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1. Li, Q.; Wang, J., Mutation Endmember Library Sparse Mixed Abundance Estimation Model for Glioma Margin Determination with Raman Spectroscopy. Analytical Chemistry 2024, 96, 8273-8281.

2. Haessler, A.;  Candlish, M.;  Hefendehl, J. K.;  Jung, N.; Windbergs, M., Mapping cellular stress and lipid dysregulation in Alzheimer-related progressive neurodegeneration using label-free Raman microscopy. Communications Biology 2024, 7, 1514.

3. Piredda, P.;  Berning, M.;  Boukamp, P.; Volkmer, A., Subcellular Raman Microspectroscopy Imaging of Nucleic Acids and Tryptophan for Distinction of Normal Human Skin Cells and Tumorigenic Keratinocytes. Analytical Chemistry 2015, 87, 6778-6785.

4. Krafft, C.;  Popp, J.;  Bronsert, P.; Miernik, A., Raman Spectroscopic Imaging of Human Bladder Resectates towards Intraoperative Cancer Assessment. Cancers 2023, 15, 2162.

5. Krafft, C.;  Belay, B.;  Bergner, N.;  Romeike, B. F. M.;  Reichart, R.;  Kalff, R.; Popp, J., Advances in optical biopsy – correlation of malignancy and cell density of primary brain tumors using Raman microspectroscopic imaging. The Analyst 2012, 137, 5533.

6. Coic, L.;  Sacré, P.-Y.;  Dispas, A.;  De Bleye, C.;  Fillet, M.;  Ruckebusch, C.;  Hubert, P.; Ziemons, E., Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations. Analytica Chimica Acta 2021, 1155, 338361.

7. Boiret, M.;  Rutledge, D. N.;  Gorretta, N.;  Ginot, Y.-M.; Roger, J.-M., Application of independent component analysis on Raman images of a pharmaceutical drug product: Pure spectra determination and spatial distribution of constituents. Journal of Pharmaceutical and Biomedical Analysis 2014, 90, 78-84.

8. Li, Y.;  Lu, H.;  Zhang, Z.; Liu, P., A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea. International Journal of Remote Sensing 2020, 41, 4684-4701.

9. Li, F.;  Liu, D.;  Guo, X.;  Zhang, Z.;  Martin, F. L.;  Lu, A.; Xu, L., Identification and visualization of environmental microplastics by Raman imaging based on hyperspectral unmixing coupled machine learning. Journal of Hazardous Materials 2024, 465, 133336.

10. Jia, X.; Hou, D., Mapping soil arsenic pollution at a brownfield site using satellite hyperspectral imagery and machine learning. Science of The Total Environment 2023, 857, 159387.

11. Fernández-Manso, A.;  Quintano, C.;  Fernández-Guisuraga, J. M.; Roberts, D., Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces. Science of The Total Environment 2024, 940, 173568.

12. Du, R.;  Chen, J.;  Xiang, Y.;  Xiang, R.;  Yang, X.;  Wang, T.;  He, Y.;  Wu, Y.;  Yin, H.;  Zhang, Z.; Chen, Y., Timely monitoring of soil water-salt dynamics within cropland by hybrid spectral unmixing and machine learning models. International Soil and Water Conservation Research 2024, 12, 726-740.

13. Cui, C.;  Li, Y.;  Liu, B.; Li, G., A New Endmember Preprocessing Method for the Hyperspectral Unmixing of Imagery Containing Marine Oil Spills. ISPRS International Journal of Geo-Information 2017, 6, 286.

14. Annam, S.; Singla, A. In Spectral unmixing of heavy metal content in agricultural soil using hyperspectral remote sensing data, 2021 Sixth International Conference on Image Information Processing (ICIIP), Shimla, India, 26-28 November 2021; Shimla, India, 2022 of Conference; pp 433-438.

15. Bouaziz, M.;  Hihi, S.;  Chtourou, M. Y.; Osunmadewa, B., Soil Salinity Detection in Semi-Arid Region Using Spectral Unmixing, Remote Sensing and Ground Truth Measurements. Journal of Geographic Information System 2020, 12, 372-386.

16. Castro, R. C.;  Ribeiro, D. S. M.;  Santos, J. L. M.; Páscoa, R. N. M. J., Comparison of near infrared spectroscopy and Raman spectroscopy for the identification and quantification through MCR-ALS and PLS of peanut oil adulterants. Talanta 2021, 230, 122373.

17. Chi, J.; Crawford, M. M., Spectral Unmixing-Based Crop Residue Estimation Using Hyperspectral Remote Sensing Data: A Case Study at Purdue University. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 7, 2531-2539.

18. Smith, J. P.;  Smith, F. C.; Booksh, K. S., Spatial and spectral resolution of carbonaceous material from hematite (α-Fe2O3) using multivariate curve resolution-alternating least squares (MCR-ALS) with Raman microspectroscopic mapping: implications for the search for life on Mars. The Analyst 2017, 142, 3140-3156.

19. Smith, J. P.;  Holahan, E. C.;  Smith, F. C.;  Marrero, V.; Booksh, K. S., A novel multivariate curve resolution-alternating least squares (MCR-ALS) methodology for application in hyperspectral Raman imaging analysis. The Analyst 2019, 144, 5425-5438.

20. Schipporeit, S.; Mergel, D., Spectral decomposition of Raman spectra of mixed‐phase TiO2 thin films on Si and silicate substrates. Journal of Raman Spectroscopy 2018, 49, 1217-1229.

21. Kunc, J.;  Hu, Y.;  Palmer, J.;  Berger, C.; de Heer, W. A., A method to extract pure Raman spectrum of epitaxial graphene on SiC. Applied Physics Letters 2013, 103, 201911.

22. Pattathal V, A.;  Sahoo, M. M.;  Porwal, A.; Karnieli, A., Deep-learning-based latent space encoding for spectral unmixing of geological materials. ISPRS Journal of Photogrammetry and Remote Sensing 2022, 183, 307-320.

23. Bioucas-Dias, J. M.;  Plaza, A.;  Dobigeon, N.;  Parente, M.;  Du, Q.;  Gader, P.; Chanussot, J., Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2012, 5, 354-379.

24. Heylen, R.;  Parente, M.; Gader, P., A Review of Nonlinear Hyperspectral Unmixing Methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 7, 1844-1868.

25. Keshava, N.; Mustard, J. F., Spectral unmixing. IEEE Signal Processing Magazine 2002, 19, 44-57.

26. Hong, D.;  Yokoya, N.;  Chanussot, J.; Zhu, X. X., An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing. IEEE Transactions on Image Processing 2019, 28, 1923-1938.

27. Jin, Q.;  Ma, Y.;  Fan, F.;  Huang, J.;  Mei, X.; Ma, J., Adversarial Autoencoder Network for Hyperspectral Unmixing. IEEE Transactions on Neural Networks and Learning Systems 2023, 34, 4555-4569.

28. Cui, C.;  Wang, X.;  Wang, S.;  Zhang, L.; Zhong, Y., Unrolling Nonnegative Matrix Factorization With Group Sparsity for Blind Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2023, 61, 1-12.

29. Gao, Y.;  Pan, B.;  Xu, X.;  Song, X.; Shi, Z., A Reversible Generative Network for Hyperspectral Unmixing With Spectral Variability. IEEE Transactions on Geoscience and Remote Sensing 2024, 62, 1-15.

30. Zhang, G.;  Mei, S.;  Wang, Y.;  Han, H.;  Feng, Y.; Du, Q., Spectral Variability-Aware Cascaded Autoencoder for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2025, 63, 1-12.

31. Qu, K.;  Luo, F.;  Wang, H.; Bao, W., A New Fast Sparse Unmixing Algorithm Based on Adaptive Spectral Library Pruning and Nesterov Optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025, 18, 6134-6151.

32. Wold, S.;  Sjöström, M.; Eriksson, L., PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 2001, 58, 109-130.

33. Altmann, Y.;  Pereyra, M.; McLaughlin, S., Bayesian Nonlinear Hyperspectral Unmixing With Spatial Residual Component Analysis. IEEE Transactions on Computational Imaging 2015, 1, 174-185.

34. Ammanouil, R.;  Ferrari, A.;  Richard, C.; Mathieu, S., Nonlinear Unmixing of Hyperspectral Data With Vector-Valued Kernel Functions. IEEE Transactions on Image Processing 2017, 26, 340-354.

35. Fang, T.;  Zhu, F.; Chen, J., Hyperspectral Unmixing Based on Multilinear Mixing Model Using Convolutional Autoencoders. IEEE Transactions on Geoscience and Remote Sensing 2024, 62, 1-16.

36. Rasti, B.;  Koirala, B.; Scheunders, P., HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing 2022, 60, 1-15.

37. Rasti, B.; Koirala, B., SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network. IEEE Geoscience and Remote Sensing Letters 2022, 19, 1-5.

38. Zhang, X.;  Sun, Y.;  Zhang, J.;  Wu, P.; Jiao, L., Hyperspectral Unmixing via Deep Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters 2018, 15, 1755-1759.

39. Wan, L.;  Chen, T.;  Plaza, A.; Cai, H., Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 11669-11682.

40. Zhao, Z.;  Liu, Z.;  Ji, M.;  Zhao, X.;  Zhu, Q.; Huang, M., ConInceDeep: A novel deep learning method for component identification of mixture based on Raman spectroscopy. Chemometrics and Intelligent Laboratory Systems 2023, 234, 104757.

41. Zhao, M.;  Wang, M.;  Chen, J.; Rahardja, S., Hyperspectral Unmixing for Additive Nonlinear Models With a 3-D-CNN Autoencoder Network. IEEE Transactions on Geoscience and Remote Sensing 2022, 60, 1-15.

42. Su, Y.;  Zhu, Z.;  Gao, L.;  Plaza, A.;  Li, P.;  Sun, X.; Xu, X., DAAN: A Deep Autoencoder-Based Augmented Network for Blind Multilinear Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2024, 62, 1-15.

43. Su, Y.;  Xu, X.;  Li, J.;  Qi, H.;  Gamba, P.; Plaza, A., Deep Autoencoders With Multitask Learning for Bilinear Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2021, 59, 8615-8629.

44. Shao, Y.;  Liu, Q.; Xiao, L., IVIU-Net: Implicit Variable Iterative Unrolling Network for Hyperspectral Sparse Unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2023, 16, 1756-1770.

45. Li, X.;  Cui, J.; Zhao, L., Blind nonlinear hyperspectral unmixing based on constrained kernel nonnegative matrix factorization. Signal, Image and Video Processing 2012, 8, 1555-1567.

46. Gu, J.;  Yang, B.; Wang, B., Nonlinear Unmixing for Hyperspectral Images via Kernel-Transformed Bilinear Mixing Models. IEEE Transactions on Geoscience and Remote Sensing 2022, 60, 1-13.

47. Borsoi, R. A.;  Imbiriba, T.;  Bermudez, J. C. M.; Richard, C., A Blind Multiscale Spatial Regularization Framework for Kernel-Based Spectral Unmixing. IEEE Transactions on Image Processing 2020, 29, 4965-4979.

48. Koirala, B.;  Zahiri, Z.;  Lamberti, A.; Scheunders, P., Robust Supervised Method for Nonlinear Spectral Unmixing Accounting for Endmember Variability. IEEE Transactions on Geoscience and Remote Sensing 2021, 59, 7434-7448.

49. Fang, Y.;  Wang, Y.;  Xu, L.;  Zhuo, R.;  Wong, A.; Clausi, D. A., BCUN: Bayesian Fully Convolutional Neural Network for Hyperspectral Spectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2022, 60, 1-14.

50. Hong, D.;  Gao, L.;  Yao, J.;  Yokoya, N.;  Chanussot, J.;  Heiden, U.; Zhang, B., Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing. IEEE Transactions on Neural Networks and Learning Systems 2022, 33, 6518-6531.

51. Ceamanos, X.;  Doute, S.;  Luo, B.;  Schmidt, F.;  Jouannic, G.; Chanussot, J., Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study. IEEE Transactions on Geoscience and Remote Sensing 2011, 49, 4341-4358.

52. Heiden, U.;  Heldens, W.;  Roessner, S.;  Segl, K.;  Esch, T.; Mueller, A., Urban structure type characterization using hyperspectral remote sensing and height information. Landscape and Urban Planning 2012, 105, 361-375.

53. Karoui, M. S.;  Benhalouche, F. z.;  Deville, Y.;  Djerriri, K.;  Briottet, X.; Bris, A. L. In Detection And Area Estimation For Photovoltaic Panels In Urban Hyperspectral Remote Sensing Data By An Original Nmf-Based Unmixing Method, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22-27 July 2018; Valencia, Spain, 2018 of Conference; pp 1640-1643.

54. Liu, J.;  Luo, B.;  Douté, S.; Chanussot, J., Exploration of Planetary Hyperspectral Images with Unsupervised Spectral Unmixing: A Case Study of Planet Mars. Remote Sensing 2018, 10, 737.

55. Marinoni, A.; Gamba, P., Accurate Detection of Anthropogenic Settlements in Hyperspectral Images by Higher Order Nonlinear Unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016, 9, 1792-1801.

56. Masudul Islam, S.;  Kumar, V.;  Kumar, S.; Agrawal, S., Spectral mixture analysis of AVIRIS-NG hyperspectral data for material identification and classification for the part of Kolkata city. Advances in Space Research 2024, 73, 1560-1572.

57. Santos, D.;  Cardoso-Fernandes, J.;  Campos de Lima, A. M.;  Teodoro, A. C. M.;  Schulz, K.;  Nikolakopoulos, K. G.; Michel, U. In The potential of spectral unmixing method applied to PRISMA hyperspectral images in the identification of Li minerals: an evaluation for prospecting purposes, Earth Resources and Environmental Remote Sensing/GIS Applications XIII, Berlin, Germany, 26 October 2022; Berlin, Germany, 2022 of Conference; p 11.

58. Yang, J.; He, Y., Automated mapping of impervious surfaces in urban and suburban areas: Linear spectral unmixing of high spatial resolution imagery. International Journal of Applied Earth Observation and Geoinformation 2017, 54, 53-64.

59. Bergner, N.;  Krafft, C.;  Geiger, K. D.;  Kirsch, M.;  Schackert, G.; Popp, J., Unsupervised unmixing of Raman microspectroscopic images for morphochemical analysis of non-dried brain tumor specimens. Analytical and Bioanalytical Chemistry 2012, 403, 719-725.

60. Hedegaard, M. A. B.;  Bergholt, M. S.; Stevens, M. M., Quantitative multi‐image analysis for biomedical Raman spectroscopic imaging. Journal of Biophotonics 2016, 9, 542-550.

61. Ilchenko, O.;  Pilgun, Y.;  Makhnii, T.;  Slipets, R.;  Reynt, A.;  Kutsyk, A.;  Slobodianiuk, D.;  Koliada, A.;  Krasnenkov, D.; Kukharskyy, V., High-speed line-focus Raman microscopy with spectral decomposition of mouse skin. Vibrational Spectroscopy 2016, 83, 180-190.

62. Yakimov, B. P.;  Venets, A. V.;  Schleusener, J.;  Fadeev, V. V.;  Lademann, J.;  Shirshin, E. A.; Darvin, M. E., Blind source separation of molecular components of the human skinin vivo: non-negative matrix factorization of Raman microspectroscopy data. The Analyst 2021, 146, 3185-3196.

63. Li, G.;  Yi, L.;  Zhang, S.;  Song, Q.;  Liang, M.;  Guan, Z.;  Zhang, Y.;  Zhuang, Y.;  Hou, B.;  Wang, X.;  Wu, X.;  Yin, P.;  Dmitriy, K.;  Yang, J.;  Hu, B.; Tian, J., Concentration Indicator Kit Based on Dandelion Propagation‐Inspired SERS Strategy. Advanced Materials 2025, 2502162, 1-7.

64. Cheng, H.;  Yi, L.;  Wu, J.;  Li, G.;  Zhao, G.;  Xiao, Z.;  Hu, B.;  Zhao, L.; Tian, J., Drug preconcentration and direct quantification in biofluids using 3D-Printed paper cartridge. Biosensors and Bioelectronics 2021, 189, 113266.

65. Li, G.;  Fan, J.;  Zhang, T.;  Gao, T.;  Chong, Y.;  Liang, M.;  Liang, S.;  Hu, B.;  Yi, L.;  Zhao, L.; Castel, H., Honeycomb-Inspired Surface-Enhanced Raman Scattering Microarray for Large-Area Automated Testing of Urease in Saliva Samples. ACS Sensors 2024, 9, 2031-2042.

66. Yang, J.;  Ye, Z.;  Xue, Q.;  Li, D.;  Liang, M.;  Li, G.;  Liu, H.;  Yi, L.;  Hu, B.;  Yin, P.;  Ge, G.;  Dmitriy, K.;  Maciuk, A.; Figadere, B., Dynamic Liquid Integrated Single-Cell SERS Platform Based on the Twisted Mixing Microfluidic Chip and Multi-Modified Nanoprobe for the Label-Free Detection of Cancer Cells. Analytical Chemistry 2025, 97, 7789-7798.

67. Abell, J. L.;  Lee, J.;  Zhao, Q.;  Szu, H.; Zhao, Y., Differentiating intrinsic SERS spectra from a mixture by sampling induced composition gradient and independent component analysis. Analyst 2012, 137, 73-6.

68. Abell, J. L.;  Lee, J.;  Zhao, Q.;  Szu, H.; Zhao, Y., Differentiating intrinsic SERS spectra from a mixture by sampling induced composition gradient and independent component analysis. The Analyst 2012, 137, 73-76.

69. Chen, J.;  Zhao, M.;  Wang, X.;  Richard, C.; Rahardja, S., Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods. IEEE Signal Processing Magazine 2023, 40, 61-74.

70. Lee, D. D.; Seung, H. S., Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401, 788-791.

71. Li, H.-C.;  Feng, X.-R.;  Zhai, D.-H.;  Du, Q.; Plaza, A., Self-Supervised Robust Deep Matrix Factorization for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2022, 60, 1-14.

72. Tian, M.;  Morais, C. L. M.;  Shen, H.;  Pang, W.;  Xu, L.;  Huang, Q.; Martin, F. L., Direct identification and visualisation of real-world contaminating microplastics using Raman spectral mapping with multivariate curve resolution-alternating least squares. Journal of Hazardous Materials 2022, 422, 126892.

73. Duarte, L. T.;  Moussaoui, S.; Jutten, C., Source Separation in Chemical Analysis : Recent achievements and perspectives. IEEE Signal Processing Magazine 2014, 31, 135-146.

74. Shi, S.;  Zhang, L.;  Altmann, Y.; Chen, J., Deep Generative Model for Spatial–Spectral Unmixing With Multiple Endmember Priors. IEEE Transactions on Geoscience and Remote Sensing 2022, 60, 1-14.

75. Matveeva, I.;  Khristoforova, Y. A.;  Bratchenko, L. A.;  Zakharov, V. P.;  Popp, J.; Gergely, C. In Analysis of Raman spectra using the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm, Biomedical Spectroscopy, Microscopy, and Imaging II, Strasbourg, France, 27 May 2022; Strasbourg, France, 2022 of Conference; p 12144.

76. Jin, Q.;  Ma, Y.;  Mei, X.; Ma, J., TANet: An Unsupervised Two-Stream Autoencoder Network for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2022, 60, 1-15.

77. Dong, X.;  Yetisen, A. K.;  Tian, H.;  Güler, İ.;  Stier, A. V.;  Li, Z.;  Köhler, M. H.;  Dong, J.;  Jakobi, M.;  Finley, J. J.; Koch, A. W., Line-Scan Hyperspectral Imaging Microscopy with Linear Unmixing for Automated Two-Dimensional Crystals Identification. ACS Photonics 2020, 7, 1216-1225.

78. Qi, L.;  Gao, F.;  Dong, J.;  Gao, X.; Du, Q., SSCU-Net: Spatial–Spectral Collaborative Unmixing Network for Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing 2022, 60, 1-15.

79. Tits, L.;  De Keersmaecker, W.;  Somers, B.;  Asner, G. P.;  Farifteh, J.; Coppin, P., Hyperspectral shape-based unmixing to improve intra- and interclass variability for forest and agro-ecosystem monitoring. ISPRS Journal of Photogrammetry and Remote Sensing 2012, 74, 163-174.

80. Lu, J.;  Li, W.;  Yu, M.;  Zhang, X.;  Ma, Y.;  Su, X.;  Yao, X.;  Cheng, T.;  Zhu, Y.;  Cao, W.; Tian, Y., Estimation of rice plant potassium accumulation based on non-negative matrix factorization using hyperspectral reflectance. Precision Agriculture 2020, 22, 51-74.

81. Yao, J.;  Su, H.; Yao, Z., Blind source separation of coexisting background in Raman spectra. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2020, 238, 118417.

82. Georgiev, D.;  Fernández-Galiana, Á.;  Vilms Pedersen, S.;  Papadopoulos, G.;  Xie, R.;  Stevens, M. M.; Barahona, M., Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders. Proceedings of the National Academy of Sciences 2024, 121, 45.

83. Piqueras, S.;  Bedia, C.;  Beleites, C.;  Krafft, C.;  Popp, J.;  Maeder, M.;  Tauler, R.; de Juan, A., Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets. Analytical Chemistry 2018, 90, 6757-6765.

84. Srilakshmi, C.;  Widjaja, E.;  Anderson, B.; Garland, M., Fourier transform Raman spectral measurements of powdered quaternary mixtures of organic compoundsExceptional pure component spectral reconstruction using band-target entropy minimization (BTEM). Talanta 2007, 72, 847-853.

85. Gao, Y.;  Dai, L.-K.;  Zhu, H.-D.;  Chen, Y.-L.; Zhou, L., Quantitative Analysis of Main Components of Natural Gas Based on Raman Spectroscopy. Chinese Journal of Analytical Chemistry 2019, 47, 67-76.

86. Han, X.;  Huang, Z.-x.;  Chen, X.-d.;  Li, Q.-f.;  Xu, K.-x.; Chen, D., On-line multi-component analysis of gases for mud logging industry using data driven Raman spectroscopy. Fuel 2017, 207, 146-153.

87. Rowlands, C. J.; Elliott, S. R., Improved blind‐source separation for spectra. Journal of Raman Spectroscopy 2011, 42, 1761-1768.

88. Winter, M. E.;  Shen, S. S.; Lewis, P. E. In A proof of the N-FINDR algorithm for the automated detection of endmembers in a hyperspectral image, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, Orlando, Florida, United States, 12-16 April 2004; Orlando, Florida, United States, 2004 of Conference; pp 31-41.

89. Chang, C. I.; Plaza, A., A fast iterative algorithm for implementation of pixel purity index. Ieee Geoscience and Remote Sensing Letters 2006, 3, 63-67.

90. González, C.;  Resano, J.;  Mozos, D.;  Plaza, A.; Valencia, D., FPGA Implementation of the Pixel Purity Index Algorithm for Remotely Sensed Hyperspectral Image Analysis. EURASIP Journal on Advances in Signal Processing 2010, 2010, 13.

91. Heylen, R.; Scheunders, P., Multidimensional Pixel Purity Index for Convex Hull Estimation and Endmember Extraction. Ieee Transactions on Geoscience and Remote Sensing 2013, 51, 4059-4069.

92. Luo, B.;  Yang, C.;  Chanussot, J.; Zhang, L., Crop Yield Estimation Based on Unsupervised Linear Unmixing of Multidate Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing 2013, 51, 162-173.

93. Ozkan, S.;  Kaya, B.; Akar, G. B., EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 482-496.

94. Fan, X.;  Ming, W.;  Zeng, H.;  Zhang, Z.; Lu, H., Deep learning-based component identification for the Raman spectra of mixtures. The Analyst 2019, 144, 1789-1798.

95. Li, M.;  Yang, B.; Wang, B., EMLM-Net: An Extended Multilinear Mixing Model-Inspired Dual-Stream Network for Unsupervised Nonlinear Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2024, 62, 1-16.

96. Luce, R.;  Hildebrandt, P.;  Kuhlmann, U.; Liesen, J., Using Separable Nonnegative Matrix Factorization Techniques for the Analysis of Time-Resolved Raman Spectra. Applied Spectroscopy 2016, 70, 1464-1475.

97. Luo, B.; Chanussot, J., Supervised Hyperspectral Image Classification Based on Spectral Unmixing and Geometrical Features. Journal of Signal Processing Systems 2010, 65, 457-468.

98. Pu, H. Y.;  Xia, W.;  Wang, B.; Jiang, G. M., A Fully Constrained Linear Spectral Unmixing Algorithm Based on Distance Geometry. Ieee Transactions on Geoscience and Remote Sensing 2014, 52, 1157-1176.

99. Yang, H. D.;  An, J. B.; Zhu, C., Subspace-Projection-Based Geometric Unmixing for Material Quantification in Hyperspectral Imagery. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 7, 1966-1975.

100. Wu, X. Y.;  Huang, B. M.;  Plaza, A.;  Li, Y. S.; Wu, C. K., Real-Time Implementation of the Pixel Purity Index Algorithm for Endmember Identification on GPUs. Ieee Geoscience and Remote Sensing Letters 2014, 11, 955-959.

101. Chang, C. I.;  Li, Y.; Wang, Y. L., Progressive Band Processing of Fast Iterative Pixel Purity Index for Finding Endmembers. Ieee Geoscience and Remote Sensing Letters 2017, 14, 1464-1468.

102. Zortea, M.; Plaza, A., A Quantitative and Comparative Analysis of Different Implementations of N-FINDR: A Fast Endmember Extraction Algorithm. Ieee Geoscience and Remote Sensing Letters 2009, 6, 787-791.

103. Shen, S. S.;  Xiong, W.;  Lewis, P. E.;  Tsai, C. T.;  Yang, C. W.; Chang, C.-I. In Convex cone-based endmember extraction for hyperspectral imagery, Imaging Spectrometry XV, San Diego, California, United States, 13 August 2010; San Diego, California, United States, 2010 of Conference; p 78120H

104. Ji, L. Y.;  Geng, X. R.;  Sun, K.;  Zhao, Y. C.; Gong, P., Modified N-FINDR endmember extraction algorithm for remote-sensing imagery. International Journal of Remote Sensing 2015, 36, 2148-2162.

105. Ayma Quirita, V. A.;  da Costa, G. A. O. P.; Beltrán, C., A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data. Remote Sensing 2022, 14, 2153.

106. Chang, C. I.;  Xiong, W.; Chen, S. Y., Convex cone volume analysis for finding endmembers in hyperspectral imagery. International Journal of Computational Science and Engineering 2016, 12, 209.

107. Sivakumar, V. L.;  Ramkumar, K.;  Vidhya, K.;  Gobinathan, B.; Gietahun, Y. W., A Comparative Analysis of Methods of Endmember Selection for Use in Subpixel Classification: A Convex Hull Approach. Comput Intell Neurosci 2022, 2022, 9.

108. Tsung-Han, C.;  Chong-Yung, C.;  Yu-Min, H.; Wing-Kin, M., A Convex Analysis-Based Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing. IEEE Transactions on Signal Processing 2009, 57, 4418-4432.

109. Ambikapathi, A.;  Chan, T. H.;  Ma, W. K.; Chi, C. Y., Chance-Constrained Robust Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing. Ieee Transactions on Geoscience and Remote Sensing 2011, 49, 4194-4209.

110. Chia-Hsiang, L.;  Wing-Kin, M.;  Wei-Chiang, L.;  Chong-Yung, C.; Ambikapathi, A., Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No-Pure-Pixel Case. IEEE Transactions on Geoscience and Remote Sensing 2015, 53, 5530-5546.

111. Jun, L.;  Agathos, A.;  Zaharie, D.;  Bioucas-Dias, J. M.;  Plaza, A.; Xia, L., Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2015, 53, 5067-5082.

112. Zhang, S. Q.;  Agathos, A.; Li, J., Robust Minimum Volume Simplex Analysis for Hyperspectral Unmixing. Ieee Transactions on Geoscience and Remote Sensing 2017, 55, 6431-6439.

113. Parente, M.; Plaza, A. In Survey of geometric and statistical unmixing algorithms for hyperspectral images, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Reykjavik, Iceland, 14-16 June 2010; Reykjavik, Iceland, 2010 of Conference; pp 1-4.

114. Das, S.;  Routray, A.; Deb, A. K., Fast Semi-Supervised Unmixing of Hyperspectral Image by Mutual Coherence Reduction and Recursive PCA. Remote Sensing 2018, 10, 1106.

115. Cheng, X.;  Cai, Z.;  Li, J.;  Wen, M.;  Wang, Y.; Zeng, D., A spatial-spectral clustering-based algorithm for endmember extraction and hyperspectral unmixing. International Journal of Remote Sensing 2020, 42, 1948-1972.

116. Arroyo-Cerezo, A.;  Medina-García, M.;  Cuadros-Rodríguez, L.;  Rutledge, D. N.; Jiménez-Carvelo, A. M., Chemometric enhancement for blind signal resolution from non-invasive spatially offset Raman spectra. Chemometrics and Intelligent Laboratory Systems 2023, 243, 105027.

117. Du, B.;  Wang, S.;  Wang, N.;  Zhang, L.;  Tao, D.; Zhang, L., Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach. Neurocomputing 2016, 204, 153-161.

118. 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. Theoretical Chemistry Accounts 2011, 130, 1249-1260.

119. Bruzzone, L.;  Bovolo, F.;  Toomik, M.;  Lu, S.; Nelson, J. D. B. In M-estimation for robust sparse unmixing of hyperspectral images, Image and Signal Processing for Remote Sensing XXII, Edinburgh, United Kingdom, 18 October 2016; Edinburgh, United Kingdom, 2016 of Conference; p 100040V.

120. Jiang, Q.;  Dong, Y.;  Peng, J.;  Yan, M.; Sun, Y., Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing. Remote Sensing 2021, 13, 2637.

121. Iordache, M.-D.;  Bioucas-Dias, J. M.; Plaza, A., Collaborative Sparse Regression for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2014, 52, 341-354.

122. Zhang, S.;  Li, J.;  Liu, K.;  Deng, C.;  Liu, L.; Plaza, A., Hyperspectral Unmixing Based on Local Collaborative Sparse Regression. IEEE Geoscience and Remote Sensing Letters 2016, 13, 631-635.

123. Zhang, S.;  Li, J.;  Li, H.-C.;  Deng, C.; Plaza, A., Spectral–Spatial Weighted Sparse Regression for Hyperspectral Image Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 3265-3276.

124. Li, C.;  Ma, Y.;  Mei, X.;  Fan, F.;  Huang, J.; Ma, J., Sparse Unmixing of Hyperspectral Data with Noise Level Estimation. Remote Sensing 2017, 9, 1166.

125. Wang, S.;  Huang, T.-Z.;  Zhao, X.-L.;  Liu, G.; Cheng, Y., Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing. Remote Sensing 2018, 10, 1046.

126. Han, H.;  Guo, K.;  Wang, M.;  Zhang, T.; Zhang, S., Fast Hyperspectral Unmixing via Reweighted Sparse Regression. IEICE Transactions on Information and Systems 2019, E102.D, 1819-1832.

127. Altmann, Y.;  Pereyra, M.; Bioucas-Dias, J., Collaborative sparse regression using spatially correlated supports--Application to hyperspectral unmixing. IEEE Trans Image Process 2015, 24, 5800-11.

128. Su, H.;  Jia, C.;  Zheng, P.; Du, Q., Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022, 15, 393-408.

129. Lu, X.;  Dong, L.; Yuan, Y., Subspace Clustering Constrained Sparse NMF for Hyperspectral Unmixing. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 3007-3019.

130. Yang, Z.;  Zhou, G.;  Xie, S.;  Ding, S.;  Yang, J. M.; Zhang, J., Blind spectral unmixing based on sparse nonnegative matrix factorization. IEEE Trans Image Process 2011, 20, 1112-25.