An image analysis technique to estimate the cell density and biomass concentration of<i>Trichoderma reesei</i>
Bibliographic record
Abstract
AIM: The objective is to develop an automated image analysis protocol to quantify the cell volume fraction of filamentous fungi (Trichoderma reesei) and estimate the biomass concentration. METHODS AND RESULTS: Both dry weight and image analyses were performed on samples collected periodically from 7-l stirred tank fermentations. Using the projected area of lactophenol blue-stained hyphae, the fraction occupied by the cells in a given volume was estimated. Combined with the biomass dry weight obtained by filtration, the method was used to estimate the density of filamentous fungi. Knowing the density of fungi, the algorithm was employed to quantitatively assess the biomass evolution during the course of fermentation even in the presence of solid particles. CONCLUSIONS: A density of 0.334 g dry weight cm(-3) was found for T. reesei RUT C-30. The image analysis protocol allowed successful estimation of biomass concentration in the presence or absence of solid particles. SIGNIFICANCE AND IMPACT OF THE STUDY: Methods to quantify biomass during the industrial production of cellulase with T. reesei are often limited due to the presence of solid substrates. The image analysis protocol presented here offers a quick and easy way to estimate biomass concentration of filamentous micro-organisms in insoluble medium.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".