Assessment of physiological responses and growth phases of different microalgae under environmental changes by Raman spectroscopy with chemometrics
Abstract
The assessment for cell physiology and growth phases of microalgae plays important roles in ecological and environmental fields since it can be used to forecast water eutrophication level worldwidely. Herein, growth phases and environmental conditions of microalgae were assessed by combining resonance Raman mapping spectroscopy with multivariate analysis methods. And, primary Raman characteristic peaks of microalgae were mined with two-dimensional synchronous spectra. Thereafter, algal growth phases and environmental conditions of microalgae were preliminary classified with different tendencies of characteristic Raman peaks by unsupervised principal component analysis (PCA) and support vector machine (SVM) methods. Our results demonstrated that resonance Raman mapping spectroscopy with PCA and SVM classification models can be used to assess algal growth phases and preliminary predict environmental conditions with characteristic Raman spectra of microalgae in water bodies.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Raman spectroscopy has been used to assess physiological responses and growth phases of different microalgae.
“Its use in assessing the physiological responses and growth phases of different microalgae has also been demonstrated [40].”
From Response to nutrient variation on lipid productivity in green microalgae captured using second derivative FTIR and Raman spectroscopy (2022) · cites this paper as [40]
Raman spectra was found applicable for the assessment of environmental conditions in microalgae cultivation.
“Although samples were used to construct models for the identification and classification of culture conditions with PCA and SVM, Raman spectra was also found applicable for the assessment of environmental conditions in microalgae cultivation.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [84]
Support vector machine was applied to identify growth phases under different culture conditions.
“Subsequently, support vector machine (SVM), which is a machine learning method was applied to identify growth phases under different culture conditions.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [84]
Assessment of cell physiology in all stages of microalgae growth under cultivation plays an important role for environmental and ecological monitoring purposes.
“An assessment of cell physiology in all stages of microalgae growth under cultivation plays an important role for environmental and ecological monitoring purposes [84].”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [84]
The results demonstrated that the combination of PCA and SVM was useful in assessing cultivation and also predicted the preliminary environmental condition of microalgae in water bodies, which is useful to check eutrophication.
“The results demonstrated that the combination of PCA and SVM was not only useful in assessing cultivation but also predicted the preliminary environmental condition of microalgae in water bodies, which is useful to check eutrophication.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [84]
Raman spectra of algal cells induced with different environmental conditions were classified by their PCA scores.
“Raman spectra of algal cells induced with different environmental conditions were classified by their PCA scores, in a process that applied dimensionality reduction prior to MVA.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [84]
Classification of culture conditions was 65% accurate.
“Classification of culture conditions was 65% accurate.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [84]
Correlation of Raman analysis of the two microalgae species and the characteristic Raman intensities were fitted to gather statistical information used in assessing growth phases and physiological responses.
“Correlation of Raman analysis of the two microalgae species and the characteristic Raman intensities were fitted to gather statistical information used in assessing growth phases and physiological responses.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [84]
Growth phases of microalgae were monitored using Raman spectroscopy in a study that investigated two freshwater species, Microcystis flos-aquae and Chlorella vulgaris.
“Growth phases of microalgae were monitored using Raman spectroscopy in a study, which investigated two freshwater species viz. Microcystis flos-aquae and Chlorella vulgaris [84].”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [84]
Cites
Chemometrics: Statistics and Computer Application in Analytical Chemistry
(2001) · Microchemical Journal · doi:10.1016/s0026-265x(00)00156-9 · Cited by 753 · see citing works
Chemometrics: statistics and computer application in analytical chemistry
(1999) · Choice Reviews Online · doi:10.5860/choice.37-0336 · Cited by 534 · see citing works
Two-dimensional correlation spectroscopy
Noda, I. (2002) · Cited by 245 · see citing works
Baseline correction for Raman spectra using an improved asymmetric least squares method
He, S., Zhang, W., Liu, L., Huang, Y., He, J., Xie, W., Wu, P., & Du, C. (2014) · Analytical Methods · doi:10.1039/c4ay00068d · Cited by 243 · see citing works
Self-citationRaman Spectroscopy of Microbial Pigments
Jehlička, J., Edwards, H. G. M., & Oren, A. (2014) · Applied and Environmental Microbiology · doi:10.1128/aem.00699-14 · Cited by 184 · see citing works
Wood, B. R., Heraud, P., Stojkovic, S., Morrison, D., Beardall, J., & McNaughton, D. (2005) · Analytical Chemistry · doi:10.1021/ac050281z · Cited by 135 · see citing works
Zhang, F., Lee, J., Liang, S., et al. (2015) · Environmental Health · doi:10.1186/s12940-015-0026-7 · Cited by 133 · see citing works
Two-Dimensional Correlation Spectroscopy
Czarnecki, M. A., & Morita, S. (2020) · Springer eBooks · doi:10.1007/978-981-15-8648-4_6 · Cited by 117 · see citing works
Li, Y., Zhang, Y., Zhang, Y., et al. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.05.075 · Cited by 115 · see citing works
Heraud, P., Beardall, J., McNaughton, D., & Wood, B. R. (2007) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2007.00861.x · Cited by 102 · see citing works
Jung, G. B., Nam, S. W., Choi, S., Lee, G., & Park, H. (2014) · Biomedical Optics Express · doi:10.1364/boe.5.003238 · Cited by 89 · see citing works
Mowe, M. A. D., Porojan, C., Abbas, F., et al. (2015) · Harmful Algae · doi:10.1016/j.hal.2015.10.011 · Cited by 83 · see citing works
He, S., Xie, W., Zhang, W., et al. (2014) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2014.08.134 · Cited by 78 · see citing works
Self-citationEnvironmental response to long-term mariculture activities in the Weihai coastal area, China
Li, H., Li, X., Li, Q., et al. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.05.167 · Cited by 75 · see citing works
Li, W., Xu, X., Fujibayashi, M., et al. (2016) · Environmental Science and Pollution Research · doi:10.1007/s11356-016-7180-5 · Cited by 68 · see citing works
He, X. N., Allen, J., Black, P. N., Baldacchini, T., Huang, X., Huang, H., Jiang, L., & Lu, Y. F. (2012) · Biomedical Optics Express · doi:10.1364/boe.3.002896 · Cited by 62 · see citing works
Raman Spectroscopy of Algae: A Review
Tomar, V. (2012) · Journal of Nanomedicine & Nanotechnology · doi:10.4172/2157-7439.1000131 · Cited by 56 · see citing works
Zhang, P., Li, Z., Lu, L., Xiao, Y., Liu, J., Guo, J., & Fang, F. (2017) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2017.03.022 · Cited by 45 · see citing works
Self-citationProbing the carotenoid content of intact Cyclotella cells by resonance Raman spectroscopy
Alexandre, M. T. A., Gundermann, K., Pascal, A. A., van Grondelle, R., Büchel, C., & Robert, B. (2013) · Photosynthesis Research · doi:10.1007/s11120-013-9942-y · Cited by 39 · see citing works
Barickman, T. C., & Kopsell, D. A. (2016) · Scientia Horticulturae · doi:10.1016/j.scienta.2016.04.007 · Cited by 39 · see citing works
Grudziński, W., Krzemińska, I., Luchowski, R., Nosalewicz, A., & Gruszecki, W. I. (2016) · Algal Research · doi:10.1016/j.algal.2016.03.021 · Cited by 37 · see citing works
Can, Ş. S., Koru, E., & Cırık, S. (2017) · Aquaculture International · doi:10.1007/s10499-017-0121-6 · Cited by 35 · see citing works
Assessment of growth phases of the diatom Ditylum brightwellii by FT-IR and Raman spectroscopy
Rüger, J., Unger, N., Schie, I. W., Brunner, E., Popp, J., & Krafft, C. (2016) · Algal Research · doi:10.1016/j.algal.2016.09.007 · Cited by 32 · see citing works
Meksiarun, P., Spegazzini, N., Matsui, H., Nakajima, K., Matsuda, Y., & Sato, H. (2015) · Applied Spectroscopy · doi:10.1366/14-07598 · Cited by 31 · see citing works
He, S., Xie, W., Zhang, P., Fang, S., Li, Z., Tang, P., Gao, X., Guo, J., Tlili, C., & Wang, D. (2017) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2017.09.036 · Cited by 31 · see citing works
Self-citationSubcommunity FTIR-spectroscopy to determine physiological cell states
Wagner, H., Dunker, S., Liu, Z., & Wilhelm, C. (2012) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2012.09.008 · Cited by 26 · see citing works
Uragami, C., Galzerano, D., Gall, A., et al. (2014) · Photosynthesis Research · doi:10.1007/s11120-014-0011-y · Cited by 26 · see citing works
Kessler, R. W., Crecelius, A. C., Schubert, U. S., & Wichard, T. (2017) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-017-0430-7 · Cited by 22 · see citing works
Physiological Response of the Green Algae Ulva lactuca (Chlorophyta) to Heavy Metals Stress
Saleh, B. (2015) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 17 · see citing works
Oren, A., Mana, L., & Jehlička, J. (2015) · FEMS Microbiology Letters · doi:10.1093/femsle/fnv021 · Cited by 17 · see citing works
Multidimensional single-cell analysis based on fluorescence microscopy and automated image analysis
Sandmann, M., Lippold, M., Saalfrank, F., Odika, C. P., & Rohn, S. (2017) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-017-0344-4 · Cited by 17 · see citing works
Vogt, F., & White, L. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.02.005 · Cited by 10 · see citing works
Yang, F.-X., Su, R., Jian, H.-M., & Yao, Q.-Z. (2016) · Ecological Indicators · doi:10.1016/j.ecolind.2016.06.039 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Smart microalgae farming with internet-of-things for sustainable agriculture
Lim, H. R., Khoo, K. S., Chia, W. Y., Chew, K. W., Ho, S.-H., & Show, P. L. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.107931 · Cited by 155 · see citing works
Grace, C. E. E., Lakshmi, P. K., Meenakshi, S., Vaidyanathan, S., Srisudha, S., & Mary, M. B. (2020) · Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2019.117382 · Cited by 104 · see citing works
Musa, M., Ayoko, G. A., Ward, A., Rösch, C., Brown, R. J., & Rainey, T. J. (2019) · Cells · doi:10.3390/cells8080851 · Cited by 97 · see citing works
Machine learning for microalgae detection and utilization
Ning, H., Li, R., & Zhou, T. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.947394 · Cited by 83 · see citing works
Esther Elizabeth Grace, C., Briget Mary, M., Vaidyanathan, S., & Srisudha, S. (2022) · Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2021.120830 · Cited by 21 · see citing works
- Authors
- He, S., Fang, S., Xie, W., Zhang, P., Li, Z., Zhou, D., Zhang, Z., Guo, J., Du, C., Du, J., & Wang, D.
- Affiliations
- Chinese Academy of Sciences (CN)
- Chongqing University (CN)
- Sichuan University (CN)
- Corresponding author
- He, Shixuan
- Zhang, Zhiyou
- Wang, Deqiang
- ORCID
- Venue
- Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 204
- Pages
- 287-294
- ISSN
- 1386-1425 (ISSN-L)
- OpenAlex
- W2809147123
- Cited by
- 40
- References
- 33
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 2 · 2023: 5 · 2022: 3 · 2021: 9 · 2020: 5 · 2019: 7
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (21507131, 41603089)
- Chongqing Science and Technology Innovation in Social Livelihood of the People (cstc2015shmszxX0002)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Keywords
- microalgae, algal growth phase, cell physiology, environmental factors, eutrophication, support vector machine classification, principal component analysis
- MeSH terms
- Cells, Cultured, Culture Media, Eutrophication, Models, Biological, Spectrum Analysis, Raman, Principal Component Analysis, Microalgae, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
