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. 2022 Jul 22;105(4):75. doi: 10.1007/s10846-022-01602-7

A Review of Pharmaceutical Robot based on Hyperspectral Technology

Xuesan Su 1, Yaonan Wang 1,✉, Jianxu Mao 1, Yurong Chen 1, ATing Yin 1, Bingrui Zhao 1, Hui Zhang 1, Min Liu 1
PMCID: PMC9306415  PMID: 35909703

Abstract

The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

Keywords: Pharmaceutical robot, Quality inspection, Hyperspectral imaging, Raman hyperspectral

Biographies

Xuesan Su

is currently pursuing the Ph.D. degree at Hunan University, and received the M.S. and B.S. degree in College of Electrical and Information Engineering, Hunan University in 2020 and 2018, respectively. His current research interests include hyperspectral image processing, machine learning.

Yaonan Wang

received the Ph.D. degree in Electrical Engineering from Hunan University, Changsha, China, in 1994. He was a Post-Doctoral Research Fellow with the Normal University of Defence Technology, Changsha, between 1994 to 1995. From 1998 to 2000, he was a Senior Humboldt Fellow in Germany. From 2001 to 2004, he was a Visiting Professor at the University of Bremen, Bremen, Germany. Between 2001 to 2020, he has been the Dean of the College of Electrical and Information Engineering, Hunan University, China. Since 1995, he has been a Professor at Hunan University, China. His current research interests include intelligent control, robotics, and image processing. Dr. Wang holds the Principle Leader at the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan, China. He is the President of China Society of Image and Graphics, Beijing, China. He is a Fellow of Chinese Academy of Engineering.

Jianxu Mao

received the B.S. degree in computer application from Nanchang University, Nanchang, China, in 1993, the M.S. degree in earth exploration and information technology from the East China Institute of Technology, Fuzhou, China, in 1999, and the Ph.D. degree in control theory and control engineering from Hunan University, Changsha, China, in 2003. From 1993 to 1999, he was with the East China Institute of Technology. Now he has been a Professor with the College of Electrical and Information Engineering, and National Engineering Research Center for Robot Visual Perception and Control Technology, Hunan University, Changsha, China. His current research interests include image processing, pattern recognition, and intelligent information processing.

Yurong Chen

is currently pursuing the Ph.D. degree at Hunan University, and received the M.S. and B.S. degree in Electrical and Computer Engineering, University of Pittsburgh, PA, USA and Changsha University of Science and Technology in 2020 and 2019, respectively. His current research interests include image processing, machine learning and domain adaption.

Bingrui Zhao

received the B.S. degree from the Northeastern University in electronic information Engineering, Shenyang, China, in 2017, and the M.S. degree in circuit and system from Xiamen University, Xiamen, China, in 2020. He is currently pursuing the Ph.D. degree with the Department of control science and engineering at Hunan University, Changsha, China. His research interests include hyperspectral image processing and hardware acceleration.

Hui Zhang

received the B.S., M.S., and Ph.D. degrees in pattern recognition and intelligent system from Hunan University, Changsha, China, in 2004, 2007, and 2012, respectively. He is currently a Professor with the School of Robotics, and National Engineering Research Center for Robot Visual Perception and Control Technology, Hunan University, Changsha, China. His current research interests include machine vision, sparse representation, and visual tracking.

Min Liu

received the bachelor’s degree from Beijing University, Beijing, China, in 2004, and the Ph.D. degree in electrical engineering from the University of California, Riverside, CA, USA, in 2012. He was a Research Scientist at the University of California, Santa Barbara, CA, USA. He is currently a Professor with the College of Electrical and Information Engineering, Hunan University, Changsha, China. His research interests include computer vision and image processing.

Author Contributions

Y. Wang, J. Mao, H. Zhang and M. Liu contributed to the design of the paper framework. X. SU, Y. Chen, A. Yin and B. Zhao contributed to the implementation and analysis of the research and to the writing of the manuscript.

Funding

This work is supported by National Natural Science Foundation of China and the award number is 62027810. And it is supported in part by the Major Research plan of the National Natural Science Foundation of China (Grant No. 92148204), the National Key RD Program of China under Grant 2018YFB1308200, in part by the National Natural Science Foundation of China under Grants 61971071, 62133005, Hunan Science Fund for Distinguished Young Scholars under Grant 2021JJ10025, Hunan key research and development program under Grants 2021GK4011, 2022GK2011, Changsha Science and Technology Major Project under Grant kh2003026, Joint Open Foundation of state Key Laboratory of Robotics under Grant 2021-KF-22-17, China University industry-University-research Innovation Fund under Grant 2020HYA06006.

Declarations

Conflict of Interests

The authors declare no conflict of interest.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Baden LR, El Sahly HM, Essink B, Kotloff K, Frey S, Novak R, Diemert D, et al. Efficacy and safety of the mRNA-1273 SARS-cov-2 vaccine. N. Engl. J. Med. 2021;384(5):403–416. doi: 10.1056/NEJMoa2035389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Castells MC, Phillips EJ. Maintaining safety with SARS-cov-2 vaccines. N. Engl. J. Med. 2021;384(7):643–649. doi: 10.1056/NEJMra2035343. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Raman, N.V.V.S.S., Mallu, U.R., Bapatu, H.R.: Analytical quality by design approach to test method development and validation in drug substance manufacturing. J. Chem. 2015 (2015)
  • 4.Mathaes R, Mahler H-C, Buettiker J-P, Roehl H, Lam P, Brown H, Luemkemann J, et al. The pharmaceutical vial capping process: container closure systems, capping equipment, regulatory framework, and seal quality tests. Eur. J. Pharm. Biopharm. 2016;99:54–64. doi: 10.1016/j.ejpb.2015.11.016. [DOI] [PubMed] [Google Scholar]
  • 5.Liu L, Qu H. Recent advancement of chemical imaging in pharmaceutical quality control: From final product testing to industrial utilization. J. Innov. Opt. Health Sci. 2020;13(01):1930014. doi: 10.1142/S1793545819300143. [DOI] [Google Scholar]
  • 6.Van der Meer FD, Van der Werff HMA, Van Ruitenbeek FJA, Hecker CA, Bakker WH, Noomen MF, Meijde MVD, Carranza EJM, De Smeth JB, Woldai T. Multi-and hyperspectral geologic remote sensing: a review. Int. J. Appl. Earth Obs. Geoinformation. 2012;14(1):112–128. doi: 10.1016/j.jag.2011.08.002. [DOI] [Google Scholar]
  • 7.Adão T, Hruška J, Pádua L, Bessa J, Peres E, Morais R, Sousa JJ. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 2017;9(11):1110. doi: 10.3390/rs9111110. [DOI] [Google Scholar]
  • 8.Freitas S, Silva H, Almeida J, Silva E. Hyperspectral imaging for real-time unmanned aerial vehicle maritime target detection. J. Intell. Robot. Syst. 2018;90(3):551–570. doi: 10.1007/s10846-017-0689-0. [DOI] [Google Scholar]
  • 9.Liu Y, Pu H, Sun D-W. Hyperspectral imaging technique for evaluating food quality and safety during various processes: a review of recent applications. Trends Food Sci. Techno. 2017;69:25–35. doi: 10.1016/j.tifs.2017.08.013. [DOI] [Google Scholar]
  • 10.Fevotte G. In situ Raman spectroscopy for in-line control of pharmaceutical crystallization and solids elaboration processes: A review. Chem. Eng. Res. Des. 2007;85(7):906–920. doi: 10.1205/cherd06229. [DOI] [Google Scholar]
  • 11.Kiefer, W.: Surface enhanced Raman spectroscopy: Analytical, biophysical and life science applications. John Wiley & Sons (2011)
  • 12.Gnyba M, Smulko J, Kwiatkowski A, Wierzba P. “Portable Raman spectrometer-design rules and applications”. Bulletin of the Polish Academy of Sciences. Tech. Sci. 2011;59(3):325–329. [Google Scholar]
  • 13.Vítek P, Ali EsamMA, Edwards HGM, Jehlička J, Cox R, Page K. Evaluation of portable Raman spectrometer with 1064 nm excitation for geological and forensic applications. Spectrochim Acta A Mol. Biomol. Spectrosc. 2012;86:320–327. doi: 10.1016/j.saa.2011.10.043. [DOI] [PubMed] [Google Scholar]
  • 14.ElMasry, G., Sun, D.-W.: Principles of hyperspectral imaging technology, pp 3–43. Academic Press (2010)
  • 15.Chang, C.-I.: Hyperspectral imaging: Techniques for spectral detection and classification. Vol. 1 Springer Science & Business Media (2003)
  • 16.Roggo Y, Edmond A, Chalus P, Ulmschneider M. Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms. Anal. Chim. Acta. 2005;535(1-2):79–87. doi: 10.1016/j.aca.2004.12.037. [DOI] [Google Scholar]
  • 17.Lu G, Fei B. Medical hyperspectral imaging: a review. J. Biomed. Opt. 2014;19(1):010901. doi: 10.1117/1.JBO.19.1.010901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Bioucas-Dias JM, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N, Chanussot J. Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. 2013;1(2):6–36. doi: 10.1109/MGRS.2013.2244672. [DOI] [Google Scholar]
  • 19.Gowen AA, O’Donnell CP, Cullen PJ, Downey G, Frias JM. Hyperspectral imaging–an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 2007;18(12):590–598. doi: 10.1016/j.tifs.2007.06.001. [DOI] [Google Scholar]
  • 20.Li Q, He X, Wang Y, Liu H, Xu D, Guo F. Review of spectral imaging technology in biomedical engineering: Achievements and challenges. J. Biomed. Opt. 2013;18(10):100901. doi: 10.1117/1.JBO.18.10.100901. [DOI] [PubMed] [Google Scholar]
  • 21.Fowler, J.E.: Compressive pushbroom and whiskbroom sensing for hyperspectral remote-sensing imaging. In: 2014 IEEE International Conference on Image Processing (ICIP), pp 684–688. IEEE (2014)
  • 22.Lawrence KC, Park B, Windham WR, Mao C. Calibration of a pushbroom hyperspectral imaging system for agricultural inspection. Trans. ASAE. 2003;46(2):513. doi: 10.13031/2013.12940. [DOI] [Google Scholar]
  • 23.Gupta, N.: Development of staring hyperspectral imagers. In: 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp 1–8. IEEE (2011)
  • 24.Hagen NA, Kudenov MW. Review of snapshot spectral imaging technologies. Opt. Eng. 2013;52(9):090901. doi: 10.1117/1.OE.52.9.090901. [DOI] [Google Scholar]
  • 25.Kester RT, Bedard N, Gao LS, Tkaczyk TS. Real-time snapshot hyperspectral imaging endoscope. J. Biomed. Opt. 2011;16(5):056005. doi: 10.1117/1.3574756. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Gao L, Wang LV. A review of snapshot multidimensional optical imaging: measuring photon tags in parallel. Phys. Rep. 2016;616:1–37. doi: 10.1016/j.physrep.2015.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Zhang H, Yi J, Wang Y, Wu L, Chen R. Review on key technologies and applications of pharmaceutical quality testing. Chin. J. Sci. Instrum. 2020;41(3):1–17. [Google Scholar]
  • 28.Yi J, Zhang H, Zhao C, Che A, Wang Y. Key technologies and progress of pharmaceutical intelligent manufacturing production line. J. Cent. South. Univ. (Sci. Technol.) 2021;52(2):421–433. [Google Scholar]
  • 29.DelSpina, B., Zhang, Y., Wang, Y.: A benchtop robot and automation solution for prefilled syringes in pharmaceutical manufacturing. In: 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), pp 228–234. IEEE (2021)
  • 30.Tamura, T., Kurebayashi, H., Tanaka, Y., Sakakibara, S., Nihei, R., Inaba, Y.: High speed intelligent handling robot for food and pharmaceutical products. In: 2009 IEEE Workshop on Advanced Robotics and its Social Impacts, pp 60–64. IEEE (2009)
  • 31.Zeng, K., Wang, Y., Mao, J., Liu, C., Zhou, X., Peng, W.: Research on filling and sealing robot positioning algorithm with gaussian mixture model. In: 2020 Chinese Automation Congress (CAC), pp 872–876. IEEE (2020)
  • 32.Zhou X, Wang Y, Xiao C, Zhu Q, Lu X, Zhang H, Ge J, Zhao H. Automated visual inspection of glass bottle bottom with saliency detection and template matching. IEEE Trans. Instrum. Meas. 2019;68(11):4253–4267. doi: 10.1109/TIM.2018.2886977. [DOI] [Google Scholar]
  • 33.Zhou X, Wang Y, Zhu Q, Mao J, Xiao C, Lu X, Zhang H. A surface defect detection framework for glass bottle bottom using visual attention model and wavelet transform. IEEE Trans. Ind. Inform. 2019;16(4):2189–2201. doi: 10.1109/TII.2019.2935153. [DOI] [Google Scholar]
  • 34.Tzafestas S. Sensor integration and fusion techniques in robotic applications. J. Intell. Robot. Syst. 2005;1:43. [Google Scholar]
  • 35.Buckmann O, Krömker M, Berger U. An application platform for the development and experimental validation of mobile robots for health care purposes. J. Intell. Robot. Syst. 1998;22(3):331–350. doi: 10.1023/A:1007945702881. [DOI] [Google Scholar]
  • 36.Zhang H, Zhao M, Li L, Zhong H, Liang Z, Yang Y, Zhou X, Wu QMJ, Wang Y. Deep multimodel cascade method based on CNN and random forest for pharmaceutical particle detection. IEEE Trans. Instrum. Meas. 2020;69(9):7028–7042. doi: 10.1109/TIM.2020.2973843. [DOI] [Google Scholar]
  • 37.Chen Y, Zhang H, Wang Y, Yang Y, Zhou X, Wu QMJ. MAMA Net: Multi-scale attention memory autoencoder network for anomaly detection. IEEE Trans. Med. Imaging. 2020;40(3):1032–1041. doi: 10.1109/TMI.2020.3045295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Opaspilai, P., Vongbunyong, S., Dheeravongkit, A.: Robotic system for depalletization of pharmaceutical products. In: 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp 133–138. IEEE (2021)
  • 39.Zhang H, Wang Y, Yi J, Zhong H, Li L, Miao Z, Jiang Y. Research on intelligent robot systems for emergency prevention and control of major pandemics. Scientia Sinica Informationis. 2020;50(7):1069–1090. doi: 10.1360/SSI-2020-0107. [DOI] [Google Scholar]
  • 40.Munson J, Freeman Stanfield C, Gujral B. A review of process analytical technology (PAT) in the US pharmaceutical industry. Curr. Pharm. Anal. 2006;2(4):405–414. doi: 10.2174/157341206778699582. [DOI] [Google Scholar]
  • 41.De B, Thomas AB, Fonteyne M, Saerens L, Remon JP, Vervaet C. Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes. Int. J. Pharm. 2011;417(1-2):32–47. doi: 10.1016/j.ijpharm.2010.12.012. [DOI] [PubMed] [Google Scholar]
  • 42.Zhang S, Song Z, Godaliyadda GM, Ye DH, Sengupta A, Buzzard GT, Boumanb CA, Simpsona GJ. A supervised learning approach for dynamic sampling (SLADS) in raman hyperspectral imaging. Electron. Imaging. 2018;2018(15):132–1. [Google Scholar]
  • 43.Zhang S, Song Z, GM DP, Ye DH, Chowdhury AU, Sengupta A, Buzzard GT, Bouman CA, Simpson GJ. Godaliyadda “Dynamic sparse sampling for confocal Raman microscopy”. Anal. Chem. 2018;90(7):4461–4469. doi: 10.1021/acs.analchem.7b04749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Giuliani A. The application of principal component analysis to drug discovery and biomedical data. Drug Discov. Today. 2017;22(7):1069–1076. doi: 10.1016/j.drudis.2017.01.005. [DOI] [PubMed] [Google Scholar]
  • 45.Maggio RM, Castellano PM, Kaufman TS. A new principal component analysis-based approach for testing “similarity” of drug dissolution profiles. Eur. J. Pharm. Sci. 2008;34(1):66–77. doi: 10.1016/j.ejps.2008.02.009. [DOI] [PubMed] [Google Scholar]
  • 46.El Y, Meriem OB, Agoub M, Moussaoui D, Gutknecht C, Dalery J, d’Amato T, Saoud M. Validity of the depressive dimension extracted from principal component analysis of the PANSS in drug-free patients with schizophrenia. Schizophr. Res. 2002;56(1-2):121–127. doi: 10.1016/S0920-9964(01)00247-X. [DOI] [PubMed] [Google Scholar]
  • 47.Gut Y, Boiret M, Bultel L, Renaud T, Chetouani A, Hafiane A, Ginot Y-M, Jennane R. Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets. J. Pharm. Biomed. Anal. 2015;105:91–100. doi: 10.1016/j.jpba.2014.11.047. [DOI] [PubMed] [Google Scholar]
  • 48.Boiret M, Gorretta N, Ginot Y-M, Roger J-M. An iterative approach for compound detection in an unknown pharmaceutical drug product: application on Raman microscopy. J. Pharm. Biomed. Anal. 2016;120:342–351. doi: 10.1016/j.jpba.2015.12.038. [DOI] [PubMed] [Google Scholar]
  • 49.Porquez JG, Slepkov AD. Application of spectral-focusing-CARS microscopy to pharmaceutical sample analysis. Aip Advances. 2018;8(9):095213. doi: 10.1063/1.5027273. [DOI] [Google Scholar]
  • 50.Gavage M, Delahaut P, Gillard N. Suitability of High-Resolution mass spectrometry for routine analysis of small molecules in food, feed and water for safety and authenticity purposes: a review. Foods. 2021;10(3):601. doi: 10.3390/foods10030601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Bolla, A.S., Patel, A.R., Priefer, R.: The silent development of counterfeit medications in developing countries–A systematic review of detection technologies. International Journal of Pharmaceutics, 119702 (2020) [DOI] [PubMed]
  • 52.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. Anal. Chim. Acta. 2021;1155:338361. doi: 10.1016/j.aca.2021.338361. [DOI] [PubMed] [Google Scholar]
  • 53.Coic L, Sacré P-Y, Dispas A, Sakira AK, Fillet M, Marini RD, Hubert P, Ziemons E. Comparison of hyperspectral imaging techniques for the elucidation of falsified medicines composition. Talanta. 2019;198:457–463. doi: 10.1016/j.talanta.2019.02.032. [DOI] [PubMed] [Google Scholar]
  • 54.Frosch T, Wyrwich E, Di Y, Domes C, Domes R, Popp J, Frosch T. Counterfeit and substandard test of the antimalarial tablet Riamet® by means of Raman hyperspectral multicomponent analysis. Molecules. 2019;24(18):3229. doi: 10.3390/molecules24183229. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Esmonde-White KA, Cuellar M, Uerpmann C, Lenain B, Lewis IR. Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing. Anal. Bioanal. Chem. 2017;409(3):637–649. doi: 10.1007/s00216-016-9824-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Strachan CJ, Rades T, Gordon KC, Rantanen J. Raman spectroscopy for quantitative analysis of pharmaceutical solids. J. Pharm. Pharmacol. 2007;59(2):179–192. doi: 10.1211/jpp.59.2.0005. [DOI] [PubMed] [Google Scholar]
  • 57.Hédoux A, Guinet Y, Descamps M. The contribution of Raman spectroscopy to the analysis of phase transformations in pharmaceutical compounds. Int. J. Pharm. 2011;417(1-2):17–31. doi: 10.1016/j.ijpharm.2011.01.031. [DOI] [PubMed] [Google Scholar]
  • 58.Szostak R, Mazurek S. Quantitative determination of acetylsalicylic acid and acetaminophen in tablets by FT-raman spectroscopy. Analyst. 2002;127(1):144–148. doi: 10.1039/b108240j. [DOI] [PubMed] [Google Scholar]
  • 59.Frosch T, Wyrwich E, Di Y, Popp J, Frosch T. Fiber-Array-Based Raman hyperspectral imaging for simultaneous, Chemically-Selective monitoring of particle size and shape of active ingredients in analgesic tablets. Molecules. 2019;24(23):4381. doi: 10.3390/molecules24234381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Johnke H, Batres G, Wilson M, Holmes AE, Sikich S. Detecting concentration of analytes with DETECHIP: A molecular sensing array. J. Sens. Technol. 2013;3:3. doi: 10.4236/jst.2013.33015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Yun-Han L, Fu-Rong H, Li S-P, Zhe C. Detection limit of glucose concentration with near-infrared absorption and scattering spectroscopy. Chin. Phys. Lett. 2008;25(3):1117. doi: 10.1088/0256-307X/25/3/084. [DOI] [Google Scholar]
  • 62.Nagy B, Farkas A, Balogh A, Pataki H, Vajna B, Nagy ZK, Marosi G. Quantification and handling of nonlinearity in Raman micro-spectrometry of pharmaceuticals. J. Pharm. Biomed. Anal. 2016;128:236–246. doi: 10.1016/j.jpba.2016.05.036. [DOI] [PubMed] [Google Scholar]
  • 63.Markl, D., Ziegler, J., Hannesschläger, G., Sacher, S., Buchsbaum, A., Leitner, M., Khinast, J.G.: Real-time data processing for in-line monitoring of a pharmaceutical coating process by optical coherence tomography. In: Biophotonics: Photonic Solutions for Better Health Care IV, vol. 9129, p 91290M. Int. Soc. Opt. Photon. (2014)
  • 64.Nguyen, T.L., Shu, M.H., Hsu, B.M.: Monitoring the coating thickness of pharmaceutical tablets with MaxGWMA control chart. In: Applied Mechanics and Materials, vol. 372, pp 325–330. Trans Tech Publications Ltd (2013)
  • 65.Möltgen C-V, Herdling T, Reich G. A novel multivariate approach using science-based calibration for direct coating thickness determination in real-time NIR process monitoring. Eur. J. Pharm. Biopharm. 2013;85(3):1056–1063. doi: 10.1016/j.ejpb.2013.09.011. [DOI] [PubMed] [Google Scholar]
  • 66.Song SW, Kim J, Eum C, Cho Y, Park CR, Woo Y-A, Kim HM, Chung H. Hyperspectral Raman line mapping as an effective tool to monitor the coating thickness of pharmaceutical tablets. Anal. Chem. 2019;91(9):5810–5816. doi: 10.1021/acs.analchem.9b00047. [DOI] [PubMed] [Google Scholar]
  • 67.Sanhueza MI, Castillo RdelP, Meléndrez MF, von Plessing C, Tereszczuk J, Osorio G, Peña-Farfal C, Fernández M, Neira JY. Confocal laser scanning microscopy as a novel tool of hyperspectral imaging for the localization and quantification of fluorescent active principles in pharmaceutical solid dosage forms. Microchem. J. 2021;168:106479. doi: 10.1016/j.microc.2021.106479. [DOI] [Google Scholar]
  • 68.Kandpal LM, Cho B-K, Tewari J, Gopinathan N. Raman spectral imaging technique for API detection in pharmaceutical microtablets. Sens. Actuators B. 2018;260:213–222. doi: 10.1016/j.snb.2017.12.178. [DOI] [Google Scholar]
  • 69.Boiret M, Gorretta N, Ginot Y-M, Roger J-M. An iterative approach for compound detection in an unknown pharmaceutical drug product: Application on Raman microscopy. J. Pharm. Biomed. Anal. 2016;120:342–351. doi: 10.1016/j.jpba.2015.12.038. [DOI] [PubMed] [Google Scholar]
  • 70.Kandpal LM, Tewari J, Gopinathan N, Boulas P, Cho B-K. In-process control assay of pharmaceutical microtablets using hyperspectral imaging coupled with multivariate analysis. Anal. Chem. 2016;88(22):11055–11061. doi: 10.1021/acs.analchem.6b02969. [DOI] [PubMed] [Google Scholar]
  • 71.Howari, F.M., Xavier, C., Sharma, M., Nazzal, Y., Salem, I.B., AlAydaroos, F.: Application of hyperspectral imaging technique to determine the quality of Photo and Thermal exposed and contaminated pharmaceutical formulations: A cost effective way of quality testing. In: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), pp 1–5. IEEE (2020)
  • 72.Al K, Mohammad MS, Boldrini B, Ostertag E, Brecht M. Characterization of pharmaceutical tablets using UV hyperspectral imaging as a rapid in-line analysis tool. Sensors. 2021;21(13):4436. doi: 10.3390/s21134436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Sanhueza MI, Castillo RdelP, Meléndrez MF, von Plessing C, Tereszczuk J, Osorio G, Peña-Farfal C, Fernández M, Neira JY. Confocal laser scanning microscopy as a novel tool of hyperspectral imaging for the localization and quantification of fluorescent active principles in pharmaceutical solid dosage forms. Microchem. J. 2021;168:106479. doi: 10.1016/j.microc.2021.106479. [DOI] [Google Scholar]
  • 74.Kandpal LM, Tewari J, Tran K, Quan E, Gopinathan N, Cho B-K. Hyperspectral imaging sensor for optimization of small molecule formulations. Medical Devices & Sensors. 2018;1(1):e10006. doi: 10.1002/mds3.10006. [DOI] [Google Scholar]
  • 75.Alexandrino GL, Khorasani MR, Amigo JM, Rantanen J, Poppi RJ. Monitoring of multiple solid-state transformations at tablet surfaces using multi-series near-infrared hyperspectral imaging and multivariate curve resolution. Eur. J. Pharm. Biopharm. 2015;93:224–230. doi: 10.1016/j.ejpb.2015.03.034. [DOI] [PubMed] [Google Scholar]
  • 76.Nishii T, Matsuzaki K, Morita S. Real-time determination and visualization of two independent quantities during a manufacturing process of pharmaceutical tablets by near-infrared hyperspectral imaging combined with multivariate analysis. Int. J. Pharm. 2020;590:119871. doi: 10.1016/j.ijpharm.2020.119871. [DOI] [PubMed] [Google Scholar]
  • 77.Scherholz ML, Wan B, McGeorge G. A rational analysis of uniformity risk for agglomerated drug substance using NIR chemical imaging. Aaps Pharmscitech. 2017;18(2):432–440. doi: 10.1208/s12249-016-0523-1. [DOI] [PubMed] [Google Scholar]
  • 78.Omar MA, Nagy DM, Halim ME. Simple ultrasensitive spectrofluorimetric method for determination of midodrine in its tablet form: Application to content uniformity testing. Luminescence. 2019;34(8):854–858. doi: 10.1002/bio.3682. [DOI] [PubMed] [Google Scholar]
  • 79.Atia NN, Marzouq MA, Hassan AI, Eltoukhi WE. Utility of chromogenic property of 2, 2-dihydroxyindane-1, 3-dione for quantification of Memantine hydrochloride in pure, pharmaceutical preparation and application to uniformity testing. Spectrochim. Acta. A. Mol. Biomol. Spectrosc. 2020;227:117640. doi: 10.1016/j.saa.2019.117640. [DOI] [PubMed] [Google Scholar]
  • 80.Ibrahim F, Wahba MEK. Liquid chromatographic determination of ergotamine tartrate in its combined tablets using fluorimetric and UV detection: Application to content uniformity testing. Sep. Sci. Technol. 2014;49(14):2228–2240. doi: 10.1080/01496395.2014.914953. [DOI] [Google Scholar]
  • 81.Abo El Abass Mohamed S, El-Awady MI. Spectrofluorimetric investigation with green analytical procedures for estimation of bambuterol and terbutaline: Application to pharmaceutical dosage forms and content uniformity testing. Luminescence. 2019;34(1):70–76. doi: 10.1002/bio.3578. [DOI] [PubMed] [Google Scholar]
  • 82.Bobiak JP, McGeorge G. Assessing hyperspectral image content of pharmaceutical products using the Herfindahl–Hirschman index and Ripley’s K-functions. Appl. Spectrosc. 2015;69(8):955–965. doi: 10.1366/14-07650. [DOI] [PubMed] [Google Scholar]
  • 83.Rocha de Oliveira R, de Juan A. Design of heterogeneity indices for blending quality assessment based on hyperspectral images and variographic analysis. Anal. Chem. 2020;92(24):15880–15889. doi: 10.1021/acs.analchem.0c03241. [DOI] [PubMed] [Google Scholar]
  • 84.Obisesan A, Kudirat SN, Bugnicourt E, Campos I, Rodriguez-Turienzo L. Determination and quantification of the distribution of CN-NL nanoparticles encapsulating glycyrrhetic acid on novel textile surfaces with hyperspectral imaging. J. Funct. Biomaterials. 2020;11(2):32. doi: 10.3390/jfb11020032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Alexandrino GL, Khorasani MR, Amigo JM, Rantanen J, Poppi RJ. Monitoring of multiple solid-state transformations at tablet surfaces using multi-series near-infrared hyperspectral imaging and multivariate curve resolution. Eur. J. Pharm. Biopharm. 2015;93:224–230. doi: 10.1016/j.ejpb.2015.03.034. [DOI] [PubMed] [Google Scholar]
  • 86.Ru C, Li Z, Tang R. A hyperspectral imaging approach for classifying geographical origins of rhizoma atractylodis macrocephalae using the fusion of spectrum-image in VNIR and SWIR ranges (VNIR-SWIR-fuSI) Sensors. 2019;19(9):2045. doi: 10.3390/s19092045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Zhang, P., De Shen, M.: Design of automatic sorting system of boxed drugs based on profibus-DP. In: Advanced Materials Research, vol. 468, pp 848–851. Trans Tech Publications Ltd (2012)
  • 88.Kaneko H, Funatsu K. Classification of drug tablets using hyperspectral imaging and wavelength selection with a GAWLS method modified for classification. Int. J. Pharm. 2015;491(1-2):130–135. doi: 10.1016/j.ijpharm.2015.06.012. [DOI] [PubMed] [Google Scholar]
  • 89.Kumar B, Phaneendra LN, Prabukumar M. Hyperspectral image classification using fuzzy-embedded hyperbolic sigmoid nonlinear principal component and weighted least squares approach. J. Appl. Remote Sens. 2020;14(2):024501. [Google Scholar]
  • 90.Liu Y, Zhou S, Han W, Liu W, Qiu Z, Li C. Convolutional neural network for hyperspectral data analysis and effective wavelengths selection. Anal. Chim. Acta. 2019;1086:46–54. doi: 10.1016/j.aca.2019.08.026. [DOI] [PubMed] [Google Scholar]
  • 91.Mazivila SJ, Olivieri AC. Chemometrics coupled to vibrational spectroscopy and spectroscopic imaging for the analysis of solid-phase pharmaceutical products: a brief review on non-destructive analytical methods. TrAC Trends Anal. Chem. 2018;108:74–87. doi: 10.1016/j.trac.2018.08.013. [DOI] [Google Scholar]
  • 92.Rodionova OY, Houmøller LP, Pomerantsev AL, Geladi P, Burger J, Dorofeyev VL, Arzamastsev AP. NIR spectrometry for counterfeit drug detection: a feasibility study. Anal. Chim. Acta. 2005;549(1-2):151–158. doi: 10.1016/j.aca.2005.06.018. [DOI] [Google Scholar]
  • 93.Nayyar GML, Breman JG, Newton PN, Herrington J. Poor-quality antimalarial drugs in southeast Asia and sub-Saharan Africa. Lancet Infect. Dis. 2012;12(6):488–496. doi: 10.1016/S1473-3099(12)70064-6. [DOI] [PubMed] [Google Scholar]
  • 94.Marini Djang’Eing’A R, Kindenge JM, Montes MdeLA, Debrus B, Lebrun P, Mantanus J, Ziemons E, Rohrbasser C, Rudaz S, Hubert P. Analytical tools to fight against counterfeit medicines. Chim. Oggi. 2010;28(5):10–14. [Google Scholar]
  • 95.Lopes MB, Wolff J-C. Investigation into classification/sourcing of suspect counterfeit HeptodinTM tablets by near infrared chemical imaging. Analytica Chimica Acta. 2009;633(1):149–155. doi: 10.1016/j.aca.2008.11.036. [DOI] [PubMed] [Google Scholar]
  • 96.De Peinder P, Vredenbregt MJ, Visser T, De Kaste D. Detection of Lipitor® counterfeits: A comparison of NIR and Raman spectroscopy in combination with chemometrics. J. Pharm. Biomed. Anal. 2008;47(4-5):688–694. doi: 10.1016/j.jpba.2008.02.016. [DOI] [PubMed] [Google Scholar]
  • 97.Puchert T, Lochmann D, Menezes JC, Reich G. Near-infrared chemical imaging (NIR-CI) for counterfeit drug identification—a four-stage concept with a novel approach of data processing (Linear Image Signature) J. Pharm. Biomed. Anal. 2010;51(1):138–145. doi: 10.1016/j.jpba.2009.08.022. [DOI] [PubMed] [Google Scholar]
  • 98.da Silva Fernandes R, da Costa FSL, Valderrama P, Março PH, de Lima KMG. Non-destructive detection of adulterated tablets of glibenclamide using NIR and solid-phase fluorescence spectroscopy and chemometric methods. J. Pharm. Biomed. Anal. 2012;66:85–90. doi: 10.1016/j.jpba.2012.03.004. [DOI] [PubMed] [Google Scholar]
  • 99.Rodionova OY, Pomerantsev AL. NIR-Based approach to counterfeit-drug detection. TrAC Trends Anal. Chem. 2010;29(8):795–803. doi: 10.1016/j.trac.2010.05.004. [DOI] [Google Scholar]
  • 100.Sacré P-Y, Lebrun P, Chavez P-F, Bleye CD, Netchacovitch L, Rozet E, Klinkenberg R, Streel B, Hubert P, Ziemons E. A new criterion to assess distributional homogeneity in hyperspectral images of solid pharmaceutical dosage forms. Analytica Chimica Acta. 2014;818:7–14. doi: 10.1016/j.aca.2014.02.014. [DOI] [PubMed] [Google Scholar]
  • 101.Firkala T, Farkas A, Vajna B, Farkas I, Marosi G. Investigation of drug distribution in tablets using surface enhanced Raman chemical imaging. J. Pharm. Biomed. Anal. 2013;76:145–151. doi: 10.1016/j.jpba.2012.12.017. [DOI] [PubMed] [Google Scholar]
  • 102.Wahl PR, Pucher I, Scheibelhofer O, Kerschhaggl M, Sacher S, Khinast JG. Continuous monitoring of API content, API distribution and crushing strength after tableting via near-infrared chemical imaging. Int. J. Pharm. 2017;518(1-2):130–137. doi: 10.1016/j.ijpharm.2016.12.003. [DOI] [PubMed] [Google Scholar]
  • 103.Piqueras S, Duponchel L, Tauler R, De Juan A. Monitoring polymorphic transformations by using in situ Raman hyperspectral imaging and image multiset analysis. Analytica Chimica Acta. 2014;819:15–25. doi: 10.1016/j.aca.2014.02.027. [DOI] [PubMed] [Google Scholar]
  • 104.Farias MAdosS, Carneiro RL. Simultaneous quantification of three polymorphic forms of carbamazepine using raman spectroscopy and multivariate calibration. Anal. Lett. 2014;47(6):1043–1051. doi: 10.1080/00032719.2013.860537. [DOI] [Google Scholar]
  • 105.Farias dosS, Antônio M, Soares FLF, Carneiro RL. Crystalline phase transition of ezetimibe in final product, after packing, promoted by the humidity of excipients: Monitoring and quantification by Raman spectroscopy. J. Pharm. Biomed. Anal. 2016;121:209–214. doi: 10.1016/j.jpba.2016.01.008. [DOI] [PubMed] [Google Scholar]
  • 106.Simone E, Saleemi AN, Nagy ZK. Application of quantitative Raman spectroscopy for the monitoring of polymorphic transformation in crystallization processes using a good calibration practice procedure. Chem. Eng. Res. Des. 2014;92(4):594–611. doi: 10.1016/j.cherd.2013.11.004. [DOI] [Google Scholar]
  • 107.Netchacovitch L, Dumont E, Cailletaud J, Thiry J, Bleye CD, Sacré P-Y, Boiret M, Evrard B, Hubert PH, Ziemons E. Development of an analytical method for crystalline content determination in amorphous solid dispersions produced by hot-melt extrusion using transmission Raman spectroscopy: a feasibility study. Int. J. Pharm. 2017;530(1-2):249–255. doi: 10.1016/j.ijpharm.2017.07.052. [DOI] [PubMed] [Google Scholar]
  • 108.Kachrimanis K, Braun DE, Griesser UJ. Quantitative analysis of paracetamol polymorphs in powder mixtures by FT-raman spectroscopy and PLS regression. J. Pharm. Biomed. Anal. 2007;43(2):407–412. doi: 10.1016/j.jpba.2006.07.032. [DOI] [PubMed] [Google Scholar]
  • 109.Shinde, S.R., Bhavsar, K., Kimbahune, S., Khandelwal, S., Ghose, A., Pal, A.: Detection of counterfeit medicines using hyperspectral sensing. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp 6155–6158. IEEE (2020) [DOI] [PubMed]
  • 110.Ou Y-Y, Tsai A-C, Zhou X-P, Wang J-F. Automatic drug pills detection based on enhanced feature pyramid network and convolution neural networks. IET Comput. Vis. 2020;14(1):9–17. doi: 10.1049/iet-cvi.2019.0171. [DOI] [Google Scholar]
  • 111.Ou, Y.-Y., Tsai, A.-C., Wang, J.-F., Lin, J.: Automatic drug pills detection based on convolution neural network. In: 2018 International Conference on Orange Technologies (ICOT), pp 1–4. IEEE (2018)
  • 112.Wilczyński S, Koprowski R, Marmion M, Duda P, Błońska-Fajfrowska B. The use of hyperspectral imaging in the VNIR (400–1000 nm) and SWIR range (1000–2500 nm) for detecting counterfeit drugs with identical API composition. Talanta. 2016;160:1–8. doi: 10.1016/j.talanta.2016.06.057. [DOI] [PubMed] [Google Scholar]
  • 113.de Moura França L, Pimentel MF, da Silva Simões S, Grangeiro JrS, Prats-Montalbán JM, Ferrer A. NIR Hyperspectral imaging to evaluate degradation in captopril commercial tablets. Eur. J. Pharm. Biopharm. 2016;104:180–188. doi: 10.1016/j.ejpb.2016.05.005. [DOI] [PubMed] [Google Scholar]
  • 114.Brock D, Zeitler JA, Funke A, Knop K, Kleinebudde P. A comparison of quality control methods for active coating processes. Int. J. Pharm. 2012;439(1-2):289–295. doi: 10.1016/j.ijpharm.2012.09.021. [DOI] [PubMed] [Google Scholar]
  • 115.Subramanian P, Lesniewski A, Kaminska I, Vlandas A, Vasilescu A, Niedziolka-Jonsson J, Pichonat E, Happy H, Boukherroub R, Szunerits S. Lysozyme detection on aptamer functionalized graphene-coated SPR interfaces. Biosens. Bioelectron. 2013;50:239–243. doi: 10.1016/j.bios.2013.06.026. [DOI] [PubMed] [Google Scholar]
  • 116.Hudovornik G, Korasa K, Vrečer F. A study on the applicability of in-line measurements in the monitoring of the pellet coating process. Eur. J. Pharm. Sci. 2015;75:160–168. doi: 10.1016/j.ejps.2015.04.007. [DOI] [PubMed] [Google Scholar]
  • 117.Korasa K, Hudovornik G, Vrečer F. Applicability of near-infrared spectroscopy in the monitoring of film coating and curing process of the prolonged release coated pellets. Eur. J. Pharm. Sci. 2016;93:484–492. doi: 10.1016/j.ejps.2016.08.038. [DOI] [PubMed] [Google Scholar]
  • 118.Pavurala N, Xu X, Krishnaiah YSR. Hyperspectral imaging using near infrared spectroscopy to monitor coat thickness uniformity in the manufacture of a transdermal drug delivery system. Int. J. Pharm. 2017;523(1):281–290. doi: 10.1016/j.ijpharm.2017.03.022. [DOI] [PubMed] [Google Scholar]
  • 119.Daikos O, Heymann K, Scherzer T. Monitoring of thickness and conversion of thick pigmented UV-cured coatings by NIR hyperspectral imaging. Progress in Organic Coatings. 2018;125:8–14. doi: 10.1016/j.porgcoat.2018.08.022. [DOI] [Google Scholar]
  • 120.Kandpal LM, Seo YW, Cho BK. Measurement of drug component distribution and hardness using hyperspectral imaging and NIR spectroscopy. J. Korean Soc. Agric. Mach. 2015;20(2):119–120. [Google Scholar]
  • 121.Yin W-J, Ru C-L, Zheng J, Lu Z, Yan J-Z, Zhang H. Fusion of spectrum and image features to identify Glycyrrhizae Radix et Rhizoma from different origins based on hyperspectral imaging technology. Zhongguo Zhong yao za zhi= Zhongguo Zhongyao Zazhi= China Journal of Chinese Materia Medica. 2021;46(4):923–930. doi: 10.19540/j.cnki.cjcmm.20201120.103. [DOI] [PubMed] [Google Scholar]
  • 122.Sandasi M, Vermaak II, Chen W, Viljoen AM. Hyperspectral imaging and chemometric modeling of Echinacea—A novel approach in the quality control of herbal medicines. Molecules. 2014;19(9):13104–13121. doi: 10.3390/molecules190913104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 123.Saputro, A.H., Aprichilia, C.: Classification system of honey floral origin based on visual near-infrared imaging. In: 2019 International Conference on Sustainable Information Engineering and Technology (SIET), pp 125–129. IEEE (2019)
  • 124.Vermaak I, Viljoen A, Lindström SW. Hyperspectral imaging in the quality control of herbal medicines–the case of neurotoxic Japanese star anise. J. Pharm. Biomed. Anal. 2013;75:207–213. doi: 10.1016/j.jpba.2012.11.039. [DOI] [PubMed] [Google Scholar]
  • 125.Djokam M, Sandasi M, Chen W, Viljoen A, Vermaak I. Hyperspectral imaging as a rapid quality control method for herbal tea blends. Appl. Sci. 2017;7(3):268. doi: 10.3390/app7030268. [DOI] [Google Scholar]
  • 126.Kong W, Liu F, Zhang C, Zhang J, Feng H. Non-destructive determination of Malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging. Sci. Rep. 2016;6(1):1–8. doi: 10.1038/srep35393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 127.Badaró AT, Garcia-Martin JF, del Carmen Lopez-Barrera M, Barbin DF, Alvarez-Mateos P. Determination of pectin content in orange peels by near infrared hyperspectral imaging. Food Chemistry. 2020;323:126861. doi: 10.1016/j.foodchem.2020.126861. [DOI] [PubMed] [Google Scholar]
  • 128.Liang L, Di L, Zhang L, Deng M, Qin Z, Zhao S, Lin H. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sens. Environ. 2015;165:123–134. doi: 10.1016/j.rse.2015.04.032. [DOI] [Google Scholar]
  • 129.Yang X, Yu Y. Estimating soil salinity under various moisture conditions: an experimental study. IEEE Trans. Geosci. Remote Sens. 2017;55(5):2525–2533. doi: 10.1109/TGRS.2016.2646420. [DOI] [Google Scholar]
  • 130.Yokoya N, Chan JC-W, Segl K. Potential of resolution-enhanced hyperspectral data for mineral mapping using simulated enMAP and Sentinel-2 images. Remote Sens. 2016;8(3):172. doi: 10.3390/rs8030172. [DOI] [Google Scholar]
  • 131.Li S, Dian R, Fang L, Bioucas-Dias JM. Fusing hyperspectral and multispectral images via coupled sparse tensor factorization. IEEE Trans. Image Process. 2018;27(8):4118–4130. doi: 10.1109/TIP.2018.2836307. [DOI] [PubMed] [Google Scholar]
  • 132.Zhang S, Li J, Wu Z, Plaza A. Spatial discontinuity-weighted sparse unmixing of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 2018;56(10):5767–5779. doi: 10.1109/TGRS.2018.2825457. [DOI] [Google Scholar]
  • 133.Sun Z, Chen J, Chao L, Ruan W, Mukherjee M. A survey of multiple pedestrian tracking based on tracking-by-detection framework. IEEE Trans. Circuits Syst. Video Technol. 2020;31(5):1819–1833. doi: 10.1109/TCSVT.2020.3009717. [DOI] [Google Scholar]
  • 134.Zhong Y, Zhang L. An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 2011;50(3):894–909. doi: 10.1109/TGRS.2011.2162589. [DOI] [Google Scholar]
  • 135.Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 2004;42(8):1778–1790. doi: 10.1109/TGRS.2004.831865. [DOI] [Google Scholar]
  • 136.Li J, Bioucas-Dias JM, Plaza A. Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans. Geosci. Remote Sens. 2010;48(11):4085–4098. [Google Scholar]
  • 137.Li J, Bioucas-Dias JM, Plaza A. Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Trans. Geosci. Remote Sens. 2011;50(3):809–823. doi: 10.1109/TGRS.2011.2162649. [DOI] [Google Scholar]
  • 138.Camps-Valls G, Gomez-Chova L, Muñoz-Marí J, Vila-Francés J, Calpe-Maravilla J. Composite kernels for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 2006;3(1):93–97. doi: 10.1109/LGRS.2005.857031. [DOI] [Google Scholar]
  • 139.Fauvel M, Chanussot J, Benediktsson JA. A spatial–spectral kernel-based approach for the classification of remote-sensing images. Pattern Recogn. 2012;45(1):381–392. doi: 10.1016/j.patcog.2011.03.035. [DOI] [Google Scholar]
  • 140.Fang L, Li S, Duan W, Ren J, Benediktsson JA. Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels. IEEE Trans. Geosci. Remote Sens. 2015;53(12):6663–6674. doi: 10.1109/TGRS.2015.2445767. [DOI] [Google Scholar]
  • 141.Liu P, Zhang H, Eom KB. Active deep learning for classification of hyperspectral images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016;10(2):712–724. doi: 10.1109/JSTARS.2016.2598859. [DOI] [Google Scholar]
  • 142.Zhong P, Gong Z, Li S, Schönlieb C-B. Learning to diversify deep belief networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2017;55(6):3516–3530. doi: 10.1109/TGRS.2017.2675902. [DOI] [Google Scholar]
  • 143.Hu, W., Huang, Y., Li, W., Zhang, F., Li, H.: Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors 2015 (2015)
  • 144.Chen Y, Jiang H, Li C, Jia X, Ghamisi P. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 2016;54(10):6232–6251. doi: 10.1109/TGRS.2016.2584107. [DOI] [Google Scholar]
  • 145.Haut JM, Paoletti ME, Plaza J, Li J, Plaza A. Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach. IEEE Trans. Geosci. Remote Sens. 2018;56(11):6440–6461. doi: 10.1109/TGRS.2018.2838665. [DOI] [Google Scholar]
  • 146.Yang X, Ye Y, Li X, Lau RYK, Zhang X, Huang X. Hyperspectral image classification with deep learning models. IEEE Trans. Geosci. Remote Sens. 2018;56(9):5408–5423. doi: 10.1109/TGRS.2018.2815613. [DOI] [Google Scholar]
  • 147.Zhu L, Chen Y, Ghamisi P, Benediktsson JA. Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2018;56(9):5046–5063. doi: 10.1109/TGRS.2018.2805286. [DOI] [Google Scholar]
  • 148.Zhan Y, Hu D, Wang Y, Yu X. Semisupervised hyperspectral image classification based on generative adversarial networks. IEEE Geosci. Remote Sens. Lett. 2017;15(2):212–216. doi: 10.1109/LGRS.2017.2780890. [DOI] [Google Scholar]
  • 149.Mou L, Ghamisi P, Zhu XX. Deep recurrent neural networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2017;55(7):3639–3655. doi: 10.1109/TGRS.2016.2636241. [DOI] [Google Scholar]
  • 150.Wu H, Prasad S. Convolutional recurrent neural networks forhyperspectral data classification. Remote Sens. 2017;9(3):298. doi: 10.3390/rs9030298. [DOI] [Google Scholar]
  • 151.Abraham, S.M., McCutcheon, M.J., Fujii, K., McGhee, J.R., Yamamoto, M., Kiyono, H.: A semi-automated immunofluorescent microscopy system for enumeration of immunoglobulin producing cells. In: Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Magnificent Milestones and Emerging Opportunities in Medical Engineering’(Cat. No. 97CH36136), vol. 6, pp 2371–2374. IEEE (1997)
  • 152.Abraham, S.M., McCutcheon, M.J., Fujii, K., McGhee, J.R., Yamarnoto, M., Kiyono, H.: Enumeration Of Immuoglobulin producing cells from mucosal compartments using an automated immunofluorescent microscopy system. In: Proceedings of the 17th Southern Biomedical Engineering Conference, pp 40–40. IEEE (1998)
  • 153.Lee, L., Bretschneider, T.R., Preiser, P.R.: Automatic analysis of Cos-7 binding assay imagery for malaria vaccination experiments. In: 2006 9th International Conference on Control, Automation, Robotics and Vision, pp 1–6. IEEE (2006)
  • 154.Shew, B.Y., Chu, S.C., Lai, L.J., Liu, S.J., Leng, C.H., Hsieh, Y.H.: Arrayed specific t-cell monitor via immuno-capture under weak-DEP Enhancement. In: SENSORS, 2008 IEEE, pp 235–238. IEEE (2008)

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