Rapid in situ measurements of algal cell concentrations using an artificial neural network and single-excitation fluorescence spectrometry
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The use of ANNs as software sensors in microalgae cultivation has been reported for the estimation of cell counts, contamination in monoalgal cultures, or biomass concentration using fluorescence spectra, multispectral absorption, and reflectance through offline monitoring.
“The use of ANNs as software sensors in microalgae cultivation has been reported for the estimation of cell counts, contamination in monoalgal cultures, or biomass concentration using fluorescence spectra, multispectral absorption, and reflectance through offline monitoring (López Expósito et al. 2017; Franco et al. 2019; Liu et al. 2020a).”
From Software sensors in the monitoring of microalgae cultivations (2024) · cites this paper as Liu et al. 2020a
The cell concentration range was 2×10^5 to 6.4×10^6 cells/mL.
“The cell concentration range was 2 × 10 5 -6.4 × 10 6 cells•mL -1 .”
From On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods (2022) · cites this paper as [39]
Comparison of measured and predicted cell count values in three validation sets showed an acceptable result only in one set, with errors in range -8 to 34% of true value, while other two sets had prediction errors in range 5 to 110% and -95 to 25% of true value.
“Comparison of the measured and predicted cell count values in three validation sets showed an acceptable result only in one set, with errors in the range -8 to 34% of the true (hemocytometer measured) value, where the other two sets had prediction errors in the range 5 to 110% and -95 to 25% of the true value.”
From On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods (2022) · cites this paper as [39]
An ANN or Genetic-Algorithm-ANN was trained to correlate cell count in C. reinhardtii cultivation measured with a hemocytometer with fluorescence emission intensity of suspension samples at 101 wavelengths in 660-760 nm with 1 nm steps, excited with 470 nm LED.
“An ANN or Genetic-Algorithm-ANN was trained to correlate cell count in a C. reinhardtii cultivation measured with a hemocytometer with the fluorescence emission intensity of suspension samples at 101 wavelengths acquired in the range 660-760 nm with 1 nm steps, excited with a 470 nm LED [39].”
From On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods (2022) · cites this paper as [39]
With increasing cell concentration, the fluorescence peak moved to a slightly longer wavelength.
“With increasing cell concentration, the peak moved to a slightly longer wavelength.”
From On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods (2022) · cites this paper as [39]
Cites
Wang, S., Zhang, N., Wu, L., & Wang, Y. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.03.103 · Cited by 674 · see citing works
Gitelson, A. A. (1992) · International Journal of Remote Sensing · doi:10.1080/01431169208904125 · Cited by 637 · see citing works
A fluorometric method for the differentiation of algal populations in vivo and in situ
Beutler, M., Wiltshire, K. H., Meyer, B., Moldaenke, C., Lüring, C., Meyerhöfer, M., Hansen, U., & Dau, H. (2002) · Photosynthesis Research · doi:10.1023/a:1016026607048 · Cited by 563 · see citing works
Interference by Pigment in the Estimation of Microalgal Biomass Concentration by Optical Density
Griffiths, M. J., Garcin, C., van Hille, R. P., & Harrison, S. T. L. (2011) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2011.02.005 · Cited by 463 · see citing works
Gregor, J., & Maršálek, B. (2003) · Water Research · doi:10.1016/j.watres.2003.10.033 · Cited by 289 · see citing works
Guide to Neural Computing Applications
(1998) · Elsevier eBooks · doi:10.1016/b978-0-340-70589-6.x5000-3 · Cited by 281 · see citing works
Passive remote sensing of phytoplankton via chlorophyll α fluorescence
Neville, R. A., & Gower, J. F. R. (1977) · Journal of Geophysical Research Atmospheres · doi:10.1029/jc082i024p03487 · Cited by 240 · see citing works
Neural network based modelling of environmental variables: A systematic approach
Maier, H. R., & Dandy, G. C. (2001) · Mathematical and Computer Modelling · doi:10.1016/s0895-7177(00)00271-5 · Cited by 187 · see citing works
Phytoplankton manual — monographs on oceanographic methodology, 6
Sakshaug, E. (1981) · Ocean Management · doi:10.1016/0302-184x(81)90053-6 · Cited by 170 · see citing works
A review on the methods for correcting the fluorescence inner-filter effect of fluorescence spectrum
Wang, T., Zeng, L., & Li, D.-L. (2017) · Applied Spectroscopy Reviews · doi:10.1080/05704928.2017.1345758 · Cited by 156 · see citing works
Self-citationA novel single-parameter approach for forecasting algal blooms
Xiao, X., He, J., Huang, H., Miller, T. R., Christakos, G., Reichwaldt, E. S., Ghadouani, A., Lin, S. C., Xu, X., & Shi, J. (2016) · Water Research · doi:10.1016/j.watres.2016.10.076 · Cited by 152 · see citing works
On-Line Monitoring of Large Cultivations of Microalgae and Cyanobacteria
Havlik, I., Lindner, P., Scheper, T., & Reardon, K. F. (2013) · Trends in Biotechnology · doi:10.1016/j.tibtech.2013.04.005 · Cited by 131 · see citing works
Synchronous fluorescence spectrometry: Conformational investigation or inner filter effect?
Pacheco, M. E., & Bruzzone, L. (2013) · Journal of Luminescence · doi:10.1016/j.jlumin.2012.12.056 · Cited by 100 · see citing works
Observations of in situ fluorescence of chlorophyll-a in Saanich Inlet
Gower, J. F. R. (1980) · Boundary-Layer Meteorology · doi:10.1007/bf00122022 · Cited by 94 · see citing works
Xiao, X., Sogge, H., Lagesen, K., Tooming‐Klunderud, A., Jakobsen, K. S., & Rohrlack, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106510 · Cited by 93 · see citing works
Multi-Wavelength Based Optical Density Sensor for Autonomous Monitoring of Microalgae
Jia, F., Kacira, M., & Ogden, K. (2015) · Sensors · doi:10.3390/s150922234 · Cited by 73 · see citing works
Artificial neural network modeling for predicting the growth of the microalga Karlodinium veneficum
García-Camacho, F., López-Rosales, L., Sánchez-Mirón, A., Belarbi, E. H., Chisti, Y., & Molina-Grima, E. (2016) · Algal Research · doi:10.1016/j.algal.2016.01.002 · Cited by 67 · see citing works
Chang, D.-W., Hobson, P., Burch, M. D., & Lin, T. (2012) · Water Research · doi:10.1016/j.watres.2012.06.050 · Cited by 64 · see citing works
Monoalgal and mixed algal cultures discrimination by using an artificial neural network
Franco, B. M., Navas, L. M., Gómez, C., Sepúlveda, C., & Acién, F. G. (2019) · Algal Research · doi:10.1016/j.algal.2019.101419 · Cited by 61 · see citing works
Lü, F., Chen, Z., Liu, W., & Shao, H. (2016) · Ecological Engineering · doi:10.1016/j.ecoleng.2016.06.072 · Cited by 60 · see citing works
Nguyen, B. T., & Rittmann, B. E. (2018) · Algal Research · doi:10.1016/j.algal.2018.03.013 · Cited by 56 · see citing works
Zhao, D., Xing, X., Liu, Y., Yang, J., & Wang, L. (2010) · International Journal of Remote Sensing · doi:10.1080/01431160902882512 · Cited by 54 · see citing works
Wang, H., Zhu, R., Zhang, J., Ni, L., Shen, H., & Xie, P. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.00869 · Cited by 45 · see citing works
Tarai, M., & Mishra, A. K. (2016) · Analytica Chimica Acta · doi:10.1016/j.aca.2016.08.041 · Cited by 37 · see citing works
Artificial neural network model for predicting production of Spirulina platensis in outdoor culture
Pappu, J. S. M., Vijayakumar, G. K., & Ramamurthy, V. (2012) · Bioresource Technology · doi:10.1016/j.biortech.2012.12.082 · Cited by 32 · see citing works
Wang, S., Wang, J., Shang, F., Wang, Y., Cheng, Q., & Liu, N. (2019) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2019.117396 · Cited by 32 · see citing works
Straile, D., Jochimsen, M. C., & Kümmerlin, R. (2013) · Freshwater Biology · doi:10.1111/fwb.12118 · Cited by 32 · see citing works
Cancilla, J. C., Díaz‐Rodríguez, P., Izquierdo, J. G., Bañares, L., & Torrecilla, J. S. (2014) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2014.02.097 · Cited by 25 · see citing works
Tao, B., Mao, Z., Pan, D., Shen, Y., Zhu, Q., & Chen, J. (2013) · Ecological Informatics · doi:10.1016/j.ecoinf.2013.04.005 · Cited by 21 · see citing works
Gitelson, A. A. (1993) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.150992 · Cited by 18 · see citing works
Martínez‐Guijarro, R., PACHES, M., Ferrer, J., & Seco, A. (2018) · Environmental Technology & Innovation · doi:10.1016/j.eti.2018.01.005 · Cited by 11 · see citing works
Neural Networks - Their Use and Abuse for Small Data Sets
Meyer, D., Balemi, A., & Wearing, C. (2002) · IGI Global eBooks · doi:10.4018/978-1-930708-26-6.ch015 · Cited by 9 · see citing works
Zhao, D., Fengshou, Z., Fei, D., ling, Z., Hao, G., & 辽宁大连116023, 国. (2005) · National Remote Sensing Bulletin · doi:10.11834/jrs.20050339 · Cited by 6 · see citing works
The relationships between optical density, cell number, and biomass (dry weight) of four microalgae
Fang, 梁. L., Qiao, 鸭. Y., Weichun, 杜. D., Xiaobin, W., Yahong, G., & Yeguang, 李. L. (2014) · Acta Ecologica Sinica · doi:10.5846/stxb201301310207 · Cited by 2 · see citing works
Method of Accurate Correction on Inner Filter Effects in Fluorescence Quenching Analysis
Xiang-dong, 陈. C., Zi-jun, 袁. Y., Feng, 高. G., & Wei-qing, 高. G. (2015) · ACTA PHOTONICA SINICA · doi:10.3788/gzxb20154410.1017001 · Cited by 2 · see citing works
The relationships between optical density,cell number,and biomass of four microalgae
Fan, L. (2014) · Cited by 1 · see citing works
Becker, S., Matthijs, H. C. P., & van Donk, E. (2011) · Hydrobiologia · doi:10.1007/s10750-011-0767-4 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 39 references couldn’t be matched to a known paper.
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Peter, A. P., Koyande, A. K., Chew, K. W., Ho, S., Chen, W., Chang, J., Rambabu, K., BANAT, F. A., & Show, P. L. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111852 · Cited by 252 · see citing works
Oruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K. K., Gadhamshetty, V., & Bhattacharyya, D. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162797 · Cited by 211 · see citing works
Microalgae with Artificial Intelligence: A Digitalized Perspective on Genetics, Systems and Products
Teng, S. Y., Yew, G. Y., Sukačová, K., Show, P. L., Máša, V., & Chang, J.-S. (2020) · Biotechnology Advances · doi:10.1016/j.biotechadv.2020.107631 · Cited by 145 · see citing works
Goswami, R. K., Agrawal, K., Upadhyaya, H. M., Gupta, V. K., & Verma, P. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.116118 · Cited by 103 · see citing works
A review on machine learning approaches for microalgae cultivation systems
Syed, T., Krujatz, F., Ihadjadene, Y., Mühlstädt, G., Hamedi, H., Mädler, J., & Urbas, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108248 · Cited by 92 · see citing works
A review of the current and emerging detection methods of marine harmful microalgae
Liu, F., Zhang, C., Wang, Y., & Chen, G. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.152913 · Cited by 83 · see citing works
Chen, J., Dai, L., Mataya, D., Cobb, K., Chen, P., & Ruan, R. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128188 · Cited by 79 · see citing works
On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods
Havlik, I., Beutel, S., Scheper, T., & Reardon, K. F. (2022) · Energies · doi:10.3390/en15030875 · Cited by 73 · see citing works
Godbole, V., Pal, M. K., & Gautam, P. (2021) · Algal Research · doi:10.1016/j.algal.2021.102436 · Cited by 74 · see citing works
Software sensors in the monitoring of microalgae cultivations
Porras Reyes, L., Havlik, I., & Beutel, S. (2024) · Reviews in Environmental Science and Bio/Technology · doi:10.1007/s11157-023-09679-8 · Cited by 48 · see citing works
“…cultivation has been reported for the estimation of cell counts, contamination in monoalgal cultures, or biomass concentration using fluorescence spectra, multispectral absorption, and reflectance through offline monitoring (López Expósito et al. 2017; Franco et al. 2019; Liu et al. 2020a).”
InfluentialShitanaka, T., Fujioka, H., Khan, M., Kaur, M., Du, Z., & Khanal, S. K. (2023) · Bioresource Technology · doi:10.1016/j.biortech.2023.129924 · Cited by 48 · see citing works
The Application of Spectroscopy Technology in the Monitoring of Microalgae Cells Concentration
Liu, J.-Y., Zeng, L.-H., & Ren, Z.-H. (2021) · Applied Spectroscopy Reviews · doi:10.1080/05704928.2020.1763380 · Cited by 33 · see citing works
Self-citationKavitha, S. K., Ravi, Y. K., Kumar, G., Yogalakshmi, K. N., & Banu, J. R. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120135 · Cited by 33 · see citing works
Khruschev, S., Plyusnina, T. Y., Antal, T. K., Pogosyan, S. I., Riznichenko, G. Y., & Rubin, A. B. (2022) · Biophysical Reviews · doi:10.1007/s12551-022-00982-2 · Cited by 32 · see citing works
Thajuddin, F., Rasheed, A. S., Elumalai, A., Palanivel, P., Thajuddin, N., & Dharumadurai, D. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.123961 · Cited by 17 · see citing works
Chen, Y., Zhang, J., Liu, J., Wang, J., Li, W., & Wang, C. (2024) · Microchemical Journal · doi:10.1016/j.microc.2024.110071 · Cited by 16 · see citing works
Self-citationHeddam, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820644-7.00027-x · Cited by 13 · see citing works
Liu, M., Hu, J., Huang, Y., He, J., Effiong, K. S., Tang, T., Huang, S., Perianen, Y. D., Wang, F., Li, M., et al. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/acaf11 · Cited by 13 · see citing works
Wang, M., Chen, T., & Wang, X. (2023) · Photonics · doi:10.3390/photonics10060627 · Cited by 12 · see citing works
Advanced imaging for microalgal biotechnology
Plouviez, M., Bhatia, N., Shurygin, B., & Solovchenko, A. E. (2024) · Algal Research · doi:10.1016/j.algal.2024.103649 · Cited by 12 · see citing works
Kalwani, M., & Vítová, M. (2026) · Algal Research · doi:10.1016/j.algal.2026.104620 · Cited by 12 · see citing works
Machine Learning Models for Chlorophyll-a Forecasting in a Freshwater Lake: Case Study of Lake Taihu
Sun, G., Zhu, W., Qian, X., Wei, C., Xie, P., Shi, Y., Cao, X., & He, Y. (2025) · Water · doi:10.3390/w17081219 · Cited by 10 · see citing works
Shan, S., Wang, X., Xu, Z., & Tong, M. (2021) · Chemosensors · doi:10.3390/chemosensors9100293 · Cited by 10 · see citing works
Liu, F., Zhang, C., Duan, Y., Ma, J., Wang, Y., & Chen, G. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154771 · Cited by 10 · see citing works
Su, J., Barathan, B. P., Su, Y., Morton, S. L., She, C., Zhang, H., & Lin, X. (2024) · Marine Biology · doi:10.1007/s00227-023-04367-1 · Cited by 9 · see citing works
Recent Advances in the Application of Artificial Intelligence in Microalgal Cultivation
Rayamajhi, V., Hussain, M., Shin, H., & Jung, S.-M. (2025) · Processes · doi:10.3390/pr13123764 · Cited by 10 · see citing works
Carvalho, J. D. S., Artifon, W., da Silva, A., de Andrade, C. J., & de Olíveira, D. (2025) · Algal Research · doi:10.1016/j.algal.2025.104216 · Cited by 10 · see citing works
Recent Advances in Microalgae Cultivation Systems: Toward Autonomous Architecture
Sangregorio‐Soto, V., Mayorga, E. Y., & Perafán, R. D. L. H. (2026) · Fermentation · doi:10.3390/fermentation12030147 · Cited by 10 · see citing works
A rapid fluorescence approach on differentiation of typical dinoflagellate of East China Sea
Shan, S., Xu, L., Chen, K., Tong, M., & Wang, X. (2022) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2022.121216 · Cited by 8 · see citing works
Huang, Y., & Chen, Y. (2024) · Water Air & Soil Pollution · doi:10.1007/s11270-024-06913-z · Cited by 7 · see citing works
Abbaszadeh, M., Punna, S., Pusuluru, S., Samuel, M. S., Khandge, R. S., Ethiraj, S., Almukhlifi, H. A., & Menaa, F. (2026) · Frontiers in Chemistry · doi:10.3389/fchem.2026.1858141 · Cited by 6 · see citing works
How can household dampness-related exposure and its related health outcomes be predicted?
Sun, C., Wang, P., Huang, X., Li, K., Hong, S., Zou, Z., Su, C., & Huang, C. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108385 · Cited by 6 · see citing works
Singh, R. P., Singh, P. K., Kumar, I., Kumar, M., Gaur, V. K., Kaushik, A., Arya, A., Saikia, M., Santos‐Villalobos, S. D. L., Kumar, A., et al. (2026) · Energy Strategy Reviews · doi:10.1016/j.esr.2026.102050 · Cited by 5 · see citing works
Lhamo, P., & Mahanty, B. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104687 · Cited by 5 · see citing works
Xie, M., Liu, Z., & Yu, L. (2025) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2025.117608 · Cited by 4 · see citing works
Microalgae Isolation and Cultivation Technology for Mass Production
Lee, T. H. W., Show, P.-L., Ong, H. C., Ling, T. C., Lan, J. C., & Chang, J. (2022) · Springer eBooks · doi:10.1007/978-94-007-6724-9_10-1 · Cited by 3 · see citing works
Medlin, L. K., García‐Portela, M., Rossignoli, A. E., & Reguera, B. (2024) · Harmful Algae · doi:10.1016/j.hal.2024.102697 · Cited by 2 · see citing works
Interpretable multi-morphology and multi-scale microalgae classification based on machine learning
Yan, H., Peng, X., Wang, C., Xia, A., Huang, Y., Zhu, X., Zhang, J., Zhu, X., & Liao, Q. (2024) · Algal Research · doi:10.1016/j.algal.2024.103812 · Cited by 2 · see citing works
Quantification of microalgae concentration based on multi-channel spectral fusion and 1D-CNN
Chen, Y., Wang, C., Liu, J., Li, W., Zhang, J., & Wang, J. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adc030 · Cited by 2 · see citing works
Self-citationAutomating microalgal raceway pond operations using real-time spectroradiometric monitoring
Gao, S., Katinas, C., Timlin, J. A., Reichardt, T. A., Edmundson, S. J., & Huesemann, M. H. (2026) · Algal Research · doi:10.1016/j.algal.2026.104706 · Cited by 2 · see citing works
Wastewater-Mediated Microalgae Isolation and Culturing Techniques
Rai, M. P., & Vasistha, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-31674-6_2 · Cited by 2 · see citing works
Study on correlation prediction model for static explosion and dynamic explosion shock wave pressure
Wang, L., & Kong, D. (2024) · Measurement and Control · doi:10.1177/00202940241227063 · Cited by 1 · see citing works
Harvesting and Lipid Extraction Techniques of Microalgae in Wastewater
Rai, M. P., & Vasistha, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-31674-6_6 · Cited by 1 · see citing works
Biotechnology advancements in CO2 capture and conversion by microalgae-based systems
Perazzoli, S., Michelon, W., & da Silva, M. L. B. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90971-6.00038-3 · Cited by 1 · see citing works
Microalgae Isolation and Cultivation Technology for Mass Production
Lee, T. H. W., Show, P.-L., Ong, H. C., Ling, T. C., Lan, J. C., & Chang, J. (2024) · Springer eBooks · doi:10.1007/978-981-97-7586-6_10
Xu, H., Wang, X., Zhou, Y., Xia, T., Chang, F., & Xu, C. (2025) · Hydrology research · doi:10.2166/nh.2025.160
Wang, M., Guo, X., Han, X., & Wang, W. (2025) · International Symposium on Bioinformatics and Computational Biology (BCB) · doi:10.1145/3747357.3747385
A COMPARISON OF CNN AND TRANSFORMER-BASED MODELS FOR ALGAL SPECIES CLASSIFICATION
ŞİMŞEK, G. K., & Ertarğın, M. (2025) · Middle East Journal of Science · doi:10.51477/mejs.1674736
How Does the Internet of Things (IoT) Help in Microalgae Pigment Production?
Sukhinov, D. V., Gotovtsev, P. M., & Sergeeva, Y. E. (2026) · Springer eBooks · doi:10.1007/978-3-032-05438-8_17
Jepsen, P. M., Rhiger, M., & Westergaard, C. R. (2026) · Aquaculture Research · doi:10.1155/are/2232484
Engineered Microalgae: A Sustainable Solution for the Remediation of Organic Contaminants
Roy, A., Kiran, B. R., & Poluri, K. M. (2026) · Springer eBooks · doi:10.1007/978-981-95-7234-2_14
Wang, L., Zhang, S., Han, Y., Zhang, R., & Zhao, W. (2026) · Microorganisms · doi:10.3390/microorganisms14051085
Peter, A. P., Mahari, W. A. W., Lim, S. Y. M., Ibrahim, A. Y., Sankaran, R., Markandan, K., & Rajasegran, T. (2026) · GCB Bioenergy · doi:10.1111/gcbb.70174
Zhou, X., Wang, X., Wei, S., Sun, Z., Hu, L., Zhao, D., Hao, J., & Zheng, X. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.135728
Shan, J., Miao, X., Liu, M., Li, H., 何志鹏, Wang, Z., & Wang, H. (2026) · Sensors · doi:10.3390/s26206487
- Authors
- Liu, J.-Y., Zeng, L.-H., Ren, Z.-H., Du, T.-M., & Liu, X.
- Affiliations
- Hebei Agricultural University (CN)
- Corresponding author
- Ren, Zhen-Hui
- ORCID
- Liu, Xuan
- Venue
- Algal Research
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 45
- Pages
- 101739
- ISSN
- 2211-9264 (ISSN-L)
- OpenAlex
- W2991238520
- Cited by
- 55
- References
- 39
- Citations by year
- 2026: 12 · 2025: 9 · 2024: 11 · 2023: 5 · 2022: 11 · 2021: 5 · 2020: 2
- FWCI
- 3.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Algal Research (publishedVersion)
- Funders
- Hebei Agricultural University
- Department of Education of Hebei Province
- Topics
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Keywords
- Chlamydomonas reinhardtii, artificial neural networks, genetic algorithm optimization, BP neural network, LED excitation
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