Application of mid-infrared chemical imaging and multivariate chemometrics analyses to characterise a population of microalgae cells
Abstract
A suite of multivariate chemometrics methods was applied to a mid-infrared imaging dataset of a eustigmatophyte, marine Nannochloropsis sp. microalgae strain. This includes the improved leader-follower cluster analysis (iLFCA) to interrogate spectra in an unsupervised fashion, a resonant Mie optical scatter correction algorithm (RMieS-EMSC) that improves data linearity, the band-target entropy minimization (BTEM) self-modeling curve resolution for recovering component spectra, and a multi-linear regression (MLR) for estimating relative concentrations and plotting chemical maps of component spectra. A novel Alpha-Stable probability calculation for microalgae cellular lipid-to-protein ratio Λi is introduced for estimating population characteristics.
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Water, protein and lipid content of microalgae cells were evaluated on a compositional basis with this technique.
“Water, protein and lipid content of microalgae cells were evaluated on a compositional basis with this technique.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [71]
Estimation of the lipid-to-protein concentration ratio from the statistical cell population distribution was achieved.
“Furthermore, the estimation of lipid-to-protein concentration ratio from the statistical cell population distribution was achieved.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [71]
The study illustrated a technique for lipid-protein estimation statistically without the need for lipid extraction.
“This study illustrated a technique for lipid-protein estimation statistically without the need for lipid extraction, and an additional supporting process i.e., BTEM-MLR was further used to produce concentrations and chemical maps.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [71]
The two iLFCA procedures took only 48 min, showing the effectiveness of the iLFCA algorithm in sorting out 1195 FTIR spectra.
“The two iLFCA procedures took only 48 min, showing the effectiveness of the iLFCA algorithm in sorting out large number (1195) of FTIR spectra.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [71]
The component spectra from the BTEM-MLR accounted for 97% of the signal from the data set.
“The component spectra from the BTEM-MLR accounted for 97% of the signal from the data set.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [71]
In the study by Tan et al., the iLFCA algorithm was effective in sorting out a large number of FTIR spectra.
“The iLFCA algorithm was effective in sorting out a large number of FTIR spectra.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [71]
Energy storage capacity of microalgae relates directly to lipids content, with C12 to C18 chain lengths being the key ones.
“Energy storage capacity of microalgae is also known to relate directly to the lipids content, with C12 to C18 chain lengths being the key ones [2,71].”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [71]
The BTEM-MLR was able to differentiate spectra contributions from major and minor comparative signals.
“The BTEM-MLR was also able to differentiate spectra contributions from major and minor comparative signals.”
From Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity (2019) · cites this paper as [71]
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- Authors
- Tan, S.-T., Balasubramanian, R. K., Das, P., Obbard, J. P., & Chew, W.
- Affiliations
- Agency for Science, Technology and Research (SG)
- Corresponding author
- Chew, Wee
- Venue
- Bioresource Technology
- Publisher
- Elsevier BV
- Published
- 2013-04
- Volume
- 134
- Pages
- 316-323
- ISSN
- 0960-8524 (ISSN-L)
- OpenAlex
- W2053967833
- Cited by
- 15
- References
- 35
- Citations by year
- 2025: 2 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 3 · 2017: 1 · 2015: 1 · 2014: 2
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioresource Technology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Institute of Chemical and Engineering Sciences
- Topics
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Keywords
- Nannochloropsis, eustigmatophytes, self-modeling curve resolution, multiple linear regression, lipid/protein ratio, marine microalgae
- MeSH terms
- Algorithms, Infrared Rays, Lipids, Proteins, Regression Analysis, Scattering, Radiation, Singapore, Soybean Oil, Multivariate Analysis, Cluster Analysis, Spectroscopy, Fourier Transform Infrared, Entropy, Microalgae, Databases, Chemical
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