Peer-reviewedCC BY-NC-SA 4.0Bioresource Technology
Opal glass method, an extraction-free way to access microalgae pigment content
Abstract
A new extraction-free and non-destructive method to evaluate pigment content (chlorophyll a, chlorophyll b, and total carotenoids) in microalgae cells is introduced. It relies on the use of a spectrophotometer and an optical diffuser, together coined as the opal glass method. First, the practical aspects of the technique are consolidated (ease of setting up, usage of disposable cuvettes, speed of data acquisition, …). Then, a database is created to link opal glass spectra to microalgae pigment contents obtained by a methanol-extraction method. Partial Least Squares (PLS -machine learning) and Convolutional Neural Networks (CNN -deep
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BibTeX
@article{pozzobon2026opal,
title = {{Opal glass method, an extraction-free way to access microalgae pigment content}},
author = {Victor Pozzobon and Clarisse Arnoudts and Wendie Levasseur},
journal = {Bioresource Technology},
year = {2026},
month = oct,
doi = {10.1016/j.biortech.2026.135966},
url = {https://hal.science/hal-05777976v1},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - JOUR
TI - Opal glass method, an extraction-free way to access microalgae pigment content
AU - Victor Pozzobon
AU - Clarisse Arnoudts
AU - Wendie Levasseur
PY - 2026
DA - 2026/10/05
JO - Bioresource Technology
DO - 10.1016/j.biortech.2026.135966
UR - https://hal.science/hal-05777976v1
AB - A new extraction-free and non-destructive method to evaluate pigment content (chlorophyll a, chlorophyll b, and total carotenoids) in microalgae cells is introduced. It relies on the use of a spectrophotometer and an optical diffuser, together coined as the opal glass method. First, the practical aspects of the technique are consolidated (ease of setting up, usage of disposable cuvettes, speed of data acquisition, …). Then, a database is created to link opal glass spectra to microalgae pigment contents obtained by a methanol-extraction method. Partial Least Squares (PLS -machine learning) and Convolutional Neural Networks (CNN -deep
SN - 0960-8524
ER -