Abstract：
:Statistical growth rate modelling can be applied in a variety of ecological and biotechnological applications. Such models are frequently based on Monod or Droop equations and, especially for the latter, require reliable determination of model input parameters such as C:N quotas. Besides growth rate modelling, a C:N quota quantification can be useful for monitoring and interpretation of physiological acclimation to abiotic and biotic disturbances (e.g., nutrient limitations). However, as high throughput C:N quota determination is difficult to perform, alternatives need to be established. Fourier-transformed infrared (FTIR) spectroscopy is used to analyze a variety of biochemical, chemical, and physiological parameters in phytoplankton. Hence, a quantification of the C:N quota should also be feasible. Therefore, using FTIR spectroscopy, six phytoplankton species from among different phylogenetic groups have been analyzed to determine the effect of nutrient limitation on C:N quota patterns. The typical species-specific response to increasing nitrogen limitation was an increase in the C:N quota. Irrespective of this species specificity, we were able to develop a reliable multi-species C:N quota prediction model based on FTIR spectroscopy using the partial least square regression (PLSR) algorithm. Our data demonstrate that the PLSR approach is more robust in C:N quota quantification (R2 = 0.93) than linear correlation of C:N quota versus growth rate (R2 ranges from 0.74 to 0.86) or biochemical information based on FTIR spectra (R2 ranges from 0.82 to 0.89). This accurate prediction of C:N values may support high throughput measurements in a broad range of future approaches.
journal_name
J Phycoljournal_title
Journal of phycologyauthors
Wagner H,Jebsen C,Wilhelm Cdoi
10.1111/jpy.12858subject
Has Abstractpub_date
2019-06-01 00:00:00pages
543-551issue
3eissn
0022-3646issn
1529-8817journal_volume
55pub_type
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