[en] Complex signal vectors, particularly spectra, are integral to many scientific domains. Interpreting these signals often involves decomposing them into contributions from independent components and subtraction or deconvolution of the channel and instrument noise. Despite the fundamental nature of this task, researchers frequently rely on costly commercial tools. To make such tools accessible to all, we present Tihi, interactive, open-source multiplatform software for interpolation, denoising, baseline correction, peak detection, and signal decomposition. Tihi provides a user-friendly graphical interface (GUI) that facilitates the analysis of spectroscopic data and more. It allows researchers to contribute to and freely distribute these tools, ensuring broad accessibility and fostering collaborative improvements. We present examples demonstrating the efficiency of the program using the spectra of different systems acquired by different spectroscopic techniques, including Raman (aspirin), IR (solid ammonia), XRD (anatase), and UV-vis (petal tip from the Puya alpestris flower). These examples showcase a variety of spectra that differ significantly, from signals with narrow profiles to signals with very broad profiles. This demonstrates the versatility of Tihi for peak identification in a wide range of spectroscopic techniques.
Centre de recherche :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Physique Science des matériaux & ingénierie Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
HAN, Kyunghoon ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
BOZIKI, Ariadni ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
TKATCHENKO, Alexandre ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
BERRYMAN, Josh ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
TIHI Toolkit: A Peak Finder and Analyzer for Spectroscopic Data
This project is funded by the grant C20/MS/14588607 of the Fonds Nationale de la Recherche (FNR) Luxembourg. The calculations presented in this paper were carried out using the HPC facilities of the University of Luxembourg (see hpc.uni.lu ). The authors thank Tobias Henkes, Alessio Fallani, Matthieu Sarkis, F. Simone Ruggeri, Carolin Müller and Gregory Cordeiro Fonseca for attention and instruction during the development process.
Morris, J. S.; Coombes, K. R.; Koomen, J.; Baggerly, K. A.; Kobayashi, R. Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics 2005, 21 ( 9), 1764- 1775, 10.1093/bioinformatics/bti254
Gibb, S.; Strimmer, K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 2012, 28 ( 17), 2270- 2271, 10.1093/bioinformatics/bts447
Yu, Y.-J.; Xia, Q.-L.; Wang, S.; Wang, B.; Xie, F.-W.; Zhang, X.-B.; Ma, Y.-M.; Wu, H.-L. Chemometric strategy for automatic chromatographic peak detection and background drift correction in chromatographic data. J. Chromatogr. A 2014, 1359, 262- 270, 10.1016/j.chroma.2014.07.053
Lu, J.; Trnka, M. J.; Roh, S.-H.; Robinson, P. J. J.; Shiau, C.; Fujimori, D. G.; Chiu, W.; Burlingame, A. L.; Guan, S. Improved peak detection and deconvolution of native electrospray mass spectra from large protein complexes. J. Am. Soc. Mass Spectrom. 2015, 26 ( 12), 2141- 2151, 10.1007/s13361-015-1235-6
Huzortey, A. A.; Anderson, B.; Owusu, A. Raman spectra recovery using a second derivative technique and range independent baseline correction algorithm. OSA Continuum 2021, 4 ( 9), 2468- 2480, 10.1364/OSAC.432785
Andreev, V. P.; Rejtar, T.; Chen, H.-S.; Moskovets, E. V.; Ivanov, A. R.; Karger, B. L. A universal denoising and peak picking algorithm for lcms based on matched filtration in the chromatographic time domain. Anal. Chem. 2003, 75 ( 22), 6314- 6326, 10.1021/ac0301806
Major, G. H.; Fairley, N.; Sherwood, P. M. A.; Linford, M. R.; Terry, J.; Fernandez, V.; Artyushkova, K. Practical guide for curve fitting in x-ray photoelectron spectroscopy. J. Vac. Sci. Technol., A 2020, 38 ( 6), 061203 10.1116/6.0000377
Brown, A. J. Spectral curve fitting for automatic hyperspectral data analysis. IEEE Trans. Geosci. Electron. 2006, 44 ( 6), 1601- 1608, 10.1109/TGRS.2006.870435
Inouye, T.; Harper, T.; Rasmussen, N. C. Application of fourier transforms to the analysis of spectral data. Nucl. Instrum. Methods 1969, 67 ( 1), 125- 132, 10.1016/0029-554X(69)90551-5
Du, P.; Kibbe, W. A.; Lin, S. M. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 2006, 22, 2059- 2065, 10.1093/bioinformatics/btl355
Nguyen, N.; Huang, H.; Oraintara, S.; Vo, A. Mass spectrometry data processing using zero-crossing lines in multi-scale of Gaussian derivative wavelet. Bioinformatics 2010, 26 ( 18), i659- i665, 10.1093/bioinformatics/btq397
Zheng, Y.; Fan, R.; Qiu, C.; Liu, Z.; Tian, D. An improved algorithm for peak detection in mass spectra based on continuous wavelet transform. Int. J. Mass Spectrom. 2016, 409, 53- 58, 10.1016/j.ijms.2016.09.020
Yang, G.; Dai, J.; Liu, X.; Chen, M.; Wu, X. Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection. Anal. Methods 2020, 12, 169- 178, 10.1039/C9AY02052G
Liu, M.; Dong, Z.; Xin, G.; Sun, Y.; Qu, R. An improved method based on a new wavelet transform for overlapped peak detection on spectrum obtained by portable raman system. Chemom. Intell. Lab. Syst. 2018, 182, 1- 8, 10.1016/j.chemolab.2018.08.002
Xi, Y.; Li, Y.; Duan, Z.; Lu, Y. A novel pre-processing algorithm based on the wavelet transform for raman spectrum. Appl. Spectrosc. 2018, 72 ( 12), 1752- 1763, 10.1177/0003702818789695
Li, D.-W.; Hansen, A. L.; Bruschweiler-Li, L.; Yuan, C.; Brüschweiler, R. Fundamental and practical aspects of machine learning for the peak picking of biomolecular nmr spectra. J. Biomol. NMR 2022, 76 ( 3), 49- 57, 10.1007/s10858-022-00393-1
Li, D.-W.; Hansen, A. L.; Yuan, C.; Bruschweiler-Li, L.; Brüschweiler, R. Deep picker is a deep neural network for accurate deconvolution of complex two-dimensional nmr spectra. Nat. Commun. 2021, 12 ( 1), 5229 10.1038/s41467-021-25496-5
Melnikov, A. D.; Tsentalovich, Y. P.; Yanshole, V. V. Deep learning for the precise peak detection in high-resolution lc-ms data. Anal. Chem. 2020, 92 ( 1), 588- 592, 10.1021/acs.analchem.9b04811
Yu, T.; Jones, D. P. Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach. Bioinformatics 2014, 30 ( 20), 2941- 2948, 10.1093/bioinformatics/btu430
Spectragryph: optical spectroscopy software. https://www.effemm2.de/spectragryph/, version v1.2.16.1, 2022; (accessed June 3, 2024).
Mnova: Mestrelab research, chemistry software solutions. https://mestrelab.com/download/mnova/, version 15.0.1; (accessed June 3, 2024).
Origin(Pro): OriginLab corporation. https://www.originlab.com/, version 2022; (accessed June 3, 2024).
Vibrational spectroscopy software OPUS. https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software.html, version 7.0.122.1124 (accessed Jan 23, 2024).
The MathWorks Inc. Statistics and Machine Learning Toolbox, 2022.
Eaton, J. W. GNU Octave Manual; Network Theory Limited, 2002.
Asymmetric least squares for multiple spectra baseline correction Anal. Chim. Acta, 2010 683 63 68 10.1016/j.aca.2010.08.033.
Liland, K. H.; Almøy, T.; Mevik, B. Optimal choice of baseline correction for multivariate calibration of spectra. Appl. Spectrosc. 2010, 64 ( 9), 1007- 1016, 10.1366/000370210792434350
Goryawala, M.; Sullivan, M.; Maudsley, A. A. Effects of apodization smoothing and denoising on spectral fitting. J. Magn. Reson. Imaging 2020, 70, 108- 114, 10.1016/j.mri.2020.04.013
Hammersley, A. P.; Riekel, C. MFIT: Multiple spectra fitting program. Synchrotron Radiat. News 1989, 2 ( 1), 24- 26, 10.1080/08940888908261193
Sadat, A.; Joye, I. J. Peak fitting applied to Fourier transform infrared and Raman spectroscopic analysis of proteins. Appl. Sci. 2020, 10, 5918 10.3390/app10175918
Courbin, F.; Magain, P.; Kirkove, M.; Sohy, S. A method for spatial deconvolution of spectra. Astrophys. J. 2000, 529 ( 2), 1136, 10.1086/308291
Nagata, K.; Sugita, S.; Okada, M. Bayesian spectral deconvolution with the exchange Monte Carlo method. Neural Networks 2012, 28, 82- 89, 10.1016/j.neunet.2011.12.001
Carley, A. F.; Joyner, R. W. The application of deconvolution methods in electron spectroscopy - a review. J. Electron Spectrosc. Relat. Phenom. 1979, 16 ( 1), 1- 23, 10.1016/0368-2048(79)85001-X
Cade-Menun, B. J. Improved peak identification in 31P-NMR spectra of environmental samples with a standardized method and peak library. Geoderma 2015, 257-258, 102- 114, 10.1016/j.geoderma.2014.12.016
Blok, H. P.; de Lange, J. C.; Schotman, J. W. A new peak search method for an automatic spectrum analysis program. Nucl. Instrum. Methods 1975, 128 ( 3), 545- 556, 10.1016/0029-554X(75)90523-6
Huang, Y.-C.; Tremouilhac, P.; Nguyen, A.; Jung, N.; Bräse, S. ChemSpectra: a web-based spectra editor for analytical data. J. Cheminform. 2021, 13, 8 10.1186/s13321-020-00481-0
Vernier spectral analysis software. https://www.vernier.com/product/spectral-analysis/ (accessed June 3, 2024).
https://cheminformer.blogs.rutgers.edu/2014/02/free-software-for-spectra-analysis/ (accessed June 3, 2024).
Arumugam, R. A. Developing web-enabled spectral library architecture using free and open source software (FOSS), Ph.D. thesis, ISRO, 2014.
Brucker topspin. https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html (accessed Jan 23, 2024).
Altuntas, C.; Tunalioglu, N. GIRAS: an open-source matlab-based software for GNSS-IR analysis. GPS Solut. 2022, 26, 16 10.1007/s10291-021-01201-3
Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367- 1372, 10.1038/nbt.1511
Ho, T.-J.; Kuo, C.-H.; Wang, S.-Y.; Chen, G.-Y.; Tseng, Y. J. True ion pick (tipick): a denoising and peak picking algorithm to extract ion signals from liquid chromatography/mass spectrometry data. J. Mass Spectrom. 2013, 48 ( 2), 234- 242, 10.1002/jms.3154
Renard, B. Y.; Kirchner, M.; Steen, H.; AJ Steen, J.; Hamprecht, F. A. Nitpick: peak identification for mass spectrometry data. BMC Bioinf. 2008, 9, 355 10.1186/1471-2105-9-355
Akansu, A. N.; Sedijn, W. A.; Selesnick, I. W. Emerging applications of wavelets: A review. Phys. Commun. 2010, 3, 1- 18, 10.1016/j.phycom.2009.07.001
Shirley, D. A. High-resolution X-ray photoemission spectrum of the valence bands of gold. Phys. Rev. B 1972, 5, 4709 10.1103/PhysRevB.5.4709
Végh, J. The Shirley background revised. J. Electron Spectrosc. Relat. Phenom. 2006, 151, 159- 164, 10.1016/j.elspec.2005.12.002
Gerth, D. A new interpretation of (Tikhonov) regularization. Inverse Probl. 2021, 37, 064002 10.1088/1361-6420/abfb4d
Coombes, K. R.; Tsavachidis, S.; Morris, J. S.; Baggerly, K. A.; Hung, M.-C.; Kuerer, H. M. 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 2005, 5 ( 16), 4107- 4117, 10.1002/pmic.200401261
Mantini, D.; Petrucci, F.; Pieragostino, D.; Del Boccio, P.; Di Nicola, M.; Di Ilio, C.; Federici, G.; Sacchetta, P.; Comani, S.; Urbani, A. LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise. BMC Bioinform. 2007, 8, 101 10.1186/1471-2105-8-101
Liu, Y.; Zhou, X.; Yu, Y. A concise iterative method using the bezier technique for baseline construction. Analyst 2015, 140, 7984- 7996, 10.1039/C5AN01184A
Baek, S.-J.; Park, A.; Ahn, Y.-J.; Choo, J. Baseline correction using asymmetrically reweighted penalized least squares smoothing. Analyst 2015, 140, 250- 257, 10.1039/C4AN01061B
MacKenzie, D. The history of statistics: the measurement of uncertainty before 1900 by Stephen M. Stigler. Technol. Cult. 1988, 29 ( 2), 299- 300, 10.1353/tech.1988.0165
Legendre, A. M. Nouvelles méthodes pour la détermination des orbites des comètes: avec un supplément contenant divers perfectionnemens de ces méthodes et leur application aux deux comètes de 1805; Courcier, 1806.
Stigler, S. M. Gauss and the invention of least squares. Ann. Stat. 1981, 9, 465- 474, 10.1214/aos/1176345451
Pyqtgraph. https://www.pyqtgraph.org/.
Lafuente, B.; Downs, R. T.; Yang, H.; Stone, N. The Power of Databases: the RRUFF Project; W. De Gruyter, 2015.
Hummer, D. R.; Noll, B. C.; Hazen, R. M.; Downs, R. T. Crystal structure of abelsonite, the only known crystalline geoporphyrin. Am. Mineral. 2017, 102 ( 5), 1129- 1132, 10.2138/am-2017-5927
John Wiley & Sons Inc. Wiley SpectraBase. http://spectrabase.com (accessed May 28, 2024).
Jamur, J. M. S. Raman spectroscopy analysis for monitoring of chemical composition of aspirin after exposure to plasma flame. Spectrosc. Eur. 2022, 34 ( 5), 18- 22
Hadjiivanov, K. I.; Klissurski, D. G. Surface chemistry of titania (anatase) and titania-supported catalysts. Chem. Soc. Rev. 1996, 25 ( 1), 61- 69, 10.1039/cs9962500061
Srinivasu, P.; Singh, S. P.; Islam, A.; Han, L. Novel approach for the synthesis of nanocrystalline anatase titania and their photovoltaic application. Adv. OptoElectron. 2011, 2011 ( 1), 539382 10.1155/2011/539382
Horn, M. S. C. F.; Schwebdtfeger, C. F.; Meagher, E. P. Refinement of the structure of anatase at several temperatures. Z. Kristallogr. Cryst. Mater. 1972, 136 ( 1-6), 273- 281, 10.1524/zkri.1972.136.16.273
Mizuno, T.; Sugahara, K.; Tsutsumi, C.; Iino, M.; Koi, S.; Noda, N.; Iwashina, T. Identification of anthocyanin and other flavonoids from the green-blue petals of puya alpestris (bromeliaceae) and a clarification of their coloration mechanism. Phytochemistry 2021, 181, 112581 10.1016/j.phytochem.2020.112581
Blum, V.; Gehrke, R.; Hanke, F.; Havu, P.; Havu, V.; Ren, X.; Reuter, K.; Scheffler, M. Ab initio molecular simulations with numeric atom-centered orbitals. Comput. Phys. Commun. 2009, 180 ( 11), 2175- 2196, 10.1016/j.cpc.2009.06.022
Knuth, F.; Carbogno, C.; Atalla, V.; Blum, V.; Scheffler, M. All-electron formalism for total energy strain derivatives and stress tensor components for numeric atom-centered orbitals. Comput. Phys. Commun. 2015, 190, 33- 50, 10.1016/j.cpc.2015.01.003
Yu, V. W.-z.; Corsetti, F.; García, A.; Huhn, W. P.; Jacquelin, M.; Jia, W.; Lange, B.; Lin, L.; Lu, J.; Mi, W.; Seifitokaldani, A.; Vázquez-Mayagoitia, Á.; Yang, C.; Yang, H.; Blum, V. ELSI: A unified software interface for Kohn-Sham electronic structure solvers. Comput. Phys. Commun. 2018, 222, 267- 285, 10.1016/j.cpc.2017.09.007
Havu, V.; Blum, V.; Havu, P.; Scheffler, M. Efficient O(N) integration for all-electron electronic structure calculation using numeric basis functions. J. Comput. Phys. 2009, 228 ( 22), 8367- 8379, 10.1016/j.jcp.2009.08.008
Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865- 3868, 10.1103/PhysRevLett.77.3865
Hermann, J.; Tkatchenko, A. Density functional model for van der waals interactions: unifying many-body atomic approaches with nonlocal functionals. Phys. Rev. Lett. 2020, 124, 146401, 10.1103/PhysRevLett.124.146401
Monkhorst, H. J.; Pack, J. D. Special points for brillouin-zone integrations. Phys. Rev. B 1976, 13 ( 12), 5188, 10.1103/PhysRevB.13.5188
Python package index - pypi.
Varrette, S.; Bouvry, P.; Cartiaux, H.; Georgatos, F. Management of an academic hpc cluster: The UL experience. In 2014 International Conference on High Performance Computing & Simulation (HPCS); IEEE, 2014; pp 959- 967.
