Assessment of the Structure and Predictive Ability of Models Developed for Monitoring Key Analytes in a Submerged Fungal Bioprocess Using Near-Infrared Spectroscopy
Bibliographic record
Abstract
The robustness of models developed for the near-infrared spectroscopic prediction of mycelial biomass, total sugars, and ammonium in a submerged Penicillium chrysogenum bioprocess was assessed by rigorously challenging them with artificially introduced analyte and background matrix variations, so that analyte concentrations were varied in an invariant matrix and vice versa. The models were also challenged by using a data set from a process operated at a different scale from that used in the original model formulation. Simple univariate and bivariate linear regression models, and partial least-squares (PLS) models with as few factors as three and four, performed sufficiently well for predicting analyte concentrations and were robust with respect to the matrix variations tested. However, models based on relatively weaker absorptions, or those that were likely to be influenced by stronger absorbers present in the same matrix, were vulnerable to changes in the matrix. A change in the scale of operation affected models that would be influenced by biomass, possibly due to an influence of the morphology of the mycelial biomass. An analysis of the loading vectors of some PLS models revealed details that were useful in understanding the type of information modeled and the behavior of these models to the variations tested.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 0.000 |
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".