265"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1214\/12-STS406","article-title":"A Review of Dimension Reduction Methods in Approximate Bayesian Computation","volume":"28","author":"Blum","year":"2013","journal-title":"Stat. Sci."},{"key":"ref_21","first-page":"189","article-title":"abctools: An R package for Tuning Approximate Bayesian Computation Analyses","volume":"7","year":"2016","journal-title":"R J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"210646","DOI":"10.1155\/2013\/210646","article-title":"Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models","volume":"2013","author":"Burr","year":"2013","journal-title":"BioMed Res. Int."},{"key":"ref_23","first-page":"50","article-title":"Approximate Bayesian Computation Applied to Metrology for Nuclear Safeguards","volume":"57","author":"Burr","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_24","unstructured":"Carlin, B., John, B., Stern, H., and Rubin, D. (1995). Bayesian Data Analysis, CRC Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1080\/01621459.2013.823775","article-title":"Estimation and Accuracy after Model Selection","volume":"109","author":"Efron","year":"2014","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lawson, J. (2014). Design and Analysis of Experiments in R, CRC Press.","DOI":"10.1201\/b17883"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.chemolab.2017.10.010","article-title":"Comparing Multiple Statistical Methods for Inverse Prediction in Nuclear Forensics Applications","volume":"17","author":"Lewis","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_28","first-page":"1740","article-title":"Strategies for Sequential Design of Experiments and Augmentation","volume":"2020","author":"Lu","year":"2020","journal-title":"Qual. Reliab. Eng."},{"key":"ref_29","unstructured":"Myers, R., and Montgomery, D. (1995). Response Surface Methodology, Wiley."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1080\/08982112.2012.758284","article-title":"Comparing Computer Experiments for the Gaussian Process Model Using Integrated Prediction Variance","volume":"25","author":"Jones","year":"2013","journal-title":"Qual. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1080\/00224065.2017.11917992","article-title":"Selecting an Informative\/Discriminating Multivariate Response for Inverse Prediction","volume":"49","author":"Thomas","year":"2017","journal-title":"J. Qual. Technol."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/12\/340\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:18Z","timestamp":1760168118000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/12\/340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,24]]},"references-count":31,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["a14120340"],"URL":"https:\/\/doi.org\/10.3390\/a14120340","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,24]]}}}