Effects of pre‐processing of Raman spectra on in vivo classification of nutrient status of microalgal cells
Abstract
Raman spectra were obtained from cells of the chlorophyte unicellular eukaryotic alga Dunaliella tertiolecta, which had been grown either under nutrient‐replete conditions or starved of nitrogen for 4 days. Spectra were rich in bands which could all be attributed to either chlorophyll a or β‐carotene. A cursory examination of the differences between the spectra of replete and starved cells indicated a decline in chlorophyll a and an increase in β‐carotene in chlorophytes. Unprocessed spectra showed pronounced baseline effects. A variety of pre‐processing techniques were used in an attempt to visualise the spectral, and hence chemical, differences in the transformed data and perform classification based upon these differences. Six types of spectral pre‐processing were compared: baseline correction with vector normalisation; Multiplicative Scatter Correction (MSC), Extended Multiplicative Signal Correction (EMSC); Standard Normal Variate (SNV); and vector normalised 1st and 2nd derivative spectra. Results for Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares (PLS) discriminant analysis were compared. All pre‐processing methods allowed spectral differences between N‐replete and N‐starved spectra to be visualised, with derivatives and EMSC scoring the lowest RMSEC and RMSEV values with PLS and also the best overall classification results. SIMCA was not suited to classifying the nutrient classes under any of the pre‐treatments, due to the small model distances involved. Copyright © 2006 John Wiley & Sons, Ltd.
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When detecting Raman scattered light, Rayleigh and Tyndall scattering must first be eliminated, elucidating faint characteristic spectra comprising astaxanthin, chlorophyll a, chlorophyll b, chlorophyll c, lipids, fatty acids, proteins, carbohydrates, nucleic acids.
“When detecting Raman scattered light, Rayleigh and Tyndall scattering must first be eliminated, elucidating faint characteristic spectra comprising astaxanthin, chlorophyll a, chlorophyll b, chlorophyll c, lipids, fatty acids, proteins, carbohydrates, nucleic acids, [18,38-44] (Fig. 2 & Fig. 3).”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [40]
To extract relevant information related to macromolecule content from MIR, NIR, and Raman spectra, various chemometric models, statistical, and mathematical treatments are required.
“To extract relevant information related to macromolecule content from MIR, NIR, and Raman spectra, various chemometric models, statistical, and mathematical treatments are required [40, 46,48].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [40]
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Self-citation
- Authors
- Heraud, P., Wood, B. R., Beardall, J., & McNaughton, D.
- Affiliations
- Monash University (AU)
- Corresponding author
- Heraud, Philip
- Venue
- Journal of Chemometrics
- Publisher
- Wiley
- Published
- 2006-05
- Volume
- 20
- Issue
- 5
- Pages
- 193-197
- ISSN
- 0886-9383 (ISSN-L)
- 1099-128X
- OpenAlex
- W1986076957
- Cited by
- 143
- References
- 11
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2023: 7 · 2022: 11 · 2021: 13 · 2020: 9 · 2019: 7 · 2018: 7 · 2017: 10 · 2016: 10 · 2015: 5 · 2014: 9 · 2013: 7 · 2012: 10
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemometrics (publishedVersion)
- 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 › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Keywords
- Raman spectroscopy, Dunaliella tertiolecta, microalgae, nitrogen starvation, chlorophyll a, beta-carotene, spectral preprocessing, baseline correction, multiplicative scatter correction, standard normal variate, derivative spectra, SIMCA, PLS-DA
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