, i The corrections made in this section will be reviewed and approved by journal production editor

M. S. Pedersen, D. Wang, J. Larsen, and U. Kjems, Two-microphone separation of speech mixtures, IEEE Trans. Neural Netw, vol.19, pp.475-492, 2008.

A. Hyvärinen and E. Oja, Independent component analysis: algorithms and applications, Neural Netw, vol.13, pp.411-430, 2000.

A. Hyvärinen, Independent component analysis: recent advances, Philos. Trans A Math. Phys. Eng. Sci, vol.371, 2012.

B. R. , Multivariate analysis in metabolomics, Curr. Metabolomics, vol.1, pp.92-107, 2013.

H. Abdi and L. J. Williams, Partial least squares methods: partial least squares correlation and partial least square regression, Methods Mol. Biol, vol.930, pp.549-579, 2013.

C. J. Gaskin and B. Happell, Int. J. Nurs. Stud, vol.51, pp.511-521, 2014.

G. Wang, Q. Ding, and Z. Hou, Independent component analysis and its applications in signal processing for analytical chemistry, Trends Anal. Chem, vol.27, pp.368-376, 2008.

D. N. Rutledge and D. Jouan-rimbaud-bouveresse, Trends Anal. Chem, pp.22-32, 2013.

C. Jutten and J. Herault, Blind source separation of sources, part I: an adaptive algorithm based on neuromimetic architecture, Signal Process, vol.24, pp.1-10, 1991.

P. Comon, Independent component analysis, Signal Process, vol.36, pp.287-314, 1994.
URL : https://hal.archives-ouvertes.fr/hal-00346684

J. R. Wessel, Testing multiple psychological processes for common neural mechanisms using EEG and independent component analysis, Brain Topogr, vol.31, pp.90-100, 2018.

F. Artoni, A. Delorme, and S. Makeig, A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of electroencephalographic preprocessing pipelines, Data Brief, vol.22, pp.787-793, 2018.

W. , C. R. Vanderburg, H. Gunshin, and J. T. , A review of independent component analysis application to microarray gene expression data, Biotechniques, vol.45, pp.501-520, 2008.

Y. B. Monakhova and S. P. Mushtakova, Multicomponent quantitative spectroscopic analysis without reference substances based on ICA modelling, Anal. Bioanal. Chem, vol.409, pp.3319-3327, 2017.

F. , J. C. Da-costa-pereira, J. , and H. D. Burrows, Direct estimation of dissolved organic carbon using synchronous fluorescence and independent component analysis (ICA): advantages of a multivariate calibration, Environ. Monit. Assess, vol.187, p.703, 2015.

L. and S. Ren, Integrating independent component analysis with artificial neural network to analyze overlapping fluorescence spectra of organic pollutants, J. Fluoresc, vol.22, pp.1595-1602, 2012.

X. Yu, Y. Zhang, G. Yin, N. Zhao, X. Xiao et al., Discrimination of three dimensional fluorescence spectra based on wavelet analysis and independent component analysis, Spectrochim. Acta A Mol. Biomol. Spectrosc, vol.124, pp.52-58, 2014.

F. Ammari, R. Bendoula, D. Jouan-rimbaud-bouveresse, D. N. Rutledge, and J. M. Roger, 3D front face solid-phase fluorescence spectroscopy combined with Independent Components Analysis to characterize organic matter in model soils, Talanta, vol.125, pp.146-152, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01173837

M. Loudiyi, R. Karoui, D. N. Rutledge, M. C. Montel, E. Rifa et al., Fluorescence spectroscopy coupled with independent components analysis to monitor molecular changes during heating and cooling of Cantal-type cheeses with different NaCl and KCl contents, J. Sci. Food Agric, vol.98, pp.963-975, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01682204

F. Ammari, L. Redjdal, and D. N. Rutledge, Detection of orange juice frauds using front-face fluorescence spectroscopy and Independent Components Analysis, Food Chem, vol.168, pp.211-217, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01173943

R. Garcia, A. Boussard, L. Rakotozafy, J. Nicolas, J. Potus et al., 3D-front-face fluorescence spectroscopy and independent components analysis: a new way to monitor bread dough development, Talanta, vol.147, pp.307-314, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01250500

F. Ammari, C. B. Cordella, N. Boughanmi, and D. N. Rutledge, Independent components analysis applied to 3D-front-face fluorescence spectra of edible oils to study the antioxidant effect of Nigella sativa L. extract on the thermal stability of heated oils, Chemometr. Intell. Lab. Syst, vol.113, pp.32-42, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01004206

F. Ammari, D. Jouan-rimbaud-bouveresse, N. Boughanmi, and D. N. Rutledge, Study of the heat stability of sunflower oil enriched in natural antioxidants by different analytical techniques and front-face fluorescence spectroscopy combined with Independent Components Analysis, Talanta, vol.99, pp.323-329, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01004528

A. Kassouf, M. E. Rakwe, H. Chebib, V. Ducruet, D. N. Rutledge et al., Independent components analysis coupled with 3D-front-face fluorescence spectroscopy to study the interaction between plastic food packaging and olive oil, Anal. Chim. Acta, vol.839, pp.14-25, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01186901

R. Saad, D. J. Bouveresse, N. Locquet, and D. N. Rutledge, Using pH variations to improve the discrimination of wines by 3D front face fluorescence spectroscopy associated to Independent Components Analysis, Talanta, vol.153, pp.278-284, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01349812

H. Pu, G. Zhang, W. He, F. Liu, H. Guang et al., Resolving fluorophores by unmixing multispectral fluorescence tomography with independent component analysis, Phys. Med. Biol, vol.59, pp.5025-5042, 2014.

A. Rohart, D. Jouan-rimbaud-bouveresse, D. N. Rutledge, and C. Michon, Spectrophotometric analysis of polysaccharide/milk protein interactions with methylene blue using Independent Components Analysis, Food Hydrocolloids, vol.43, pp.769-776, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01269247

Z. Han, J. Wan, L. Deng, and K. Liu, Oil Adulteration identification by hyperspectral imaging using QHM and ICA, PLoS One, vol.11, p.146547, 2016.

I. Toumi, S. Caldarelli, and B. Torrésani, A review of blind source separation in NMR spectroscopy, Prog. Nucl. Magn. Reson. Spectrosc, vol.81, pp.37-64, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01060561

R. Kalyanam, D. Boutte, K. Hutchison, and V. Calhoun, Application of ICA to realistically simulated 1 H-MRS data, Brain Behav, vol.5, p.345, 2015.

R. Kalyanam, D. Boutte, C. Gasparovic, K. E. Hutchison, and V. D. Calhoun, Group independent component analysis of MR spectra, Brain Behav, vol.3, pp.229-242, 2013.

M. Spiteri, E. Jamin, F. Thomas, A. Rebours, M. Lees et al., Fast and global authenticity screening of honey using 1 H-NMR profiling, Food Chem, vol.189, pp.60-66, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01251255

Y. B. Monakhova, A. M. Tsikin, T. Kuballa, D. W. Lachenmeier, and S. P. Mushtakova, Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products, Magn. Reson. Chem, vol.52, pp.231-240, 2014.

Y. B. Monakhova, D. W. Lachenmeier, T. Kuballa, and S. P. Mushtakova, Standardless multicomponent qNMR analysis of compounds with overlapped resonances based on the combination of ICA and PULCON, Magn. Reson. Chem, vol.53, pp.821-828, 2015.

Y. B. Monakhova, D. N. Rutledge, A. Roßmann, H. Waiblinger, M. Mahler et al., Determination of rice type by 1 H NMR spectroscopy in combination with different chemometric tools, J. Chemom, vol.28, pp.83-92, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01251884

A. Kassouf, J. Maalouly, D. N. Rutledge, H. Chebib, and V. Ducruet, Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA), Waste Manag, vol.34, pp.2131-2138, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01173836

A. Kassouf, A. Ruellan, D. Jouan-rimbaud-bouveresse, D. N. Rutledge, S. Domenek et al., Attenuated total reflectance-mid infrared spectroscopy (ATR-MIR) coupled with independent components analysis (ICA): a fast method to determine plasticizers in polylactide (PLA), Talanta, vol.147, pp.569-580, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01219018

M. Mecozzi, M. Pietroletti, M. Scarpiniti, R. Acquistucci, and M. E. Conti, Monitoring of marine mucilage formation in Italian seas investigated by infrared spectroscopy and independent component analysis, Environ. Monit. Assess, vol.184, pp.6025-6036, 2012.

Y. B. Monakhova, A. M. Tsikin, S. P. Mushtakova, and M. Mecozzi, Independent component analysis and multivariate curve resolution to improve spectral interpretation of complex spectroscopic data sets: application to infrared spectra of marine organic matter aggregates, Microchem. J, vol.118, pp.211-222, 2015.

Y. Chuang, I. Yang, Y. M. Lo, C. Tsai, and S. Chen, Integration of independent component analysis with near-infrared spectroscopy for analysis of bioactive components in the medicinal plant Gentiana scabra Bunge, J. Food Drug Anal, vol.22, pp.336-344, 2014.

P. Mishra, C. Cordella, D. N. Rutledge, P. Barreiro, J. Roger et al., Application of Independent Components Analysis with the JADE algorithm and NIR hyperspectral imaging for revealing food adulteration, J. Food Eng, vol.168, pp.7-15, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01250504

Z. Yuan, Spatiotemporal and time-frequency analysis of functional near infrared spectroscopy brain signals using independent component analysis, J. Biomed. Opt, vol.18, p.106011, 2013.

I. Schelkanova and V. Toronov, Independent component analysis of broadband near-infrared spectroscopy data acquired on adult human head, Biomed. Opt. Express, vol.3, pp.64-74, 2012.

J. J. González-vidal, R. Pérez-pueyo, M. J. Soneira, and S. Ruiz-moreno, Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures, Appl. Spectrosc, vol.69, pp.314-322, 2015.

M. R. Almeida, L. P. Logrado, J. J. Zacca, D. N. Correa, and R. J. Poppi, Raman hyperspectral imaging in conjunction with independent component analysis as a forensic tool for explosive analysis: the case of an ATM explosion, Talanta, vol.174, pp.628-632, 2017.

C. A. Teixeira and R. J. Poppi, Discriminating blue ballpoint pens inks in questioned documents by Raman imaging and mean-field approach independent component analysis (MF-ICA), Microchem. J, vol.144, pp.411-418, 2019.

P. Meksiarun, M. Ishigaki, V. A. Huck-pezzei, C. W. Huck, K. Wongravee et al., Comparison of multivariate analysis methods for extracting the paraffin component from the paraffin-embedded cancer tissue spectra for Raman imaging, Sci. Rep, vol.7, p.44890, 2017.

M. Boiret, D. N. Rutledge, N. Gorretta, Y. M. Ginot, and J. M. Roger, Application of independent component analysis on Raman images of a pharmaceutical drug product: pure spectra determination and spatial distribution of constituents, J. Pharm. Biomed. Anal, vol.90, pp.78-84, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00948530

H. Mitsutake, S. R. Castro, E. De-paula, R. J. Poppi, D. N. Rutledge et al., Comparison of different chemometric methods to extract chemical and physical information from Raman images of homogeneous and heterogeneous semi-solid pharmaceutical formulations, Int. J. Pharm, vol.552, pp.119-129, 2018.

W. Yu, W. Cai, and X. Shao, Chemometric approach for fast analysis of prometryn in human hair by GC-MS, J. Sep. Sci, vol.36, pp.2277-2282, 2013.

M. Zarghani and H. Parastar, Joint approximate diagonalization of eigenmatrices as a highthroughput approach for analysis of hyphenated and comprehensive two-dimensional gas chromatographic data, J. Chromatogr., A, vol.1524, pp.188-201, 2017.

B. Debrus, P. Lebrun, J. M. Kindenge, F. Lecomte, A. Ceccato et al., Innovative high-performance liquid chromatography method development for the screening of 19 antimalarial drugs based on a generic approach, using design of experiments, independent component analysis and design space, J. Chromatogr., A, vol.1218, pp.5205-5215, 2011.

X. Domingo-almenara, A. Perera, N. Ramírez, N. Cañellas, X. Correig et al., Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation, J. Chromatogr., A, vol.1409, pp.226-233, 2015.

Y. Liu, K. Smirnov, M. Lucio, R. D. Gougeon, H. Alexandre et al., MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics, BMC Bioinf, vol.17, p.114, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01430376

Y. Izadmanesh, E. Garreta-lara, J. B. Ghasemi, S. Lacorte, V. Matamoros et al., Chemometric analysis of comprehensive two dimensional gas chromatography-mass spectrometry metabolomics data, J. Chromatogr., A, vol.1488, pp.113-125, 2017.

M. Navarro-reig, J. Jaumot, A. Baglai, G. Vivó-truyols, P. J. Schoenmakers et al., Untargeted comprehensive two-dimensional liquid chromatography coupled with high-resolution mass spectrometry analysis of rice metabolome using multivariate curve resolution, Anal. Chem, vol.89, pp.7675-7683, 2017.

O. Monago-maraña, R. L. Pérez, G. M. Escandar, A. Muñoz-de-la-peña, and T. Galeano-díaz, Combination of liquid chromatography with multivariate curve resolution-alternating leastsquares (MCR-ALS) in the quantitation of polycyclic aromatic hydrocarbons present in paprika samples, J. Agric. Food Chem, vol.64, pp.8254-8262, 2016.

F. Ciepiela and M. Jakubowska, Faradaic and capacitive current estimation by means of Independent Components Analysis and 1kHz sampling, Talanta, vol.170, pp.158-164, 2017.

T. Aguilera, J. Lozano, J. A. Paredes, F. J. Alvarez, and J. I. Suárez, Electronic nose based on independent component analysis combined with partial least squares and artificial neural networks for wine prediction, Sensors, vol.12, pp.8055-8072, 2012.

D. Bouveresse, H. Benabid, and D. N. Rutledge, Independent component analysis as a pretreatment method for parallel factor analysis to eliminate artefacts from multiway data, Anal. Chim. Acta, vol.589, pp.216-224, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01273702

H. Santosa, M. J. Hong, S. P. Kim, and K. S. Hong, Noise reduction in functional near-infrared spectroscopy signals by independent component analysis, Rev. Sci. Instrum, vol.84, p.73106, 2013.

U. Kairov, L. Cantini, A. Greco, A. Molkenov, U. Czerwinska et al., Determining the optimal number of independent components for reproducible transcriptomic data analysis, BMC Genomics, vol.18, issue.1, p.712, 2017.
URL : https://hal.archives-ouvertes.fr/inserm-02064099

J. C. Pereira, J. C. Azevedo, H. G. Knapik, and H. D. Burrows, Unsupervised component analysis: PCA, POA and ICA data exploring -connecting the dots, Spectrochim. Acta A Mol. Biomol. Spectrosc, vol.165, pp.69-84, 2016.

A. Kassouf, D. Jouan-rimbaud-bouveresse, and D. N. Rutledge, Determination of the optimal number of components in independent components analysis, Talanta, vol.179, pp.538-545, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01757714

D. Bouveresse, A. Moya-gonzález, F. Ammari, and D. N. Rutledge, Two novel methods for the determination of the number of components in independent components analysis models, Chemometr. Intell. Lab. Syst, vol.112, pp.24-32, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01004543

C. Hsu, M. Chen, and L. Chen, Integrating independent component analysis and support vector machine for multivariate process monitoring, Comput. Ind. Eng, vol.59, pp.145-156, 2010.

L. Wang, D. Yang, C. Fang, Z. Chen, P. J. Lesniewski et al., Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array, Talanta, vol.131, pp.395-403, 2015.

J. Puuronen and A. Hyvärinen, A Bayesian inverse solution using independent component analysis, Neural Netw, vol.50, pp.47-59, 2014.

Y. B. Monakhova, R. Godelmann, T. Kuballa, S. P. Mushtakova, and D. N. Rutledge, Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): application to NMR fingerprinting of wine, Talanta, vol.141, pp.60-65, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01251886

H. Kaneko, M. Arakawa, and K. Funatsu, Development of a new regression analysis method using independent component analysis, J. Chem. Inf. Model, vol.48, pp.534-541, 2008.

M. G. Gustafsson, Independent component analysis yields chemically interpretable latent variables in multivariate regression, J. Chem. Inf. Model, vol.45, pp.1244-1255, 2005.

L. Gao and S. Ren, Integrating independent component analysis with artificial neural network to analyze overlapping fluorescence spectra of organic pollutants, J. Fluoresc, vol.22, pp.1595-15602, 2012.

R. P. Woods, L. K. Hansen, and S. Strother, How many separable sources? Model selection in independent components analysis, PLoS One, vol.10, p.118877, 2015.

H. Han and X. L. Li, Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery, BMC Bioinf, vol.12, p.7, 2011.

H. Saberkari, M. Shamsi, M. Joroughi, F. Golabi, and M. H. Sedaaghi, Cancer classification in microarray data using a hybrid selective independent component analysis and ?-support vector machine algorithm, J. Med. Signals Sens, vol.4, pp.91-298, 2014.

H. Parastar, M. Jalali-heravi, and R. Tauler, Is independent component analysis appropriate for multivariate resolution in analytical chemistry?, Trends Anal. Chem, vol.31, pp.134-143, 2012.

Y. B. Monakhova, S. A. Astakhov, A. Kraskov, and S. P. Mushtakova, Independent components in spectroscopic analysis of complex mixtures, Chemometr. Intell. Lab. Syst, vol.103, pp.108-115, 2010.

Y. Shi, W. Zeng, N. Wang, and L. Zhao, A new method for independent component analysis with priori information based on multi-objective optimization, J. Neurosci. Methods, vol.283, pp.72-82, 2017.

L. Liu, C. Li, Y. Lei, J. Yin, and J. Zhao, Feature extraction for hyperspectral remote sensing image using weighted PCA-ICA, Arab. J. Geosci, vol.10, p.307, 2017.