Principal Component Regression for Broadband Microwave-Microfluidic Chemometrics on Small Sample Counts
Bibliographic record
Abstract
While broadband dielectric spectroscopy enables label-free analysis of biological and chemical materials, extracting multiple concentrations from the data has remained a challenge. This work is one of the first demonstrations of simultaneous concentration extraction of three liquid constituents in a solution using broadband microwave spectroscopic data. Furthermore, the used methods eliminate the need for de-embedding or characterizing the measurement setup, simplifying the process. Advanced regression techniques such as Principal Component Regression (PCR) and Principal Least Squares (PLS) are applied to determine the concentrations of sodium chloride, glucose, and ethanol in water. As input data,$S$-parameters are measured between 0.5 and 26.5 GHz on a broadband coplanar waveguide sensor with a microfluidic container to transport the liquids. The training datasets consist of 27 and 34 samples, respectively. The mean absolute percentage error for sodium chloride predictions ranged from 3-5% for the different methods, while the minimal errors for glucose and ethanol predictions were 6-7% and 4-6%, respectively.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".