Analyzing Raman spectroscopic data

References

[1] Bocklitz TW, Guo S, Ryabchykov O, Vogler N, Popp J. Raman based molecular imaging and analytics: a magic bullet for biomedical applications!? Anal Chem. 2016;88:133–51.10.1021/acs.analchem.5b04665Search in Google Scholar PubMed

[2] Ehrentreich F, Sümmchen L. Spike removal and denoising of Raman spectra by wavelet transform methods. Anal Chem. 2001;73:4364–73.10.1021/ac0013756Search in Google Scholar PubMed

[3] Schulze HG, Turner RF. A two-dimensionally coincident second difference cosmic ray spike removal method for the fully automated processing of Raman spectra. Appl Spectrosc. 2014;68:185–91.10.1366/13-07216Search in Google Scholar PubMed

[4] Ryabchykov O, Bocklitz T, Ramoji A, Neugebauer U, Foerster M, Kroegel C, et al. Automatization of spike correction in Raman spectra of biological samples. Chemometrics Intell Lab Syst. 2016;155:1–6.10.1016/j.chemolab.2016.03.024Search in Google Scholar

[5] Dörfer T, Bocklitz T, Tarcea N, Schmitt M, Popp J. Checking and improving calibration of Raman spectra using chemometric approaches. Z Phys Chem Int J Res Phy Chem Chem Phy. 2011;225:753–64.10.1524/zpch.2011.0077Search in Google Scholar

[6] Bocklitz T, Dörfer T, Heinke R, Schmitt M, Popp J. Spectrometer calibration protocol for Raman spectra recorded with different excitation wavelengths. Spectrochimica Acta A: Mol Biomol Spectrosc. 2015;149:544–9.10.1016/j.saa.2015.04.079Search in Google Scholar PubMed

[7] McCreery RL. Raman spectroscopy for chemical analysis Vol. 157. New York: John Wiley & Sons, 200010.1002/0471721646Search in Google Scholar

[8] Berg RW, Nørbygaard T. Wavenumber calibration of CCD detector Raman spectrometers controlled by a sinus arm drive. Appl Spectrosc Rev. 2006;41:165–83.10.1080/05704920500510786Search in Google Scholar

[9] Carrabba MM. Wavenumber standards for Raman spectrometry. In: Griffiths P, Chalmers JM, editor(s). Handbook of vibrational spectroscopy. Chichester: Wiley online library, 2006Search in Google Scholar

[10] E1840-96, A., Standard guide for Raman shift standards for spectrometer calibration. ASTM International, West Conshohocken, PA, 2014. 03.06.Search in Google Scholar

[11] Fryling M, Frank CJ, McCreery RL. Intensity calibration and sensitivity comparisons for CCD/Raman spectrometers. Appl Spectrosc. 1993;47:1965–74.10.1366/0003702934066226Search in Google Scholar

[12] Davis W, Forney G, Bukowski R. National institute of standards and technology, Gaithersburg MD, USA.Search in Google Scholar

[13] Rodriguez JD, Westenberger BJ, Buhse LF, Kauffman JF. Standardization of Raman spectra for transfer of spectral libraries across different instruments. Analyst. 2011;136:4232–40.10.1039/c1an15636eSearch in Google Scholar PubMed

[14] Guo S, Heinke R, Stöckel S, Rösch P, Bocklitz T, Popp J. Towards an improvement of model transferability for Raman spectroscopy in biological applications. Vib Spectrosc. 2017;91:111–8.10.1016/j.vibspec.2016.06.010Search in Google Scholar

[15] Bocklitz T, Walter A, Hartmann K, Rosch P, Popp J. How to pre-process Raman spectra for reliable and stable models? Anal Chim Acta. 2011;704:47–56.10.1016/j.aca.2011.06.043Search in Google Scholar PubMed

[16] Savitzky A, Golay MJE. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 1964;36:1627–39.10.1021/ac60214a047Search in Google Scholar

[17] Lieber CA, Mahadevan-Jansen A. Automated method for subtraction of fluorescence from biological Raman spectra. Appl Spectrosc. 2003;57:1363–7.10.1366/000370203322554518Search in Google Scholar

[18] Eilers PH, Boelens HF. Baseline correction with asymmetric least squares smoothing. Leiden Univ Med Centre Rep. 2005;1:1.Search in Google Scholar

[19] Ryan CG, Clayton E, Griffin WL, Sie SH, Cousens DR. SNIP, a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications. Nucl Instrum Methods Phys Res B: Beam Interact Mater Atoms. 1988;34:396–402.10.1016/0168-583X(88)90063-8Search in Google Scholar

[20] Martens H, Stark E. Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy. J Pharm Biomed Anal. 1991;9:625–35.10.1016/0731-7085(91)80188-FSearch in Google Scholar PubMed

[21] Guo S, Bocklitz T, Popp J. Optimization of Raman-spectrum baseline correction in biological application. Analyst. 2016;141:2396–404.10.1039/C6AN00041JSearch in Google Scholar PubMed

[22] Gautam R, Vanga S, Ariese F, Umapathy S. Review of multidimensional data processing approaches for Raman and infrared spectroscopy. EPJ Tech Instrum. 2015;2:8.10.1140/epjti/s40485-015-0018-6Search in Google Scholar

[23] Black MJ, Anandan P. The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput Vis Image Understand. 1996;63:75–104.10.1006/cviu.1996.0006Search in Google Scholar

[24] Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–82.Search in Google Scholar

[25] Malinowski ER. Factor analysis in chemistry, 2 ed. New York: Wiley, 1991Search in Google Scholar

[26] Zhang X, Tauler R. Application of multivariate curve resolution alternating least squares (MCR-ALS) to remote sensing hyperspectral imaging. Anal Chim Acta. 2013;762:25–38.10.1016/j.aca.2012.11.043Search in Google Scholar PubMed

[27] Piqueras S, Krafft C, Beleites C, Egodage K, Von Eggeling F, Guntinas-Lichius O, et al. Combining multiset resolution and segmentation for hyperspectral image analysis of biological tissues. Anal Chim Acta. 2015;881:24–36.10.1016/j.aca.2015.04.053Search in Google Scholar PubMed

[28] Brereton RG, Jansen J, Lopes J, Marini F, Pomerantsev A, Rodionova O, et al. Chemometrics in analytical chemistry – Part I: history, experimental design and data analysis tools. Anal Bioanal Chem. 2017;409:5891–9.10.1007/s00216-017-0517-1Search in Google Scholar PubMed

[29] Bruce LM, Koger CH, Li J. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Trans Geosci Remote Sens. 2002;40:2331–8.10.1109/TGRS.2002.804721Search in Google Scholar

[30] Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Series B Methodol. 1996;58:267–88.10.1111/j.2517-6161.1996.tb02080.xSearch in Google Scholar

[31] Chun H, Keleş S. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J R Stat Soc Series B Stat Methodol. 2010;72:3–25.10.1111/j.1467-9868.2009.00723.xSearch in Google Scholar PubMed PubMed Central

[32] Zhang Z, Chow TW, Zhao M. M-Isomap: orthogonal constrained marginal isomap for nonlinear dimensionality reduction. IEEE Trans Syst Man Cybern. 2013;43:180–91.10.1109/TSMCB.2012.2202901Search in Google Scholar PubMed

[33] Silva VD, Tenenbaum JB. Global versus local methods in nonlinear dimensionality reduction. In: Advances in neural information processing systems. Cambridge, MA, USA: MIT Press, 2003.Search in Google Scholar

[34] Shan R, Cai W, Shao X. Variable selection based on locally linear embedding mapping for near-infrared spectral analysis. Chemometrics Intell Lab Syst. 2014;131:31–6.10.1016/j.chemolab.2013.12.002Search in Google Scholar

[35] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313:504–7.10.1126/science.1127647Search in Google Scholar PubMed

[36] Wang W, Huang Y, Wang Y, Wang L. Generalized autoencoder: a neural network framework for dimensionality reduction. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2014.10.1109/CVPRW.2014.79Search in Google Scholar

[37] Kalivas JH, Palmer J. Characterizing multivariate calibration tradeoffs (bias, variance, selectivity, and sensitivity) to select model tuning parameters. J Chemom. 2014;28:347–57.10.1002/cem.2555Search in Google Scholar

[38] Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Stat Surv. 2010;4:40–79.10.1214/09-SS054Search in Google Scholar

[39] Guo S, Bocklitz T, Neugebauer U, Popp J. Common mistakes in cross-validating classification models. Anal Methods. 2017;9:4410–7.10.1039/C7AY01363ASearch in Google Scholar

[40] Hastie T, Tibshirani R, Friedman J. The elements of statistical learning; data mining, inference and prediction. New York: Springer, 2008.10.1007/978-0-387-84858-7Search in Google Scholar

[41] Hedegaard M, Matthäus C, Hassing S, Krafft C, Diem M, Popp J. Spectral unmixing and clustering algorithms for assessment of single cells by Raman microscopic imaging. Theor Chem Acc. 2011;130:1249–60.10.1007/s00214-011-0957-1Search in Google Scholar

[42] Bezdek JC, Ehrlich R, Full W. FCM: the fuzzy c-means clustering algorithm. Comput Geosci. 1984;10:191–203.10.1016/0098-3004(84)90020-7Search in Google Scholar

[43] Bocklitz T, Putsche M, Stüber C, Käs J, Niendorf A, Rösch P, et al. A comprehensive study of classification methods for medical diagnosis. J Raman Spectrosc. 2009;40:1759–65.10.1002/jrs.2529Search in Google Scholar

[44] Acquarelli J, Van Laarhoven T, Gerretzen J, Tran TN, Buydens LM, Marchiori E. Convolutional neural networks for vibrational spectroscopic data analysis. Anal Chim Acta. 2017;954:22–31.10.1016/j.aca.2016.12.010Search in Google Scholar PubMed

[45] Breiman L. Random forests. Mach Learn. 2001;45:5–32.10.1023/A:1010933404324Search in Google Scholar

[46] Mevik B-H, Wehrens R, Liland KH. pls: partial least squares and principal component regression. R Package Version. 2011;2(3).Search in Google Scholar

[47] Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27:1413–32.10.1007/s11222-016-9696-4Search in Google Scholar

[48] Héberger K. Sum of ranking differences compares methods or models fairly. TrAC Trends Anal Chem. 2010;29:101–9.10.1016/j.trac.2009.09.009Search in Google Scholar

[49] Refaeilzadeh P, Tang L, Liu H. Cross-validation, in Encyclopedia of database systems. New York: Springer, 2009:532–8.10.1007/978-0-387-39940-9_565Search in Google Scholar

[50] Xu QS, Liang YZ, Du YP. Monte Carlo cross‐validation for selecting a model and estimating the prediction error in multivariate calibration. J Chemom. 2004;18:112–20.10.1002/cem.858Search in Google Scholar

[51] Wan C, Harrington PDB. Screening GC-MS data for carbamate pesticides with temperature-constrained-cascade correlation neural networks. Anal Chim Acta. 2000;408:1–12.10.1016/S0003-2670(99)00865-XSearch in Google Scholar

[52] De Boves Harrington P. Statistical validation of classification and calibration models using bootstrapped Latin partitions. TrAC Trends Anal Chem. 2006;25:1112–24.10.1016/j.trac.2006.10.010Search in Google Scholar

[53] Qi N, Zhang Z, Xiang Y, Yang Y, Liang X, Harrington PD. Terahertz time-domain spectroscopy combined with support vector machines and partial least squares-discriminant analysis applied for the diagnosis of cervical carcinoma. Anal Methods. 2015;7:2333–8.10.1039/C4AY02665ASearch in Google Scholar

[54] Krstajic D, Buturovic LJ, Leahy DE, Thomas S. Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminform. 2014;6:10.10.1186/1758-2946-6-10Search in Google Scholar PubMed PubMed Central

[55] Copas JB. Regression, prediction and shrinkage. J R Stat Soc Series B Methodol. 1983;45:311–54.10.1111/j.2517-6161.1983.tb01258.xSearch in Google Scholar

[56] Shahbazikhah P, Kalivas JH. A consensus modeling approach to update a spectroscopic calibration. Chemometrics Intell Lab Syst. 2013;120:142–53.10.1016/j.chemolab.2012.06.006Search in Google Scholar

[57] Guo S, Heinke R, Stöckel S, Rösch P, Popp J, Bocklitz T. Model transfer for Raman‐spectroscopy‐based bacterial classification. J Raman Spectrosc. 2018;49:627–37.10.1002/jrs.5343Search in Google Scholar

[58] Kalivas JH, Siano GG, Andries E, Goicoechea HC. Calibration maintenance and transfer using Tikhonov regularization approaches. Appl Spectrosc. 2009;63:800–9.10.1366/000370209788701206Search in Google Scholar PubMed

[59] Liang C, Yuan H-F, Zhao Z, Song C-F, Wang J-J. A new multivariate calibration model transfer method of near-infrared spectral analysis. Chemometrics Intell Lab Syst. 2016;153:51–7.10.1016/j.chemolab.2016.01.017Search in Google Scholar

[60] Bloemberg TG, Gerretzen J, Lunshof A, Wehrens R, Buydens LM. Warping methods for spectroscopic and chromatographic signal alignment: a tutorial. Anal Chim Acta. 2013;781:14–32.10.1016/j.aca.2013.03.048Search in Google Scholar PubMed

[61] Kalivas JH, Brownfield B, Karki BJ. Sample‐wise spectral multivariate calibration desensitized to new artifacts relative to the calibration data using a residual penalty. J Chemom. 2017;31:e287310.1002/cem.2873Search in Google Scholar

[62] Bevilacqua M, Marini F. Local classification: locally weighted-partial least squares-discriminant analysis (LW-PLS-DA). Anal Chim Acta. 2014;838:20–30.10.1016/j.aca.2014.05.057Search in Google Scholar PubMed

[63] Kalivas JH. Overview of two‐norm (L2) and one‐norm (L1) Tikhonov regularization variants for full wavelength or sparse spectral multivariate calibration models or maintenance. J Chemom. 2012;26:218–30.10.1002/cem.2429Search in Google Scholar

[64] Castanedo F. A review of data fusion techniques. Sci World J. 2013;2013:19.10.1155/2013/704504Search in Google Scholar PubMed PubMed Central

[65] Márquez C, López MI, Ruisánchez I, Callao MP. FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud. Talanta. 2016;161:80–6.10.1016/j.talanta.2016.08.003Search in Google Scholar PubMed

[66] Teglia CM, Azcarate SM, Alcaráz MR, Goicoechea HC, Culzoni MJ. Exploiting the synergistic effect of concurrent data signals: low-level fusion of liquid chromatographic with dual detection data. Talanta. 2018;186:481–8.10.1016/j.talanta.2018.04.090Search in Google Scholar PubMed

[67] Borràs E, Ferré J, Boqué R, Mestres M, Aceña L, Busto O. Data fusion methodologies for food and beverage authentication and quality assessment – a review. Anal Chim Acta. 2015;891:1–14.10.1016/j.aca.2015.04.042Search in Google Scholar PubMed

[68] Durrant-Whyte H, Stevens M, Nettleton E. Data fusion in decentralised sensing networks. In: 4th International Conference on Information Fusion, 2001.Search in Google Scholar

[69] Bocklitz T, Bräutigam K, Urbanek A, Hoffmann F, Von Eggeling F, Ernst G, et al. Novel workflow for combining Raman spectroscopy and MALDI-MSI for tissue based studies. Anal Bioanal Chem. 2015;407:7865–73.10.1007/s00216-015-8987-5Search in Google Scholar PubMed

[70] Bocklitz T, Crecelius AC, Matthaus C, Tarcea N, Von Eggeling F, Schmitt M, et al. Deeper understanding of biological tissue: quantitative correlation of MALDI-TOF and Raman imaging. Anal Chem. 2013;85:10829–34.10.1021/ac402175cSearch in Google Scholar PubMed