A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Lipid monitoring of Chlorella vulgaris using non-invasive near-infrared spectral imaging (2024)


Pääkkönen, S., Pölönen, I., Calderini, M., Yli-Tuomola, A., Ruokolainen, V., Vihinen-Ranta, M., & Salmi, P. (2024). Lipid monitoring of Chlorella vulgaris using non-invasive near-infrared spectral imaging. Journal of Applied Phycology, Early online. https://doi.org/10.1007/s10811-024-03397-6


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatPääkkönen, Salli; Pölönen, Ilkka; Calderini, Marco; Yli-Tuomola, Aliisa; Ruokolainen, Visa; Vihinen-Ranta, Maija; Salmi, Pauliina

Lehti tai sarjaJournal of Applied Phycology

ISSN0921-8971

eISSN1573-5176

Julkaisuvuosi2024

Ilmestymispäivä04.12.2024

VolyymiEarly online

KustantajaSpringer Nature

JulkaisumaaAlankomaat

Julkaisun kielienglanti

DOIhttps://doi.org/10.1007/s10811-024-03397-6

Linkki tutkimusaineistoonhttps://doi.org/https://doi.org/10.23729/96494a42-bc7f-4e0f-9310-8ac8babae9b4

Julkaisun avoin saatavuusAvoimesti saatavilla

Julkaisukanavan avoin saatavuusOsittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX)https://jyx.jyu.fi/handle/123456789/98940


Tiivistelmä

Microalgal lipids are molecules of biotechnological interest for their application in sustainable food and energy production. However, lipid production is challenged by the time-consuming and laborious monitoring of lipid content in microalgae. This study aimed to predict the lipid content of Chlorella vulgaris cultivations based on non-invasively collected near-infrared (NIR) range hyperspectral data. A gravimetric analysis of total lipids was used as reference data (between 2 and 22% per dry weight) to compare three different models to determining the lipid content. A one-dimensional convolutional neural network and partial least squares models performed at a similar level. Both models could predict the lipid content of Chlorella dry weight with an error of 4%pt (root mean squared error). The index-based linear regression model performed the weakest of the three models, with the error of the prediction being 6%pt. Nile Red staining was used to visualise lipids on a microscope and lipid class analysis to resolve the lipid classes that explained most of the increase in lipids in Chlorella. A SHAP algorithm (SHapley Additive exPlanations) was used to analyse the wavebands of NIR spectra that were important for predicting the total lipid content. The results show that spectral data, when combined with an adequate algorithm, could be used to monitor microalgae lipids non-invasively in a closed system, in a way that has not previously been demonstrated with an imaging system.


YSO-asiasanatmikroleväthyperspektrikuvantaminenlipiditkoneoppiminenbiotekniikkakestävä energia

Vapaat asiasanathyperspectral imaging; microalgae; lipid content; machine learning; Nile Red staining


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointiKyllä

VIRTA-lähetysvuosi2024

Alustava JUFO-taso1


Viimeisin päivitys 2025-31-01 klo 08:54