Quantitative analyses aspect of vibrational spectroscopy and its applications in agriculture
Bibliographic record
Abstract
The three main branches of vibrational spectroscopy, i.e., mid-infrared, near-infrared, and Raman spectroscopy, are widely accepted techniques for qualitative analyses in the agriculture and food sectors. Mid-infrared and Raman spectroscopy probe the same ‘fingerprinting’ area for most organic as well inorganic matter. Such advantages make them ideal classification tools, with little need for sample preparation in routine analysis. Utilization of these vibrational spectroscopy techniques for quantitative analysis is more complicated and requires data handling to render meaningful interpretation of the spectra. A brief introduction to the theoretical background of infrared absorption and Raman inelastic scattering processes is presented. This article presents a detailed discussion of the fundamental principles behind quantitative analysis using absorption and emission spectroscopy techniques. Different measurement modes and their relationship to the acquisition and transformation of spectral data are explained with practical applications related to the quantitative theory. This paper also talks about the rationale for pre-treating the data. A review of the available procedures to pre-process different kinds of spectra and to build calibration concludes the paper. Linear calibration methods, e.g., classic linear regression, principal component regression, partial least squares regression, support vector machines, and nonlinear methods such as artificial neural network, are briefly reviewed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".