Vibrational Spectroscopy of Food and Food Products
Bibliographic record
Abstract
Abstract Vibrational spectroscopy may be applied to the qualitative and quantitative analysis of complex food systems. Mid infrared (MIR) spectroscopy and Raman spectroscopy are valuable tools for identification and structural characterization of food components and for elucidating structure–function relation of food biopolymers such as proteins. Fourier transform infrared(FT‐IR) spectrometers and chemometrics have broadened the scope of MIR spectroscopy for quality control analysis, but such applications remain generally limited to homogeneous fluid products such as beverages, juices, fats, and oils. In contrast, near infrared (NIR) and FT‐NIR spectroscopy in conjunction with multivariate calibration techniques is becoming increasingly popular in the food industry for rapid and routine analysis of proximate composition of various foods as well as for authentication and detection of adulteration. Increasing application of Raman spectroscopy in food analysis is expected with recent developments in NIR‐FT Raman spectrometers, fibre optic sampling, and confocal Raman microscopy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".