A. Van-belkum, M. Welker, D. Pincus, J. P. Charrier, and V. Girard, Matrix-assisted laser desorption ionization time-of-flight mass spectrometry in clinical microbiology: What are the current issues?, Ann Lab Med, vol.37, issue.6, p.475, 2017.

E. N. Ilina, A. D. Borovskaya, M. M. Malakhova, V. A. Vereshchagin, A. A. Kubanova et al., Direct bacterial profiling by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry for identification of pathogenic neisseria, J Mol Diagn, vol.11, issue.1, pp.75-86, 2009.

N. Singhal, M. Kumar, P. K. Kanaujia, and J. S. Virdi, Maldi-tof mass spectrometry: an emerging technology for microbial identification and diagnosis, Front Microbiol, vol.6, p.791, 2015.
DOI : 10.3389/fmicb.2015.00791

URL : https://doi.org/10.3389/fmicb.2015.00791

C. Yang, Z. He, and W. Yu, Comparison of public peak detection algorithms for maldi mass spectrometry data analysis, BMC Bioinformatics, vol.10, issue.1, p.4, 2009.

C. K. Larive and J. V. Sweedler, Celebrating the 75th anniversary of the acs division of analytical chemistry: A special collection of the most highly cited analytical chemistry papers published between 1938 and 2012, Anal Chem, vol.85, issue.9, pp.4201-4203, 2013.

A. Savitzky and M. J. Golay, Smoothing and differentiation of data by simplified least squares procedures, Anal Chem, vol.36, issue.8, pp.1627-1666, 1964.
DOI : 10.1021/ac60214a047

P. A. Gorry, General least-squares smoothing and differentiation by the convolution (savitzky-golay) method, Anal Chem, vol.62, issue.6, pp.570-573, 1990.
DOI : 10.1021/ac00205a007

M. Browne, N. Mayer, and T. R. Cutmore, A multiscale polynomial filter for adaptive smoothing, Digit Signal Process, vol.17, issue.1, pp.69-75, 2007.
DOI : 10.1016/j.dsp.2006.01.006

P. Barak, Smoothing and differentiation by an adaptive-degree polynomial filter, Anal Chem, vol.67, issue.17, pp.2758-62, 1995.
DOI : 10.1021/ac00113a006

M. J. Shensa, The discrete wavelet transform: wedding the a trous and mallat algorithms, IEEE Trans Signal Process, vol.40, issue.10, pp.2464-82, 1992.

G. P. Nason and B. W. Silverman, The stationary wavelet transform and some statistical applications, Wavelets and Statistics, pp.281-99, 1995.
DOI : 10.1007/978-1-4612-2544-7_17

K. R. Coombes, S. Tsavachidis, J. S. Morris, K. A. Baggerly, and H. M. Kuerer, Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform, Proteomics, vol.5, pp.4107-4124, 2005.

A. Antoniadis, J. Bigot, and S. Lambert-lacroix, Peaks detection and alignment for mass spectrometry data, J Société Française Stat, vol.151, issue.1, pp.17-37, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00629178

R. Perez-pueyo, M. J. Soneira, and S. Ruiz-moreno, Morphology-based automated baseline removal for raman spectra of artistic pigments, Appl Spectrosc, vol.64, issue.6, pp.595-600, 2010.
DOI : 10.1366/000370210791414281

URL : https://upcommons.upc.edu/bitstream/2117/8320/1/PerezPueyoRosanna.pdf

M. Morhá? and V. Matou?ek, Peak clipping algorithms for background estimation in spectroscopic data, Appl Spectrosc, vol.62, issue.1, pp.91-106, 2008.

V. Mazet, C. Carteret, D. Brie, J. Idier, and B. Humbert, Background removal from spectra by designing and minimising a non-quadratic cost function, Chemometr Intell Lab Syst, vol.76, issue.2, pp.121-154, 2005.
DOI : 10.1016/j.chemolab.2004.10.003

A. F. Ruckstuhl, M. P. Jacobson, R. W. Field, and J. A. Dodd, Baseline subtraction using robust local regression estimation, J Quant Spectrosc Radiative Transf, vol.68, issue.2, pp.179-93, 2001.
DOI : 10.1016/s0022-4073(00)00021-2

Z. Li, D. J. Zhan, J. J. Wang, J. Huang, Q. S. Xu et al., Morphological weighted penalized least squares for background correction, Analyst, vol.138, issue.16, pp.4483-92, 2013.
DOI : 10.1039/c3an00743j

J. Dubrovkin, Evaluation of the peak location uncertainty in second-order derivative spectra. Case study: symmetrical lines, J Emerg Technol Comput Appl Sci, vol.3, p.9, 2014.

A. Mohammad-djafari, J. F. Giovannelli, G. Demoment, and J. Idier, Regularization, maximum entropy and probabilistic methods in mass spectrometry data processing problems, Int J Mass Spectrom, vol.215, issue.1, pp.175-93, 2002.

B. Y. Renard, M. Kirchner, H. Steen, J. A. Steen, and F. A. Hamprecht, Nitpick: peak identification for mass spectrometry data, BMC Bioinformatics, vol.9, issue.1, p.355, 2008.

M. Slawski, R. Hussong, A. Tholey, T. Jakoby, B. Gregorius et al., Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching, BMC Bioinformatics, vol.13, issue.1, p.291, 2012.

J. F. Giovannelli and A. Coulais, Positive deconvolution for superimposed extended source and point sources, Astron Astrophys, vol.439, pp.401-413, 2005.

M. Hirsch, B. Schölkopf, and M. Habeck, A blind deconvolution approach for improving the resolution of cryo-em density maps, J Comput Biol, vol.18, issue.3, pp.335-381, 2011.

E. Lange, C. Gröpl, K. Reinert, O. Kohlbacher, and A. Hildebrandt, High-Accuracy Peak Picking Of Proteomics Data Using Wavelet Techniques, Biocomputing, pp.243-54, 2006.

P. Du, W. A. Kibbe, and S. M. Lin, Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching, Bioinformatics, vol.22, issue.17, pp.2059-65, 2006.

S. Mallat and S. Zhong, Characterization of signals from multiscale edges, IEEE Trans Pattern Anal Mach Intell, vol.7, pp.710-742, 1992.

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, IEEE J Sel Top Sign Process, vol.1, issue.4, pp.586-97, 2007.

R. Tibshirani, Regression shrinkage and selection via the lasso, J R Stat Soc Ser B Methodol, vol.58, issue.1, pp.267-88, 1996.

Y. H. Dai and R. Fletcher, Projected barzilai-borwein methods for large-scale box-constrained quadratic programming, Numer Math, vol.100, issue.1, pp.21-47, 2005.

J. Barzilai and J. M. Borwein, Two-point step size gradient methods, IMA J Numer Anal, vol.8, issue.1, pp.141-149, 1988.

R. Fletcher, On the barzilai-borwein method, Optimization and Control with Applications, pp.235-56, 2005.

M. Raydan, The barzilai and borwein gradient method for the large scale unconstrained minimization problem, SIAM J Optim, vol.7, issue.1, pp.26-33, 1997.

M. Raydan, On the barzilai and borwein choice of steplength for the gradient method, IMA J Numer Anal, vol.13, issue.3, pp.321-327, 1993.

J. J. Moré and G. Toraldo, On the solution of large quadratic programming problems with bound constraints, SIAM J Optim, vol.1, issue.1, pp.93-113, 1991.

D. Kim, S. Sra, and I. S. Dhillon, A non-monotonic method for large-scale non-negative least squares, Optim Methods Softw, 2012.

E. G. Birgin, J. M. Martínez, and M. Raydan, Nonmonotone spectral projected gradient methods on convex sets, SIAM J Optim, vol.10, issue.4, pp.1196-211, 2000.

S. Boyd and L. Vandenberghe, Convex optimization, 2009.

C. Mercier, A. Klich, C. Truntzer, V. Picaud, J. Giovannelli et al., Variance component analysis to assess protein quantification in biomarker discovery. application to MALDI-TOF mass spectrometry, Biom J, vol.60, issue.2, pp.262-74, 2017.
URL : https://hal.archives-ouvertes.fr/cea-01683000

C. Ryan, C. E. Griffin, W. Sie, S. Cousens, and D. , Snip, a statistics-sensitive background treatment for the quantitative analysis of pixe spectra in geoscience applications, Nucl Inst Methods Phys Res Sec B: Beam Interactions with Mater Atoms, vol.34, issue.3, pp.396-402, 1988.

M. Morhá?, J. Kliman, V. Matou?ek, `. Veselsk, M. Veselsk`y et al., Background elimination methods for multidimensional coincidence ?-ray spectra

, Nucl Inst Methods Phys Res Sec A: Accelerators, Spectrometers, Detectors and Assoc Equip, vol.401, issue.1, pp.113-145, 1997.

S. Gibb and K. Strimmer, Maldiquant: a versatile R package for the analysis of mass spectrometry data, Bioinformatics, vol.28, issue.17, pp.2270-2271, 2012.

S. D. Conte and C. Boor, Elementary numerical analysis: an algorithmic approach: McGraw-Hill High Educ, 1980.

R. S. Varga, Ger?gorin and his circles, 2004.

B. N. Parlett, The symmetric eigenvalue problem, SIAM, vol.7, p.134, 1980.