IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v143y2020ics0167947319302014.html
   My bibliography  Save this article

Bayesian modeling and computation for analyte quantification in complex mixtures using Raman spectroscopy

Author

Listed:
  • Han, Ningren
  • Ram, Rajeev J.

Abstract

A two-stage algorithm based on Bayesian modeling and computation for quantifying analyte concentration in complex mixtures with Raman spectroscopy is proposed. A hierarchical Bayesian model is constructed for spectral signal analysis, and reversible-jump Markov chain Monte Carlo (RJMCMC) computation is carried out for model selection and spectral variable estimation. Processing is performed in two stages. In the first stage, the peak representation for a target analyte spectrum is learned. In the second, the peak variables learned from the first stage are used to estimate the concentration of the target analyte in a mixture. Numerical experiments validated the performance over a wide range of simulation conditions and established the algorithm accuracy over conventional multivariate regression algorithms for analyte quantification (when constrained to a small training sample size). In addition, the algorithm was applied to analyze experimental spontaneous Raman spectroscopy data collected for glucose concentration estimation in a biopharmaceutical process monitoring application. The results show that this algorithm can be a promising complementary tool alongside conventional multivariate regression algorithms in Raman spectroscopy-based mixture quantification studies, especially when collection of a large training dataset is challenging or resource-intensive.

Suggested Citation

  • Han, Ningren & Ram, Rajeev J., 2020. "Bayesian modeling and computation for analyte quantification in complex mixtures using Raman spectroscopy," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:csdana:v:143:y:2020:i:c:s0167947319302014
    DOI: 10.1016/j.csda.2019.106846
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947319302014
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2019.106846?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    2. McGrory, C.A. & Pettitt, A.N. & Titterington, D.M. & Alston, C.L. & Kelly, M., 2016. "Transdimensional sequential Monte Carlo using variational Bayes — SMCVB," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 246-254.
    3. Mingjun Zhong & Mark Girolami & Karen Faulds & Duncan Graham, 2011. "Bayesian methods to detect dye‐labelled DNA oligonucleotides in multiplexed Raman spectra," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(2), pages 187-206, March.
    4. David I. Hastie & Peter J. Green, 2012. "Model choice using reversible jump Markov chain Monte Carlo," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 309-338, August.
    5. Jeffrey W. Miller & Matthew T. Harrison, 2018. "Mixture Models With a Prior on the Number of Components," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 340-356, January.
    6. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sylvia Frühwirth-Schnatter & Gertraud Malsiner-Walli, 2019. "From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 33-64, March.
    2. You, Na & Dai, Hongsheng & Wang, Xueqin & Yu, Qingyun, 2024. "Sequential estimation for mixture of regression models for heterogeneous population," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    3. Im, Yunju & Tan, Aixin, 2021. "Bayesian subgroup analysis in regression using mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    4. Bettina Grün & Gertraud Malsiner-Walli & Sylvia Frühwirth-Schnatter, 2022. "How many data clusters are in the Galaxy data set?," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 325-349, June.
    5. Grazian, Clara & Villa, Cristiano & Liseo, Brunero, 2020. "On a loss-based prior for the number of components in mixture models," Statistics & Probability Letters, Elsevier, vol. 158(C).
    6. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    7. Shuang Zhang & Xingdong Feng, 2022. "Distributed identification of heterogeneous treatment effects," Computational Statistics, Springer, vol. 37(1), pages 57-89, March.
    8. Anna Sokolova, 2023. "Marginal Propensity to Consume and Unemployment: a Meta-analysis," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 813-846, December.
    9. Hasan, Iftekhar & Horvath, Roman & Mares, Jan, 2020. "Finance and wealth inequality," Journal of International Money and Finance, Elsevier, vol. 108(C).
    10. Galharret, Jean-Michel & Philippe, Anne, 2023. "Bayesian analysis for mediation and moderation using g−priors," Econometrics and Statistics, Elsevier, vol. 27(C), pages 161-172.
    11. Philipp Piribauer & Jesús Crespo Cuaresma, 2016. "Bayesian Variable Selection in Spatial Autoregressive Models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(4), pages 457-479, October.
    12. Ley, Eduardo & Steel, Mark F. J., 2007. "On the effect of prior assumptions in Bayesian model averaging with applications to growth regression," Policy Research Working Paper Series 4238, The World Bank.
    13. Forte, Anabel & Peiró-Palomino, Jesús & Tortosa-Ausina, Emili, 2015. "Does social capital matter for European regional growth?," European Economic Review, Elsevier, vol. 77(C), pages 47-64.
    14. N. T. Longford & Pierpaolo D'Urso, 2011. "Mixture models with an improper component," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2511-2521, January.
    15. Aart Kraay & Norikazu Tawara, 2013. "Can specific policy indicators identify reform priorities?," Journal of Economic Growth, Springer, vol. 18(3), pages 253-283, September.
    16. Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
    17. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    18. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    19. Francisco Richter & Bart Haegeman & Rampal S. Etienne & Ernst C. Wit, 2020. "Introducing a general class of species diversification models for phylogenetic trees," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 261-274, August.
    20. Robert Richardson, 2022. "Spatial Generalized Linear Models with Non-Gaussian Translation Processes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 4-21, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:143:y:2020:i:c:s0167947319302014. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.