Vibrational Spectroscopy of Food and Food Products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 it